# OpenCV

Open Source Computer Vision Library

Repository: https://github.com/opencv/opencv
Canonical: https://ross.abutalabs.com/products/opencv
Homepage: https://opencv.org
Language: C++
License: Apache-2.0
License Family: permissive
Topics: opencv, c-plus-plus, computer-vision, deep-learning, image-processing
Last push: 2026-08-26T10:38:33+00:00

## Health v2 (maintenance only)
Score: 89/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 69, longevity 100
- inputs: {"age_days": 5158, "days_push": 7, "days_rel": 45, "gap_med": 165.5, "n_releases_24m": 5}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 90613, forks 56973 (observed 2026-08-28T04:12:23.302474+00:00)

## What it is
OpenCV is the de facto open-source computer vision library, providing thousands of optimized algorithms for image and video processing, feature detection, object recognition, and machine learning. It ships with C++, Python, Java, and JavaScript bindings and runs across desktop, mobile, and embedded platforms.

## Use cases
- detect faces and objects in images and video
- process and filter webcam video streams in real time
- calibrate cameras and correct lens distortion
- extract features and stitch panoramas from photos
- run DNN inference for image classification on edge devices
- read barcodes and QR codes from images
- track motion in surveillance footage

## When to choose
- you need a battle-tested, high-performance computer vision library with broad language and platform support
- you are building real-time image or video pipelines in C++ or Python
- you want hardware acceleration (CUDA, OpenCL) for vision workloads
- you need classic CV algorithms plus DNN inference in one dependency

## When to avoid
- you only need simple image edits like cropping or resizing in a web app (a lighter image library suffices)
- you require state-of-the-art deep learning models rather than classical CV algorithms (use PyTorch or a dedicated model framework)
- your project is JavaScript-only in the browser and bundle size is critical

## Facets
- artifact type: library
- maturity: stable
- function: computer-vision, image-processing, video-processing, machine-learning, deep-learning
- domain: computer-vision, image-processing, machine-learning, cross-platform
- platform: cpp, python, cross-platform, windows
- tags: opencv, c-plus-plus, image-analysis, object-detection, camera-calibration, feature-detection, video, linux, macos, android, ios, gpu

## Member repositories
- opencv/opencv (main) score 89
- opencv/opencv_contrib (plugin) score 76
- opencv/opencv-python (mirror) score 93

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:12:23.302474+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:13:50.981258+00:00, confidence not recorded.
  - readme: https://github.com/opencv/opencv (fetched 2026-08-28T04:12:23.302474+00:00, sha aac69c83891b)
- Data as of 2026-08-30T08:39:29.467469+00:00.