This repository is dedicated to exploring the fascinating world of computer vision, with a special focus on 3D aspects. It serves as a resource and guide for those interested in understanding how computers perceive and interpret visual information from the world around us.
We delve into the fundamentals of the camera model, explaining how cameras capture 3D scenes and convert them into 2D images. This section covers intrinsic and extrinsic parameters, distortion correction, and camera calibration techniques.
Explore the geometry of multiple viewpoints, essential for reconstructing the structure of a scene from different angles. This includes discussions on the essential and fundamental matrices, stereo vision, and epipolar geometry.
Discover methods for reconstructing 3D models of scenes and objects from images. We cover stereo reconstruction, structure from motion (SfM), and photogrammetry, providing examples and code for creating detailed 3D models.
Learn about detecting and matching features across images, a critical step for many applications like object recognition and motion tracking. We explore algorithms like SIFT, ORB, and various feature matching techniques.
Before you begin, ensure you meet the following requirements:
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Python 3.x: Most of the projects in this repository are implemented in Python. Make sure you have Python 3.x installed on your system. You can download it from python.org.
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Python Libraries: Several Python libraries are essential for computer vision projects, including NumPy, OpenCV, Matplotlib, SciPy, and scikit-image. You can install these using pip:
pip install numpy opencv-python matplotlib scipy scikit-image
This guide will help you set up your Python environment and run the project on your machine. Follow these steps to get everything ready:
First, clone the repository to your local machine:
git clone https://github.com/yourusername/computer-vision.git
cd computer-vision
pip install -r requirements.txt