# o0o0o0o0o0o0o/image-processing-from-scratch

This project contains some interesting image processing algorithms that were wrote in python and c++ from scratch.

Repository: https://github.com/o0o0o0o0o0o0o/image-processing-from-scratch
Canonical: https://ross.abutalabs.com/products/image-processing-from-scratch
Language: C++
License: MIT
License Family: permissive
Last push: 2024-02-13T05:00:54+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2744, "days_push": 932, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1091, forks 300 (observed 2026-08-28T04:03:33.109049+00:00)

## What it is
A collection of classic image processing and computer vision algorithms implemented from scratch in Python and C++, including Canny edge detection, Hough transform, SIFT, watershed segmentation, and image matting. It is designed as an educational resource for understanding the principles behind these algorithms rather than a production library.

## Use cases
- learn how canny edge detection works internally
- understand sift feature extraction implementation
- study image segmentation algorithms like watershed and meanshift
- see a from-scratch implementation of hough transform
- learn closed-form image matting
- understand haze removal with dark channel prior
- study ransac-based image stitching

## When to choose
- you want to learn the internals of classic computer vision algorithms
- you need readable reference implementations to complement OpenCV
- you are teaching or studying image processing fundamentals

## When to avoid
- you need a production-ready, performant image processing library
- you want a maintained API or stable package to integrate into an application
- you need GPU acceleration or batch processing of many images

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: image-processing, computer-vision, math
- domain: computer-vision, image-processing, education
- platform: python, cpp, cross-platform
- tags: from-scratch, educational, edge-detection, feature-extraction, image-segmentation, sift, hough-transform, opencv, algorithm, algorithms

## Member repositories
- o0o0o0o0o0o0o/image-processing-from-scratch (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:33.109049+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 2026-08-30T06:49:09.616180+00:00, confidence not recorded.
  - readme: https://github.com/o0o0o0o0o0o0o/image-processing-from-scratch (fetched 2026-08-28T04:03:33.109049+00:00, sha 559583c79048)
- Data as of 2026-08-30T08:39:29.467469+00:00.