Learn OPENCV with Real Code Examples

Read and display an image

Convert an image to grayscale

Apply Gaussian blur or edge detection

Face detection using Haar cascades

Real-time object tracking from a webcam

Ensure correct file paths for images/videos

Check image format and color channels

Verify OpenCV version compatibility

Handle empty frames from camera/video streams

Optimize performance for real-time applications

Verify image/video loads correctly

Check transformation results visually

Test object detection on multiple datasets

Benchmark processing speed

Validate real-time pipeline stability

Desktop applications using Python/C++

Embedded systems and robotics

Web apps via OpenCV.js

Mobile apps with OpenCV Android/iOS SDK

Cloud-based image/video processing pipelines

NumPy for matrix operations

Matplotlib for visualization

TensorFlow/PyTorch for deep learning integration

Dlib for advanced CV and facial recognition

MediaPipe for hand and face tracking

Deep learning models via DNN module

Integration with ROS for robotics

Webcam and IP camera streams

Video processing pipelines with FFmpeg

GUI applications using Tkinter or PyQt

Use pipelines for repeatable CV workflows

Preprocess images consistently

Optimize for real-time constraints

Leverage existing pre-trained models

Profile code to remove bottlenecks

Detect faces or objects in varied lighting

Track multiple objects in real-time

Process high-resolution videos efficiently

Integrate OpenCV with ML/DL models

Deploy pipelines across platforms (desktop, mobile, cloud)