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

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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!

Yeah! Buy the DRAGON a COFFEE!

Or leave a tip! ヾ(°∇°*)


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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