OpenCV Tutorials

OpenCV Tutorials Roadmap

Section 1: Introduction to OpenCV

  • What is OpenCV?
    • Open Source Computer Vision Library.
    • Provides tools for image processing, computer vision, and machine learning.
  • Why Use OpenCV?
    • Cross-platform support.
    • Extensive functionalities.
    • Active community and tutorials.
  • Installing OpenCV:
    • Installing via package managers (pip, apt, conda).
    • Building from source (optional for customization).
  • First Program:
    • Loading and displaying an image.
    • Using OpenCV functions in your language (Python or C++).

Section 2: Basic Image Operations

  • Reading and Writing Images:
    • cv2.imread(), cv2.imwrite().
  • Displaying Images:
    • cv2.imshow()
    • Handling wait and destroy windows.
  • Image Properties:
    • Shape, size, data type.
  • Color Spaces:
    • RGB, BGR, Grayscale, HSV.
    • Converting color spaces (cv2.cvtColor()).

Section 3: Image Processing Techniques

  • Thresholding:
    • Binary, adaptive, Otsu's thresholding.
  • Filtering and Smoothing:
    • Gaussian Blur, Median Blur.
    • Blur techniques to reduce noise.
  • Edge Detection:
    • Canny Edge Detector.
    • Sobel, Laplacian filters.
  • Morphological Operations:
    • Erosion, Dilation, Opening, Closing.
  • Image Contours and Shapes:
    • Finding contours (cv2.findContours()).
    • Drawing contours.

Section 4: Geometric Transformations

  • Resizing and Cropping:
    • cv2.resize().
  • Translation, Rotation, and Affine Transforms:
    • Using transformation matrices.
  • Perspective Warping:
    • Homography matrices.

Section 5: Image Segmentation and Masking

  • Color-Based Segmentation:
    • Using color thresholds in HSV space.
  • Masking:
    • Creating masks and applying them to images.
  • Watershed Algorithm and Clustering:
    • Segmenting complex objects.

Section 6: Feature Detection and Matching

  • Keypoint Detectors:
    • SIFT, SURF, ORB, AKAZE.
  • Descriptor Matching:
    • Brute-force matcher, FLANN.
  • Applications:
    • Image stitching, object recognition.

Section 7: Object Detection and Recognition

  • Template Matching:
    • Finding objects based on templates.
  • Haar Cascades:
    • Pre-trained classifiers for face detection, eye detection, etc.
  • Deep Learning Integration:
    • Using DNN module for object detection models (YOLO, SSD, Faster R-CNN).

Section 8: Video Processing

  • Reading Video Streams:
    • cv2.VideoCapture().
  • Writing Video Files:
    • cv2.VideoWriter().
  • Frame Processing:
    • Real-time processing of video frames.

Section 9: Deep Learning with OpenCV

  • Using DNN Module:
    • Loading pre-trained models (Caffe, TensorFlow, ONNX).
    • Running inference on images and videos.
  • Training Models:
    • Training custom deep learning models (advanced).

Section 10: Advanced Topics and Further Learning

  • 3D Reconstruction and Stereo Vision:
    • Depth maps, point clouds.
  • Augmented Reality (AR):
    • Overlaying virtual objects onto real-world images.
  • Real-time Applications:
    • Object tracking, gesture recognition, robotics.
  • Resources for Further Learning:
    • Official OpenCV documentation.
    • Online tutorials and courses (Coursera, Udemy).
    • Community forums and GitHub repositories.