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zhs><@keSsUGPBx4W>$O1%xVvLHXW5tFw?nKXI6X2ylM}bS?wV+t3709wTH~C_K=y? z9x}7qL!J{yr4!7o_K=ybZ}mK@J!D?Bhs><@keSsUGPBx4W>$O1%xVw0XC0O2)68lQ znOW^2GrgwOYin+GX0?Z$W3`9OtoD$Z)gCgl+C%OiN9DdXv)V&uR(r_IY7d#|KB~T+ z=2mA`d&oIfd&tac51Co*A+M#Qau1nV?IAO(J!EFJhs><@keSsUGE=JeZMBD-W3`9O ztoD%W>ZrV)W>$O1%xVvrS?wV+t3709wTH~C_K=xUeO9VH<@ zkeSsUGPBx4W>$O1%xVvrS?wV+rTTnUd&oRT + + + + + + + blobAnalysis + Detects blob in the source image + + + + + Calling Sequence + + [blob] = blobAnalysis(srcImg) + [blob] = blobAnalysis(srcImg, Name, Value) + + + + + + Parameters + + srcImg: + The input image Matrix. + ROI: + This defines a particular in the image in of which you want the features. + filterByThreshold: + This filter compares the intensity of a binary image at the center of a + filterByArea: + Extracted blobs have an area between minArea (inclusive) and maxArea (exclusive). + filterByCircularity: + Extracted blobs have circularity ( 4∗π∗Areaperimeter∗perimeter) between + filterByConvexity: + Extracted blobs have convexity (area / area of blob convex hull) between minConvexity + + + + + Description + +The function uses SimpleBlobDetector function to detect the blobs then it checks for different Name +Value pair arguments and accordingly returns the parameters of the blob such as 2D coordinates of the +blob, size of the blob. + + +The Name-Value pair may be any of following typesbool filterByArea, vector [minArea maxArea]bool filterByCircularity, vector [minCircularity maxCircularity]bool filterByConvexity, vector [minConvexity maxConvexity]double ROI, vector bool filterByThreshold, vector [minThreshold maxThreshold] + + + + + + + Examples + + + + + See also + + drawKeypoints + + + + + Authors + + Deepshikha + + + diff --git a/help/en_US/build_help.sce b/help/en_US/build_help.sce new file mode 100644 index 0000000..81154f3 --- /dev/null +++ b/help/en_US/build_help.sce @@ -0,0 +1,17 @@ +// Copyright (C) 2015 - IIT Bombay - FOSSEE +// +// Author: Shamika Mohanan +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in +// This file must be used under the terms of the BSD. +// This source file is licensed as described in the file LICENSE, which +// you should have received as part of this distribution. The terms +// are also available at +// https://opensource.org/licenses/BSD-3-Clause + +help_lang_dir = get_absolute_file_path('build_help.sce'); + +tbx_build_help(TOOLBOX_TITLE, help_lang_dir); + +clear help_lang_dir; + diff --git a/help/en_US/build_help.sce~ b/help/en_US/build_help.sce~ new file mode 100644 index 0000000..0205c56 --- /dev/null +++ b/help/en_US/build_help.sce~ @@ -0,0 +1,17 @@ +// Copyright (C) 2015 - IIT Bombay - FOSSEE +// +// Author: Shamika Mohanan +// Organization: FOSSEE, IIT Bombay +// Email: harpreet.mertia@gmail.com +// This file must be used under the terms of the BSD. +// This source file is licensed as described in the file LICENSE, which +// you should have received as part of this distribution. The terms +// are also available at +// https://opensource.org/licenses/BSD-3-Clause + +help_lang_dir = get_absolute_file_path('build_help.sce'); + +tbx_build_help(TOOLBOX_TITLE, help_lang_dir); + +clear help_lang_dir; + diff --git a/help/en_US/decorrstretch.xml b/help/en_US/decorrstretch.xml new file mode 100644 index 0000000..ab9169a --- /dev/null +++ b/help/en_US/decorrstretch.xml @@ -0,0 +1,74 @@ + + + + + + + + decorrstretch + Applies decorrelation stretch to an image. + + + + + Calling Sequence + + outputImage = decorrstretch(image); + + + + + + Parameters + + image: + Input image. + + + + + Description + +This function applies decorrelation stretch to an input image which causes the image to enhance +by way of amplifying image difference. + + + + + + + Examples + + + + + See also + + imread + imshow + + + + + Authors + + Dhruti Shah + + + diff --git a/help/en_US/detectAndComputeORB.xml b/help/en_US/detectAndComputeORB.xml new file mode 100644 index 0000000..e785072 --- /dev/null +++ b/help/en_US/detectAndComputeORB.xml @@ -0,0 +1,106 @@ + + + + + + + + detectAndComputeORB + Oriented FAST and rotated BRIEF (ORB) is used to detect and compute the corners in an image. + + + + + Calling Sequence + + [orb] = detectAndComputeORB(srcImg) + [orb] = detectAndComputeORB(srcImg, Name(same as the ones given under description), Value, ...) + + + + + + Parameters + + srcImg: + The input image Matrix + ROI: + This defines a particular in the image in of which you want the features. + maxFeatures: + The maximum number of features to retain. + scaleFactor: + Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical pyramid, + nLevels: + The number of pyramid levels. The smallest level will have linear size equal to + edgeThreshold: + This is size of the border where the features are not detected. It should roughly match + firstLevel: + It should be 0 in the current implementation. + scoreType: + The default HARRIS_SCORE (flag value = 0) means that Harris algorithm is used to rank features + patchSize: + Size of the patch used by the oriented BRIEF descriptor. Of course, on smaller pyramid layers + fastThreshold: + The threshold value used for feature detection. + + + + + Description + +Oriented FAST and rotated BRIEF (ORB) is a fast robust local feature detector, first presented +by Ethan Rublee in 2011,that can be used in computer vision tasks like object recognition +or 3D reconstruction. +It is based on the FAST keypoint detector and the visual descriptor BRIEF (Binary Robust +Independent Elementary Features). +Its aim is to provide a fast and efficient alternative to SIFT. + + +The Name-Value pair may be any of following types edgeThreshold fastThreshold firstLevel maxFeatures nLevels patchSize scaleFactor scoreType + + + + + + + Examples + + + + + See also + + imread + drawMatch + drawKeypoints + matchFeatures + + + + + Authors + + Siddhant Narang + + + diff --git a/help/en_US/detectBRISKFeatures.xml b/help/en_US/detectBRISKFeatures.xml new file mode 100644 index 0000000..d278da3 --- /dev/null +++ b/help/en_US/detectBRISKFeatures.xml @@ -0,0 +1,94 @@ + + + + + + + + detectBRISKFeatures + This function is used to detect BRISK(Binary Robust Invariant Scalable Keypoints) Features in a grayscale Image. + + + + + Calling Sequence + + result = detectBRISKFeatures(Image); + result = detectBRISKFeatures(Image, Name, Value, ...) + + + + + + Parameters + + result: + BRISKPoints struct which contains Location of KeyPoints, Orientation, Metric, SignOfLaplacian, Scale and Count of the features. + Image : + Input image, specified as a A-by-N 2D grayscale. + MinContrast : + (Optional) The minimum difference in intensity between a corner and its surrounding region. (Default: 0.2). The value must be between 0 and 1. + NumOctaves : + (Optional)The number of Octaves that the detector uses. (Default - 3) The value must be an integer scalar in between 1 and 4. + MinQuality : + (Optional) This specifies the minimum quality accepted for corners. (Default - 0.1) The value must be between 0 and 1. + ROI : + (Optional) Region Of Interest. This is taken as a vector [u v width height]. When specified, the function detects the key points within region of area width*height with u and v being the top left corner coordinates. + + + + + Description + +This function returns the BRISK features detected in a 2D grayscale image. + + + + + + + Examples + + + + + See also + + imread + drawMatch + drawKeypoints + matchFeatures + extractFeatures + + + + + Authors + + Shashank Shekhar + Siddhant Narang + + + diff --git a/help/en_US/detectFASTFeatures.xml b/help/en_US/detectFASTFeatures.xml new file mode 100644 index 0000000..1766720 --- /dev/null +++ b/help/en_US/detectFASTFeatures.xml @@ -0,0 +1,113 @@ + + + + + + + + detectFASTFeatures + This function is used to detect the corner points using FAST Alogrithm + + + + + Calling Sequence + + [ Location Count Metric ] = detectFASTFeatures( Image, Name, Value... ) + + + + + + Parameters + + Image: + Input Image, should be a 2-D grayscale. The Input Image should be real + MinQuality [Optional Input Argument]: + Minimum Accepted Quality of Corners, can be specified as a scalar value between [0,1]. Default: 0.1 + MinContrast [Optional Input Argument]: + Minimum Intensity difference for Corners to be detected, can be specified as a scalar value between[0,1]. Default: 0.2 + ROI [Optional Input Argument]: + Specify a rectangular region of operation. Format [ x y width height ]. Default: [1 1 size(Image,2) size(Image,1)] + Location: + Set of x,y coordinates for the deteccted points + Count: + Number of corner points detected + Metric: + Value describing the strength of each detected Point + + + + + Description + +The detectFASTFeatures function uses the Features from Accelerated Segment Test (FAST) algorithm to find feature points. + + + + + + + Examples + + + + + Examples + + + + + See also + + imread + drawMatch + drawKeypoints + matchFeatures + + + + + Authors + + Umang Agrawal + Sridhar Reddy + Siddhant Narang + + + diff --git a/help/en_US/detectHarrisFeatures.xml b/help/en_US/detectHarrisFeatures.xml new file mode 100644 index 0000000..f55af8a --- /dev/null +++ b/help/en_US/detectHarrisFeatures.xml @@ -0,0 +1,86 @@ + + + + + + + + detectHarrisFeatures + This function is used to find corner points in an image using Harris algorithm. + + + + + Calling Sequence + + points = detectHarrisFeatures(I); + points = detectHarrisFeatures(I, Name, Value, ...); + + + + + + Parameters + + points: + Structure of corner points + I: + Input image to detectHarrisFeatures() + MinQuality: + (Optional) Minimum accepted quality of corners (Default- 0.01) + FilterSize: + (Optional) Dimension of Gaussian Filter (Default: 5) + ROI: + (Optional) Rectangular region for corner detection + SensitivityFactor: + (Optional) SensitivityFactor of Harris algorithm (Default- 0.04) + + + + + Description + +This function detects corners in an image I. These corner points are used to extract features and hence recognize the contents of an image. + + + + + + + Examples + + + + + See also + + imread + drawKeypoints + + + + + Authors + + Rohit Suri + Sridhar Reddy + Siddhant Narang + + + diff --git a/help/en_US/detectSURFFeatures.xml b/help/en_US/detectSURFFeatures.xml new file mode 100644 index 0000000..eb7cb5e --- /dev/null +++ b/help/en_US/detectSURFFeatures.xml @@ -0,0 +1,95 @@ + + + + + + + + detectSURFFeatures + This function is used to detect SURF(Speeded Up Robust Features) Features in a grayscale Image. + + + + + Calling Sequence + + result = detectSURFFeatures(Image); + result = detectSURFFeatures(Image, Name, Value, ...) + + + + + + Parameters + + result: + SURFPoints struct which contains Location of KeyPoints, Orientation, Metric, SignOfLaplacian, Scale and Count of the features. + Image : + Input image, specified as a A-by-N 2D grayscale. + MetricThreshold : + (Optional) With default value equal to 1000, it is to be specified as a scalar. Every interest point detected has a strength associated with it. In case, only the stronget ones are needed, this parameter has to be given a larger value. To get more no of interest points/blobs, it is to be reduced. + NumOctaves : + (Optional)With default value equal to 3, it is to be specified as a scalar. Larger the number of octaves, larger is the size of blobs detected. This is because higher octave use large sized filters. Value must be an integer scalar in between 1 and 4. + NumScaleLevels : + (Optional)With default value equal to 4, it is to be specified as a scalar. It denotes the number of scale level for each octave. The Value must be an integer scalar greater than or equal to 3. + ROI : + (Optional) Region Of Interest. This is taken as a vector [u v width height]. When specified, the function detects the key points within region of area width*height with u and v being the top left corner coordinates. + + + + + Description + +This function return the SURF(Speeded Up Robust Features) Interest Points for a 2D Grayscale image. It is scale- and rotation- invariant point detector and descriptor and its application include Camera Calibration, 3D Reconstruction, Object Recognition to name a few. + + + + + + + Examples + + + + + See also + + imread + drawMatch + drawKeypoints + matchFeatures + extractFeatures + + + + + Authors + + Shashank Shekhar + Siddhant Narang + + + diff --git a/help/en_US/disparity.xml b/help/en_US/disparity.xml new file mode 100644 index 0000000..28b659c --- /dev/null +++ b/help/en_US/disparity.xml @@ -0,0 +1,98 @@ + + + + + + + + disparity + This function returns the disparity map between two images which + + + + + Calling Sequence + + map, map1 = disparity(img1, img2) + map, map1 = disparity(img1, img2, Name(same as the ones given under parameters), Value, ....) + + + + + + Parameters + + method: + Disparity estimation algorithm, specified as the comma-separated pair consisting of 'Method' + numDisparities: + Maximum disparity minus minimum disparity. This parameter must be divisible by 16. + SADWindowSize: + Matched block size. It must be an odd number >=1 + uniquenessRatio: + Margin in percentage by which the best (minimum) computed cost function value should + textureThreshold: + This controls the detailing in the disparity map. + disp12MaxDiff: + Maximum allowed difference (in integer pixel units) in the left-right disparity check. + Returns: + disparity map + + + + + Description + +Validates disparity using the left-right check. The matrix "cost" should be computed by the +stereo correspondence algorithm. + + + + + + + Examples + + + + + See also + + imread + genCheckerboardPoints + detectCheckerboardCorne + stereoCalibrateAndRect + reconstructScene + + + + + Authors + + Siddhant Narang + + + diff --git a/help/en_US/disparity.xml~ b/help/en_US/disparity.xml~ new file mode 100644 index 0000000..7c6bb1f --- /dev/null +++ b/help/en_US/disparity.xml~ @@ -0,0 +1,95 @@ + + + + + + + + disparity + This function returns the disparity map between two images which + + + + + Calling Sequence + + + + + + Parameters + + method: + Disparity estimation algorithm, specified as the comma-separated pair consisting of 'Method' and // character vector 'blockMatching' or 'semi-blockMatching'. + numDisparities: + Maximum disparity minus minimum disparity. This parameter must be divisible by 16. + SADWindowSize: + Matched block size. It must be an odd number >=1 + uniquenessRatio: + Margin in percentage by which the best (minimum) computed cost function value should “win†+ textureThreshold: + This controls the detailing in the disparity map. + disp12MaxDiff: + Maximum allowed difference (in integer pixel units) in the left-right disparity check. + Returns: + disparity map + + + + + Description + +Validates disparity using the left-right check. The matrix "cost" should be computed by the +stereo correspondence algorithm + + + + + + + Examples + + + + + See also + + imread + genCheckerboardPoints + detectCheckerboardCorne + stereoCalibrateAndRect + reconstructScene + + + + + Authors + + Siddhant Narang + + + diff --git a/help/en_US/filter.xml b/help/en_US/filter.xml new file mode 100644 index 0000000..3cdcf94 --- /dev/null +++ b/help/en_US/filter.xml @@ -0,0 +1,88 @@ + + + + + + + + filter + Convolves an image with the kernel. + + + + + Calling Sequence + + img = filter(input_image, depth, kernelMatrix, -1, -1, 1); + + + + + + Parameters + + input_image: + Input image. + depth: + Destination image depth, given below are the possible values- CV_8U CV_16U/CV_16S CV_32F CV_64F + kernelMatrix: + The matrix which defines the kernel of the filter to be applied on the input image. + anchor_x: + X-coordinate of the anchor. + anchor_y: + Y-coordinate of the anchor. + delta: + Value added to the filtered results before storing them. + + + + + Description + +The function applies an arbitrary linear filter to an image. In-place operation is supported. +When the aperture is partially outside the image, the function interpolates outlier pixel values +according to the specified border mode. +The function does actually compute correlation, not the convolution: + +dst(x, y) = $$\sum_{0 < x' < kernel.cols}_{0 < y' < kernel.rows} kernel(x', y') * src(x + x' - anchor.y + y'- anchor.y) + + + + + + + + Examples + + + + + See also + + imread + + + + + Authors + + Sukul Bagai + + + diff --git a/help/en_US/ftrans2.xml b/help/en_US/ftrans2.xml new file mode 100644 index 0000000..f10835c --- /dev/null +++ b/help/en_US/ftrans2.xml @@ -0,0 +1,72 @@ + + + + + + + + ftrans2 + 2-D FIR filter using frequency transformation. + + + + + Calling Sequence + + h = ftrans2(b, t); + + + + + + Parameters + + b: + A bandpass filter + t: + Transformation matrix + + + + + Description + +Produces the two-dimensional FIR filter h that corresponds to the one-dimensional FIR filter b +using the transform t. +(ftrans2 returns h as a computational molecule, which is the appropriate form to use with filter2.) +b must be a one-dimensional, Type I (even symmetric, odd-length) filter such as can be returned by +fir1, fir2, or firpm in the Signal Processing Toolbox software. The transform matrix t contains +coefficients that define the frequency transformation to use. If t is m-by-n and b has length Q, +then h is size ((m - 1) * (Q - 1) / 2 + 1)-by-((n - 1) * (Q - 1) / 2 + 1). + + + + + + + Examples + + + + + Authors + + Vinay + + + diff --git a/help/en_US/getParams.xml b/help/en_US/getParams.xml new file mode 100644 index 0000000..b11fe8d --- /dev/null +++ b/help/en_US/getParams.xml @@ -0,0 +1,91 @@ + + + + + + + + getParams + This function is used view the parameters of a trained classifier. + + + + + Calling Sequence + + param = getParams(classifier, modelName); + + + + + + Parameters + + classifier: + Image category classifier + modelName: + Name of the model to which the classifier belongs to. + + + + + Description + +This function can be used to view the parameters of a trained classifier and +make changes accordingly in the training process to get accurate results. + + + + + + + Examples + + + + + Examples + + + + + See also + + trainEMClassifier + trainLRClassifier + trainKNNClassifier + trainNBClassifier + trainSVMClassifier + trainSVMSGDClassifier + trainANNClassifier + trainRTreesClassifier + trainDTreesClassifier + + + + + Authors + + Siddhant Narang + + + diff --git a/help/en_US/getParams.xml~ b/help/en_US/getParams.xml~ new file mode 100644 index 0000000..b11fe8d --- /dev/null +++ b/help/en_US/getParams.xml~ @@ -0,0 +1,91 @@ + + + + + + + + getParams + This function is used view the parameters of a trained classifier. + + + + + Calling Sequence + + param = getParams(classifier, modelName); + + + + + + Parameters + + classifier: + Image category classifier + modelName: + Name of the model to which the classifier belongs to. + + + + + Description + +This function can be used to view the parameters of a trained classifier and +make changes accordingly in the training process to get accurate results. + + + + + + + Examples + + + + + Examples + + + + + See also + + trainEMClassifier + trainLRClassifier + trainKNNClassifier + trainNBClassifier + trainSVMClassifier + trainSVMSGDClassifier + trainANNClassifier + trainRTreesClassifier + trainDTreesClassifier + + + + + Authors + + Siddhant Narang + + + diff --git a/help/en_US/graydiffweight.xml b/help/en_US/graydiffweight.xml new file mode 100644 index 0000000..178db5d --- /dev/null +++ b/help/en_US/graydiffweight.xml @@ -0,0 +1,80 @@ + + + + + + + + graydiffweight + Calculate weights for image pixels based on grayscale intensity difference. + + + + + Calling Sequence + + W = graydiffweight(srcImage, refGrayVal) + + + + + + Parameters + + srcImage: + Input image. + refGrayVal: + Reference grayscale intensity value, specified as a scalar. + + + + + Description + +This function computes the pixel weight for each pixel in the grayscale image I. The weight is the absolute +value of the difference between the intensity of the pixel and the reference grayscale intensity +specified by the scalar refGrayVal. Pick a reference grayscale intensity value that is representative +of the object you want to segment. The weights are returned in the array W, which is the same size as +input image I. The weight of a pixel is inversely related to the absolute value of the grayscale intensity +difference at the pixel location. If the difference is small (intensity value close to refGrayVal), the +weight value is large. If the difference is large (intensity value very different from refGrayVal), the +weight value is small. + + + + + + + Examples + + + + + See also + + imread + + + + + Authors + + Dhruti Shah + + + diff --git a/help/en_US/imabsdiff.xml b/help/en_US/imabsdiff.xml new file mode 100644 index 0000000..130a44e --- /dev/null +++ b/help/en_US/imabsdiff.xml @@ -0,0 +1,82 @@ + + + + + + + + imabsdiff + Calculates the per-element absolute difference between two images. + + + + + Calling Sequence + + image = imabsdiff(img1, img2) + + + + + + Parameters + + img1: + First input image. + img2: + Second input image. + + + + + Description + +The function absdiff calculates- Absolute difference between two arrays when they have the same size and type- +dst(I) = saturate(|src1(I) − src2(I)|) +Absolute difference between an array and a scalar when the second array is constructed +from Scalar or has as many elements as the number of channels in src1- +dst(I) = saturate(|src1(I) − src2|) +Absolute difference between a scalar and an array when the first array is constructed from Scalar or has +as many elements as the number of channels in src2- +dst(I) = saturate(|src1 − src2(I)|) +where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each channel is processed // independently. + + + + + + + Examples + + + + + See also + + imread + + + + + Authors + + Siddhant Narang + + + diff --git a/help/en_US/imboxfilt3.xml b/help/en_US/imboxfilt3.xml new file mode 100644 index 0000000..621aa10 --- /dev/null +++ b/help/en_US/imboxfilt3.xml @@ -0,0 +1,113 @@ + + + + + + + + imboxfilt3 + Blurs an image using the box filter. + + + + + Calling Sequence + + blurredImg = imboxfilt3(srcImg) + blurredImg = imboxfilt3(srcImg, filterWidth, filterHeight) + + + + + + Parameters + + srcImage: + Input image. + filterWidth: + The width of the filter to be applied. + filterHeight: + The height of the filter to be applied. + + + + + Description + +The function smoothes an image using the kernel: + + + +K = $$\alpha $$\begin{bmatrix} +1 & 1 & 1 & \dots & 1 & 1 \\ +1 & 1 & 1 & \dots & 1 & 1 \\ +$$\hdots \\ +1 & 1 & 1 & \dots & 1 & 1 +$$\end{bmatrix} + + + + + +where + + + +$$\alpha = \begin{cases} $$\frac{1}{ksize.width * ksize.height} & \hspace{2mm} when\hspace{2mm}normalize = true \\ 1 & {otherwise} +\end{cases}$ + + + + + +Unnormalized box filter is useful for computing various integral characteristics over each pixel +neighborhood, such as covariance matrices of image derivatives (used in dense optical flow algorithms, +and so on). If you need to compute pixel sums over variable-size windows. + + + + + + + Examples + + + + + Examples + + + + + See also + + imread + + + + + Authors + + Yash S. Bhalgat + + + diff --git a/help/en_US/imboxfilt3.xml~ b/help/en_US/imboxfilt3.xml~ new file mode 100644 index 0000000..d7f935b --- /dev/null +++ b/help/en_US/imboxfilt3.xml~ @@ -0,0 +1,107 @@ + + + + + + + + imboxfilt3 + Blurs an image using the box filter. + + + + + Calling Sequence + + + + + + Parameters + + srcImage: + Input image. + filterWidth: + The width of the filter to be applied. + filterHeight: + The height of the filter to be applied. + + + + + Description + +The function smoothes an image using the kernel: + + + +K = $$\alpha $$\begin{bmatrix} +1 & 1 & 1 & \dots & 1 & 1 \\ +1 & 1 & 1 & \dots & 1 & 1 \\ +$$\hdots \\ +1 & 1 & 1 & \dots & 1 & 1 +$$\end{bmatrix} + + +where + + + + +$$\alpha = \begin{cases} $$\frac{1}{ksize.width * ksize.height} & \hspace{2mm} when\hspace{2mm}normalize = true \\ 1 & {otherwise} +\end{cases}$ + + + + +Unnormalized box filter is useful for computing various integral characteristics over each pixel +neighborhood, such as covariance matrices of image derivatives (used in dense optical flow algorithms, +and so on). If you need to compute pixel sums over variable-size windows. + + + + + + + Examples + + + + + Examples + + + + + See also + + imread + + + + + Authors + + Yash S. Bhalgat + + + diff --git a/help/en_US/imcontrast.xml b/help/en_US/imcontrast.xml new file mode 100644 index 0000000..7adf4e0 --- /dev/null +++ b/help/en_US/imcontrast.xml @@ -0,0 +1,77 @@ + + + + + + + + imcontrast + Adjusts image contrast. + + + + + Calling Sequence + + new_image = imcontrast(srcImg, aplha, beta) + + + + + + Parameters + + srcImage: + Input image. + alpha: + Pixel multiplier controls the weight of each pixel. + beta: + Added to every pixel to change the range of pixel values. + + + + + Description + +This function is used to change the contrast of the image using +using the values alpha and beta. + + + + + + + Examples + + + + + See also + + imread + + + + + Authors + + Sukul Bagai + Siddhant Narang + + + diff --git a/help/en_US/imcrop.xml b/help/en_US/imcrop.xml new file mode 100644 index 0000000..8b31e4a --- /dev/null +++ b/help/en_US/imcrop.xml @@ -0,0 +1,79 @@ + + + + + + + + imcrop + Crops the image. + + + + + Calling Sequence + + new_image = imcrop(srcImg, xcoor, ycoor, width, height) + + + + + + Parameters + + srcImage: + Input image. + xcoor: + The x-coordinate of the starting point of the region of interest, ie region to be cropped. + ycoor: + The y-coordinate of the starting point of the region of interest. + width: + The total width of the region of interest wrt to the starting point. + height: + The total height of the region of interest wrt to the starting point. + + + + + Description + +This function can be used to crop the image to a given region of interest. + + + + + + + Examples + + + + + See also + + imread + + + + + Authors + + Sukul Bagai + + + diff --git a/help/en_US/imextendedmax.xml b/help/en_US/imextendedmax.xml new file mode 100644 index 0000000..5f6dd02 --- /dev/null +++ b/help/en_US/imextendedmax.xml @@ -0,0 +1,75 @@ + + + + + + + + imextendedmax + Extended-maxima transform. + + + + + Calling Sequence + + BW = imextendedmax(srcImage, Hmax) + + + + + + Parameters + + srcImage: + Input image. + Hmax: + H-maxima transform, specified as a real, nonnegative scalar. + + + + + Description + +This functoin returns the extended-maxima transform for I, which is the regional maxima of the +H-maxima transform. Regional maxima are connected components of pixels with a constant intensity +value, and whose external boundary pixels all have a lower value. H is a nonnegative scalar. + + + + + + + Examples + + + + + See also + + imread + + + + + Authors + + Vinay Bhat + + + diff --git a/help/en_US/imfindcircles.xml b/help/en_US/imfindcircles.xml new file mode 100644 index 0000000..d0add81 --- /dev/null +++ b/help/en_US/imfindcircles.xml @@ -0,0 +1,82 @@ + + + + + + + + imfindcircles + Finds circles in an image. + + + + + Calling Sequence + + [points] = imcontrast(srcImg, Rmin, Rmax) + [points radii] = imcontrast(srcImg, Rmin, Rmax) + + + + + + Parameters + + srcImage: + Input image. + Rmin: + The minimum value of the radius of the circles to find. + Rmax: + The maximum value of the radius of the circle to find. + points: + The returned coordinates of the center of all the circles found. + radii: + The returned values of the radiis of all the circles found. + + + + + Description + +This function can be used to find the circles of a definite range of radii +in an image. + + + + + + + Examples + + + + + See also + + imread + + + + + Authors + + Tess Zacharias + + + diff --git a/help/en_US/imwrite.xml b/help/en_US/imwrite.xml new file mode 100644 index 0000000..0572a59 --- /dev/null +++ b/help/en_US/imwrite.xml @@ -0,0 +1,73 @@ + + + + + + + + imwrite + Writes to a specified location. + + + + + Calling Sequence + + new_image = imwrite(srcImg, location) + + + + + + Parameters + + srcImg: + input image. + location: + The relative path of where you want to write the image. + + + + + Description + +This function is used write the image to a specified path + + + + + + + Examples + name of the image + + ]]> + + + + See also + + imread + + + + + Authors + + Sukul Bagai + + + diff --git a/help/en_US/integralFilter.xml b/help/en_US/integralFilter.xml new file mode 100644 index 0000000..b5a48d2 --- /dev/null +++ b/help/en_US/integralFilter.xml @@ -0,0 +1,81 @@ + + + + + + + + integralFilter + Integral Image based Filter. + + + + + Calling Sequence + + filter = integralFilter(intimage,bbox,weights,filterSize); + + + + + + Parameters + + intimage: + Integral Image, which can be obtained from the function integralImage. + bbox: + Bounding box of the filter object, which can be obtained from integralKernel function. + weight: + Weights of the bounding box, which can be obtained from integralKernel function. + filterSize: + Size of the filter, which can be obtained from integralKernel function. + + + + + Description + +This function filters image using box filters and integral images. + + + + + + + Examples + + + + + See also + + integralImage + integralKernel + imread + + + + + Authors + + Tanmay Chaudhari + + + diff --git a/help/en_US/isFilter.xml b/help/en_US/isFilter.xml new file mode 100644 index 0000000..92bbf92 --- /dev/null +++ b/help/en_US/isFilter.xml @@ -0,0 +1,77 @@ + + + + + + + + isFilter + Decides if a filter is separable or not. + + + + + Calling Sequence + + [isFilter s u] = isFilter(filter, size); + + + + + + Parameters + + filter: + Input filter (datatype- mat(double)). + size: + Size of the filter. + + + + + Description + +This function uses SVD to compute if the filter is separable or not. It takes as input the +filter matrix and is the filter is separable it returns the calculated singular and the left +singular values. + + + + + + + Examples + + + + + Examples + + + + + Authors + + Siddhant Narang + + + diff --git a/help/en_US/master_help.xml b/help/en_US/master_help.xml new file mode 100644 index 0000000..7962ab5 --- /dev/null +++ b/help/en_US/master_help.xml @@ -0,0 +1,85 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +]> + + + FOSSEE Image Processing Toolbox + + + +FOSSEE Image Processing Toolbox +&a6dbd35989820ebb5c796f995827da098; +&a7d219c26f9ace1d0a2f8552215507f6f; +&a976c5310205b661ca99613ee7032b5b1; +&a4a1e4fb236ebb505264c877754b6ef90; +&a9ac0094e789c5f226a4b308b0001abc2; +&a0931905e8f69405ab75f2c1d43d72fd8; +&af8d15f24157d0714cbfc1af4193dd7b1; +&a39b7627b986b999ce794d5ba0114844e; +&a7ef3d9042644c8366d33f0d9c6ff06cd; +&aa65b10f5debdda434b059e2eee47a80e; +&abaabec0228a36a19c11cf51d83737ce4; +&ad5bed013c69534648a6115bc0c3a09e1; +&a52f3b2468a8da46db3ff4c91172f62e8; +&ad4dfa0c3b90a5832f31d5d53907deebf; +&aa4cdc5e56e130a8f36dad69ce43dd281; +&a5a603975657458a7416db0a246b88bfe; +&ace0e74e22dac1aedbe0146f4f683519f; +&a4ba9aa4c8e5f298f08a1e85aec575990; +&a8acc2c79cdff8fb96e4e835e89d808c2; +&a05e6388a1ea2eaeea60794e89f4f2d7f; +&a334a0fc9d9f2de45a70ac7d0d9986377; +&acaf9e1c03376a50309258afbf9b2fb63; +&a2ca670b3de8b850e90e0e0834a0253dc; +&a8811795017c692701cc4b0dd9b49c6e2; +&a61a39a460fe8210fe93090d7a918c19d; +&a72ec37b7bf9fcfe6834dbae1a0ad8582; +&a2f826cc4060e581d71c8b85f91ddf723; +&a121886eeb3f1d84e514690d7a751b1e1; +&a329b4a9e5e2c67947ef555c181771f73; +&abb221d483f3441095d99be301b6f5ee3; +&a34bdfc3eff9e80872c09a9f61d8ef274; +&a43492df2f75f820fb7d9eedcfb8b66a5; + + diff --git a/help/en_US/matchFeatures.xml b/help/en_US/matchFeatures.xml new file mode 100644 index 0000000..a8e30b6 --- /dev/null +++ b/help/en_US/matchFeatures.xml @@ -0,0 +1,124 @@ + + + + + + + + matchFeatures + This function is used to find the corresponding matches between two features sets + + + + + Calling Sequence + + [ indexPairs ] = matchFeatures( features1, features2, Name, Value... ) + [ indexPairs matchMetric ] = matchFeatures( features1, features2, Name, Value... ) + + + + + + Parameters + + features1: + Feature Set 1, a set of M1-by-N Matrix, M1 corresponding to number of features and N corresponds to length of each feature vector + features2: + Feature Set 2, a set of M2-by-N Matrix, M2 corresponding to number of features and N corresponds to length of each feature vector + Method [Optional Input Argument]: + Method of matching to be used. Values: ['Exhaustive' (default) | 'Approximate'] + MatchThreshold [Optional Input Argument]: + Matching Threshold for selecting the percentage of strongest matches. Values in range [0 100], default - 10.0 + Unique [Optional Input Argument]: + Boolean value for selecting only unique matches between features. Default-False(0) + Metric [Optional Input Argument]: + Metric to be used for matching features. Values: ['SSD'(default)|'SAD'|'Hamming'|'Hamming_2'] + indexPairs: + P-by-2 matrix containing the indices of corresponding features matched between two input feature sets + matchMetric: + P-by-1 Vector containing the distance metric between matched Features + + + + + Description + +MatchFeatures function takes in the Feature Descriptors value of two images as its input and finds the best match between each feature vector of the first image to that of the second image and returns the corresponding indices of each feature matrix + + + + + + + Examples + + + + + Examples + + + + + See also + + imread + extractFeatures + drawMatch + matches + + + + + Authors + + Umang Agrawal + Sridhar Reddy + Siddhant Narang + + + diff --git a/help/en_US/mlPredict.xml b/help/en_US/mlPredict.xml new file mode 100644 index 0000000..7dd3df7 --- /dev/null +++ b/help/en_US/mlPredict.xml @@ -0,0 +1,96 @@ + + + + + + + + mlPredict + This function is used to predict the class of an image using an image classifier. + + + + + Calling Sequence + + label = mlpredict(classifier, image, modelName) + label = mlpredict(classifier, image, modelName, numberOfNeighnours) // only in case of KNN + + + + + + Parameters + + label: + Evaluated label of the input image + classifier: + Image classifier + image: + Input image + numberOfNeighbours: + Number of neighbours to consider during prediction. + Returns: + Label + + + + + Description + +This function predicts the class of an image based on the classifier provided. + + + + + + + Examples + + + + + Examples + + + + + See also + + imread + load + + + + + Authors + + Siddhant Narang + + + diff --git a/help/en_US/reconstructScene.xml b/help/en_US/reconstructScene.xml new file mode 100644 index 0000000..258ede2 --- /dev/null +++ b/help/en_US/reconstructScene.xml @@ -0,0 +1,93 @@ + + + + + + + + reconstructScene + This function converts a 2-D image to a 3-D image. + + + + + Calling Sequence + + reconstructScene(Q, disp_mat, handleMissingValues) + + + + + + Parameters + + Q(Depth Map): + It is a matrix whose values define the different characteristics of a camera image. + Disparity Map: + Refers to the apparent pixel difference or motion between a pair of stereo image. + handleMissingValues: + Flag whose default value is false, which controls the handling of missing values. + Returns: + 3D image + + + + + Description + +The function transforms a single-channel disparity map to a 3-channel image representing a 3D surface. +That is, for each pixel (x,y) and the corresponding disparity d = disparity(x,y). +The matrix Q can be an arbitrary 4×4 matrix (for example, the one computed by stereoRectify). To reproject +a sparse set of points {(x,y,d),...} to 3D space, use perspectiveTransform. + + + + + + + Examples + + + + + See also + + imread + genCheckerboardPoints + detectCheckerboardCorne + stereoCalibrateAndRect + disparity + + + + + Authors + + Siddhant Narang + + + diff --git a/help/en_US/scharr.xml b/help/en_US/scharr.xml new file mode 100644 index 0000000..0672c2d --- /dev/null +++ b/help/en_US/scharr.xml @@ -0,0 +1,82 @@ + + + + + + + + scharr + Calculates the first x- or y- image derivative using Scharr operator. + + + + + Calling Sequence + + new_image = imcontrast(srcImg, aplha, beta) + + + + + + Parameters + + srcImg: + input image. + ddepth: + output image depth. The possible ddepth values are the following CV_8U CV_16U/CV_16S CV_32F CV_64F + dx: + order of the derivative x. + dy: + order of the derivative y. + scale: + Scale factor for the computed derivative values. + delta: + Delta value that is added to the results. + + + + + Description + +This function is used to find the derivative of the source image using the +Scharr operator. + + + + + + + Examples + + + + + See also + + imread + + + + + Authors + + Sukul Bagai + + + diff --git a/help/en_US/scharr.xml~ b/help/en_US/scharr.xml~ new file mode 100644 index 0000000..ccdcc35 --- /dev/null +++ b/help/en_US/scharr.xml~ @@ -0,0 +1,82 @@ + + + + + + + + scharr + Calculates the first x- or y- image derivative using Scharr operator. + + + + + Calling Sequence + + new_image = imcontrast(srcImg, aplha, beta) + + + + + + Parameters + + srcImg: + input image. + ddepth: + output image depth. 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+ + + + +
+ << FOSSEE Image Processing Toolbox + + + FOSSEE Image Processing Toolbox + + + decorrstretch >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > blobAnalysis + +

+

blobAnalysis

+

Detects blob in the source image

+ + +

Calling Sequence

+ 
[blob] = blobAnalysis(srcImg)
+[blob] = blobAnalysis(srcImg, Name, Value)
+ +

Parameters

+
srcImg: +

The input image Matrix.

+
ROI: +

This defines a particular in the image in of which you want the features.

+
filterByThreshold: +

This filter compares the intensity of a binary image at the center of a

+
filterByArea: +

Extracted blobs have an area between minArea (inclusive) and maxArea (exclusive).

+
filterByCircularity: +

Extracted blobs have circularity ( 4∗π∗Areaperimeter∗perimeter) between

+
filterByConvexity: +

Extracted blobs have convexity (area / area of blob convex hull) between minConvexity

+ +

Description

+

The function uses SimpleBlobDetector function to detect the blobs then it checks for different Name +Value pair arguments and accordingly returns the parameters of the blob such as 2D coordinates of the +blob, size of the blob.

+

The Name-Value pair may be any of following types

  • bool filterByArea, vector [minArea maxArea]

  • bool filterByCircularity, vector [minCircularity maxCircularity]

  • bool filterByConvexity, vector [minConvexity maxConvexity]

  • double ROI, vector

  • bool filterByThreshold, vector [minThreshold maxThreshold]

+

+ +

Examples

+ 
img_2 = imread("/images/blob.jpg", 0);
+
+lis1 = blobAnalysis(img_2, "filterByThreshold", [200, 255]);
+lis2 = blobAnalysis(img_2, "filterByCircularity", [0.1, 0.9]);
+lis3 = blobAnalysis(img_2, "filterByArea", [1500, 1600]);
+
+dimage1 = drawKeypoints(img_2, lis1.Points, "color", [0, 255, 0]);
+dimage2 = drawKeypoints(img_2, lis2.Points, "color", [0, 255, 0]);
+dimage3 = drawKeypoints(img_2, lis3.Points, "color", [0, 255, 0]);
+ +

See also

+
+ +

Authors

+
  • Deepshikha
+
+ +
+ + + + + + +
Report an issue
+ << FOSSEE Image Processing Toolbox + + + FOSSEE Image Processing Toolbox + + + decorrstretch >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/c_code.css b/help/en_US/scilab_en_US_help/c_code.css new file mode 100644 index 0000000..948d2ee --- /dev/null +++ b/help/en_US/scilab_en_US_help/c_code.css @@ -0,0 +1,54 @@ +.ccomment { + font-style: italic; + color: #b22222 +} + +.cdefault { + font-style: normal; + color: #000000 +} + +.copenclose { + font-style: normal; + color: #000000 +} + +.coperator { + font-style: normal; + color: #000000 +} + +.cstring { + font-style: normal; + color: #a6557a +} + +.ctype { + font-style: normal; + color: #55a655 +} + +.cpreprocessor { + font-style: normal; + color: #9965a6 +} + +.cid { + font-style: normal; + color: #000000 +} + +.ckeyword { + font-style: normal; + color: #ad3ff2 +} + +.cmodifier { + font-style: normal; + color: #ad3ff2 +} + +.cnumber { + font-style: normal; + color: #008b8b +} diff --git a/help/en_US/scilab_en_US_help/decorrstretch.html b/help/en_US/scilab_en_US_help/decorrstretch.html new file mode 100644 index 0000000..367e224 --- /dev/null +++ b/help/en_US/scilab_en_US_help/decorrstretch.html @@ -0,0 +1,84 @@ + + + decorrstretch + + + +
+ + + + +
+ << blobAnalysis + + + FOSSEE Image Processing Toolbox + + + detectAndComputeORB >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > decorrstretch + +

+

decorrstretch

+

Applies decorrelation stretch to an image.

+ + +

Calling Sequence

+ 
outputImage = decorrstretch(image);
+ +

Parameters

+
image: +

Input image.

+ +

Description

+

This function applies decorrelation stretch to an input image which causes the image to enhance +by way of amplifying image difference.

+

+ +

Examples

+ 
i = imread("images/lena.jpg");
+outputImage = decorrstretch(i);
+imshow(i);
+ +

See also

+
+ +

Authors

+
  • Dhruti Shah
+
+ +
+ + + + + + +
Report an issue
+ << blobAnalysis + + + FOSSEE Image Processing Toolbox + + + detectAndComputeORB >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/detectAndComputeORB.html b/help/en_US/scilab_en_US_help/detectAndComputeORB.html new file mode 100644 index 0000000..5c4c884 --- /dev/null +++ b/help/en_US/scilab_en_US_help/detectAndComputeORB.html @@ -0,0 +1,114 @@ + + + detectAndComputeORB + + + +
+ + + + +
+ << decorrstretch + + + FOSSEE Image Processing Toolbox + + + detectBRISKFeatures >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > detectAndComputeORB + +

+

detectAndComputeORB

+

Oriented FAST and rotated BRIEF (ORB) is used to detect and compute the corners in an image.

+ + +

Calling Sequence

+ 
[orb] = detectAndComputeORB(srcImg)
+[orb] = detectAndComputeORB(srcImg, Name(same as the ones given under description), Value, ...)
+ +

Parameters

+
srcImg: +

The input image Matrix

+
ROI: +

This defines a particular in the image in of which you want the features.

+
maxFeatures: +

The maximum number of features to retain.

+
scaleFactor: +

Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical pyramid,

+
nLevels: +

The number of pyramid levels. The smallest level will have linear size equal to

+
edgeThreshold: +

This is size of the border where the features are not detected. It should roughly match

+
firstLevel: +

It should be 0 in the current implementation.

+
scoreType: +

The default HARRIS_SCORE (flag value = 0) means that Harris algorithm is used to rank features

+
patchSize: +

Size of the patch used by the oriented BRIEF descriptor. Of course, on smaller pyramid layers

+
fastThreshold: +

The threshold value used for feature detection.

+ +

Description

+

Oriented FAST and rotated BRIEF (ORB) is a fast robust local feature detector, first presented +by Ethan Rublee in 2011,that can be used in computer vision tasks like object recognition +or 3D reconstruction. +It is based on the FAST keypoint detector and the visual descriptor BRIEF (Binary Robust +Independent Elementary Features). +Its aim is to provide a fast and efficient alternative to SIFT.

+

The Name-Value pair may be any of following types

  • edgeThreshold

  • fastThreshold

  • firstLevel

  • maxFeatures

  • nLevels

  • patchSize

  • scaleFactor

  • scoreType

+

+ +

Examples

+ 
img_1 = imread("images/checkerBoard.jpg", 0);
+img_2 = imread("images/chess.jpg", 0);
+lis1 = detectAndComputeORB(img_1);
+lis2 = detectAndComputeORB(img_2);
+dimage = drawKeypoints(img_2, lis2.Points);
+[matches, distance] = matchFeatures(lis1.Features, lis1.Features, "Method", "Exhaustive", "Metric", "Hamming");
+matchedImage = drawMatch(img_1, img_2, lis1.Points, lis2.Points, matches, distance);
+ +

See also

+
+ +

Authors

+
  • Siddhant Narang
+
+ +
+ + + + + + +
Report an issue
+ << decorrstretch + + + FOSSEE Image Processing Toolbox + + + detectBRISKFeatures >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/detectBRISKFeatures.html b/help/en_US/scilab_en_US_help/detectBRISKFeatures.html new file mode 100644 index 0000000..38dd2f1 --- /dev/null +++ b/help/en_US/scilab_en_US_help/detectBRISKFeatures.html @@ -0,0 +1,104 @@ + + + detectBRISKFeatures + + + +
+ + + + +
+ << detectAndComputeORB + + + FOSSEE Image Processing Toolbox + + + detectFASTFeatures >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > detectBRISKFeatures + +

+

detectBRISKFeatures

+

This function is used to detect BRISK(Binary Robust Invariant Scalable Keypoints) Features in a grayscale Image.

+ + +

Calling Sequence

+ 
result = detectBRISKFeatures(Image);
+result = detectBRISKFeatures(Image, Name, Value, ...)
+ +

Parameters

+
result: +

BRISKPoints struct which contains Location of KeyPoints, Orientation, Metric, SignOfLaplacian, Scale and Count of the features.

+
Image : +

Input image, specified as a A-by-N 2D grayscale.

+
MinContrast : +

(Optional) The minimum difference in intensity between a corner and its surrounding region. (Default: 0.2). The value must be between 0 and 1.

+
NumOctaves : +

(Optional)The number of Octaves that the detector uses. (Default - 3) The value must be an integer scalar in between 1 and 4.

+
MinQuality : +

(Optional) This specifies the minimum quality accepted for corners. (Default - 0.1) The value must be between 0 and 1.

+
ROI : +

(Optional) Region Of Interest. This is taken as a vector [u v width height]. When specified, the function detects the key points within region of area width*height with u and v being the top left corner coordinates.

+ +

Description

+

This function returns the BRISK features detected in a 2D grayscale image.

+

+ +

Examples

+ 
stacksize('max');
+img_1 = imread("images/table.jpg", 0);
+img_2 = imread("images/table1.jpg", 0);
+lis1 = detectBRISKFeatures(img_1);
+lis2 = detectBRISKFeatures(img_2);
+features_1 = extractFeatures(img_1, lis1.KeyPoints, "BRISKPoints", "Metric", lis1.Metric, "Orientation", lis1.Orientation, "Scale", lis1.Scale);
+features_2 = extractFeatures(img_2, lis2.KeyPoints, "BRISKPoints", "Metric", lis2.Metric, "Orientation", lis2.Orientation, "Scale", lis2.Scale);
+[matches, distance] = matchFeatures(features_1.Features, features_2.Features);
+matchedImage = drawMatch(img_1, img_2, lis1.KeyPoints, lis2.KeyPoints, matches, distance);
+ +

See also

+
+ +

Authors

+
  • Shashank Shekhar
    • +
  • Siddhant Narang
+
+ +
+ + + + + + +
Report an issue
+ << detectAndComputeORB + + + FOSSEE Image Processing Toolbox + + + detectFASTFeatures >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/detectFASTFeatures.html b/help/en_US/scilab_en_US_help/detectFASTFeatures.html new file mode 100644 index 0000000..2553dd5 --- /dev/null +++ b/help/en_US/scilab_en_US_help/detectFASTFeatures.html @@ -0,0 +1,118 @@ + + + detectFASTFeatures + + + +
+ + + + +
+ << detectBRISKFeatures + + + FOSSEE Image Processing Toolbox + + + detectHarrisFeatures >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > detectFASTFeatures + +

+

detectFASTFeatures

+

This function is used to detect the corner points using FAST Alogrithm

+ + +

Calling Sequence

+ 
[ Location Count Metric ] = detectFASTFeatures( Image, Name, Value... )
+ +

Parameters

+
Image: +

Input Image, should be a 2-D grayscale. The Input Image should be real

+
MinQuality [Optional Input Argument]: +

Minimum Accepted Quality of Corners, can be specified as a scalar value between [0,1]. Default: 0.1

+
MinContrast [Optional Input Argument]: +

Minimum Intensity difference for Corners to be detected, can be specified as a scalar value between[0,1]. Default: 0.2

+
ROI [Optional Input Argument]: +

Specify a rectangular region of operation. Format [ x y width height ]. Default: [1 1 size(Image,2) size(Image,1)]

+
Location: +

Set of x,y coordinates for the deteccted points

+
Count: +

Number of corner points detected

+
Metric: +

Value describing the strength of each detected Point

+ +

Description

+

The detectFASTFeatures function uses the Features from Accelerated Segment Test (FAST) algorithm to find feature points.

+

+ +

Examples

+ 
stacksize("max");
+img_1 = imread("images/table.jpg", 0);
+img_2 = imread("images/table1.jpg", 0);
+lis1 = detectFASTFeatures(img_1);
+lis2 = detectFASTFeatures(img_2);
+features_1 = extractFeatures(img_1, lis1.Location, "cornerPoints", "Metric", lis1.Metric);
+features_2 = extractFeatures(img_2, lis2.Location, "cornerPoints", "Metric", lis2.Metric)
+dimage = drawKeypoints(img_1, lis1.Location);
+[matches, distance] = matchFeatures(features_1.Features, features_2.Features);
+matchedImage = drawMatch(img_1, img_2, lis1.Location, lis2.Location, matches, distance);
+ +

Examples

+ 
stacksize("max");
+img_1 = imread("sample_image1.jpg", 0);
+img_2 = imread("sample_image2.jpg", 0);
+lis1 = detectFASTFeatures(img_1, "MinConstrast", 0.2);
+lis2 = detectFASTFeatures(img_2, "MinConstrast", 0.2);
+features_1 = extractFeatures(img_1, lis1.Location, "cornerPoints", "Metric", lis1.Metric);
+features_2 = extractFeatures(img_2, lis2.Location, "cornerPoints", "Metric", lis2.Metric)
+dimage = drawKeypoints(img_1, lis1.Location);
+[matches, distance] = matchFeatures(features_1.Features, features_2.Features);
+matchedImage = drawMatch(img_1, img_2, lis1.Location, lis2.Location, matches, distance);
+ +

See also

+
+ +

Authors

+
  • Umang Agrawal
    • +
  • Sridhar Reddy
    • +
  • Siddhant Narang
+
+ +
+ + + + + + +
Report an issue
+ << detectBRISKFeatures + + + FOSSEE Image Processing Toolbox + + + detectHarrisFeatures >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/detectHarrisFeatures.html b/help/en_US/scilab_en_US_help/detectHarrisFeatures.html new file mode 100644 index 0000000..40d0312 --- /dev/null +++ b/help/en_US/scilab_en_US_help/detectHarrisFeatures.html @@ -0,0 +1,96 @@ + + + detectHarrisFeatures + + + +
+ + + + +
+ << detectFASTFeatures + + + FOSSEE Image Processing Toolbox + + + detectSURFFeatures >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > detectHarrisFeatures + +

+

detectHarrisFeatures

+

This function is used to find corner points in an image using Harris algorithm.

+ + +

Calling Sequence

+ 
points = detectHarrisFeatures(I);
+points = detectHarrisFeatures(I, Name, Value, ...);
+ +

Parameters

+
points: +

Structure of corner points

+
I: +

Input image to detectHarrisFeatures()

+
MinQuality: +

(Optional) Minimum accepted quality of corners (Default- 0.01)

+
FilterSize: +

(Optional) Dimension of Gaussian Filter (Default: 5)

+
ROI: +

(Optional) Rectangular region for corner detection

+
SensitivityFactor: +

(Optional) SensitivityFactor of Harris algorithm (Default- 0.04)

+ +

Description

+

This function detects corners in an image I. These corner points are used to extract features and hence recognize the contents of an image.

+

+ +

Examples

+ 
img_1 = imread("images/checkerBoard.jpg", 0);
+lis1 = detectHarrisFeatures(img_1);
+dimage = drawKeypoints(img_1, lis1.Location, "color", [0, 255, 0]);
+ +

See also

+
+ +

Authors

+
  • Rohit Suri
    • +
  • Sridhar Reddy
    • +
  • Siddhant Narang
+
+ +
+ + + + + + +
Report an issue
+ << detectFASTFeatures + + + FOSSEE Image Processing Toolbox + + + detectSURFFeatures >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/detectSURFFeatures.html b/help/en_US/scilab_en_US_help/detectSURFFeatures.html new file mode 100644 index 0000000..4e68e11 --- /dev/null +++ b/help/en_US/scilab_en_US_help/detectSURFFeatures.html @@ -0,0 +1,105 @@ + + + detectSURFFeatures + + + +
+ + + + +
+ << detectHarrisFeatures + + + FOSSEE Image Processing Toolbox + + + disparity >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > detectSURFFeatures + +

+

detectSURFFeatures

+

This function is used to detect SURF(Speeded Up Robust Features) Features in a grayscale Image.

+ + +

Calling Sequence

+ 
result = detectSURFFeatures(Image);
+result = detectSURFFeatures(Image, Name, Value, ...)
+ +

Parameters

+
result: +

SURFPoints struct which contains Location of KeyPoints, Orientation, Metric, SignOfLaplacian, Scale and Count of the features.

+
Image : +

Input image, specified as a A-by-N 2D grayscale.

+
MetricThreshold : +

(Optional) With default value equal to 1000, it is to be specified as a scalar. Every interest point detected has a strength associated with it. In case, only the stronget ones are needed, this parameter has to be given a larger value. To get more no of interest points/blobs, it is to be reduced.

+
NumOctaves : +

(Optional)With default value equal to 3, it is to be specified as a scalar. Larger the number of octaves, larger is the size of blobs detected. This is because higher octave use large sized filters. Value must be an integer scalar in between 1 and 4.

+
NumScaleLevels : +

(Optional)With default value equal to 4, it is to be specified as a scalar. It denotes the number of scale level for each octave. The Value must be an integer scalar greater than or equal to 3.

+
ROI : +

(Optional) Region Of Interest. This is taken as a vector [u v width height]. When specified, the function detects the key points within region of area width*height with u and v being the top left corner coordinates.

+ +

Description

+

This function return the SURF(Speeded Up Robust Features) Interest Points for a 2D Grayscale image. It is scale- and rotation- invariant point detector and descriptor and its application include Camera Calibration, 3D Reconstruction, Object Recognition to name a few.

+

+ +

Examples

+ 
stacksize("max");
+img_1 = imread("images/table.jpg", 0);
+img_2 = imread("images/table1.jpg", 0);
+lis1 = detectSURFFeatures(img_1);
+lis2 = detectSURFFeatures(img_2);
+dimage = drawKeypoints(img_2, lis2.KeyPoints);
+features_1 = extractFeatures(img_1, lis1.KeyPoints, "SURFPoints", "Metric", lis1.Metric, "Orientation", lis1.Orientation, "Scale", lis1.Scale, "SignOfLaplacian", lis1.SignOfLaplacian);
+features_2 = extractFeatures(img_2, lis2.KeyPoints, "SURFPoints", "Metric", lis2.Metric, "Orientation", lis2.Orientation, "Scale", lis2.Scale, "SignOfLaplacian", lis2.SignOfLaplacian);
+[matches, distance] = matchFeatures(features_1.Features, features_2.Features, "Method", "Approximate");
+matchedImage = drawMatch(img_1, img_2, lis1.KeyPoints, lis2.KeyPoints, matches, distance);
+ +

See also

+
+ +

Authors

+
  • Shashank Shekhar
    • +
  • Siddhant Narang
+
+ +
+ + + + + + +
Report an issue
+ << detectHarrisFeatures + + + FOSSEE Image Processing Toolbox + + + disparity >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/disparity.html b/help/en_US/scilab_en_US_help/disparity.html new file mode 100644 index 0000000..d71971e --- /dev/null +++ b/help/en_US/scilab_en_US_help/disparity.html @@ -0,0 +1,108 @@ + + + disparity + + + +
+ + + + +
+ << detectSURFFeatures + + + FOSSEE Image Processing Toolbox + + + filter >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > disparity + +

+

disparity

+

This function returns the disparity map between two images which

+ + +

Calling Sequence

+ 
map, map1 = disparity(img1, img2)
+map, map1 = disparity(img1, img2, Name(same as the ones given under parameters), Value, ....)
+ +

Parameters

+
method: +

Disparity estimation algorithm, specified as the comma-separated pair consisting of 'Method'

+
numDisparities: +

Maximum disparity minus minimum disparity. This parameter must be divisible by 16.

+
SADWindowSize: +

Matched block size. It must be an odd number >=1

+
uniquenessRatio: +

Margin in percentage by which the best (minimum) computed cost function value should

+
textureThreshold: +

This controls the detailing in the disparity map.

+
disp12MaxDiff: +

Maximum allowed difference (in integer pixel units) in the left-right disparity check.

+
Returns: +

disparity map

+ +

Description

+

Validates disparity using the left-right check. The matrix "cost" should be computed by the +stereo correspondence algorithm.

+

+ +

Examples

+ 
stacksize("max");
+img_1 = imread("images/left1.jpg", 0);
+img_2 = imread("images/right1.jpg", 0);
+w1 = genCheckerboardPoints([10, 7], 8);
+ip1 = detectCheckerboardCorner(img_1, [7, 10]);
+ip2 = detectCheckerboardCorner(img_2, [7, 10]);
+ip1l = list(ip1);
+ip2l = list(ip2);
+op = stereoCalibrateAndRect(w1, ip1l, ip2l, size(img_1));
+[map map1] = disparity(img_1, img_2);
+img = reconstructScene(op.DepthMap, map1, 1);
+ +

See also

+
+ +

Authors

+
  • Siddhant Narang
+
+ +
+ + + + + + +
Report an issue
+ << detectSURFFeatures + + + FOSSEE Image Processing Toolbox + + + filter >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/disparity.html~ b/help/en_US/scilab_en_US_help/disparity.html~ new file mode 100644 index 0000000..74e469b --- /dev/null +++ b/help/en_US/scilab_en_US_help/disparity.html~ @@ -0,0 +1,107 @@ + + + disparity + + + +
+ + + + +
+ << detectSURFFeatures + + + FOSSEE Image Processing Toolbox + + + filter >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > disparity + +

+

disparity

+

This function returns the disparity map between two images which

+ + +

Calling Sequence

+ 
[map map1] = disparity(img_1, img_2);
+ +

Parameters

+
method: +

Disparity estimation algorithm, specified as the comma-separated pair consisting of 'Method' and // character vector 'blockMatching' or 'semi-blockMatching'.

+
numDisparities: +

Maximum disparity minus minimum disparity. This parameter must be divisible by 16.

+
SADWindowSize: +

Matched block size. It must be an odd number >=1

+
uniquenessRatio: +

Margin in percentage by which the best (minimum) computed cost function value should “winâ€

+
textureThreshold: +

This controls the detailing in the disparity map.

+
disp12MaxDiff: +

Maximum allowed difference (in integer pixel units) in the left-right disparity check.

+
Returns: +

disparity map

+ +

Description

+

Validates disparity using the left-right check. The matrix "cost" should be computed by the +stereo correspondence algorithm

+

+ +

Examples

+ 
stacksize("max");
+img_1 = imread("left1.jpg", 0);
+img_2 = imread("right1.jpg", 0);
+w1 = genCheckerboardPoints([10, 7], 8);
+ip1 = detectCheckerboardCorner(img_1, [7, 10]);
+ip2 = detectCheckerboardCorner(img_2, [7, 10]);
+ip1l = list(ip1);
+ip2l = list(ip2);
+op = stereoCalibrateAndRect(w1, ip1l, ip2l, size(img_1));
+[map map1] = disparity(img_1, img_2);
+img = reconstructScene(op.DepthMap, map1, 1);
+ +

See also

+
+ +

Authors

+
  • Siddhant Narang
+
+ +
+ + + + + + +
Report an issue
+ << detectSURFFeatures + + + FOSSEE Image Processing Toolbox + + + filter >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/filter.html b/help/en_US/scilab_en_US_help/filter.html new file mode 100644 index 0000000..2613b80 --- /dev/null +++ b/help/en_US/scilab_en_US_help/filter.html @@ -0,0 +1,96 @@ + + + filter + + + +
+ + + + +
+ << disparity + + + FOSSEE Image Processing Toolbox + + + ftrans2 >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > filter + +

+

filter

+

Convolves an image with the kernel.

+ + +

Calling Sequence

+ 
img = filter(input_image, depth, kernelMatrix, -1, -1, 1);
+ +

Parameters

+
input_image: +

Input image.

+
depth: +

Destination image depth, given below are the possible values-

  • CV_8U

  • CV_16U/CV_16S

  • CV_32F

  • CV_64F

+
kernelMatrix: +

The matrix which defines the kernel of the filter to be applied on the input image.

+
anchor_x: +

X-coordinate of the anchor.

+
anchor_y: +

Y-coordinate of the anchor.

+
delta: +

Value added to the filtered results before storing them.

