This repository contains the solutions to the Computer Vision assignments for ENGN6528. The lab work involves implementing various computer vision algorithms in Python and analyzing the results.
Each assignment is self-contained and includes code, results, images, and detailed reports explaining all outputs. The assignments are structured as follows:
- Basic Python Operations: Covers fundamental operations like array creation, slicing, random number generation, and element-wise operations.
- NumPy Operations: Focuses on array manipulations, linear space generation, broadcasting, and conditional operations.
- Goal: Build a solid understanding of Python and NumPy essentials for computer vision tasks.
[Add details for your second assignment here].
[Describe the problem statement, the approach you took, any specific techniques used like image processing, feature extraction, or machine learning].
- Camera Calibration: Estimates intrinsic parameters (focal length, principal point) and distortion coefficients using known 3D world points and their 2D image projections.
- Homography Estimation: Applies the Direct Linear Transformation (DLT) method to compute a homography matrix for image warping based on corresponding points between two images.
- Techniques: Camera matrix decomposition, image transformation, and 3D point visualization.
- Results: Calibration output, homography warping results, and visualizations of selected points and 3D structures.
To run these assignments, you will need the following:
- Python (v3.x recommended)
For Python users, install the required libraries using:
pip install numpy opencv-python matplotlib