Projects sharing features with Headtrackr

jeelizFaceFilter is a browser-based computer vision engine and WebGL face tracking library designed for AR filters and real-time facial movement tracking. It functions as a neural network face detector that identifies multiple faces and monitors mouth movements and rotation within a web browser. The system distinguishes itself through a model-swappable detection pipeline, allowing the exchange of neural network weights to balance accuracy and performance across different camera angles and devices. It features real-time lighting synchronization to match the illumination of 3D overlays with the

This repository is a comprehensive collection of reference implementations and sample libraries for the Universal Windows Platform. It provides practical examples of how to use Windows Runtime APIs to build cross-device applications, including detailed guidance on XAML-based declarative user interfaces and DirectX-integrated rendering. The project distinguishes itself by providing a wide array of hardware integration suites, covering low-level communication with USB, Serial, I2C, SPI, and GPIO peripherals. It includes specialized implementations for mixed reality holographic rendering, advanc

This project is a computer vision system designed for real-time facial recognition and identity tracking using live camera feeds. It provides a framework for capturing, registering, and identifying multiple individuals simultaneously by comparing live video input against a local database of pre-registered facial descriptors. The system distinguishes itself through a performance-oriented processing pipeline that balances computational load during live analysis. By combining deep neural network feature extraction with centroid-based object tracking, the software maintains consistent identity la

Pigo is a computer vision library written in Go for locating human faces in images and video streams. It provides tools for face detection, facial landmark identification, and pupil and eye localization. The project is implemented in pure Go to ensure portable execution without external dependencies. It supports compilation to WebAssembly, enabling face detection and image processing to run directly in web browsers without a backend. The library's capabilities include real-time face detection using classifier cascades and gaze tracking localization. It maps anatomical points on the face to a

This project is a computer vision tool designed to calculate the pitch, yaw, and roll of a human head in real time. It functions as a facial landmark detection library that identifies sixty-eight specific points on a face to enable precise orientation tracking from video streams. The system utilizes a high-performance inference engine to execute pre-trained machine learning models, ensuring that geometric analysis is performed efficiently on input frames. By mapping detected facial coordinates to a three-dimensional model, the software determines the camera-relative rotation of the head throu

SeetaFaceEngine is a C++ face recognition engine designed to detect, align, and identify human faces. It functions as a native library that performs facial analysis without relying on external third-party software libraries. The system utilizes a convolutional neural network framework for facial feature extraction and identity matching, representing identities as numerical feature-vector embeddings. It employs a funnel-structured cascade schema for real-time face localization and stacked auto-encoder networks to normalize facial orientation through landmark alignment. The toolkit integrates

This is a Python facial recognition library designed to detect, encode, and identify human faces in images and video. It functions as a biometric identification tool that converts facial features into numerical encodings to compare and match identities. The library provides a computer vision command line interface for batch processing face detection and recognition tasks across image directories. It also supports a GPU accelerated vision API that utilizes CUDA and NVIDIA hardware to increase the speed of facial analysis and identification. Its capabilities cover human face detection and faci

ar-cutpaste is an augmented reality asset extraction tool and prototype designed to isolate objects from a live camera feed and transfer them into image editing software. It functions as a mobile-to-desktop bridge that uses machine learning to remove backgrounds from live images, creating digital cutouts for use in image composition. The system establishes a local server connection to transmit image data and spatial coordinates from a mobile device to a design application. This bridge uses a remote socket mechanism and a secure password to inject captured assets directly into a desktop worksp

Hide screen when boss is approaching.

AR.js is a web-based augmented reality framework used to create immersive experiences directly in a browser using WebGL and WebRTC. It provides a cross-platform runtime that executes on mobile devices and head-mounted displays without requiring a dedicated application installation. The framework includes a marker-based engine for detecting physical patterns or barcodes to anchor digital overlays, a location-based tool for positioning entities via GPS coordinates, and a face tracking library for anchoring content to human facial features. It supports multi-marker simultaneous tracking, custom

jsqrcode is a JavaScript library for decoding QR codes within a web browser. It functions as a QR code scanner and decoder capable of extracting encoded text and data from various visual sources. The library supports data extraction from static images via URLs and data URLs, as well as from canvas elements. It also provides real-time scanning capabilities by processing live video streams from a device camera through the media devices API. Its internal processing covers pattern detection, pixel sampling, and binary-matrix decoding. It utilizes Reed-Solomon error correction to recover missing

ccv is a computer vision library written in C designed for high-performance visual analysis. It serves as a framework for image classification, object detection, and the identification of faces, pedestrians, and vehicles. The library distinguishes itself through hardware-accelerated vision and deep learning inference optimizations. It utilizes a quantized tensor processor to transform floating-point data into eight-bit integers and implements integer-quantized attention mechanisms to reduce memory bandwidth and increase data throughput. The project covers a broad range of capabilities, inclu

OpenCV-Face-Recognition is a computer vision system designed to detect human faces and verify identities within live video streams and static images. It functions as a Python-based toolkit that processes visual data to locate facial boundaries and match detected features against a pre-trained database of profiles. The system utilizes a structured computer vision pipeline that integrates object detection with statistical analysis to perform identity verification. It employs pre-trained classifiers to scan for facial patterns and encodes facial textures into binary histograms to create represen

This is a webcam-based client for a local llama.cpp server that enables real-time object detection and vision-language model inference directly from a browser. It captures frames from the user's webcam at configurable intervals and sends them to a locally running inference server for analysis, displaying both detection results and textual scene descriptions as they are produced. The application distinguishes itself by combining object detection with vision-language scene description in a single real-time interface, all processed through a local llama.cpp server for private, offline operation.

