#OpenCV

BossSensor project leverages image classification to automatically hide computer screens when a boss is approaching. Using Python 3.5 and OpenCV, the system requires a webcam and a comprehensive facial image dataset for training. End-users benefit from quick reaction to proximity alerts, making it suitable for OSX users seeking privacy in supervised environments. The installation involves setting a virtual environment and transitioning the Keras backend to TensorFlow, ensuring software compatibility without overstated features.

The GoCV package provides OpenCV 4 support for Go developers on Linux, macOS, and Windows, enabling efficient image and video processing with hardware acceleration via CUDA for Nvidia GPUs and Intel OpenVINO support. It includes examples and installation guides to streamline integration and leverage the latest OpenCV capabilities. The package is designed to be compatible with the newest Go releases, offering a reliable solution for developers looking to implement high-performance computer vision applications using Go, without unnecessary promotional language.

This lightweight and customizable framework offers high-performance implementations of image processing algorithms optimized for deep learning. Supporting a variety of hardware platforms, it enables the addition of new hardware and algorithm support with ease. Functions aligned with OpenCV simplify deployment by reducing dependencies and enhancing performance through optimized memory and computation. It supports major CPU/GPUs and plans to expand with image decoding and VSLAM capabilities. Integration with ppl.nn enhances its utility for comprehensive deep learning applications.

FER is a Python tool for facial expression recognition supporting version 3.6 and above, leveraging OpenCV and TensorFlow for efficient emotion detection in images and videos. It utilizes OpenCV's Haar Cascade and MTCNN for improved accuracy. Performance can be boosted with TensorFlow-GPU support. The tool includes a Keras model for flexibility but allows custom models too. Suitable for applications in research and security, FER integrates easily into various projects, offering dependable results.

This repository provides additional data to support the OpenCV library, aimed at improving computer vision features. It includes access to documentation, forums, and issue tracking, supporting learning and collaboration. Contribution guidelines focus on organized pull requests, appropriate branch selection, thorough testing, and adherence to coding standards for maintaining high quality and consistency.

HyperLPR3 is a high-efficiency, cross-platform framework designed for license plate recognition, compatible with Windows, MacOS, Linux, Raspberry Pi, and Android. It provides real-time processing for 720p images with up to 97% accuracy, particularly beneficial for access control solutions. Supporting various license plate types, including single-line blue and yellow plates and new energy plates, the framework facilitates end-to-end recognition without character segmentation, ensuring a quick setup for diverse applications.

AI-Shorts-Creator is an innovative tool that helps content creators, podcasters, and video enthusiasts automatically extract engaging segments from videos. By utilizing GPT-4 for transcript analysis, it identifies and crops the most exciting moments using FFmpeg and OpenCV. This tool simplifies video editing, ensuring compatibility across various formats and enhancing content with precision-cropped highlights.

This project provides a system for real-time human head pose estimation with ONNX Runtime and OpenCV. Key steps include: face detection with a bounding box, facial landmark identification using a deep learning model, and pose computation via a PnP algorithm. It runs on Ubuntu 22.04 and requires ONNX Runtime 1.17.1 and OpenCV 4.5.4. The code is compatible with video files and webcams, and includes detailed setup and usage instructions. Licensed under the MIT license, it utilizes publicly available datasets, enhancing both accessibility and practical application.

The OpenCV repository offers accessible CPU-only pre-built packages for Python, simplifying developer integration. It includes installation guidance tailored to various environments, ensuring cross-platform compatibility. Built-in Haar cascade files and developmental options allow extensive customization while supporting current Python versions. Users can find both troubleshooting help and full package details through GitHub documentation. This collection supports OpenCV's commitment to free usage, serving as a resourceful tool for advanced computer vision innovations.

Learn how to extend OpenCV functionality with experimental modules developed in this repository. These modules are developed separately to ensure compatibility and stability before possible integration into the main library. Discover the process of building OpenCV with these additional modules using CMake for enhanced image processing capabilities. Keep track of ongoing module development to ensure optimal performance with the newest OpenCV versions. Properly document your contributions to facilitate smooth integration and visibility within the OpenCV ecosystem.

OpenCV provides a wide range of open-source tools focused on computer vision and AI. It offers comprehensive documentation, active forums, and encourages community contributions under clear guidelines. The platform extends its capabilities through the opencv_contrib package and offers educational courses. Resources are tailored for practitioners ranging from novices to experts to enhance their computer vision skills, promoting community interaction and project showcasing.

The Ubuntu 20.04 image for Jetson Nano integrates updated AI software including OpenCV, TensorFlow, and PyTorch, enhancing performance for machine learning tasks. This image provides easy installation and the latest features such as WiFi support and TensorRT improvements, serving as a solid AI development platform.

Discover practical image processing techniques using OpenCV, covering basics like arithmetic operations and thresholding, to advanced applications including OCR recognition and geometric transformations. Gain insights into VisionPro and explore a range of examples detailed with blog references for a thorough understanding of image processing in both theory and practice.

This node editor application is designed for effective image processing, focusing on task verification and analysis. It offers a user-friendly interface for creating and managing nodes and supports visualization. Compatible with libraries like opencv-python and mediapipe, it facilitates advanced image operations. Installation methods include scripts, Docker, and pip, catering to diverse user needs. The platform provides features like node creation and configuration management, suitable for tasks ranging from filtering to deploying complex AI models.

Graph-Cut RANSAC, now part of OpenCV, provides robust estimation techniques for tasks like homography, fundamental matrix, and 6D pose fitting with enhanced spatial coherence. Easily installed via PyPI or compiled from C++ source, it aids computer vision projects by improving accuracy in outlier management. Ideal for developers and researchers addressing geometric challenges with practical examples and tutorials.

Facemoji is a Unity-based Android application that leverages OpenCV and Dlib for facial expression tracking, integrated with AI voice interaction through Turing Robot and Iflytek. It translates expressions into animated Live2D models, enhancing the chat experience in Chinese. This application captures GIFs of user expressions and supports features like voice and text conversations, encyclopedic queries, storytelling, and more.

Auto Maple is a Python-based bot that uses machine learning and computer vision, integrating TensorFlow and OpenCV to simulate and automate gameplay in MapleStory. It offers precision in navigation and command execution, solves in-game puzzles efficiently, and incorporates community-created resources for a streamlined gaming experience.

Explore a curated selection of models optimized for OpenCV DNN, featuring detailed performance benchmarks on platforms like x86-64, ARM, and RISC-V. This guide provides hardware setup insights for devices from Intel, NVIDIA, and ARM, detailing inference times and showcasing usage examples from face detection to QR code parsing. Ideal for those interested in machine learning, these models offer accessible high-performance solutions for image processing and recognition, ensuring compatibility and efficient application.

The project offers accessible implementations of diverse multi-object tracking algorithms in Python, including tools like CentroidTracker, IOUTracker, CentroidKF_Tracker, and SORT. It integrates with OpenCV-based detectors such as TF_SSDMobileNetV2, Caffe_SSDMobileNet, and YOLOv3. Installation is straightforward using pip or GitHub, with additional guidance provided for GPU use via CUDA-enabled OpenCV. The repository includes practical examples and pre-trained model weights for broader application.