Lecture 3.9 - Convolutions and Blurring.ipynb

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   "source": [
    "## Convolutions and Blurring"
   ]
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    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "image = cv2.imread('images/elephant.jpg')\n",
    "cv2.imshow('Original Image', image)\n",
    "cv2.waitKey(0)\n",
    "\n",
    "# Creating our 3 x 3 kernel\n",
    "kernel_3x3 = np.ones((3, 3), np.float32) / 9\n",
    "\n",
    "# We use the cv2.fitler2D to conovlve the kernal with an image \n",
    "blurred = cv2.filter2D(image, -1, kernel_3x3)\n",
    "cv2.imshow('3x3 Kernel Blurring', blurred)\n",
    "cv2.waitKey(0)\n",
    "\n",
    "# Creating our 7 x 7 kernel\n",
    "kernel_7x7 = np.ones((7, 7), np.float32) / 49\n",
    "\n",
    "blurred2 = cv2.filter2D(image, -1, kernel_7x7)\n",
    "cv2.imshow('7x7 Kernel Blurring', blurred2)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
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    "### Other commonly used blurring methods in OpenCV"
   ]
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    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "image = cv2.imread('images/elephant.jpg')\n",
    "\n",
    "# Averaging done by convolving the image with a normalized box filter. \n",
    "# This takes the pixels under the box and replaces the central element\n",
    "# Box size needs to odd and positive \n",
    "blur = cv2.blur(image, (3,3))\n",
    "cv2.imshow('Averaging', blur)\n",
    "cv2.waitKey(0)\n",
    "\n",
    "# Instead of box filter, gaussian kernel\n",
    "Gaussian = cv2.GaussianBlur(image, (7,7), 0)\n",
    "cv2.imshow('Gaussian Blurring', Gaussian)\n",
    "cv2.waitKey(0)\n",
    "\n",
    "# Takes median of all the pixels under kernel area and central \n",
    "# element is replaced with this median value\n",
    "median = cv2.medianBlur(image, 5)\n",
    "cv2.imshow('Median Blurring', median)\n",
    "cv2.waitKey(0)\n",
    "\n",
    "# Bilateral is very effective in noise removal while keeping edges sharp\n",
    "bilateral = cv2.bilateralFilter(image, 9, 75, 75)\n",
    "cv2.imshow('Bilateral Blurring', bilateral)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
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   "source": [
    "## Image De-noising - Non-Local Means Denoising"
   ]
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   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "image = cv2.imread('images/elephant.jpg')\n",
    "\n",
    "# Parameters, after None are - the filter strength 'h' (5-10 is a good range)\n",
    "# Next is hForColorComponents, set as same value as h again\n",
    "# \n",
    "dst = cv2.fastNlMeansDenoisingColored(image, None, 6, 6, 7, 21)\n",
    "\n",
    "cv2.imshow('Fast Means Denoising', dst)\n",
    "cv2.waitKey(0)\n",
    "\n",
    "cv2.destroyAllWindows()"
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    "**There are 4 variations of Non-Local Means Denoising:**\n",
    "\n",
    "- cv2.fastNlMeansDenoising() - works with a single grayscale images\n",
    "- cv2.fastNlMeansDenoisingColored() - works with a color image.\n",
    "- cv2.fastNlMeansDenoisingMulti() - works with image sequence captured in short period of time (grayscale images)\n",
    "- cv2.fastNlMeansDenoisingColoredMulti() - same as above, but for color images."
   ]
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