+ +

Description

+

The function applies an arbitrary linear filter to an image. In-place operation is supported. +When the aperture is partially outside the image, the function interpolates outlier pixel values +according to the specified border mode. +The function does actually compute correlation, not the convolution: +

+

+ +

Examples

+ 
img1 = imread("images/right1.jpg", 0);
+img2 = imread("images/left1.jpg", 0);
+image = imabsdiff(img1, img2);
+ +

See also

+
+ +

Authors

+
  • Sukul Bagai
+
+ +
+ + + + + + +
Report an issue
+ << disparity + + + FOSSEE Image Processing Toolbox + + + ftrans2 >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/ftrans2.html b/help/en_US/scilab_en_US_help/ftrans2.html new file mode 100644 index 0000000..a11063e --- /dev/null +++ b/help/en_US/scilab_en_US_help/ftrans2.html @@ -0,0 +1,86 @@ + + + ftrans2 + + + +
+ + + + +
+ << filter + + + FOSSEE Image Processing Toolbox + + + getParams >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > ftrans2 + +

+

ftrans2

+

2-D FIR filter using frequency transformation.

+ + +

Calling Sequence

+ 
h = ftrans2(b, t);
+ +

Parameters

+
b: +

A bandpass filter

+
t: +

Transformation matrix

+ +

Description

+

Produces the two-dimensional FIR filter h that corresponds to the one-dimensional FIR filter b +using the transform t. +(ftrans2 returns h as a computational molecule, which is the appropriate form to use with filter2.) +b must be a one-dimensional, Type I (even symmetric, odd-length) filter such as can be returned by +fir1, fir2, or firpm in the Signal Processing Toolbox software. The transform matrix t contains +coefficients that define the frequency transformation to use. If t is m-by-n and b has length Q, +then h is size ((m - 1) * (Q - 1) / 2 + 1)-by-((n - 1) * (Q - 1) / 2 + 1).

+

+ +

Examples

+ 
b = [1, 1, 1]
+h = ftrans2(b);
+ +

Authors

+
  • Vinay
+
+ +
+ + + + + + +
Report an issue
+ << filter + + + FOSSEE Image Processing Toolbox + + + getParams >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/getParams.html b/help/en_US/scilab_en_US_help/getParams.html new file mode 100644 index 0000000..23e25be --- /dev/null +++ b/help/en_US/scilab_en_US_help/getParams.html @@ -0,0 +1,96 @@ + + + getParams + + + +
+ + + + +
+ << ftrans2 + + + FOSSEE Image Processing Toolbox + + + graydiffweight >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > getParams + +

+

getParams

+

This function is used view the parameters of a trained classifier.

+ + +

Calling Sequence

+ 
param = getParams(classifier, modelName);
+ +

Parameters

+
classifier: +

Image category classifier

+
modelName: +

Name of the model to which the classifier belongs to.

+ +

Description

+

This function can be used to view the parameters of a trained classifier and +make changes accordingly in the training process to get accurate results.

+

+ +

Examples

+ 
load("argset3.dat", "knnclassi3"); // Use the scilab load function to load a trained classifier.
+params = getParamsKNN(knnclassi3, "KNN")
+ +

Examples

+ 
load("emclassi4.dat", "emclassi4"); // Use the scilab load function to load a trained classifier.
+params = getParams(emclassi4, "EM");
+ +

See also

+
+ +

Authors

+
  • Siddhant Narang
+
+ +
+ + + + + + +
Report an issue
+ << ftrans2 + + + FOSSEE Image Processing Toolbox + + + graydiffweight >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/graydiffweight.html b/help/en_US/scilab_en_US_help/graydiffweight.html new file mode 100644 index 0000000..40f72b6 --- /dev/null +++ b/help/en_US/scilab_en_US_help/graydiffweight.html @@ -0,0 +1,90 @@ + + + graydiffweight + + + +
+ + + + +
+ << getParams + + + FOSSEE Image Processing Toolbox + + + imabsdiff >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > graydiffweight + +

+

graydiffweight

+

Calculate weights for image pixels based on grayscale intensity difference.

+ + +

Calling Sequence

+ 
W = graydiffweight(srcImage, refGrayVal)
+ +

Parameters

+
srcImage: +

Input image.

+
refGrayVal: +

Reference grayscale intensity value, specified as a scalar.

+ +

Description

+

This function computes the pixel weight for each pixel in the grayscale image I. The weight is the absolute +value of the difference between the intensity of the pixel and the reference grayscale intensity +specified by the scalar refGrayVal. Pick a reference grayscale intensity value that is representative +of the object you want to segment. The weights are returned in the array W, which is the same size as +input image I. The weight of a pixel is inversely related to the absolute value of the grayscale intensity +difference at the pixel location. If the difference is small (intensity value close to refGrayVal), the +weight value is large. If the difference is large (intensity value very different from refGrayVal), the +weight value is small.

+

+ +

Examples

+ 
image = imread("images/lena.jpg");
+W = graydiffweight(image, 120);
+ +

See also

+
+ +

Authors

+
  • Dhruti Shah
+
+ +
+ + + + + + +
Report an issue
+ << getParams + + + FOSSEE Image Processing Toolbox + + + imabsdiff >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/imabsdiff.html b/help/en_US/scilab_en_US_help/imabsdiff.html new file mode 100644 index 0000000..74359df --- /dev/null +++ b/help/en_US/scilab_en_US_help/imabsdiff.html @@ -0,0 +1,92 @@ + + + imabsdiff + + + +
+ + + + +
+ << graydiffweight + + + FOSSEE Image Processing Toolbox + + + imboxfilt3 >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > imabsdiff + +

+

imabsdiff

+

Calculates the per-element absolute difference between two images.

+ + +

Calling Sequence

+ 
image = imabsdiff(img1, img2)
+ +

Parameters

+
img1: +

First input image.

+
img2: +

Second input image.

+ +

Description

+

The function absdiff calculates- Absolute difference between two arrays when they have the same size and type- +dst(I) = saturate(|src1(I) − src2(I)|) +Absolute difference between an array and a scalar when the second array is constructed +from Scalar or has as many elements as the number of channels in src1- +dst(I) = saturate(|src1(I) − src2|) +Absolute difference between a scalar and an array when the first array is constructed from Scalar or has +as many elements as the number of channels in src2- +dst(I) = saturate(|src1 − src2(I)|) +where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each channel is processed // independently.

+

+ +

Examples

+ 
img1 = imread("images/left1.jpg", 0);
+img2 = imread("images/right1.jpg", 0);
+image = imabsdiff(img1, img2);
+ +

See also

+
+ +

Authors

+
  • Siddhant Narang
+
+ +
+ + + + + + +
Report an issue
+ << graydiffweight + + + FOSSEE Image Processing Toolbox + + + imboxfilt3 >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/imboxfilt3.html b/help/en_US/scilab_en_US_help/imboxfilt3.html new file mode 100644 index 0000000..7e0e79c --- /dev/null +++ b/help/en_US/scilab_en_US_help/imboxfilt3.html @@ -0,0 +1,98 @@ + + + imboxfilt3 + + + +
+ + + + +
+ << imabsdiff + + + FOSSEE Image Processing Toolbox + + + imcontrast >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > imboxfilt3 + +

+

imboxfilt3

+

Blurs an image using the box filter.

+ + +

Calling Sequence

+ 
blurredImg = imboxfilt3(srcImg)
+blurredImg = imboxfilt3(srcImg, filterWidth, filterHeight)
+ +

Parameters

+
srcImage: +

Input image.

+
filterWidth: +

The width of the filter to be applied.

+
filterHeight: +

The height of the filter to be applied.

+ +

Description

+

The function smoothes an image using the kernel:

+

+

+

where

+

+

+

Unnormalized box filter is useful for computing various integral characteristics over each pixel +neighborhood, such as covariance matrices of image derivatives (used in dense optical flow algorithms, +and so on). If you need to compute pixel sums over variable-size windows.

+

+ +

Examples

+ 
image = imread("images/lena.jpg");
+blurredImage = imboxfilt3(input_img);
+ +

Examples

+ 
image = imread("images/blob.jpg");
+blurredImage = imboxfilt3(input_img, 5, 5);
+ +

See also

+
+ +

Authors

+
  • Yash S. Bhalgat
+
+ +
+ + + + + + +
Report an issue
+ << imabsdiff + + + FOSSEE Image Processing Toolbox + + + imcontrast >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/imcontrast.html b/help/en_US/scilab_en_US_help/imcontrast.html new file mode 100644 index 0000000..6f8c8b5 --- /dev/null +++ b/help/en_US/scilab_en_US_help/imcontrast.html @@ -0,0 +1,87 @@ + + + imcontrast + + + +
+ + + + +
+ << imboxfilt3 + + + FOSSEE Image Processing Toolbox + + + imcrop >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > imcontrast + +

+

imcontrast

+

Adjusts image contrast.

+ + +

Calling Sequence

+ 
new_image = imcontrast(srcImg, aplha, beta)
+ +

Parameters

+
srcImage: +

Input image.

+
alpha: +

Pixel multiplier controls the weight of each pixel.

+
beta: +

Added to every pixel to change the range of pixel values.

+ +

Description

+

This function is used to change the contrast of the image using +using the values alpha and beta.

+

+ +

Examples

+ 
image = imread("images/lena.jpg");
+new_image = imcontrast(image, 1.2, 2);
+ +

See also

+
+ +

Authors

+
  • Sukul Bagai
    • +
  • Siddhant Narang
+
+ +
+ + + + + + +
Report an issue
+ << imboxfilt3 + + + FOSSEE Image Processing Toolbox + + + imcrop >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/imcrop.html b/help/en_US/scilab_en_US_help/imcrop.html new file mode 100644 index 0000000..85af9b2 --- /dev/null +++ b/help/en_US/scilab_en_US_help/imcrop.html @@ -0,0 +1,89 @@ + + + imcrop + + + +
+ + + + +
+ << imcontrast + + + FOSSEE Image Processing Toolbox + + + imextendedmax >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > imcrop + +

+

imcrop

+

Crops the image.

+ + +

Calling Sequence

+ 
new_image = imcrop(srcImg, xcoor, ycoor, width, height)
+ +

Parameters

+
srcImage: +

Input image.

+
xcoor: +

The x-coordinate of the starting point of the region of interest, ie region to be cropped.

+
ycoor: +

The y-coordinate of the starting point of the region of interest.

+
width: +

The total width of the region of interest wrt to the starting point.

+
height: +

The total height of the region of interest wrt to the starting point.

+ +

Description

+

This function can be used to crop the image to a given region of interest.

+

+ +

Examples

+ 
image = imread("images/lena.jpg");
+new_image = imcrop(image, 10, 10, 100, 100);
+ +

See also

+
+ +

Authors

+
  • Sukul Bagai
+
+ +
+ + + + + + +
Report an issue
+ << imcontrast + + + FOSSEE Image Processing Toolbox + + + imextendedmax >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/imextendedmax.html b/help/en_US/scilab_en_US_help/imextendedmax.html new file mode 100644 index 0000000..5d21c2b --- /dev/null +++ b/help/en_US/scilab_en_US_help/imextendedmax.html @@ -0,0 +1,85 @@ + + + imextendedmax + + + +
+ + + + +
+ << imcrop + + + FOSSEE Image Processing Toolbox + + + imfindcircles >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > imextendedmax + +

+

imextendedmax

+

Extended-maxima transform.

+ + +

Calling Sequence

+ 
BW = imextendedmax(srcImage, Hmax)
+ +

Parameters

+
srcImage: +

Input image.

+
Hmax: +

H-maxima transform, specified as a real, nonnegative scalar.

+ +

Description

+

This functoin returns the extended-maxima transform for I, which is the regional maxima of the +H-maxima transform. Regional maxima are connected components of pixels with a constant intensity +value, and whose external boundary pixels all have a lower value. H is a nonnegative scalar.

+

+ +

Examples

+ 
image = imread("images/lena.jpg");
+new_image = imextendedmax(image, 80);
+ +

See also

+
+ +

Authors

+
  • Vinay Bhat
+
+ +
+ + + + + + +
Report an issue
+ << imcrop + + + FOSSEE Image Processing Toolbox + + + imfindcircles >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/imfindcircles.html b/help/en_US/scilab_en_US_help/imfindcircles.html new file mode 100644 index 0000000..7737106 --- /dev/null +++ b/help/en_US/scilab_en_US_help/imfindcircles.html @@ -0,0 +1,92 @@ + + + imfindcircles + + + +
+ + + + +
+ << imextendedmax + + + FOSSEE Image Processing Toolbox + + + imwrite >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > imfindcircles + +

+

imfindcircles

+

Finds circles in an image.

+ + +

Calling Sequence

+ 
[points] = imcontrast(srcImg, Rmin, Rmax)
+[points radii] = imcontrast(srcImg, Rmin, Rmax)
+ +

Parameters

+
srcImage: +

Input image.

+
Rmin: +

The minimum value of the radius of the circles to find.

+
Rmax: +

The maximum value of the radius of the circle to find.

+
points: +

The returned coordinates of the center of all the circles found.

+
radii: +

The returned values of the radiis of all the circles found.

+ +

Description

+

This function can be used to find the circles of a definite range of radii +in an image.

+

+ +

Examples

+ 
image = imread("blob.jpg");
+[points radii] = imfindcircles(image, 4, 5);
+nimage = drawKeypoints(image, points);
+ +

See also

+
+ +

Authors

+
  • Tess Zacharias
+
+ +
+ + + + + + +
Report an issue
+ << imextendedmax + + + FOSSEE Image Processing Toolbox + + + imwrite >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/imwrite.html b/help/en_US/scilab_en_US_help/imwrite.html new file mode 100644 index 0000000..8039508 --- /dev/null +++ b/help/en_US/scilab_en_US_help/imwrite.html @@ -0,0 +1,83 @@ + + + imwrite + + + +
+ + + + +
+ << imfindcircles + + + FOSSEE Image Processing Toolbox + + + integralFilter >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > imwrite + +

+

imwrite

+

Writes to a specified location.

+ + +

Calling Sequence

+ 
new_image = imwrite(srcImg, location)
+ +

Parameters

+
srcImg: +

input image.

+
location: +

The relative path of where you want to write the image.

+ +

Description

+

This function is used write the image to a specified path

+

+ +

Examples

+ 
image = imread("images/lena.jpg");
+imwrite(image, "./imageName.jpg"); // imageName -> name of the image
+ +

See also

+
+ +

Authors

+
  • Sukul Bagai
+
+ +
+ + + + + + +
Report an issue
+ << imfindcircles + + + FOSSEE Image Processing Toolbox + + + integralFilter >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/index.html b/help/en_US/scilab_en_US_help/index.html new file mode 100644 index 0000000..0b6c5de --- /dev/null +++ b/help/en_US/scilab_en_US_help/index.html @@ -0,0 +1,241 @@ + + + + + + +
+ + + + +
+ + + + + +
+
+
+ + + + +

+

FOSSEE Image Processing Toolbox

+
    + +
  • FOSSEE Image Processing Toolbox +
    • blobAnalysis — Detects blob in the source image
      • + + + + + +
    • decorrstretch — Applies decorrelation stretch to an image.
      • + + + + + +
    • detectAndComputeORB — Oriented FAST and rotated BRIEF (ORB) is used to detect and compute the corners in an image.
      • + + + + + +
    • detectBRISKFeatures — This function is used to detect BRISK(Binary Robust Invariant Scalable Keypoints) Features in a grayscale Image.
      • + + + + + +
    • detectFASTFeatures — This function is used to detect the corner points using FAST Alogrithm
      • + + + + + +
    • detectHarrisFeatures — This function is used to find corner points in an image using Harris algorithm.
      • + + + + + +
    • detectSURFFeatures — This function is used to detect SURF(Speeded Up Robust Features) Features in a grayscale Image.
      • + + + + + +
    • disparity — This function returns the disparity map between two images which
      • + + + + + +
    • filter — Convolves an image with the kernel.
      • + + + + + +
    • ftrans2 — 2-D FIR filter using frequency transformation.
      • + + + + + +
    • getParams — This function is used view the parameters of a trained classifier.
      • + + + + + +
    • graydiffweight — Calculate weights for image pixels based on grayscale intensity difference.
      • + + + + + +
    • imabsdiff — Calculates the per-element absolute difference between two images.
      • + + + + + +
    • imboxfilt3 — Blurs an image using the box filter.
      • + + + + + +
    • imcontrast — Adjusts image contrast.
      • + + + + + +
    • imcrop — Crops the image.
      • + + + + + +
    • imextendedmax — Extended-maxima transform.
      • + + + + + +
    • imfindcircles — Finds circles in an image.
      • + + + + + +
    • imwrite — Writes to a specified location.
      • + + + + + +
    • integralFilter — Integral Image based Filter.
      • + + + + + +
    • isFilter — Decides if a filter is separable or not.
      • + + + + + +
    • matchFeatures — This function is used to find the corresponding matches between two features sets
      • + + + + + +
    • mlPredict — This function is used to predict the class of an image using an image classifier.
      • + + + + + +
    • reconstructScene — This function converts a 2-D image to a 3-D image.
      • + + + + + +
    • scharr — Calculates the first x- or y- image derivative using Scharr operator.
      • + + + + + +
    • sepFilter2D — Applies a separable linear filter to an image.
      • + + + + + +
    • stereoCalibrateAndRect — This function returns a set of transformation matrices which helps to callibrate camera
      • + + + + + +
    • trainEMClassifier — This function is used to train an image classifier using the EM algorithm.
      • + + + + + +
    • trainKNNClassifier — This function is used to train an image classifier using the KNN algorithm.
      • + + + + + +
    • trainLRClassifier — This function is used to train an image classifier using the LR algorithm.
      • + + + + + +
    • triangulatePoints — Returns the worldPoint coordinates of the feature points.
      • + + + + + +
    • whitepoint — Returns a three element vector defining the illumination xyz values for different lighting conditions.
+
+ +
+ + + + + + +
Report an issue
+ + + + + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/integralFilter.html b/help/en_US/scilab_en_US_help/integralFilter.html new file mode 100644 index 0000000..c55d429 --- /dev/null +++ b/help/en_US/scilab_en_US_help/integralFilter.html @@ -0,0 +1,91 @@ + + + integralFilter + + + +
+ + + + +
+ << imwrite + + + FOSSEE Image Processing Toolbox + + + isFilter >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > integralFilter + +

+

integralFilter

+

Integral Image based Filter.

+ + +

Calling Sequence

+ 
filter = integralFilter(intimage,bbox,weights,filterSize);
+ +

Parameters

+
intimage: +

Integral Image, which can be obtained from the function integralImage.

+
bbox: +

Bounding box of the filter object, which can be obtained from integralKernel function.

+
weight: +

Weights of the bounding box, which can be obtained from integralKernel function.

+
filterSize: +

Size of the filter, which can be obtained from integralKernel function.

+ +

Description

+

This function filters image using box filters and integral images.

+

+ +

Examples

+ 
i = imread("images/lena.jpg");
+intImg = integralImage(i);
+kernel = integralKernel([2 2 11 11], 1/51);
+filter = integralFilter(intImg, kernel.bbox, kernel.weights, kernel.filterSize);
+ +

See also

+
+ +

Authors

+
  • Tanmay Chaudhari
+
+ +
+ + + + + + +
Report an issue
+ << imwrite + + + FOSSEE Image Processing Toolbox + + + isFilter >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/isFilter.html b/help/en_US/scilab_en_US_help/isFilter.html new file mode 100644 index 0000000..050079f --- /dev/null +++ b/help/en_US/scilab_en_US_help/isFilter.html @@ -0,0 +1,86 @@ + + + isFilter + + + +
+ + + + +
+ << integralFilter + + + FOSSEE Image Processing Toolbox + + + matchFeatures >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > isFilter + +

+

isFilter

+

Decides if a filter is separable or not.

+ + +

Calling Sequence

+ 
[isFilter s u] = isFilter(filter, size);
+ +

Parameters

+
filter: +

Input filter (datatype- mat(double)).

+
size: +

Size of the filter.

+ +

Description

+

This function uses SVD to compute if the filter is separable or not. It takes as input the +filter matrix and is the filter is separable it returns the calculated singular and the left +singular values.

+

+ +

Examples

+ 
filter = [1 0; 0 1]; // Any matrix of dimensions n x n.
+[isfilter] = isFilter(filter);
+ +

Examples

+ 
filter = [1 0; 0 1]; // Any matrix of dimensions n x n.
+[isfilter s u] = isFilter(filter);
+ +

Authors

+
  • Siddhant Narang
+
+ +
+ + + + + + +
Report an issue
+ << integralFilter + + + FOSSEE Image Processing Toolbox + + + matchFeatures >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/jhelpidx.xml b/help/en_US/scilab_en_US_help/jhelpidx.xml new file mode 100644 index 0000000..aa5a66e --- /dev/null +++ b/help/en_US/scilab_en_US_help/jhelpidx.xml @@ -0,0 +1,3 @@ + + + \ No newline at end of file diff --git a/help/en_US/scilab_en_US_help/jhelpmap.jhm b/help/en_US/scilab_en_US_help/jhelpmap.jhm new file mode 100644 index 0000000..d00b229 --- /dev/null +++ b/help/en_US/scilab_en_US_help/jhelpmap.jhm @@ -0,0 +1,38 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/help/en_US/scilab_en_US_help/jhelpset.hs b/help/en_US/scilab_en_US_help/jhelpset.hs new file mode 100644 index 0000000..7c93217 --- /dev/null +++ b/help/en_US/scilab_en_US_help/jhelpset.hs @@ -0,0 +1,28 @@ + + + +FOSSEE Image Processing Toolbox + +top + + + +TOC + +javax.help.TOCView +jhelptoc.xml + + +Index + +javax.help.IndexView +jhelpidx.xml + + +Search + +javax.help.SearchView +JavaHelpSearch + + \ No newline at end of file diff --git a/help/en_US/scilab_en_US_help/jhelptoc.xml b/help/en_US/scilab_en_US_help/jhelptoc.xml new file mode 100644 index 0000000..3d47f35 --- /dev/null +++ b/help/en_US/scilab_en_US_help/jhelptoc.xml @@ -0,0 +1,40 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/help/en_US/scilab_en_US_help/matchFeatures.html b/help/en_US/scilab_en_US_help/matchFeatures.html new file mode 100644 index 0000000..a3cdf19 --- /dev/null +++ b/help/en_US/scilab_en_US_help/matchFeatures.html @@ -0,0 +1,129 @@ + + + matchFeatures + + + +
+ + + + +
+ << isFilter + + + FOSSEE Image Processing Toolbox + + + mlPredict >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > matchFeatures + +

+

matchFeatures

+

This function is used to find the corresponding matches between two features sets

+ + +

Calling Sequence

+ 
[ indexPairs ] = matchFeatures( features1, features2, Name, Value... )
+[ indexPairs matchMetric ] = matchFeatures( features1, features2, Name, Value... )
+ +

Parameters

+
features1: +

Feature Set 1, a set of M1-by-N Matrix, M1 corresponding to number of features and N corresponds to length of each feature vector

+
features2: +

Feature Set 2, a set of M2-by-N Matrix, M2 corresponding to number of features and N corresponds to length of each feature vector

+
Method [Optional Input Argument]: +

Method of matching to be used. Values: ['Exhaustive' (default) | 'Approximate']

+
MatchThreshold [Optional Input Argument]: +

Matching Threshold for selecting the percentage of strongest matches. Values in range [0 100], default - 10.0

+
Unique [Optional Input Argument]: +

Boolean value for selecting only unique matches between features. Default-False(0)

+
Metric [Optional Input Argument]: +

Metric to be used for matching features. Values: ['SSD'(default)|'SAD'|'Hamming'|'Hamming_2']

+
indexPairs: +

P-by-2 matrix containing the indices of corresponding features matched between two input feature sets

+
matchMetric: +

P-by-1 Vector containing the distance metric between matched Features

+ +

Description

+

MatchFeatures function takes in the Feature Descriptors value of two images as its input and finds the best match between each feature vector of the first image to that of the second image and returns the corresponding indices of each feature matrix

+

+ +

Examples

+ 
stacksize("max");
+
+img_1 = imread("images/table.jpg", 0);
+img_2 = imread("images/table1.jpg", 0);
+
+lis1 = detectSURFFeatures(img_1);
+lis2 = detectSURFFeatures(img_2);
+
+dimage = drawKeypoints(img_2, lis2.KeyPoints);
+
+features_1 = extractFeatures(img_1, lis1.KeyPoints, "SURFPoints", "Metric", lis1.Metric, "Orientation", lis1.Orientation, "Scale", lis1.Scale, "SignOfLaplacian", lis1.SignOfLaplacian);
+features_2 = extractFeatures(img_2, lis2.KeyPoints, "SURFPoints", "Metric", lis2.Metric, "Orientation", lis2.Orientation, "Scale", lis2.Scale, "SignOfLaplacian", lis2.SignOfLaplacian);
+
+[matches, distance] = matchFeatures(features_1.Features, features_2.Features, "Method", "Approximate");
+matchedImage = drawMatch(img_1, img_2, lis1.KeyPoints, lis2.KeyPoints, matches, distance);
+ +

Examples

+ 
stacksize('max');
+
+img_1 = imread("sample_image1.jpg", 0);
+img_2 = imread("sample_image2.jpg", 0);
+
+lis1 = detectBRISKFeatures(img_1);
+lis2 = detectBRISKFeatures(img_2);
+
+features_1 = extractFeatures(img_1, lis1.KeyPoints, "BRISKPoints", "Metric", lis1.Metric, "Orientation", lis1.Orientation, "Scale", lis1.Scale);
+features_2 = extractFeatures(img_2, lis2.KeyPoints, "BRISKPoints", "Metric", lis2.Metric, "Orientation", lis2.Orientation, "Scale", lis2.Scale);
+
+[matches, distance] = matchFeatures(features_1.Features, features_2.Features);
+matchedImage = drawMatch(img_1, img_2, lis1.KeyPoints, lis2.KeyPoints, matches, distance);
+ +

See also

+
+ +

Authors

+
  • Umang Agrawal
    • +
  • Sridhar Reddy
    • +
  • Siddhant Narang
+
+ +
+ + + + + + +
Report an issue
+ << isFilter + + + FOSSEE Image Processing Toolbox + + + mlPredict >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/mlPredict.html b/help/en_US/scilab_en_US_help/mlPredict.html new file mode 100644 index 0000000..cfdf8eb --- /dev/null +++ b/help/en_US/scilab_en_US_help/mlPredict.html @@ -0,0 +1,101 @@ + + + mlPredict + + + +
+ + + + +
+ << matchFeatures + + + FOSSEE Image Processing Toolbox + + + reconstructScene >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > mlPredict + +

+

mlPredict

+

This function is used to predict the class of an image using an image classifier.

+ + +

Calling Sequence

+ 
label = mlpredict(classifier, image, modelName)
+label = mlpredict(classifier, image, modelName, numberOfNeighnours) // only in case of KNN
+ +

Parameters

+
label: +

Evaluated label of the input image

+
classifier: +

Image classifier

+
image: +

Input image

+
numberOfNeighbours: +

Number of neighbours to consider during prediction.

+
Returns: +

Label

+ +

Description

+

This function predicts the class of an image based on the classifier provided.

+

+ +

Examples

+ 
load("argset3.dat", "knnclassi3"); // Use the scilab load function to load a trained classifier.
+img = imread("bike.jpg");
+resp = mlPredict(knnclassi3, img, "knn", 5);
+img = imread("car.jpg");
+resp1 = mlPredict(knnclassi3, img, "knn", 5);
+ +

Examples

+ 
load("emclassi4.dat", "emclassi4"); // Use the scilab load function to load a trained classifier.
+img = imread("images/bike.jpg");
+[prob, resp] = mlPredict(emclassi4, img, "em");
+img = imread("images/plane.jpg");
+[prob1, resp1] = mlPredict(emclassi4, img, "em");
+ +

See also

+
+ +

Authors

+
  • Siddhant Narang
+
+ +
+ + + + + + +
Report an issue
+ << matchFeatures + + + FOSSEE Image Processing Toolbox + + + reconstructScene >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/reconstructScene.html b/help/en_US/scilab_en_US_help/reconstructScene.html new file mode 100644 index 0000000..29fa736 --- /dev/null +++ b/help/en_US/scilab_en_US_help/reconstructScene.html @@ -0,0 +1,103 @@ + + + reconstructScene + + + +
+ + + + +
+ << mlPredict + + + FOSSEE Image Processing Toolbox + + + scharr >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > reconstructScene + +

+

reconstructScene

+

This function converts a 2-D image to a 3-D image.

+ + +

Calling Sequence

+ 
reconstructScene(Q, disp_mat, handleMissingValues)
+ +

Parameters

+
Q(Depth Map): +

It is a matrix whose values define the different characteristics of a camera image.

+
Disparity Map: +

Refers to the apparent pixel difference or motion between a pair of stereo image.

+
handleMissingValues: +

Flag whose default value is false, which controls the handling of missing values.

+
Returns: +

3D image

+ +

Description

+

The function transforms a single-channel disparity map to a 3-channel image representing a 3D surface. +That is, for each pixel (x,y) and the corresponding disparity d = disparity(x,y). +The matrix Q can be an arbitrary 4×4 matrix (for example, the one computed by stereoRectify). To reproject +a sparse set of points {(x,y,d),...} to 3D space, use perspectiveTransform.

+

+ +

Examples

+ 
stacksize("max")
+img_1 = imread("images/left1.jpg", 0);
+img_2 = imread("images/right1.jpg", 0);
+w1 = genCheckerboardPoints([10, 7], 8);
+ip1 = detectCheckerboardCorner(img_1, [7, 10]);
+ip2 = detectCheckerboardCorner(img_2, [7, 10]);
+ip1l = list(ip1);
+ip2l = list(ip2);
+op = stereoCalibrateAndRect(w1, ip1l, ip2l, size(img_1));
+[map map1] = disparity(img_1, img_2);
+img = reconstructScene(op.DepthMap, map1, 1);
+ +

See also

+
+ +

Authors

+
  • Siddhant Narang
+
+ +
+ + + + + + +
Report an issue
+ << mlPredict + + + FOSSEE Image Processing Toolbox + + + scharr >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/scharr.html b/help/en_US/scilab_en_US_help/scharr.html new file mode 100644 index 0000000..9ec5d1c --- /dev/null +++ b/help/en_US/scilab_en_US_help/scharr.html @@ -0,0 +1,92 @@ + + + scharr + + + +
+ + + + +
+ << reconstructScene + + + FOSSEE Image Processing Toolbox + + + sepFilter2D >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > scharr + +

+

scharr

+

Calculates the first x- or y- image derivative using Scharr operator.

+ + +

Calling Sequence

+ 
new_image = imcontrast(srcImg, aplha, beta)
+ +

Parameters

+
srcImg: +

input image.

+
ddepth: +

output image depth. The possible ddepth values are the following

  • CV_8U
  • CV_16U/CV_16S
  • CV_32F
  • CV_64F

+
dx: +

order of the derivative x.

+
dy: +

order of the derivative y.

+
scale: +

Scale factor for the computed derivative values.

+
delta: +

Delta value that is added to the results.

+ +

Description

+

This function is used to find the derivative of the source image using the +Scharr operator.

+

+ +

Examples

+ 
image = imread("lena.jpg");
+new_image = scharr(image, "CV_8U", 2, 3, 1.5, 2);
+ +

See also

+
+ +

Authors

+
  • Sukul Bagai
+
+ +
+ + + + + + +
Report an issue
+ << reconstructScene + + + FOSSEE Image Processing Toolbox + + + sepFilter2D >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/scilab_code.css b/help/en_US/scilab_en_US_help/scilab_code.css new file mode 100644 index 0000000..658f42e --- /dev/null +++ b/help/en_US/scilab_en_US_help/scilab_code.css @@ -0,0 +1,96 @@ +.scilabcomment { + font-style: italic; + color: #01a801 +} + +.scilabdefault { + font-style: normal; + color: #000000 +} + +.scilabspecial { + font-style: normal; + color: #ffaa00 +} + +.scilabconstants { + font-style: normal; + color: #da70d6 +} + +.scilaboperator { + font-style: normal; + color: #5c5c5c +} + +.scilabnumber { + font-style: normal; + color: #bc8f8f +} + +.scilabfkeyword { + font-style: normal; + color: #b01813 +} + +.scilabskeyword { + font-style: normal; + color: #a020f0 +} + +.scilabckeyword { + font-style: normal; + color: #5f9ea0 +} + +.scilabcommand { + font-style: normal; + color: #32b9b9 +} + +.scilabmacro { + font-style: normal; + color: #ae5cb0 +} + +a.scilabcommand { + font-style: normal; + text-decoration: underline; + color: #32b9b9 +} + +a.scilabmacro { + font-style: normal; + text-decoration: underline; + color: #ae5cb0 +} + +.scilabstring { + font-style: normal; + color: #bc8f8f +} + +.scilabid { + font-style: normal; + color: #000000 +} + +.scilabinputoutputargs { + font-weight: bold; + color: #834310 +} + +.scilabfunctionid { + font-weight: bold; + color: #000000 +} + +.scilabfield { + font-style: normal; + color: #aaaaaa +} + +.scilabopenclose { + font-style: normal; + color: #4a55db +} diff --git a/help/en_US/scilab_en_US_help/section_c28e37d78ba50cc67245894dd02440a0.html b/help/en_US/scilab_en_US_help/section_c28e37d78ba50cc67245894dd02440a0.html new file mode 100644 index 0000000..4815db1 --- /dev/null +++ b/help/en_US/scilab_en_US_help/section_c28e37d78ba50cc67245894dd02440a0.html @@ -0,0 +1,241 @@ + + + + + + +
+ + + + +
+ + + FOSSEE Image Processing Toolbox + + + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox + +

+

FOSSEE Image Processing Toolbox

+
  • blobAnalysis — Detects blob in the source image
    • + + + + + +
  • decorrstretch — Applies decorrelation stretch to an image.
    • + + + + + +
  • detectAndComputeORB — Oriented FAST and rotated BRIEF (ORB) is used to detect and compute the corners in an image.
    • + + + + + +
  • detectBRISKFeatures — This function is used to detect BRISK(Binary Robust Invariant Scalable Keypoints) Features in a grayscale Image.
    • + + + + + +
  • detectFASTFeatures — This function is used to detect the corner points using FAST Alogrithm
    • + + + + + +
  • detectHarrisFeatures — This function is used to find corner points in an image using Harris algorithm.
    • + + + + + +
  • detectSURFFeatures — This function is used to detect SURF(Speeded Up Robust Features) Features in a grayscale Image.
    • + + + + + +
  • disparity — This function returns the disparity map between two images which
    • + + + + + +
  • filter — Convolves an image with the kernel.
    • + + + + + +
  • ftrans2 — 2-D FIR filter using frequency transformation.
    • + + + + + +
  • getParams — This function is used view the parameters of a trained classifier.
    • + + + + + +
  • graydiffweight — Calculate weights for image pixels based on grayscale intensity difference.
    • + + + + + +
  • imabsdiff — Calculates the per-element absolute difference between two images.
    • + + + + + +
  • imboxfilt3 — Blurs an image using the box filter.
    • + + + + + +
  • imcontrast — Adjusts image contrast.
    • + + + + + +
  • imcrop — Crops the image.
    • + + + + + +
  • imextendedmax — Extended-maxima transform.
    • + + + + + +
  • imfindcircles — Finds circles in an image.
    • + + + + + +
  • imwrite — Writes to a specified location.
    • + + + + + +
  • integralFilter — Integral Image based Filter.
    • + + + + + +
  • isFilter — Decides if a filter is separable or not.
    • + + + + + +
  • matchFeatures — This function is used to find the corresponding matches between two features sets
    • + + + + + +
  • mlPredict — This function is used to predict the class of an image using an image classifier.
    • + + + + + +
  • reconstructScene — This function converts a 2-D image to a 3-D image.
    • + + + + + +
  • scharr — Calculates the first x- or y- image derivative using Scharr operator.
    • + + + + + +
  • sepFilter2D — Applies a separable linear filter to an image.
    • + + + + + +
  • stereoCalibrateAndRect — This function returns a set of transformation matrices which helps to callibrate camera
    • + + + + + +
  • trainEMClassifier — This function is used to train an image classifier using the EM algorithm.
    • + + + + + +
  • trainKNNClassifier — This function is used to train an image classifier using the KNN algorithm.
    • + + + + + +
  • trainLRClassifier — This function is used to train an image classifier using the LR algorithm.
    • + + + + + +
  • triangulatePoints — Returns the worldPoint coordinates of the feature points.
    • + + + + + +
  • whitepoint — Returns a three element vector defining the illumination xyz values for different lighting conditions.
+
+ +
+ + + + + + +
Report an issue
+ + + FOSSEE Image Processing Toolbox + + + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/sepFilter2D.html b/help/en_US/scilab_en_US_help/sepFilter2D.html new file mode 100644 index 0000000..01e0a09 --- /dev/null +++ b/help/en_US/scilab_en_US_help/sepFilter2D.html @@ -0,0 +1,97 @@ + + + sepFilter2D + + + +
+ + + + +
+ << scharr + + + FOSSEE Image Processing Toolbox + + + stereoCalibrateAndRect >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > sepFilter2D + +

+

sepFilter2D

+

Applies a separable linear filter to an image.

+ + +

Calling Sequence

+ 
new_image = sepFilter2D(image, ddepth, kernel_x, kernel_y, anchor_x, anchor_y, delta, border);
+ +

Parameters

+
image: +

Input image.

+
ddepth: +

Destination image depth, given below are the possible values

  • CV_8U

  • CV_16U/CV_16S

  • CV_32F

  • CV_64F

+
kernel_x: +

Coefficients for filtering each row.

+
kernel_y: +

Coefficients for filtering each column.

+
anchor_x: +

X-coordinate of the anchor.

+
anchor_y: +

Y-coordinate of the anchor.

+
delta: +

Value added to the filtered results before storing them.

+
borderType: +

Pixel extrapolation method, given below are the possible argument values

  • BORDER_CONSTANT 0

  • BORDER_REPLICATE 1

  • BORDER_REFLECT 2

  • BORDER_WRAP 3

  • BORDER_REFLECT_101 4

  • BORDER_TRANSPARENT 5

+ +

Description

+

The function applies a separable linear filter to the image. That is, first, every row of src +is filtered with the 1D kernel kernelX. +Then, every column of the result is filtered with the 1D kernel kernelY.

+

+ +

Examples

+ 
image = imread("images/lena.jpg");
+new_image = sepFilter2D(image, CV_8U, [1, 2, 1], [2, 1, 2], -1, -1, 2, 1)
+ +

See also

+
+ +

Authors

+
  • Sukul Bagai
+
+ +
+ + + + + + +
Report an issue
+ << scharr + + + FOSSEE Image Processing Toolbox + + + stereoCalibrateAndRect >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/stereoCalibrateAndRect.html b/help/en_US/scilab_en_US_help/stereoCalibrateAndRect.html new file mode 100644 index 0000000..ba7487a --- /dev/null +++ b/help/en_US/scilab_en_US_help/stereoCalibrateAndRect.html @@ -0,0 +1,105 @@ + + + stereoCalibrateAndRect + + + +
+ + + + +
+ << sepFilter2D + + + FOSSEE Image Processing Toolbox + + + trainEMClassifier >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > stereoCalibrateAndRect + +

+

stereoCalibrateAndRect

+

This function returns a set of transformation matrices which helps to callibrate camera

+ + +

Calling Sequence

+ 
params = stereoCalibrateAndRect(objectpoints, imagepoints1, imagepoints2, imageSize)
+ +

Parameters

+
objectpoints: +

Vector of vectors of the calibration pattern points.

+
imagepoints1: +

Vector of vectors of the projections of the calibration pattern points, observed

+
imagePoints2: +

Vector of vectors of the projections of the calibration pattern points, observed

+
imageSize: +

Size of the image used only to initialize intrinsic camera matrix.

+
Returns: +

A struct with the following values

  • cameraMatrix1

  • distortionCoefficients1

  • cameraMatrix2

  • distortionCoefficients2

  • rotationMatrix

  • TranslationVector

  • DepthMap

  • ProjectionMatrix1

  • ProjectionMatrix2

+ +

Description

+

The function estimates transformation between two cameras making a stereo pair and also +computes the rotation matrices for each camera that (virtually) make both camera image +planes the same plane. Consequently, this makes all the epipolar lines parallel and thus +simplifies the dense stereo correspondence problem

+

+ +

Examples

+ 
stacksize("max");
+img_1 = imread("images/left1.jpg", 0);
+img_2 = imread("images/right1.jpg", 0);
+w1 = genCheckerboardPoints([10, 7], 8);
+ip1 = detectCheckerboardCorner(img_1, [7, 10]);
+ip2 = detectCheckerboardCorner(img_2, [7, 10]);
+ip1l = list(ip1);
+ip2l = list(ip2);
+op = stereoCalibrateAndRect(w1, ip1l, ip2l, size(img_1));
+[map map1] = disparity(img_1, img_2);
+img = reconstructScene(op.DepthMap, map1, 1);
+ +

See also

+
+ +

Authors

+
  • Siddhant Narang
+
+ +
+ + + + + + +
Report an issue
+ << sepFilter2D + + + FOSSEE Image Processing Toolbox + + + trainEMClassifier >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/style.css b/help/en_US/scilab_en_US_help/style.css new file mode 100644 index 0000000..0fe8923 --- /dev/null +++ b/help/en_US/scilab_en_US_help/style.css @@ -0,0 +1,350 @@ +body { + color:#000000; + background-color:#ffffff; + font-family:sans-serif; + font-size:100%; + margin:5px; + padding:0; + background : url("/img/body.png"); + background-repeat : repeat-x; +} + +.para { + padding-left: 10px; +} + +.refname { + color: #ff6c0a; +} +.refpurpose { + font-size: 110%; +} + +.synopsis { + border: 1px solid black; + width:80%; + padding: 0.5em; +} + +.editbar { + text-align: right; +} + +.term { + color:#800000; + font-size:100%; +} + +h3 { + color: #000063; + font-weight: bold; + font-size:130%; + margin-bottom: 10px; +} + +.programlisting { + font-family: monospace; + font-size: 100%; + background-color:#EEEEFF; + border-color:#CCCCCC; + border-style:solid; + border-width:2px medium; + width:80%; + color:#333333; + line-height:120%; + padding:10px; +} + +.literal { + font-family: monospace; + font-size: 100%; +} + +.option { + font-family: monospace; + font-style: italic; + font-size: 100%; +} + +.command { + font-family: monospace; + font-size: 100%; + color: #32b9b9; +} + +.function { + font-family: monospace; + font-size: 100%; + color: #32b9b9; +} + +.varname { + font-family: monospace; + font-weight: bold; + font-size: 100%; +} + +.constant { + font-family: monospace; + font-size: 100%; + color: #da70d6; +} + +a { + color: blue; + text-decoration:none; +} + +a:hover { + text-decoration:underline; +} + +.itemizedlist { + list-style-type: disk; +} + +.inline-list li { + display: inline; + list-style-type: disk; +} + +.vert-list { + list-style-type: disk; +} + +pre { + margin-bottom: 0px; + margin-top: 0px; +} + +.leftpart { + position:absolute; + float:left; + width: 186px; + padding: 5px; + font-size: smaller; +} + +.content { + margin-top: 100px; + margin-left: 196px +} + +.container { + margin: 0 auto; + width:1024px; +} + +ul.toc li.list-active { + list-style-type:disc; + font-weight:bold; +} + +ul.toc li.parent { + font-size: 115%; + padding: 5px 0px 5px 11px; + border-bottom: 1px solid #cccccc; + margin-bottom: 5px; +} + +ul.toc li.root { + font-size: 135%; + padding: 5px 0px 5px 11px; + border-bottom: 1px solid #cccccc; + margin-bottom: 5px; +} + +ul.toc li { + font-size: 85%; + margin: 1px 0 1px 1px; + padding: 1px 0 1px 11px; + background-repeat: no-repeat; + background-position: center left; + list-style-type: circle; +} + +.next { + float:right; + text-align: right; +} + +.center { + text-align: center; +} + +.screen { + font-family: monospace; + font-size: 100%; + background-color:#FFFFFF; + border-color:#CCCCCC; + border-style:solid; + border-width:2px medium; + width:80%; + color:#333333; + line-height:120%; + padding:10px; +} + +/* Top and bottom navigation controls on manual pages --------------------- */ +div.manualnavbar { + background-color: #E0E0E0; + color: inherit; + padding: 4px; + margin-bottom: 10px; +} +div.manualnavbar .prev { + padding-right: 4px; +} +div.manualnavbar .next { + text-align: right; + padding-left: 4px; +} + +div.manualnavbar .top { + text-align: center; + display: block; +} + +div.manualnavbar hr { + color: #cccccc; + background-color: #cccccc; +} + +/* Footer navigation area ------------------------------------------------- */ + +#pagefooter { + position: relative; + font-size: 75%; + color: inherit; + background-color: #e5e5e5; + width: 100%; +} + +#pagefooterleft { + top: 0px; + left: 0px; + padding: 6px; + margin-right: 30%; +} + +#pagefooterright { + text-align: right; + margin-left: 50%; + padding: 6px; +} +#footnav { + color: inherit; + background-color: #9999cc; + border-width: 1px 0px; + border-color: #333366; + border-style: solid; + text-align: right; + padding-right: 6px; +} + + + + + #global{ +/* width: 90%; */ + max-width: 90em; +/* min-width: 850px; */ + margin-left: auto; + margin-right: auto; + } + + #myHeader{ + background-color:#000000; + color:white; + margin-bottom : 10px; + position : relative; + text-align: center; +/* width : 1024px;*/ + height : 100px; + padding-left : 20px; + background : url("http://atoms.scilab.org/images/homepage/cadre_head.png"); + background-repeat : no-repeat; + background-position : 0px 0px; + + } + + #myFooter{ + background-color:#E5E5E5; + font-color:black; +/* width: 90%;*/ + max-width: 90em; +/* min-width: 850px; + margin-left: 5%; + margin-right: 5%;*/ + margin-top:10px; + padding:10px; + } + + #mainArea{ + width: 100%; + overflow: hidden; + } + + #myLeftArea{ + color:white; + float: left; + width: 180px; + padding:5px; + } + + #myMiddleArea{ + color:black; + margin-left: 200px; + padding: 10px 20px; + } + + #myRightArea{ + color:white; + float: right; + width: 200px; + padding: 12px 20px; + } + + +div#cadre_head +{ + position : relative; + text-align: center; +/* width : 1024px;*/ + height : 100px; + padding-left : 20px; + background : url("/img/cadre_head.png"); + background-repeat : no-repeat; + background-position : 0px 0px; +} + + +div#slogan{ + position: absolute; + top : 50px; + left:251px; + color:#0000AA; + font: 120%, Georgia,Serif; +} +div#cadre_help +{ + position: absolute; + top:45px; + right:0px; + font-size:0.8em; + color:#0000AA; +} + +table.revhistory +{ + width:80%; + border-color:#CCCCCC; + border-style:solid; + border-width:2px medium; + margin-bottom: 10px; +} + +table.revhistory tr.title td +{ + background-color: #9999CC; +} \ No newline at end of file diff --git a/help/en_US/scilab_en_US_help/trainEMClassifier.html b/help/en_US/scilab_en_US_help/trainEMClassifier.html new file mode 100644 index 0000000..3033d54 --- /dev/null +++ b/help/en_US/scilab_en_US_help/trainEMClassifier.html @@ -0,0 +1,104 @@ + + + trainEMClassifier + + + +
+ + + + +
+ << stereoCalibrateAndRect + + + FOSSEE Image Processing Toolbox + + + trainKNNClassifier >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > trainEMClassifier + +

+

trainEMClassifier

+

This function is used to train an image classifier using the EM algorithm.

+ + +

Calling Sequence

+ 
classifier = trainEMClassifier(imgSets, bag, classifierName)
+classifier = trainEMClassifier(imgSets, bag, classifierName, numberOfClusters)
+ +

Parameters

+
classifier: +

Image category classifier

+
imgSets: +

Input imageSet to train the classifier on

+
bag: +

The bagOfFeatures of the imageSet provided

+
numberOfClusters: +

Defines the number of clusters to classify the image set into.

+ +

Description

+

The EM algorithm is a unsupervised learning algorithm. +This function trains a EM classifier which can be used to predict classes of images given to it as +input using the predictEM() function.

+

+ +

Examples

+ 
imgSet = imageSet('images/trainset_2/', 'recursive');
+[trainingSet testSet] = partition(imgSet,[0.8]);
+bag = bagOfFeatures(trainingSet);
+EMClassifier = trainEMClassifier(trainingSet, bag, classifierName);
+ +

Examples

+ 
imgSet = imageSet('images/trainset_3/', 'recursive');
+[trainingSet testSet] = partition(imgSet,[0.8]);
+bag = bagOfFeatures(trainingSet);
+numberOfClusters = 3;
+EMClassifier = trainEMClassifier(trainingSet, bag, classifierName, numberOfClusters);
+save("var.dat", "EMClassifier");
+ +

See also

+
+ +

Authors

+
  • Siddhant Narang
+
+ +
+ + + + + + +
Report an issue
+ << stereoCalibrateAndRect + + + FOSSEE Image Processing Toolbox + + + trainKNNClassifier >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/trainKNNClassifier.html b/help/en_US/scilab_en_US_help/trainKNNClassifier.html new file mode 100644 index 0000000..efd91f4 --- /dev/null +++ b/help/en_US/scilab_en_US_help/trainKNNClassifier.html @@ -0,0 +1,103 @@ + + + trainKNNClassifier + + + +
+ + + + +
+ << trainEMClassifier + + + FOSSEE Image Processing Toolbox + + + trainLRClassifier >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > trainKNNClassifier + +

+

trainKNNClassifier

+

This function is used to train an image classifier using the KNN algorithm.

+ + +

Calling Sequence

+ 
classifier = trainKNNClassifier(imgSets, bag, classifierName)
+classifier = trainKNNClassifier(imgSets, bag, classifierName, algorithmType)
+ +

Parameters

+
classifier: +

Image category classifier

+
imgSets: +

Input imageSet to train the classifier on

+
bag: +

The bagOfFeatures of the imageSet provided

+
algorithmType: +

+ +

Description

+

This function trains a KNN classifier which can be used to predict classes of images given to it as +input using the predictKNN() function.

+

+ +

Examples

+ 
imgSet = imageSet('images/trainset_2/','recursive');
+[trainingSet testSet] = partition(imgSet,[0.8]);
+bag = bagOfFeatures(trainingSet);
+KNNClassifier = trainKNNClassifier(trainingSet, bag, classifierName);
+ +

Examples

+ 
imgSet = imageSet('images/trainset_3/','recursive');
+[trainingSet testSet] = partition(imgSet,[0.8]);
+bag = bagOfFeatures(trainingSet);
+algorithmType = 1;
+KNNClassifier = trainKNNClassifier(trainingSet, bag, classifierName, algorithmType);
+save("var.dat", "KNNClassifier");
+ +

See also

+
+ +

Authors

+
  • Siddhant Narang
+
+ +
+ + + + + + +
Report an issue
+ << trainEMClassifier + + + FOSSEE Image Processing Toolbox + + + trainLRClassifier >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/trainLRClassifier.html b/help/en_US/scilab_en_US_help/trainLRClassifier.html new file mode 100644 index 0000000..51c7713 --- /dev/null +++ b/help/en_US/scilab_en_US_help/trainLRClassifier.html @@ -0,0 +1,114 @@ + + + trainLRClassifier + + + +
+ + + + +
+ << trainKNNClassifier + + + FOSSEE Image Processing Toolbox + + + triangulatePoints >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > trainLRClassifier + +

+

trainLRClassifier

+

This function is used to train an image classifier using the LR algorithm.

+ + +

Calling Sequence

+ 
classifier = trainLRClassifier(imgSets, bag, classifierName)
+classifier = trainLRClassifier(imgSets, bag, classifierName, learningRate)
+classifier = trainLRClassifier(imgSets, bag, classifierName, learningRate, iteration)
+classifier = trainLRClassifier(imgSets, bag, classifierName, learningRate, iteration, regularization)
+classifier = trainLRClassifier(imgSets, bag, classifierName, learningRate, iteration, regularization, trainMethod)
+classifier = trainLRClassifier(imgSets, bag, classifierName, learningRate, iteration, regularization, trainMethod, minibatch)
+ +

Parameters

+
classifier: +

Image category classifier

+
imgSets: +

Input imageSet to train the classifier on

+
bag: +

The bagOfFeatures of the imageSet provided

+
learningRate: +

Defines the rate at which the classifier will learn.

+
iteration: +

Number of iterations the training function will perform.

+
regularization: +

Controls the kind of regularization to be applied. The types are

  • REG_DISABLE- Regularization disabled, flag value = -1.
  • REG_L1- L1 norm, flag value = 0.
  • REG_L2- L2 norm, flag value = 1.

+
trainMethod: +

Controls the kind of training method to be applied. The types are

  • BATCH- flag value = 1.
  • MINI_BATCH- flag value = 0.

+
minibatch: +

Specifies the number of training samples taken in each step of Mini-Batch Gradient Descent.

+ +

Description

+

This function trains a LR classifier which can be used to predict classes of images given to it as +input using the predictLR() function.

+

+ +

Examples

+ 
imgSet = imageSet('images/trainset_2/','recursive');
+[trainingSet testSet] = partition(imgSet,[0.8]);
+bag = bagOfFeatures(trainingSet);
+lrclassi = trainLRClassifier(im, bag, "lrclassi", 1, 150, 0, 1, 5);
+ +

Examples

+ 
imgSet = imageSet('images/trainset_3/','recursive');
+[trainingSet testSet] = partition(imgSet,[0.8]);
+bag = bagOfFeatures(trainingSet);
+lrclassi = trainLRClassifier(im, bag, "lrclassi", 1, 150, 0);
+save("var.dat", "lrclassi");
+ +

See also

+
+ +

Authors

+
  • Siddhant Narang
+
+ +
+ + + + + + +
Report an issue
+ << trainKNNClassifier + + + FOSSEE Image Processing Toolbox + + + triangulatePoints >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/triangulatePoints.html b/help/en_US/scilab_en_US_help/triangulatePoints.html new file mode 100644 index 0000000..5f5baa9 --- /dev/null +++ b/help/en_US/scilab_en_US_help/triangulatePoints.html @@ -0,0 +1,89 @@ + + + triangulatePoints + + + +
+ + + + +
+ << trainLRClassifier + + + FOSSEE Image Processing Toolbox + + + whitepoint >> + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > triangulatePoints + +

+

triangulatePoints

+

Returns the worldPoint coordinates of the feature points.

+ + +

Calling Sequence

+ 
points4D = triangulatePoints(projMat1, projMat2, points1, points2)
+ +

Parameters

+
projMat1: +

3X4 projection matrix for the first Camera matrix

+
projMat2: +

3X4 projection matrix for the second Camera matrix

+
points1: +

2xN array of feature points in the 1st image. In case of c++ version it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1

+
points2: +

2xN array of feature points in the 2nd image. In case of c++ version it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1

+
points4D: +

4XN array or matrix of reconstructed points

+ +

Description

+

Returns 4D location of the matched feature points from the two projected matrices. The 4D location of the feature points is reconstructed using triangulation.

+

+ +

Examples

+ 
[projMat1] = [12 21 21 19; 34 12 0 2; 112 431 890 32.1]
+[projMat2] = [16 17 32 1; 64 90 12 11; 123 43.5 895 9.8]
+[points1] = [1 2 3 4;5 6 7 8]
+[points2] = [32 1 3 5; 9 8 3 4]
+[points4D] = triangulatePoints(projMat1, projMat2, points1, points2)
+ +

Authors

+
  • Deepshikha
+
+ +
+ + + + + + +
Report an issue
+ << trainLRClassifier + + + FOSSEE Image Processing Toolbox + + + whitepoint >> + +
+
+
+ + diff --git a/help/en_US/scilab_en_US_help/whitepoint.html b/help/en_US/scilab_en_US_help/whitepoint.html new file mode 100644 index 0000000..a2a5594 --- /dev/null +++ b/help/en_US/scilab_en_US_help/whitepoint.html @@ -0,0 +1,89 @@ + + + whitepoint + + + +
+ + + + +
+ << triangulatePoints + + + FOSSEE Image Processing Toolbox + + + +
+
+
+ + + + FOSSEE Image Processing Toolbox >> FOSSEE Image Processing Toolbox > whitepoint + +

+

whitepoint

+

Returns a three element vector defining the illumination xyz values for different lighting conditions.

+ + +

Calling Sequence

+ 
xyz = whitepoint(string);
+ +

Parameters

+
string: +

The following are the possible values for this argument and their descriptions

  • d65- CIE standard illuminant D65, [0.9504, 1.0000, 1.0888]. Reperesents noon daylight with correlated color temperature of 6504 K.

  • d50- CIE standard illuminant D50, [0.9642, 1.0000, 0.8251]. Represents warm daylight at sunrise or sunset with correlated color temperature of 5003 K.

  • a- CIE standard illuminant A, [1.0985, 1.0000, 0.3558]. Reperesents typical, domestic, tungsten-filament lighting with correlated color temperature of 2856 K.

  • c- CIE standard illuminant C, [0.9807, 1.0000, 1.1822]. Simulates average or north sky daylight with correlated color temperature of 6774 K.

  • icc- Profile Connection Space (PCS) illuminant used in ICC profiles. Approximation of [0.962, 1.000, 0.8249] using fixed-point, signed, 32-bit numbers with 16 fractional bits. Actual value: [31595,32768, 27030]/32768.

  • c- CIE standard illuminant C, [0.9807, 1.0000, 1.1822]. Represents average or north sky daylight with correlated color temperature of 6774 K.

  • d55- CIE standard illuminant D55, [0.9568, 1.0000, 0.9214]. Represents mid-morning or mid-afternoon daylight with correlated color temperature of 5500 K.

+ +

Description

+

This function is used to get a vector of xyz color space representation for different lighting +conditions which can be used to get images with different lighting properties.

