methyldragon / opencv-python-reference

An (almost) fully comprehensive reference for OpenCV! 📷🤖

opencv-python-reference

An (almost) fully comprehensive reference for OpenCV!

Comes with Code Snippets, Resources, Pictures (Some are animated), Explanations, Parameter Descriptions, and more!

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Sometimes it's just nice to have everything you need, all the bits and pieces of code all easily retrievable from a single source. It's like making lego bricks for your code!

I found that this kind of pre-loading of all the retrieval work makes my workflow a lot more streamlined, and I save a lot of time for the time invested into preparing these references. It also makes it so that I'm forced to read through all the documentation and understand it.

So, here's a reference that covers almost every function and functionality in OpenCV!

-CH3EERS

Support my efforts!

Or leave a tip! ヾ(°∇°*)

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Reference Map

1. Basics and Image Processing
   • OpenCV Basic Commands
     • Windows, Operations, Drawing, Interaction
   • Basic Image Operations
   • Basic Image Processing
     • Transformations, Convolutions, Morphological Transformations, Gradients, Pyramids
   • Advanced Image Processing
     • Canny, Histogram Equalisation, Hough Lines and Circles, Blob Detection, Denoising, Inpainting, Fourier, HDR, Template Matching, Watershed
   • Contours
   • Histograms
   • Saliency API
2. Feature Detection and Description
   • Harris Corner
   • Shi-Tomasi
   • Key Points
   • Keypoint Detectors
     • FAST
     • SIFT
     • SURF
     • BRIEF
     • ORB
     • Final Notes
   • Feature Matching
     • Brute Force
     • FLANN
     • Homography
3. Video and Image Analysis, and Object Tracking
   • Optical Flow
     • Lucas-Kanade
     • Dense
   • Background Subtraction
   • Camera Calibration
     • Camera Properties
     • Calibration
     • Undistortion
   • Pose Estimation
   • Depth Map
   • Meanshift
   • Camshift
   • Centroid Tracking
   • Single and Multi-Object Tracking
   • Single and Multi-Object Tracking with dlib (and Multiprocessing!)
   • Footfall Tracking
4. Machine Learning and AI Detectors
   • k-Nearest Neighbours (With OCR example)
   • K-Means Clustering (With Colour Quantisation example)
   • Support Vector Machines (With OCR example)
   • Non-maximum Suppression
   • Haar Cascades
   • Facial Landmarks with dlib
   • Caffe Face Detection
   • Loading Neural Nets with OpenCV
   • YOLO
   • Mask R-CNN
   • Applications
     • Face Recognition
     • Facial Clustering
5. Optimisation
   • Enabling optimisation
   • Measuring performance
   • Cython

Credits

All credits and sources are listed inside the tutorials and references themselves.

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