Why does my image look blue when using OpenCV and Matplotlib?

OpenCV loads images in BGR format by default, while Matplotlib and most web libraries expect RGB. This skill provides the correct cv2.cvtColor(img, cv2.COLOR_BGR2RGB) patterns to fix this.

How do I prevent memory leaks when processing video streams?

This skill provides robust patterns for using VideoCapture, including try-finally blocks and context manager wrappers to ensure camera and file resources are always released.

Does this skill help with image coordinate systems?

Yes, it clarifies the common confusion between NumPy indexing (row, col) and OpenCV drawing/resizing functions (x, y) to prevent out-of-bounds errors and drawing misalignments.

Can I use this for deep learning models?

Absolutely. It includes patterns for the cv2.dnn module, allowing you to run inference on pre-trained models from TensorFlow, Torch, and Caffe directly within OpenCV.

Python OpenCV Master

Name: Python OpenCV Master
Author: JosiahSiegel

byJosiahSiegel

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Data Science & ML

Streamlines computer vision development with expert patterns for image processing, video analysis, and deep learning inference using OpenCV.

About

Python OpenCV Master is a specialized capability designed to navigate the complexities of computer vision development. It proactively addresses common library pitfalls such as the BGR vs. RGB color format 'gotcha' and coordinate system inconsistencies while providing optimized implementation patterns for image filtering, edge detection, and feature matching. By offering memory-safe practices for video capture and performance-oriented guidance for the cv2.dnn module, it ensures developers can build robust, production-ready vision systems directly within their Claude-assisted workflows.

Key Features

Proactive BGR to RGB color format correction
Advanced image processing and morphological operation patterns
Deep learning inference guidance using the cv2.dnn module
7 GitHub stars
Memory-safe VideoCapture and VideoWriter implementation
Optimized feature detection (SIFT, ORB, AKAZE) and matching

Use Cases

Developing real-time video analysis and object tracking applications
Implementing complex feature matching for image recognition and alignment
Building automated image processing pipelines and batch enhancement tools

About

Key Features

Proactive BGR to RGB color format correction
Advanced image processing and morphological operation patterns
Deep learning inference guidance using the cv2.dnn module
7 GitHub stars
Memory-safe VideoCapture and VideoWriter implementation
Optimized feature detection (SIFT, ORB, AKAZE) and matching

Use Cases

Developing real-time video analysis and object tracking applications
Implementing complex feature matching for image recognition and alignment
Building automated image processing pipelines and batch enhancement tools