+

+ +

Examples

+ 
xyz = whitepoint("a");
+img = imread("images/lena.jpeg");
+image = applycform(img, "srgb2xyz");
+temp1 = image(:, :, 1)
+temp3 = image(:, :, 3)
+image(:, :, 1) = temp3 * xyz(1);
+image(:, :, 2) = image(:, :, 2) * xyz(2);
+image(:, :, 3) = temp1 * xyz(3);
+imshow(image);
+ +

See also

+
+ +

Authors

+
  • Tess Zacharias
+
+ +
+ + + + + + +
Report an issue
+ << triangulatePoints + + + FOSSEE Image Processing Toolbox + + + +
+
+
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That is, first, every row of src +is filtered with the 1D kernel kernelX. +Then, every column of the result is filtered with the 1D kernel kernelY. + + + + + + + Examples + + + + + See also + + imread + + + + + Authors + + Sukul Bagai + + + diff --git a/help/en_US/stereoCalibrateAndRect.xml b/help/en_US/stereoCalibrateAndRect.xml new file mode 100644 index 0000000..1a5b0ad --- /dev/null +++ b/help/en_US/stereoCalibrateAndRect.xml @@ -0,0 +1,95 @@ + + + + + + + + stereoCalibrateAndRect + This function returns a set of transformation matrices which helps to callibrate camera + + + + + Calling Sequence + + params = stereoCalibrateAndRect(objectpoints, imagepoints1, imagepoints2, imageSize) + + + + + + Parameters + + objectpoints: + Vector of vectors of the calibration pattern points. + imagepoints1: + Vector of vectors of the projections of the calibration pattern points, observed + imagePoints2: + Vector of vectors of the projections of the calibration pattern points, observed + imageSize: + Size of the image used only to initialize intrinsic camera matrix. + Returns: + A struct with the following values cameraMatrix1 distortionCoefficients1 cameraMatrix2 distortionCoefficients2 rotationMatrix TranslationVector DepthMap ProjectionMatrix1 ProjectionMatrix2 + + + + + Description + +The function estimates transformation between two cameras making a stereo pair and also +computes the rotation matrices for each camera that (virtually) make both camera image +planes the same plane. Consequently, this makes all the epipolar lines parallel and thus +simplifies the dense stereo correspondence problem + + + + + + + Examples + + + + + See also + + imread + genCheckerboardPoints + detectCheckerboardCorner + disparity + reconstructScene + + + + + Authors + + Siddhant Narang + + + diff --git a/help/en_US/trainEMClassifier.xml b/help/en_US/trainEMClassifier.xml new file mode 100644 index 0000000..56aab5a --- /dev/null +++ b/help/en_US/trainEMClassifier.xml @@ -0,0 +1,99 @@ + + + + + + + + trainEMClassifier + This function is used to train an image classifier using the EM algorithm. + + + + + Calling Sequence + + classifier = trainEMClassifier(imgSets, bag, classifierName) + classifier = trainEMClassifier(imgSets, bag, classifierName, numberOfClusters) + + + + + + Parameters + + classifier: + Image category classifier + imgSets: + Input imageSet to train the classifier on + bag: + The bagOfFeatures of the imageSet provided + numberOfClusters: + Defines the number of clusters to classify the image set into. + + + + + Description + +The EM algorithm is a unsupervised learning algorithm. +This function trains a EM classifier which can be used to predict classes of images given to it as +input using the predictEM() function. + + + + + + + Examples + + + + + Examples + + + + + See also + + imageSet + partition + bagOfFeatures + mlPredict + save + + + + + Authors + + Siddhant Narang + + + diff --git a/help/en_US/trainKNNClassifier.xml b/help/en_US/trainKNNClassifier.xml new file mode 100644 index 0000000..222d4a1 --- /dev/null +++ b/help/en_US/trainKNNClassifier.xml @@ -0,0 +1,98 @@ + + + + + + + + trainKNNClassifier + This function is used to train an image classifier using the KNN algorithm. + + + + + Calling Sequence + + classifier = trainKNNClassifier(imgSets, bag, classifierName) + classifier = trainKNNClassifier(imgSets, bag, classifierName, algorithmType) + + + + + + Parameters + + classifier: + Image category classifier + imgSets: + Input imageSet to train the classifier on + bag: + The bagOfFeatures of the imageSet provided + algorithmType: + + + + + + Description + +This function trains a KNN classifier which can be used to predict classes of images given to it as +input using the predictKNN() function. + + + + + + + Examples + + + + + Examples + + + + + See also + + imageSet + partition + bagOfFeatures + mlPredict + save + + + + + Authors + + Siddhant Narang + + + diff --git a/help/en_US/trainLRClassifier.xml b/help/en_US/trainLRClassifier.xml new file mode 100644 index 0000000..c851c8b --- /dev/null +++ b/help/en_US/trainLRClassifier.xml @@ -0,0 +1,109 @@ + + + + + + + + trainLRClassifier + This function is used to train an image classifier using the LR algorithm. + + + + + Calling Sequence + + classifier = trainLRClassifier(imgSets, bag, classifierName) + classifier = trainLRClassifier(imgSets, bag, classifierName, learningRate) + classifier = trainLRClassifier(imgSets, bag, classifierName, learningRate, iteration) + classifier = trainLRClassifier(imgSets, bag, classifierName, learningRate, iteration, regularization) + classifier = trainLRClassifier(imgSets, bag, classifierName, learningRate, iteration, regularization, trainMethod) + classifier = trainLRClassifier(imgSets, bag, classifierName, learningRate, iteration, regularization, trainMethod, minibatch) + + + + + + Parameters + + classifier: + Image category classifier + imgSets: + Input imageSet to train the classifier on + bag: + The bagOfFeatures of the imageSet provided + learningRate: + Defines the rate at which the classifier will learn. + iteration: + Number of iterations the training function will perform. + regularization: + Controls the kind of regularization to be applied. The types areREG_DISABLE- Regularization disabled, flag value = -1.REG_L1- L1 norm, flag value = 0.REG_L2- L2 norm, flag value = 1. + trainMethod: + Controls the kind of training method to be applied. The types areBATCH- flag value = 1.MINI_BATCH- flag value = 0. + minibatch: + Specifies the number of training samples taken in each step of Mini-Batch Gradient Descent. + + + + + Description + +This function trains a LR classifier which can be used to predict classes of images given to it as +input using the predictLR() function. + + + + + + + Examples + + + + + Examples + + + + + See also + + imageSet + partition + bagOfFeatures + mlPredict + save + + + + + Authors + + Siddhant Narang + + + diff --git a/help/en_US/trainLRClassifier.xml~ b/help/en_US/trainLRClassifier.xml~ new file mode 100644 index 0000000..6df7af4 --- /dev/null +++ b/help/en_US/trainLRClassifier.xml~ @@ -0,0 +1,106 @@ + + + + + + + + trainLRClassifier + This function is used to train an image classifier using the LR algorithm. + + + + + Calling Sequence + + classifier = trainLRClassifier(imgSets, bag, classifierName) + classifier = trainLRClassifier(imgSets, bag, classifierName, learningRate) + classifier = trainLRClassifier(imgSets, bag, classifierName, learningRate, iteration) + classifier = trainLRClassifier(imgSets, bag, classifierName, learningRate, iteration, regularization) + classifier = trainLRClassifier(imgSets, bag, classifierName, learningRate, iteration, regularization, trainMethod) + classifier = trainLRClassifier(imgSets, bag, classifierName, learningRate, iteration, regularization, trainMethod, minibatch) + + + + + + Parameters + + classifier: + Image category classifier + imgSets: + Input imageSet to train the classifier on + bag: + The bagOfFeatures of the imageSet provided + learningRate: + Defines the rate at which the classifier will learn. + iteration: + Number of iterations the training function will perform. + regularization: + Controls the kind of regularization to be applied. The types are- + trainMethod: + Controls the kind of training method to be applied. The types are- + minibatch: + Specifies the number of training samples taken in each step of Mini-Batch Gradient Descent. + + + + + Description + +This function trains a LR classifier which can be used to predict classes of images given to it as +input using the predictLR() function. + + + + + + + Examples + + + + + Examples + + + + + See also + + imageSet + partition + bagOfFeatures + + + + + Authors + + Siddhant Narang + + + diff --git a/help/en_US/triangulatePoints.xml b/help/en_US/triangulatePoints.xml new file mode 100644 index 0000000..d1cc016 --- /dev/null +++ b/help/en_US/triangulatePoints.xml @@ -0,0 +1,75 @@ + + + + + + + + triangulatePoints + Returns the worldPoint coordinates of the feature points. + + + + + Calling Sequence + + points4D = triangulatePoints(projMat1, projMat2, points1, points2) + + + + + + Parameters + + projMat1: + 3X4 projection matrix for the first Camera matrix + projMat2: + 3X4 projection matrix for the second Camera matrix + points1: + 2xN array of feature points in the 1st image. In case of c++ version it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1 + points2: + 2xN array of feature points in the 2nd image. In case of c++ version it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1 + points4D: + 4XN array or matrix of reconstructed points + + + + + Description + +Returns 4D location of the matched feature points from the two projected matrices. The 4D location of the feature points is reconstructed using triangulation. + + + + + + + Examples + + + + + Authors + + Deepshikha + + + diff --git a/help/en_US/whitepoint.xml b/help/en_US/whitepoint.xml new file mode 100644 index 0000000..e3a7c91 --- /dev/null +++ b/help/en_US/whitepoint.xml @@ -0,0 +1,81 @@ + + + + + + + + whitepoint + Returns a three element vector defining the illumination xyz values for different lighting conditions. + + + + + Calling Sequence + + xyz = whitepoint(string); + + + + + + Parameters + + string: + The following are the possible values for this argument and their descriptions d65- CIE standard illuminant D65, [0.9504, 1.0000, 1.0888]. Reperesents noon daylight with correlated color temperature of 6504 K.d50- CIE standard illuminant D50, [0.9642, 1.0000, 0.8251]. Represents warm daylight at sunrise or sunset with correlated color temperature of 5003 K.a- CIE standard illuminant A, [1.0985, 1.0000, 0.3558]. Reperesents typical, domestic, tungsten-filament lighting with correlated color temperature of 2856 K. c- CIE standard illuminant C, [0.9807, 1.0000, 1.1822]. Simulates average or north sky daylight with correlated color temperature of 6774 K. icc- Profile Connection Space (PCS) illuminant used in ICC profiles. Approximation of [0.962, 1.000, 0.8249] using fixed-point, signed, 32-bit numbers with 16 fractional bits. Actual value: [31595,32768, 27030]/32768. c- CIE standard illuminant C, [0.9807, 1.0000, 1.1822]. Represents average or north sky daylight with correlated color temperature of 6774 K.d55- CIE standard illuminant D55, [0.9568, 1.0000, 0.9214]. Represents mid-morning or mid-afternoon daylight with correlated color temperature of 5500 K. + + + + + Description + +This function is used to get a vector of xyz color space representation for different lighting +conditions which can be used to get images with different lighting properties. + + + + + + + Examples + + + + + See also + + imshow + imread + applycform + + + + + Authors + + Tess Zacharias + + + diff --git a/help/en_US/whitepoint.xml~ b/help/en_US/whitepoint.xml~ new file mode 100644 index 0000000..47453c0 --- /dev/null +++ b/help/en_US/whitepoint.xml~ @@ -0,0 +1,81 @@ + + + + + + + + whitepoint + Returns a three element vector defining the illumination xyz values for different lighting conditions. + + + + + Calling Sequence + + xyz = whitepoint(string); + + + + + + Parameters + + string: + The following are the possible values for this argument and their descriptions d65- CIE standard illuminant D65, [0.9504, 1.0000, 1.0888]. Reperesents noon daylight with correlated color temperature of 6504 K.d50- CIE standard illuminant D50, [0.9642, 1.0000, 0.8251]. Represents warm daylight at sunrise or sunset with correlated color temperature of 5003 K.a- CIE standard illuminant A, [1.0985, 1.0000, 0.3558]. Reperesents typical, domestic, tungsten-filament lighting with correlated color temperature of 2856 K. c- CIE standard illuminant C, [0.9807, 1.0000, 1.1822]. Simulates average or north sky daylight with correlated color temperature of 6774 K. icc- Profile Connection Space (PCS) illuminant used in ICC profiles. Approximation of [0.962, 1.000, 0.8249] using fixed-point, signed, 32-bit numbers with 16 fractional bits. Actual value: [31595,32768, 27030]/32768. c- CIE standard illuminant C, [0.9807, 1.0000, 1.1822]. Represents average or north sky daylight with correlated color temperature of 6774 K.d55- CIE standard illuminant D55, [0.9568, 1.0000, 0.9214]. Represents mid-morning or mid-afternoon daylight with correlated color temperature of 5500 K. + + + + + Description + +This function is used to get a vector of xyz color space representation for different lighting +conditions which can be used to get images with different lighting properties. + + + + + + + Examples + + + + + See also + + imshow + imread + applycform + + + + + Authors + + Tess Zacharias + + + diff --git a/images/bike.jpg b/images/bike.jpg new file mode 100644 index 0000000000000000000000000000000000000000..6423fbef2048f67a0f6fa4728c132408bf615417 GIT binary patch literal 7729 zcmb_>by$>N*X|%Oq)3CrAR!IX%?JpCC@BrnDWH^eDUv@J$^j(>q)WO>Is~bqbC9lq zp@*4+@AtjmiNDS{*SDT)J^PQn*1Gn+)_v{7&SF;q57bqks{(Lv005l34}e_&C<6!y z2!I6mgg_vWh=`Dwl$wl`goKoVl8T&~`5}mf`5_Y%D~BK#D;qyM6BD;25C0QkF)=X^ zm$bZ;h@7CPn8@ECI7CE5q$H&DWMuRrkC+~b{67n}6F@--_=1Cvi^B%EM}dP&frIS< 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FOSSEE +// +// This file must be used under the terms of the CeCILL. +// This source file is licensed as described in the file COPYING, which +// you should have received as part of this distribution. The terms +// are also available at +// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt +// Author: Deepshikha +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + +function [blob] = blobAnalysis(srcImg, varargin) +// Detects blob in the source image +// +// Calling Sequence +// [blob] = blobAnalysis(srcImg) +// [blob] = blobAnalysis(srcImg, Name, Value) +// +// Parameters +// srcImg: The input image Matrix. +// ROI: This defines a particular in the image in of which you want the features. +// filterByThreshold: This filter compares the intensity of a binary image at the center of a +// blob to blobColor. If they differ, the blob is filtered out. Use blobColor = 0 to extract +// dark blobs and blobColor = 255 to extract light blobs. +// filterByArea: Extracted blobs have an area between minArea (inclusive) and maxArea (exclusive). +// filterByCircularity: Extracted blobs have circularity ( 4∗π∗Areaperimeter∗perimeter) between +// minCircularity (inclusive) and maxCircularity (exclusive). +// filterByConvexity: Extracted blobs have convexity (area / area of blob convex hull) between minConvexity +// (inclusive) and maxConvexity (exclusive). +// +// Description +// The function uses SimpleBlobDetector function to detect the blobs then it checks for different Name +// Value pair arguments and accordingly returns the parameters of the blob such as 2D coordinates of the +// blob, size of the blob. +// +// The Name-Value pair may be any of following typesbool filterByArea, vector [minArea maxArea]bool filterByCircularity, vector [minCircularity maxCircularity]bool filterByConvexity, vector [minConvexity maxConvexity]double ROI, vector bool filterByThreshold, vector [minThreshold maxThreshold] +// +// Examples +// img_2 = imread("/images/blob.jpg", 0); +// +// lis1 = blobAnalysis(img_2, "filterByThreshold", [200, 255]); +// lis2 = blobAnalysis(img_2, "filterByCircularity", [0.1, 0.9]); +// lis3 = blobAnalysis(img_2, "filterByArea", [1500, 1600]); +// +// dimage1 = drawKeypoints(img_2, lis1.Points, "color", [0, 255, 0]); +// dimage2 = drawKeypoints(img_2, lis2.Points, "color", [0, 255, 0]); +// dimage3 = drawKeypoints(img_2, lis3.Points, "color", [0, 255, 0]); +// +// See also +// imread, drawKeypoints +// +// Authors +// Deepshikha + +[lhs,rhs] = argn(0) + + // To check the number of input and output arguments + + if rhs < 1 then + error(msprintf(" Not enough input arguments")) + elseif rhs > 10 then + error(msprintf(" Too many input arguments to the function")) + elseif lhs < 1 then + error(msprintf(" Not enough output arguments")) + elseif lhs > 1 then + error(msprintf(" Too many output arguments")) + end + + srcMat = mattolist(srcImg) + + if modulo(rhs,2) == 0 then + error(msprintf("Number of input arguments must be odd")) + end + + select rhs + case 1 then + output = raw_blobAnalysis(srcMat) + + case 3 then + if typeof(varargin(1)) <> "string" + error(msprintf("argument at position 2 must be string")) + end + output = raw_blobAnalysis(srcMat, varargin(1), varargin(2)) + + case 5 then + if typeof(varargin(1)) <> "string" + error(msprintf("argument at position 2 must be string")) + end + if typeof(varargin(3)) <> "string" + error(msprintf("argument at position 4 must be string")) + end + output = raw_blobAnalysis(srcMat, varargin(1), varargin(2), varargin(3), varargin(4)) + + case 7 then + if typeof(varargin(1)) <> "string" + error(msprintf("argument at position 2 must be string")) + end + if typeof(varargin(3)) <> "string" + error(msprintf("argument at position 4 must be string")) + end + if typeof(varargin(5)) <> "string" + error(msprintf("argument at position 6 must be string")) + end + output = raw_blobAnalysis(srcMat, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6)) + + case 9 then + if typeof(varargin(1)) <> "string" + error(msprintf("argument at position 2 must be string")) + end + if typeof(varargin(3)) <> "string" + error(msprintf("argument at position 4 must be string")) + end + if typeof(varargin(5)) <> "string" + error(msprintf("argument at position 6 must be string")) + end + if typeof(varargin(7)) <> "string" + error(msprintf("argument at position 8 must be string")) + end + output = raw_blobAnalysis(srcMat, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(8)) + + case 11 then + if typeof(varargin(1)) <> "string" + error(msprintf("argument at position 2 must be string")) + end + if typeof(varargin(3)) <> "string" + error(msprintf("argument at position 4 must be string")) + end + if typeof(varargin(5)) <> "string" + error(msprintf("argument at position 6 must be string")) + end + if typeof(varargin(7)) <> "string" + error(msprintf("argument at position 8 must be string")) + end + output = raw_blobAnalysis(srcMat, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(8), varargin(9), varargin(10)) + end + + blob = struct("Points", output(1), "Size", output(2)) + +endfunction + diff --git a/macros/decorrstretch.bin b/macros/decorrstretch.bin new file mode 100644 index 0000000000000000000000000000000000000000..94d0503eaab12fe4f5bdbb0b81a7ac1aa4391831 GIT binary patch literal 3972 zcmds)&2G~`6on0lzmU3ZpmmDvHnfo_rK_@F0TLTv!wawji8teQ`WPS~HmCx`3K7od z&Phj8Pl%AZsimvAp8GTBp1CtlR?6YHuA8NIS9 z^B9k>6~(ITJCeteeaWxcY__El*^rDRrKFKyXFcABlCh)?!W{ksNh=`^dnF-`c;@lP z7o8mI670O=k4_H9k!Lf^t!X6oiNv_2&UNSdsT#-u9@juV;>rJ6cJkbpY)jCwg9qKY zr_%8U7k*%b7wg}YC&h;Uk%TeF)gmq)DXy>P1I@EX#wK;H-;vIGUqan*O#NV^9&j@< z&)Uv$PdagZ>m3FgHNs6?cP=KkFYrptkH9+^m;(=MlN*fa?ipizuQ_5rDVF`khj`}j zVK2OQX%2kgf&7Sj0WO>)w@3I~R6j4vC-^54*9~Xxadc{BP}*yFs65#NY4a!yiB9>A{$LZ1{qSHGC#>7}+0e#3A7E9TH>A z_1pe;RQf&59x;d6xAvfPM^XYn6C*Je;e!>U4W57lY)a0|l$eIXs-H`aXDrHcwRmo?# zd;Oyve2=e7^yw||ML36yA0+j9(-#VaVq<{~EY$o)YmkeFzC&M>6TdV3CojPEOhO!3 zUrWdXC$5*8!O!>etJY&rsR58a1MX>J;R_pFa%STmXP)yyethBG-z|K}%U*$Jv)QUg zRlAxJ)W<8>i7D%PT&Dqg&&9AFv7FOEmEQ}v_+NgXE7ttJ40%{Ld*11Sg=6jt_xHzehHx567;lx; 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The terms +// are also available at +// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt +// Author: Dhruti Shah +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + +function new_image = decorrstretch(image) +// Applies decorrelation stretch to an image. +// +// Calling Sequence +// outputImage = decorrstretch(image); +// +// Parameters +// image: Input image. +// +// Description +// This function applies decorrelation stretch to an input image which causes the image to enhance +// by way of amplifying image difference. +// +// Examples +// i = imread("images/lena.jpg"); +// outputImage = decorrstretch(i); +// imshow(i); +// +// See also +// imread +// imshow +// +// Authors +// Dhruti Shah + + [lhs rhs] = argn(0) + if rhs < 1 + error(msprintf("Not enough input arguments")); + elseif rhs > 1 + error(msprintf("Too many input arguments")); + end + if lhs > 1 + error(msprintf("Too many output arguments")); + end + + image_list = mattolist(image) + out = opencv_decorrstretch(image_list) + + sz = size(out) + for i = 1:sz + new_image(:, :, i) = out(i) + end + +endfunction diff --git a/macros/detectAndComputeORB.bin b/macros/detectAndComputeORB.bin new file mode 100644 index 0000000000000000000000000000000000000000..b2be40699f7c59115fb67b9cea52448786f6b84a GIT binary patch literal 47820 zcmeI*d#t2qT?g=Osj}U6mv(#Et!H;Rt?jmM*QP8;69IcfK)fJ=R%x-)QX`f~BpPq6 z(P$tT5{X74(LhYRMdK|RF9}|v(P+dQX@U_ol46_IqP4A+ztsBsoO!>y&%8Tx&dj^? 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The terms +// are also available at +// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt +// Author: Siddhant Narang +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + + +function [orb] = detectAndComputeORB(srcImg, varargin) +// Oriented FAST and rotated BRIEF (ORB) is used to detect and compute the corners in an image. +// +// Calling Sequence +// [orb] = detectAndComputeORB(srcImg) +// [orb] = detectAndComputeORB(srcImg, Name(same as the ones given under description), Value, ...) +// +// Parameters +// srcImg: The input image Matrix +// ROI: This defines a particular in the image in of which you want the features. +// maxFeatures: The maximum number of features to retain. +// scaleFactor: Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical pyramid, +// where each next level has 4x less pixels than the previous, but such a big scale factor will +// degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor will +// mean that to cover certain scale range you will need more pyramid levels and so the speed will suffer. +// nLevels: The number of pyramid levels. The smallest level will have linear size equal to +// input_image_linear_size / pow(scaleFactor, nLevels). +// edgeThreshold: This is size of the border where the features are not detected. It should roughly match +// the patchSize parameter. +// firstLevel: It should be 0 in the current implementation. +// scoreType: The default HARRIS_SCORE (flag value = 0) means that Harris algorithm is used to rank features +// (the score is written to KeyPoint score and is used to retain best nfeatures features); FAST_SCORE (flag +// value = 1) is alternative value of the parameter that produces slightly less stable keypoints, but it is +// a little faster to compute. +// patchSize: Size of the patch used by the oriented BRIEF descriptor. Of course, on smaller pyramid layers +// the perceived image area covered by a feature will be larger. +// fastThreshold: The threshold value used for feature detection. +// +// Description +// Oriented FAST and rotated BRIEF (ORB) is a fast robust local feature detector, first presented +// by Ethan Rublee in 2011,that can be used in computer vision tasks like object recognition +// or 3D reconstruction. +// It is based on the FAST keypoint detector and the visual descriptor BRIEF (Binary Robust +// Independent Elementary Features). +// Its aim is to provide a fast and efficient alternative to SIFT. +// +// The Name-Value pair may be any of following types edgeThreshold fastThreshold firstLevel maxFeatures nLevels patchSize scaleFactor scoreType +// +// Examples +// img_1 = imread("images/checkerBoard.jpg", 0); +// img_2 = imread("images/chess.jpg", 0); +// lis1 = detectAndComputeORB(img_1); +// lis2 = detectAndComputeORB(img_2); +// dimage = drawKeypoints(img_2, lis2.Points); +// [matches, distance] = matchFeatures(lis1.Features, lis1.Features, "Method", "Exhaustive", "Metric", "Hamming"); +// matchedImage = drawMatch(img_1, img_2, lis1.Points, lis2.Points, matches, distance); +// +// See also +// imread +// drawMatch +// drawKeypoints +// matchFeatures +// +// Authors +// Siddhant Narang + +[lhs,rhs] = argn(0) + + // To check the number of input and output arguments + + if rhs < 1 then + error(msprintf(" Not enough input arguments")) + elseif rhs > 10 then + error(msprintf(" Too many input arguments to the function")) + elseif lhs < 1 then + error(msprintf(" Not enough output arguments")) + elseif lhs > 1 then + error(msprintf(" Too many output arguments")) + end + + srcMat = mattolist(srcImg) + + if modulo(rhs,2) == 0 then + error(msprintf("Number of input arguments must be odd")) + end + + select rhs + case 1 then + output = raw_detectAndComputeORB(srcMat) + + case 3 then + if typeof(varargin(1)) <> "string" + error(msprintf("argument at position 2 must be string")) + end + output = raw_detectAndComputeORB(srcMat, varargin(1), varargin(2)) + + case 5 then + if typeof(varargin(1)) <> "string" + error(msprintf("argument at position 2 must be string")) + end + if typeof(varargin(3)) <> "string" + error(msprintf("argument at position 4 must be string")) + end + output = raw_detectAndComputeORB(srcMat, varargin(1), varargin(2), varargin(3), varargin(4)) + + case 7 then + if typeof(varargin(1)) <> "string" + error(msprintf("argument at position 2 must be string")) + end + if typeof(varargin(3)) <> "string" + error(msprintf("argument at position 4 must be string")) + end + if typeof(varargin(5)) <> "string" + error(msprintf("argument at position 6 must be string")) + end + output = raw_detectAndComputeORB(srcMat, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6)) + + case 9 then + if typeof(varargin(1)) <> "string" + error(msprintf("argument at position 2 must be string")) + end + if typeof(varargin(3)) <> "string" + error(msprintf("argument at position 4 must be string")) + end + if typeof(varargin(5)) <> "string" + error(msprintf("argument at position 6 must be string")) + end + if typeof(varargin(7)) <> "string" + error(msprintf("argument at position 8 must be string")) + end + output = raw_detectAndComputeORB(srcMat, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(8)) + + case 11 then + if typeof(varargin(1)) <> "string" + error(msprintf("argument at position 2 must be string")) + end + if typeof(varargin(3)) <> "string" + error(msprintf("argument at position 4 must be string")) + end + if typeof(varargin(5)) <> "string" + error(msprintf("argument at position 6 must be string")) + end + if typeof(varargin(7)) <> "string" + error(msprintf("argument at position 8 must be string")) + end + if typeof(varargin(9)) <> "string" + error(msprintf("argument at position 10 must be string")) + end + output = raw_detectAndComputeORB(srcMat, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(9), varargin(10)) + + case 13 then + if typeof(varargin(1)) <> "string" + error(msprintf("argument at position 2 must be string")) + end + if typeof(varargin(3)) <> "string" + error(msprintf("argument at position 4 must be string")) + end + if typeof(varargin(5)) <> "string" + error(msprintf("argument at position 6 must be string")) + end + if typeof(varargin(7)) <> "string" + error(msprintf("argument at position 8 must be string")) + end + if typeof(varargin(9)) <> "string" + error(msprintf("argument at position 10 must be string")) + end + if typeof(varargin(11)) <> "string" + error(msprintf("argument at position 12 must be string")) + end + output = raw_detectAndComputeORB(srcMat, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(9), varargin(10), varargin(11), varargin(12)) + + case 15 then + if typeof(varargin(1)) <> "string" + error(msprintf("argument at position 2 must be string")) + end + if typeof(varargin(3)) <> "string" + error(msprintf("argument at position 4 must be string")) + end + if typeof(varargin(5)) <> "string" + error(msprintf("argument at position 6 must be string")) + end + if typeof(varargin(7)) <> "string" + error(msprintf("argument at position 8 must be string")) + end + if typeof(varargin(9)) <> "string" + error(msprintf("argument at position 10 must be string")) + end + if typeof(varargin(11)) <> "string" + error(msprintf("argument at position 12 must be string")) + end + if typeof(varargin(13)) <> "string" + error(msprintf("argument at position 14 must be string")) + end + output = raw_detectAndComputeORB(srcMat, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(9), varargin(10), varargin(11), varargin(13), varargin(14)) + + case 17 then + if typeof(varargin(1)) <> "string" + error(msprintf("argument at position 2 must be string")) + end + if typeof(varargin(3)) <> "string" + error(msprintf("argument at position 4 must be string")) + end + if typeof(varargin(5)) <> "string" + error(msprintf("argument at position 6 must be string")) + end + if typeof(varargin(7)) <> "string" + error(msprintf("argument at position 8 must be string")) + end + if typeof(varargin(9)) <> "string" + error(msprintf("argument at position 10 must be string")) + end + if typeof(varargin(11)) <> "string" + error(msprintf("argument at position 12 must be string")) + end + if typeof(varargin(13)) <> "string" + error(msprintf("argument at position 14 must be string")) + end + if typeof(varargin(15)) <> "string" + error(msprintf("argument at position 16 must be string")) + end + output = raw_detectAndComputeORB(srcMat, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(9), varargin(10), varargin(11), varargin(13), varargin(14), varargin(15), varargin(16)) + + case 19 then + if typeof(varargin(1))<> "string" + error(msprintf("argument at position 2 must be string")) + end + if typeof(varargin(3))<> "string" + error(msprintf("argument at position 4 must be string")) + end + if typeof(varargin(5))<> "string" + error(msprintf("argument at position 6 must be string")) + end + if typeof(varargin(7))<> "string" + error(msprintf("argument at position 8 must be string")) + end + if typeof(varargin(9))<> "string" + error(msprintf("argument at position 10 must be string")) + end + if typeof(varargin(11))<> "string" + error(msprintf("argument at position 12 must be string")) + end + if typeof(varargin(13))<> "string" + error(msprintf("argument at position 14 must be string")) + end + if typeof(varargin(15))<> "string" + error(msprintf("argument at position 16 must be string")) + end + if typeof(varargin(17))<> "string" + error(msprintf("argument at position 18 must be string")) + end + output = raw_detectAndComputeORB(srcMat, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(9), varargin(10), varargin(11), varargin(13), varargin(14), varargin(15), varargin(16), varargin(17), varargin(18)) + end + + orb = struct("Points", output(1), "Features", output(2)) +endfunction \ No newline at end of file diff --git a/macros/detectBRISKFeatures.bin b/macros/detectBRISKFeatures.bin new file mode 100644 index 0000000000000000000000000000000000000000..c2addfa4365ff3bc69f3cefb98589b5bdd4571e9 GIT binary patch literal 12612 zcmeI(-;P|x5eD!HA;DR%7qj@!!t9uhu>@JbiA-!G6p2NGD=zW?x#S6W2O>qv6%UY` zAi;xhg?NW32uMPR5J@0cQB3`o^R0W2&zXN<%Nt>-I6d7}UsZio-P1EWoY|UfZO*>> z`16lG`(kTjc4@k``?P;&CX>kzN}kx5ZcNYaT$&xu-u}-6wYGg>eOP5tdkt`F*T}&%^{r&if^F|JmunvnDPvvk$MarBBcEYzaND`Jvm!m009; zq0?hy{`r#BvxY}w^@ROp*}#<(iGTLiaRBG>67d@2h=1c?KUHJ;kv1=Ou*2)MPH(K{ zL-k(7oqia~f4}CRDM_3+JKy5GUt;`a7t^O*Ez}UVng6h z*A6GW6hG?1pLx9%jNjFG9#7A9>qGV8YiKXPsW#?DHW`dyQ+#@QjZ07K z_%M&7)G@Ua8xDgN9CjIt$2whnnm30(dbqT1Zm%0h-+H*`M^1dhqGKE#bE#c?q<)_1 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The terms +// are also available at +// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt +// Author: Shashank Shekhar & Siddhant Narang +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + +function [varargout] = detectBRISKFeatures(image, varargin) +// This function is used to detect BRISK(Binary Robust Invariant Scalable Keypoints) Features in a grayscale Image. +// +// Calling Sequence +// result = detectBRISKFeatures(Image); +// result = detectBRISKFeatures(Image, Name, Value, ...) +// +// Parameters +// result: BRISKPoints struct which contains Location of KeyPoints, Orientation, Metric, SignOfLaplacian, Scale and Count of the features. +// Image : Input image, specified as a A-by-N 2D grayscale. +// MinContrast : (Optional) The minimum difference in intensity between a corner and its surrounding region. (Default: 0.2). The value must be between 0 and 1. +// NumOctaves : (Optional)The number of Octaves that the detector uses. (Default - 3) The value must be an integer scalar in between 1 and 4. +// MinQuality : (Optional) This specifies the minimum quality accepted for corners. (Default - 0.1) The value must be between 0 and 1. +// ROI : (Optional) Region Of Interest. This is taken as a vector [u v width height]. When specified, the function detects the key points within region of area width*height with u and v being the top left corner coordinates. +// +// Description +// This function returns the BRISK features detected in a 2D grayscale image. +// +// Examples +// stacksize('max'); +// img_1 = imread("images/table.jpg", 0); +// img_2 = imread("images/table1.jpg", 0); +// lis1 = detectBRISKFeatures(img_1); +// lis2 = detectBRISKFeatures(img_2); +// features_1 = extractFeatures(img_1, lis1.KeyPoints, "BRISKPoints", "Metric", lis1.Metric, "Orientation", lis1.Orientation, "Scale", lis1.Scale); +// features_2 = extractFeatures(img_2, lis2.KeyPoints, "BRISKPoints", "Metric", lis2.Metric, "Orientation", lis2.Orientation, "Scale", lis2.Scale); +// [matches, distance] = matchFeatures(features_1.Features, features_2.Features); +// matchedImage = drawMatch(img_1, img_2, lis1.KeyPoints, lis2.KeyPoints, matches, distance); +// +// See also +// imread +// drawMatch +// drawKeypoints +// matchFeatures +// extractFeatures +// +// Authors +// Shashank Shekhar +// Siddhant Narang + + image_list = mattolist(image); + [lhs, rhs] = argn(0) + if rhs > 9 then + error(msprintf("Too many input arguments")) + end + if lhs > 1 then + error(msprintf("Too many output arguments")) + end + select rhs + case 1 then + [a b c d e]= ocv_detectBRISKFeatures(image_list) + case 3 then + [a b c d e]= ocv_detectBRISKFeatures(image_list, varargin(1), varargin(2)) + case 5 then + [a b c d e]= ocv_detectBRISKFeatures(image_list, varargin(1), varargin(2), varargin(3), varargin(4)) + case 7 then + [a b c d e]= ocv_detectBRISKFeatures(image_list, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6)) + case 9 then + [a b c d e]= ocv_detectBRISKFeatures(image_list, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(8)) + end + varargout(1) = struct('KeyPoints', a, 'Orientation', b, 'Metric', c ,'Scale', d, 'Count', e); +endfunction diff --git a/macros/detectFASTFeatures.bin b/macros/detectFASTFeatures.bin new file mode 100644 index 0000000000000000000000000000000000000000..af7e5afea756e8a8962af4016a169e3eec756ea9 GIT binary patch literal 13628 zcmeI(%Wh;>6$W5)?-<+dHnClyTyA4B2u;+31f_)n#6l1dlZ+SuLtcOx60g9J5hGqE zGoFGF2w;Rd+(w9IefIh7UB~CR${qKG$d*>lK6|h0zt&!RS9N85W45t2`|w}yzxQx` zb+$j<*n8#VZ+&HDFRWIYj29O zXU&{vbcT<8^WLqIn-;NU-q_kpMb_B1Mqhl64_Vu+H8LMoeE%+bzeud+yl>Z&E`0Qp zqmM5;MZu|tXJc#R|5@^@^`ry!ZL-DhFBb93I9M7-_YZ1JU+9f)d|dK;g9SVMV#8eU z=Y#j;2@gGg>1=yXT=ZCb&^=%4o@06GW9FUT@r6yg&B6aGMfCrz_+W<*uGYU+WbMzj zX5M-`|GRD;j=}2n8i&ZcJR5($#@0XRbnvOQ0Ry(o^D{c$sWCY;_tc~H!y@`p2Xv8#4c_eC=;YV3{*qtT z*W{W{d`VUfho015Y9#rzPNuC3IpG62e0-+cv-Js1 zc*z@k_G`sF+t&2_U@LyQTHA0t=;p1(H@Wi1cjpmoQ-5j}FaPMKSAEd+Y7zbZ@RLp5 z{HDg%P@A7%$2J{&r&nCw@jp+slltUaFypISnM=K^qvR0=c*Pc()SmdPtEc2Gn0SXv zWRrV#@RM!pXsoxBxAy;5i`P|$`fcj)S2czk`a{*So+PK&Dr__vk zk$3ZIB7M-_Gnd*4zjgJePVm#!_AKMrCqoDQtxWd3y8wQCJ4N9QW}aUv@{IonHKt45 z(VKger+T!;NAK3jJ}8RKW%`VK=`m#U%wGI*&9UR2BERvQP4o7Gy0}+F{wi{8k>hXb z>dhL{gBIQ8(kF9gO#E$r;K|3}DgV}!hx8Y@O+Lhx9?|b9$pIcd4)u!E7#{2R)CimM z#TGqmB<|b;@uqJ%6T+KV-34<7r)Q}ra&6p+(;6Lo3E!A6WAX4cvBckR)f|2_c5jZ) zL-*9y799A?Klq3%IH@zdecZ{IBf~c_;td~}77sk~%TD&tQ104T>2N>p=Pel2@>%}q z#h?Awj|t{YT@?4_po+xG{Lb-u&P(_An$=Y7#&&FMB5YT3+c$p7}!`tm0?p?4v$5dJKLx*rbnNskg)zKfPN|9I3_l z1xI${Gt5$lWXATFo}8T5@NE1-jmiDJ;?b|@AFAN=O{ieEloEHY=k?c2XC zz4YOu-}}9Kezhn%=|g1k!sz89J=*+h-81Wnmrl6AonH3M_5G(SvexN`A^mXqLXmjQ z(~~<^y`G#A%lPUXdXm1r=QfE z++DV}U^i5g>PTJN3+hsRrN1reRb!Z?{`z^7|KqAp{eca(*oC_}eLb@37*Bdldkzns zE8=Tw-+1h7ZTW_L9Fyzg{^PQ_Ebjdpxo+I)4|Z)#=AU=m;v_l&nD_Q`An?la%R~N_{H;} zsEvC_=Q4YF+5VxP7U_%c%jiu07wPQd_@gfFp?Ki}x1rBnEmr*+QG@EK&BvmeNFCbN{r;er=m(y9uCZR_Gnvm>K1(g?T|+&j z?GsNj&YSz;X|1$%5xkRIHKfMmx8>{a0cYQ3_muXt*3JF!dT`I#cNy+C_rvpk=>DQV z-rNsQd->R1=;nSnf6vtmuls(O?;+`SuBuHvl^u8B?Ay=P*m~~L`Y{>&@)SPr_?Ef1 zM<%xWGRu4(?CTh+rA23dbd=j?aopvw#&&p_!0L%8yt%*J#&nf3+heN09!$jZg)?szU>!9O-G%ULZq_&$6K zzW0mBv2%|7*7vt37mG!$SkJyy z`}4UwUHf_GleHff$=#bp{oMHs<@*MCNxxL@>0SDu@z{P4fBI7Pin;V${N1h}WbyHvMZuT9W54PBPhm>%B&OBbY_h$zH|wQ(nVxW> z|M<=YS-Sb5m%UpB@acLwPuTk?{ee#PcHSpWzXuTeQPG9i2YhQ|pRC<|d41qF(?hYd zt3EyT!<~)UbZe~#TD{g%{my!Sw~r@IYul5x)!EkM!kK7oAiksQoo{#R+3RE2Cq8`V zI6Zs|_G7>09*Wn#o!4>N7p|&<-OF%>XL^Y`c{Vt29_d%==sQJMsiS#}o*mBfe%w0x z==t7!EFFvTE!WAn`g}I|o*ezUY|HoCMK}2_5LO;9-}80%;$zj!ypG9Z=~z^EK8vRA zZq@i{)g5I0{p{eh-o9Vs)Egi4Jf9g3>*-GF?qlBfJSg`V_v_q?#{R9GoY;;_A35^Q zVCR;*jJe#?$IhXgA9UHDW8crgd+ZEsb-+B|@B2K%K7YuMos*Hrvp1XWuMHfg&MdJe br~d8AnMJp!GkWa*cdDh69LU2@yYc@3!Y6Mh literal 0 HcmV?d00001 diff --git a/macros/detectFASTFeatures.sci b/macros/detectFASTFeatures.sci new file mode 100644 index 0000000..1c154a5 --- /dev/null +++ b/macros/detectFASTFeatures.sci @@ -0,0 +1,85 @@ +// Copyright (C) 2015 - IIT Bombay - FOSSEE +// +// This file must be used under the terms of the CeCILL. +// This source file is licensed as described in the file COPYING, which +// you should have received as part of this distribution. The terms +// are also available at +// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt +// Author: Siddhant Narang +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + +function [cornerPoints] = detectFASTFeatures(image,varargin) +// This function is used to detect the corner points using FAST Alogrithm +// +// Calling Sequence +// [ Location Count Metric ] = detectFASTFeatures( Image, Name, Value... ) +// +// Parameters +// Image: Input Image, should be a 2-D grayscale. The Input Image should be real +// MinQuality [Optional Input Argument]: Minimum Accepted Quality of Corners, can be specified as a scalar value between [0,1]. Default: 0.1 +// MinContrast [Optional Input Argument]: Minimum Intensity difference for Corners to be detected, can be specified as a scalar value between[0,1]. Default: 0.2 +// ROI [Optional Input Argument]: Specify a rectangular region of operation. Format [ x y width height ]. Default: [1 1 size(Image,2) size(Image,1)] +// Location: Set of x,y coordinates for the deteccted points +// Count: Number of corner points detected +// Metric: Value describing the strength of each detected Point +// +// Description +// The detectFASTFeatures function uses the Features from Accelerated Segment Test (FAST) algorithm to find feature points. +// +// Examples +// stacksize("max"); +// img_1 = imread("images/table.jpg", 0); +// img_2 = imread("images/table1.jpg", 0); +// lis1 = detectFASTFeatures(img_1); +// lis2 = detectFASTFeatures(img_2); +// features_1 = extractFeatures(img_1, lis1.Location, "cornerPoints", "Metric", lis1.Metric); +// features_2 = extractFeatures(img_2, lis2.Location, "cornerPoints", "Metric", lis2.Metric) +// dimage = drawKeypoints(img_1, lis1.Location); +// [matches, distance] = matchFeatures(features_1.Features, features_2.Features); +// matchedImage = drawMatch(img_1, img_2, lis1.Location, lis2.Location, matches, distance); +// +// Examples +// stacksize("max"); +// img_1 = imread("sample_image1.jpg", 0); +// img_2 = imread("sample_image2.jpg", 0); +// lis1 = detectFASTFeatures(img_1, "MinConstrast", 0.2); +// lis2 = detectFASTFeatures(img_2, "MinConstrast", 0.2); +// features_1 = extractFeatures(img_1, lis1.Location, "cornerPoints", "Metric", lis1.Metric); +// features_2 = extractFeatures(img_2, lis2.Location, "cornerPoints", "Metric", lis2.Metric) +// dimage = drawKeypoints(img_1, lis1.Location); +// [matches, distance] = matchFeatures(features_1.Features, features_2.Features); +// matchedImage = drawMatch(img_1, img_2, lis1.Location, lis2.Location, matches, distance); +// +// See also +// imread +// drawMatch +// drawKeypoints +// matchFeatures +// +// Authors +// Umang Agrawal +// Sridhar Reddy +// Siddhant Narang + + [lhs rhs]=argn(0); + if lhs>3 + error(msprintf(" Too many output arguments")); + elseif rhs-1>6 + error(msprintf(" Too many input arguments")); + elseif modulo(rhs-1,2)<>0 + error(msprintf("Either Argument Name or its Value missing")); + end + imageList=mattolist(image); + select rhs-1 + case 0 then + [location count metric]=ocv_detectFASTFeatures(imageList); + case 2 then + [location count metric]=ocv_detectFASTFeatures(imageList,varargin(1),varargin(2)); + case 4 then + [location count metric]=ocv_detectFASTFeatures(imageList,varargin(1),varargin(2),varargin(3),varargin(4)); + case 6 then + [location count metric]=ocv_detectFASTFeatures(imageList,varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6)); + end + cornerPoints=struct('Type','cornerPoints','Location',location,'Metric',metric,'Count',count); +endfunction diff --git a/macros/detectHarrisFeatures.bin b/macros/detectHarrisFeatures.bin new file mode 100644 index 0000000000000000000000000000000000000000..414fd5480f901c44c3d2eff722d6256b41bf5463 GIT binary patch literal 9156 zcmeI&%WfQ15C-4@0yxHY923U_<8dxT5CV!&fP@7SMuHU^gkZ^vEqh*q7hug}AfA9N zJOJVnAc!Q~R)|pFxPQB6JUxy_Apyill|Fsy{#TvTr`uUx8Lli1uOA%@);4c0FAlc` zE1T!*x7_RX?kigA4>nc?S2xx+hNF2rzx#`~c5-obW#@M6-&2c&#lh;@#@J-te6i5$ z9V@+4MQ;|JFZ$}%ty{mH=%#qnD+9+Bo8W!RAi_Fn$%af(MQY3DCI^>PnBTqMc zcJT+zJY8e*j}_rXhpohxIdY3d?RZW$bE|b_k4$2V9-jH@l{bAp5q^A_UoRq?81cmc zD}J6Y3YIn89pkhAYh#}sH0l5A;*0U|A~6~VZ{yfLTVwWSVREUFfh}Eex~6trx!!DI zkS`r^z=AF;o-X>SWG>Y8nWDt>LXGcrOo`$924^)9cjmur=3yUv!