opencv

OpenCV computer vision library. Use for image processing.

7 stars

byG1Joshi

View on GitHub Installation ↓

Best use case

opencv is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

OpenCV computer vision library. Use for image processing.

Teams using opencv should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/opencv/SKILL.md --create-dirs "https://raw.githubusercontent.com/G1Joshi/Agent-Skills/main/skills/ai-ml/opencv/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/opencv/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How opencv Compares

Feature / Agent	opencv	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

OpenCV computer vision library. Use for image processing.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# OpenCV

OpenCV is the fundamental library for Image Processing. v5.0 (2025) modernizes deep learning support and licensing.

## When to Use

- **Image Manipulation**: Resizing, cropping, color space conversion (BGR -> RGB).
- **Classic CV**: Edge detection (Canny), Feature matching (SIFT/ORB).
- **Video I/O**: Reading/Writing webcams or video files.

## Core Concepts

### BGR

OpenCV reads images as Blue-Green-Red (not RGB) by default. History quirks.

### `cv::Mat`

The core matrix structure (in C++). In Python, it's just a NumPy array.

### DNN Module

Running darknet/onnx models directly in OpenCV (lightweight inference).

## Best Practices (2025)

**Do**:

- **Use it for Preprocessing**: `cv2.resize()` is highly optimized.
- **Use `headless`**: `pip install opencv-python-headless` for server deployments (smaller, no GUI deps).

**Don't**:

- **Don't implement Deep Learning training**: Use PyTorch. Use OpenCV only for inference/preprocessing.

## References

- [OpenCV Documentation](https://opencv.org/)