This project is a deep learning face classification system that detects human faces and classifies gender and emotion. It utilizes convolutional neural networks and computer vision tools to analyze facial attributes in both static images and live video streams. The system includes specialized classifiers for emotions based on the FER2013 dataset and gender based on IMDB datasets. These models are integrated into a containerized web service, allowing the classification logic to be exposed as an API that processes image data via network requests. The technical surface covers the entire pipelin

This project is an AI-driven suite of tools designed to repurpose long-form video content into short-form clips. It integrates a speech-to-text engine for automated transcription, a highlighting system that ranks engaging segments based on emotional hooks, and a video processor that converts horizontal footage into vertical formats. The system distinguishes itself through intelligent video cropping that utilizes face tracking and motion smoothing to keep subjects centered. It also employs an analysis system to extract viral highlights by scoring segments for engagement and practical value. T

Gifify is a tool for converting video files into optimized animated GIFs. It functions as a video to GIF converter and optimization utility that extracts specific clips from video files and burns text or subtitle overlays directly into the frames. The project differentiates itself through specialized GIF optimization, using lossy compression, color count limiting, and custom color palette generation to reduce file sizes. It also provides precise control over the output by allowing users to adjust playback speed, reverse playback direction, and resize dimensions. The software covers a broad s

lite.ai.toolkit is a C++ computer vision toolkit designed for edge AI deployment. It enables the execution of pre-trained models for object detection, image classification, and segmentation on resource-constrained devices. The project features a multi-backend inference engine that supports the ONNX model runtime, allowing AI models to run across different hardware targets. It includes a GPU-accelerated pipeline specifically for NVIDIA hardware to reduce latency and increase processing speed. The toolkit covers a broad range of facial analysis capabilities, including emotion detection, gender

EasyVtuber is 2D avatar animation software that transforms a single static image into a real-time animated character. It functions as a face tracking animation tool and live streaming avatar driver, mapping facial movements from webcams or iOS devices to drive virtual expressions and head motion. The project distinguishes itself through a neural animation pipeline that includes AI video upscaling and frame interpolation to increase visual smoothness and resolution. It utilizes a transparent video streaming system via Spout2, allowing rendered frames with alpha channels to be sent directly to

Deepface is a comprehensive deep learning library for facial recognition and demographic analysis. It provides a modular pipeline that handles the entire lifecycle of facial processing, including detection, geometric alignment, and the transformation of facial images into high-dimensional numerical vector embeddings for identity verification and similarity comparison. The library distinguishes itself through a model ensemble approach, which combines predictions from multiple pre-trained neural networks to improve classification accuracy and reduce bias. It also integrates advanced security fe

This project is a computer vision framework designed for the detection, identification, and tracking of human subjects within video streams. It provides an integrated system for locating individuals, generating biometric models from image datasets, and maintaining identity labels across consecutive video frames. The system distinguishes itself through its ability to maintain identity persistence across multiple camera feeds. By utilizing deep learning inference to extract feature vector embeddings and applying motion prediction algorithms, it links unique identity signatures across disparate

This project is a neural style transfer framework that provides a suite of computer vision tools for applying artistic styles to images and video. It functions as a system for training feedforward neural networks, an iterative style optimizer, and a real-time video stylizer. The framework supports two primary methods of stylization: a feedforward model that applies styles in a single pass and an iterative optimization method that generates stylized images by minimizing content and style loss without a pre-trained model. It also enables real-time processing of live webcam feeds using trained m

This project is a web-based 3D experience and interactive gallery designed as a personal portfolio. It uses a virtual room simulation to create a spatial representation of a physical environment where users can navigate and explore digital art and creative work. The environment is rendered directly in the browser as an interactive 3D gallery, allowing users to engage with a spatial showcase through first-person navigation. The project utilizes WebGL-based GPU rendering and custom shader-based lighting to display complex spatial environments. It incorporates asynchronous asset loading and an

facenet-pytorch is a facial recognition library for PyTorch that provides pretrained neural networks for detecting faces and extracting facial embeddings. It includes an MTCNN face detector for locating faces and landmarks, alongside an InceptionResnet face encoder to convert facial images into high-dimensional vectors for identity verification. The project provides tools for identity recognition by comparing facial embeddings using cosine similarity. It also supports facial video tracking to maintain identity consistency across consecutive frames and allows for the fine-tuning of pretrained

VDO.Ninja is a low-latency peer-to-peer media routing service and video streaming platform designed to integrate remote audio and video feeds into professional production workflows. It functions as a WebRTC broadcast integration tool and studio controller, allowing for the direct transmission of high-definition media between publishers and viewers with minimal delay. The platform distinguishes itself through extensive protocol bridging, converting between WebRTC, WHIP, WHEP, SRT, and RTMP to ensure compatibility across diverse network environments and professional studio software. It includes

This project is a multi-purpose web platform serving as a canvas 2D game engine, a frontend engineering blog, and a markdown static site generator. It functions as a web graphics showcase and a technical knowledge base for sharing architectural decisions and performance optimizations. The platform implements a high-performance graphics framework using low-level drawing APIs, integrating skeletal animation and web physics for collision detection and realistic motion of two-dimensional objects. It simultaneously operates as a system for converting markdown source files into pre-rendered HTML pa

Faceai is a computer vision toolkit designed for facial analysis, identity recognition, and image processing. It provides integrated engines for detecting human faces in static images and live video streams, matching facial encodings against identity databases, and mapping facial landmarks to understand geometric structure and alignment. The project enables real-time augmented reality applications, such as applying virtual makeup and digital accessories by scaling assets to detected facial coordinates. It also includes a suite for digital image restoration capable of removing noise, erasing w