&AG^WNyXU&GV5O z%kyeu3pTKdui$JRUcu*y8qdP#*@mwN*q|@Q)T>T-J~cNFZek2xY`|6y=7RNP?l(#&`H+pgIr_6=#ybd`(SW zdwSx3v`AchvXQe`-Hz5+`+O-T@yJ0ea+r*fT|R@aIE=gYirYEsChs;b7s_Tkw%6)j zZ2U)Fu5`rH#*qvR@J|>0Sb39iR_R(3Fpk}J9KmZ66WCsBFoywOa9~3n^k8&TJM-+g zimzRd%XRM;9q*Z*rzmqZ>|H6E#KwGjExnoNL9h`Y-)hQ#Fr?CUO#ysa1OvHCtf;i$TjyK`fVH8YsloCh+X#a=(C03|4>nWYFr--_I)q#{ov1v+1gY82a3odIlEdL zgX3eLCU!qN6sxyBgC~}SqR-0-+kWC?ca|Qxz4&juBDR-{=!y09B6iLeC0{x7VcmJ| zsvm+^o_TPhV-7FS=wSeI@;#brKt~t^e_+eS zYCg(oaX9R+uWb$|N_{))dSw6F@qN025{Lch({iF$JX84V>ce7B`qqe`{kBGY}T5cXDMoSX-I^ zwBfDymllU>{e90gdh+uQe7PaH*t@#UPw83fMQr+A&yv4Vbg3x%?AjYWnh=faTYa7@`ZwY{UaN)CbLz0Q8n>}JmyLt!t=6fx{=7H! z?r(oozSY}4@PEAvgx-Ah9`BhGbM?%)js9FV4(dDas_DCh8sD3~JH7o0q>H?l|KE27 z!oR%l#(Q^juHGHDae6Kr2lc;iJL!LCk-OFZD(v@hy+G)D=Qn-t%=K=OGnR9h^O^Sv z=Obf(zcufiWZpT+dx!nn)jc`y@;Rq@UwW~~`|R1edRJx7ob~2e3%|~bw-7q>&|<}^#6BL>`~9@yZ4&$Ul(hG AcmMzZ literal 0 HcmV?d00001 diff --git a/macros/detectHarrisFeatures.sci b/macros/detectHarrisFeatures.sci new file mode 100644 index 0000000..8e7817d --- /dev/null +++ b/macros/detectHarrisFeatures.sci @@ -0,0 +1,68 @@ +// Copyright (C) 2015 - IIT Bombay - FOSSEE +// +// This file must be used under the terms of the CeCILL. +// This source file is licensed as described in the file COPYING, which +// you should have received as part of this distribution. The terms +// are also available at +// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt +// Author: Siddhant Narang +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + +function [cornerPoints] = detectHarrisFeatures(image,varargin) +// This function is used to find corner points in an image using Harris algorithm. +// +// Calling Sequence +// points = detectHarrisFeatures(I); +// points = detectHarrisFeatures(I, Name, Value, ...); +// +// Parameters +// points: Structure of corner points +// I: Input image to detectHarrisFeatures() +// MinQuality: (Optional) Minimum accepted quality of corners (Default- 0.01) +// FilterSize: (Optional) Dimension of Gaussian Filter (Default: 5) +// ROI: (Optional) Rectangular region for corner detection +// SensitivityFactor: (Optional) SensitivityFactor of Harris algorithm (Default- 0.04) +// +// Description +// This function detects corners in an image I. These corner points are used to extract features and hence recognize the contents of an image. +// +// Examples +// img_1 = imread("images/checkerBoard.jpg", 0); +// lis1 = detectHarrisFeatures(img_1); +// dimage = drawKeypoints(img_1, lis1.Location, "color", [0, 255, 0]); +// +// See also +// imread +// drawKeypoints +// +// Authors +// Rohit Suri +// Sridhar Reddy +// Siddhant Narang + + [lhs rhs]=argn(0); + if lhs>1 + error(msprintf("Too many output arguments")); + elseif rhs>9 + error(msprintf("Too many input arguments")); + elseif modulo(rhs, 2) == 0 + error(msprintf("Either Argument Name or its Value missing")); + end + imageList=mattolist(image); + select rhs-1 + case 0 then + [location metric count]=ocv_detectHarrisFeatures(imageList); + //[location metric]=ocv_detectHarrisFeatures(imageList); + case 2 then + [location metric count]=ocv_detectHarrisFeatures(imageList,varargin(1),varargin(2)); + case 4 then + [location metric count]=ocv_detectHarrisFeatures(imageList,varargin(1),varargin(2),varargin(3),varargin(4)); + case 6 then + [location metric count]=ocv_detectHarrisFeatures(imageList,varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6)); + case 8 then + [location metric count]=ocv_detectHarrisFeatures(imageList,varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6),varargin(7),varargin(8)); + end + //cornerPoints=struct('Type','cornerPoints','Location',location,'Metric',metric,'Count',count); + cornerPoints=struct('Location',location, 'Metric', metric); +endfunction diff --git a/macros/detectORB.bin b/macros/detectORB.bin new file mode 100644 index 0000000000000000000000000000000000000000..e26e1cad6e7543b3f7497907f0f7fa7679c35087 GIT binary patch literal 40036 zcmeI5e~4X2701{3Yr9Fd$u|3|Z(o{hlTC~H?tC|M@4jDmHgCK; zybFi>?)*4ðT5nYnM@t{$zAuBxA!SbcJGVtRdj^r`uiTejM@dT40qc}7#Ul{_xG zR#@FkZPhaqYZg^)yv1!K`vBrLd8}Dz>-}Q3+32EI)g2A 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z&y1AMrtsatpF4g0e0H;y^<&SIaxS~^%^5x(Dus{gpSk`bB0f^iWAAhL_+=@4RR7HN zR}t}%at^!msw{pSFNKflpSeC95g#d^xjx|V@oNts-Otm)^4^$dC6Bbg$DEb*BmW=| zs~f9QONmc^+XoaGpn4j+EyS_8sIrkFa$(&m-(yKF=d8*5`Tb z3SKWdy&qU>$kY3QG5cKh{Q$`2dd~f?@;}>W_FEA0^Y{0x{ZHEOy-wMkG5DXint edgeThreshold int fastThreshold int firstLevel int setMaxFeatures int nLevels int patchSize int scaleFactor int scoreType +// +// Examples +// [srcImg] = imread('images/table.jpg'); +// [orb] = orbAnalysis(srcImg); +// [orb] = orbAnalysis(srcImg, "filterByArea", [0.01 1]); +// +// Authors +// Siddhant Narang + +[lhs,rhs] = argn(0) + + // To check the number of input and output arguments + + if rhs<1 then + error(msprintf(" Not enough input arguments")) + elseif rhs>10 then + error(msprintf(" Too many input arguments to the function")) + elseif lhs<1 then + error(msprintf(" Not enough output arguments")) + elseif lhs>1 then + error(msprintf(" Too many output arguments")) + end + + srcMat = mattolist(srcImg) + + if modulo(rhs,2) == 0 then + error(msprintf("Number of input arguments must be odd")) + end + + select rhs + case 1 then + output = raw_detectORB(srcMat) + + case 3 then + if typeof(varargin(1))<> "string" + error(msprintf("argument at position 2 must be string")) + end + output = raw_detectORB(srcMat, varargin(1), varargin(2)) + + case 5 then + if typeof(varargin(1))<> "string" + error(msprintf("argument at position 2 must be string")) + end + if typeof(varargin(3))<> "string" + error(msprintf("argument at position 4 must be string")) + end + output = raw_detectORB(srcMat, varargin(1), varargin(2), varargin(3), varargin(4)) + + case 7 then + if typeof(varargin(1))<> "string" + error(msprintf("argument at position 2 must be string")) + end + if typeof(varargin(3))<> "string" + error(msprintf("argument at position 4 must be string")) + end + if typeof(varargin(5))<> "string" + error(msprintf("argument at position 6 must be string")) + end + output = raw_detectORB(srcMat, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6)) + + case 9 then + if typeof(varargin(1))<> "string" + error(msprintf("argument at position 2 must be string")) + end + if typeof(varargin(3))<> "string" + error(msprintf("argument at position 4 must be string")) + end + if typeof(varargin(5))<> "string" + error(msprintf("argument at position 6 must be string")) + end + if typeof(varargin(7))<> "string" + error(msprintf("argument at position 8 must be string")) + end + output = raw_detectORB(srcMat, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(8)) + + case 11 then + if typeof(varargin(1))<> "string" + error(msprintf("argument at position 2 must be string")) + end + if typeof(varargin(3))<> "string" + error(msprintf("argument at position 4 must be string")) + end + if typeof(varargin(5))<> "string" + error(msprintf("argument at position 6 must be string")) + end + if typeof(varargin(7))<> "string" + error(msprintf("argument at position 8 must be string")) + end + if typeof(varargin(9))<> "string" + error(msprintf("argument at position 10 must be string")) + end + output = raw_detectORB(srcMat, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(9), varargin(10)) + + case 13 then + if typeof(varargin(1))<> "string" + error(msprintf("argument at position 2 must be string")) + end + if typeof(varargin(3))<> "string" + error(msprintf("argument at position 4 must be string")) + end + if typeof(varargin(5))<> "string" + error(msprintf("argument at position 6 must be string")) + end + if typeof(varargin(7))<> "string" + error(msprintf("argument at position 8 must be string")) + end + if typeof(varargin(9))<> "string" + error(msprintf("argument at position 10 must be string")) + end + if typeof(varargin(11))<> "string" + error(msprintf("argument at position 12 must be string")) + end + output = raw_detectORB(srcMat, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(9), varargin(10), varargin(11), varargin(12)) + + case 15 then + if typeof(varargin(1))<> "string" + error(msprintf("argument at position 2 must be string")) + end + if typeof(varargin(3))<> "string" + error(msprintf("argument at position 4 must be string")) + end + if typeof(varargin(5))<> "string" + error(msprintf("argument at position 6 must be string")) + end + if typeof(varargin(7))<> "string" + error(msprintf("argument at position 8 must be string")) + end + if typeof(varargin(9))<> "string" + error(msprintf("argument at position 10 must be string")) + end + if typeof(varargin(11))<> "string" + error(msprintf("argument at position 12 must be string")) + end + if typeof(varargin(13))<> "string" + error(msprintf("argument at position 14 must be string")) + end + output = raw_detectORB(srcMat, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(9), varargin(10), varargin(11), varargin(13), varargin(14)) + + case 17 then + if typeof(varargin(1))<> "string" + error(msprintf("argument at position 2 must be string")) + end + if typeof(varargin(3))<> "string" + error(msprintf("argument at position 4 must be string")) + end + if typeof(varargin(5))<> "string" + error(msprintf("argument at position 6 must be string")) + end + if typeof(varargin(7))<> "string" + error(msprintf("argument at position 8 must be string")) + end + if typeof(varargin(9))<> "string" + error(msprintf("argument at position 10 must be string")) + end + if typeof(varargin(11))<> "string" + error(msprintf("argument at position 12 must be string")) + end + if typeof(varargin(13))<> "string" + error(msprintf("argument at position 14 must be string")) + end + if typeof(varargin(15))<> "string" + error(msprintf("argument at position 16 must be string")) + end + output = raw_detectORB(srcMat, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(9), varargin(10), varargin(11), varargin(13), varargin(14), varargin(15), varargin(16)) + + case 19 then + if typeof(varargin(1))<> "string" + error(msprintf("argument at position 2 must be string")) + end + if typeof(varargin(3))<> "string" + error(msprintf("argument at position 4 must be string")) + end + if typeof(varargin(5))<> "string" + error(msprintf("argument at position 6 must be string")) + end + if typeof(varargin(7))<> "string" + error(msprintf("argument at position 8 must be string")) + end + if typeof(varargin(9))<> "string" + error(msprintf("argument at position 10 must be string")) + end + if typeof(varargin(11))<> "string" + error(msprintf("argument at position 12 must be string")) + end + if typeof(varargin(13))<> "string" + error(msprintf("argument at position 14 must be string")) + end + if typeof(varargin(15))<> "string" + error(msprintf("argument at position 16 must be string")) + end + if typeof(varargin(17))<> "string" + error(msprintf("argument at position 18 must be string")) + end + output = raw_detectORB(srcMat, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(9), varargin(10), varargin(11), varargin(13), varargin(14), varargin(15), varargin(16), varargin(17), varargin(18)) + end + + orb = struct("Points", output(1), "Size", output(2)) +endfunction \ No newline at end of file diff --git a/macros/detectSURFFeatures.bin b/macros/detectSURFFeatures.bin new file mode 100644 index 0000000000000000000000000000000000000000..75345628e448fb6cf5967c4317acffbd1d0ab463 GIT binary patch literal 15540 zcmeI(OOG8_5eM+$?M!UPcKix6V^541!Le)zBo+`9FA!qOnhg>g#1~=1mq6^1Sn?Uz zAhAaT5?(>bJj4oN>NoEHxaPQf?wyI9#8yT*>C<)U`LC*T`u4p(b$aLY$(?uJe)rzJ zQ=2RW$)JCwMsiWp{kdF_`FF`g4_&cO z-a6dq`Fss=nI~W7(S5z<_)E`9{%Str6c|pX`1VeEaI; z%U>qkv*d3u z{&Vs2m%h|`{21*-=C6}}>yhPK)?r4Up3*yP@J$^2x5wxcb8>ji@pc`F7cbw$7QXC< z`dRGoCeNo44>Wj32VczTvPQPoLEl5ZUTgHt^&R@nX+1oA5FefKMSSBvl{n=%dvdVg 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a/macros/detectSURFFeatures.sci b/macros/detectSURFFeatures.sci new file mode 100644 index 0000000..f2d6b49 --- /dev/null +++ b/macros/detectSURFFeatures.sci @@ -0,0 +1,74 @@ +// Copyright (C) 2015 - IIT Bombay - FOSSEE +// +// This file must be used under the terms of the CeCILL. +// This source file is licensed as described in the file COPYING, which +// you should have received as part of this distribution. The terms +// are also available at +// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt +// Author: Shashank Shekhar & Siddhant Narang +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + +function [varargout] = detectSURFFeatures(image, varargin) +// This function is used to detect SURF(Speeded Up Robust Features) Features in a grayscale Image. +// +// Calling Sequence +// result = detectSURFFeatures(Image); +// result = detectSURFFeatures(Image, Name, Value, ...) +// +// Parameters +// result: SURFPoints struct which contains Location of KeyPoints, Orientation, Metric, SignOfLaplacian, Scale and Count of the features. +// Image : Input image, specified as a A-by-N 2D grayscale. +// MetricThreshold : (Optional) With default value equal to 1000, it is to be specified as a scalar. Every interest point detected has a strength associated with it. In case, only the stronget ones are needed, this parameter has to be given a larger value. To get more no of interest points/blobs, it is to be reduced. +// NumOctaves : (Optional)With default value equal to 3, it is to be specified as a scalar. Larger the number of octaves, larger is the size of blobs detected. This is because higher octave use large sized filters. Value must be an integer scalar in between 1 and 4. +// NumScaleLevels : (Optional)With default value equal to 4, it is to be specified as a scalar. It denotes the number of scale level for each octave. The Value must be an integer scalar greater than or equal to 3. +// ROI : (Optional) Region Of Interest. This is taken as a vector [u v width height]. When specified, the function detects the key points within region of area width*height with u and v being the top left corner coordinates. +// +// Description +// This function return the SURF(Speeded Up Robust Features) Interest Points for a 2D Grayscale image. It is scale- and rotation- invariant point detector and descriptor and its application include Camera Calibration, 3D Reconstruction, Object Recognition to name a few. +// +// Examples +//stacksize("max"); +// img_1 = imread("images/table.jpg", 0); +// img_2 = imread("images/table1.jpg", 0); +// lis1 = detectSURFFeatures(img_1); +// lis2 = detectSURFFeatures(img_2); +// dimage = drawKeypoints(img_2, lis2.KeyPoints); +// features_1 = extractFeatures(img_1, lis1.KeyPoints, "SURFPoints", "Metric", lis1.Metric, "Orientation", lis1.Orientation, "Scale", lis1.Scale, "SignOfLaplacian", lis1.SignOfLaplacian); +// features_2 = extractFeatures(img_2, lis2.KeyPoints, "SURFPoints", "Metric", lis2.Metric, "Orientation", lis2.Orientation, "Scale", lis2.Scale, "SignOfLaplacian", lis2.SignOfLaplacian); +// [matches, distance] = matchFeatures(features_1.Features, features_2.Features, "Method", "Approximate"); +// matchedImage = drawMatch(img_1, img_2, lis1.KeyPoints, lis2.KeyPoints, matches, distance); +// +// See also +// imread +// drawMatch +// drawKeypoints +// matchFeatures +// extractFeatures +// +// Authors +// Shashank Shekhar +// Siddhant Narang + + image_list = mattolist(image); + [ lhs, rhs ] = argn(0) + if rhs > 9 then + error(msprintf("Too many input arguments")) + end + if lhs > 1 then + error(msprintf("Not enough input arguments")) + end + select rhs + case 1 then + [a b c d e f] = ocv_detectSURFFeatures(image_list) + case 3 then + [a b c d e f] = ocv_detectSURFFeatures(image_list, varargin(1), varargin(2)) + case 5 then + [a b c d e f] = ocv_detectSURFFeatures(image_list, varargin(1), varargin(2), varargin(3), varargin(4)) + case 7 then + [a b c d e f] = ocv_detectSURFFeatures(image_list, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6)) + case 9 then + [a b c d e f] = ocv_detectSURFFeatures(image_list, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(8)) + end + varargout(1) = struct('KeyPoints', a, 'Orientation', b, 'Metric', c ,'SignOfLaplacian', d,'Scale', e, 'Count', f ); +endfunction diff --git a/macros/disparity.bin b/macros/disparity.bin new file mode 100644 index 0000000000000000000000000000000000000000..fd1000033f8d491a5c414e8003f1add8b19ea9c9 GIT binary patch literal 15176 zcmeI&Pj6jU6$Nl7KuO&uO-vFyJiC2yqbRsmgbbw!0!=Gu5iN*613*GBs01_SO!y2) z3_Aj544Lx@_z)CPKrlcE5FbFc67pXV4$ku4qFbI;jl?X}n5=iK-1^Tn0* z8_ScGjmo*R;oPq- zEiFA)crR3Yt=gSxe|YfV!IiqMR9o-H*gRitwc4}QHmaFlt7go)NwtET&9QZFE*RFp zWy4az`}Y4sDUxb&mP zz3N*ZUg3=gJNSIL22bC5>xS{NfrU%3#<*oI+pC>zVlxIeHq+`S&gjX%#z%5ueb&Kn zPm62SqQ@typV+JWurF5r{#A8htu-3%!7=}S&BOX=HTl;!Pgnh5kLC4~H4h_sqVc(I zY^S>>K03~;mygw4G!-9=V7KG;-Bz`5`d;;i{iWSXhH}3J^3-7y-j1|>c)7hGhgAuSM2zwWw`&osgu+}=G0i*+8or>!+hA|{QtSb z!e>AK$>R^YbuG{ARk3Y%^RWL}*tFQMp4de{F{g%b$$fNYYuw(Tv+?#k!vn8q5x?;5 z5!m8zHHL36=jGTse&A~S->t?T#xQ@$Td*_FXL24)wFAeTYvOJ9Jl{Cu`Es?tmZto& zZFM)79DTm}i3?V^@N112l5b<-9Db?Y%thyLP&;y9oXyp0cqM1?Bd)}PpY=nm=tx`k zv|SXxJzw7`9RB#XN8le+kNndXr|oLiC$5{-HxDOx=4or(n&i5z$@VN}7e4H*Pu#{A z#TPfMvCkDPC;8zo_2ZhHCoa6y1H60=Xya5HiPtr`f|WIi*|XuPYHeQbcXpm-|E?w{ z?^TO7^i(6$YHW zi@x`2Y^NGcvQKlCs;A_C_-q#!-r66FXZDyl_#bM*U!FnX$u@oU0Fxc<^v#JMwzbBF zxt!LRvjbK%XJei5c8xy1(bTh*CgI^4on5!8!GbF$V|da92E1q-n--s*(a^j#e85WF z>Q;QtEwylV9ey+yH^nlM-c;k~;QX_3F^LUC$ zJgrW!ti=T;KdCqNZC=DHmM)$&5Ic)c{h)P@$9K+B~M%3oUz~!^JYC9T+Q9B#+NmzKjVBP zF4+6!M|{bvzRzZDp2Eemgm3t%Uvu!2kHh+GYeFpcxxe)B5QDt)$%nDTDjqzv9d2sDx?mnfCo$uZ{A7>7i+=Eqk|Qx?t$wtJlf90s`)I~rbWm63ep@KOa82p zSG=M@_A>3+Mu%|0y{+B4eOMPySD*iXlrDI}O03Bl9*ISs*(VnE$&oSh`JCxopm%Zv z-~CoyU#b?&Y4u@R%T^q>s-3H5e5bo+KKk%YllTc=xMH_n+#jh%N4Wgwd0Mx#^QGm2 z;}{ob?@_oU2YjltMfvclO{)#{fSr2q&TF`5%;BM))djuW)6Dw{V>DGy_{kqFKUIw{ zf6?m0)qkbAzfk>Q9zR}VxWc6OkZ3SZFfLi@joOr@Ojmmwg>AlgFVxLrmO`g2{e}`I#ESNpAUV`Gr?< z?aHow*Nc-{SL4goQfIy&s}niP^E$OB&S^EZ5N+WkZZQqV6KlrD&OI2zvrN3hm~YpZ zIOUT)-if`v$4{NMnuv!V{;jb#=Z@Gnt0j*^F5y1pIhL2Zg~M-a+w$O3K4>Z@v`)Uv zd1gj$?;`Zsvvb8UId@*?^NpCq;atn-S!3Zo)Fc`$azx9$~EOz!{cNP2O%KAXD-P@X4y0*_;a-=qX*=cNz{K4K2m))Znw(rZ$y=vCU ziFX@tUappy)q?z~Ip1GgHFcCTOl@s;W6oLk&brhrTXm-1;2XC#=O|s&G9K60Z%kHJ zH=Y}cGo~rd)?AuQ)+WORbw6)STZ7B1^YcK1{5@ZbaDSzmar*!KSo$x`^#5$(ME^Ic z&&Q{$4fXd^$B`{Nt0z}~uh*s(-)q&P z;ZKjH;pN$x(rWmv!sg@k&d1}_5Z0sT$=_e4&Ak7Cns*lWF_!Kxf?>^mx%^jCJdiz*4HQiHF?-n*6U+R3&?yROB<(3vcXFiUa z>SJ78s4n{$YYSlsYmYYfo+erm)raTb+-y+8+N- z-mj^}&dWZ=>D6|h$NED1qmMCN2;-nV=KFB=*fZ5Xi9Pm>>2CdhrK^AY`ogjH*quFl z?01D}kA1cCK|8VJ^Tyd{k|*!^(cd4Zx5xTCHW%8beT-WRVH~vAy%Ww}_fGaC_WDaV zclY7f3O~=&Kh>8mpI!XfqcfiSyjx`V== z)>mG=1 +// uniquenessRatio: Margin in percentage by which the best (minimum) computed cost function value should +// “win†the second best value to consider the found match correct. Normally, a value within the 5-15 +// range is good enough. +// textureThreshold: This controls the detailing in the disparity map. +// disp12MaxDiff: Maximum allowed difference (in integer pixel units) in the left-right disparity check. +// Returns: disparity map +// +// Description +// Validates disparity using the left-right check. The matrix "cost" should be computed by the +// stereo correspondence algorithm. +// +// Examples +// stacksize("max"); +// img_1 = imread("images/left1.jpg", 0); +// img_2 = imread("images/right1.jpg", 0); +// w1 = genCheckerboardPoints([10, 7], 8); +// ip1 = detectCheckerboardCorner(img_1, [7, 10]); +// ip2 = detectCheckerboardCorner(img_2, [7, 10]); +// ip1l = list(ip1); +// ip2l = list(ip2); +// op = stereoCalibrateAndRect(w1, ip1l, ip2l, size(img_1)); +// [map map1] = disparity(img_1, img_2); +// img = reconstructScene(op.DepthMap, map1, 1); +// +// See also +// imread +// genCheckerboardPoints +// detectCheckerboardCorne +// stereoCalibrateAndRect +// reconstructScene +// +// Authors +// Siddhant Narang + + img_list1 = mattolist(img1); + img_list2 = mattolist(img2); + + [lhs rhs] = argn(0); + + if rhs > 14 then + error(msprintf("Too many input arguments")); + end + if rhs < 2 then + error(msprintf("Not enough input arguments")); + end + if lhs > 2 then + error(msprintf("Too many output arguments")); + end + + if rhs == 2 then + [tmap, tmap1] = raw_disparity(img_list1, img_list2); + end + if rhs == 4 then + [tmap, tmap1] = raw_disparity(img_list1, img_list2, varargin(1), varargin(2)); + end + if rhs == 6 then + [tmap, tmap1] = raw_disparity(img_list1, img_list2, varargin(1), varargin(2), varargin(3), varargin(4)); + end + if rhs == 8 then + [tmap, tmap1] = raw_disparity(img_list1, img_list2, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6)); + end + if rhs == 10 then + [tmap, tmap1] = raw_disparity(img_list1, img_list2, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(8)); + end + if rhs == 12 then + [tmap, tmap1] = raw_disparity(img_list1, img_list2, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(8), varargin(9), varargin(10)); + end + if rhs == 14 then + [tmap, tmap1] = raw_disparity(img_list1, img_list2, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5), varargin(6), varargin(7), varargin(8), varargin(9), varargin(10), varargin(11), varargin(12)); + end + + channel = size(tmap); + + for i = 1 : channel + map(:, :, i) = tmap(i); + end + + channel = size(tmap1); + + for i = 1 : channel + map1(:, :, i) = tmap1(i); + end +endfunction \ No newline at end of file diff --git a/macros/filter.bin b/macros/filter.bin new file mode 100644 index 0000000000000000000000000000000000000000..20e97355dfd5a2a952238402ccfddab83a65f9bf GIT binary patch literal 6528 zcmd7X+io015C-5RoDvgn;w3(@-ryt_gv1yKATDAFZg>Z-kRUI?D+2GpSwhMM0s>qi zLVeEstM+(?tPzM$vpU(HuN~7onp~O8HYe-%FU)7tYwhyN=Eb$G+1@do^-h1iwKZOytj|~a z%9`&Ns7rVk1dghzx?uiU+ZGMSaP+**!sn8oY;poXHQZ` zV%+KUY^})yfAGHA>BZeHk;ghN6RYLq?b6GmSH$2GKC(8($NjEe_P7%NT1os4YOMdp z=GJbN+%8G&HC&9JuQ6;tba~`}-M+%w)~yE;1Ha*JSswN~pC2n%-zTy9{nYlu3 z$Hacn?LVxr<4Z0zWu87C%Q==K9P1Ntof901&j*+0`bCc25_-AuYRw!D{hYaeU=K49 zTP$&6BbPXI>1(zJC3j2sWUp{&exroHzT-z1UJh%_PwwFJjJ|K*kIzmm&f_;FdjEXM zw>730`;C%i;>30g%b0$d*nN!7gF6{c_*(o~bB8?1H9S?g&z1cAgyY2X)Bh1pd)M#D zWqLmM*C*v~k57B==AQYkxNpyJ?k#6<)?vHW<=Cw!Tll<(@-EB!AnzIXi1X@r|qFSy*<=GO8)3HSWs>q(D8{AS$t{#bvt{bK*j^|pM$lwMEoiIsh1FCUxa&)Kq1 zdZ>>_f3^En)5UKVeZs!|jX;-}`c)pi1A`p!c=vu(!nc3!t*a+})A#3t=b^Kd+S7Nj zg$EySh-c2mQ*W`$nUoh#S+_Q}AJ)QSa`87FJDb+>d+ngcxew&SUG@lH*35%A&FT6J2U+@mQA&wqS0tunmX7ZH{LW-eDAn4g6x{ ztjOEPF~nh%r{)a4i}s-XDvLpnA11$g_*y&?pAs>^kcs{b0*l3^n!lK z9^xce+jDB2Prr6pvtQ!yO&`OQd&s$HbF_6h`_{4_ZSQBV z>Xq;Tm)g=d;=q!1J(<32_Yep2+VAv?F`c|aJSHYP`^Q$y0+2REsu=qDIyWn(QZk4dR(>-6QCwa4kA56IMUjMw#bUhXr_=)#UPdD}( z$X=&*_{IOMVvfaz?=-RP4=#605*x-bJmEu*mJ{>v@_LQM5Knyg<46BmNuP&$1M^Wv?&P7zTaeH#ctOw&z2=QR)-Eztle1OMW>E;_z2{a_!bw zEYgLEIEU8QW?yQ0;v0#x)5mhI(=Cf7 zHLCC5!l7RH)jq_Fb$29Q@q!Ea368>FxD5xbUY)l}ZkJ?lYPcAuC-SW=Smai--1;%T z&(*l)vcGq+E3<{q`OfF}+Na!IZJ)P&;|z!o4_jx_6Bhd4N*~@lTTa-hckCPOALU!i zM>ZQP^ZC~Lbf^EN_s_D?W6zZ2-{$7ob38Wj&$gdrYw4)w0UXEdS{!5Fja!>X-;nnT z^6`(zt CV_8U CV_16U/CV_16S CV_32F CV_64F +// kernelMatrix: The matrix which defines the kernel of the filter to be applied on the input image. +// anchor_x: X-coordinate of the anchor. +// anchor_y: Y-coordinate of the anchor. +// delta: Value added to the filtered results before storing them. +// +// Description +// The function applies an arbitrary linear filter to an image. In-place operation is supported. +// When the aperture is partially outside the image, the function interpolates outlier pixel values +// according to the specified border mode. +// The function does actually compute correlation, not the convolution: +// +// dst(x, y) = $$\sum_{0 < x' < kernel.cols}_{0 < y' < kernel.rows} kernel(x', y') * src(x + x' - anchor.y + y'- anchor.y) +// +// +// Examples +// img1 = imread("images/right1.jpg", 0); +// img2 = imread("images/left1.jpg", 0); +// image = imabsdiff(img1, img2); +// +// See also +// imread +// +// Authors +// Sukul Bagai + + input_image1 = mattolist(input_image); + a = raw_filter2D(input_image1, depth, kernel_matrix, anchor_x, anchor_y, delta); + dimension = size(a) + + for i = 1: dimension + out(:, :, i) = a(i); + end +endfunction diff --git a/macros/ftrans2.bin b/macros/ftrans2.bin new file mode 100644 index 0000000000000000000000000000000000000000..96fa9525d257e94103dd315604bb8211bda2272f GIT binary patch literal 4828 zcmcK8+iq256b0Z7o;TRino@MPP%Rh)EC!8U;=M26of>@yAI7IMp6(1D;+3(EZ?i^S zEV>FLvXhzrG!J8rIoH3sy1Bnv?(UtxIsLu9SS-#(wx$Qe`h3nV%$!|Yu9mAy+q=`_ z?_OLiHe+`!ayN1_!p~9UkK^NG>)$f|8u@8h)8CHlMD`;akyV85)yQ7N`EKM=#6BB3 zbl-~5GpBPg@=k;vfAp3SXMC}}60v_d;*6dc_UU~Xp(hW&&bKn>_s?Kw2Q!)C`!VNp z*zDKe8=3Q0zxRf<^V6{7^1aA~$VZXpcq{YUk^0T_WKNgfF9YWCA$M5DoR=SY%ZnZR zJH!01p=S2VsXlshShFYWV8P3@uEofv`0M|N_%FU^sTV)vTJjVTYS>jGdR$LeAHiaQ(JpoA7swvD8dfT)}lGvYcICX*gwm&)1H!t zIX~w7z{x-Sq?qO1xUjXB?Cdv(aZc6e<9X+#?^*|3!83P9G;g?yTdwU}x$#vV)S!Cf z=l`2w-*ZQw->DzZCEIdI2QKT@)Ol+HL%B6~)|$Yf4($19-R63Nz8pMfa^;6@*S#kW zvL`-2bc)5gSf-qb4GWQrIGwXAwsJhjCp$T|R@Fbq9zTsshYx$xdjrSnaw|f|9rbmD zP475dGzOnNYdm=E!J{trk0Q_V9F|XamG=xj>fd{ff4yQ4Mq_&w&uri%&fvtYoYkfN zVT6kuMOsh3ya&q}KE#~k&6&7(8P^>bt-F2e;>0IxZ0CB~Ic#+3%d2(yGV?JeKK!lG z+=|t{x^`dA_NKNt#+Q9O6=yjpR_|Ts_@N^wys;bK*Yaf}e^Q_Q`$BDDA?)R6?tQ`+ z8*6#Ma1arzT9V_2Fx5dwk1*ojmQ)Zyxol|GtWh_2n&Q zYrqdX`}M`14ZrXgi#;{3kH)he>vt_iIe8v`r!^)X%=_$%-x_!(;i#?=?&gT=qey$$ zy#3WWjr*K^IjN7iXOr&FiLbw5P>;_d#nRqrJnJbwG2lInlvno)X}`eC?p);7FuyyD z^k8s)n%)P4p68Ix`@>jYY{uL)b}^sk^Z$xHeh&x635Tx@7=KS*o(pxtg?pm4z*{-r z$XssnFy0%+k2A`%_s?|xNzUq{cSCDbAI|%nwqL}?C$7X)liQK{%JgLZYUVKc+gktj znd_Y}&g11l_xtGT2{jP2cQ60)r`I1G`B;1GTg|uIKk*~ymm)7j=n@!=-mj~zz3FG( zdYz-i&KYO06c1l>-;#d^17CD5FZcJa>}>DtPa>X%6K*(-xV=6TAV+H)u;A+3h*;*v zv+~RF+7>tear)r?yIJdpgUC3?ISk_*SIdLzo2Pt}KPgAGxN>#M%tNAbbduwvSQnq_ zaaa!r9sMEiCt-cGj~?7-Gxyy;hkecO8TmSI@4-E;>C@yJllAXL5|rbO$h7a~I2+ew zZ~Jg#(sJwNTwd)v7%y%ucXu~-9y155SYF8-zHtx9{VdxYuerICkuGlBN867@dbrU( uRJYF=$E*8bG~x4{{@%%I#&ixNcPsj^*y+5t-Zu}Q3x@6J- literal 0 HcmV?d00001 diff --git a/macros/ftrans2.sci b/macros/ftrans2.sci new file mode 100644 index 0000000..7fa2077 --- /dev/null +++ b/macros/ftrans2.sci @@ -0,0 +1,55 @@ +// Copyright (C) 2015 - IIT Bombay - FOSSEE +// +// This file must be used under the terms of the CeCILL. +// This source file is licensed as described in the file COPYING, which +// you should have received as part of this distribution. The terms +// are also available at +// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt +// Author: Vinay +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + +function h = ftrans2(b, varargin) +// 2-D FIR filter using frequency transformation. +// +// Calling Sequence +// h = ftrans2(b, t); +// +// Parameters +// b: A bandpass filter +// t: Transformation matrix +// +// Description +// Produces the two-dimensional FIR filter h that corresponds to the one-dimensional FIR filter b +// using the transform t. +// (ftrans2 returns h as a computational molecule, which is the appropriate form to use with filter2.) +// b must be a one-dimensional, Type I (even symmetric, odd-length) filter such as can be returned by +// fir1, fir2, or firpm in the Signal Processing Toolbox software. The transform matrix t contains +// coefficients that define the frequency transformation to use. If t is m-by-n and b has length Q, +// then h is size ((m - 1) * (Q - 1) / 2 + 1)-by-((n - 1) * (Q - 1) / 2 + 1). +// +// Examples +// b = [1, 1, 1] +// h = ftrans2(b); +// +// Authors +// Vinay + + [lhs, rhs] = argn(0) + + _b = mattolist(b); + + select rhs + case 1 then + out = raw_ftrans2(_b) + + case 2 then + out = raw_ftrans2(_b, varargin(1)) + end + + channel = size(out) + + for i = 1: channel + h(:,:,i) = out(i) + end +endfunction diff --git a/macros/getParams.bin b/macros/getParams.bin new file mode 100644 index 0000000000000000000000000000000000000000..9048ddac2fa7db348de30af2f353b728db043a2a GIT binary patch literal 18584 zcmeI)OK)9Q6$fy)yb?Ec95=QTY!^2K2mvbU0&PV_iwX=-s}7JDATi=&AjZsL#3y3F zmjK$T38_$M3ll2B`q}4iZy(=t@A0(*O?xe^o_+UYJ^t&l_dfTgt1CP2E^jSwY~MQm z_vF&j(ql!}4IUrb|MYl`U0Pq>-d?-Bwz746!p5~HSJv-d+*mo@eQSAbY3XvU`%=+s zMYoFHdhp=Em3r3D(stKp&eKJkMVE?pii}??(q~;e-&|`~is)^e40H7D7U{!>EPXy- zFIq0z>Yf|*G-jTs{^&$Dy{xk){IZrTx>*+;=)PWLK0e4ie4?B6&v$)v_7{xkO3sIc z5gvG<$Xczo@waQ7+%FcvLOa-Lhu`gLn?K2J{d?UwzShCv-A;Dwgb_QwUNqLe@6=rU zt%W0e@Mp{2j(@PH<2Q<;b2Sg!PgEP9NjgPe&-g7ozS^xDtM50v`F}6n=mq;=LOwqF z5+}ykMedEF%o*eR==j5K-589bUmrVi+c@pV`y}4hm}i~#Zc*})or%FdFJoiu!;|0Q zZ*s&Y%#vG?Ka>Y#;qyTk=ho?iw_Fgr9Rk*kE~0M#IO3zLHxpBc(?US3?*mMk9R-be6}9X%*&Hp zYs7&$WRXcvx1*EYBDzNJ z=c-Mo?-k)6{NkfLC)s0uaL{gTc%eSy8=Kp`%Upc(OzyK=|0Mj_N=JWucA|S}J*;nBC9T=um7XEPtOxwSR9z~)J3 z1@l|m@gA#Zbfj;~!)H1tAF6rSzF2MgjjaLrm_HQ5t*w~@i`d=f58KApfJOcv`RwDE z|3B#BLY~USq1>cDCF$(2d<#2(}< z(m&fJZ`$)g`i5#(|Gd$^=z^dJVjo_{%N}M%U_C4lW*bcOgiMNmaq2x zBJVX$i^nwG|5f}>d#)d&yEB9LD9#?@HTRm+>OKu0Se%E9QtvQ#E>xR8;NnguhrGAQ z9ayf#el^Toz7ccI&ZvFQVZAx2O}Wpd2I7U}@#959@5w(Z88zIS^Va6QE89DHb^f24 zle3XK9ezI3!PB|ni2u#?wWFm}+}9(zd|pTQhtKlX>SpA$&q*@PC)1P6tt zX??%EIqU~G1lwu;s%Z_jORvKA`$fU_C)LL1g`z&TzH^WVu%j2Of=lqUR{j{vS>e-; zZyUSLcQAUXsE;oU$HeX@4c9Vz|L>3W(h)Y|5jIFJeo+5W4}M!ow4Zka-#ONl{Xgrx zzf2s8MQ2dFFqcduGO8?GqDhBja`vU88r-4|Jp}9HL|P=ja|D#??N0;7O-GUv}X+ge_kDK*j>w zBmd08Hh#NWxWRWC#%EV|KcsLoOMs~UwU<3{$D75fA09pUp}$Nitm$hF3cr|ZreLsPhUE`Q5_-A zb~v$E_zYLh;n<+^${xt`pAW!`#XPlZqF6`5p^=Ls+M z6MoUf+~f6f{(45fdKr11-b+T7^MU&2{A29PMJMIYSLfstUTpJZtNnp(rjWb#B>1{wI%odgTtMkA5fjnQL>&pS91x zCHo9L&$7?Ji$6aq;^&_iEo#8Qo-z~ziNi$=IH)^|8gQ@|JxVn|j;DsGwe9z5f2hx5 z`xixLss-=exswy}(A_}o%=vieUQtsH^~m1j`xmu~o@#&gr0fTI@1#!OsB!P5PVSHM zpAX2*y~=(-2UuzACrh0$&pz=)kvaBJx}MxC=07Vq)47l4oy9Zt`S?CK-#K8`K6KXm zP8)yTMcLbtFJ$2M%c4d69px#1O2yj}XV@A2QM&-6aaJA4?WE>3#iq;A6*Ch8;ngSFpCz(2KI zTisO4%uzG(&wJWFuJDi_?8+STz2m~0pVeUeobAT&lXHE3ZJouNo^+9u?OrriXM0*? z(6{BEf2YDHWY|k)VPHS#^UImleUZM2#b7&&r&`_e~C5pl@PoOl(}J`O{?Blf|QaC2uU& z7dyUqxA{4%PO*jlu;G)$3!7n=diSW;sk>zh`_Q6R9blmLjjdJAhp_V$Ba6DVs9SAq zJkPq7nv;EE=#16=&Tv-!dbW$-)POuwyR*0H-|Ob8jq3Yu(aj?IsEgJN{br)oNq*5? z4aWyw$UenJPyEQi-@NQ$Wb|vfG5VdQe)W0I!kFHaS;;-bt!lT zzh@uem|z3)_j-|B_;ry!=Lzo#zEbpDk$t@V4pI!6FPHL8#T@-|OT4J%>Nj5U*LlF0 zKDfPDe67jZ2EXXy?lJWL>S=%Tv@!W7?fbuJ$HRLDb|QCYb<5K|c+Bt9 Ne^c{< 1 + error(msprintf("Too many output arguments")); + elseif rhs < 2 + error(msprintf("Not enough input arguments")); + elseif rhs > 2 + error(msprintf("Too many input arguments")); + end + + select modelName +// case "SVM" then +// temp = raw_getParamsSVM(classifier_list); +// param = struct(); +// case "svm" then +// temp = raw_getParamsSVM(classifier_list); +// param = struct(); + +// case "SVMSGD" then +// temp = raw_getParamsSVMSGD(classifier_list, image); +// param = struct(); +// case "svmsgd" then +// temp = raw_getParamsSVMSGD(classifier_list, image); +// param = struct(); + + case "EM" then + temp = raw_getParamsEM(classifier_list); + param = struct("No. of Clusters", temp(1), "Means", temp(2), "Weights", temp(3)); + case "em" then + temp = raw_getParamsEM(classifier_list); + param = struct("No. of Clusters", temp(1), "Means", temp(2), "Weights", temp(3)); + + case "LR" then + temp = raw_getParamsLR(classifier_list); + param = struct("Iterations", temp(1), "Learning Rate", temp(2), "MiniBatchSize", temp(3), "Regularization", temp(4), "Train Method", temp(5), "Learnt Thetas", temp(6)); + case "lr" then + temp = raw_getParamsLR(classifier_list); + param = struct("Iterations", temp(1), "Learning Rate", temp(2), "MiniBatchSize", temp(3), "Regularization", temp(4), "Train Method", temp(5), "Learnt Thetas", temp(6)); + + case "KNN" then + temp = raw_getParamsKNN(classifier_list); + param = struct("Algorithm Type", temp(1), "No of Neighbours", temp(2), "Emax", temp(3)); + case "knn" then + temp = raw_getParamsKNN(classifier_list); + param = struct("Algorithm Type", temp(1), "No of Neighbours", temp(2), "Emax", temp(3)); + +// case "RT" then +// temp = raw_getParamsRT(classifier_list); +// param = struct("No. of Active Variables", temp(1), "CVfolds", temp(2), "maxCatgories", temp(3),"maxDepth", temp(4),"minSample", temp(5),"reg accuracy", temp(6),"isPruned", temp(7),"UseSurrogates", temp(8),"UseSE1Rule", temp(9)); +// case "rt" then +// temp = raw_getParamsRT(classifier_list); +// param = struct("No. of Active Variables", temp(1), "CVfolds", temp(2), "maxCax xtgories", temp(3),"maxDepth", temp(4),"minSample", temp(5),"reg accuracy", temp(6),"isPruned", temp(7),"UseSurrogates", temp(8),"UseSE1Rule", temp(9)); + +// case "ANN" then +// temp = raw_getParamsANN(classifier_list); +// param = struct(); +// case "ann" then +// temp = raw_getParamsANN(classifier_list); + +// case "DTree" then +// temp = raw_getParamsDTree(classifier_list); +// case "dtree" then +// temp = raw_getParamsDTree(classifier_list); +// param = struct(); + +// case "Prob" then +// temp = raw_getParamsNB(classifier_list,image_list); +// param = struct("Active Variables", temp(1)); +// case "prob" then +// temp = raw_getParamsNB(classifier_list,image_list); +// param = struct("Active Variables", temp(1)); + + else + mprintf("\nThe given modelName-%s is invalid\n", modelName); + end +endfunction diff --git a/macros/graydiffweight.bin b/macros/graydiffweight.bin new file mode 100644 index 0000000000000000000000000000000000000000..6fa0b875ff9e29a99960820d152c4fa0396d2caa GIT binary patch literal 5680 zcmciG%Whmn5C-6J5Vrx_lL@|n?c^ju!kDZA5=*u$cmTEt2}Obh@5xJm0A+_FKv_kY z`po%9oo3EB77in|x=(do{`#xBk9`(L^LyLN-Q8hlxwv_9y83O;X0xpthqoKyYWMW? 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The terms +// are also available at +// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt +// Author: Yash S. Bhalgat +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + +function [out] = graydiffweight(image, refgrayval) +// Calculate weights for image pixels based on grayscale intensity difference. +// +// Calling Sequence +// W = graydiffweight(srcImage, refGrayVal) +// +// Parameters +// srcImage: Input image. +// refGrayVal: Reference grayscale intensity value, specified as a scalar. +// +// Description +// This function computes the pixel weight for each pixel in the grayscale image I. The weight is the absolute +// value of the difference between the intensity of the pixel and the reference grayscale intensity +// specified by the scalar refGrayVal. Pick a reference grayscale intensity value that is representative +// of the object you want to segment. The weights are returned in the array W, which is the same size as +// input image I. The weight of a pixel is inversely related to the absolute value of the grayscale intensity +// difference at the pixel location. If the difference is small (intensity value close to refGrayVal), the +// weight value is large. If the difference is large (intensity value very different from refGrayVal), the +// weight value is small. +// +// Examples +// image = imread("images/lena.jpg"); +// W = graydiffweight(image, 120); +// +// See also +// imread +// +// Authors +// Dhruti Shah + + image1 = mattolist(image); + + a = raw_graydiffweight(image1, refgrayval); + + dimension = size(a) + + for i = 1:dimension + out(:,:,i)=a(i); + end +endfunction; diff --git a/macros/imabsdiff.bin b/macros/imabsdiff.bin new file mode 100644 index 0000000000000000000000000000000000000000..19d0e193dea352ef03da179c8c9bafa1f9ee7adc GIT binary patch literal 6944 zcmdtn-HsGh5C`x9qv8O|?wH*HXIF=hm~|yYQDcl6Oi0uhFnaG5S3HH!;S2aCzJoWK z7!zagBU~A=eoOy{L({#iD*<$pN}sM%pZ}^l-7_=Wnx5P~Jv(z|_x{i6$z*b@Xtp(7 zU%P*6|L*wy-ukhDt=-wy`tc$DH_uHbXG-stqU%MwMStA6b7xxD&7xC9FL!Iaouaj( z?IN=7H;VAwpX=6(x?btl?AYJxuJgLmBhTi^BKFP}k>xLb$jEzEBu>N@`>t$>(V8q< z*7TR%I&${I=gU32;u6Pt5m~XkRTNxmxmwGWUW<{K*%1pH_J1f0*?Or+K8Yc*#<6ztF0Ykr@)3t#dAp~NuTS=h5<@LlYqt6QvFiai!zQ&$UGU))8|vQX5&xGv z{;y?EY|&#U@^GV{x`*GdOIs7TwsOfU{(?We)tV1@Mdv6!OMR6uyKMd5)fpCe@{^~0 z(goHrC)#h^vFDHcCVBS`P&sk`sqo`pIBjrtS=P7 zQf$kj-~$spn1n|b&plmdm^tgqxo4A1`ZzrGxmXlEGHtK$$4|~RxTTKfjU9DhpDf-l zh3Q^h?OpAz@lQsLldpTe>AhD(&Yn7&-@d(%i}`c$ZxY$ts0000FEys4hW1iFHiPpg zwWjNyEWi2=&$%>~4>|JW($k4U-dX2dlymTtt*^1X4mwNO7>bdP^f+H+Vuw!F_~Jv!$$pEwcN(=3D?M`TCy$&f zZxjzjzr~gfdM(dd{ov@_Y2VxHc6I*0P-`}StDaCJF~f?@)Icnm?PaYK1G#+1z`NSp zOV6<>5AQ|amLK+p{D|?zBEH!r8=UKQ|2^vUx+a$NRQi&Rd3_YI94kBjG#>B1EjDr# zliCc$WIylynJ>8^ShabHCs?$6`R*09xYX?mqtrLw#S#nO>Z=x+6Y)P^)aKByVX!Z; zm5&phj_0$pBhNB+>9_p@5B9|)4|${ZeJ0PkpGroYY`E%a`oT#2_0!4f1B>ZmeLnOX z%U@*+hR+r~Q$!Z=>BX=+UTi3+)L6LaG`e_k6 z7mE0hFA_gYeRqX15~O(yPIU0>r6%SoYkR?#4}AJLN8rMT8F;e@_Tt*wKDU_7cltkz z?GE892Rdtu#o5IGvST`Y(^Zq{e7q(w$>+OjCV5;cN`IWF^}H|=gHLL5P@T!$tI6b} zeobz4cDy(DYw}_74pS3-56`32BWFNreZK7EJV*`Imi^p2W7O3gr62bO@xcEZaFcVMk)AW=pgoUw*0#>|;NHI-ig!%5jb(dw zap-T9(dS8G`LZ0I+L+GgYnu;*)5hX`MvvV)({Ejq<3YOgVQ{&KEDYW)nmyp@E+aM4 vyI)YK4;}M8#3M3htG4z(t2r3)* 1 + error(msprintf("Too many output arguments.\n")); + end + + if rhs > 2 + error(msprintf("Too many input arguments, maximum number of arguments is 2.\n")); + elseif rhs < 2 + error(msprintf("The function needs atleast 2 arguments.\n")); + end + + img_list1 = mattolist(img1); + img_list2 = mattolist(img2); + + temp = raw_imabsdiff(img_list1, img_list2); + + channel = size(temp) + + for i = 1: channel + image(:, :, i) = temp(i) + end +endfunction \ No newline at end of file diff --git a/macros/imboxfilt3.bin b/macros/imboxfilt3.bin new file mode 100644 index 0000000000000000000000000000000000000000..9b9b5bfc8d107feeeb464bd1aef2cf1b17d1abb2 GIT binary patch literal 7052 zcmd7X*=`(F5C-50VKL6?NxXpVWK05t5V05p0wN{cfP{DeZc#3H65b360q=kiAR!?X zBDO0;sBhB$aNM4@$0(4DRO;$Jwfyy0oipS1cb(XooD+c|QfInY z{C+E}aPcGkyx-TlSWlP0Mu+QUrziHL?!|+vtsMqFxW8So)SQ}jOXnk}=WR{>dXCrn z3njP_Q~oyI)w&OscS?VdSYGe)_iIQ`z9}BmjFa^BFrS~hKJdez{_$<=uG<+0xWvH^ z<)^V1b)Y|X891p6-NkFBxMv&^*o?Aw`S zv*?+7zHIedU1HMBfxe}lQfK;7>Eb(&*vShcKj-%KlKABuXHR}AE?sxK`{(OQ@Fk9T zcr_dD*|s*BTl-br@P1h_Jr4)4q(}09QR~6;Ox*`d|9Y%jd%L?Hb`P^Xk{+cuhxzj( z!E~wgK9=_WFdY5${|i^!mzAnv9v5vL|5oqwHF*g5O7G6i!P4?#Pnc2M9A!?Q?Bd#E zuaq3N&&p&rPpw`*H4(D-u|?+O<&u(hxT&zy`B#3WoImZ z`w320dsv<}2mJOqTR!-;7ueFBYkZv(_Lx|{&n6|F=}FF3v0pB69r6o*eB0kD!Ipim zHnws*%YsjSxcnY>R+0ElN>6W+Zi5%&bdNNn+#OjM2xM=sqP|x){@$T22__)LLZDECB5$?>2I?OB{cDj4m)Q{_Y z2jYcoc%kRJSlxWw^RebWy>pKX_xfml*orM4tY(j$zs<~+eAf6RkM-CG7eBUql3Ol) zN}SAUd=guKgMp8GIpl;xU8$R%IGF);z=JpI@~5w#lrEOqtg*}f3g2RjB_Gj`{MLp= z-Kj+^`GYZh_W5EXr*(PxW$&mpwP!u?c+^(=i!{dhBTxRK+f60!6l z-y!@a-LvojuV-xAznSWG#$V2_Uy2X=K(5r4eEh>VU9O=S+wHBZ0cLUa#!P)xxe^;z zYw(>b!JYoIxmwp3OOpG!(&Y{pxD-D*#Ls&QG27Yim zv&7y!#jezuzEnD&KWauZ7x^CG-|iPP!uIR371LV2e_+%b>)vB}9-RE>W)21dzu(UJ z_{~-?O831d_1^Taig&Emac7>{67gJbm29qT^*(g2wFc|;&Sp`pd*|rR4fPGZb05M! z<@bPkt!MV&?)UWQ^)B6fzv8I(MoIR=BE7jE_3n)^y7^ylhNp1$Xz54gtX!s_56|D@ z>D{_(c^cxpo#CzVrPK2cg`qiU_vy)%<@NQ`tNYx6%9-K3)x&nr;qoBgTAuRaJBlq{ z%>H;^rh5-kvz~p#p+0<^=kuO18H!a)(+|Ic>{ +// K = $$\alpha $$\begin{bmatrix} +// 1 & 1 & 1 & \dots & 1 & 1 \\ +// 1 & 1 & 1 & \dots & 1 & 1 \\ +// $$\hdots \\ +// 1 & 1 & 1 & \dots & 1 & 1 +// $$\end{bmatrix} +// +// +// +// where +// +// +// $$\alpha = \begin{cases} $$\frac{1}{ksize.width * ksize.height} & \hspace{2mm} when\hspace{2mm}normalize = true \\ 1 & {otherwise} +// \end{cases}$ +// +// +// +// Unnormalized box filter is useful for computing various integral characteristics over each pixel +// neighborhood, such as covariance matrices of image derivatives (used in dense optical flow algorithms, +// and so on). If you need to compute pixel sums over variable-size windows. +// +// Examples +// image = imread("images/lena.jpg"); +// blurredImage = imboxfilt3(input_img); +// +// Examples +// image = imread("images/blob.jpg"); +// blurredImage = imboxfilt3(input_img, 5, 5); +// +// See also +// imread +// +// Authors +// Yash S. Bhalgat + + [lhs, rhs] = argn(0) + + srcMat = mattolist(srcImg) + + select rhs + case 1 then + out = raw_imboxfilt3(srcMat) + + case 2 then + out = raw_imboxfilt3(srcMat, varargin(1)) + + case 3 then + out = raw_imboxfilt3(srcMat, varargin(1), varargin(2)) + end + channel = size(out) + + for i = 1: channel + dstMat(:,:,i) = out(i) + end + +endfunction diff --git a/macros/imcontrast.bin b/macros/imcontrast.bin new file mode 100644 index 0000000000000000000000000000000000000000..0e5d4314492de04e80af8185d0fcb54753ee0af6 GIT binary patch literal 3180 zcmb`K-EPxB5QSa->xR&}#7#&Hp{;}|y(&mta6un~dmfj^0145H3h^f)A;S4AA8WJS zEkz>I$m^LiGiPRY-ECEmrhCo2Tr}<4?`}~P8xmX3HrD~yWn0Wg=k@lQTlZ2OVE!b#MqO7pG&~l z|GC!ArRA{R(Hj46$_swd3wek|4(uOfC)WcByjWu=FKfp>)*3uE^h?R1B**(iHt@Fp zt&XR{QWN!oJr^(f0nZl_Vo}FTLM_zCyKP%q!;2oFKa!Zcmctqxu|D>FaX-mrf1c>! z;~3P9Uy397JTvzR{`kO`zQQZ)N9YsI@Kuk)PM!F>R>y`reSIu}D>!0P1G#u79~k$G zwd(?#ng{y~UupzH407Yg`?6mXi+ZuSE_~?~IPmZ%2N-nlh&hLKpZ2(Tj;`6~hciTc zp62K?hMqIWllkpSa=Bw)bA>l^^E{0U``|qjLnKZJX@bP`W^N{ufx8B7k*zg zbLs+%4i28LXH9-MdC#6|jqiK;!XLb63x@pQi5JJfFT{hzH`n`r=^t2gC9nHQ{M3Fz z7xo`ssc)q@V)ty^3(g8Pr7-B@9Jx7x_58xKX+3$pH{-pWoAIW6@c*JY!ISvZ=dIWPhch<#?MleY{s(iU=QD|OKhc`l_~5&g{O`X5 z<_#yGLx1Ntq=Tpa#17wn)-g}12F~uU|JK3V^YZu3XP3F(?(OCg_s_i$Y{+q^_u)Im z9`k-8F8<8Se(Znp!w2kr2~W77|1Q02=Lrw{(hvKhXPyY>11xj#8T!=6@%h4rKd}(< z(YxHe=a>9t?l5)PG}B2HU#r^R!Z4GY67EpM;~fq>-%1q!tX#itb+;**Qe2YQkW219 z7GiRDvk)^cmxop2zDP~f;+k$~9oN*1>sjJT$Y?Iql*5Kx7S+Kx74J&@JxF=B0~hu) zbH4?-?)Uk1H}^~YfKmUk#4~-Vb(K!ldOxwJ-r%4gJ!5aCbU<>kWa@t2pE+FEzg&;6 U(!b08C!j8Rw=eOzVNbLE14B#xdH?_b literal 0 HcmV?d00001 diff --git a/macros/imcontrast.sci b/macros/imcontrast.sci new file mode 100644 index 0000000..06c36aa --- /dev/null +++ b/macros/imcontrast.sci @@ -0,0 +1,46 @@ +// Copyright (C) 2015 - IIT Bombay - FOSSEE +// +// This file must be used under the terms of the CeCILL. +// This source file is licensed as described in the file COPYING, which +// you should have received as part of this distribution. The terms +// are also available at +// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt +// Author: Sukul Bagai & Siddhant Narang +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + +function contrastMat = imcontrast(srcImg, alpha, beta) +// Adjusts image contrast. +// +// Calling Sequence +// new_image = imcontrast(srcImg, aplha, beta) +// +// Parameters +// srcImage: Input image. +// alpha: Pixel multiplier controls the weight of each pixel. +// beta: Added to every pixel to change the range of pixel values. +// +// Description +// This function is used to change the contrast of the image using +// using the values alpha and beta. +// +// Examples +// image = imread("images/lena.jpg"); +// new_image = imcontrast(image, 1.2, 2); +// +// See also +// imread +// +// Authors +// Sukul Bagai +// Siddhant Narang + + srcMat = mattolist(srcImg) + + temp = raw_imcontrast(srcMat, alpha, beta) + + sz = size(temp) + for i = 1: sz + contrastMat(:, :, i) = temp(i) + end +endfunction diff --git a/macros/imcrop.bin b/macros/imcrop.bin new file mode 100644 index 0000000000000000000000000000000000000000..444c08b28f7f9f90d94fba96714aeb9682ac9cdd GIT binary patch literal 4272 zcmd^?&2JMi5XDmvUuhDOg`_D-%BJ)XO6j2>!HqKlapnwvE`J6{NE|A}*9kSeU%M|F zdDo4qa)L-B+v7Lm@td*ZEk>QmczaR%bz800hJ@MUVSkzeu4!&M=^T&x>C9p=?~KQj zx_Ysj(U(PcHfX>PdPRRQt52L!}3Wxr8}*LBM)$SN+^vsX5lA4$PtL%qR1##Td-K#CuhD z)!$T~SUxL0cq9f|Vuc56Y0xkN(&rI&9#9zWun)*cU#m2eZyEzTg%g*kNy8 zV?5EPb3yC4oO^KSS@%5O#D;%x

