Open CV
OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library.
It contains more than 2500 optimized algorithms, which can be used for a wide range of tasks, including detecting and
recognizing faces, identifying objects, classifying human actions in videos, tracking camera movements, extracting 3D
models of objects, stitching images together, finding similar images in a database, removing red-eye, and tracking eye
movements. OpenCV was designed for computational efficiency and with a strong focus on real-time applications. It is
cross-platform and free for use under the open-source BSD license.
| Subtopics |
Description |
| What is OpenCV? |
Open-source computer vision library |
| Installation |
pip install opencv-python or C++ setup |
| Basic Usage |
Reading, displaying, and writing images/videos |
| Subtopics |
Description |
| Color Spaces |
BGR vs RGB, Grayscale, HSV |
| Drawing Functions |
Lines, circles, rectangles, text |
| Image Operations |
Resize, crop, rotate, flip, threshold |
| Filters |
Gaussian, median, bilateral filtering |
| Subtopics |
Description |
| Morphological Operations |
Erosion, dilation, opening, closing |
| Geometric Transformations |
Affine, perspective, warp |
| Contours |
Finding and drawing contours, hierarchy, approximation |
| Histograms |
Equalization, back projection |
📹 4. Video Processing
| Subtopics |
Description |
| Capture from Webcam |
cv2.VideoCapture() |
| Write to File |
cv2.VideoWriter() |
| Frame Operations |
Frame-by-frame processing, object tracking basics |
🧠 5. Advanced Computer Vision
| Subtopics |
Description |
| Edge Detection |
Canny, Sobel, Laplacian |
| Object Detection |
Haar cascades, HOG+SVM |
| Feature Matching |
ORB, SIFT, SURF (SIFT/SURF may require opencv-contrib) |
| Camera Calibration |
Intrinsics, distortion correction |
🧪 6. Machine Learning & Deep Learning with OpenCV
| Subtopics |
Description |
| DNN Module |
Loading TensorFlow/Caffe models |
| Face Detection |
Pretrained SSDs or YOLO integration |
| Object Classification |
Using OpenCV DNN or with TensorFlow/PyTorch |
🤖 7. Real-World Projects
| Examples |
Description |
| Face Recognition |
With OpenCV + face_recognition lib |
| Motion Detection |
Background subtraction and contours |
| Lane Detection |
Edge detection + perspective transform |
| Augmented Reality |
Marker detection, pose estimation |
| Subtopics |
Description |
| Efficient Coding |
Use vectorized ops, avoid loops |
| Multithreading |
Using threading to improve real-time performance |
| GPU Acceleration |
Use OpenCV CUDA modules or cv2.UMat |