Care sunt principalele funcționalități ale esimov/pigo?

Principalele funcționalități ale esimov/pigo sunt: Face Detection, WebAssembly-Based Detection, Facial Landmark Analysis, Facial Landmark Detection, Go Computer Vision, Eye Localization, Pupil and Eye Localization, Web-Based Computer Vision.

Care sunt câteva alternative open-source pentru esimov/pigo?

Alternativele open-source pentru esimov/pigo includ: ageitgey/face_recognition — This is a Python facial recognition library designed to detect, encode, and identify human faces in images and video.… serengil/deepface — Deepface is a comprehensive deep learning library for facial recognition and demographic analysis. It provides a… accord-net/framework — This project is a scientific computing framework for the .NET ecosystem, providing a comprehensive suite of libraries… eduardolundgren/tracking.js — tracking.js is a browser computer vision library written in JavaScript for performing real-time image analysis and… 1adrianb/face-alignment — This is a PyTorch-based computer vision library for detecting 2D and 3D facial landmark coordinates. It functions as a… jeeliz/jeelizfacefilter — jeelizFaceFilter is a browser-based computer vision engine and WebGL face tracking library designed for AR filters and…

Pigo

Pigo este o bibliotecă de computer vision scrisă în Go pentru localizarea fețelor umane în imagini și fluxuri video. Oferă instrumente pentru detectarea feței, identificarea reperelor faciale și localizarea pupilelor și a ochilor.

Proiectul este implementat în Go pur pentru a asigura execuția portabilă fără dependențe externe. Suportă compilarea în WebAssembly, permițând detectarea feței și procesarea imaginilor să ruleze direct în browserele web, fără un backend.

Capabilitățile bibliotecii includ detectarea feței în timp real folosind cascade de clasificatoare și localizarea privirii (gaze tracking). Aceasta mapează punctele anatomice de pe față pentru a analiza geometria structurală și expresiile folosind ansambluri de arbori randomizați.

Features

Face Detection - Provides computer vision capabilities to locate human faces in images and video streams.
WebAssembly-Based Detection - Executes face detection directly in the browser via WebAssembly for real-time performance without a backend.
Facial Landmark Analysis - Provides tools for mapping and analyzing specific anatomical points on the human face for structural geometry.
Facial Landmark Detection - Provides a tool for identifying anatomical points on a face to analyze structural geometry and expressions.
Go Computer Vision - Implements fast image recognition and facial detection features within the Go language.
Eye Localization - Detects the position and boundaries of eyes within a previously detected face.
Pupil and Eye Localization - Pinpoints the precise coordinates of eyes and pupils within a detected face.
Web-Based Computer Vision - Provides a computer vision implementation that runs face detection directly in web browsers without a backend.
Cascade Classifier Detections - Implements face detection using a series of filters through cascade classifiers with Haar-like features.
Client-Side Image Processing - Executes face detection logic directly in the web browser to avoid sending data to a server.
Go Libraries - A library written in Go for locating human faces in images and video streams.
Pure Go Portings - Implemented entirely in pure Go to ensure portable execution without external C dependencies.
WebAssembly Compilation - Compiles detection logic into WebAssembly binaries to enable high-performance image processing directly in the browser.
Eye Gaze Tracking - Pinpoints the exact coordinates of pupils and eyes to determine where a person is looking.
Random Forest Ensembles - Uses randomized tree ensembles to identify facial landmarks and pupil coordinates based on pixel intensity.
AI and Machine Learning - Face detection library.

Istoric stele

esimovpigo

Name: esimov/pigo
Author: esimov

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4,710 stele323 fork-uriGoMIT4 vizualizări

Pigo

Features

Face Detection - Provides computer vision capabilities to locate human faces in images and video streams.
WebAssembly-Based Detection - Executes face detection directly in the browser via WebAssembly for real-time performance without a backend.
Facial Landmark Analysis - Provides tools for mapping and analyzing specific anatomical points on the human face for structural geometry.
Facial Landmark Detection - Provides a tool for identifying anatomical points on a face to analyze structural geometry and expressions.
Go Computer Vision - Implements fast image recognition and facial detection features within the Go language.
Eye Localization - Detects the position and boundaries of eyes within a previously detected face.
Pupil and Eye Localization - Pinpoints the precise coordinates of eyes and pupils within a detected face.
Web-Based Computer Vision - Provides a computer vision implementation that runs face detection directly in web browsers without a backend.
Cascade Classifier Detections - Implements face detection using a series of filters through cascade classifiers with Haar-like features.
Client-Side Image Processing - Executes face detection logic directly in the web browser to avoid sending data to a server.
Go Libraries - A library written in Go for locating human faces in images and video streams.
Pure Go Portings - Implemented entirely in pure Go to ensure portable execution without external C dependencies.
WebAssembly Compilation - Compiles detection logic into WebAssembly binaries to enable high-performance image processing directly in the browser.
Eye Gaze Tracking - Pinpoints the exact coordinates of pupils and eyes to determine where a person is looking.
Random Forest Ensembles - Uses randomized tree ensembles to identify facial landmarks and pupil coordinates based on pixel intensity.
AI and Machine Learning - Face detection library.

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