  • %g&lM32VoB0VoqLajBQhm=5hk|Q1ZXzB=YsT z;;U3kj^eq^@#P%RQg_^Ma9&DMALwJfU=!>2yjqBM>*~zwTzyn?XgcD2bX8RuP@fc4~`rhNWhYVXOg0}+cSmF1!v=`3iSG}`FhC0c literal 0 HcmV?d00001 diff --git a/macros/imcrop.sci b/macros/imcrop.sci new file mode 100644 index 0000000..a84c0bd --- /dev/null +++ b/macros/imcrop.sci @@ -0,0 +1,46 @@ +// Copyright (C) 2015 - IIT Bombay - FOSSEE +// +// This file must be used under the terms of the CeCILL. +// This source file is licensed as described in the file COPYING, which +// you should have received as part of this distribution. The terms +// are also available at +// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt +// Author: Sukul Bagai +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + +function cropImgs = imcrop(srcImg, xcoor, ycoor, width, height) +// Crops the image. +// +// Calling Sequence +// new_image = imcrop(srcImg, xcoor, ycoor, width, height) +// +// Parameters +// srcImage: Input image. +// xcoor: The x-coordinate of the starting point of the region of interest, ie region to be cropped. +// ycoor: The y-coordinate of the starting point of the region of interest. +// width: The total width of the region of interest wrt to the starting point. +// height: The total height of the region of interest wrt to the starting point. +// +// Description +// This function can be used to crop the image to a given region of interest. +// +// Examples +// image = imread("images/lena.jpg"); +// new_image = imcrop(image, 10, 10, 100, 100); +// +// See also +// imread +// +// Authors +// Sukul Bagai + + srcMat = mattolist(srcImg) + cropImg = raw_imcrop(srcMat, xcoor, ycoor, width, height) + channel = size(cropImg) // for converting to hyper matrix + + for i = 1: channel + cropImgs(:, :, i) = (cropImg(i)) + end + cropImgs = double(cropImgs) +endfunction diff --git a/macros/imextendedmax.bin b/macros/imextendedmax.bin new file mode 100644 index 0000000000000000000000000000000000000000..785a3e5f99ba404bf4defed51d2a42e596fef422 GIT binary patch literal 3548 zcmb`~U2mI35Cvct#Ft4-YZNCCyEtuyN~v;DkhtW6KY@f0Li}9*1PBlk7j4DY6(XD` z^>JQY+f=2NM!WCsnKNf*XVdL;HaS~Njt?h?v+>#8tKZ>hG`bc$oG*SEFRs=*dntSK z(}Rbz>s`4TA07RRYQ6Er%cId-@p~_J8oL|&_2S~!d6`GeJ3b@Sbq?=aU7f|ZXv#CjOc?$d_rmYV>7@p5(E8J#2<+XkuOzZHxJ<+vvqxBfFS7`ei!18z07jt8&U$TWW!$ z{(NxDr*W#Iz3^4``2G-I{SV)~V4M}rV&LEpV{MqTJyjf*p2OFxy%*KbdPS_CRz2~& zy4!m_KlN(v?dA3cOz%f=YNG$@1H(@Z+9Si-z^bnvfpw3HTV`&r^^+fc#>Q*!!Psjo z7{2|k4&3Fum3hzO%gp(>mp$YDt#2Qdzd0|1)l2fhD+UhZu;+V6$_wM~Fu&e6oc3Yq zg4tJ_a;XfzNcxbY2Y(6k_ZsTo#zMg%*O^w#7x3%q?rZu8d{%YxLi+?S1`8Dol z?TQOz?L!BjKT|LL-TRA||M2Yk9{E04F70X<(=u4cR9Usu3nlq z^`6|^%<{?edz;=}*rAsB^!8-^fFDerrFI85#>dB#qk-4cTJ)Fu@hX9rzsH}-ai1@k z8r+Y;s=+6*=~I!OEVN(r%;y~P zUK1fl556I)i@a_dbW;)b@W`7YWD6o}o`@V*Y{4&z$L6~*pid0QzOiThgVynLTeK{K zCmy)q?R!H!`rD!7KF8I-I*Lj-! zU*!2$ofTDepzfYwdd597h>RM8fgXVYV%zo}Jh?@Uvd7>=0TzpjhYFU~J&OpU(j{BVDfft5Nj zYxEGFT2p7AeJ{mh>$3+P=Yx*>g1@p4?2wZyKDp-%1_Qj$7VbTx_y7az<#PfV>*tvt zeBlRMaBw9SF+>04g>PeXAHnVO9ox{Kb;6m$C$r;x@ax>eIw0qqMrQxvIVf?_gB#+B z{Z;+rEW`#|`WBwEj!j+BkHtW~Raz$x^2RT?d9HY?SoAp-3!Jw^#0NXN8@fIa8N)sC z#M%~Bon-T8MHtC3f+5VY>fL`>C;lBE)aj(F{Vej$f?9#sJ>_gK*GgY2 z`n7ti*BScKaoSV&Z8+!T;FbK4v;P;Ev!bsvm^0Xh<~W_pvdG63sU_3YV|ZU3@8iES z#wUKm`K7Ii;?~!lFHy0;waZMTqAB( sJnuoC?a(uww-;e8N2_?62)_R;zvN&zzvN(+^J7MM2KKyjJMg6Q;=E1%dRe^6*Pgu?H4kR7bmUaBngjEqMmbJ{@>%|zUk&J~ zr}X&I^QzBEr{wI_?7ZUJmw$T8IEl)Pe{;ZQx1CFUa8_~D#y_8R2dZ}ua>lkeqr=`A z9d*@g_kC2JrBByhO?4Ni*;gKaTQC1?iaWeI6XQ?b1O1*>%`u;GcPe{6KZUz1S;a8# zY|V>W#4FpXbzXn?*$J#AuUX+2C+_d-#46voB{T2m199DDIybZ43M%%q?7P0F`Q7K1 z|BB6TyW<2X?uEcA7jeI| zyz7@7^-xTpb3CZW|L>q#+t~Lfn}Z+mb2##2!O?&&;L-XrXwHr2nM&i(`T;VzsQE== z@a2)O{XstIb@G2Kw}$~cxjqm0G5b}on&t05)(qiP4U*JLbK?w$Z1GNpH!{AeC(d!I zCv=_HywT-XujDftZ#Bb>$!edVdA!P<%}Ud3HhqHyy2LB_kDeKqsDi;&SdQ#lrZPEAb_i)9K&S)^1Y_1Q-lV0x^ z%}Vv%9UP7ZYx}>jw^7~8W4>NDk44|yU7cnyy*8ZlT`yO))ceAn>&~cLm(3{N=vjXP D?oa@B literal 0 HcmV?d00001 diff --git a/macros/imwrite.sci b/macros/imwrite.sci new file mode 100644 index 0000000..fa1edb3 --- /dev/null +++ b/macros/imwrite.sci @@ -0,0 +1,43 @@ +// Copyright (C) 2015 - IIT Bombay - FOSSEE +// +// This file must be used under the terms of the CeCILL. +// This source file is licensed as described in the file COPYING, which +// you should have received as part of this distribution. The terms +// are also available at +// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt +// Author: Sukul Bagai +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + +function imwrite(img, pstData) +// Writes to a specified location. +// +// Calling Sequence +// new_image = imwrite(srcImg, location) +// +// Parameters +// srcImg: input image. +// location: The relative path of where you want to write the image. +// +// Description +// This function is used write the image to a specified path +// +// Examples +// image = imread("images/lena.jpg"); +// imwrite(image, "./imageName.jpg"); // imageName -> name of the image +// +// See also +// imread +// +// Authors +// Sukul Bagai + + [lhs rhs] = argn(0) + if rhs < 2 + error(msprintf("Not enough input arguments")); + elseif rhs > 2 + error(msprintf("Too many input arguments")); + end + image = mattolist(img) + raw_imwrite(image, pstData) +endfunction diff --git a/macros/integralFilter.bin b/macros/integralFilter.bin new file mode 100644 index 0000000000000000000000000000000000000000..ddbe4a6fcbf10a78e7dc857b04834dae3aafd5b7 GIT binary patch literal 5512 zcmds*%W4!s6ox0>o#6DJojUKSEXK?Mab8&`r0T?m42=E`^RB?JQ^h!}4x zG5%kuzo$~^O4@9M3>>=p+|OU9PEU6+7XQ^*h~awWdVFg;B+e7e^#3 z2BTuO-R;DD*3;9&#gxbWxKng{qxl`8nS$8sz2(Hy?W3*MjN+UZjYO-WFPod2gK*!` zwJRz`ZBbujo!P)cjvoB!z?%;|YJ|TbpM|d269;{KUKcsOmesnaHFiH_=eXG7gWQOT zd|h&Uof9pHSR+3b*7kiz_vmw9mj|-7pyL?MGp#>Y%cbDk*x@I|=!SS;1Pw)tB6wpF zxnn~->L)%pBG2Vb{n#vvu)i$2B68hYR%>EX=f{Al_mmv5b-nP3YadUw#xBOCuC}W| zZ@>LRox~Z7sGk_@qkDlhy#T)GQ4e{u221X#0UhUx+&xG=)P^oKV~0Ne?FSwD$UTG{ zU)VUVeH*W|Kd3+5_qfkr%9ryeXZL~e`zd>P&V&3N(|sS?xqfob)r5U|_H2i*Ga@hm zW9s@Jd*K>$xPBHcb#Es9K_u+krm&!RC#x94s_owL| z9%Q_q1-!tjt`E~0X8cqBUYO_nA7pF=X*H)gyI}M^^eue~kMjcVb0W?bYtJ(<In%lMwwHfD7d-3jFE>kwU6&KN#Ir)T z6}~66#_pRi;<-&sY64g05VHh(V+zKO!*wmX$Buo$20!G@|A%+-ZC~JljC`2S!~q|4 zF6(+lZ9;bN9ebtXXE7*_=1fKL1eL*gq5BIO- z59X=&3O-naExPd86Z`S`A)nd#xl-@tGby%pxRN_Iow6K8ul|*3&W3o{GUMBW{9b^? ze(}94TjTpM_*v35#TN|eRjv?NxnIE;n}dj}%G&z96~JJ;B65Gf31Z1G5(K&p9{LC_|vCN@OQ7%I|rq=Rm#@ym4+WUUlf6lF`|Eq!Q4XKSF+#QBZ;?q ze6~Fr4g1A>`lYSO`J4Eks?IG7eAnl(T`$ff_uVem$$i6%b#mYHVx6)*UeTP|@$HeC z`L;+8GC#R8YqrgtZIw=Lq*m3;{C&;_=!ZSes5)NU_sHXW%-a+9+g45XCim@>y4)u` H&shHjC_|}b literal 0 HcmV?d00001 diff --git a/macros/integralFilter.sci b/macros/integralFilter.sci new file mode 100644 index 0000000..8b78b3f --- /dev/null +++ b/macros/integralFilter.sci @@ -0,0 +1,55 @@ +// Copyright (C) 2015 - IIT Bombay - FOSSEE +// +// This file must be used under the terms of the CeCILL. +// This source file is licensed as described in the file COPYING, which +// you should have received as part of this distribution. The terms +// are also available at +// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt +// Author: Tanmay Chaudhari +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + +function out = integralFilter(intimage, bbox, weights, filterSize) +// Integral Image based Filter. +// +// Calling Sequence +// filter = integralFilter(intimage,bbox,weights,filterSize); +// +// Parameters +// intimage: Integral Image, which can be obtained from the function integralImage. +// bbox: Bounding box of the filter object, which can be obtained from integralKernel function. +// weight: Weights of the bounding box, which can be obtained from integralKernel function. +// filterSize: Size of the filter, which can be obtained from integralKernel function. +// +// Description +// This function filters image using box filters and integral images. +// +// Examples +// i = imread("images/lena.jpg"); +// intImg = integralImage(i); +// kernel = integralKernel([2 2 11 11], 1/51); +// filter = integralFilter(intImg, kernel.bbox, kernel.weights, kernel.filterSize); +// +// See also +// integralImage +// integralKernel +// imread +// +// Authors +// Tanmay Chaudhari + + + [lhs rhs] = argn(0) + if rhs < 4 + error(msprintf("Not enough input arguments")); + elseif rhs > 4 + error(msprintf("Too many input arguments")); + end + if lhs > 1 + error(msprintf("Too many output arguments")); + end + + inputimage1 = mattolist(inputimage); + a = raw_integralFilter(inputimage1, bbox, weights, filterSize); + out(:, :, 1) = a(1); +endfunction \ No newline at end of file diff --git a/macros/isFilter.bin b/macros/isFilter.bin new file mode 100644 index 0000000000000000000000000000000000000000..d431e73e29551a6a5c0dc00296f3aaf7848e3e98 GIT binary patch literal 4140 zcmeI#-)>Y@6bA4Ck?3Gs+E`jVGX;T|T8$xvv?O}LGjPET@yaLif>+^vh$h4v2x_<@ z*6&x(n(Q3sOiMzzVv?1;_Fn(Lwaz|gwq~z4x998K{@}MZnM^K(ZcMvrcXfAfK9FCG zY;8K94vtT^v)ju~_xE3YGMQ}f8TvM~ANuR$y;2&GB&%Rvft_V{N4*yzF`;qW_WggCwKn%LULk%HAMb4s}*0q3yY(i@geWmu^p?i>f`e`B-UZSo@FoA7=HJAAN=!kJ0#D8(08HAH|&Ck zrT8y;EDGamIb+-Qhu`w?LwI&$xqRQtU&b%Y^iaM0t+FFiqdC$W`nBE)&yTz+zC24_ zcIH8>c1~b`+L_Q-@rHO+?)u;2NSALlyLyJ?;Y<(2RjU|y zap73_KMBwFx8x`XI_xVC_1CNpeb6DNFZZ%j@AHs+=&%zPFE2d2+g?`f;=d~{{I+>l zKK$!x&0^a}Hu50DtI=MraCT{rc~XF-1S^g|!$tG)W`j9%^O;;l8O_AO4Z zBv%Lf!T}GPngRVi4$(dA*U}$*C$4&@9mEH$3iGfF{$sz!`{mt#-hR>3PpjwTL-p^o zKBoB=x9$M5X^v{f%wGFVW}eJ8av#)P!d?x3#Gf-hO2*y{A@h1UR6pOs`AFVb_SO4O ze0erM4dwH9LKlf;{Uy|G&X$=zF(rfa6D7T+N?Bx0=#82U; zUL^l-LcSSbj3in2iD&*+e+Faw_FYf6aOGdm+PeiNV%B?DoSWHK9CwiWg-^Wq2pha! z%KzHzdbhPR-xvbAtPg5sv)*;P|N8?J2V*v3vemok&Uo+ir+&|pWYt`K`}IBa?Z*VD zzC8{vhIy|!t-c-PtiGjT40}3AuV86~rM{_AzdrWyKa2v!>F-VegRwa*{0iesIrHsn z#^Gnxo3a1NZ=)OW>2t+)_TETX&Du+}8YhFOL1ck7!Y! HIalkygD)=+ literal 0 HcmV?d00001 diff --git a/macros/isFilter.sci b/macros/isFilter.sci new file mode 100644 index 0000000..3f3fd1a --- /dev/null +++ b/macros/isFilter.sci @@ -0,0 +1,47 @@ +// Copyright (C) 2015 - IIT Bombay - FOSSEE +// +// This file must be used under the terms of the CeCILL. +// This source file is licensed as described in the file COPYING, which +// you should have received as part of this distribution. The terms +// are also available at +// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt +// Author: Siddhant Narang +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + +function varargout = isFilter(data, sz) +// Decides if a filter is separable or not. +// +// Calling Sequence +// [isFilter s u] = isFilter(filter, size); +// +// Parameters +// filter: Input filter (datatype- mat(double)). +// size: Size of the filter. +// +// Description +// This function uses SVD to compute if the filter is separable or not. It takes as input the +// filter matrix and is the filter is separable it returns the calculated singular and the left +// singular values. +// +// Examples +// filter = [1 0; 0 1]; // Any matrix of dimensions n x n. +// [isfilter] = isFilter(filter); +// +// Examples +// filter = [1 0; 0 1]; // Any matrix of dimensions n x n. +// [isfilter s u] = isFilter(filter); +// +// Authors +// Siddhant Narang + + [lhs rhs] = argn(0) + if rhs > 2 then + error(msprintf("Too many input arguments. Two expected")) + end + if rhs < 2 then + error(msprintf("Insufficient input arguments. Two expected")) + end + + varargout = raw_isFilter("Data", data, "Size", sz); +endfunction \ No newline at end of file diff --git a/macros/matchFeatures.bin b/macros/matchFeatures.bin new file mode 100644 index 0000000000000000000000000000000000000000..09740d42a4eef8169d09bbce07abbb63d9e9d4c1 GIT binary patch literal 17244 zcmeI)%Z^-E6$W5C0b+OTj@|guq1;Z}7-WoPB!g`sA<+mBicl0mNEk3=!Z;E_%y@wy zF+<`Z7&G7rfEh2qP2naW+y*GZtgrg~<=w~U)TQ0sj<8Ews$F}p>%Z38d!K5TFRX2y zI{VJ)wXMsO^-I?uyiOlGcI=s=jfScwaJ;y%UcgbrlV&|=7sfL zb;o;anSZ7?zpHEGVsHBTkI$5@()Uu)SBtI}{pSAt`)BKYqv%pMhv#z9#iCP1TSeA4 zi_DR0`SFrnFA}G9GWd<@!%wzdXO|2fcJNO+GEe7=MX^gZdMcEu+=Y5yD`Jn1=u9ln z*Bo8ex4W^=i6ir^uhyeeYHbg=Tk%aS z#?k$9jp+;B?Z(ju8~j&_-tKH#<9o2US7Y+GyZ0gN-s$`qi~nPtEWg+r<2zn!pIVYv zUxfD$o$q|3Kl8up=HVEeK3U_CxNZ~$uh(lFti10BuOWQ?*x@7hu!XT)vGF9~Tj9>I z)??yN-N8j&B!{q)%hY0W^j6K0Tc(z7cD(cIldkj=n?AR7$R~4c|N5-=`Cu%+WNsCS z>uNVvmswBEC;xgbbsBlR>S8~88sD=Y*{9SyEVGxf+xL4BhWPnSU-sa{w5*@mM_T{% zqukPKPqvo5uf-J)`)p->p3nSsPLU%Mn_?$tZQ1&kY4|%>W zo0pscITM2w9Z&0Lynf#7aBlZ>wW)5?BhHQASMR%rz!lck;i?|gKb-$kJm&D}SG?x` zUGr*IZqmo;7d-chQk(PqXnSesT$)z{**EyZhwOi1bbgv2I$!a{H#poW`g_G=zk`$b zqc7N`XYtbOzQB%{^=)L}M&?ElzE5}W_DaJN-I`P9Wd%_QEOdl-SVnZMC zH+Nk+$%-%e=Fj8GdEd`|E|>Ki2Cxzz43cNDi5CvZlUi-}3pn5*2X|w7$Qa`%%U5#Y zZR?UfJjq#X;LRHSZ4Q&0@GPoF^1;WLEk3oj%&S)~W9NBZ_iB){dR|?UyH(W6^UW)@ z_U#&5|8>7;PoKqrhb{Rxr@m4<|0o>H;m3=Qk7RBYxdQ};^oV`R>qb%X`@I^o z+wRNk$+Nn(c9i*@=ZDzjqs7r!?1?e^TR-1Jc}b6Dk60G_WzP}!bR<`I%Ex8S31TprNi)*_mb)C5Qrmn5CGtU?6sek;&$?0n~9`kdo*XHH*c&ASfz){R>@*lQr znG3GgR>^DfAcn}w37zaE7GvwFDeKl2;k_td^PZhrT+7r9LeKT?cfQH=W3kVH=kMyPioiadA6-DiZ%5B3;bi}GCncHhwU1h z^O=9iSMFocr=~K-C&uBmElL`$;oH-DKdH9AY(n)TI=K2`knabYJDHVm9FT{m@dAWUu27ZG4PR3EzV>1 z`uRxAEe7l2bft%ldZFp9?1#iZQ)ShrI!&V>t0CHP+_Jy!^L%-C^~8U+=t_ep3AW zRu}x^FMrzon{PusgkO#0Ppqkt{no@#tf}>7VvW4s?APfSK5ZWxkKHL-zBU)}Z%nTB z$#)FdPvMvR2Y0#4*|W&rD!N)9;37|KW^Y?$Ye=TAlka*#Ong&Qf2wzOauyh~jn^3O zGPd`t`$p++_vBUTU5~XrebDuO5WU^cLAx&>R=sEMIjVY(y`!l2*m@lG-r7E>dan|m zonP(vTU~A6t@qro?BjYf_iy`t>MLiSIeh6|{QBZvQQIp+I+r;UmOY1@eT($P_hobj z`(<_7v)cNHnSC2h?S5vw%HK)w_xG%ClupibRMp9Ubphe#VYcNy^RMptJH8G_md?L_hp}J;Df*I5&dzlo3q|N+uW(o@!LJh z=e`d$HS1rxQ9lpvALeiO*4;B;AkS(f&o=xs$9B$TeC~MBZ9R3A`^_rPK4ZSh?P2$3DKWgqK?LF2~0HuH%_VFFHTe@1p%=tmpc5FyvPs%ft4Iy}n;H zHN)-)HHO_X`)wanI9@4&<9qR0VkozVb-zl#wdYHo8Pr<#TxZ29ccS)O>esjPbQN8# z4?}mQMYe`y`n9!6J@tFxpzDeL#N1+M9(JpoRj@hAdWw%nRZp>Z6!jEa%hXe)X|`9r zT4Q_F-J+bqk8*c-QtrR~9({T@-Tm9~(D!cHFAk!<`nXrw&T2XLR6StdLuz}i$X?~X z$3N$?@9v)~vYxwM|DG5=_dk4(vX@2|U+8j2$@#ur7YgYA$WdB6)_@v@EI96RoaFtvuR7DW%<_(M;0 zw0ihvufmsqhacbKH+@L{KTwpH{jz?*^3ZQi{08N}WzXH?{YB3f;YDnD_dVL=Z0`p# z-H%sl>Dc)>YkWCT^vg0rw--$5ohOH{@qdSJpD7~8*Dn^)^SPqLD`qx4+xRzmY9GO? z&F|H2&RXh(Ovd<=H#W?Zm%HRZE%D{Tr57g~7q?#M|CVP6Q+|_KpG-CVquou1FvdmmevErYH4h1LHHu2(7+^(B8}*uGmeSIZ5y z$9ASQvU&RQ`OSwDu12h<@6@h5H+4T<`%cZ_v-WzC9IO9+Zah=^&I0yo#-5^Pv;Wx7 zjK^xa-B(iY=Hxs&qFeo28~XQT=E>v5hf)uz&-Ys2l#A2XFFvx1&zI^GeC-3@FYx6E zp7Jo3|5}>9n*2?B>sHMse{{-o!!!N<2QDucC8zlF`@#P23R7n-PyaWDEze|a``U$p z-#rf1AD!*KKb5<%zBSo6wWolKZS=R2)&}C-`FhQZ^UK|O`spCzr0W5ufBURB#(rlx zwm0w9vD>?=>R@*s-tVjR^t<=N!~4w6cS}FKUnx51y4s7?6iCci=O6v_}Kd-myA%p*K L6q!fg?B4$es$|@D literal 0 HcmV?d00001 diff --git a/macros/matchFeatures.sci b/macros/matchFeatures.sci new file mode 100644 index 0000000..b61830a --- /dev/null +++ b/macros/matchFeatures.sci @@ -0,0 +1,98 @@ +// Copyright (C) 2015 - IIT Bombay - FOSSEE +// +// This file must be used under the terms of the CeCILL. +// This source file is licensed as described in the file COPYING, which +// you should have received as part of this distribution. The terms +// are also available at +// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt +// Author: Siddhant Narang +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + +function [indexPairs,varargout]=matchFeatures(features1,features2,varargin) +// This function is used to find the corresponding matches between two features sets +// +// Calling Sequence +// [ indexPairs ] = matchFeatures( features1, features2, Name, Value... ) +// [ indexPairs matchMetric ] = matchFeatures( features1, features2, Name, Value... ) +// +// Parameters +// features1: Feature Set 1, a set of M1-by-N Matrix, M1 corresponding to number of features and N corresponds to length of each feature vector +// features2: Feature Set 2, a set of M2-by-N Matrix, M2 corresponding to number of features and N corresponds to length of each feature vector +// Method [Optional Input Argument]: Method of matching to be used. Values: ['Exhaustive' (default) | 'Approximate'] +// MatchThreshold [Optional Input Argument]: Matching Threshold for selecting the percentage of strongest matches. Values in range [0 100], default - 10.0 +// Unique [Optional Input Argument]: Boolean value for selecting only unique matches between features. Default-False(0) +// Metric [Optional Input Argument]: Metric to be used for matching features. Values: ['SSD'(default)|'SAD'|'Hamming'|'Hamming_2'] +// indexPairs: P-by-2 matrix containing the indices of corresponding features matched between two input feature sets +// matchMetric: P-by-1 Vector containing the distance metric between matched Features +// +// Description +// MatchFeatures function takes in the Feature Descriptors value of two images as its input and finds the best match between each feature vector of the first image to that of the second image and returns the corresponding indices of each feature matrix +// +// Examples +//stacksize("max"); +// +// img_1 = imread("images/table.jpg", 0); +// img_2 = imread("images/table1.jpg", 0); +// +// lis1 = detectSURFFeatures(img_1); +// lis2 = detectSURFFeatures(img_2); +// +// dimage = drawKeypoints(img_2, lis2.KeyPoints); +// +// features_1 = extractFeatures(img_1, lis1.KeyPoints, "SURFPoints", "Metric", lis1.Metric, "Orientation", lis1.Orientation, "Scale", lis1.Scale, "SignOfLaplacian", lis1.SignOfLaplacian); +// features_2 = extractFeatures(img_2, lis2.KeyPoints, "SURFPoints", "Metric", lis2.Metric, "Orientation", lis2.Orientation, "Scale", lis2.Scale, "SignOfLaplacian", lis2.SignOfLaplacian); +// +// +// [matches, distance] = matchFeatures(features_1.Features, features_2.Features, "Method", "Approximate"); +// matchedImage = drawMatch(img_1, img_2, lis1.KeyPoints, lis2.KeyPoints, matches, distance); +// Examples +// stacksize('max'); +// +// img_1 = imread("sample_image1.jpg", 0); +// img_2 = imread("sample_image2.jpg", 0); +// +// lis1 = detectBRISKFeatures(img_1); +// lis2 = detectBRISKFeatures(img_2); +// +// features_1 = extractFeatures(img_1, lis1.KeyPoints, "BRISKPoints", "Metric", lis1.Metric, "Orientation", lis1.Orientation, "Scale", lis1.Scale); +// features_2 = extractFeatures(img_2, lis2.KeyPoints, "BRISKPoints", "Metric", lis2.Metric, "Orientation", lis2.Orientation, "Scale", lis2.Scale); +// +// [matches, distance] = matchFeatures(features_1.Features, features_2.Features); +// matchedImage = drawMatch(img_1, img_2, lis1.KeyPoints, lis2.KeyPoints, matches, distance); +// +// See also +// imread +// extractFeatures +// drawMatch +// matches +// +// Authors +// Umang Agrawal +// Sridhar Reddy +// Siddhant Narang + + [lhs rhs]=argn(0); + if lhs>2 + error(msprintf("Too many output arguments")); + elseif rhs>10 + error(msprintf("Too many input arguments")); + elseif modulo(rhs, 2) <> 0 + error(msprintf("Either Argument Name or its Value missing")); + end + select rhs-2 + case 0 then + [indexPairs, matchmetric]=raw_matchFeatures(features1,features2); + case 2 then + [indexPairs, matchmetric]=raw_matchFeatures(features1,features2,varargin(1),varargin(2)); + case 4 then + [indexPairs, matchmetric]=raw_matchFeatures(features1,features2,varargin(1),varargin(2),varargin(3),varargin(4)); + case 6 then + [indexPairs, matchmetric]=raw_matchFeatures(features1,features2,varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6)); + case 8 then + [indexPairs, matchmetric]=raw_matchFeatures(features1,features2,varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6),varargin(7),varargin(8)); + end + if lhs==2 then + varargout(1)=matchmetric; + end +endfunction diff --git a/macros/mlPredict.bin b/macros/mlPredict.bin new file mode 100644 index 0000000000000000000000000000000000000000..0706ad0ecab6e6a5b26147ab4ffeba462d5a69ca GIT binary patch literal 9940 zcmeI2eQz5@5XQqhbz>*J)-S2;@}eR_fwY8IA*6zY5C{YagoJ>KKl*w2GJFj_MMK)DWT9cA*d3kbxI%>EwN@dRmX|Vm@TyRUKPo1A3UBCv)J_>mtWg%}E=7nClP4 z7hCZMN^Q9=KDy3|u$Q*uPTRUKsf|9z<)qs1d8dCfe-a1!4vP6p;#c+Wja)Bwr!h8< zey{<1+ce7khDYB8k$w4I?M3#*euER3;iK^&CvM7jd?g0>PVC4%a)L3$igC!2+r)-= zfU$8#23fFtUIgE{LmT|aE3Z5H;`+F)xtSkw+$A!>0DBp8LwxjL3s2+4`o(tJXdSk- zC=XweJUZ`6Cp89r$l)t(>vWAM(vkYXm{=Pe1M(Za@EKE-pSIaDRde2c{3bt$2eyMB z@nEj1dOZK>d|Z)@aW3#aj9(w;dXJKa;GgE9Z6Rlh@;b$adDHwZ(#tq7U_Qss`Az;3 z7uOE*4H@3OMvOTaz&oh6A#2^SHiHFYx8)l)V~_RY2X@&19m#;X*ED%*EY+N}$pdum z<~q?qA3k6Qb}~=2$$Gv}KVy%&{~i_9hj$F+lPUUSG;j(9mf_)R<*kKIN2Ph5~= zJhAk8cHFDZjeh3ki5_Eb-_S=Lf$#j_8FAI?1P>i=#&%PGo>Lneu^)Z-%^cWl46dk+ zt?-x&dGxbC{vsW&Vc5l-@Q@*{@VyVvKBGS31{K8_A087SdqK~aMa&&*{;%RWrbRJ? 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The terms +// are also available at +// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt +// Author: Sukul Bagai +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + +function new_image = scharr(image, ddepth, dx, dy, scale, delta) +// Calculates the first x- or y- image derivative using Scharr operator. +// +// Calling Sequence +// new_image = imcontrast(srcImg, aplha, beta) +// +// Parameters +// srcImg: input image. +// ddepth: output image depth. The possible ddepth values are the following CV_8U CV_16U/CV_16S CV_32F CV_64F +// dx: order of the derivative x. +// dy: order of the derivative y. +// scale: Scale factor for the computed derivative values. +// delta: Delta value that is added to the results. +// +// Description +// This function is used to find the derivative of the source image using the +// Scharr operator. +// +// Examples +// image = imread("lena.jpg"); +// new_image = scharr(image, "CV_8U", 2, 3, 1.5, 2); +// +// See also +// imread +// +// Authors +// Sukul Bagai + + image_list = mattolist(image) + + out = raw_scharr(image_list, ddepth, dx, dy, scale, delta) + + sz = size(out) + + for i = 1: sz + new_image(:, :, i) = out(i) + end + +endfunction diff --git a/macros/sepFilter2D.bin b/macros/sepFilter2D.bin new file mode 100644 index 0000000000000000000000000000000000000000..c473f66c9dcd964fe7b316d10ae78ae59c73f067 GIT binary patch literal 8928 zcmdto+iq1=6b4{CW3d~kloofZEi_t{fI*`dkjRBz=o5IQ7rumVW1^4XWB3l9jT#dJ zqFxzse7Js?&aSoDMxwwGmNe!3rD*?8jk=KLW4Fn)l4 zb+*2}(krR{kzT3eyZMRy=bjyRpC zKD>|Ao18iP(Xla%h1VS0^yAqsB14Zk{mr3GhP-k7CyR_7D;6+K;~&E6A3JG$SiKK{hAQ|-N?F`w{VDDrNxx>~lzVt%mruGQT0 z^&Zpr%AiYKB0j<%PU6h`iE5jJWBe2|KX;3k;XB>%1$$VqBPVqC`WErE`r> zd}FqHoMHU%;NzXfQr`MHU8df%N1ZQuKHY9l&wipttW9f+4#b|l+uOMHZ~a+cbmJj2 zZ~Ay)oONo>7+L!XIl8QIx^vFr%U;s%hZk!6c5yf(?v^;jBn}wZzwA?DZ)1A4`eK5C zx!@2ib3P`f&nq|Q20lFDv*!g{x`H{~c;rXhc)Jfz+5M+Jwzz-xKlpfgh%e`T>P~#Y z&c3ame5k!(-=4AB!3uV1x430Z#138R);sd)TO;xx-mICNo$cC*Pk7`i`Au%KZrVJm zt2SSs4EF~5BOhG&^mXANU-mWjf{(ioJG;X;f5nc+7@Oo_NS1Cq`sT6V7J=v4CdlJ3v-@eA|?R466`ZFoA@75eyxb$bbGa>h| zUY<& za>0Y#-H4a_ev4PRc{9(CSDW9?x!roJJGGhhlQ>fsVoscW4I97E%?B%b)rj9cKP_7@ zOPeA>;4RN&5r6%rlLrPX^J5zazkclR|51A2 z=iJP1RrvYl{eIE>_|}*xHx8{YnB$9>KOSu0=P$jBbmF_^|ITH;T`D5S*Efpj*)2-E zV&*?A?RDOWlc|4e1yY?(erX@Lnxe_&87D6O7dF)c5M& zD@Qf`*Vx2H+Rpm mu^a1ULKj&NxkY>}hEq5%gW=Twpg8lz1-sniv(B8G+J6Ei*Tm5P literal 0 HcmV?d00001 diff --git a/macros/sepFilter2D.sci b/macros/sepFilter2D.sci new file mode 100644 index 0000000..d025eb0 --- /dev/null +++ b/macros/sepFilter2D.sci @@ -0,0 +1,64 @@ +// Copyright (C) 2015 - IIT Bombay - FOSSEE +// +// This file must be used under the terms of the CeCILL. +// This source file is licensed as described in the file COPYING, which +// you should have received as part of this distribution. The terms +// are also available at +// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt +// Author: Sukul Bagai +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + +function new_image = sepFilter2D(image, ddepth, kernel_x, kernel_y, anchor_x, anchor_y, delta, border) +// Applies a separable linear filter to an image. +// +// Calling Sequence +// new_image = sepFilter2D(image, ddepth, kernel_x, kernel_y, anchor_x, anchor_y, delta, border); +// +// Parameters +// image: Input image. +// ddepth: Destination image depth, given below are the possible values CV_8U CV_16U/CV_16S CV_32F CV_64F +// kernel_x: Coefficients for filtering each row. +// kernel_y: Coefficients for filtering each column. +// anchor_x: X-coordinate of the anchor. +// anchor_y: Y-coordinate of the anchor. +// delta: Value added to the filtered results before storing them. +// borderType: Pixel extrapolation method, given below are the possible argument values BORDER_CONSTANT 0 BORDER_REPLICATE 1 BORDER_REFLECT 2 BORDER_WRAP 3 BORDER_REFLECT_101 4 BORDER_TRANSPARENT 5 +// +// Description +// The function applies a separable linear filter to the image. That is, first, every row of src +// is filtered with the 1D kernel kernelX. +// Then, every column of the result is filtered with the 1D kernel kernelY. +// +// Examples +// image = imread("images/lena.jpg"); +// new_image = sepFilter2D(image, CV_8U, [1, 2, 1], [2, 1, 2], -1, -1, 2, 1) +// +// See also +// imread +// +// Authors +// Sukul Bagai + + [lhs, rhs] = argn(0) + if rhs > 8 then + error(msprintf("Too many input arguments")) + end + if rhs < 8 then + error(msprintf("Too less input arguments")) + end + if lhs > 1 then + error(msprintf("Too many output arguments")) + end + + image_list = mattolist(image) + + out = raw_sepFilter2D(image_list, ddepth, kernel_x, kernel_y, anchor_x, anchor_y, delta, border) + + sz = size(out) + + for i = 1 : sz + new_image(:, :, i) = out(i) + end + +endfunction diff --git a/macros/stereoCalibrateAndRect.bin b/macros/stereoCalibrateAndRect.bin new file mode 100644 index 0000000000000000000000000000000000000000..2337e91379086ee3e8c03a7960d8246ddbed228d GIT binary patch literal 14804 zcmeI(&2C*+6$fyqd?ileI&Km>Tsyr@tI7m~+VD|9i&E4 zzCs7gc?v`;1fvS7ju2-3?DKcGx6ir0c49EaSK2!JV}1VDTKnvCe7wE0He1{J{Mwz3 z>$BCJwJ$bretPr9&hGBbm6eq%)$Xm%R_|`_9{YWx*0!!cw{~Oa-qyzE&Jl`@yQ}MK z2h&$t#%BJRWAmTfdU36ttp4+*d$zu@*-h`hy|%LQZ0Uc#+Pl?utNnRzZ|_!JH>zz9 zb7<~Y+pKn@+D8twqLa_0buJ&X^4H^~uGDHoVr*n7>=i+L-=! z4K4fFN>^-{S3ka4)2G)J-JNRps)e2&y3wGg8;wT(^=h#fEg3B>o6LnKSid^Z#ZNhc zFY@V+Uc6+qxRdjs8XkIMzcIUl#d`Jew*Js@dzeq`_%S6fe)qoopm^d>br1W&^z+K2 z_!GS7V-q_+sdct}yPAB_w@#maWdE}I=*5M+V9B2N^{D!2j1!082FKQxsknVn_RudD zpANPK^Y7FgIdFQe8vC-wE^FlCr=J}^tTq*oDIWX8S@@ngfe+T~d$AhZA65&#)jjO< zkIjE6`W|b1Ew+4&jJF29(_-|!f$vWp-sFfMTlDGCw@0()M*BUdhL?959Dp z4CzX~k<(DbC3iaiueR{<5+D z#h_!Xru%(1@$Kj7qC5LqzgLaP#^XL2`*UW94|2S{hnvSk79U;dac%d(+!I<_bm}0y zbn3$lohuoAO<2o5*UEUI=G%SoekDKJ+-K4DXU1UXzKk|C$u6{Pz-MhFVXZwE z!5SuT()8!m;$zMw_K)C`a|zwA547akYxs#b_=yL(=3?s`)yLP*$JWRRUG6!2WP^GB zP2v5w;XchKJmkXqN;Q0Rzfx^XZq}ByZCSc8U2K6*iw*qQ%y#p}^3+*PA2wo^eHjMm z%%j5x=Zom;Y2)Qy@c$C8i|DI7lyh=Q-Ss&+Rc9A1W}e;o!Ur{?KGf})Pm9jn*&D`c zIcv-A2g}lp>4NV+$|p5%U0m$xiHEt176a$Kt9trHkxM(d2r?*J+afcJqyx>$2|G2Jy!9}8hY#SXyvxJ zv&9;D=yDF7!VrDziVfzxYm%SA1Pz~p11ug?dsq#9>=Xm~&53jTTgDE$B1inl3C{0T zpG?@&%?@#Haflz$4|jBAW^Rn%GXdE-^O1`ePyb$!j`*JE!sumxVjVw%N9J4n=nQY{ zb7do0uJDV$$xUPHz5nRRN7w2h6Ayjn@g=XLUu@YJyn~6k#3k4nqjM!YzC~7ip+mpL zlMU$6T4Sp#Kds4Aee+pEA1q>f(QAK%~WUg>!SHfZA~ zUb@4}-`0Ng^oub%>)H3!&mT)}@_>BfhjsmKwaA=TpG>@H<^4<5@;vdV`t5o!p&P!@ z6JN=NEq*a{@8=J>^e1O)xL>=iEo^Lc#g^PRm$ikB{K2p8&uR}_w0X5253-X3>Nfjo z;$e*)*@NI^46~_sVX*vhHM;zQ)winuS~I>{{XUMb)LgKGcW-ZMA$iYE{nOf>J&G@C zA%3!bEQjI)zxa`HeB{?jzVz|`Z}`H7*nS#cVA97Wdtvg~7+vl*vzy1X!#JUi*{N%NwW3&ECe@tfR!KE7stY~LfJ z-^=*h`}}gv@l!m}vpc@GWA@3@785y05B+%X^4%QYA5=>mds||AZ|7KCzF%_aZE0IO z=oAn2EGB9)@v`Q=np*e%RUdt7myLwS{pMQr+0PbsXvXjp zZ?>~1_a}Qs>wo-3F9xAw54!W%m!l)Oao+n+^zo5T%j8;ka?TA-XU&VqEA@vMUmfgG z(|Nw2PrT?8yYPtv+w;z!4;p%Y$v<_1=C8v(LPpl`lcmXvZ&u4soz#aM$G+q6UB|yl zmK^jw$=9l()#!3{-k9Aw@_EVO=ODGTa%aI>?194{N)h>fY$baUFS;*%`0O$J-l&F; zy>C||=hbTQncsB4#j`Y=HJGLT;P_yevzEBX)%4NBl^imU+ z7~6+obQXNGM<#YJl$`AAiHTT|pRs$YIEkG#F=qR+F-1p*`0CqFzglr#-;eX_a^tE~r!$-xN82)CE^OL$JhSsLO5iIq4wY<;Yd>44+d%+2sBi{}lqZ#>* zu%(%;&R^Od`R37{_w}+T_aRqjA9>+w&)XQ;D^1nX%B=-!>kHP7ZP)`Xnf>>W5o7IU z2Hn-#jczVOYu7XAuGUt&xeTp|>FM+2_I|CmdGgyqj{3i-d2;mp^4Vi~JoPQ=)V&hV zqFj?-$u;MU%UnCiwu0h}xwdsI*M3*Brok3&n4&Od2-&F=`!b^H0KYV@!Ipi$Azn$|9Q2` zoIk*K>P+!8a{j1{^>e8kwUW9~GnaL9v~CKL{ohCR2XndqRyDbw{Pzs%nbo^M&!Ktu zqHl2A`=`$Fc@84q^D!NH*WfwFy9zq=%^Rc3-;g{n$4=|)b2X0+pJ(a3Uz&Q> zXze(QpM2D&o^x9H=jmtcn0gj#?SK(%VG1X4>~WroD?L-sf~^kkYP~B-p732f)clyf zsc-4%n|dy7^{Mld{{BGL)bnpEi|tSPy;kyW>RJ40>r4En-U~z@y6v6Weg9Y4|LpZ@ cDEp cameraMatrix1 distortionCoefficients1 cameraMatrix2 distortionCoefficients2 rotationMatrix TranslationVector DepthMap ProjectionMatrix1 ProjectionMatrix2 +// +// +// Description +// The function estimates transformation between two cameras making a stereo pair and also +// computes the rotation matrices for each camera that (virtually) make both camera image +// planes the same plane. Consequently, this makes all the epipolar lines parallel and thus +// simplifies the dense stereo correspondence problem +// +// Examples +// stacksize("max"); +// img_1 = imread("images/left1.jpg", 0); +// img_2 = imread("images/right1.jpg", 0); +// w1 = genCheckerboardPoints([10, 7], 8); +// ip1 = detectCheckerboardCorner(img_1, [7, 10]); +// ip2 = detectCheckerboardCorner(img_2, [7, 10]); +// ip1l = list(ip1); +// ip2l = list(ip2); +// op = stereoCalibrateAndRect(w1, ip1l, ip2l, size(img_1)); +// [map map1] = disparity(img_1, img_2); +// img = reconstructScene(op.DepthMap, map1, 1); +// +// See also +// imread +// genCheckerboardPoints +// detectCheckerboardCorner +// disparity +// reconstructScene +// +// Authors +// Siddhant Narang + + [lhs rhs] = argn(0); + + if lhs > 1 + error(msprintf("Too many output arguments\n")); + elseif rhs > 8 + error(msprintf("Too many input arguments, maximum number of arguments is 7\n")); + elseif rhs < 4 + error(msprintf("The function needs atleast 3 arguments\n")); + end + + if rhs == 4 + [a b c d e f g h i] = raw_stereoCalibrateAndRect(objectpoints, imagepoints1, imagepoints2, imageSize); + elseif rhs == 5 + [a b c d e f g h i] = raw_stereoCalibrateAndRect(objectpoints, imagepoints1, imagepoints2, imageSize, varargin(1)); + elseif rhs == 6 + [a b c d e f g h i] = raw_stereoCalibrateAndRect(objectpoints, imagepoints1, imagepoints2, imageSize, varargin(1), varargin(2)); + elseif rhs == 7 + [a b c d e f g h i] = raw_stereoCalibrateAndRect(objectpoints, imagepoints1, imagepoints2, imageSize, varargin(1), varargin(2), varargin(3)); + elseif rhs == 8 + [a b c d e f g h i] = raw_stereoCalibrateAndRect(objectpoints, imagepoints1, imagepoints2, imageSize, varargin(1), varargin(2), varargin(3), varargin(4)); + end + + params = struct('cameraMatrix1', a, 'distortionCoefficients1', b, 'cameraMatrix2', c,'distortionCoefficients2', d, 'rotationMatrix', e, 'TranslationVector', f, "DepthMap", g, "ProjectionMatrix1", h, "ProjectionMatrix2", i); + +endfunction \ No newline at end of file diff --git a/macros/trainEMClassifier.bin b/macros/trainEMClassifier.bin new file mode 100644 index 0000000000000000000000000000000000000000..d637888b917f0650b3d5eb34e5b12f389720c948 GIT binary patch literal 8600 zcmeI%-ELG>6bJD7nKD2rle7h=14xKL^a4@A_yJs~G4aCag)hK^`3k;=k3kbf5=20+ z47h&n`FD0tXQtC~VFFIFa`xGKeg4_Uzf|dJc;=yE#1PiZ+T)6pf3lpD8j&uI0x|cBM$1*2&<{cvRy-5xMB_ zp0WA$B6F)nbdhC$t;jqZ2rnN!@zISpe#8bnu`kBxd{i<2USy44`tkCYF7o^*!>136 zf?ExD$JXh1Qo5ptuD<=PT4(#MB0d@iTjS{dqQ>+cCgy9M41fC=Cr4NF69qy@-;NRDiT)flCEt=Q&JN^e{hwjt|ACpsavDfamiw(~rbs8V- zOS{ExfX=x3AOHm-gi zX4&mykbKSBU%@GL5PlfJ6!C{&spq~g({QDitQhcnPh8&Fn|1#10e>)|$DF*-AxCr= z_iOA%&5`ZbQ_c))Vms(;jK6G0#uJXPH*d@rG4d_-lDKncU<(hs_{awf{UZ7g@~Oq^ zPA8W*>5H#&6+hwD$BI33eBo1kiM%;HEoSrTf_yL_ccI9>Gf&RgyfNM4_nz~>IQ2us-^7jN?7UUQ{LOb6w>t({`u_4imOPv_e;77uLLPu;fhy;t)shWziJE7y8F zbmD_&%gcYchJ{+Ro_dupb9zqh5+8SS;e#a}{?U<~;e!ibla~)_%n~&B< zGWgkK$GBa;QSW#XGu*^WKVEwCOs|@E9~CPfedKJph>dBa3i+4|AWFM59Ri?54f z)A#AilI26{Gj}_9z%F@`6S%;Xt=8{p_7AJ?uyLfmTFf%HXy2Q5|8MKB!rZOJV_nSu z@41~GIjx7Nga7y3{_m^(J*QXOKh7Hc(7QU$-pbz3Ud;IueC+0QoFBT z<=j(Sd}ZGqfzP>L@iYBRk5`ZQrk(#eo2}Eg$k`d0_|W$3)cQf+6Qd(``6v#*CBT>N z+)HvFiOd_dHZ4zCujO6bmOI|RORs$=SMGSZ+rTYyxg*0EZtNrT{8Vq`+0I$R*Q~+U z8huDSuNKYvjrE_hQ_JDm8jsGM7~kEvKinMe{=E9Mc>K02W<7yE@BHx8BTE0JwN|K; 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The terms +// are also available at +// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt +// Author: Siddhant Narang +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + +function classifier = trainKNNClassifier(imgSets, bag, classifierName, varargin) +// This function is used to train an image classifier using the KNN algorithm. +// +// Calling Sequence +// classifier = trainKNNClassifier(imgSets, bag, classifierName) +// classifier = trainKNNClassifier(imgSets, bag, classifierName, algorithmType) +// +// Parameters +// classifier: Image category classifier +// imgSets: Input imageSet to train the classifier on +// bag: The bagOfFeatures of the imageSet provided +// algorithmType: +// +// Description +// This function trains a KNN classifier which can be used to predict classes of images given to it as +// input using the predictKNN() function. +// +// Examples +// imgSet = imageSet('images/trainset_2/','recursive'); +// [trainingSet testSet] = partition(imgSet,[0.8]); +// bag = bagOfFeatures(trainingSet); +// KNNClassifier = trainKNNClassifier(trainingSet, bag, classifierName); +// +// Examples +// imgSet = imageSet('images/trainset_3/','recursive'); +// [trainingSet testSet] = partition(imgSet,[0.8]); +// bag = bagOfFeatures(trainingSet); +// algorithmType = 1; +// KNNClassifier = trainKNNClassifier(trainingSet, bag, classifierName, algorithmType); +// save("var.dat", "KNNClassifier"); +// +// See also +// imageSet +// partition +// bagOfFeatures +// mlPredict +// save +// +// Authors +// Siddhant Narang + + bag_list = bagStructToList(bag); + imgSets_list = imageSetToList(imgSets); + + // Handling variable arguments. + [lhs rhs] = argn(0) + if lhs > 1 + error(msprintf("Too many output arguments")); + elseif rhs < 3 + error(msprintf("Not enough input arguments")); + elseif rhs > 4 + error(msprintf("Too many input arguments")); + end + if rhs == 3 + temp = raw_trainKNNClassifier(imgSets_list, bag_list, classifierName); + elseif rhs == 4 + temp = raw_trainKNNClassifier(imgSets_list, bag_list, classifierName, varargin(1)); + end + + classifier = struct("ClassifierType", temp(1), "ClassifierLocation", temp(2), "BagofFeaturesLocation", temp(3), "Description", temp(4)) + +endfunction diff --git a/macros/trainLRClassifier.bin b/macros/trainLRClassifier.bin new file mode 100644 index 0000000000000000000000000000000000000000..7960561a279fe7604fbb0b38f33670f74eca796a GIT binary patch literal 15460 zcmeI&-EJLM6$fww1yVP494oOCj-9$`ghGL;gpXddCFQF^REWD)Tvvhz;1Y=^;DWc} zf|o$55tK-f(!v#B*3W1DKKqQ%*mDkXt0q3u*6i7Peg4y?L;)x7&@(F3+rQ&Tg9COxF8PZ*R;do?D-+ zPd2x9W+T(y2ODc^S4z)w)!wRhx7vG$hlf||x>aqr8$)xe+D^60)%L2HzfsK?zLp*> z-V(pYPxE-tr$4FwdNq8J;XZxi+trM1RwD~9{nx7*r$a-_MptxXqm3QWK~D7ZF)|;N z&wr|Bj$HE5vX?CU?8n2VZ&nL#)jjT;C*z}%6**+}^&iwc-CwH4M*U!`AK9;0pS)#! zzSr@vw~ulBWZxJJzfv;T&S!YUZnEM2t!nIhrdr~Zv9UJ4-(f`u-(Rd2|C5trWwGVc z?tkzTA9%o39OXxwNAlI2SoCpwyT+chyjc~u@WD1%X5F|qo;ug&<}WTV9-W)ZU>sU8 zkk{&t8ku@!Tuff5mi!-z(XWc;VjYaDAHjXhItcBmF$%x+m9<8%8u!<3Z!~8=wf3AL z+O_way1$6);}O^7g8e@^;F>dqJh1=Eg*FH3_V}EDRzCc>+Y^&hIa83KmKb+#$@${T z)n6u$zShzIt@Mzs{-{r6M^0)|YFEyXw;P(&veVA5vC&zRY?#ow=p5GCWewe|re^ZH zvGp$gn_xS@!Kz2^3ZET{6VEX+ zlLvkL+{X@b`e$pp=wXLBIN@pSMYG?HwV246*h~hS>Ecg|%?tJLPlh@4@}IuM!#I6I z=bYf>SuPmHFZbjO`G2jG&9^+Cx7e;~6Mxu1mVV-=FF#?Uv5jy1rav{VzwXIWH-l;X zAuBe%T79z6k_YGD7WvVKAKh!!@b$<0a?p9s`bV{g)$Y{2wW|KBpZ}`%&uX5l(Y#di ze8ofevN6RA4|cGXpD<)+e93+(w~V8q2d^6C-nd-CWBgusWuJFW@aF%^)y813Dpqiz zk6w1h-L${Es-=(G{t~%T9e9*$=9guIT+2RWt-vE^Sb6C@Avd#D>?PUb%%K-Q zSGjC2NdC6xK(fc2mBy@_>^JPr9^Kb%{X07tr<+Y|Oy4+~_-CG6`puDRtUYVUi^y<& zshYeXr^O1NJvenJ`($iEiza7jdfCy=x96{SI(j@~+aEhy<-3NDFZ_3(XT0{@BR*_l z2b=NmQ+)Lo#b&6sqF-iR(?d7D*btmtQwys*XiH*~`YJAbb&p>>(2yrL7c^Khc`EPoRh|L48f(VrFbp?rM0sXEIJdOyPu)*!ldq{oYOrUCZ&zz+;OIVin0TRM<5#Q2=5JJ= zJ>RJYXMFrj402uM3;N`Terx|Lbx&^m6>D^-CeA=)z{m1E2rf8SvmbUt+)QVQa|`-4^RiQ2dHJ% zvU}s|k$P&cagVQE>tky}Uw?@?v97Z}WFMzbjM$hn2)j}T>=E`+bk+mD$Y5vchIzh@ z*<->J8`^p}c0Y)W=w%~6{3i!bva@$*&km0oxF{}}ujymIE_Jltd*qzt9F+YZZsE&0 z5W-j|Ac^ZGb>l^z1?%$=SrbBbEH@S6r@4cM|Tf2M5pWgkq zXw-i`f3Bl-&kk4TkdlArddt*t*VLRHL*ECsc5H30Z*Cks%Z#gy8F$Cn(aAqyOY8{F z!O;7s;0kAW!M(+@U1R3Z%Qv*iKV#wme=(Lz<`OS4&iNGI7pmnaKJ~HViEkZ2xX5Sp z?05ByG`V{6drQ1EwRZiKIatWek4lNS`mvwf)A-QM?0*$1>(%<$`ZiNdfL-!CIT$?grp6@CpIm(RkMM1C z*BKv1_p9~sh2faoecW&@w~u~be}0P1_v#KdYR9er93G|?teW5VN>1|omFko8{c6GA z+)!O__l==FsHGYD-n^xmtnc61Tks9Mx}k<;AAG9%>PA}w_BOU}wsj-@B0KVDd5kuX zal^AMZ=&5^ph1yuVoeW#gT*pffaC&Vc82rfX~S zqd%YRp2)*rmoM`0t!fu}SRfqzR?EZrT;4pFT$aDdW#^e?b6Kp_)bVHf>6x?L&wpF| zHq)$ literal 0 HcmV?d00001 diff --git a/macros/trainLRClassifier.sci b/macros/trainLRClassifier.sci new file mode 100644 index 0000000..dfeb6a4 --- /dev/null +++ b/macros/trainLRClassifier.sci @@ -0,0 +1,94 @@ +// Copyright (C) 2015 - IIT Bombay - FOSSEE +// +// This file must be used under the terms of the CeCILL. +// This source file is licensed as described in the file COPYING, which +// you should have received as part of this distribution. The terms +// are also available at +// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt +// Author: Siddhant Narang +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + +function classifier = trainLRClassifier(imgSets, bag, classifierName, varargin) +// This function is used to train an image classifier using the LR algorithm. +// +// Calling Sequence +// classifier = trainLRClassifier(imgSets, bag, classifierName) +// classifier = trainLRClassifier(imgSets, bag, classifierName, learningRate) +// classifier = trainLRClassifier(imgSets, bag, classifierName, learningRate, iteration) +// classifier = trainLRClassifier(imgSets, bag, classifierName, learningRate, iteration, regularization) +// classifier = trainLRClassifier(imgSets, bag, classifierName, learningRate, iteration, regularization, trainMethod) +// classifier = trainLRClassifier(imgSets, bag, classifierName, learningRate, iteration, regularization, trainMethod, minibatch) +// +// Parameters +// classifier: Image category classifier +// imgSets: Input imageSet to train the classifier on +// bag: The bagOfFeatures of the imageSet provided +// learningRate: Defines the rate at which the classifier will learn. +// iteration: Number of iterations the training function will perform. +// regularization: Controls the kind of regularization to be applied. The types areREG_DISABLE- Regularization disabled, flag value = -1.REG_L1- L1 norm, flag value = 0.REG_L2- L2 norm, flag value = 1. +// +// trainMethod: Controls the kind of training method to be applied. The types areBATCH- flag value = 1.MINI_BATCH- flag value = 0. +// +// minibatch: Specifies the number of training samples taken in each step of Mini-Batch Gradient Descent. +// Will only be used if trainMethod flag value is set to 0 training algorithm. +// It has to take values less than the total number of training samples. +// +// +// Description +// This function trains a LR classifier which can be used to predict classes of images given to it as +// input using the predictLR() function. +// +// Examples +// imgSet = imageSet('images/trainset_2/','recursive'); +// [trainingSet testSet] = partition(imgSet,[0.8]); +// bag = bagOfFeatures(trainingSet); +// lrclassi = trainLRClassifier(im, bag, "lrclassi", 1, 150, 0, 1, 5); +// +// Examples +// imgSet = imageSet('images/trainset_3/','recursive'); +// [trainingSet testSet] = partition(imgSet,[0.8]); +// bag = bagOfFeatures(trainingSet); +// lrclassi = trainLRClassifier(im, bag, "lrclassi", 1, 150, 0); +// save("var.dat", "lrclassi"); +// +// See also +// imageSet +// partition +// bagOfFeatures +// mlPredict +// save +// +// Authors +// Siddhant Narang + + bag_list = bagStructToList(bag); + imgSets_list = imageSetToList(imgSets); + + // Handling variable arguments. + [lhs rhs] = argn(0) + if lhs > 1 + error(msprintf("Too many output arguments")); + elseif rhs < 3 + error(msprintf("Not enough input arguments")); + elseif rhs > 8 + error(msprintf("Too many input arguments")); + end + if rhs == 3 + temp = raw_trainLRClassifier(imgSets_list, bag_list, classifierName); + elseif rhs == 4 + temp = raw_trainLRClassifier(imgSets_list, bag_list, classifierName, varargin(1)); + elseif rhs == 5 + temp = raw_trainLRClassifier(imgSets_list, bag_list, classifierName, varargin(1), varargin(2)); + elseif rhs == 6 + temp = raw_trainLRClassifier(imgSets_list, bag_list, classifierName, varargin(1), varargin(2), varargin(3)); + msprintf("6 arguments"); + elseif rhs == 7 + temp = raw_trainLRClassifier(imgSets_list, bag_list, classifierName, varargin(1), varargin(2), varargin(3), varargin(4)); + elseif rhs == 8 + temp = raw_trainLRClassifier(imgSets_list, bag_list, classifierName, varargin(1), varargin(2), varargin(3), varargin(4), varargin(5)); + end + + classifier = struct("ClassifierType", temp(1), "ClassifierLocation", temp(2), "BagofFeaturesLocation", temp(3), "Description", temp(4)) + +endfunction diff --git a/macros/triangulatePoints.bin b/macros/triangulatePoints.bin new file mode 100644 index 0000000000000000000000000000000000000000..e36e8bae766cb17c8a8e026e0124b73906dbd42f GIT binary patch literal 5092 zcmeI$&2Cgj5CvdE2*D=&8H`QLfJs7^!?wNm6eraC0E`p zp4e;4v*85Y_SVJS)yc8hVw<$}<8$lpe9gh9?_$&?S;WmalKEN%i<xa_mm$6(JFy?Asz zx1-du_bJ9s^L@8Qah)?9aVL1=i$m(l4lgy(2Sa{xqx+^LdD|!0^i0pg8XZi|FMKKQrBznPY?poW zre?727=FZ1pP}#ciWuhf{Wn2hJ3gXI?(F0d-~ZLA7T)zgmcTzK8SdrqyZ?uVz1P`zmw6ZPVe~wlTf^&i z)A^pZuW-G6JrXX@S(YpNv0P6!AAEn&=84I- z^>Bwxo;^pH)XcoMiH=YHlCOL|Z)>M&BR0aYS-7}*QP(H9&Ee|hBZ0t8Fjw?3;$cE zmVZ?(^pX7C%X-LNrFU|!PS;%B^A1%5cL@v|J$I?i-AC*q?j-v^_FL886~ELyX6}cm ye_%#_!QJEN}V48s7rLxJ&>5 literal 0 HcmV?d00001 diff --git a/macros/triangulatePoints.sci b/macros/triangulatePoints.sci new file mode 100644 index 0000000..ac94b1e --- /dev/null +++ b/macros/triangulatePoints.sci @@ -0,0 +1,42 @@ +// Copyright (C) 2015 - IIT Bombay - FOSSEE +// +// This file must be used under the terms of the CeCILL. +// This source file is licensed as described in the file COPYING, which +// you should have received as part of this distribution. The terms +// are also available at +// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt +// Author: Deepshikha +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + +function points4D = triangulatePoints(projMat1, projMat2, points1, points2) +// Returns the worldPoint coordinates of the feature points. +// +// Calling Sequence +// points4D = triangulatePoints(projMat1, projMat2, points1, points2) +// +// Parameters +// projMat1: 3X4 projection matrix for the first Camera matrix +// projMat2: 3X4 projection matrix for the second Camera matrix +// points1: 2xN array of feature points in the 1st image. In case of c++ version it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1 +// points2: 2xN array of feature points in the 2nd image. In case of c++ version it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1 +// points4D: 4XN array or matrix of reconstructed points +// +// Description +// Returns 4D location of the matched feature points from the two projected matrices. The 4D location of the feature points is reconstructed using triangulation. +// +// Examples +// [projMat1] = [12 21 21 19; 34 12 0 2; 112 431 890 32.1] +// [projMat2] = [16 17 32 1; 64 90 12 11; 123 43.5 895 9.8] +// [points1] = [1 2 3 4;5 6 7 8] +// [points2] = [32 1 3 5; 9 8 3 4] +// [points4D] = triangulatePoints(projMat1, projMat2, points1, points2) +// +// Authors +// Deepshikha + + + points4D = raw_triangulatePoints(projMat1, projMat2, points1, points2) + +endfunction + diff --git a/macros/whitepoint.bin b/macros/whitepoint.bin new file mode 100644 index 0000000000000000000000000000000000000000..9f1f0a9045c53b75ba6937873158453ae84ca789 GIT binary patch literal 9072 zcmeI&%WfQ15C-50*NFomF&D=;4sAO~iDMZQ5Lwvoz$36=!L#uqYEb$E|LA;>gx4XsOhvPu;8jQ{CgFySu)9d-LA<#`dE#*SoXX>_*Yf?%tXGJm9Tw zY(Ch2cz=6qedA8{-G7+RX73gMlcMj69u+-5IXSsq_wAwwMO#H1MfZ#F*ZMpD-6DMQ z2;cp~{{B&ar%xWAFEZ~G*}GT72A*8-#I#;SFZVmOCKJE$htD4pFQ3s7D|yfSu*b$u z(QeUR|12i5vB7ro70WP2_Ut3FsX?13Tja#8PKkp|a#4fCC3o_X%f2|iDB??;uB_RW z&tKKE#2>${pI`c(eq6F@BxZd6tz!>=tUgcs{jTnOCQiDWMPjv={2$j^+;UNy@Z`N; z^rR?0Yk9i9Rcro#?en24ui%Ki)SLa-`2NiodQ(0RN|xQ9%dYs@@JwIqYKE-4qBdS~ zVn~lYi=(xZ8t{+k%Z1JOdKrwxK0EBSF{oj1&UR`Z8PBjZjFT-n*v~m*8wS!NdGJlP z#otlCm-^?7IX_}xFSuiq+;I`T;Xd1~oyhT>I=kCXtZ&u2T0A@K_u&2aqCbkr#3#Mv zA6uyfzhXhDpFQ%?6Bm1bmfiULvfulv?k;EXW|4SYZxltwti>N*WBhpug`U-#9^aY2 z>Y=uJ<1R;c@ru(kdCz*4e%X_=oXoh?znw#5x}x+Vb0qVK&-kU!R(h6tMi!rM^6W+q zM%o-=L+xXmJYDC|IyJB5qOSISt6r$hQPJl`V>k&fxdso(Pu;|GTts$RxiMezPwx+F zEynA>V|)fnLp-V-EWTD0nTzmPq6^&3dp$nrOyMzoo5EsOy828`z13zJECw6o*@h9g zW=9X>55L7)D;t@d;jY*9yejxk4=beE>qYWeHGJ#Eb>aH&I@`f2Jhyl?W9LO`7MYe` z+oxCfHp~jOrH9y5vJkC95oAI(cDubGbDat94=v=O<1HC`N-J|w$0p&rfXP(}$VNuXd)>A!CnBS7eXQs^H&jN>9xzaR&d^M@6eJrGNU<&X@EN2J!R|*{83M?1d+1 z*vWShnb`1~j~}+k?AJ4S`tXHd4d(3e15fV!kj2k?98RK7#@aVO`^d96rt)GZ-_+hS zWLx>2dKQOx#4(m@>c!XaY{G+cb>6uQuf)!LXTgv2{j7-XW%%5KIm=gk^IEu-LJ;^en{TVuXyK0sfl{ncad}Hjd#(QUT}5P%jky~ zllyV4!;`O?$uSsnS6kSN?6PuWzSK%T)y~V`%ZiY zheIsm59h>1ST2*v}D=oic)Ve&=;5Z)PcVJ$3;JuPyXEZ>;9xD ze&B??)K*xspZ8i@*Rl9g|I{|Ux>34qeDT@FFP4L%#L%wk?-!;1Q+|B2zltCF ztsnVL)q9zE+gOG*NvxmsKHA!{ot#_TiS07+=v{P&7+ot~Fmzr{t&OReBh$uw*&3!7 z=haY+@TcO5jW!+_YHJARW`c9k6{*1(KGm@Z|@4f zlg^7_N^Uq$B6q8|$KNvNK6)j78q>VD;WxN<47{c;{K_GE?&9~&kr|9{@2A=mhj~Na zn!cJ^#||5612a?qPvxJoRqTb#)>`+l8~ML5|CS6K_zu)>_B@N<{oA6c|07eZMNVQN G=l&lXv+#5P literal 0 HcmV?d00001 diff --git a/macros/whitepoint.sci b/macros/whitepoint.sci new file mode 100644 index 0000000..d6d4515 --- /dev/null +++ b/macros/whitepoint.sci @@ -0,0 +1,46 @@ +// Copyright (C) 2015 - IIT Bombay - FOSSEE +// +// This file must be used under the terms of the CeCILL. +// This source file is licensed as described in the file COPYING, which +// you should have received as part of this distribution. The terms +// are also available at +// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt +// Author: Tess Zacharias +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + +function xyz = whitepoint(input_string) +// Returns a three element vector defining the illumination xyz values for different lighting conditions. +// +// Calling Sequence +// xyz = whitepoint(string); +// +// Parameters +// string: The following are the possible values for this argument and their descriptions d65- CIE standard illuminant D65, [0.9504, 1.0000, 1.0888]. Reperesents noon daylight with correlated color temperature of 6504 K.d50- CIE standard illuminant D50, [0.9642, 1.0000, 0.8251]. Represents warm daylight at sunrise or sunset with correlated color temperature of 5003 K.a- CIE standard illuminant A, [1.0985, 1.0000, 0.3558]. Reperesents typical, domestic, tungsten-filament lighting with correlated color temperature of 2856 K. c- CIE standard illuminant C, [0.9807, 1.0000, 1.1822]. Simulates average or north sky daylight with correlated color temperature of 6774 K. icc- Profile Connection Space (PCS) illuminant used in ICC profiles. Approximation of [0.962, 1.000, 0.8249] using fixed-point, signed, 32-bit numbers with 16 fractional bits. Actual value: [31595,32768, 27030]/32768. c- CIE standard illuminant C, [0.9807, 1.0000, 1.1822]. Represents average or north sky daylight with correlated color temperature of 6774 K.d55- CIE standard illuminant D55, [0.9568, 1.0000, 0.9214]. Represents mid-morning or mid-afternoon daylight with correlated color temperature of 5500 K. +// +// +// Description +// This function is used to get a vector of xyz color space representation for different lighting +// conditions which can be used to get images with different lighting properties. +// +// Examples +// xyz = whitepoint("a"); +// img = imread("images/lena.jpeg"); +// image = applycform(img, "srgb2xyz"); +// temp1 = image(:, :, 1) +// temp3 = image(:, :, 3) +// image(:, :, 1) = temp3 * xyz(1); +// image(:, :, 2) = image(:, :, 2) * xyz(2); +// image(:, :, 3) = temp1 * xyz(3); +// imshow(image); +// +// See also +// imshow +// imread +// applycform +// +// Authors +// Tess Zacharias + + xyz = raw_whitepoint(input_string) +endfunction diff --git a/sci_gateway/cpp/opencv_decorrstretch.cpp b/sci_gateway/cpp/opencv_decorrstretch.cpp new file mode 100644 index 0000000..377a3cd --- /dev/null +++ b/sci_gateway/cpp/opencv_decorrstretch.cpp @@ -0,0 +1,145 @@ +/*************************************************** +Author : Dhruti Shah +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + Mat dstretch(Mat& input) + { + CV_Assert(input.channels() > 1); + + Mat dataMu, dataSigma, eigDataSigma, scale, stretch; + Mat targetMean = Mat(); + + Mat data = input.reshape(1, input.rows*input.cols); + + meanStdDev(input, dataMu, dataSigma); + + PCA pca(data, Mat(), CV_PCA_DATA_AS_ROW); + + sqrt(pca.eigenvalues, eigDataSigma); + scale = Mat::diag(1/eigDataSigma); + stretch = Mat::diag(dataSigma); + + stretch.convertTo(stretch, CV_32F); + + Mat zmudata; + Mat repMu = repeat(dataMu.t(), data.rows, 1); + subtract(data, repMu, zmudata, Mat(), CV_32F); + + Mat transformed = zmudata*(pca.eigenvectors.t()*scale*pca.eigenvectors*stretch); + add(transformed, repMu, transformed, Mat(), CV_32F); + + Mat dstr32f = transformed.reshape(input.channels(), input.rows); + return dstr32f; + } + + int opencv_decorrstretch(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + char **type = NULL; + int i,j,k; + + //checking input argument + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + Mat lab; + try + { + cvtColor(image, lab, CV_BGR2Lab); + } + catch(Exception &e) + { + Scierror(999, "%s", e.what()); + return 0; + } + Mat dstrlab32f = dstretch(lab); + Mat dstrlab8u; + try + { + dstrlab32f.convertTo(dstrlab8u, CV_8UC3); + } + catch(Exception &e) + { + Scierror(999, "%s", e.what()); + return 0; + } + + Mat dstrlab2bgr; + try + { + cvtColor(dstrlab8u, dstrlab2bgr, CV_Lab2BGR); + } + catch(Exception &e) + { + Scierror(999, "%s", e.what()); + return 0; + } + + Mat dstrbgr32f; + try + { + dstrbgr32f = dstretch(image); + } + catch(Exception &e) + { + Scierror(999, "%s", e.what()); + return 0; + } + + Mat dstrbgr8u; + try + { + dstrbgr32f.convertTo(dstrbgr8u, CV_8UC3); + } + catch(Exception &e) + { + Scierror(999, "%s", e.what()); + return 0; + } + + string tempstring = type2str(dstrbgr8u.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,dstrbgr8u,1); + free(checker); + + // Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + + // Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + } +/* ==================================================================== */ +} + diff --git a/sci_gateway/cpp/opencv_detectAndComputeORB.cpp b/sci_gateway/cpp/opencv_detectAndComputeORB.cpp new file mode 100644 index 0000000..82ad94f --- /dev/null +++ b/sci_gateway/cpp/opencv_detectAndComputeORB.cpp @@ -0,0 +1,660 @@ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include "opencv2/features2d/features2d.hpp" +#include + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_detectAndComputeORB(char *fname, unsigned long fname_len) + { + + // Error management variable + SciErr sciErr; + + //Variables + int i, j; + int intErr; + int iRows1 = 0; + int iCols1 = 0; + + int iRows = 0; + int iCols = 0; + int *piLen = NULL; + int *piAddr = NULL; + char **pstData = NULL; + char *currentArg = NULL; + bool *providedArgs = NULL; + + double edgeThreshold = 20; + double fastThreshold = 31; + double firstLevel = 0; + double maxFeatures = 80; + double nLevels = 8; + double patchSize = 30; + double scaleFactor = 1.5f; + double scoreType = ORB::HARRIS_SCORE; + int WTA_K = 2; + + double *ROImat = NULL; + int iRet; + + // checking input argument + // 1 : image + // 2 : edgeThreshold + // 3 : fastThreshold + // 4 : firstLevel + // 5 : ROI + // 6 : maxFeatures + // 7 : nLevels + // 8 : patchSize + // 9 : scaleFactor + // 10 : scoreType + CheckInputArgument(pvApiCtx, 1, 19); + CheckOutputArgument(pvApiCtx, 1, 1); + + // retrieve image + Mat image; + retrieveImage(image, 1); + + // For the optional arguments + int nbInputArguments = *getNbInputArgument(pvApiCtx); + //sciprint("%d\n",nbInputArguments); + + if((nbInputArguments%2) == 0) + { + Scierror(999, "%d: The number of arguments must be odd\n"); + return 0; + } + + providedArgs = (bool*) malloc(sizeof(bool) * 9); + memset(providedArgs, 0, 9); + + for(int iter = 2; iter <= nbInputArguments; iter += 2) + { + sciErr = getVarAddressFromPosition(pvApiCtx, iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + /// Three calls to getMatrixOfString + //First Call + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Second call to get length of string + piLen = (int*) malloc(sizeof(int) * iRows1 * iCols1); + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows1, &iCols1, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // third call + pstData = (char**) malloc(sizeof(char*) * iRows1 * iCols1); + for(int k = 0; k < iRows1 * iCols1; ++k) + { + pstData[k] = (char*) malloc(sizeof(char) * piLen[k] + 1); + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows1, &iCols1, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + currentArg = pstData[0]; + + // getting edgeThreshold + if(strcmp(currentArg, "edgeThreshold") == 0) + { + if(iter + 1 <= nbInputArguments and !providedArgs[0]) + { + if(isIntegerType(pvApiCtx, piAddr) || isDoubleType(pvApiCtx, piAddr)) + { + if(isScalar(pvApiCtx, piAddr)) + { + double dData = 0; + iRet = getScalarDouble(pvApiCtx, piAddr, &dData); + if(!iRet) + { + edgeThreshold = dData; + } + } + else + { + Scierror(999, "Error: The input argument ""edgeThreshold"" is not of scalar type.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument ""edgeThreshold"" is not of type Integer.\n"); + return 0; + } + providedArgs[0] = 1; + } + else if(providedArgs[0]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + if(strcmp(currentArg, "fastThreshold") == 0) + { + if(iter + 1 <= nbInputArguments and !providedArgs[1]) + { + if(isIntegerType(pvApiCtx, piAddr) || isDoubleType(pvApiCtx, piAddr)) + { + if(isScalar(pvApiCtx, piAddr)) + { + double dData = 0; + iRet = getScalarDouble(pvApiCtx, piAddr, &dData); + if(!iRet) + { + fastThreshold = dData; + } + } + else + { + Scierror(999, "Error: The input argument ""fastThreshold"" is not of scalar type.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument ""fastThreshold"" is not of type Integer.\n"); + return 0; + } + providedArgs[1] = 1; + } + else if(providedArgs[1]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + if(strcmp(currentArg, "firstLevel") == 0) + { + if(iter + 1 <= nbInputArguments and !providedArgs[2]) + { + if(isIntegerType(pvApiCtx, piAddr) || isDoubleType(pvApiCtx, piAddr)) + { + if(isScalar(pvApiCtx, piAddr)) + { + double dData = 0; + iRet = getScalarDouble(pvApiCtx, piAddr, &dData); + if(!iRet) + { + firstLevel = dData; + } + } + else + { + Scierror(999, "Error: The input argument ""firstLevel"" is not of scalar type.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument ""firstLevel"" is not of type Integer.\n"); + return 0; + } + providedArgs[2] = 1; + } + else if(providedArgs[2]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + else if(strcmp(currentArg,"ROI")==0) + { + // val_position=i+1; + + if(providedArgs[3] == 1) + { + Scierror(999,"Do not enter the same parameter\n"); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx, i + 1, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isDoubleType(pvApiCtx, piAddr) || isVarComplex(pvApiCtx, piAddr)) + { + Scierror(999,"Enter a List of 4 arguments\n"); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &ROImat); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows*iCols!=4) + { + Scierror(999,"Invalid Argument\n"); + return 0; + } + + if(ROImat[0]<0 || ROImat[1]<0 || ROImat[2]<0 || ROImat[3]<0) + { + Scierror(999,"Arguments cannot be negative\n"); + return 0; + } + + if(ROImat[0]>image.cols || ROImat[1]>image.rows || ROImat[0]+ROImat[2]>image.cols + || ROImat[1]+ROImat[3]>image.rows) + { + Scierror(999,"Please make sure the arguments are within the image range\n"); + return 0; + } + + providedArgs[3] = 1; + } + + if(strcmp(currentArg, "maxFeatures") == 0) + { + if(iter + 1 <= nbInputArguments and !providedArgs[4]) + { + if(isIntegerType(pvApiCtx, piAddr) || isDoubleType(pvApiCtx, piAddr)) + { + if(isScalar(pvApiCtx, piAddr)) + { + double dData = 0; + iRet = getScalarDouble(pvApiCtx, piAddr, &dData); + if(!iRet) + { + maxFeatures = dData; + } + } + else + { + Scierror(999, "Error: The input argument ""maxFeatures"" is not of scalar type.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument ""maxFeatures"" is not of type Integer.\n"); + return 0; + } + providedArgs[4] = 1; + } + else if(providedArgs[4]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + if(strcmp(currentArg, "nLevels") == 0) + { + if(iter + 1 <= nbInputArguments and !providedArgs[5]) + { + if(isIntegerType(pvApiCtx, piAddr) || isDoubleType(pvApiCtx, piAddr)) + { + if(isScalar(pvApiCtx, piAddr)) + { + double dData = 0; + iRet = getScalarDouble(pvApiCtx, piAddr, &dData); + if(!iRet) + { + nLevels = dData; + } + } + else + { + Scierror(999, "Error: The input argument ""nLevels"" is not of scalar type.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument ""nLevels"" is not of type Integer.\n"); + return 0; + } + providedArgs[5] = 1; + } + else if(providedArgs[5]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + if(strcmp(currentArg, "patchSize") == 0) + { + if(iter + 1 <= nbInputArguments and !providedArgs[6]) + { + if(isIntegerType(pvApiCtx, piAddr) || isDoubleType(pvApiCtx, piAddr)) + { + if(isScalar(pvApiCtx, piAddr)) + { + double dData = 0; + iRet = getScalarDouble(pvApiCtx, piAddr, &dData); + if(!iRet) + { + patchSize = dData; + } + } + else + { + Scierror(999, "Error: The input argument ""patchSize"" is not of scalar type.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument ""patchSize"" is not of type Integer.\n"); + return 0; + } + providedArgs[6] = 1; + } + else if(providedArgs[6]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + if(strcmp(currentArg, "scaleFactor") == 0) + { + if(iter + 1 <= nbInputArguments and !providedArgs[7]) + { + // if(isDoubleType(pvApiCtx, piAddr)) + // { + if(isScalar(pvApiCtx, piAddr)) + { + double dData = 0; + iRet = getScalarDouble(pvApiCtx, piAddr, &dData); + if(!iRet) + { + scaleFactor = dData; + } + } + else + { + Scierror(999, "Error: The input argument ""scaleFactor"" is not of scalar type.\n"); + return 0; + } + // } + // else + // { + // Scierror(999, "Error: The input argument ""scaleFactor"" is not of type Integer.\n"); + // return 0; + // } + providedArgs[7] = 1; + } + else if(providedArgs[7]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + if(strcmp(currentArg, "scoreType") == 0) + { + if(iter + 1 <= nbInputArguments and !providedArgs[8]) + { + if(isIntegerType(pvApiCtx, piAddr) || isDoubleType(pvApiCtx, piAddr)) + { + if(isScalar(pvApiCtx, piAddr)) + { + double dData = 0; + iRet = getScalarDouble(pvApiCtx, piAddr, &dData); + if(!iRet) + { + if(dData == 0 || dData == 1 || dData == 32) + { + Scierror(999, "Error: The input argument ""scoreType"" should be 0, 1 or 32.\n"); + return 0; + } + scoreType = dData; + } + } + else + { + Scierror(999, "Error: The input argument ""scoreType"" is not of scalar type.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument ""scoreType"" is not of type Integer.\n"); + return 0; + } + providedArgs[8] = 1; + } + else if(providedArgs[8]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + } + /// End of error check and input get; + + /// Creating model and setting its params; + Ptr orb = ORB::create(maxFeatures, scaleFactor, nLevels, + edgeThreshold, firstLevel, WTA_K, + scoreType, patchSize, fastThreshold); + + sciprint("\nET:%d\n", orb->getEdgeThreshold()); + /// to store the keypoints of orb detected + vector keyPoints; + + // if(providedArgs[0]) + // { + // orb->setEdgeThreshold(edgeThreshold); + // } + // if(providedArgs[1]) + // { + // orb->setFastThreshold(fastThreshold); + // } + // if(providedArgs[2]) + // { + // orb->setFirstLevel(firstLevel); + // } + + // if(providedArgs[4]) + // { + // orb->setMaxFeatures(maxFeatures); + // } + // if(providedArgs[5]) + // { + // orb->setNLevels(nLevels); + // } + // if(providedArgs[6]) + // { + // orb->setPatchSize(patchSize); + // } + // if(providedArgs[7]) + // { + // orb->setScaleFactor(scaleFactor); + // } + // if(providedArgs[8]) + // { + // orb->setScoreType(scoreType); + // } + + // Handling ROI argument + if(providedArgs[3]==0) + { + ROImat=(double *)malloc(sizeof(double)*1*4); + ROImat[0]=0; + ROImat[1]=0; + ROImat[2]=image.cols; + ROImat[3]=image.rows; + } + + Rect masker(ROImat[0], ROImat[1], ROImat[2], ROImat[3]); + Mat mask(image.size(), CV_8UC1, Scalar::all(0)); + + // Creating ROI + try + { + mask(masker).setTo(Scalar::all(255)); + } + catch(Exception &e) + { + const char *err = e.what(); + Scierror(999, "%s", err); + } + + Mat descriptor; + + try + { + image.convertTo(image, CV_8U); + // mask.rows -= 500; + // mask.cols -= 500; + // sciprint("M:%d %d I:%d %d", mask.rows, mask.cols, image.rows, image.cols); + orb->detectAndCompute(image, mask, keyPoints, descriptor); + + // orb->detect(image, keyPoints); + // orb->compute(image, keyPoints, descriptor); + // int dcols = descriptor.cols; + // for(int i = 0; i < dcols; i++) + // { + // sciprint("%d ", descriptor.at(i)); + // } + // sciprint("%d %d", keyPoints[104].pt.x, keyPoints[104].pt.y); + } + catch(Exception &e) + { + const char *err = e.what(); + Scierror(999, "%s", err); + return 0; + } + + int total_keypoints = (int)keyPoints.size(); + + double *key_value = (double*)malloc(sizeof(double) * (int)keyPoints.size() * 2); + + vector< KeyPoint >::iterator it; + i = 0; + for(it = keyPoints.begin(); it != keyPoints.end(); ++it) + { + KeyPoint temp = keyPoints[i]; + + // x coordinate + key_value[i + 0 * total_keypoints] = it->pt.x; + + // y coordinate + key_value[i + 1 * total_keypoints] = it->pt.y; + i++; + } + + // Converting descriptor from Mat to double* + int dcols = descriptor.cols; + int drows = descriptor.rows; + // for(int i = 0; i < dcols; i++) + // { + // sciprint("%f ", descriptor.at(i)); + // } + double *_descriptors = (double*) malloc(sizeof(double) * dcols * drows); + + for(int i = 0; i < drows * dcols; i++) + { + _descriptors[i] = int(descriptor.at(i)); + } + + // Creating output arguments + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 2, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 1, total_keypoints, 2, key_value); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 2, drows, dcols, _descriptors); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + } +} \ No newline at end of file diff --git a/sci_gateway/cpp/opencv_detectBRISKFeatures.cpp b/sci_gateway/cpp/opencv_detectBRISKFeatures.cpp new file mode 100644 index 0000000..2813190 --- /dev/null +++ b/sci_gateway/cpp/opencv_detectBRISKFeatures.cpp @@ -0,0 +1,449 @@ +/*************************************************** +Author : Shashank Shekhar +***************************************************/ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_detectBRISKFeatures(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int *piAddr = NULL; + int *piAddrVal = NULL; + int argPresence[4]; + + for(int i=0;i<4;i++) + argPresence[i]=0; + + int val_position; + int iRows = 0; + int iCols = 0; + int *piLen = NULL; + char ** pstData = NULL; + char *currentArg = NULL; + + +// DECLARING INPUT ARGUMENTS + + double *ROImat = NULL; + double NumOctaves = 0; + double MinContrast = 0; + double MinQuality = 0; + + +// DECLARING OUTPUT ARGUMENTS + + vector myPoints; + vector validKeyPoints; + int *LocationList = NULL; + double *LocationData = NULL; + double *OrientationData = NULL; + double *ScaleData = NULL; + double *MetricData = NULL; + int size; + +// ARGUMENT CHECK + + CheckInputArgument(pvApiCtx, 1,9); + CheckOutputArgument(pvApiCtx,1,5); + +// RETRIEVING IMAGE + + Mat image; + retrieveImage(image,1); + +// GRAYSCALE VALIDATION + + if(image.channels()!