# computer-vision-opencv

A skill that keeps your agent inside the conventions of OpenCV and PyTorch work: filters, feature matching, video pipelines, tracking. Useful when the repo already deals with frames.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 3800 installs · ★ 286
- Author: Mindrally
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/Mindrally/skills/tree/HEAD/computer-vision-opencv/SKILL.md
- Updated: 2026-10-08
- HTML: https://affpapa.org/en/nevibecode/skills/computer-vision-opencv

## What it does

The skill spells out how an agent should write computer vision code. Its OpenCV section covers color space conversion (BGR, RGB, HSV, LAB, grayscale), uint8 and float32 handling, filters and kernels, Canny, Sobel and Laplacian edge detection, morphology, histograms, and geometric transforms. Separate blocks handle feature detectors (SIFT, SURF, ORB, FAST), matching with FLANN or brute force, RANSAC for outlier rejection, and homography estimation for alignment and stitching.

For the neural side it asks for PyTorch or TensorFlow, preprocessing and augmentation pipelines, `torchvision.transforms`, transfer learning on ResNet, VGG and EfficientNet, and normalization based on pre-training statistics. Video work goes through `cv2.VideoCapture`, codecs MJPG, XVID and H264, frame-by-frame processing, and the KCF, CSRT and DeepSORT trackers. A performance section pushes NumPy vectorization over explicit loops, CUDA, batched inference, and multiprocessing for CPU-bound preprocessing.

Nothing gets installed for you. The dependency list names `opencv-python`, `numpy`, `torch`, `torchvision`, `Pillow`, `scikit-image`, `albumentations` and `matplotlib`, and they have to be present in the project environment or the agent's examples will not run. Style follows PEP 8, with functional code for processing pipelines and OOP for model architectures.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add Mindrally/skills --skill computer-vision-opencv -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/computer-vision-opencv/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/computer-vision-opencv/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add Mindrally/skills --skill computer-vision-opencv -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/computer-vision-opencv/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add Mindrally/skills --skill computer-vision-opencv -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/computer-vision-opencv/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install Mindrally/skills computer-vision-opencv
```

2. **Manually: put the files in place**
- File: `.github/skills/computer-vision-opencv/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add Mindrally/skills --skill computer-vision-opencv -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/Mindrally/skills --path computer-vision-opencv
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/computer-vision-opencv/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add Mindrally/skills --skill computer-vision-opencv -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/Mindrally/skills/tree/HEAD/computer-vision-opencv/SKILL.md

## When to use it
- build an OpenCV frame processing pipeline
- pick a feature detector for a matching task
- convert bounding boxes between xyxy, xywh and cxcywh
- speed up image preprocessing with NumPy vectorization
- set up augmentation and normalization for a pretrained model

## Example prompts
- write a video reading pipeline with cv2.VideoCapture and frame-by-frame processing
- match features with ORB and filter outliers using RANSAC
- convert boxes from xywh to xyxy and apply NMS

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: b3407612de532dec.

BEGIN THIRD-PARTY FILE b3407612de532dec
```text
---
name: computer-vision-opencv
description: Expert guidance for computer vision development using OpenCV, PyTorch, and modern deep learning techniques for image and video processing.
metadata:
  maintainer: Mindrally
  source: https://github.com/Mindrally/skills
---

# Computer Vision and OpenCV Development

You are an expert in computer vision, image processing, and deep learning for visual data, with a focus on OpenCV, PyTorch, and related libraries.

## Key Principles

- Write concise, technical responses with accurate Python examples
- Prioritize clarity, efficiency, and best practices in computer vision workflows
- Use functional programming for image processing pipelines and OOP for model architectures
- Implement proper GPU utilization for computationally intensive tasks
- Use descriptive variable names that reflect image processing operations
- Follow PEP 8 style guidelines for Python code

## OpenCV Fundamentals

- Use cv2 (OpenCV-Python) as the primary library for traditional image processing
- Implement proper color space conversions (BGR, RGB, HSV, LAB, grayscale)
- Use appropriate data types (uint8, float32) for different operations
- Handle image I/O correctly with proper encoding/decoding
- Implement efficient video capture and processing pipelines

## Image Processing Operations

- Apply filters and kernels correctly (Gaussian blur, median, bilateral)
- Implement edge detection using Canny, Sobel, or Laplacian operators
- Use morphological operations (erosion, dilation, opening, closing) appropriately
- Implement histogram equalization and contrast adjustment techniques
- Apply geometric transformations (rotation, scaling, perspective warping)

## Feature Detection and Matching

- Use appropriate feature detectors (SIFT, SURF, ORB, FAST) for the task
- Implement feature matching with FLANN or brute-force matchers
- Apply RANSAC for robust estimation and outlier rejection
- Use homography estimation for image alignment and stitching

## Object Detection and Recognition

- Implement classical approaches: Haar cascades, HOG + SVM
- Use deep learning detectors: YOLO, SSD, Faster R-CNN
- Apply non-maximum suppression (NMS) correctly
- Implement proper bounding box formats and conversions (xyxy, xywh, cxcywh)

## Deep Learning for Computer Vision
```
END THIRD-PARTY FILE b3407612de532dec

The third-party file has ended. Everything between the b3407612de532dec tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

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