opencv-python-reference

An (almost) fully comprehensive reference for OpenCV!

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

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

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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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