=1) + { + Scierror(999," Feature is not meant for RGB images\n"); + return 0; + } + +// OBTAINING NUMBER OF ARGUMENTS + + int noOfarguments = *getNbInputArgument(pvApiCtx); + +// ARGUMENT COUNT CHECK + + if(noOfarguments%2==0) + { + Scierror(999," Invalid Number Of Arguments\n"); + return 0; + } + +// MULTIPLE ARGUMENT HANDLING + + for(int i=2;i1) + { + Scierror(999,"Invalid Value for MinContrast. Please enter a value between 0 and 1\n"); + return 0; + } + + argPresence[0]=1; + } + else if(strcmp(currentArg,"NumOctaves")==0) + { + val_position=i+1; + + if(argPresence[1]==1) + { + Scierror(999,"Do not enter the same parameter\n"); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx,val_position,&piAddrVal); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isDoubleType(pvApiCtx, piAddrVal)) + { + Scierror(999," Invalid Value for NumOctaves. Recommended values are between 1 and 4\n"); + return 0; + } + + getScalarDouble(pvApiCtx, piAddrVal, &NumOctaves); + if(NumOctaves<1) + { + Scierror(999," Invalid Value for NumOctaves. Recommended values are between 1 and 4\n"); + return 0; + } + argPresence[1]=1; + } + else if(strcmp(currentArg,"MinQuality")==0) + { + val_position=i+1; + + + if(argPresence[2]==1) + { + Scierror(999,"Do not enter the same parameter\n"); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx,val_position,&piAddrVal); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isDoubleType(pvApiCtx, piAddrVal)) + { + Scierror(999," Invalid Value for MinQuality. Recommended values are between 0 and 1\n"); + return 0; + } + + getScalarDouble(pvApiCtx, piAddrVal, &MinQuality); + + if(MinQuality<0 || MinQuality>1) + { + Scierror(999," Invalid Value for MinQuality. Please enter a value between 0 and 1 \n"); + return 0; + } + + argPresence[2]=1; + } + else if(strcmp(currentArg,"ROI")==0) + { + val_position=i+1; + + if(argPresence[3]==1) + { + Scierror(999,"Do not enter the same parameter\n"); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx,val_position,&piAddrVal); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isDoubleType(pvApiCtx, piAddrVal) || isVarComplex(pvApiCtx, piAddrVal)) + { + Scierror(999,"Enter a List of 4 arguments\n"); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddrVal, &iRows, &iCols, &ROImat); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows*iCols!=4) + { + Scierror(999,"Invalid Argument\n"); + return 0; + } + + if(ROImat[0]<0 || ROImat[1]<0 || ROImat[2]<0 || ROImat[3]<0) + { + Scierror(999,"Arguments cannot be negative\n"); + return 0; + } + + if(ROImat[0]>image.cols || ROImat[1]>image.rows || ROImat[0]+ROImat[2]>image.cols + || ROImat[1]+ROImat[3]>image.rows) + { + Scierror(999,"Please make sure the arguments are within the image range\n"); + return 0; + } + + argPresence[3]=1; + } + else + { + Scierror(999, "Error: \"%s\" Invalid input argument.\n", currentArg); + return 0; + } + + } + + +// DEFAULT ARGUMENTS + + if(argPresence[0]==0) + MinContrast=0.2; + if(argPresence[1]==0) + NumOctaves=4; + if(argPresence[2]==0) + MinQuality=0.1; + if(argPresence[3]==0) + { + ROImat=(double *)malloc(sizeof(double)*1*4); + ROImat[0]=0; + ROImat[1]=0; + ROImat[2]=image.cols; + ROImat[3]=image.rows; + } + +// CREATING REGION OF INTEREST + + Rect masker(ROImat[0], ROImat[1], ROImat[2], ROImat[3]); + + Mat mask(image.size(), CV_8UC1, Scalar::all(0)); + mask(masker).setTo(Scalar::all(255)); + +// PROCESSING BRISK + + + // Setting Threshold -------------------------------------------------> + + double Threshold = MinContrast * 255; + + // Limiting NumOctaves -----------------------------------------------> + + int g = image.rows>image.cols?image.rows:image.cols; + int maxNumOctaves = int(floor(log2(g))); + if(NumOctaves>maxNumOctaves) + NumOctaves=maxNumOctaves; + + //----------------------------------------------------------------------- + + // BRISK myobject(Threshold, NumOctaves); + // myobject.operator()(image, mask, myPoints, noArray()); + + Ptr myobject = BRISK::create(); + try + { + image.convertTo(image, CV_8U); + myobject->detect(image, myPoints, mask); + } + catch(cv::Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + return 0; + } + + + // Selecting Points according to MinQuality ---------------------------> + + size = myPoints.size(); + + double maxMetric = 0; + double minMetric = 0; + + for(int i=0;imaxMetric) + maxMetric=myPoints[i].response; + } + minMetric = maxMetric*MinQuality; + + int finalSize = 0; + for(int i=0;i=minMetric) + finalSize++; + } + + //------------------------------------------------------------------------ + +// LOCATION ----> + + MetricData = (double *)malloc(sizeof(double)*finalSize); + OrientationData = (double *)malloc(sizeof(double)*finalSize); + ScaleData = (double *)malloc(sizeof(double)*finalSize); + int k=0; + // Appending 1x2 Matrices containing the coordinates of the keypoints, as well as assiging other attributes + for(int i=0;i=minMetric) + { + validKeyPoints.push_back(myPoints[i]); + OrientationData[i] = 0; + MetricData[i] = myPoints[i].response; + ScaleData[i] = myPoints[i].size; + } + } + + int validKeyPointsSize = validKeyPoints.size(); + LocationData = (double *)malloc(sizeof(double) * validKeyPointsSize * 2); + + for(int i = 0; i < validKeyPointsSize; i++) + { + LocationData[i] = myPoints[i].pt.x; + LocationData[i + validKeyPointsSize] = myPoints[i].pt.y; + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, finalSize, 2, LocationData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 2, finalSize, 1, OrientationData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 3, finalSize,1, MetricData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 4,finalSize,1, ScaleData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + createScalarInteger32(pvApiCtx,nbInputArgument(pvApiCtx) + 5, finalSize); + +// ASSIGNING ----> + + for(int i=1;i<=5;i++) + { + AssignOutputVariable(pvApiCtx, i) = nbInputArgument(pvApiCtx) + i; + } + + ReturnArguments(pvApiCtx); + } + +} \ No newline at end of file diff --git a/sci_gateway/cpp/opencv_detectFASTFeatures.cpp b/sci_gateway/cpp/opencv_detectFASTFeatures.cpp new file mode 100644 index 0000000..b15739e --- /dev/null +++ b/sci_gateway/cpp/opencv_detectFASTFeatures.cpp @@ -0,0 +1,594 @@ +/************************************************************************************************************************************************************************************************* +* Author: Umang Agrawal * +* Code: detectFASTFeatures.cpp * +* Function Format: detectFASTFeatures( Image, Optional Arguments... ) * +* Requirements: Image should be a grayscale image * +* Optional Arguments: 1. 'MinContrast' Value: Range 0 < x < 1 * +* 2. 'MinQuality' Value: Range 0 < x < 1 * +* 3. 'ROI' Value: [ x_cordinate, y_cordinate, width, height ] * +*************************************************************************************************************************************************************************************************/ + + +#include +#include +#include +#include +#include "opencv2/core/core.hpp" +#include "opencv2/features2d/features2d.hpp" +#include "opencv2/highgui/highgui.hpp" + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + //#include "../common.h" + + // Function using OpenCV to detect FAST Features + int opencv_detectFASTFeatures(char *fname, unsigned long fname_len) + { + int *piAddr = NULL; + + //Variable Declarations + SciErr sciErr; //Error Variable + int sca_read; //Error Variable + + int *imgAddr = NULL; //Address of the Image List Argument + int *piAddrChild = NULL; //Address of the Child of the Image Matrix List + int no_item_list; //Number of items in the list + + int iPrec = 0; //Precesision of the image in the lsit + int iRows=0, iCols=0; //Rows & Columns in the image + + int inp_params; //Number of Input Arguments + int *piAddr1 = NULL; //Address of Argument Head + int *piAddr2 = NULL; //Address of Value_1 of Argument + //int *piAddr3 = NULL; //Address of Value_2 of Argument + //int *piAddr4 = NULL; //Address of Value_3 of Argument + //int *piAddr5 = NULL; //Address of Value_4 of Argument + int Rows,Cols; //Rows & Columns of String Matrix + int *len = NULL; //Length of each String Element + char **arg; //String Argument + double *roi_arg = NULL; //ROI Argument Matrix Address + int rows,cols; //Rows and Columns of ROI value matrix + + //int *point = NULL; //Points returned by the FAST Algorithm + + double arg_val; //Variables for Contrast/Quality Argument + double x = 0, y = 0, width, height; //Variables for ROI Argument + + double thresh; //Effective Threshold + double thresh_c = 0; //Threshold due to arguments + double thresh_q = 0; //Threshold due to arguments + + int count_q = 0; //Count of Argument Quality Call + int count_c = 0; //Count of Argument Contrast Call + int count_r = 0; //Count of Argument ROI call + + vector keypoints; //Keypoint Vector to store the points returned by FAST Algorithm + + int i,j; //Iterators + + double count = 0; //Count of number of Features Detected + double *metric = NULL; + + Mat image; //Image Container + Mat cropped; //Image Container + + //Checks on Input and Output Arguments + CheckInputArgument(pvApiCtx, 1, 7); + CheckOutputArgument(pvApiCtx, 1, 3); + + + //Image Retrieval + //Get Address of the Image List + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &imgAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Get Number of items in the list + sciErr = getListItemNumber(pvApiCtx, imgAddr, &no_item_list); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Check if it is a Grayscal image by checking whether 1 item is present in the list + if( no_item_list == 1) + { + //Address of the Grayscale Matrix + sciErr = getListItemAddress(pvApiCtx, imgAddr, 1, &piAddrChild); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Check whether it is an integer Matrix or not + if(isIntegerType(pvApiCtx, piAddrChild)) + { + //If integer matrix getting precision + sciErr = getMatrixOfIntegerPrecision(pvApiCtx, piAddrChild, &iPrec); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Based on precision Read the matrix + switch(iPrec) + { + case SCI_UINT8: //for unsigned integer 8 + { + unsigned char *pstDatagray = NULL; + sciErr = getMatrixOfUnsignedInteger8InList(pvApiCtx, imgAddr, 1, &iRows, &iCols, &pstDatagray); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + image = Mat(iRows,iCols,CV_8UC1); + for(i=0;i(i,j)=pstDatagray[i+iRows*j]; + break; + } + case SCI_UINT16: //for unsigned integer 16 + { + short unsigned int *pstDatagray = NULL; + sciErr = getMatrixOfUnsignedInteger16InList(pvApiCtx, imgAddr, 1, &iRows, &iCols, &pstDatagray); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + image = Mat(iRows,iCols,CV_16UC1); + for(i=0;i(i,j)=pstDatagray[i+iRows*j]; + break; + } + case SCI_INT32: //for unsigned integer 32 + { + int *pstDatagray = NULL; + sciErr = getMatrixOfInteger32InList(pvApiCtx, imgAddr, 1, &iRows, &iCols, &pstDatagray); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + image = Mat(iRows,iCols,CV_32SC1); + for(i=0;i(i,j)=pstDatagray[i+iRows*j]; + break; + } + } + } + else + { + //If not Integer then must be double + double *pstDatagray = NULL; + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &imgAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDoubleInList(pvApiCtx, imgAddr, 1, &iRows, &iCols, &pstDatagray); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + image = Mat(iRows,iCols,CV_64FC1); + for(i=0;i(i,j)=pstDatagray[i+iRows*j]; + } + } + //If not single element in list than it is not a grayscale image and hence error + else + { + Scierror(999,"Expecting a single Matrix in grayscale format\n"); + return 0; + } + + //Get the Number of Arguments + inp_params = *getNbInputArgument(pvApiCtx); + + //Based on Number of Arguments Evaluate each Argument + for(i=2; i=1) + { + Scierror(999,"Not a valid range.Should be between 0 and 1.\n"); + return 0; + } + //Threshold Calculation + else thresh_c = 256*arg_val; + i++; //Incrementing iterator to count for the value argument read + count_c += 1; //Indicating MinContrast has been called once + } + else + { + Scierror(999,"Specified MinContrast twice. Check Arguments\n"); + return 0; + } + } + //MinQuality Argument + else if(strcmp(arg[0],"MinQuality") == 0) + { + if(count_q == 0) + { + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Checking the type of Value + if( !isDoubleType(pvApiCtx, piAddr2)) + { + Scierror(999,"Not a valid type of value.\n"); + return 0; + } + //Reading the Value of argument + sca_read = getScalarDouble(pvApiCtx, piAddr2, &arg_val); + if(sca_read) + { + Scierror(999,"Not a valid type of value.\n"); + return 0; + } + //Cheack for valid range + if(arg_val<0 && arg_val>=1) + { + Scierror(999,"Not a valid range.Should be between 0 and 1.\n"); + return 0; + } + //Threshold Calculation + else thresh_q = 240*arg_val; + i++; //Incrementing iterator to count for the value argument read + count_q += 1; //Indicating MinQuality has been called once + } + else + { + Scierror(999,"Specified MinQuality twice. Check Arguments\n"); + return 0; + } + } + //ROI Argument + else if(strcmp(arg[0],"ROI") == 0) + { + if(count_r == 0) + { + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Checking the type of argument + if( !isDoubleType(pvApiCtx, piAddr2)) + { + Scierror(999,"Not a valid type of value.\n"); + return 0; + } + //Reading the Value of the argument + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &rows, &cols, &roi_arg); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Assigning the elements of the list to their proper function + x = roi_arg[0]; + y = roi_arg[1]; + width = roi_arg[2]; + height = roi_arg[3]; + + + //Code for reading the value associated with ROI individually rather than in a list form + + /*sca_read = getScalarDouble(pvApiCtx, piAddr2, &x); + if(sca_read) + { + Scierror(999,"Not a valid type of value.\n"); + return 0; + } + sciErr = getVarAddressFromPosition(pvApiCtx, i+2, &piAddr3); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sca_read = getScalarDouble(pvApiCtx, piAddr3, &y); + if(sca_read) + { + Scierror(999,"Not a valid type of value.\n"); + return 0; + } + sciErr = getVarAddressFromPosition(pvApiCtx, i+3, &piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sca_read = getScalarDouble(pvApiCtx, piAddr4, &width); + if(sca_read) + { + Scierror(999,"Not a valid type of value.\n"); + return 0; + } + sciErr = getVarAddressFromPosition(pvApiCtx, i+4, &piAddr5); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sca_read = getScalarDouble(pvApiCtx, piAddr5, &height); + if(sca_read) + { + Scierror(999,"Not a valid type of value.\n"); + return 0; + }*/ + + //Checking for valid crop area + if(x>=image.cols || y>=image.rows || x<0 || y<0) + { + sciprint("Invalid x or y value\n"); + return 0; + } + if(width<=0 || height<=0 || x+width > image.cols || y+height > image.rows) + { + sciprint("Invalid width or height value\n"); + return 0; + } + //Croppint the image + Rect myROI(x, y, width, height); + Mat croppedRef(image, myROI); + croppedRef.copyTo(cropped); + i++; //Incrementing iterator to count for the value argument read + count_r += 1; //Indicating ROI has been called once + } + else + { + Scierror(999,"Specified ROC twice. Check Arguments\n"); + return 0; + } + } + else + { + Scierror(999,"Invalid Argument\n"); + return 0; + } + + } + + //Calculating the effective threshold by selecting the maximum threshold + if(thresh_c > 0 && thresh_c >= thresh_q) + thresh = thresh_c; + else if(thresh_q > 0 && thresh_q > thresh_c) + thresh = thresh_q; + else thresh = 256 * 0.2; + + //Implementing FAST Algorithm + int t = int(thresh); + Ptr fd = FastFeatureDetector::create(t, true); + image.convertTo(image, CV_8U); + if(count_r != 0) + { + try + { + fd -> detect(cropped, keypoints); + } + catch(Exception &e) + { + const char *err = e.what(); + Scierror(999, "%s", err); + return 0; + } + } + else + { + try + { + int t = int(thresh); + fd -> detect(image, keypoints); + } + catch(Exception &e) + { + const char *err = e.what(); + Scierror(999, "%s", err); + return 0; + } + } + + count = keypoints.size(); + + metric = (double*)malloc(sizeof(double)*keypoints.size()*1); + for( i=0; i +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include "opencv2/imgproc/imgproc.hpp" +#include +#include + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + /* Calling syntax: detectHarrisFeatures(I) or detectHarrisFeatures(I, Name, Value) + which uses additional options specified by one or more Name, Value pair arguments. + Arguments Allowed : MinQuality, FilterSize, ROI, SensitivityFactor + MinQuality : The minimum accepted quality of corners represents a fraction of the + maximum corner metric value in the image. Larger values can be used + to remove erroneous corners. + FilterSize : Gaussian filter dimension + ROI : Rectangular region for corner detection + SensitivityFactor : A scalar value, K, where 0 < K < 0.25, specifying the sensitivity + factor used in the Harris detection algorithm. */ + + int opencv_detectHarrisFeatures(char *fname, unsigned long fname_len) + { + // Error management variables + SciErr sciErr; + int intErr; + + //------Local variables------// + double *location = NULL; + double *metric = NULL; + int *piAddr = NULL; + int *piLen = NULL; + int nbInputArguments; + char **pstData = NULL; + char *currentArg = NULL; + bool *providedArgs = NULL; + double *matrixOfRoi; // ROI[xStart, yStart, width, height] + int iRows, iCols; + Mat sourceImage, dst, dstThresholded, ucharDstThresholded, extended; + vector > contours; + double filterSize = 5, minQuality = 1, sensitivityFactor = 0.04, blockSize = 2, maxDst, localMax; + double sensitivityFactor1 = 0.05; + int blockSize1 = 2, filterSize1 = 3; + bool *included = NULL; + int pointCount = 0, localMaxPos; + double tempForSwapping; + int coordinateMin, coordinatePos; + + //------Check number of parameters------// + CheckInputArgument(pvApiCtx, 1, 9); + CheckOutputArgument(pvApiCtx, 1, 3); + + //------Get input arguments------// + retrieveImage(sourceImage, 1); + if(sourceImage.channels() != 1) + { + Scierror(999, "The input image is not a grayscale image."); + return 0; + } + matrixOfRoi = (double*) malloc(sizeof(double) * 4); + providedArgs = (bool*) malloc(sizeof(bool) * 4); + memset(providedArgs, 0, 4); + matrixOfRoi[0] = 0; + matrixOfRoi[1] = 0; + matrixOfRoi[2] = sourceImage.cols; + matrixOfRoi[3] = sourceImage.rows; + + nbInputArguments = *getNbInputArgument(pvApiCtx); + // The following loop is for checking if optional arguments have been provided + for(int iter = 2; iter <= nbInputArguments; iter++) + { + // Getting address of next argument + sciErr = getVarAddressFromPosition(pvApiCtx, iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Extracting name of next argument takes three calls to getMatrixOfString + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + currentArg = pstData[0]; + // providedArgs[] makes sure that no optional argument is provided more than once + if(strcmp(currentArg, "MinQuality")==0) + { + if(iter+1<=nbInputArguments && !providedArgs[0]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr, &minQuality); + if(intErr) + { + return intErr; + } + // Checking if values are in proper range. Same for all optional arguments + if(minQuality < 0 || minQuality > 1) + { + Scierror(999, "Error: Please provide proper value for \"%s\". Permissible range is [0, 1].\n", currentArg); + return 0; + } + providedArgs[0] = 1; + } + else if(providedArgs[0]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + else if(strcmp(currentArg, "FilterSize")==0) + { + if(iter+1 <= nbInputArguments && !providedArgs[1]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr, &filterSize); + if(intErr) + { + return intErr; + } + providedArgs[1] = 1; + if(filterSize!=1 && filterSize!=3 && filterSize!=5 && filterSize!=7) + { + Scierror(999, "Error: Please provide proper value for \"%s\". Permissible values are {1, 3, 5, 7}.\n", currentArg); + return 0; + } + } + else if(providedArgs[1]) + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + else if(strcmp(currentArg, "ROI")==0) + { + if(iter+1 <= nbInputArguments && !providedArgs[2]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isDoubleType(pvApiCtx, piAddr) || isVarComplex(pvApiCtx, piAddr)) + { + Scierror(999, "%s: Wrong type for input argument #%d: A real matrix expected.\n", fname, iter); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &matrixOfRoi); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(iRows!=1 || iCols!=4) + { + Scierror(999, "Incorrect dimension of matrix for argument ROI.\n"); + return 0; + } + providedArgs[2] = 1; + if(matrixOfRoi[0] < 0 || matrixOfRoi[0] > sourceImage.cols || matrixOfRoi[1] < 0 || + matrixOfRoi[1] > sourceImage.rows || matrixOfRoi[2] < 0 || matrixOfRoi[0] + matrixOfRoi[2] > sourceImage.cols + || matrixOfRoi[3] < 0 || matrixOfRoi[1] + matrixOfRoi[3] > sourceImage.rows) + { + Scierror(999, "Error: Please provide proper values for \"%s\".\n", currentArg); + return 0; + } + } + else if(providedArgs[2]) + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + else if(strcmp(currentArg, "SensitivityFactor")==0) + { + if(iter+1 <= nbInputArguments && !providedArgs[3]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr, &sensitivityFactor); + if(intErr) + { + return intErr; + } + providedArgs[3] = 1; + if(sensitivityFactor < 0 || sensitivityFactor > 0.25) + { + Scierror(999, "Error: Please provide proper value for \"%s\". Permissible values are [0, 0.25].\n", currentArg); + return 0; + } + } + else if(providedArgs[3]) + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: \"%s\" name for input argument is not valid.\n", currentArg); + return 0; + } + } + + //------Actual processing------// + dst = Mat::zeros( sourceImage.size(), CV_32FC1 ); + try + { + sourceImage.convertTo(sourceImage, CV_8U); + //(Rect(matrixOfRoi[0], matrixOfRoi[1], matrixOfRoi[2], matrixOfRoi[3]) + cornerHarris(sourceImage, dst, blockSize1, filterSize1, sensitivityFactor1, BORDER_DEFAULT); + } + catch(Exception &e) + { + const char *err = e.what(); + Scierror(999, "%s", err); + return 0; + } + + pointCount = dst.rows * dst.cols; + + Mat dst_norm, dst_norm_scaled; + vector loc; + normalize( dst, dst_norm, 0, 255, NORM_MINMAX, CV_32FC1, Mat() ); + convertScaleAbs( dst_norm, dst_norm_scaled ); + + for( int j = 0; j < dst_norm.rows; j++ ) + { + for( int i = 0; i < dst_norm.cols; i++ ) + { + if( (int) dst_norm.at(j,i) > 200 ) + { + loc.push_back(i); + loc.push_back(j); + } + } + } + + int size1 = loc.size() / 2; + location = (double*) malloc(sizeof(double) * size1 * 2); + metric = (double*) malloc(sizeof(double) * size1); + for(int i = 0; i < size1; i++) + { + location[i] = loc[i]; + location[i + size1] = loc[i + 1]; + metric[i] = dst_norm.at(loc[i + 1],loc[i]); + } + //------Create output arguments------// + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx)+1, size1, 2, location); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx)+2, size1, 1, metric); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfInteger32(pvApiCtx, nbInputArgument(pvApiCtx)+3, 1, 1, &pointCount); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //------Return Arguments------// + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx)+1; + AssignOutputVariable(pvApiCtx, 2) = nbInputArgument(pvApiCtx)+2; + AssignOutputVariable(pvApiCtx, 3) = nbInputArgument(pvApiCtx)+3; + ReturnArguments(pvApiCtx); + return 0; + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_detectORB.cpp b/sci_gateway/cpp/opencv_detectORB.cpp new file mode 100644 index 0000000..aa9f0bf --- /dev/null +++ b/sci_gateway/cpp/opencv_detectORB.cpp @@ -0,0 +1,636 @@ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include "opencv2/features2d/features2d.hpp" +#include + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_detectORB(char *fname, unsigned long fname_len) + { + + // Error management variable + SciErr sciErr; + + //Variables + int i, j; + int intErr; + int iRows1 = 0; + int iCols1 = 0; + + int iRows = 0; + int iCols = 0; + int *piLen = NULL; + int *piAddr = NULL; + char **pstData = NULL; + char *currentArg = NULL; + bool *providedArgs = NULL; + + double edgeThreshold = 0; + double fastThreshold = 0; + double firstLevel = 0; + double maxFeatures = 0; + double nLevels = 0; + double patchSize = 0; + double scaleFactor = 0; + double scoreType = 0; + + int iRet; + + // checking input argument + // 1 : image + // 2 : edgeThreshold + // 3 : fastThreshold + // 4 : firstLevel + // 5 : ROI + // 6 : setMaxFeatures + // 7 : nLevels + // 8 : patchSize + // 9 : scaleFactor + // 10 : scoreType + CheckInputArgument(pvApiCtx, 1, 19); + CheckOutputArgument(pvApiCtx, 1, 1); + + // retrieve image + Mat image; + retrieveImage(image, 1); + + // For the optional arguments + int nbInputArguments = *getNbInputArgument(pvApiCtx); + //sciprint("%d\n",nbInputArguments); + + if((nbInputArguments%2) == 0) + { + Scierror(999, "%d: The number of arguments must be odd\n"); + return 0; + } + + providedArgs = (bool*) malloc(sizeof(bool) * 9); + memset(providedArgs, 0, 9); + + for(int iter = 2; iter <= nbInputArguments; ++iter) + { + sciErr = getVarAddressFromPosition(pvApiCtx, iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + /// Three calls to getMatrixOfString + //First Call + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Second call to get length of string + piLen = (int*) malloc(sizeof(int) * iRows1 * iCols1); + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows1, &iCols1, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // third call + pstData = (char**) malloc(sizeof(char*) * iRows1 * iCols1); + for(int k = 0; k < iRows1 * iCols1; ++k) + { + pstData[k] = (char*) malloc(sizeof(char) * piLen[k] + 1); + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows1, &iCols1, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + currentArg = pstData[0]; + + // getting edgeThreshold + if(strcmp(currentArg, "edgeThreshold") == 0) + { + if(iter + 1 <= nbInputArguments and !providedArgs[0]) + { + if(isIntegerType(pvApiCtx, piAddr) || isDoubleType(pvApiCtx, piAddr)) + { + if(isScalar(pvApiCtx, piAddr)) + { + double dData = 0; + iRet = getScalarDouble(pvApiCtx, piAddr, &dData); + if(!iRet) + { + edgeThreshold = dData; + } + } + else + { + Scierror(999, "Error: The input argument ""edgeThreshold"" is not of scalar type.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument ""edgeThreshold"" is not of type Integer.\n"); + return 0; + } + providedArgs[0] = 1; + } + else if(providedArgs[0]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + if(strcmp(currentArg, "fastThreshold") == 0) + { + if(iter + 1 <= nbInputArguments and !providedArgs[1]) + { + if(isIntegerType(pvApiCtx, piAddr) || isDoubleType(pvApiCtx, piAddr)) + { + if(isScalar(pvApiCtx, piAddr)) + { + double dData = 0; + iRet = getScalarDouble(pvApiCtx, piAddr, &dData); + if(!iRet) + { + fastThreshold = dData; + } + } + else + { + Scierror(999, "Error: The input argument ""fastThreshold"" is not of scalar type.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument ""fastThreshold"" is not of type Integer.\n"); + return 0; + } + providedArgs[1] = 1; + } + else if(providedArgs[1]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + if(strcmp(currentArg, "firstLevel") == 0) + { + if(iter + 1 <= nbInputArguments and !providedArgs[2]) + { + if(isIntegerType(pvApiCtx, piAddr) || isDoubleType(pvApiCtx, piAddr)) + { + if(isScalar(pvApiCtx, piAddr)) + { + double dData = 0; + iRet = getScalarDouble(pvApiCtx, piAddr, &dData); + if(!iRet) + { + firstLevel = dData; + } + } + else + { + Scierror(999, "Error: The input argument ""firstLevel"" is not of scalar type.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument ""firstLevel"" is not of type Integer.\n"); + return 0; + } + providedArgs[2] = 1; + } + else if(providedArgs[2]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + else if(strcmp(currentArg,"ROI")==0) + { + // val_position=i+1; + + if(providedArgs[3] == 1) + { + Scierror(999,"Do not enter the same parameter\n"); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx, i + 1, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isDoubleType(pvApiCtx, piAddr) || isVarComplex(pvApiCtx, piAddr)) + { + Scierror(999,"Enter a List of 4 arguments\n"); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &ROImat); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows*iCols!=4) + { + Scierror(999,"Invalid Argument\n"); + return 0; + } + + if(ROImat[0]<0 || ROImat[1]<0 || ROImat[2]<0 || ROImat[3]<0) + { + Scierror(999,"Arguments cannot be negative\n"); + return 0; + } + + if(ROImat[0]>image.cols || ROImat[1]>image.rows || ROImat[0]+ROImat[2]>image.cols + || ROImat[1]+ROImat[3]>image.rows) + { + Scierror(999,"Please make sure the arguments are within the image range\n"); + return 0; + } + + providedArgs[3] = 1; + } + + if(strcmp(currentArg, "maxFeatures") == 0) + { + if(iter + 1 <= nbInputArguments and !providedArgs[4]) + { + if(isIntegerType(pvApiCtx, piAddr) || isDoubleType(pvApiCtx, piAddr)) + { + if(isScalar(pvApiCtx, piAddr)) + { + double dData = 0; + iRet = getScalarDouble(pvApiCtx, piAddr, &dData); + if(!iRet) + { + maxFeatures = dData; + } + } + else + { + Scierror(999, "Error: The input argument ""maxFeatures"" is not of scalar type.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument ""maxFeatures"" is not of type Integer.\n"); + return 0; + } + providedArgs[4] = 1; + } + else if(providedArgs[4]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + if(strcmp(currentArg, "nLevels") == 0) + { + if(iter + 1 <= nbInputArguments and !providedArgs[5]) + { + if(isIntegerType(pvApiCtx, piAddr) || isDoubleType(pvApiCtx, piAddr)) + { + if(isScalar(pvApiCtx, piAddr)) + { + double dData = 0; + iRet = getScalarDouble(pvApiCtx, piAddr, &dData); + if(!iRet) + { + nLevels = dData; + } + } + else + { + Scierror(999, "Error: The input argument ""nLevels"" is not of scalar type.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument ""nLevels"" is not of type Integer.\n"); + return 0; + } + providedArgs[5] = 1; + } + else if(providedArgs[5]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + if(strcmp(currentArg, "patchSize") == 0) + { + if(iter + 1 <= nbInputArguments and !providedArgs[6]) + { + if(isIntegerType(pvApiCtx, piAddr) || isDoubleType(pvApiCtx, piAddr)) + { + if(isScalar(pvApiCtx, piAddr)) + { + double dData = 0; + iRet = getScalarDouble(pvApiCtx, piAddr, &dData); + if(!iRet) + { + patchSize = dData; + } + } + else + { + Scierror(999, "Error: The input argument ""patchSize"" is not of scalar type.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument ""patchSize"" is not of type Integer.\n"); + return 0; + } + providedArgs[6] = 1; + } + else if(providedArgs[6]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + if(strcmp(currentArg, "scaleFactor") == 0) + { + if(iter + 1 <= nbInputArguments and !providedArgs[7]) + { + if(isIntegerType(pvApiCtx, piAddr) || isDoubleType(pvApiCtx, piAddr)) + { + if(isScalar(pvApiCtx, piAddr)) + { + double dData = 0; + iRet = getScalarDouble(pvApiCtx, piAddr, &dData); + if(!iRet) + { + scaleFactor = dData; + } + } + else + { + Scierror(999, "Error: The input argument ""scaleFactor"" is not of scalar type.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument ""scaleFactor"" is not of type Integer.\n"); + return 0; + } + providedArgs[7] = 1; + } + else if(providedArgs[7]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + if(strcmp(currentArg, "scoreType") == 0) + { + if(iter + 1 <= nbInputArguments and !providedArgs[8]) + { + if(isIntegerType(pvApiCtx, piAddr) || isDoubleType(pvApiCtx, piAddr)) + { + if(isScalar(pvApiCtx, piAddr)) + { + double dData = 0; + iRet = getScalarDouble(pvApiCtx, piAddr, &dData); + if(!iRet) + { + if(dData == 0 || dData == 1 || dData == 32) + { + Scierror(999, "Error: The input argument ""scoreType"" should be 0, 1 or 32.\n"); + return 0; + } + scoreType = dData; + } + } + else + { + Scierror(999, "Error: The input argument ""scoreType"" is not of scalar type.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument ""scoreType"" is not of type Integer.\n"); + return 0; + } + providedArgs[8] = 1; + } + else if(providedArgs[8]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + } + /// End of error check and input get; + + /// Creating model and setting its params; + Ptr orb = ORB::create(); + + /// to store the keypoints of orb detected + vector keyPoints; + + if(providedArgs[0]) + { + orb->setEdgeThreshold(edgeThreshold); + } + if(providedArgs[1]) + { + orb->setFastThreshold(fastThreshold); + } + if(providedArgs[2]) + { + orb->setFirstLevel(firstLevel); + } + + if(providedArgs[4]) + { + orb->setMaxFeatures(maxFeatures); + } + if(providedArgs[5]) + { + orb->setNLevels(nLevels); + } + if(providedArgs[6]) + { + orb->setPatchSize(patchSize); + } + if(providedArgs[7]) + { + orb->setScaleFactor(scaleFactor); + } + if(providedArgs[8]) + { + orb->setScoreType(scoreType); + } + + // Handling ROI argument + if(providedArgs[3]==0) + { + ROImat=(double *)malloc(sizeof(double)*1*4); + ROImat[0]=0; + ROImat[1]=0; + ROImat[2]=image.cols; + ROImat[3]=image.rows; + } + + Rect masker(ROImat[0], ROImat[1], ROImat[2], ROImat[3]); + Mat mask(image.size(), CV_8UC1, Scalar::all(0)); + + // Creating ROI + try + { + mask(masker).setTo(Scalar::all(255)); + } + catch(Exception &e) + { + const char *err = e.what(); + Scierror(999, "%s", err); + } + + Mat descriptor; + + try + { + image.convertTo(image, CV_8U); + orb->detectAndCompute(image, keyPoints, mask, descriptor); + } + catch(Exception &e) + { + const char *err = e.what(); + Scierror(999, "%s", err); + return 0; + } + + int total_keypoints = (int)keyPoints.size(); + + double *key_value = (double*)malloc(sizeof(double) * (int)keyPoints.size() * 2); + + vector< KeyPoint >::iterator it; + for(it = keyPoints.begin(); it != keyPoints.end(); ++it) + { + KeyPoint temp = keyPoints[i]; + + // x coordinate + key_value[i + 0 * total_keypoints] = it->pt.x; + + // y coordinate + key_value[i + 1 * total_keypoints] = it->pt.y; + } + + // Converting descriptor from Mat to double* + int dcols = descriptor.cols; + double *_descriptors = (double*) malloc(sizeof(double) * dcols); + + for(int i = 0; i < dcols; i++) + { + _descriptors[i] = descriptor.at(i); + } + + // Creating output arguments + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 2, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 1, total_keypoints, 2, key_value); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 2, 1, dcols, _descriptors); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + } +} \ No newline at end of file diff --git a/sci_gateway/cpp/opencv_detectSURFFeatures.cpp b/sci_gateway/cpp/opencv_detectSURFFeatures.cpp new file mode 100644 index 0000000..aca5c07 --- /dev/null +++ b/sci_gateway/cpp/opencv_detectSURFFeatures.cpp @@ -0,0 +1,446 @@ +/*************************************************** +Author : Shashank Shekhar +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/features2d/features2d.hpp" +#include +//#include "opencv2/nonfree/features2d.hpp" +#include "opencv2/highgui/highgui.hpp" +//#include "opencv2/nonfree/nonfree.hpp" +#include "opencv2/opencv.hpp" +#include + +using namespace cv; +using namespace std; +using namespace cv::xfeatures2d; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + //#include "../common.cpp" + +int opencv_detectSURFFeatures(char *fname, unsigned long fname_len) +{ + + SciErr sciErr; + + int *piAddr = NULL; + int *piAddrVal = NULL; + int val_position; + int *piLen = NULL; + int iRows, iCols = 0; + char ** pstData = NULL; + char *currentArg = NULL; + int valOfCA; + int argPresence[4]; + for(int i=0;i<4;i++) + argPresence[i]=0; + + vector myPoints; + + int *LocationList = NULL; + int *OrientationList = NULL; + int *MetricList = NULL; + int *LaplacianList = NULL; + int *ScaleList = NULL; + + double *LocationData = NULL; + double *OrientationData = NULL; + double *MetricData = NULL; + double *LaplacianData = NULL; + double *ScaleData = NULL; + + int noOfarguments = *getNbInputArgument(pvApiCtx); + + CheckInputArgument(pvApiCtx, 1,9); + CheckOutputArgument(pvApiCtx,1,6); + + Mat image; + retrieveImage(image,1); + + if(image.channels()!=1) + { + Scierror(999," Feature is not meant for RGB images\n"); + return 0; + } + +//********************************************* Valid Argument Count Check ***************************************** + + if(noOfarguments%2==0) + { + Scierror(999," Invalid No of Arguments \n"); + return 0; + } + +//********************************************* Optional Input Arguments ***************************************** + + double MetricThreshold = 0; + double NumOctaves = 0; + double NumScaleLevels = 0; + double *ROImat = NULL; + +//********************************************* Retrieval of Name, Value Argument Pair **************************** + + + for(int i=2;i<=noOfarguments;i=i+2) + { + sciErr = getVarAddressFromPosition(pvApiCtx,i,&piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isStringType(pvApiCtx, piAddr)) + { + Scierror(999,"Invalid Argument\n"); + return 0; + } + + //first call to get rows and columns + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int j = 0 ; j < iRows * iCols ; j++) + { + pstData[j] = (char*)malloc(sizeof(char) * (piLen[j] + 1));//+ 1 for null termination + } + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + currentArg = pstData[0]; + free(pstData); + iRows=0; + iCols=0; + free(piLen); + + if(strcmp(currentArg,"MetricThreshold")==0) + { + + val_position=i+1; + + if(argPresence[0]==1) + { + Scierror(999,"Do not enter the same parameter\n"); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx,val_position,&piAddrVal); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isDoubleType(pvApiCtx, piAddrVal) || isVarComplex(pvApiCtx,piAddrVal) || !isScalar(pvApiCtx, piAddrVal)) + { + Scierror(999," Invalid Value for MetricThreshold. Please enter a non negative Double value\\n"); + return 0; + } + + getScalarDouble(pvApiCtx, piAddrVal, &MetricThreshold); + + if(MetricThreshold<0) + { + Scierror(999," Invalid Value for MetricThreshold. Please enter a non negative Double value\\n"); + return 0; + } + argPresence[0]=1; + } + else if(strcmp(currentArg,"NumOctaves")==0) + { + val_position=i+1; + + if(argPresence[1]==1) + { + Scierror(999,"Do not enter the same parameter\n"); + return 0; + } + + + sciErr = getVarAddressFromPosition(pvApiCtx,val_position,&piAddrVal); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isDoubleType(pvApiCtx, piAddrVal) || isVarComplex(pvApiCtx,piAddrVal) || !isScalar(pvApiCtx, piAddrVal)) + { + + Scierror(999," Invalid Value for NumOctaves. Recommended Values are between 1 and 4\n"); + return 0; + } + + getScalarDouble(pvApiCtx, piAddrVal, &NumOctaves); + + if(NumOctaves<1) + { + Scierror(999," Invalid Value for NumOctaves. Recommended Values are between 1 and 4\n"); + return 0; + } + + argPresence[1]=1; + } + else if(strcmp(currentArg,"NumScaleLevels")==0) + { + val_position=i+1; + + if(argPresence[2]==1) + { + Scierror(999,"Do not enter the same parameter\n"); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx,val_position,&piAddrVal); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isDoubleType(pvApiCtx, piAddrVal) || isVarComplex(pvApiCtx,piAddrVal) || !isScalar(pvApiCtx, piAddrVal)) + { + Scierror(999," Invalid Value for NumScaleLevels. Recommended values are between 3 and 6\n"); + return 0; + } + + getScalarDouble(pvApiCtx, piAddrVal, &NumScaleLevels); + + if(NumScaleLevels<3) + { + Scierror(999," Invalid Value for NumScaleLevels. Please enter an integer value greater than or equal to 3\n"); + return 0; + } + + argPresence[2]=1; + } + else if(strcmp(currentArg,"ROI")==0) + { + val_position=i+1; + + if(argPresence[3]==1) + { + Scierror(999,"Do not enter the same parameter\n"); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx,val_position,&piAddrVal); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isDoubleType(pvApiCtx, piAddrVal) || isVarComplex(pvApiCtx, piAddrVal)) + { + Scierror(999,"Enter a List of 4 double arguments\n"); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddrVal, &iRows, &iCols, &ROImat); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows*iCols!=4) + { + Scierror(999,"Invalid Argument\n"); + return 0; + } + + + if(ROImat[0]<0 || ROImat[1]<0 || ROImat[2]<0 || ROImat[3]<0) + { + sciprint("Arguments cannot be negative\n"); + return 0; + } + + if(ROImat[0]>image.cols || ROImat[1]>image.rows || ROImat[0]+ROImat[2]>image.cols + || ROImat[1]+ROImat[3]>image.rows) + { + Scierror(999,"Please make sure the arguments are within the image range\n"); + return 0; + } + + argPresence[3]=1; + } + else + { + Scierror(999, "Error: \"%s\" name for input argument is not valid.\n", currentArg); + return 0; + } + + } +//********************************************* End of OIA ******************************************************** + + int nOctaveLayers; + + if(argPresence[2]==1) + nOctaveLayers = NumScaleLevels-2; + + if(argPresence[0]==0) + MetricThreshold=100; + if(argPresence[1]==0) + NumOctaves=4; + if(argPresence[2]==0) + nOctaveLayers = 3; + if(argPresence[3]==0) + { + ROImat=(double *)malloc(sizeof(double)*4); + ROImat[0]=0; + ROImat[1]=0; + ROImat[2]=image.cols; + ROImat[3]=image.rows; + } + + Rect masker(ROImat[0], ROImat[1], ROImat[2], ROImat[3]); + + Mat mask(image.size(), CV_8UC1, Scalar::all(0)); + mask(masker).setTo(Scalar::all(255)); + + // SURF my_object(MetricThreshold, NumOctaves, nOctaveLayers); + Ptr detector = SURF::create(MetricThreshold, NumOctaves, nOctaveLayers, 1, false); + + // my_object.operator()(image, mask, myPoints, noArray()); + try + { + image.convertTo(image, CV_8UC1); + detector->detect(image, myPoints); + } + catch(Exception &e) + { + const char *err = e.what(); + Scierror(999, "%s", err); + return 0; + } + + int size = myPoints.size(); + +//*************************************************************** Location *************************************************************** + + ScaleData = (double *)malloc(sizeof(double)*size); + OrientationData = (double *)malloc(sizeof(double)*size); + MetricData = (double *)malloc(sizeof(double)*size); + LaplacianData = (double*)malloc(sizeof(double)*size); + + iRows=size; + iCols=2; + int k=0; + + LocationData = (double *)malloc(sizeof(double)*iRows*iCols); + + for(int i=0;i +#include +#include +#include +#include +#include +#include +#include + + +using namespace cv; +using namespace std; +using namespace cv::ximgproc; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_disparity(char *fname, unsigned long fname_len) + { + SciErr sciErr; + + /*----- Local Variables -----*/ + int *piAddr = NULL; + int *piAddrVal = NULL; + int val_position = 0; + int *piLen = NULL; + int iRows = 0, iCols = 0; + char ** pstData = NULL; + char *currentArg = NULL; + int valOfCA = 0; + int argPresence[6]; + for(int i = 0; i < 6; i++) + argPresence[i] = 0; + + /*----- Variables used in procesing -----*/ + Mat left_for_matcher, right_for_matcher; + Mat left_disp,right_disp; + Mat filtered_disp; + //conf_map = Scalar(255); + Rect ROI; + Ptr wls_filter; + int vis_mult = 1.0; + //char *dst_path = "./disparity.jpg" + + /*----- Optional Input Arguments -----*/ + char **method = NULL; + int numDisparities = 160; + int SADWindowSize = 7; + int uniquenessRatio = 0; + int textureThreshold = 507; + int disp12MaxDiff = 1; + + int noOfarguments = *getNbInputArgument(pvApiCtx); + + /*----- Check for input and output arguments -----*/ + CheckInputArgument(pvApiCtx, 2, 14); + CheckOutputArgument(pvApiCtx, 2, 2); + + /*----- Retrieving two images -----*/ + Mat image_1; + retrieveImage(image_1, 1); + image_1.convertTo(image_1, CV_8UC1); + + Mat image_2; + retrieveImage(image_2, 2); + image_2.convertTo(image_2, CV_8UC1); + + if(image_1.channels() != 1) + { + Scierror(999, "Image 1 not a RGB image.\n"); + return 0; + } + + if(image_2.channels() != 1) + { + Scierror(999, "Image 2 not a RGB image.\n"); + return 0; + } + + if(noOfarguments % 2 != 0) + { + Scierror(999," Invalid No of Arguments \n"); + return 0; + } + + /*----- Retrieving Optional Input Arguments -----*/ + for(int i = 3; i <= noOfarguments; i = i + 2) + { + sciErr = getVarAddressFromPosition(pvApiCtx,i,&piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isStringType(pvApiCtx, piAddr)) + { + Scierror(999,"Invalid Argument\n"); + return 0; + } + + // First call to get rows and columns + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + // Second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int j = 0 ; j < iRows * iCols ; j++) + { + pstData[j] = (char*)malloc(sizeof(char) * (piLen[j] + 1));//+ 1 for null termination + } + + // Third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + currentArg = pstData[0]; + free(pstData); + iRows = 0; + iCols = 0; + free(piLen); + + if(strcmp(currentArg, "numDisparities") == 0) + { + val_position = i + 1; + + if(argPresence[0] == 1) + { + Scierror(999,"Do not enter the same parameter\n"); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx, val_position, &piAddrVal); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isIntegerType(pvApiCtx, piAddrVal) || + isVarComplex(pvApiCtx,piAddrVal) || + !isScalar(pvApiCtx, piAddrVal)) + { + Scierror(999," Invalid Value for numDisparities. Please enter a non negative Integer value.\n"); + return 0; + } + + getScalarInteger32(pvApiCtx, piAddrVal, &numDisparities); + + if(numDisparities < 0 && numDisparities % 16 != 0) + { + Scierror(999, "Invalid Value for numDisparities. Please enter a non negative Integer value which is divisible by 16.\n"); + return 0; + } + argPresence[0] = 1; + } + + if(strcmp(currentArg, "SADWindowSize") == 0) + { + val_position = i + 1; + + if(argPresence[1] == 1) + { + Scierror(999,"Do not enter the same parameter\n"); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx, val_position, &piAddrVal); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isIntegerType(pvApiCtx, piAddrVal) || + isVarComplex(pvApiCtx,piAddrVal) || + !isScalar(pvApiCtx, piAddrVal)) + { + Scierror(999, "Invalid Value for SADWindowSize. Please enter a non negative Integer value.\n"); + return 0; + } + + getScalarInteger32(pvApiCtx, piAddrVal, &SADWindowSize); + + if(SADWindowSize < 1 && SADWindowSize % 2 == 0) + { + Scierror(999, "Invalid Value for SADWindowSize. Please enter a non negative Integer value, which is odd and greater than 1.\n"); + return 0; + } + argPresence[1] = 1; + } + + if(strcmp(currentArg, "Method") == 0) + { + if(argPresence[2] != 0) + { + Scierror(999,"Method Argument has been called twice\n"); + return 0; + } + + val_position = i + 1; + + sciErr = getVarAddressFromPosition(pvApiCtx, val_position, &piAddrVal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + // Check for Argument type + if(!isStringType(pvApiCtx, piAddrVal)) + { + Scierror(999, "%s: Wrong type of 2nd argument #%d. A string is expected.\n", fname, 1); + return 0; + } + + // Matrix of Stings + sciErr = getMatrixOfString(pvApiCtx, piAddrVal, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + // Second call to retrieve the length of the string + sciErr = getMatrixOfString(pvApiCtx, piAddrVal, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(piLen); + return 0; + } + method = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int j = 0; j < iRows * iCols; j++) + { + method[j] = (char*)malloc(sizeof(char) * (piLen[j] + 1)); + } + + // Third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddrVal, &iRows, &iCols, piLen, method); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(piLen); + free(method); + return 0; + } + argPresence[2] = 1; + } + + if(strcmp(currentArg, "uniquenessRatio") == 0) + { + val_position = i + 1; + + if(argPresence[3] == 1) + { + Scierror(999,"Do not enter the same parameter\n"); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx, val_position, &piAddrVal); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isIntegerType(pvApiCtx, piAddrVal) || + isVarComplex(pvApiCtx,piAddrVal) || + !isScalar(pvApiCtx, piAddrVal)) + { + Scierror(999," Invalid Value for uniquenessRatio. Please enter a non negative Integer value.\n"); + return 0; + } + + getScalarInteger32(pvApiCtx, piAddrVal, &uniquenessRatio); + + if(uniquenessRatio < 0) + { + Scierror(999," Invalid Value for uniquenessRatio. Please enter a non negative Integer value.\n"); + return 0; + } + argPresence[3] = 1; + } + + if(strcmp(currentArg, "textureThreshold") == 0) + { + val_position = i + 1; + + if(argPresence[4] == 1) + { + Scierror(999,"Do not enter the same parameter\n"); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx, val_position, &piAddrVal); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isIntegerType(pvApiCtx, piAddrVal) || + isVarComplex(pvApiCtx,piAddrVal) || + !isScalar(pvApiCtx, piAddrVal)) + { + Scierror(999," Invalid Value for textureThreshold. Please enter a non negative Integer value.\n"); + return 0; + } + + getScalarInteger32(pvApiCtx, piAddrVal, &textureThreshold); + + if(textureThreshold < 0) + { + Scierror(999," Invalid Value for textureThreshold. Please enter a non negative Integer value.\n"); + return 0; + } + argPresence[4] = 1; + } + + if(strcmp(currentArg, "disp12MaxDiff") == 0) + { + val_position = i + 1; + + if(argPresence[5] == 1) + { + Scierror(999,"Do not enter the same parameter\n"); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx, val_position, &piAddrVal); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isIntegerType(pvApiCtx, piAddrVal) || + isVarComplex(pvApiCtx,piAddrVal) || + !isScalar(pvApiCtx, piAddrVal)) + { + Scierror(999," Invalid Value for disp12MaxDiff. Please enter a non negative Integer value.\n"); + return 0; + } + + getScalarInteger32(pvApiCtx, piAddrVal, &disp12MaxDiff); + + if(disp12MaxDiff < 0) + { + Scierror(999," Invalid Value for disp12MaxDiff. Please enter a non negative Integer value.\n"); + return 0; + } + argPresence[5] = 1; + } + } + + if(argPresence[2] == 0) + { + method = (char**)malloc(sizeof(char*) * 1 * 1); + method[0] = (char*)malloc(sizeof(char) * 13); + strcpy(method[0],"blockMatching"); + } + + /*----- Matching -----*/ + if(strcmp(method[0], "blockMatching") == 0) + { + Ptr left_matcher = StereoBM::create(numDisparities, SADWindowSize); + left_matcher -> setTextureThreshold(textureThreshold); + left_matcher -> setUniquenessRatio(uniquenessRatio); + left_matcher -> setDisp12MaxDiff(disp12MaxDiff); + wls_filter = createDisparityWLSFilter(left_matcher); + Ptr right_matcher = createRightMatcher(left_matcher); + try + { + sciprint("\n%d %d\n", image_1.rows, image_1.cols); + left_matcher -> compute(image_1, image_2,left_disp); + right_matcher -> compute(image_2, image_1, right_disp); + } + catch(Exception &e) + { + const char *err = e.what(); + Scierror(999, "%s", err); + return 0; + } + } + + else if(strcmp(method[0], "semi-blockMatching") == 0) + { + Ptr left_matcher = StereoSGBM::create(0, numDisparities, SADWindowSize); + left_matcher -> setP1(24 * SADWindowSize * SADWindowSize); + left_matcher -> setP2(96 * SADWindowSize * SADWindowSize); + left_matcher -> setPreFilterCap(63); + left_matcher -> setMode(StereoSGBM::MODE_SGBM_3WAY); + left_matcher -> setDisp12MaxDiff(disp12MaxDiff); + left_matcher -> setUniquenessRatio(uniquenessRatio); + wls_filter = createDisparityWLSFilter(left_matcher); + Ptr right_matcher = createRightMatcher(left_matcher); + try + { + left_matcher -> compute(image_1, image_2, left_disp); + right_matcher -> compute(image_2, image_1, right_disp); + } + catch(Exception &e) + { + const char *err = e.what(); + Scierror(999, "%s", err); + return 0; + } + } + + /*----- Filtering -----*/ + int lambda = 8000; + int sigma = 1.5; + wls_filter->setLambda(lambda); + wls_filter->setSigmaColor(sigma); + try + { + wls_filter->filter(left_disp, image_1, filtered_disp, right_disp); + } + catch(Exception &e) + { + const char *err = e.what(); + Scierror(999, "%s", err); + return 0; + } + + //Mat conf_map = Mat(left.rows, left.cols, CV_8U); + //conf_map = wls_filter->getConfidenceMap(); + + Mat filtered_disp_vis; + try + { + getDisparityVis(filtered_disp, filtered_disp_vis, vis_mult); + } + catch(Exception &e) + { + const char *err = e.what(); + Scierror(999, "%s", err); + return 0; + } + + try + { + left_disp.convertTo(left_disp, CV_8U, 255 / (numDisparities * 16.)); + } + catch(Exception &e) + { + const char *err = e.what(); + Scierror(999, "%s", err); + return 0; + } + + // imwrite(dst_path, filtered_disp_vis); + + /*----- Creating output images -----*/ + string tempstring = type2str(filtered_disp_vis.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, filtered_disp_vis, 1); + free(checker); + + tempstring = type2str(left_disp.type()); + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, left_disp, 2); + free(checker); + + /*----- Returning output -----*/ + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + AssignOutputVariable(pvApiCtx, 2) = nbInputArgument(pvApiCtx) + 2; + ReturnArguments(pvApiCtx); + return 0; + } +} \ No newline at end of file diff --git a/sci_gateway/cpp/opencv_filter2D.cpp b/sci_gateway/cpp/opencv_filter2D.cpp new file mode 100644 index 0000000..72725fd --- /dev/null +++ b/sci_gateway/cpp/opencv_filter2D.cpp @@ -0,0 +1,193 @@ +/*************************************************** +Author : Sukul Bagai +**************************************************** +Usage : return_image = filter2D(input_image,"",kernel_matrix,anchor_x,anchor_y,delta); +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_filter2D(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + char **ddepth = NULL; + int i,j,k; + double *kernel, anchorX, anchorY, delta; + + //checking input argument + CheckInputArgument(pvApiCtx, 6, 6); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for ddepth + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + ddepth = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + ddepth[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, ddepth); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //for kernel matrix + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr3, &iRows, &iCols ,&kernel); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + int n=iRows; + double kernelArray[n][n]; + //assigning values to actual kernelMatrix + for(i=0;i= n || anchorY >=n) + { + sciprint("Invalid anchor point given. Centre point (-1,-1) was used instead"); + anchorY = -1; + anchorX = -1; + } + Mat new_image(image.rows,image.cols,image.type()); + Point pt(anchorX,anchorY); + + int actualddepth; + + if(strcmp(ddepth[0],"CV_8U")==0) + actualddepth=CV_8U; + else if(strcmp(ddepth[0],"CV_16U")==0) + actualddepth=CV_16U; + else if(strcmp(ddepth[0],"CV_16S")==0) + actualddepth=CV_16S; + else if(strcmp(ddepth[0],"CV_32F")==0) + actualddepth=CV_32F; + else if(strcmp(ddepth[0],"CV_64F")==0) + actualddepth=CV_64F; + else + { + sciprint("Invalid type %s used. CV_8U was used instead by default",ddepth[0]); + actualddepth = CV_8U; + } + + //applying function + try + { + image.convertTo(image, CV_8U); + filter2D(image,new_image,actualddepth,kernelMatrix,pt,delta); + } + catch(Exception &e) + { + Scierror(999, "%s", e.what()); + return 0; + } + + //returning image + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_ftrans2.cpp b/sci_gateway/cpp/opencv_ftrans2.cpp new file mode 100644 index 0000000..3d45ffc --- /dev/null +++ b/sci_gateway/cpp/opencv_ftrans2.cpp @@ -0,0 +1,208 @@ +/******************************************************** +Author: Vinay + +Function: h = ftrans2(b, t) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include +using namespace cv; +using namespace std; + +void rotate180(Mat &m) { + double temp; + for (int i=0; i<(m.rows+1)/2; i++) { + int k = m.cols; + if ((i+1)>=((m.rows+1)/2) && m.rows%2!=0) { + k = (m.cols+1)/2; + } + for (int j=0; j(i, j); + m.at(i, j) = m.at(m.rows-i-1, m.cols-j-1); + m.at(m.rows-i-1, m.cols-j-1) = temp; + } + } + +} + +Mat fftshift(Mat m) { + int a = m.rows/2; + int b = m.cols/2; + Mat r = Mat::zeros(m.size(), m.type()); + for (int i=0; i((i+a)%m.rows, (j+b)%m.cols) = m.at(i, j); + } + } + return r; +} + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_ftrans2(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int cRows=0,cCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + + //checking input argument + CheckInputArgument(pvApiCtx, 1, 2); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat b, bcpy, t; + Mat P0, P1, P2, h, hh; + + retrieveImage(bcpy, 1); + bcpy.convertTo(b, CV_64F); + + if (b.cols == 1) { + transpose(b, b); + } + else if (b.rows == 1) { + ; + } + else { + sciprint("b at argument 1 expected to be vector"); + return 0; + } + + if (b.cols%2 == 0) { + sciprint("b at argument 1 must be of odd length"); + return 0; + } + + Mat c = b.clone(); + rotate180(c); + + int zeroCount = 0; + double eps = 1.5e-5; + + for (int i=0; i(0, i) - c.at(0, i)) > eps) { + zeroCount = b.cols; + break; + } + if (b.at(0, i) == 0) { + zeroCount++; + } + } + if (zeroCount == b.cols) { + sciprint("b at argument 1 must be nonzero and symmetric"); + return 0; + } + + + if (nbInputArgument(pvApiCtx) == 2) { + Mat tcpy; + retrieveImage(tcpy, 2); + tcpy.convertTo(t, CV_64F); + + } + else { + Mat tcpy; + double data[3][3] = {{1.0/8, 2.0/8, 1.0/8},{2.0/8, -4.0/8, 2.0/8},{1.0/8, 2.0/8, 1.0/8}}; + tcpy = Mat(3, 3, CV_64F, &data); + t = tcpy.clone(); + } + + rotate180(b); + + fftshift(b).copyTo(b); + + rotate180(b); + + int inset1 = (t.rows-1)/2; + int inset2 = (t.cols-1)/2; + int n = (b.cols-1)/2; + + Mat a = Mat::zeros(1, n+1, CV_64F); + a.at(0, 0) = b.at(0,0); + for (int i=1; i<=n; i++) { + a.at(0, i) = 2*b.at(0, i); + } + + P0 = Mat::ones(1, 1, CV_64F); + t.copyTo(P1); + h = (P1*a.at(0, 1)); + + h.at(inset1,inset2) = h.at(inset1,inset2) + a.at(0, 0); + + for (int i=2; i<=n; i++) { + Mat src = t.clone(); + + int additionalRows = P1.rows-1, additionalCols = P1.cols-1; + + copyMakeBorder(src, src, (additionalRows+1)/2, additionalRows/2, (additionalCols+1)/2, additionalCols/2, BORDER_CONSTANT, Scalar(0)); + + Point anchor(P1.cols - P1.cols/2 - 1, P1.rows - P1.rows/2 - 1); + int borderMode = BORDER_CONSTANT; + Mat P11; + try + { + flip(P1, P11,-1); + filter2D(src, P2, src.depth(), P11, anchor, 0, borderMode); + } + catch(Exception &e) + { + sciprint("%s", e.what()); + return 0; + } + + P2 = P2 * 2; + + for (int x=0; x(x + 2*inset1, y+ 2*inset2) = P2.at(x + 2*inset1, y+ 2*inset2) - P0.at(x, y); + } + } + h.copyTo(hh); + h = (P2*a.at(0, i)); + for (int x=0; x(x + inset1, y + inset2) = h.at(x + inset1, y + inset2) + hh.at(x, y); + } + } + + P1.copyTo(P0); + P2.copyTo(P1); + + } + + rotate180(h); + + + Mat hcopy; + h.copyTo(hcopy); + + string tempstring = type2str(hcopy.type()); + char *imtype; + imtype = (char *)malloc(tempstring.size() + 1); + memcpy(imtype, tempstring.c_str(), tempstring.size() + 1); + returnImage(imtype,hcopy,1); + free(imtype); + + + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} + diff --git a/sci_gateway/cpp/opencv_getParamsEM.cpp b/sci_gateway/cpp/opencv_getParamsEM.cpp new file mode 100644 index 0000000..67da458 --- /dev/null +++ b/sci_gateway/cpp/opencv_getParamsEM.cpp @@ -0,0 +1,214 @@ +/*************************************************** +Author : Siddhant Narang +***************************************************/ +#include +#include +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/imgproc/imgproc.hpp" +#include "opencv2/features2d.hpp" +#include "opencv2/xfeatures2d.hpp" +#include "opencv2/ml.hpp" +#include "opencv2/opencv.hpp" + + +using namespace cv; +using namespace cv::xfeatures2d; +using namespace std; +using namespace cv::ml; + + +inline bool file_exists_check(const std::string& name) +{ + struct stat buffer; + return (stat (name.c_str(), &buffer) == 0); +} + + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + int opencv_getParamsEM(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int *piAddr = NULL; + int *piChild = NULL; + int iRows, iCols; + char **pstData = NULL; + int *piLen = NULL; + char **classifierDescription = NULL; + int classifierDescriptionCount; + + double nClusters = 0; + Mat means; + Mat weights; + double *_means; + double *_weights; + + /*------ Check number of parameters ------*/ + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1); + + /*------ Get input arguments ------*/ + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isListType(pvApiCtx, piAddr)) + { + Scierror(999, "Error: The input argument #1 is not of type classifier.\n"); + return 0; + } + + /*----- Extracting object type and checking if type is classifier -----*/ + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + pstData[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1));//+ 1 for null termination + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!(strcmp(pstData[0],"EMClassifier"))) + { + Scierror(999, "Error: The input argument #1 is not of type classifier.\n"); + return 0; + } + + /*----- Extracting classifier location from classifier object -----*/ + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows!=1 || iCols!=1) + { + Scierror(999, "1x1 Matrix expected for classifier argument."); + return 0; + } + string classifierLocation = string(pstData[0]); + + if(!file_exists_check(classifierLocation)) + { + Scierror(999, "Error: Input "".yml"" File not found in the pwd.\n"); + return 0; + } + + Ptr em = Algorithm::load(classifierLocation); + + try + { + if(!em->isTrained()) + { + Scierror(999,"\nEM Model not trained\n"); + return 0; + } + nClusters = em->getClustersNumber(); + + means = em->getMeans(); + // Converting from type matrix to double + _means = (double*) malloc(means.cols * sizeof(double)); + for(int i = 0; i < means.cols; i++) + { + _means[i] = means.at(i); + } + + weights = em->getWeights(); + // Converting from type matrix to double + _weights = (double*) malloc(weights.cols * sizeof(double)); + for(int i = 0; i < weights.cols; i++) + { + _weights[i] = weights.at(i); + } + } + catch(Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + } + + /*----- Creating output arguments -----*/ + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 6, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 1, 1, 1, &nClusters); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 2, 1, means.cols, _means); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 3, 1, weights.cols, _weights); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + /*------ Return Arguments ------*/ + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx)+1; + ReturnArguments(pvApiCtx); + return 0; + } +} \ No newline at end of file diff --git a/sci_gateway/cpp/opencv_getParamsKNN.cpp b/sci_gateway/cpp/opencv_getParamsKNN.cpp new file mode 100644 index 0000000..d5457f7 --- /dev/null +++ b/sci_gateway/cpp/opencv_getParamsKNN.cpp @@ -0,0 +1,207 @@ +/*************************************************** +Author : Siddhant Narang +***************************************************/ +#include +#include +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/imgproc/imgproc.hpp" +#include "opencv2/features2d.hpp" +#include "opencv2/xfeatures2d.hpp" +#include "opencv2/ml.hpp" +#include "opencv2/opencv.hpp" + + +using namespace cv; +using namespace cv::xfeatures2d; +using namespace std; +using namespace cv::ml; + + +inline bool file_exists_check(const std::string& name) +{ + struct stat buffer; + return (stat (name.c_str(), &buffer) == 0); +} + + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + int opencv_getParamsKNN(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int *piAddr = NULL; + int *piChild = NULL; + int iRows, iCols; + char **pstData = NULL; + int *piLen = NULL; + char **classifierDescription = NULL; + int classifierDescriptionCount; + + char *algoType = NULL; + double k = 0; + double emax = 0; + + /*------ Check number of parameters ------*/ + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1); + + /*------ Get input arguments ------*/ + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isListType(pvApiCtx, piAddr)) + { + Scierror(999, "Error: The input argument #1 is not of type classifier.\n"); + return 0; + } + + /*----- Extracting object type and checking if type is classifier -----*/ + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + pstData[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1)); //+ 1 for null termination + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!(strcmp(pstData[0],"KNNClassifier"))) + { + Scierror(999, "Error: The input argument #1 is not of type classifier.\n"); + return 0; + } + + /*----- Extracting classifier loacation from classifier object -----*/ + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows!=1 || iCols!=1) + { + Scierror(999, "1x1 Matrix expected for classifier argument."); + return 0; + } + string classifierLocation = string(pstData[0]); + + if(!file_exists_check(classifierLocation)) + { + Scierror(999, "Error: Input "".yml"" File not found in the pwd.\n"); + return 0; + } + + /*----- Getting all the parameters -----*/ + try + { + Ptr knearest = Algorithm::load(classifierLocation); + if(!knearest->isTrained()) + { + Scierror(999,"\nKNN Model not trained\n"); + return 0; + } + int a = knearest->getAlgorithmType(); + if(a == 1) + { + algoType = "BRUTE_FORCE"; + } + else + { + algoType = "KDTREE"; + } + k = knearest->getDefaultK(); + } + catch(Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + } + + /*------ Create output arguments ------*/ + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 3, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfStringInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, + 1, 1, 1, &algoType); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, + 2, 1, 1, &k); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 3, 1, 1, &emax); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + /*------ Return Arguments ------*/ + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx)+1; + ReturnArguments(pvApiCtx); + return 0; + } +} \ No newline at end of file diff --git a/sci_gateway/cpp/opencv_getParamsLR.cpp b/sci_gateway/cpp/opencv_getParamsLR.cpp new file mode 100644 index 0000000..6fd3335 --- /dev/null +++ b/sci_gateway/cpp/opencv_getParamsLR.cpp @@ -0,0 +1,254 @@ +/*************************************************** +Author : Siddhant Narang +***************************************************/ +#include +#include +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/imgproc/imgproc.hpp" +#include "opencv2/features2d.hpp" +#include "opencv2/xfeatures2d.hpp" +#include "opencv2/ml.hpp" +#include "opencv2/opencv.hpp" + + +using namespace cv; +using namespace cv::xfeatures2d; +using namespace std; +using namespace cv::ml; + + +inline bool file_exists_check(const std::string& name) +{ + struct stat buffer; + return (stat (name.c_str(), &buffer) == 0); +} + + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + int opencv_getParamsLR(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int *piAddr = NULL; + int *piChild = NULL; + int iRows, iCols; + char **pstData = NULL; + int *piLen = NULL; + char **classifierDescription = NULL; + int classifierDescriptionCount; + + double iterations = 0; + double learningRate = 0; + double miniBatchSize = 0; + char *regularization = NULL; + char *trainMethod = NULL; + Mat glm; + double *_glm; + + /*------ Check number of parameters ------*/ + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1); + + /* ------Get input arguments------ */ + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isListType(pvApiCtx, piAddr)) + { + Scierror(999, "Error: The input argument #1 is not of type classifier.\n"); + return 0; + } + + /*----- Extracting object type and checking if type is classifier -----*/ + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + pstData[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1));//+ 1 for null termination + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!(strcmp(pstData[0],"LRClassifier"))) + { + Scierror(999, "Error: The input argument #1 is not of type classifier.\n"); + return 0; + } + + /*----- Extracting classifier location from classifier object -----*/ + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows!=1 || iCols!=1) + { + Scierror(999, "1x1 Matrix expected for classifier argument."); + return 0; + } + string classifierLocation = string(pstData[0]); + + /*----- Check if .yml file is in pwd -----*/ + if(!file_exists_check(classifierLocation)) + { + Scierror(999, "Error: Input "".yml"" File not found in the pwd.\n"); + return 0; + } + + /*------ Processing steps -----*/ + try + { + Ptr lr = Algorithm::load(classifierLocation); + if(!lr->isTrained()) + { + Scierror(999,"\nLR Model not trained\n"); + return 0; + } + iterations = lr->getIterations(); + learningRate = lr->getLearningRate(); + + int reg = lr->getRegularization(); + if(reg == -1) + { + regularization = "REG_DISABLED"; + } + else if(reg == 0) + { + regularization = "REG_L1"; + } + else + { + regularization = "REG_L2"; + } + miniBatchSize = lr->getMiniBatchSize(); + + int tm = lr->getTrainMethod(); + if(tm == 0) + { + trainMethod = "BATCH"; + } + else + { + trainMethod = "MINI_BATCH"; + } + + glm = lr->get_learnt_thetas(); + + // Converting learnt thetas matrix from type matrix to double. + _glm = (double*) malloc(glm.cols * sizeof(double)); + for(int i = 0; i < glm.cols; i++) + { + _glm[i] = glm.at(i); + } + } + catch(Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + } + + /*----- Creating Output Arguments ------*/ + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 6, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 1, 1, 1, &iterations); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 2, 1, 1, &learningRate); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 3, 1, 1, &miniBatchSize); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfStringInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 4, 1, 1, ®ularization); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfStringInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 5, 1, 1, &trainMethod); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 6, 1, glm.cols, _glm); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + /*------ Return Arguments ------*/ + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx)+1; + ReturnArguments(pvApiCtx); + return 0; + } +} \ No newline at end of file diff --git a/sci_gateway/cpp/opencv_graydiffweight.cpp b/sci_gateway/cpp/opencv_graydiffweight.cpp new file mode 100644 index 0000000..6bfd752 --- /dev/null +++ b/sci_gateway/cpp/opencv_graydiffweight.cpp @@ -0,0 +1,97 @@ +/******************************************************** +Authors: Dhruti Shah +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + int opencv_graydiffweight(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int i,j,k; + double refGrayVal; + + //checking input argument + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + + Mat image; + retrieveImage(image, 1); + //cvtColor( image, image, CV_BGR2GRAY ); + + //for value of refGrayVal + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &refGrayVal); + if(intErr) + { + return intErr; + } + + try + { + image.convertTo(image, CV_8U); + } + catch(Exception &e) + { + sciprint("%s", e.what()); + return 0; + } + + double value1, value2; + Mat new_image = Mat::zeros(image.size(),image.type()); + for(int i=0; i(i, j); + value2 = abs(value1 - refGrayVal); + new_image.at(i, j) = value2; + } + } + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} + diff --git a/sci_gateway/cpp/opencv_imabsdiff.cpp b/sci_gateway/cpp/opencv_imabsdiff.cpp new file mode 100644 index 0000000..1327fe1 --- /dev/null +++ b/sci_gateway/cpp/opencv_imabsdiff.cpp @@ -0,0 +1,59 @@ +/******************************************************** +Function :imabsdiff +Syntax :C=imabsdiff(A,B) +Author : Tess Zacharias +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include "string.h" +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + int opencv_imabsdiff(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + Mat A,B,C; + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1) ; + retrieveImage(A,1); + retrieveImage(B,2); + Size s1=A.size(); + Size s2=B.size(); + if(s1!=s2) + { + sciprint("\nBoth input should be same size"); + return 0; + } + try + { + absdiff(A,B,C); + } + catch(Exception &e) + { + sciprint("%s", e.what()); + return 0; + } + int temp = nbInputArgument(pvApiCtx) + 1; + string tempstring = type2str(C.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,C,1); + free(checker); + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } +} diff --git a/sci_gateway/cpp/opencv_imboxfilt3.cpp b/sci_gateway/cpp/opencv_imboxfilt3.cpp new file mode 100644 index 0000000..deb8862 --- /dev/null +++ b/sci_gateway/cpp/opencv_imboxfilt3.cpp @@ -0,0 +1,140 @@ +/*************************************************** +Author : Yash S. Bhalgat +**************************************************** +Usage : + 1) box_filtered_image = imboxfilt3(input_img); + In this usage, the default filter size of 3x3 is used. + + 2) box_filtered_image = imboxfilt3(input_img, filter_height, filter_width); + +Example : + img = imread("lena.jpg"); + imshow(img); + box_filtered_img = imboxfilt3(img, 9, 9); + imshow(box_filtered_img); +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_imboxfilt3(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int i,j,k; + + //Default filter size + double filter_height = 3; + double filter_width = 3; + + //checking input argument + CheckInputArgument(pvApiCtx, 1, 3); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + int inputarg = *getNbInputArgument(pvApiCtx); + + + Mat image; + retrieveImage(image,1); + + + if(inputarg >= 2){ + //for value of filter_height + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &filter_height); + if(intErr) + return intErr; + + //for value of filter_width + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &filter_width); + if(intErr) + return intErr; + } + + + //taking the cases which can lead to an error + Mat filtered_image(image.rows,image.cols,image.type()); + + if(filter_height < 0) + { + sciprint("Positive Value Required for Height. 3 value was used instead"); + filter_height = 3; + } + if(filter_width < 0) + { + sciprint("Positive Value Required for Width. 3 value was used instead"); + filter_width = 3; + } + if((int)filter_height % 2 == 0) + { + filter_height += 1; + sciprint("Odd Value Required for Height. %f value was used instead",&filter_height); + } + if((int)filter_width % 2 == 0) + { + filter_width += 1; + sciprint("Odd Value Required for Width. %f value was used instead",&filter_width); + } + + //temporary size variable, to use in function + Size sz(filter_height, filter_width); + + //opencv function called + //if both filter_size is not given, default value of 3x3 is used + try + { + boxFilter(image, filtered_image, -1, sz); + } + catch(Exception &e) + { + sciprint("%s", e.what()); + return 0; + } + + //returning image + string tempstring = type2str(filtered_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,filtered_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_imcontrast.cpp b/sci_gateway/cpp/opencv_imcontrast.cpp new file mode 100644 index 0000000..52113da --- /dev/null +++ b/sci_gateway/cpp/opencv_imcontrast.cpp @@ -0,0 +1,99 @@ +/******************************************************** +Author: Sukul Bagai +********************************************************/ +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "../common.h" + + int opencv_imcontrast(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int i,j,k; + double alpha, beta; + + //checking input argument + CheckInputArgument(pvApiCtx, 3, 3); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + + Mat image; + retrieveImage(image, 1); + + //for value of alpha + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &alpha); + if(intErr) + { + return intErr; + } + + //for value of beta + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &beta); + if(intErr) + { + return intErr; + } + + + //we copy the original image into the new_image, and change values of each pixel using value of alpha and beta given by the user + Mat new_image = Mat::zeros(image.size(), image.type()); + + //changes the attributes of each individual pixel + for( int y = 0; y < image.rows; y++ ) + { + for( int x = 0; x < image.cols; x++ ) + { + for( int c = 0; c < 3; c++ ) + { + new_image.at(y,x)[c] = + saturate_cast(alpha * (image.at(y, x)[c]) + beta); + } + } + } + + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + +} +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_imcrop.cpp b/sci_gateway/cpp/opencv_imcrop.cpp new file mode 100644 index 0000000..c577f97 --- /dev/null +++ b/sci_gateway/cpp/opencv_imcrop.cpp @@ -0,0 +1,138 @@ +/******************************************************** +Author: Sukul Bagai +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_imcrop(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int iRows=0,iCols=0; + int *piAddr = NULL; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int i,j,k; + double x, y, width, height; + + //checking input argument + CheckInputArgument(pvApiCtx, 5, 5); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image, 1); + + //for value of top-left x-coordinate + sciErr = getVarAddressFromPosition(pvApiCtx,2,&piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &x); + if(intErr) + { + return intErr; + } + + //for value top-left y-coordinate + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &y); + if(intErr) + { + return intErr; + } + + //for value of width + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &width); + if(intErr) + { + return intErr; + } + + //for value of height + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5, &height); + if(intErr) + { + return intErr; + } + + if(x>=image.cols || y>=image.rows || x<0 || y<0) + { + sciprint("Invalid x or y value\n"); + return 0; + } + if(width<=0 || height<=0 || x+width > image.cols || y+height > image.rows) + { + sciprint("Invalid width or height value\n"); + return 0; + } + //defining a temporary rectangle, that denotes the area that has to be cropped into the new image + Rect myROI(x, y, width, height); + + // Crop the full image to that image contained by the rectangle myROI + // Note that this doesn't copy the data + Mat croppedRef(image, myROI); + + Mat cropped; + // Copy the data into new matrix + try + { + croppedRef.copyTo(cropped); + } + catch(Exception &e) + { + Scierror(999, "%s", e.what()); + return 0; + } + + string tempstring = type2str(cropped.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,cropped,1); + free(checker); + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_imextendedmax.cpp b/sci_gateway/cpp/opencv_imextendedmax.cpp new file mode 100644 index 0000000..92a7809 --- /dev/null +++ b/sci_gateway/cpp/opencv_imextendedmax.cpp @@ -0,0 +1,119 @@ +/******************************************************** +Author: Vinay Bhat +******************************************************** +Usage: return_image = imextendedmax(input_image, h) +Example: + im = imextendedmax(image, 80) +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +using namespace cv; +using namespace std; +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + void imextendedmax_imreconstruct(Mat, Mat, Mat&); + + int opencv_imextendedmax(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int *piAddr = NULL; + double h; + + //checking input argument + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + // Get the input image from the Scilab environment + Mat image; + retrieveImage(image, 1); + + // Get the address of 2nd argument, the H-Maxima transform scalar + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Get the H-Maxima transform scalar + intErr = getScalarDouble(pvApiCtx, piAddr, &h); + if(intErr) + { + return intErr; + } + + if (h < 0) + { + sciprint("Please enter a nonnegative scalar for H-maxima transform.\n"); + return 0; + } + + Mat gray_image, dst, fin_image, m, m2; + // cvtColor(image, gray_image, CV_BGR2GRAY); + try + { + image.convertTo(image, CV_8U); + max((image - h), 0, m); + imextendedmax_imreconstruct(image, m, dst); + subtract(dst, 1, m2); + imextendedmax_imreconstruct(dst, m2, m); + subtract(dst, m, m2); + } + catch(Exception &e) + { + sciprint("%s", e.what()); + return 0; + } + + fin_image = m2 * 255; + + string tempstring = type2str(fin_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, fin_image, 1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + } + + void imextendedmax_imreconstruct(Mat g, Mat f, Mat &dest) + { + Mat m0, m1, m; + m1 = f; + do + { + m0 = m1.clone(); + try + { + dilate(m0, m, Mat()); + min(g, m, m1); + } + catch(Exception &e) + { + Scierror(999, "%s", e.what()); + return; + } + + } while(countNonZero(m1 != m0) != 0); + dest = m1.clone(); + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_imfindcircles.cpp b/sci_gateway/cpp/opencv_imfindcircles.cpp new file mode 100644 index 0000000..99b7655 --- /dev/null +++ b/sci_gateway/cpp/opencv_imfindcircles.cpp @@ -0,0 +1,155 @@ +/******************************************************** +Function: imfindcircles +Syntax: B = imfindcircles(A) +Author: Tess Zacharias +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_imfindcircles(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int intErr = 0; + int *piAddrNew = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + Mat src_gray, image; + double Rmin, Rmax; + + //checking input argument + CheckInputArgument(pvApiCtx, 3, 3); + CheckOutputArgument(pvApiCtx, 1, 2) ; + retrieveImage(image, 1); + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr2, &Rmin); + if(intErr) + { + return intErr; + } + if(Rmin != round(Rmin) || Rmin <= 0) + { + sciprint("The value of minium Radius must be an integer\n"); + return 0; + } + if(Rmin < 5) + { + sciprint("The value of minium Radius too small\n"); + return 0; + } + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &Rmax); + if(intErr) + { + return intErr; + } + if(Rmax != round(Rmax) || Rmax <= 0) + { + sciprint("The value of maximum Radius must be an integer\n"); + return 0; + } + + try + { + GaussianBlur(image, image, Size(9, 9), 2, 2); + } + catch(Exception &e) + { + Scierror(999, "%s", e.what()); + return 0; + } + + vector circles; + try + { + image.convertTo(image, CV_8U); + HoughCircles(image, circles, CV_HOUGH_GRADIENT, 2, 10, 200, 100, Rmin, Rmax); + } + catch(Exception &e) + { + Scierror(999, "%s", e.what()); + return 0; + } + if(circles.size() == 0) + { + Scierror(999, "\nNo circles of the given range of radii found in image.\n"); + return 0; + } + + int k = circles.size(); + double *radius = NULL; + radius = ( double *)malloc(sizeof(double) * k); + double *c1 = NULL; + c1 = (double *)malloc(sizeof(double) * k); + double *c2 = NULL; + c2 = ( double *)malloc(sizeof(double) * k); + + for(size_t i= 0; i < circles.size(); i++ ) + { + c1[i] = cvRound(circles[i][0]); + c2[i] = cvRound(circles[i][1]); + radius[i] = cvRound(circles[i][2]); + } + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 2, &piAddrNew); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+1, piAddrNew, 1, 1, k, c1); + free(c1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx)+1, piAddrNew, 2, 1, k, c2); + free(c2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + if(Lhs == 2) + { + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) +2, 1, k, radius); + free(radius); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + AssignOutputVariable(pvApiCtx, 2) = nbInputArgument(pvApiCtx) + 2; + } + ReturnArguments(pvApiCtx); + return 0; + } +} + diff --git a/sci_gateway/cpp/opencv_imwrite.cpp b/sci_gateway/cpp/opencv_imwrite.cpp new file mode 100644 index 0000000..84c4e77 --- /dev/null +++ b/sci_gateway/cpp/opencv_imwrite.cpp @@ -0,0 +1,93 @@ +/******************************************************** +Author: Sukul Bagai +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_imwrite(char *fname, unsigned long fname_len) + { + + SciErr sciErr; + int iRows = 0,iCols = 0; + int *piAddr = NULL; + int *piAddr1 = NULL; + int *piLen = NULL; + char **pstData = NULL; + int i, j, k; + + //checking input argument + CheckInputArgument(pvApiCtx, 2, 2); + //CheckOutputArgument(pvApiCtx, 0, 0) ; + Mat image; + retrieveImage(image, 1); + + //Now, retriving path + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr1); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr1, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr1, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int i = 0 ; i < iRows * iCols ; i++) + { + pstData[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + } + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr1, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //image path will contain the path where the image has to be written + string image_path = pstData[0]; + + //writes to the path + try + { + imwrite(image_path, image); + } + catch(Exception &e) + { + Scierror(999, "%s", e.what()); + return 0; + } + return 0; + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_integralFilter.cpp b/sci_gateway/cpp/opencv_integralFilter.cpp new file mode 100644 index 0000000..eaaa7a8 --- /dev/null +++ b/sci_gateway/cpp/opencv_integralFilter.cpp @@ -0,0 +1,252 @@ +/*************************************************** +Author : Tanmay Chaudhari + ***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + // #include "../common.h" + // #include "../common.cpp" + + int opencv_integralFilter(char *fname, unsigned long fname_len) + { + //Error management variable + SciErr sciErr; + + //Variable declaration + int i, j, k; + int iType = 0; + int iComplex = 0; + int rowsOfintImage = 0; + int colsOfintImage = 0; + int rowsOfBbox = 0; + int rowsOfWeights = 0; + int colsOfBbox = 0; + int colsOfWeights = 0; + int rowsOfFilter = 0; + int colsOfFilter = 0; + int sR = 0; + int sC = 0; + int eR = 0; + int eC = 0; + int *piAddr = NULL; + int *outSize = NULL; + double bboxSum = 0; + double *integralFilter = NULL; + double *intImage = NULL; + double *bbox = NULL; + double *weights = NULL; + double *filterSize = NULL; + + //Check input output argument + checkInputArgument(pvApiCtx, 4, 4); + checkOutputArgument(pvApiCtx, 1, 1); + + //Get variable address of the first input arguent + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Check type + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + + //Get complexity + iComplex = isVarComplex(pvApiCtx, piAddr); + + //Check complexity + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &rowsOfintImage, &colsOfintImage, &intImage); + if(sciErr.iErr || rowsOfintImage == 0 || colsOfintImage == 0) + { + printError(&sciErr, 0); + return 0; + } + + //Get variable address of the second input arguent + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Check type + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + + //Get complexity + iComplex = isVarComplex(pvApiCtx, piAddr); + + //Check complexity + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &rowsOfBbox, &colsOfBbox, &bbox); + if(sciErr.iErr || colsOfBbox != 4 || rowsOfBbox == 0) + { + printError(&sciErr, 0); + return 0; + } + + //Get variable address of the third input arguent + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Check type + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + + //Get complexity + iComplex = isVarComplex(pvApiCtx, piAddr); + + //Check complexity + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &rowsOfWeights, &colsOfWeights, &weights); + if(sciErr.iErr || colsOfWeights == 0 || rowsOfWeights != 1 || colsOfWeights != rowsOfBbox) + { + printError(&sciErr, 0); + return 0; + } + + //Get variable address of the fourth input arguent + sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Check type + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + + //Get complexity + iComplex = isVarComplex(pvApiCtx, piAddr); + + //Check complexity + if(iComplex) + { + Scierror(999, "%s: Wrong type for input argument: A complex number is not expected.\n"); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &rowsOfFilter, &colsOfFilter, &filterSize); + if(sciErr.iErr || colsOfFilter != 2 || rowsOfFilter != 1) + { + printError(&sciErr, 0); + return 0; + } + + outSize = (int*)malloc(sizeof(int) * 2); + if(filterSize[0] != 0 && filterSize[1] != 0) + { + outSize[0] = rowsOfintImage - filterSize[0]; + outSize[1] = colsOfintImage - filterSize[1]; + } + else + { + outSize[0] = rowsOfintImage - 1; + outSize[1] = colsOfintImage - 1; + } + + if(outSize[0] <= 0 || outSize[1] <= 0) + outSize[0] = outSize[1] = 0; + + integralFilter = (double*)malloc(sizeof(double) * outSize[0] * outSize[1]); + + for( i = 0; i < outSize[0] * outSize[1]; i++) + integralFilter[i] = 0; + + for( i = 1; i <= outSize[1]; i++) + { + for( j = 1; j <= outSize[0]; j++) + { + for( k = 0; k < rowsOfBbox; k++) + { + sR = j + bbox[1 * rowsOfBbox + k] - 1; + sC = i + bbox[0 * rowsOfBbox + k] - 1; + eR = sR + bbox[3 * rowsOfBbox + k]; + eC = sC + bbox[2 * rowsOfBbox + k]; + + bboxSum = intImage[(eC - 1) * rowsOfintImage + (eR - 1)] - intImage[(sC - 1) * rowsOfintImage + (eR - 1)]; + bboxSum -= intImage[(eC - 1) * rowsOfintImage + (sR - 1)]; + bboxSum += intImage[(sC - 1) * rowsOfintImage + (sR - 1)]; + + integralFilter[(j - 1) * outSize[1] + (i - 1)] = integralFilter[(j - 1) * outSize[1] + (i - 1)] + weights[k] * bboxSum; + } + } + } + + //Creating list to store integral filter in it + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 1, outSize[0], outSize[1], integralFilter); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Return output arguments + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + + return 0; + } + /* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_isFilter.cpp b/sci_gateway/cpp/opencv_isFilter.cpp new file mode 100644 index 0000000..55cf7c4 --- /dev/null +++ b/sci_gateway/cpp/opencv_isFilter.cpp @@ -0,0 +1,294 @@ +/********************************************************************************* +*Author : Kevin George +* +*-> To execute, isfilter("Data",..,"Size",..) +* +* +*********************************************************************************/ +#include +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include "opencv2/imgproc/imgproc.hpp" +#include "opencv2/features2d/features2d.hpp" +#include +#include +#include + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_isFilter(char *fname, unsigned long fname_len) + { + //-> Error Management variables + SciErr sciErr; + int intErr=0; + + //-> Address of Various Arguments + int *piAddr = NULL; + + //-> Local variables + double size; + double *data = NULL; + + Mat s; + Mat u; + Mat v; + + int num_InputArgs; //-> gives total number of arguments + int iRows, iCols; + int *piLen = NULL; + char **pstData = NULL; //-> why double pointer?? and what is it + char *currentArg = NULL; //-> Stores current string representing 'name' of name,value pair arguments + bool *providedArgs = NULL; //-> Used to check that optional argument is not entered more than once + + //-> Checks the number of arguments + //-> pvApiCtx is a Scilab environment pointer + //-> Checks number of input and output arguments + CheckInputArgument(pvApiCtx, 4, 4); + CheckOutputArgument(pvApiCtx, 1, 1); + + //-> Count number of input arguments + num_InputArgs = *getNbInputArgument(pvApiCtx); + + providedArgs = (bool*) malloc(sizeof(bool) * 2); + +//***************************************************** Getting Input Arguments ************************************************************* + for(int iter = 1; iter <= num_InputArgs; iter++) + { + //-> Getting address of next argument + sciErr = getVarAddressFromPosition(pvApiCtx, iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //-> Extracting name of next argument takes three calls to getMatrixOfString + //-> First call to get rows and columns + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + //-> Second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + //-> Third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + currentArg = pstData[0]; + free(pstData); + iRows=0; + iCols=0; + free(piLen); + + + +//****************************************************** Name,Value - Data ***************************************************************** + + if(strcmp(currentArg, "Data")==0) + { + if(iter+1<= num_InputArgs && !providedArgs[0]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &data); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + /*if(iRows*iCols!=) + { + Scierror(999,"Invalid Argument\n"); + return 0; + } */ + + providedArgs[0] = 1; + } + + else if(providedArgs[0]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + + } + +//****************************************************** Name,Value - Size ***************************************************************** + else if(strcmp(currentArg, "Size")==0) + { + if(iter+1<= num_InputArgs && !providedArgs[1]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + intErr = getScalarDouble(pvApiCtx, piAddr, &size); + if(intErr) + { + return intErr; + } + + //-> Checking if values are in proper range. Same for all optional arguments + if( size < 0) + { + Scierror(999," Invalid Value for NumPyramidLevels. Please enter a non negative Double value\\n"); + return 0; + } + providedArgs[1] = 1; + } + + else if(providedArgs[1]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + + } + + } +//***************************************************** Actual Processing ************************************************************* + + int size2 = int(size); + float *kdata = new float[size2*size2]; + + for(int i = 0;i(i, j); + if(s.at(i, j) != 0) + count++; + } + } + + double *_u = (double *) malloc(u.rows * u.cols * sizeof(double)); + for(int i = 0; i < u.rows; i++) + { + for(int j = 0; j < u.cols; j++) + { + _u[(i * u.rows) + j] = u.at(i, j); + } + } + + double ans = -1; + if (count == 1) + { + sciprint("\nFilter is seperable\n"); + ans = 1; + } + + else + { + sciprint("\nFilter is not seperable\n"); + ans = 0; + } + + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 3, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 1, 1, 1, &ans); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(_s != NULL) + { + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 2, s.rows, s.cols, _s); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + if(_u != NULL) + { + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 3, u.rows, u.cols, _u); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + //------Return Arguments------// + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } +} \ No newline at end of file diff --git a/sci_gateway/cpp/opencv_matchFeatures.cpp b/sci_gateway/cpp/opencv_matchFeatures.cpp new file mode 100644 index 0000000..c867f07 --- /dev/null +++ b/sci_gateway/cpp/opencv_matchFeatures.cpp @@ -0,0 +1,592 @@ +/******************************************************************************************************************************************************************************************************** +* Author: Umang Agrawal & Siddhant Narang * +* Code: matchFeatures.cpp * +* Function Format: [ index_Pairs_of_Matched_fetaures Metric ] = matchFeatures( feature_Vector_1, feature_Vector_2, Optional Arguments ) * +* Optional Arguments: Name Value * +* 'Method' [ 'Exhaustive' : 'Brute Force Matching', 'Approximate' : FANN Based Matching' ] * +* 'Metric' [ 'SSD', 'SAD', 'Hamming', 'Hamming_2' ] * +* 'Unique' [ Boolean True or False ] * +* 'MatchThreshold' [ Percentage: 0 - 100 ] * +********************************************************************************************************************************************************************************************************/ +#include +#include +#include +#include +#include "opencv2/core/core.hpp" +#include "opencv2/features2d/features2d.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/imgproc/imgproc.hpp" + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_matchFeatures(char *fname, unsigned long fname_len) + { + // Error Handling + SciErr sciErr; + + // Address of Various Scilab API + int *piAddr1 = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + + // Number of input and output Arguments + int inputArguments = 0; + + int Rows_1,Cols_1; + int Rows_2,Cols_2; + + // Input arguments + double *feature_1 = NULL; + double *feature_2 = NULL; + + int iRows, iCols; + int *pilen = NULL; + char **arg = NULL; + char **method = NULL; + char **metric = NULL; + double unique = 0; + double thresh = 10; + + int count_method = 0,count_metric = 0, count_unique = 0, count_thresh = 0; + + double max_dist = 0, min_dist; + double *indexPairs = NULL; + double *matchMetric = NULL; + + vector < vector > matches1; + vector matches; + vector valid_m; + + // Checks on Number of Input and Output Arguments + CheckInputArgument(pvApiCtx, 2, 10); + CheckOutputArgument(pvApiCtx, 1, 2); + + inputArguments = *getNbInputArgument(pvApiCtx); + + if( inputArguments%2 == 0 ) + { + // Feature Matrix 1 + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if( !isVarMatrixType(pvApiCtx, piAddr1)) + { + Scierror(999,"Expecting a Matrix of MxN order containing the Binary Feature Set of Image 1 (Query Image)\n"); + return 0; + } + if( !isDoubleType(pvApiCtx, piAddr1)) + { + Scierror(999,"Feature Matrix should be of Double Type\n"); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr1, &Rows_1, &Cols_1, &feature_1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Feature Matrix 2 + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if( !isVarMatrixType(pvApiCtx, piAddr2)) + { + Scierror(999,"Expecting a Matrix of MxN order containing the Binary Feature Set of Image 1 (Query Image)\n"); + return 0; + } + if( !isDoubleType(pvApiCtx, piAddr2)) + { + Scierror(999,"Feature Matrix should be of Double Type\n"); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &Rows_2, &Cols_2, &feature_2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + else + { + Scierror(999,"Required Arguments are Missing\n"); + return 0; + } + + for( int i=3; i<=inputArguments; i++) + { + sciErr = getVarAddressFromPosition(pvApiCtx, i, &piAddr3); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + // Check for Argument type + if( !isStringType(pvApiCtx, piAddr3)) + { + Scierror(999, "%s: Wrong type of argument #%d. A string is expected.\n", fname, 1); + return 0; + } + // Matrix of Stings + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + pilen = (int*)malloc(sizeof(int) * iRows * iCols); + + // second call to retrieve the length of the string + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, pilen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(pilen); + return 0; + } + arg = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int j=0;j< iRows * iCols; j++) + { + arg[j] = (char*)malloc(sizeof(char) * (pilen[j] + 1)); + } + + // third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr3, &iRows, &iCols, pilen, arg); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(pilen); + free(arg); + return 0; + } + + if(strcmp(arg[0],"Method") == 0) + { + if(count_method != 0) + { + Scierror(999,"Method argument has been called twice.\n"); + return 0; + } + free(arg); + free(pilen); + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Check for Argument type + if( !isStringType(pvApiCtx, piAddr4)) + { + Scierror(999, "%s: Wrong type of value for Method Argument. A string is expected.\n"); + return 0; + } + + // Matrix of Stings + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + pilen = (int*)malloc(sizeof(int) * iRows * iCols); + + // second call to retrieve the length of the string + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, pilen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(pilen); + return 0; + } + method = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int j=0;j< iRows * iCols; j++) + { + method[j] = (char*)malloc(sizeof(char) * (pilen[j] + 1)); + } + + // third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, pilen, method); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(pilen); + free(method); + return 0; + } + i++; + count_method += 1; + } + + else if(strcmp(arg[0],"Metric") == 0) + { + if(count_metric != 0) + { + Scierror(999,"Metric argument has been called twice.\n"); + return 0; + } + free(arg); + free(pilen); + + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Check for Argument type + if( !isStringType(pvApiCtx, piAddr4)) + { + Scierror(999, "%s: Wrong type of Value for Metric Argument. A string is expected.\n"); + return 0; + } + + //Matrix of Stings + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + pilen = (int*)malloc(sizeof(int) * iRows * iCols); + + //second call to retrieve the length of the string + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, pilen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(pilen); + return 0; + } + metric = (char**)malloc(sizeof(char*) * iRows * iCols); + for(int j=0;j< iRows * iCols; j++) + { + metric[j] = (char*)malloc(sizeof(char) * (pilen[j] + 1)); + } + + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr4, &iRows, &iCols, pilen, metric); + if(sciErr.iErr) + { + printError(&sciErr, 0); + free(pilen); + free(metric); + return 0; + } + i++; + count_metric += 1; + } + + else if(strcmp(arg[0],"Unique") == 0) + { + if(count_unique != 0) + { + Scierror(999,"Unique argument has been called twice.\n"); + return 0; + } + free(arg); + free(pilen); + + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Check for Argument type + if( !(isDoubleType(pvApiCtx, piAddr4)) ) + { + Scierror(999,"Unique Value must be a logic scalar\n"); + return 0; + } + if(getScalarDouble(pvApiCtx, piAddr4, &unique)) + { + Scierror(999,"Cannot Read Upright Value\n"); + return 0; + } + if( !(int(unique)==0 || int(unique)==1) ) + { + Scierror(999,"Unique Value must be a logic scalar\n"); + return 0; + } + i++; + count_unique += 1; + } + + else if(strcmp(arg[0],"MatchThreshold") == 0) + { + if(count_thresh != 0) + { + Scierror(999,"Match Threshold argument has been called twice.\n"); + return 0; + } + free(arg); + free(pilen); + + sciErr = getVarAddressFromPosition(pvApiCtx, i+1, &piAddr4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //Check for Argument type + if( !(isDoubleType(pvApiCtx, piAddr4)) ) + { + Scierror(999,"Match Threshold Value must be a scalar Double\n"); + return 0; + } + if(getScalarDouble(pvApiCtx, piAddr4, &thresh)) + { + Scierror(999,"Cannot Read Threshold Value\n"); + return 0; + } + if( !(thresh>0 && thresh<=100) ) + { + Scierror(999,"Threshold Value must be in the range of 1 to 100\n"); + return 0; + } + i++; + count_thresh += 1; + } + + else + { + Scierror(999, "Invalid Argument Name. Provide Valid Arguments\n"); + return 0; + } + } + + if( count_metric == 0 ) + { + metric = (char**)malloc(sizeof(char*) * 1 * 1); + metric[0] = (char*)malloc(sizeof(char) * 4); + strcpy(metric[0], "SSD"); + } + + if( count_method == 0) + { + method = (char**)malloc(sizeof(char*) * 1 * 1); + method[0] = (char*)malloc(sizeof(char) * 11); + strcpy(method[0], "Exhaustive"); + } + + Mat descriptor_2(Rows_2, Cols_2, CV_32FC1); + if( strcmp(method[0],"Exhaustive") == 0) + { + Mat descriptor_1(Rows_1, Cols_1, CV_8UC1); + Mat descriptor_2(Rows_2, Cols_2, CV_8UC1); + + // Converting feature_1 vector from double matrix to cv::mat. + for(int i = 0; i < Rows_1; i++) + { + for(int j = 0; j < Cols_1; j++) + { + descriptor_1.at(i,j) = int(feature_1[j*Rows_1 + i]); + } + } + + // Converting feature_2 vector from double matrix to cv::mat. + for(int i = 0; i < Rows_2; i++) + { + for(int j = 0; j < Cols_2; j++) + { + descriptor_2.at(i,j) = int(feature_2[j*Rows_2 + i]); + } + } + + if( strcmp(metric[0],"SSD") == 0) + { + Ptr matcher = DescriptorMatcher::create("BruteForce"); + try + { + matcher->match(descriptor_1, descriptor_2, matches); + } + catch(Exception &e) + { + const char *err = e.what(); + Scierror(999, "%s", err); + return 0; + } + // BFMatcher matcher(NORM_L2, int(unique)); + // matcher.match(descriptor_1, descriptor_2, matches); + } + else if( strcmp(metric[0],"SAD") == 0) + { + Ptr matcher = DescriptorMatcher::create("BruteForce-L1"); + try + { + matcher->match(descriptor_1, descriptor_2, matches); + } + catch(Exception &e) + { + const char *err = e.what(); + Scierror(999, "%s", err); + return 0; + } + // BFMatcher matcher(NORM_L1, int(unique)); + // matcher.match(descriptor_1, descriptor_2, matches); + } + else if( strcmp(metric[0],"Hamming") == 0) + { + Ptr matcher = DescriptorMatcher::create("BruteForce-Hamming"); + try + { + matcher->knnMatch(descriptor_1, descriptor_2, matches1, 2); + } + catch(Exception &e) + { + const char *err = e.what(); + Scierror(999, "%s", err); + return 0; + } + // BFMatcher matcher(NORM_HAMMING, int(unique)); + // matcher.match(descriptor_1, descriptor_2, matches); + } + else if( strcmp(metric[0],"Hamming_2") == 0) + { + sciprint("\nInside\n"); + Ptr matcher = DescriptorMatcher::create("BruteForce-Hamming(2)"); + try + { + matcher->match(descriptor_1, descriptor_2, matches); + } + catch(Exception &e) + { + const char *err = e.what(); + Scierror(999, "%s", err); + return 0; + } + // BFMatcher matcher(NORM_HAMMING2, int(unique)); + // matcher.match(descriptor_1, descriptor_2, matches); + } + else + { + Scierror(999,"Enter a Valid value for Metric Argument\n"); + return 0; + } + } + + else if( strcmp(method[0],"Approximate") == 0) + { + Mat descriptor_1(Rows_1, Cols_1, CV_32FC1); + //descriptor_2(Rows_2, Cols_2, CV_32FC1); + + // Converting feature_1 vector from double matrix to cv::mat. + for(int i=0; i(i,j) = float(feature_1[j*Rows_1 + i]); + } + } + + // Converting feature_1 vector from double matrix to cv::mat + for(int i=0; i(i,j) = float(feature_2[j*Rows_2 + i]); + } + } + FlannBasedMatcher matcher; + // Ptr matcher = DescriptorMatcher::create("FlannBased"); + try + { + matcher.knnMatch(descriptor_1, descriptor_2, matches1, 2); + } + catch(Exception &e) + { + const char *err = e.what(); + Scierror(999, "%s", err); + return 0; + } + } + else + { + Scierror(999,"Enter a Valid value for Method Argument\n"); + return 0; + } + + if(matches1.size() != 0) + { + for(int i = 0; i < (int)matches1.size(); i++) + { + if((matches1[i][0].distance < 0.7 * (matches1[i][1].distance)) && + ((int)matches1[i].size() <= 2 && (int)matches1[i].size() > 0)) + { + valid_m.push_back(matches1[i][0]); + } + } + } + + if(matches.size() != 0) + { + int size = (int) matches.size(); + for(int i = 0; i < (int)matches.size(); i++) + { + if((matches[i].distance < 0.7 * (matches[i + 1].distance))) + { + valid_m.push_back(matches[i]); + } + } + } + + // Extracting Index Pairs from the match struct. + indexPairs = (double*)malloc(sizeof(double) * valid_m.size() * 2); + for( int i=0; i +#include +#include +#include +#include +#include +#include +#include + + +inline bool file_exists_check(const std::string& name) +{ + /*----- Checks is the input file is in the given wd. -----*/ + struct stat buffer; + return (stat (name.c_str(), &buffer) == 0); +} + + +using namespace std; +using namespace cv; +using namespace cv::xfeatures2d; +using namespace cv::ml; + + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + int opencv_predictEM(char *fname, unsigned long fname_len) + { + // Error management variables + SciErr sciErr; + + //------Local variables------// + int upright = 1; + Ptr matcher = DescriptorMatcher::create("FlannBased"); + Ptr detector = SURF::create(400, 4, 2, 1, upright); + Ptr extractor = detector; + Ptr bowDE = makePtr(extractor, matcher); + + char *classifierLocation = NULL; + Mat dictionary, features; + Vec response; + Mat prob; + vector keyPoints; + int iPrec = 0; + int dictionarySize; + int *piAddr = NULL; + int *piChild = NULL; + int iRows, iCols; + char **pstData = NULL; + int *piLen = NULL; + char **classifierDescription = NULL; + int classifierDescriptionCount; + char *bagOfFeaturesLocation = NULL; + int descriptionCount; + Mat input; + + //------Check number of parameters------// + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1); + + //------Get input arguments------// + retrieveImage(input,2); + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isListType(pvApiCtx, piAddr)) + { + Scierror(999, "Error: The input argument #1 is not of type classifier.\n"); + return 0; + } + + // Extracting object type and checking if type is classifier + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + + for(int iter = 0; iter < iRows * iCols; iter++) + { + pstData[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1));//+ 1 for null termination + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!(strcmp(pstData[0],"EMClassifier"))) + { + Scierror(999, "Error: The input argument #1 is not of type classifier.\n"); + return 0; + } + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows!=1 || iCols!=1) + { + Scierror(999, "1x1 Matrix expected for classifier argument."); + return 0; + } + classifierLocation = pstData[0]; + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 3, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 3, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 3, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows != 1 || iCols != 1) + { + Scierror(999, "1x1 Matrix expected for bagOfFeatures argument."); + return 0; + } + + bagOfFeaturesLocation = pstData[0]; + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 4, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 4, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + classifierDescription = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + classifierDescription[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 4, &iRows, &iCols, piLen, classifierDescription); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Handling missing yml file + if(!file_exists_check(classifierLocation)) + { + Scierror(999, "Error: Input "".yml"" File not found in the pwd.\n"); + return 0; + } + + //------Actual processing------// + FileStorage fs(bagOfFeaturesLocation, FileStorage::READ); + fs["dictionary"] >> dictionary; + fs.release(); + dictionarySize = dictionary.rows; + try + { + bowDE->setVocabulary(dictionary); + } + catch(Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + } + + // Convert input image to and detect keypoints. + try + { + input.convertTo(input, CV_8U); + detector->detect(input, keyPoints); + } + catch(Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + } + + // Compute features. + try + { + bowDE->compute(input, keyPoints, features); + } + catch(Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + } + + // Load the trained classifier + Ptr em = Algorithm::load(classifierLocation); + try + { + if(!em->isTrained()) + { + Scierror(999, "Error: the model is not trained!.\n"); + return 0; + } + features.convertTo(features, CV_32F); + response = em->predict2(features, prob); + } + catch(cv::Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + } + + double *resp = (double*) malloc(sizeof(double) * 2); + int i = 0; + for(i = 0; i < 2; i++) + { + resp[i] = response[i]; + } + + double *prob1 = (double*) malloc(sizeof(double) * 2); + // int i = 0; + int nClusters = em->getClustersNumber(); + for(i = 0; i < nClusters; i++) + { + prob1[i] = prob.at(i); + } + + //------Create output arguments------// + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 2, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 1, 1, (int)nClusters, prob1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 2, 1, 2, resp); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //------Return Arguments------// + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } + /* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_predictKNN.cpp b/sci_gateway/cpp/opencv_predictKNN.cpp new file mode 100644 index 0000000..e9db31b --- /dev/null +++ b/sci_gateway/cpp/opencv_predictKNN.cpp @@ -0,0 +1,336 @@ +/*************************************************** +Author : Siddhant Narang +***************************************************/ +#include +#include +#include +#include +#include +#include +#include +#include + + +using namespace std; +using namespace cv; +using namespace cv::xfeatures2d; +using namespace cv::ml; + + +inline bool file_exists_check(const std::string& name) +{ + /*----- Checks if the input file is in the mentioned wd -----*/ + struct stat buffer; + return (stat (name.c_str(), &buffer) == 0); +} + + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + int opencv_predictKNN(char *fname, unsigned long fname_len) + { + // Error management variables + SciErr sciErr; + + //------Local variables------// + int upright = 1; + Ptr matcher = DescriptorMatcher::create("FlannBased"); + Ptr detector = SURF::create(400, 4, 2, 1, upright); + Ptr extractor = detector; + Ptr bowDE = makePtr(extractor, matcher); + //String *classifierLocation = NULL; + Mat dictionary,features; + Mat response; + std::vector keyPoints; + int k = 4; + int iRet; + int dictionarySize; + int *piAddr = NULL; + int *piChild = NULL; + int iRows, iCols; + char **pstData = NULL; + int *piLen = NULL; + char **classifierDescription = NULL; + int classifierDescriptionCount; + char *bagOfFeaturesLocation = NULL; + int descriptionCount; + Mat input; + + //------Check number of parameters------// + CheckInputArgument(pvApiCtx, 3, 3); + CheckOutputArgument(pvApiCtx, 1, 1); + + //------Get input arguments------// + retrieveImage(input, 2); + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isListType(pvApiCtx, piAddr)) + { + Scierror(999, "Error: The input argument #1 is not of type classifier.\n"); + return 0; + } + + // Extracting object type and checking if type is classifier + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + pstData[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1));//+ 1 for null termination + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!(strcmp(pstData[0],"KNNClassifier"))) + { + Scierror(999, "Error: The input argument #1 is not of type classifier.\n"); + return 0; + } + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows!=1 || iCols!=1) + { + Scierror(999, "1x1 Matrix expected for classifier argument."); + return 0; + } + //*classifierLocation = (String*) malloc(100 * sizeof(String)); + String classifierLocation = String(pstData[0]); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 3, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 3, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 3, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows!=1 || iCols!=1) + { + Scierror(999, "1x1 Matrix expected for bagOfFeatures argument."); + return 0; + } + bagOfFeaturesLocation = pstData[0]; + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 4, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 4, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + classifierDescription = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + classifierDescription[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 4, &iRows, &iCols, piLen, classifierDescription); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + // Handling the argument 3. + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(isIntegerType(pvApiCtx, piAddr)||isDoubleType(pvApiCtx, piAddr)) + { + if(isScalar(pvApiCtx, piAddr)) + { + double dData = 0; + iRet = getScalarDouble(pvApiCtx, piAddr, &dData); + if(!iRet) + { + k = (int)dData; + } + } + else + { + Scierror(999, "Error: The input argument #3 is not of scalar type.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument #3 is not of type Integer.\n"); + return 0; + } + + // Handling missing yml file + if(!file_exists_check(classifierLocation)) + { + Scierror(999, "Error: Input "".yml"" File not found in the pwd.\n"); + return 0; + } + + // sciprint("arguments done"); + //------Actual processing------// + sciprint("\n%s\n", bagOfFeaturesLocation); + FileStorage fs(bagOfFeaturesLocation, FileStorage::READ); + fs["dictionary"] >> dictionary; + fs.release(); + dictionarySize = dictionary.rows; + try + { + bowDE->setVocabulary(dictionary); + } + catch(Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + return 0; + } + // sciprint("\n%d %d\n", input.rows, input.cols); + // Convert input image to and detect keypoints. + try + { + input.convertTo(input, CV_8U); + detector->detect(input, keyPoints); + } + catch(Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + return 0; + } + // Compute features. + try + { + bowDE->compute(input, keyPoints, features); + } + catch(Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + return 0; + } + // Load and train classifier. + try + { + Ptr knearest = Algorithm::load(classifierLocation); + if(!knearest->isTrained()) + { + Scierror(999, "Error: the model is not trained!.\n"); + return 0; + } + features.convertTo(features, CV_32F); + knearest->setIsClassifier(1); + knearest->findNearest(features, k, response); + } + catch(cv::Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + return 0; + } + + //------Create output arguments------// + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfStringInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 1, 1, 1, &classifierDescription[(int)response.at(0, 0)]); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //------Return Arguments------// + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } + /* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_predictLR.cpp b/sci_gateway/cpp/opencv_predictLR.cpp new file mode 100644 index 0000000..c4cafc5 --- /dev/null +++ b/sci_gateway/cpp/opencv_predictLR.cpp @@ -0,0 +1,295 @@ +/*************************************************** +Author : Siddhant Narang +***************************************************/ +#include +#include +#include +#include +#include +#include +#include +#include + +inline bool file_exists_check(const std::string& name) +{ + struct stat buffer; + return (stat (name.c_str(), &buffer) == 0); +} + +using namespace std; +using namespace cv; +using namespace cv::xfeatures2d; +using namespace cv::ml; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + int opencv_predictLR(char *fname, unsigned long fname_len) + { + // Error management variables + SciErr sciErr; + + //------Local variables------// + int upright = 1; + Ptr matcher = DescriptorMatcher::create("FlannBased"); + Ptr detector = SURF::create(400, 4, 2, 1, upright); + Ptr extractor = detector; + Ptr bowDE = makePtr(extractor, matcher); + char *classifierLocation = NULL; + Mat dictionary, features; + Mat response; + vector keyPoints; + int iPrec = 0; + int dictionarySize; + int *piAddr = NULL; + int *piChild = NULL; + int iRows, iCols; + char **pstData = NULL; + int *piLen = NULL; + char **classifierDescription = NULL; + int classifierDescriptionCount; + char *bagOfFeaturesLocation = NULL; + int descriptionCount; + Mat input; + + //------Check number of parameters------// + CheckInputArgument(pvApiCtx, 2, 2); + CheckOutputArgument(pvApiCtx, 1, 1); + + //------Get input arguments------// + retrieveImage(input,2); + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isListType(pvApiCtx, piAddr)) + { + Scierror(999, "Error: The input argument #1 is not of type classifier.\n"); + return 0; + } + + // Extracting object type and checking if type is classifier + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + pstData[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1));//+ 1 for null termination + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!(strcmp(pstData[0],"LRClassifier"))) + { + Scierror(999, "Error: The input argument #1 is not of type classifier.\n"); + return 0; + } + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows!=1 || iCols!=1) + { + Scierror(999, "1x1 Matrix expected for classifier argument."); + return 0; + } + classifierLocation = pstData[0]; + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 3, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 3, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 3, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows!=1 || iCols!=1) + { + Scierror(999, "1x1 Matrix expected for bagOfFeatures argument."); + return 0; + } + bagOfFeaturesLocation = pstData[0]; + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 4, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 4, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + classifierDescription = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + classifierDescription[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 4, &iRows, &iCols, piLen, classifierDescription); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Handling missing yml file + if(!file_exists_check(classifierLocation)) + { + Scierror(999, "Error: Input "".yml"" File not found in the pwd.\n"); + return 0; + } + + //------Actual processing------// + FileStorage fs(bagOfFeaturesLocation, FileStorage::READ); + fs["dictionary"] >> dictionary; + fs.release(); + dictionarySize = dictionary.rows; + try + { + bowDE->setVocabulary(dictionary); + } + catch(Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + } + + // Convert input image to and detect keypoints. + try + { + input.convertTo(input, CV_8U); + detector->detect(input, keyPoints); + } + catch(Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + } + // Compute features. + try + { + bowDE->compute(input, keyPoints, features); + } + catch(Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + } + + // Load classifier + Ptr lr = Algorithm::load(classifierLocation); + + try + { + if(!lr->isTrained()) + { + Scierror(999, "Error: the model is not trained!.\n"); + return 0; + } + features.convertTo(features, CV_32F); + lr->predict(features, response); + } + catch(cv::Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + } + + //------Create output arguments------// + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 1, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + createMatrixOfStringInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 1, 1, 1, &classifierDescription[response.at(0)]); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //------Return Arguments------// + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx)+1; + ReturnArguments(pvApiCtx); + return 0; + } + /* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_reconstructScene.cpp b/sci_gateway/cpp/opencv_reconstructScene.cpp new file mode 100644 index 0000000..a6060e7 --- /dev/null +++ b/sci_gateway/cpp/opencv_reconstructScene.cpp @@ -0,0 +1,126 @@ +/*************************************************** +Author : Siddhant Narang +***************************************************/ +#include +#include +#include +#include +#include +#include +#include +#include + + +using namespace cv; +using namespace std; +using namespace cv::ximgproc; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_reconstructScene(char *fname, unsigned long fname_len) + { + SciErr sciErr; + + /*----- Local Variables -----*/ + int *piAddr = NULL; + int *piLen = NULL; + int iRows = 0, iCols = 0; + int iType = 0; + + /*----- Check for input and output arguments -----*/ + CheckInputArgument(pvApiCtx, 3, 3); + CheckOutputArgument(pvApiCtx, 1, 1); + + /*----- Taking input arguments -----*/ + double *Q = NULL; + int handleMissigValues = 0; + + + // Input Arguments one + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //check type + sciErr = getVarType(pvApiCtx, piAddr, &iType); + if(sciErr.iErr || iType != sci_matrix) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &Q); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + double temp[iCols][iRows]; + int k = 0, j = 0; + for(int i = 0; i < iRows; i++) + { + for(int i = 0; i < iRows; i++) + { + temp[i][j] = Q[(j * 4) + i]; + } + } + Mat _Q(iRows, iCols, CV_8UC1, &temp); + + // Input Arguments two + Mat _disparity; + retrieveImage(_disparity, 2); + + // Input Arguments three + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + getScalarInteger32(pvApiCtx, piAddr, &handleMissigValues); + sciprint("%d", handleMissigValues); + + if(handleMissigValues < 0 && handleMissigValues > 1) + { + Scierror(999, "Invalid Value for handleMissigValues. Please enter a non negative Integer value which is divisible by 16.\n"); + return 0; + } + + Mat image3DOCV; + try + { + reprojectImageTo3D(_disparity, image3DOCV, _Q, true, CV_64F); + sciprint("%d", image3DOCV.rows); + } + catch(Exception &e) + { + Scierror(999, "%s", e.what()); + return 0; + } + + /*----- Creating output images -----*/ + string tempstring = type2str(image3DOCV.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker, image3DOCV, 1); + free(checker); + + /*----- Returning output -----*/ + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } +} \ No newline at end of file diff --git a/sci_gateway/cpp/opencv_scharr.cpp b/sci_gateway/cpp/opencv_scharr.cpp new file mode 100644 index 0000000..2c2c08b --- /dev/null +++ b/sci_gateway/cpp/opencv_scharr.cpp @@ -0,0 +1,165 @@ +/**************************************************************** +Author: Sukul Bagai +***************************************************************** +return_image = scharr(input_image, ddepth, dx, dy, scale, delta); +*****************************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_scharr(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int intErr = 0; + int iRows = 0,iCols = 0; + int *piLen = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + char **ddepth = NULL; + int i,j,k; + double dx,dy,scale,delta; + + + //checking input argument + CheckInputArgument(pvApiCtx, 6, 6); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + + //for ddepth + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + ddepth = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + ddepth[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, ddepth); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //for value of dx + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr3, &dx); + if(intErr) + return intErr; + + //for value of dy + sciErr = getVarAddressFromPosition(pvApiCtx,4,&piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr4, &dy); + if(intErr) + return intErr; + + //for value of scale + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr5); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr5, &scale); + if(intErr) + return intErr; + + //for value of delta + sciErr = getVarAddressFromPosition(pvApiCtx,6,&piAddr6); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + intErr = getScalarDouble(pvApiCtx, piAddr6, &delta); + if(intErr) + return intErr; + + Mat new_image(image.rows,image.cols,image.type()); + + try + { + if(strcmp(ddepth[0], "CV_8U") == 0) + Scharr(image, new_image, CV_8U, dx, dy, scale, delta); + else if(strcmp(ddepth[0], "CV_16U") == 0) + Scharr(image, new_image, CV_16U, dx, dy, scale, delta); + else if(strcmp(ddepth[0],"CV_16S") == 0) + Scharr(image, new_image, CV_16S, dx, dy, scale, delta); + else if(strcmp(ddepth[0], "CV_32F") == 0) + Scharr(image, new_image, CV_32F, dx, dy, scale, delta); + else if(strcmp(ddepth[0], "CV_64F") == 0) + Scharr(image, new_image, CV_64F, dx, dy, scale, delta); + } + catch(Exception &err) + { + sciprint("%s", err.what()); + return 0; + } + + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_sepFilter2D.cpp b/sci_gateway/cpp/opencv_sepFilter2D.cpp new file mode 100644 index 0000000..3994ada --- /dev/null +++ b/sci_gateway/cpp/opencv_sepFilter2D.cpp @@ -0,0 +1,271 @@ +/*************************************************** +Author : Sukul Bagai +**************************************************** +Usage : return_image = sepFilter2D(input_image,"",kernel_x,kernel_y,anchor_x,anchor_y,delta,""); +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_sepFilter2D(char *fname, unsigned long fname_len) + { + SciErr sciErr; + int intErr=0; + int iRows=0,iCols=0; + int *piLen = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + int *piAddr5 = NULL; + int *piAddr6 = NULL; + int *piAddr7 = NULL; + int *piAddr8 = NULL; + char **ddepth = NULL; + char **borderType = NULL; + int i,j,k; + double *kernelX,*kernelY,anchorX,anchorY,delta,border,actualddepth; + + //checking input argument + CheckInputArgument(pvApiCtx, 8, 8); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + Mat image; + retrieveImage(image,1); + cout<<"here1"; + //for ddepth + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + //Now, we will retrieve the string from the input parameter. For this, we will require 3 calls + //first call to retrieve dimensions + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + //second call to retrieve length of each string + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + ddepth = (char**)malloc(sizeof(char*) * iRows * iCols); + for(i = 0 ; i < iRows * iCols ; i++) + ddepth[i] = (char*)malloc(sizeof(char) * (piLen[i] + 1));//+ 1 for null termination + //third call to retrieve data + sciErr = getMatrixOfString(pvApiCtx, piAddr2, &iRows, &iCols, piLen, ddepth); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //for kernel_x matrix + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr3, &iRows, &iCols ,&kernelX); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + int n; + + if(iRows == 1) + n=iCols; + else + n=iRows; + double kernelArray_x[n]; + //assigning values to actual kernelMatrix + for(i=0;i= n || anchorY >=n) + { + sciprint("Invalid anchor point given. Centre point (-1,-1) was used instead"); + anchorY = -1; + anchorX = -1; + } + Mat new_image(image.rows,image.cols,image.type()); + Point pt(anchorX,anchorY); + + if(strcmp(ddepth[0],"CV_8U")==0) + actualddepth=CV_8U; + else if(strcmp(ddepth[0],"CV_16U")==0) + actualddepth=CV_16U; + else if(strcmp(ddepth[0],"CV_16S")==0) + actualddepth=CV_16S; + else if(strcmp(ddepth[0],"CV_32F")==0) + actualddepth=CV_32F; + else if(strcmp(ddepth[0],"CV_64F")==0) + actualddepth=CV_64F; + else + { + sciprint("Invalid type %s used. CV_8U was used instead by default",ddepth[0]); + actualddepth = CV_8U; + } + if(strcmp(borderType[0], "BORDER_CONSTANT") == 0) + border = BORDER_CONSTANT; + else if(strcmp(borderType[0], "BORDER_REPLICATE") == 0) + border = BORDER_REPLICATE; + else if(strcmp(borderType[0], "BORDER_REFLECT") == 0) + border = BORDER_REFLECT; + else if(strcmp(borderType[0], "BORDER_REFLECT_101") == 0) + border = BORDER_REFLECT_101; + else if(strcmp(borderType[0], "BORDER_WRAP") == 0) + border = BORDER_WRAP; + else + border = BORDER_DEFAULT; + + //applying function + try + { + sepFilter2D(image,new_image,actualddepth,kernelMatrix_x,kernelMatrix_y,pt,delta,border); + } + catch(Exception &e) + { + sciprint("%s", e.what()); + return 0; + } + + + //returning image + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_stereoCalibrateAndRect.cpp b/sci_gateway/cpp/opencv_stereoCalibrateAndRect.cpp new file mode 100644 index 0000000..8d76d36 --- /dev/null +++ b/sci_gateway/cpp/opencv_stereoCalibrateAndRect.cpp @@ -0,0 +1,609 @@ +/*************************************************** +Author : Siddhant Narang +***************************************************/ + +#include +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include "opencv2/core/types_c.h" +#include + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + double* readDoubleArrayFromScilab(int cnt) + { + SciErr sciErr; + int *piAddr = NULL; + double val = 0; + int intErr = 0; + int iRows = 0, iCols = 0; + double *pdblReal; + + sciErr = getVarAddressFromPosition(pvApiCtx, cnt, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &pdblReal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(isDoubleType(pvApiCtx, piAddr)) + { + return pdblReal; + } + else + { + sciprint("Error: the %d argument is not of the type double!", cnt); + return 0; + } + } + + + int opencv_stereoCalibrateAndRect(char *fname, unsigned long fname_len) + { + /*----- Local variables -----*/ + int i = 0, j = 0, k = 0, n = 0, m = 0; + int iRows = 0, iCols = 0; + int *piAddr = NULL; + double *pdblReal = NULL; + double x, y, rms; + SciErr sciErr; + + /*----- Processing Variables -----*/ + vector >imagePoints1(1), imagePoints2(1); + vector >objectPoints(1); + Mat cameraMatrix1; + Mat cameraMatrix2; + Mat distCoeffsActual1; + Mat distCoeffsActual2; + Mat rotationMatrix; + Mat translationVector; + Mat fundamentalMatrix; + int num; + double width = 0, height = 0; + + /*----- Check for input and output arguments -----*/ + CheckInputArgument(pvApiCtx, 4, 8); + CheckOutputArgument(pvApiCtx, 9, 9); + + /*----- Parsing input -----*/ + n = *getNbInputArgument(pvApiCtx); + + // First + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &pdblReal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Stores + for(i = 0; i < iRows; ++i) + { + objectPoints[0].push_back(Point3f(float(pdblReal[(0 * iRows) + i]), + float(pdblReal[(1 * iRows) + i]), + 0.0)); + } + int pointCount = iRows; + + // Second Input + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isListType(pvApiCtx, piAddr)) + { + Scierror(999,"The imagePoints1 Argument must be a list of points.\n"); + return 0; + } + sciErr = getListItemNumber(pvApiCtx, piAddr, &num); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Get items from list + for(int i = 1; i <= num; i++) + { + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr, i, &iRows, &iCols, &pdblReal); + + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + for(j = 0; j < iRows; ++j) + { + imagePoints1[i-1].push_back(Point2f(float(pdblReal[(0 * iRows) + j]), + float(pdblReal[(1 * iRows) + j]))); + } + } + + // Third input + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isListType(pvApiCtx, piAddr)) + { + Scierror(999,"\nthe imagePoints2 Argument must be a list of points \n"); + return 0; + } + sciErr = getListItemNumber(pvApiCtx, piAddr, &num); + if(sciErr.iErr) + { + printError(&sciErr, 0); + + return 0; + } + + // Get items from list + for(int i = 1; i <= num; i++) + { + sciErr = getMatrixOfDoubleInList(pvApiCtx, piAddr,i,&iRows, &iCols, &pdblReal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + for(j = 0; j < iRows; ++j) + { + imagePoints2[i - 1].push_back(Point2f(float(pdblReal[(0 * iRows) + j]), + float(pdblReal[(1 * iRows) + j]))); + } + } + + // Fourth + sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &pdblReal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(iCols > 2 && iRows > 1) + { + Scierror(999, "The image size should be of a grayscale image.\n"); + return 0; + } + + sciprint("\nImage size obtained\n"); + width = pdblReal[0]; + height = pdblReal[1]; + + /*----- Getting optional arguments -----*/ + int p; + switch(n) + { + case 5: + pdblReal = readDoubleArrayFromScilab(4); + for(i = 0; i < 3; i++) + { + for(j = 0; j < 3; j++) + { + cameraMatrix1.at(i, j) = pdblReal[(j * 3) + i]; + } + } + break; + + case 6: + pdblReal = readDoubleArrayFromScilab(4); + for(i = 0; i < 3; i++) + { + for(j = 0; j < 3; j++) + { + cameraMatrix1.at(i, j) = pdblReal[(j * 3) + i]; + } + } + + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &pdblReal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows== 1) + { + p = iCols; + } + else if (iCols == 1) + { + p = iRows; + } + else + { + Scierror(1,"Distortion Points matrix (arg 5) must be a 1 X N or N X 1 matrix"); + return 0; + } + if(p == 4 or p == 5 or p == 8); + else + { + Scierror(1," N must be 4 or 5 or 8"); + return 0; + } + break; + + case 7: + pdblReal = readDoubleArrayFromScilab(4); + for(i = 0; i < 3; i++) + { + for(j = 0; j < 3; j++) + { + cameraMatrix1.at(i, j) = pdblReal[(j * 3) + i]; + } + } + + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &pdblReal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows == 1) + { + p = iCols; + } + else if (iCols == 1) + { + p = iRows; + } + else + { + Scierror(1,"Distortion Points matrix (arg 5) must be a 1 X N or N X 1 matrix"); + return 0; + } + if(p == 4 or p == 5 or p == 8); + else + { + Scierror(1," N must be 4 or 5 or 8"); + return 0; + } + for(i = 0; i < p; i++){ + distCoeffsActual1.at(0, i) = pdblReal[i]; + } + pdblReal=readDoubleArrayFromScilab(6); + for(i = 0; i < 3; i++) + { + for(j = 0; j < 3; j++) + { + cameraMatrix2.at(i,j) = pdblReal[(j * 3) + i]; + } + } + break; + + case 8: + pdblReal = readDoubleArrayFromScilab(4); + for(i = 0; i < 3; i++) + { + for(j = 0; j < 3; j++) + { + cameraMatrix1.at(i, j) = pdblReal[(j * 3) + i]; + } + } + + sciErr = getVarAddressFromPosition(pvApiCtx,5,&piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &pdblReal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows == 1) + { + p = iCols; + } + else if (iCols == 1) + { + p = iRows; + } + else + { + Scierror(1,"Distortion Points matrix (arg 5) must be a 1 X N or N X 1 matrix"); + return 0; + } + if(p == 4 or p == 5 or p == 8); + else{ + Scierror(1," N must be 4 or 5 or 8"); + return 0; + } + for(i = 0; i < p; i++) + { + distCoeffsActual1.at(0, i) = pdblReal[i]; + } + + pdblReal = readDoubleArrayFromScilab(6); + for(i = 0; i < 3; i++) + { + for(j = 0; j < 3; j++) + { + cameraMatrix2.at(i,j) = pdblReal[(j * 3) + i]; + } + } + sciErr = getVarAddressFromPosition(pvApiCtx,7,&piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &pdblReal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows == 1) + p = iCols; + else if (iCols == 1) + p = iRows; + else + { + Scierror(1,"Distortion Points matrix (arg 5) must be a 1 X N or N X 1 matrix"); + return 0; + } + if(p == 4 or p == 5 or p == 8); + else + { + Scierror(1," N must be 4 or 5 or 8"); + return 0; + } + for(i = 0; i < p; i++) + { + distCoeffsActual2.at(0, i) = pdblReal[i]; + } + break; + } + + Size imageSize (width, height); + Mat E; + try + { + rms = stereoCalibrate(objectPoints, imagePoints1, imagePoints2, + cameraMatrix1, distCoeffsActual1, + cameraMatrix2, distCoeffsActual2, + imageSize, rotationMatrix, + translationVector, E, fundamentalMatrix, + CV_CALIB_SAME_FOCAL_LENGTH | CV_CALIB_ZERO_TANGENT_DIST, + cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, 1e-5) + ); + } + catch(cv::Exception& e) + { + sciprint("%s", e.what()); + return 0; + } + + Mat R1, R2, P1, P2, Q; + try + { + stereoRectify(cameraMatrix1, distCoeffsActual1, + cameraMatrix2, distCoeffsActual2, + imageSize, rotationMatrix, + translationVector, R1, R2, P1, P2, Q, + CALIB_ZERO_DISPARITY); + } + catch(cv::Exception& e) + { + sciprint("%s", e.what()); + return 0; + } + + sciprint("Calibration done with RMS error=%f\n", rms); + + /*----- Return Arguments to Scilab -----*/ + double *pstdata1 = NULL; + double *pstdata2 = NULL; + double *pstdata3 = NULL; + double *pstdata4 = NULL; + double *pstdata5 = NULL; + double *pstdata6 = NULL; + double *pstdata7 = NULL; + double *pstdata8 = NULL; + double *pstdata9 = NULL; + + pstdata1 = (double*)malloc(sizeof(double) * 3 * 3); + + for(i = 0; i < 3; i++) + { + for(j = 0; j < 3; j++) + { + pstdata1[(j * 3) + i] = cameraMatrix1.at(i, j); + } + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 3, 3, pstdata1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstdata2 = (double*)malloc(sizeof(double) * 4 * 1); + for(i = 0; i < 4; i++) + { + pstdata2[i] = distCoeffsActual1.at(0, i); + } + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 2, 1, 4, pstdata2); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstdata3 = (double*)malloc(sizeof(double) * 3 * 3); + + for(i = 0; i < 3; i++) + { + for(j = 0; j < 3; j++) + { + pstdata3[(j * 3) + i] = cameraMatrix2.at(i,j); + } + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 3, 3, 3, pstdata3); + if(sciErr.iErr){ + printError(&sciErr, 0); + return 0; + } + + pstdata4 = (double*)malloc(sizeof(double) * 4 * 1); + for(i = 0; i < 4; i++) + { + pstdata4[i] = distCoeffsActual2.at(0, i); + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 4, 1, 4, pstdata4); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstdata5 = (double*)malloc(sizeof(double) *rotationMatrix.rows*rotationMatrix.cols); + for(i = 0; i < rotationMatrix.rows; i++) + { + for(j = 0; j < rotationMatrix.cols; j++) + { + pstdata5[(j * rotationMatrix.rows) + i] = rotationMatrix.at(i, j); + } + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 5, rotationMatrix.rows, rotationMatrix.cols, pstdata5); + if(sciErr.iErr){ + printError(&sciErr, 0); + return 0; + } + + pstdata6 = (double*)malloc(sizeof(double) * translationVector.rows * translationVector.cols); + + for(i = 0; i < translationVector.rows; i++) + { + for(j = 0; j < translationVector.cols; j++) + { + pstdata6[(j * translationVector.rows) + i] = translationVector.at(i, j); + } + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 6, translationVector.rows, translationVector.cols, pstdata6); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstdata7 = (double*)malloc(sizeof(double) * Q.rows * Q.cols); + for(i = 0; i < Q.rows; i++) + { + for(j = 0; j < Q.cols; j++) + { + pstdata7[(j * Q.rows) + i] = Q.at(i, j); + } + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 7, Q.rows, Q.cols, pstdata7); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstdata8 = (double*)malloc(sizeof(double) * P1.rows * P1.cols); + for(i = 0; i < P1.rows; i++) + { + for(j = 0; j < P1.cols; j++) + { + pstdata8[(j * P1.rows) + i] = P1.at(i, j); + } + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 8, P1.rows, P1.cols, pstdata8); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstdata9 = (double*)malloc(sizeof(double) * P2.rows * P2.cols); + for(i = 0; i < P2.rows; i++) + { + for(j = 0; j < P2.cols; j++) + { + pstdata9[(j * P2.rows) + i] = P2.at(i, j); + } + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 9, P2.rows, P2.cols, pstdata9); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + for(int i = 1; i <= 9; i++) + { + AssignOutputVariable(pvApiCtx, i) = nbInputArgument(pvApiCtx) + i; + } + + /*----- Returning the Output Variables as arguments to the Scilab environment -----*/ + ReturnArguments(pvApiCtx); + return 0; + } +} diff --git a/sci_gateway/cpp/opencv_trainEMClassifier.cpp b/sci_gateway/cpp/opencv_trainEMClassifier.cpp new file mode 100644 index 0000000..e491ae0 --- /dev/null +++ b/sci_gateway/cpp/opencv_trainEMClassifier.cpp @@ -0,0 +1,519 @@ +/*************************************************** +Author : Siddhant Narang +***************************************************/ +#include +#include +#include +#include +#include +#include +#include + + +inline bool file_exists_check(const std::string& name) +{ + /*----- This function checks if the input file exists in the mentioned wd -----*/ + struct stat buffer; + return (stat (name.c_str(), &buffer) == 0); +} + + +using namespace std; +using namespace cv; +using namespace cv::xfeatures2d; +using namespace cv::ml; + + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + + + int opencv_trainEMClassifier(char *fname, unsigned long fname_len) + { + // Error management variables + SciErr sciErr; + + //------Local variables------// + int upright = 1; + Ptr matcher = DescriptorMatcher::create("FlannBased"); + Ptr detector = SURF::create(400, 4, 2, 1, upright); + Ptr extractor = detector; + Ptr bowDE = makePtr(extractor, matcher); + char *fileName = NULL; + Mat dictionary,inp,features; + vector keyPoints; + + int iRet = 0; + int iPrec = 0; + int iBool = 0; + int *piAddr = NULL; + int *piChild = NULL; + int iRows, iCols; + char **pstData = NULL; + int *piLen = NULL; + int *count = NULL; + char **description = NULL; + char ***location = NULL; + char *bagOfFeaturesLocation = NULL; + int descriptionCount; + + int nClusters = 10; + int iType = 0; + char *classifierName = NULL; + + char *classifierLocation = NULL; + char *objectType = "EMClassifier"; + + //------Check number of parameters------// + CheckInputArgument(pvApiCtx, 3, 4); + CheckOutputArgument(pvApiCtx, 1, 1); + + //------Get input arguments------// + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isListType(pvApiCtx, piAddr)) + { + Scierror(999, "Error: The input argument #1 is not of type imageSet.\n"); + return 0; + } + + // Extracting object type and checking if type is imageSet + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + pstData[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1)); // + 1 for null termination + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!(strcmp(pstData[0],"imageSet")==0)) + { + Scierror(999, "Error: The input argument #1 is not of type imageSet.\n"); + return 0; + } + + // Extracting Description attribute of input argument + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + description = (char**)malloc(sizeof(char*) * iRows * iCols); + + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + description[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1));//+ 1 for null termination + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, description); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + descriptionCount = iRows; + + // Extracting Count attribute of input argument + sciErr = getMatrixOfInteger32InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &count); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + location = (char***) malloc(sizeof(char**) * descriptionCount); + + // Extracting Location of images from input argument. + sciErr = getListItemAddress(pvApiCtx, piAddr, 4, &piChild); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + for(int iter = 1; iter <= descriptionCount; iter++) + { + sciErr = getMatrixOfStringInList(pvApiCtx, piChild, iter, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piChild, iter, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + location[iter-1] = (char**)malloc(sizeof(char*) * iRows * iCols); + + for(int colIter = 0 ; colIter < iRows * iCols ; colIter++) + { + location[iter-1][colIter] = (char*)malloc(sizeof(char) * (piLen[colIter] + 1));//+ 1 for null termination + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piChild, iter, &iRows, &iCols, piLen, location[iter-1]); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + // Check is imageSet is in pwd. + if(!file_exists_check(location[0][1])) + { + Scierror(999, "Error: Image set not found in the pwd.\n"); + return 0; + } + + // Second argument + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isListType(pvApiCtx, piAddr)) + { + Scierror(999, "Error: The input argument #2 is not of type bagOfFeatures.\n"); + return 0; + } + + // Extracting object type and checking if type is bagOfFeatures + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + pstData[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1)); //+ 1 for null termination + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!(strcmp(pstData[0],"bagOfFeatures") == 0)) + { + Scierror(999, "Error: The input argument #2 is not of type bagOfFeatures.\n"); + return 0; + } + + // Extracting name of next argument takes three calls to getMatrixOfString + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows != 1 || iCols != 1) + { + Scierror(999, "1x1 Matrix expected for bagOfFeatures argument."); + return 0; + } + bagOfFeaturesLocation = pstData[0]; + + // Check is bagOfFeatures.yml is in pwd. + if(!file_exists_check(bagOfFeaturesLocation)) + { + Scierror(999, "Error: Bag of Features "".yml"" file not found in the pwd.\n"); + return 0; + } + + // Handling 3rd argument. + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(isStringType(pvApiCtx, piAddr)) + { + if(isScalar(pvApiCtx, piAddr)) + { + char* stringData = NULL; + + iRet = getAllocatedSingleString(pvApiCtx, piAddr, &stringData); + if(!iRet) + { + classifierName = stringData; + } + else + { + Scierror(999, "Error: Could not assign input arg #3 to local var.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument #3 is not scalar type.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument #3 is not a string.\n"); + return 0; + } + + // Handling more than 3 arguments. + if(*getNbInputArgument(pvApiCtx) > 3) + { + sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(isIntegerType(pvApiCtx, piAddr) || isDoubleType(pvApiCtx, piAddr)) + { + if(isScalar(pvApiCtx, piAddr)) + { + double dData = 0; + iRet = getScalarDouble(pvApiCtx, piAddr, &dData); + if(!iRet) + { + nClusters = dData; + } + } + else + { + Scierror(999, "Error: The input argument #4 is not of scalar type.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument #4 is not of type Integer.\n"); + return 0; + } + } + + classifierLocation = classifierName; + strcat(classifierLocation, ".yml"); + + //------Actual processing------// + FileStorage fs(bagOfFeaturesLocation, FileStorage::READ); + fs["dictionary"] >> dictionary; + fs.release(); + if(dictionary.rows == 0 || dictionary.cols == 0) + { + sciprint("Error: The provided file for bagOfFeatures may be invalid.\n"); + } + sciprint("Training an EM classifier for %d clusters.\n", nClusters); + sciprint("-------------------------------------------------------\n\n"); + int dictionarySize = dictionary.rows; + sciprint("%d", dictionary.rows); + Mat trainingData(0, dictionarySize, CV_32FC1); + bowDE->setVocabulary(dictionary); + + for(int i = 0; i < descriptionCount; i++) + { + sciprint("# Encoding features for Class %d ...", i + 1); + for(int j = 0; j < count[i]; j++) + { + features.release(); + keyPoints.clear(); + fileName = location[i][j]; + inp = imread(fileName); + try + { + detector->detect(inp, keyPoints); + } + catch(cv::Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + } + try + { + bowDE->compute(inp, keyPoints, features); + } + catch(cv::Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + } + trainingData.push_back(features); + // labels.push_back((float) i); + } + sciprint("done.\n"); + } + sciprint("\n# Training the EM classifier..."); + + Ptr em = EM::create(); + Mat labels; + + try + { + em->setClustersNumber(nClusters); + } + catch(cv::Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + return 0; + } + + const cv::TermCriteria& termCrit=cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, + 100, 0.0000001); + try + { + em->setTermCriteria(termCrit); + } + catch(cv::Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + return 0; + } + + try + { + em->trainEM(trainingData, noArray(), labels, noArray()); + } + catch(cv::Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + return 0; + } + + sciprint("\n# Saving the EM classifier..."); + try + { + em->save(classifierLocation); + } + catch(cv::Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + return 0; + } + sciprint("done.\n"); + + //------Create output arguments------// + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 4, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfStringInList(pvApiCtx, nbInputArgument(pvApiCtx)+1, piAddr, 1, 1, 1, &objectType); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfStringInList(pvApiCtx, nbInputArgument(pvApiCtx)+1, piAddr, 2, 1, 1, &classifierLocation); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfStringInList(pvApiCtx, nbInputArgument(pvApiCtx)+1, piAddr, 3, 1, 1, &bagOfFeaturesLocation); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfStringInList(pvApiCtx, nbInputArgument(pvApiCtx)+1, piAddr, 4, descriptionCount, 1, description); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //------Return Arguments------// + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx)+1; + ReturnArguments(pvApiCtx); + return 0; + } + /* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_trainKNNClassifier.cpp b/sci_gateway/cpp/opencv_trainKNNClassifier.cpp new file mode 100644 index 0000000..a6010e9 --- /dev/null +++ b/sci_gateway/cpp/opencv_trainKNNClassifier.cpp @@ -0,0 +1,517 @@ +/*************************************************** +Author : Siddhant Narang +***************************************************/ +#include +#include +#include +#include +#include +#include +#include +#include + + +inline bool file_exists_check(const std::string& name) +{ + /*----- Checks if the input file is in the mentioned wd ------*/ + struct stat buffer; + return (stat (name.c_str(), &buffer) == 0); +} + + +using namespace std; +using namespace cv; +using namespace cv::xfeatures2d; +using namespace cv::ml; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + + + int opencv_trainKNNClassifier(char *fname, unsigned long fname_len) + { + // Error management variables + SciErr sciErr; + + /*------ Local variables ------*/ + int upright = 1; + Ptr matcher = DescriptorMatcher::create("FlannBased"); + Ptr detector = SURF::create(400, 4, 2, 1, upright); + Ptr extractor = detector; + Ptr bowDE = makePtr(extractor, matcher); + char *fileName = NULL; + Mat dictionary,inp,features; + vector keyPoints; + + int iRet = 0; + + int iBool = 0; + + int *piAddr = NULL; + int *piChild = NULL; + int iRows, iCols; + char **pstData = NULL; + int *piLen = NULL; + int *count = NULL; + char **description = NULL; + char ***location = NULL; + char *bagOfFeaturesLocation = NULL; + int descriptionCount; + + int algorithmType = 1; + + char *classifierLocation; + char *classifierName = NULL; + char *objectType = "KNNClassifier"; + + /*------ Check number of parameters ------*/ + CheckInputArgument(pvApiCtx, 3, 4); + CheckOutputArgument(pvApiCtx, 1, 1); + + /*------ Get input arguments ------*/ + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isListType(pvApiCtx, piAddr)) + { + Scierror(999, "Error: The input argument #1 is not of type imageSet.\n"); + return 0; + } + + /*----- Extracting object type and checking if type is imageSet -----*/ + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + pstData[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1)); // + 1 for null termination + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!(strcmp(pstData[0],"imageSet")==0)) + { + Scierror(999, "Error: The input argument #1 is not of type imageSet.\n"); + return 0; + } + + /*----- Extracting Description attribute of input argument -----*/ + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + description = (char**)malloc(sizeof(char*) * iRows * iCols); + + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + description[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1));//+ 1 for null termination + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, description); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + descriptionCount = iRows; + + /*----- Extracting Count attribute of input argument -----*/ + sciErr = getMatrixOfInteger32InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &count); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + location = (char***) malloc(sizeof(char**) * descriptionCount); + + /*----- Extracting Location of images from input argument. -----*/ + sciErr = getListItemAddress(pvApiCtx, piAddr, 4, &piChild); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + for(int iter = 1; iter<=descriptionCount; iter++) + { + sciErr = getMatrixOfStringInList(pvApiCtx, piChild, iter, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piChild, iter, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + location[iter-1] = (char**)malloc(sizeof(char*) * iRows * iCols); + + for(int colIter = 0 ; colIter < iRows * iCols ; colIter++) + { + location[iter-1][colIter] = (char*)malloc(sizeof(char) * (piLen[colIter] + 1));//+ 1 for null termination + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piChild, iter, &iRows, &iCols, piLen, location[iter-1]); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + // Check is imageSet is in its specified location. + if(!file_exists_check(location[0][1])) + { + Scierror(999, "Error: Image set not found in the pwd.\n"); + return 0; + } + + // Second argument + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isListType(pvApiCtx, piAddr)) + { + Scierror(999, "Error: The input argument #2 is not of type bagOfFeatures.\n"); + return 0; + } + + /*----- Extracting object type and checking if type is bagOfFeatures -----*/ + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + pstData[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1)); //+ 1 for null termination + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!(strcmp(pstData[0],"bagOfFeatures")==0)) + { + Scierror(999, "Error: The input argument #2 is not of type bagOfFeatures.\n"); + return 0; + } + // Extracting name of next argument takes three calls to getMatrixOfString + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows != 1 || iCols != 1) + { + Scierror(999, "1x1 Matrix expected for bagOfFeatures argument."); + return 0; + } + + bagOfFeaturesLocation = pstData[0]; + + // Check is bagOfFeatures.yml is in its specified location. + if(!file_exists_check(bagOfFeaturesLocation)) + { + Scierror(999, "Error: Bag of Features "".yml"" file not found in the pwd.\n"); + return 0; + } + + // Handling 3rd argument. + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(isStringType(pvApiCtx, piAddr)) + { + if(isScalar(pvApiCtx, piAddr)) + { + char* stringData = NULL; + + iRet = getAllocatedSingleString(pvApiCtx, piAddr, &stringData); + if(!iRet) + { + classifierName = stringData; + } + else + { + Scierror(999, "Error: Could not assign input arg #3 to local var.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument #3 is not scalar type.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument #3 is not a string.\n"); + return 0; + } + + // Handling more than 3 arguments. + if(*getNbInputArgument(pvApiCtx) > 3) + { + sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(isIntegerType(pvApiCtx, piAddr) || isDoubleType(pvApiCtx, piAddr)) + { + + if(isScalar(pvApiCtx, piAddr)) + { + double dData = 0; + iRet = getScalarDouble(pvApiCtx, piAddr, &dData); + if(!iRet) + { + if(dData < 1 || dData > 2) + { + Scierror(999, "Error: The input argument ""algorithmType"" should be in range [1, 2].\n"); + return 0; + } + algorithmType = dData; + } + } + else + { + Scierror(999, "Error: The input argument #4 is not of scalar type.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument #4 is not of type Integer.\n"); + return 0; + } + } + + classifierLocation = classifierName; + strcat(classifierLocation, ".yml"); + + //------Actual processing------// + FileStorage fs(bagOfFeaturesLocation, FileStorage::READ); + fs["dictionary"] >> dictionary; + fs.release(); + if(dictionary.rows == 0 || dictionary.cols == 0) + { + sciprint("Error: The provided file for bagOfFeatures may be invalid.\n"); + } + sciprint("Training an KNN classifier for %d classes.\n",descriptionCount); + sciprint("-------------------------------------------------------\n\n"); + for(int i = 0; i < descriptionCount; i++) + { + sciprint("# Class %d: %s\n", i + 1, description[i]); + } + sciprint("\n"); + int dictionarySize = dictionary.rows; + Mat labels(0, 1, CV_32FC1); + Mat trainingData(0, dictionarySize, CV_32FC1); + bowDE->setVocabulary(dictionary); + + for(int i =0 ; i < descriptionCount; i++) + { + sciprint("# Encoding features for Class %d ...",i+1); + for(int j=0; jdetect(inp,keyPoints); + } + catch(cv::Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + return 0; + } + try + { + bowDE->compute(inp, keyPoints,features); + } + catch(cv::Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + return 0; + } + trainingData.push_back(features); + labels.push_back((float) i); + } + sciprint("done.\n"); + } + sciprint("\n# Training the KNN classifier..."); + + Ptr knearest = KNearest::create(); + + try + { + knearest->setAlgorithmType(algorithmType); + } + catch(cv::Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + return 0; + } + + try + { + knearest->train(trainingData, ml::ROW_SAMPLE, labels); + transpose(trainingData, trainingData); + } + catch(cv::Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + return 0; + } + + try + { + knearest->save(classifierLocation); + } + catch(cv::Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + return 0; + } + + /*------ Create output arguments ------*/ + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 4, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfStringInList(pvApiCtx, nbInputArgument(pvApiCtx)+1, piAddr, 1, 1, 1, &objectType); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfStringInList(pvApiCtx, nbInputArgument(pvApiCtx)+1, piAddr, 2, 1, 1, &classifierLocation); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfStringInList(pvApiCtx, nbInputArgument(pvApiCtx)+1, piAddr, 3, 1, 1, &bagOfFeaturesLocation); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfStringInList(pvApiCtx, nbInputArgument(pvApiCtx)+1, piAddr, 4, descriptionCount, 1, description); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + /*------ Return Arguments ------*/ + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx)+1; + ReturnArguments(pvApiCtx); + return 0; + } + /* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_trainLRClassifier.cpp b/sci_gateway/cpp/opencv_trainLRClassifier.cpp new file mode 100644 index 0000000..885b132 --- /dev/null +++ b/sci_gateway/cpp/opencv_trainLRClassifier.cpp @@ -0,0 +1,696 @@ +/*************************************************** +Author : Siddhant Narang +***************************************************/ +#include +#include +#include +#include +#include +#include + + +inline bool file_exists_check(const std::string& name) +{ + struct stat buffer; + return (stat (name.c_str(), &buffer) == 0); +} + + +using namespace std; +using namespace cv; +using namespace cv::xfeatures2d; +using namespace cv::ml; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + + + int opencv_trainLRClassifier(char *fname, unsigned long fname_len) + { + // Error management variables + SciErr sciErr; + + //------Local variables------// + int upright = 1; + Ptr matcher = DescriptorMatcher::create("FlannBased"); + Ptr detector = SURF::create(400, 4, 2, 1, upright); + Ptr extractor = detector; + Ptr bowDE = makePtr(extractor, matcher); + char *fileName = NULL; + Mat dictionary,inp,features; + vector keyPoints; + + int iRet = 0; + int iPrec = 0; + int iBool = 0; + int *piAddr = NULL; + int *piChild = NULL; + int iRows, iCols; + char **pstData = NULL; + int *piLen = NULL; + int *count = NULL; + char **description = NULL; + char ***location = NULL; + char *bagOfFeaturesLocation = NULL; + int descriptionCount; + + double learningRate = 1.0; + double iteration = 100; + double regularization = 1; + double trainMethod = 0; + double miniBatchSize = 10; + + char *classifierName = NULL; + char *classifierLocation = NULL; + char *objectType = "LRClassifier"; + //------Check number of parameters------// + CheckInputArgument(pvApiCtx, 2, 8); + CheckOutputArgument(pvApiCtx, 1, 1); + + //------Get input arguments------// + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isListType(pvApiCtx, piAddr)) + { + Scierror(999, "Error: The input argument #1 is not of type imageSet.\n"); + return 0; + } + + // Extracting object type and checking if type is imageSet + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + pstData[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1)); // + 1 for null termination + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!(strcmp(pstData[0],"imageSet")==0)) + { + Scierror(999, "Error: The input argument #1 is not of type imageSet.\n"); + return 0; + } + + // Extracting Description attribute of input argument + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + description = (char**)malloc(sizeof(char*) * iRows * iCols); + + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + description[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1));//+ 1 for null termination + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, description); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + descriptionCount = iRows; + + // Extracting Count attribute of input argument + sciErr = getMatrixOfInteger32InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &count); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + location = (char***) malloc(sizeof(char**) * descriptionCount); + + // Extracting Location of images from input argument. + sciErr = getListItemAddress(pvApiCtx, piAddr, 4, &piChild); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + for(int iter = 1; iter<=descriptionCount; iter++) + { + sciErr = getMatrixOfStringInList(pvApiCtx, piChild, iter, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piChild, iter, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + location[iter-1] = (char**)malloc(sizeof(char*) * iRows * iCols); + + for(int colIter = 0 ; colIter < iRows * iCols ; colIter++) + { + location[iter-1][colIter] = (char*)malloc(sizeof(char) * (piLen[colIter] + 1));//+ 1 for null termination + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piChild, iter, &iRows, &iCols, piLen, location[iter-1]); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + } + + // Check is imageSet is in pwd. + if(!file_exists_check(location[0][1])) + { + Scierror(999, "Error: Image set not found in the pwd.\n"); + return 0; + } + + // Second argument + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(!isListType(pvApiCtx, piAddr)) + { + Scierror(999, "Error: The input argument #2 is not of type bagOfFeatures.\n"); + return 0; + } + + // Extracting object type and checking if type is bagOfFeatures + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, NULL, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*)malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, NULL); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**)malloc(sizeof(char*) * iRows * iCols); + + for(int iter = 0 ; iter < iRows * iCols ; iter++) + { + pstData[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1)); //+ 1 for null termination + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, pstData); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!(strcmp(pstData[0],"bagOfFeatures")==0)) + { + Scierror(999, "Error: The input argument #2 is not of type bagOfFeatures.\n"); + return 0; + } + // Extracting name of next argument takes three calls to getMatrixOfString + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + piLen = (int*) malloc(sizeof(int) * iRows * iCols); + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + pstData = (char**) malloc(sizeof(char*) * iRows * iCols); + for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++) + { + pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1); + } + + sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows!=1 || iCols!=1) + { + Scierror(999, "1x1 Matrix expected for bagOfFeatures argument."); + return 0; + } + bagOfFeaturesLocation = pstData[0]; + + // Check is bagOfFeatures.yml is in pwd. + if(!file_exists_check(bagOfFeaturesLocation)) + { + Scierror(999, "Error: Bag of Features "".yml"" file not found in the pwd.\n"); + return 0; + } + + // Handling 3rd argument. + sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(isStringType(pvApiCtx, piAddr)) + { + if(isScalar(pvApiCtx, piAddr)) + { + char* stringData = NULL; + + iRet = getAllocatedSingleString(pvApiCtx, piAddr, &stringData); + if(!iRet) + { + classifierName = stringData; + } + else + { + Scierror(999, "Error: Could not assign input arg #3 to local var.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument #3 is not scalar type.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument #3 is not a string.\n"); + return 0; + } + + // Handling more than 3 arguments. + if(*getNbInputArgument(pvApiCtx) > 3) + { + sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(isDoubleType(pvApiCtx, piAddr)) + { + + if(isScalar(pvApiCtx, piAddr)) + { + double dData = 0; + iRet = getScalarDouble(pvApiCtx, piAddr, &dData); + if(!iRet) + { + learningRate = dData; + } + } + else + { + Scierror(999, "Error: The input argument #4 is not of scalar type.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument #4 is not of type Double.\n"); + return 0; + } + } + + // Handling more than 4 arguments. + if(*getNbInputArgument(pvApiCtx) > 4) + { + sciErr = getVarAddressFromPosition(pvApiCtx, 5, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(isIntegerType(pvApiCtx, piAddr) || isDoubleType(pvApiCtx, piAddr)) + { + + if(isScalar(pvApiCtx, piAddr)) + { + double dData = 0; + iRet = getScalarDouble(pvApiCtx, piAddr, &dData); + if(!iRet) + { + iteration = dData; + } + } + else + { + Scierror(999, "Error: The input argument #5 is not of scalar type.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument #5 is not of type Integer.\n"); + return 0; + } + } + + // Handling more than 5 arguments. + if(*getNbInputArgument(pvApiCtx) > 5) + { + sciErr = getVarAddressFromPosition(pvApiCtx, 6, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(isIntegerType(pvApiCtx, piAddr) || isDoubleType(pvApiCtx, piAddr)) + { + if(isScalar(pvApiCtx, piAddr)) + { + double dData = 0; + iRet = getScalarDouble(pvApiCtx, piAddr, &dData); + if(!iRet) + { + if(dData < -1 || dData > 1) + { + Scierror(999, "Error: The input argument ""regularization"" should be in range [-1, 1].\n"); + return 0; + } + + regularization = dData; + } + } + else + { + Scierror(999, "Error: The input argument #6 is not of scalar type.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument #6 is not of type Integer.\n"); + return 0; + } + } + + // Handling more than 6 arguments. + if(*getNbInputArgument(pvApiCtx) > 6) + { + sciErr = getVarAddressFromPosition(pvApiCtx, 7, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(isIntegerType(pvApiCtx, piAddr) || isDoubleType(pvApiCtx, piAddr)) + { + + if(isScalar(pvApiCtx, piAddr)) + { + double dData = 0; + iRet = getScalarDouble(pvApiCtx, piAddr, &dData); + if(!iRet) + { + if(!(dData >= 0 && dData <= 1)) + { + Scierror(999, "Error: The input argument ""trainMethod"" should be in range [0, 1].\n"); + return 0; + } + trainMethod = dData; + } + } + else + { + Scierror(999, "Error: The input argument #7 is not of scalar type.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument #7 is not of type Integer.\n"); + return 0; + } + } + + // Handling more than 7 arguments. + if(*getNbInputArgument(pvApiCtx) > 7) + { + sciErr = getVarAddressFromPosition(pvApiCtx, 8, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(isIntegerType(pvApiCtx, piAddr) || isDoubleType(pvApiCtx, piAddr)) + { + + if(isScalar(pvApiCtx, piAddr)) + { + double dData = 0; + iRet = getScalarDouble(pvApiCtx, piAddr, &dData); + if(!iRet) + { + miniBatchSize = dData; + } + } + else + { + Scierror(999, "Error: The input argument #8 is not of scalar type.\n"); + return 0; + } + } + else + { + Scierror(999, "Error: The input argument #8 is not of type Integer.\n"); + return 0; + } + } + + classifierLocation = classifierName; + strcat(classifierLocation, ".yml"); + + //------Actual processing------// + FileStorage fs(bagOfFeaturesLocation, FileStorage::READ); + fs["dictionary"] >> dictionary; + fs.release(); + if(dictionary.rows==0 || dictionary.cols==0) + { + sciprint("Error: The provided file for bagOfFeatures may be invalid.\n"); + } + sciprint("Training an image category classifier for %d classes.\n",descriptionCount); + sciprint("-------------------------------------------------------\n\n"); + for(int i=0;isetVocabulary(dictionary); + for(int i=0; idetect(inp,keyPoints); + } + catch(cv::Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + } + try + { + bowDE->compute(inp, keyPoints,features); + } + catch(cv::Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + } + trainingData.push_back(features); + labels.push_back((float) i); + } + sciprint("done.\n"); + } + sciprint("\n# Training the Logistic Regression classifier..."); + + Ptr p = LogisticRegression::create(); + Ptr tdata = TrainData::create(trainingData, ml::ROW_SAMPLE, labels); + + try + { + p->setLearningRate(learningRate); + } + catch(cv::Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + } + + try + { + p->setIterations(iteration); + } + catch(cv::Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + } + + try + { + p->setRegularization(regularization); + } + catch(cv::Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + } + + try + { + p->setTrainMethod(trainMethod); + } + catch(cv::Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + } + + try + { + if(trainMethod == 1) + { + p->setMiniBatchSize(miniBatchSize); + } + } + catch(cv::Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + } + + try + { + p->train(tdata); + trainingData = tdata->getSamples(); + transpose(trainingData, trainingData); + } + catch(cv::Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + } + + try + { + p->save(classifierLocation); + } + catch(cv::Exception &e) + { + const char *err = e.what(); + sciprint("%s", err); + } + + sciprint("done.\n"); + //------Create output arguments------// + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 4, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfStringInList(pvApiCtx, nbInputArgument(pvApiCtx)+1, piAddr, 1, 1, 1, &objectType); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfStringInList(pvApiCtx, nbInputArgument(pvApiCtx)+1, piAddr, 2, 1, 1, &classifierLocation); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfStringInList(pvApiCtx, nbInputArgument(pvApiCtx)+1, piAddr, 3, 1, 1, &bagOfFeaturesLocation); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = createMatrixOfStringInList(pvApiCtx, nbInputArgument(pvApiCtx)+1, piAddr, 4, descriptionCount, 1, description); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + //------Return Arguments------// + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx)+1; + ReturnArguments(pvApiCtx); + return 0; + } + /* ==================================================================== */ +} diff --git a/sci_gateway/cpp/opencv_whitepoint.cpp b/sci_gateway/cpp/opencv_whitepoint.cpp new file mode 100644 index 0000000..e43af0e --- /dev/null +++ b/sci_gateway/cpp/opencv_whitepoint.cpp @@ -0,0 +1,121 @@ +/******************************************************** +Function: whitepoint +Syntax : B = whitepoint(string) +String : 'd65', 'd50', 'a', 'icc', 'c', 'd55' +Author : Tess Zacharias +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include +#include "string.h" + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include "sciprint.h" + #include "../common.h" + + int opencv_whitepoint(char *fname, unsigned long fname_len) + { + // Error management variable + SciErr sciErr; + + //variable info + int* piAddr1 = NULL; + int error; + int iRet = 0; + char* pstData = NULL; + + //checking input argument + CheckInputArgument(pvApiCtx, 1, 1); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr1); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(isStringType(pvApiCtx, piAddr1)) + { + if(isScalar(pvApiCtx, piAddr1)) + { + iRet = getAllocatedSingleString(pvApiCtx, piAddr1, &pstData); + } + } + + double *output = NULL; + int k = 0; + output = (double *)malloc(sizeof(double) * 3); + + if(strcmp(pstData, "d65") == 0) + { + output[0] = 0.9504; + output[1] = 1.0000; + output[2] = 1.0889; + } + + else if(strcmp(pstData, "d50") == 0) + { + output[0] = 0.9642; + output[1] = 1.0000; + output[2] = 0.8251; + } + + else if(strcmp(pstData, "icc") == 0) + { + output[0] = 0.9642; + output[1] = 1.0000; + output[2] = 0.8249; + } + + else if(strcmp(pstData, "a") == 0) + { + output[0] = 1.0985; + output[1] = 1.0000; + output[2] = 0.3558; + } + + else if(strcmp(pstData, "d55") == 0) + { + output[0] = 0.9568; + output[1] = 1.0000; + output[2] = 0.9214; + } + + else if(strcmp(pstData, "c") == 0) + { + output[0] = 0.9807; + output[1] = 1.0000; + output[2] = 1.1823; + } + + else + { + sciprint("Expected input number 1: to match one of these strings:'icc', 'd50', 'd55', 'd65', 'c', 'a'"); + return 0; + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 1, 3, output); + free(output); // Data have been copied into Scilab memory + if (sciErr.iErr) + { + //Make sure everything is cleanup in case of error + printError(&sciErr, 0); + return 0; + } + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + ReturnArguments(pvApiCtx); + return 0; + } +} From ed9ab2c5b4464b74725fd7113160c3eeeae34315 Mon Sep 17 00:00:00 2001 From: Siddhant Narang Date: Wed, 8 Nov 2017 23:15:29 +0530 Subject: [PATCH 2/4] Add files via upload --- sci_gateway/cpp/opencv_blobAnalysis.cpp | 479 ++++++++++++++++++++++++ 1 file changed, 479 insertions(+) create mode 100644 sci_gateway/cpp/opencv_blobAnalysis.cpp diff --git a/sci_gateway/cpp/opencv_blobAnalysis.cpp b/sci_gateway/cpp/opencv_blobAnalysis.cpp new file mode 100644 index 0000000..be917d5 --- /dev/null +++ b/sci_gateway/cpp/opencv_blobAnalysis.cpp @@ -0,0 +1,479 @@ +/******************************************************** +Author : Deepshikha +[key_value] : blobAnalysis(source_image) + +// 1 :image +Optional arguments :- +// 2 : filterByArea +// 3 : filterByThreshold +// 4 : filterByCircularity +// 5 : filterByConvexity +********************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include "opencv2/features2d/features2d.hpp" +#include + +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_blobAnalysis(char *fname, unsigned long fname_len) + { + + // Error management variable + SciErr sciErr; + + //Variables + // int i, j; + int i = 0; + int intErr; + int iRows1 = 0; + int iCols1 = 0; + + int iRows = 0; + int iCols = 0; + int *piLen = NULL; + int *piAddr = NULL; + char **pstData = NULL; + char *currentArg = NULL; + bool *providedArgs = NULL; + + double *area = NULL; + double *threshold = NULL; + double *circularity = NULL; + double *convexity = NULL; + double *ROImat = NULL; + double *key_value = NULL; + + // checking input argument + // 1 : image + // 2 : filterByArea + // 3 : filterByThreshold + // 4 : filterByCircularity + // 5 : ROI + // 6 : filterByConvexity + CheckInputArgument(pvApiCtx, 1, 11); + CheckOutputArgument(pvApiCtx, 1, 1); + + // retrieve image + Mat image; + retrieveImage(image, 1); + + // For the optional arguments + int nbInputArguments = *getNbInputArgument(pvApiCtx); + //sciprint("%d\n",nbInputArguments); + + if((nbInputArguments%2) == 0) + { + Scierror(999, "%d: The number of arguments must be odd\n"); + return 0; + } + + providedArgs = (bool*) malloc(sizeof(bool) * 5); + memset(providedArgs, 0, 5); + + for(int iter = 2; iter <= nbInputArguments; ++iter) + { + sciErr = getVarAddressFromPosition(pvApiCtx, iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + /// Three calls to getMatrixOfString + //First Call + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows, &iCols, NULL, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // Second call to get length of string + piLen = (int*) malloc(sizeof(int) * iRows1 * iCols1); + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows1, &iCols1, piLen, NULL); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // third call + pstData = (char**) malloc(sizeof(char*) * iRows1 * iCols1); + for(int k = 0; k < iRows1 * iCols1; ++k) + { + pstData[k] = (char*) malloc(sizeof(char) * piLen[k] + 1); + } + + sciErr = getMatrixOfString(pvApiCtx, piAddr, &iRows1, &iCols1, piLen, pstData); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + currentArg = pstData[0]; + + // getting filterbyArea + if(strcmp(currentArg, "filterByArea") == 0) + { + if(iter + 1 <= nbInputArguments and !providedArgs[0]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows1, &iCols1, &area); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(iRows1 != 1 or iCols1 != 2) + { + Scierror(999, "Incorrect dimension of matrix for argument .\n"); + return 0; + } + providedArgs[0] = 1; + } + else if(providedArgs[0]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + /// filterbyThreshold + else if(strcmp(currentArg, "filterByThreshold") == 0) + { + if(iter+1 <= nbInputArguments && !providedArgs[1]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows1, &iCols1, &threshold); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(iRows1 != 1 or iCols1 != 2) + { + Scierror(999, "Incorrect dimension of matrix for argument .\n"); + return 0; + } + + // Checking if values are in proper range. Same for all optional arguments + providedArgs[1] = 1; + } + else if(providedArgs[1]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + /// filterbycircularity + else if(strcmp(currentArg, "filterByCircularity") == 0) + { + if(iter+1 <= nbInputArguments && !providedArgs[2]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows1, &iCols1, &circularity); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(iRows1 != 1 or iCols1 != 2) + { + Scierror(999, "Incorrect dimension of matrix for argument .\n"); + return 0; + } + + // Checking if values are in proper range. Same for all optional arguments + providedArgs[2] = 1; + } + else if(providedArgs[2]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + else if(strcmp(currentArg,"ROI")==0) + { + // val_position=i+1; + + if(providedArgs[3] == 1) + { + Scierror(999,"Do not enter the same parameter\n"); + return 0; + } + + sciErr = getVarAddressFromPosition(pvApiCtx, i + 1, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(!isDoubleType(pvApiCtx, piAddr) || isVarComplex(pvApiCtx, piAddr)) + { + Scierror(999,"Enter a List of 4 arguments\n"); + return 0; + } + + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &ROImat); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows*iCols!=4) + { + Scierror(999,"Invalid Argument\n"); + return 0; + } + + if(ROImat[0]<0 || ROImat[1]<0 || ROImat[2]<0 || ROImat[3]<0) + { + Scierror(999,"Arguments cannot be negative\n"); + return 0; + } + + if(ROImat[0]>image.cols || ROImat[1]>image.rows || ROImat[0]+ROImat[2]>image.cols + || ROImat[1]+ROImat[3]>image.rows) + { + Scierror(999,"Please make sure the arguments are within the image range\n"); + return 0; + } + + providedArgs[3] = 1; + } + + /// filterbyConvexity + else if(strcmp(currentArg, "filterByConvexity") == 0) + { + if(iter + 1 <= nbInputArguments and !providedArgs[4]) + { + sciErr = getVarAddressFromPosition(pvApiCtx, ++iter, &piAddr); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows1, &iCols1, &convexity); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + if(iRows1 != 1 or iCols1 != 2) + { + Scierror(999, "Incorrect dimension of matrix for argument .\n"); + return 0; + } + + providedArgs[4] = 1; + } + else if(providedArgs[4]) // Send an error message if an argument is provided more than once. Same for all optional arguments. + { + Scierror(999, "Please provide optional arguments only once.\n"); + return 0; + } + else // Send an error message if name of argument is given but type is incorrect. Same for all optional arguments. + { + Scierror(999, "Incorrect number of arguments provided. Please check the documentation for more information.\n"); + return 0; + } + } + + else + { + Scierror(9,"Invalid argument passed"); + return 0; + } + } + /// End of error check and input get; + + /// params for holding the parameters values; + SimpleBlobDetector::Params params; + + /// if area is provided + if(providedArgs[0] == 1) + { + params.filterByArea = true; + params.minArea = area[0]; + params.maxArea = area[1]; + } + + /// if threshold is provided + if(providedArgs[1] == 1) + { + sciprint("%d %d", int(threshold[0]), int(threshold[1])); + params.minThreshold = int(threshold[0]); + params.maxThreshold = int(threshold[1]); + } + + /// if Circularity is provided + if(providedArgs[2] == 1) + { + params.filterByCircularity = true; + params.minCircularity = circularity[0]; + params.maxCircularity = circularity[1]; + } + + if(providedArgs[3]==0) + { + ROImat=(double *)malloc(sizeof(double)*1*4); + ROImat[0]=0; + ROImat[1]=0; + ROImat[2]=image.cols; + ROImat[3]=image.rows; + } + + Rect masker(ROImat[0], ROImat[1], ROImat[2], ROImat[3]); + Mat mask(image.size(), CV_8UC1, Scalar::all(0)); + + // Creating ROI + try + { + mask(masker).setTo(Scalar::all(255)); + } + catch(Exception &e) + { + const char *err = e.what(); + Scierror(999, "%s", err); + } + + /// if convexity is provided + if(providedArgs[4] == 1) + { + params.filterByConvexity = true; + params.minConvexity = convexity[0]; + params.maxConvexity = convexity[1]; + } + + Ptr detector = SimpleBlobDetector::create(params); + /// to store the keypoints of blobs detected + vector keyPoints; + Mat descriptors; + + try + { + image.convertTo(image, CV_8UC1); + /// fuction to detect blob in the image + detector->detect(image, keyPoints); + } + catch(Exception &e) + { + const char *err = e.what(); + Scierror(999, "%s", err); + return 0; + } + + key_value = (double*)malloc(sizeof(double) * (int)keyPoints.size() * 2); + + double *size_value = NULL; + size_value = (double*)malloc(sizeof(double) * (int)keyPoints.size() * 1); + + int total_keypoints = (int)keyPoints.size(); + + vector< KeyPoint >::iterator it; + + i = 0; + for(it = keyPoints.begin(); it != keyPoints.end(); ++it) + { + KeyPoint temp = keyPoints[i]; + + // x coordinate + key_value[i + 0 * total_keypoints] = it->pt.x; + + // y coordinate + key_value[i + 1 * total_keypoints] = it->pt.y; + + // size + size_value[i + 0 * total_keypoints] = (int)it->size; + + //area + //key_value[i + 3 * total_keypoints] = it->Area; + i++; + } + + // Creating output arguments + sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 2, &piAddr); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 1, total_keypoints, 2, key_value); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 2, total_keypoints, 1, size_value); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + // sciErr = createMatrixOfDoubleInList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, piAddr, 3, 1, dcols, _descriptors); + // if(sciErr.iErr) + // { + // printError(&sciErr, 0); + // return 0; + // } + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + } +/* ==================================================================== */ +} From 26775288c4b2b5742233f458ba6781f7defe70f3 Mon Sep 17 00:00:00 2001 From: Siddhant Narang Date: Wed, 8 Nov 2017 23:23:16 +0530 Subject: [PATCH 3/4] Add files via upload --- sci_gateway/cpp/opencv_triangulatePoints.cpp | 166 +++++++++++++++++++ 1 file changed, 166 insertions(+) create mode 100644 sci_gateway/cpp/opencv_triangulatePoints.cpp diff --git a/sci_gateway/cpp/opencv_triangulatePoints.cpp b/sci_gateway/cpp/opencv_triangulatePoints.cpp new file mode 100644 index 0000000..4ee38bd --- /dev/null +++ b/sci_gateway/cpp/opencv_triangulatePoints.cpp @@ -0,0 +1,166 @@ +/*************************************************** +Author : Deepshikha +***************************************************/ + +#include +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" +#include "opencv2/opencv.hpp" +#include "opencv2/calib3d/calib3d.hpp" +#include +using namespace cv; +using namespace std; + +extern "C" +{ + #include "api_scilab.h" + #include "Scierror.h" + #include "BOOL.h" + #include + #include + #include "../common.h" + + int opencv_triangulatePoints(char *fname, unsigned long fname_len) + { + //variables + int i,j,n,m; + int iRows=0,iCols=0; + int *piAddr1 = NULL; + int *piAddr2 = NULL; + int *piAddr3 = NULL; + int *piAddr4 = NULL; + + double *pdblReal = NULL; + + SciErr sciErr; + + /*------------------------------------------- Checking Input Argument ----------------------------------------------------*/ + + CheckInputArgument(pvApiCtx, 4, 4); + CheckOutputArgument(pvApiCtx, 1, 1) ; + + /*------------------------------------- Projection matrix 1 :- First argument of the input -------------------------------------*/ + + sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr1); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr1, &iRows, &iCols, &pdblReal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows!=3 or iCols!=4){ + Scierror(iRows,"Projection matrix (arg 1) must be a 3 X 4 matrix"); + return 0; + } + Mat projMat1(iRows, iCols, CV_64F); + for(i = 0; i < iRows; i++) + for(j = 0; j < iCols; j++){ + projMat1.at(i,j) = pdblReal[i + (j * iRows)]; + } + + /* ------------------------------------ Projection matrix 2 :- Second argument of the input -------------------------------------*/ + + sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr2); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr2, &iRows, &iCols, &pdblReal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + if(iRows!=3 or iCols!=4){ + Scierror(iRows,"Projection matrix (arg 2) must be a 3 X 4 matrix"); + return 0; + } + Mat projMat2(iRows, iCols, CV_64F); + for(i=0;i(i,j) = pdblReal[i + (j * iRows)]; + + + /* ------------------------------------ Points matrix 1 :- Third argument of the input -------------------------------------*/ + + sciErr = getVarAddressFromPosition(pvApiCtx,3,&piAddr3); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr3, &iRows, &iCols, &pdblReal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + n = iCols; + if(iRows!=2){ + Scierror(iRows,"Points matrix (arg 3) must be a 2 X N matrix"); + return 0; + } + Mat newPoint1(2, iCols, CV_64F); + for(i = 0; i < 2; i++) + for(j = 0; j < iCols; j++) + newPoint1.at(i,j) = pdblReal[ i + ( j * iRows )]; + + + /* ------------------------------------ Points matrix 2 :- Fourth argument of the input -------------------------------------*/ + + sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr4); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + sciErr = getMatrixOfDouble(pvApiCtx, piAddr4, &iRows, &iCols, &pdblReal); + if(sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + m = iCols; + if(iRows!=2){ + Scierror(iRows,"Points matrix (arg 4) must be a 2 X N matrix"); + return 0; + } + Mat newPoint2(iRows, iCols, CV_64F); + for(i = 0; i < iRows; i++) + for(j = 0; j < iCols; j++) + newPoint2.at(i,j) = pdblReal[ i + ( j * iRows )]; + + if(n!=m){ + Scierror(n,"No of columns of arg 3 and arg 4 must be same"); + return 0; + } + Mat new_image(4, n, CV_64F); + + triangulatePoints(projMat1, projMat2, newPoint1, newPoint2, new_image); + + string tempstring = type2str(new_image.type()); + char *checker; + checker = (char *)malloc(tempstring.size() + 1); + memcpy(checker, tempstring.c_str(), tempstring.size() + 1); + returnImage(checker,new_image,1); + free(checker); + + + //Assigning the list as the Output Variable + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + //Returning the Output Variables as arguments to the Scilab environment + ReturnArguments(pvApiCtx); + return 0; + + } +/* ==================================================================== */ +} \ No newline at end of file From 8dfbdbdfd38c73dc8a02d1a25678c4a6a724fe18 Mon Sep 17 00:00:00 2001 From: Siddhant Narang Date: Wed, 8 Nov 2017 23:24:03 +0530 Subject: [PATCH 4/4] Update triangulatePoints.sci --- macros/triangulatePoints.sci | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/macros/triangulatePoints.sci b/macros/triangulatePoints.sci index ac94b1e..6adc814 100644 --- a/macros/triangulatePoints.sci +++ b/macros/triangulatePoints.sci @@ -36,7 +36,7 @@ function points4D = triangulatePoints(projMat1, projMat2, points1, points2) // Deepshikha - points4D = raw_triangulatePoints(projMat1, projMat2, points1, points2) + points4D = opencv_triangulatePoints(projMat1, projMat2, points1, points2) endfunction