Search Results for "opencv lane detection"
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Showing 68 open source projects for "opencv lane detection"
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OpenCV
Open Source Computer Vision Library
OpenCV (Open Source Computer Vision Library) is a comprehensive open-source library for computer vision, machine learning, and image processing. It enables developers to build real-time vision applications ranging from facial recognition to object tracking. OpenCV supports a wide range of programming languages including C++, Python, and Java, and is optimized for both CPU and GPU operations. -
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AIMr
The best AI Aimbot for Fortnite, Valorant, CS2, R6, COD, Apex, & more
...The software includes various aiming enhancements, such as recoil control, silent aim, and prediction capabilities, aimed at making gameplay smoother and more competitive. AIMr also provides visual customization options like field-of-view displays and detection indicators, allowing players to tailor their interface. The system is compatible with games that use human-shaped models, and although it functions effectively out of the box, optimizing it with CUDA-accelerated OpenCV is recommended for maximum performance. -
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Frigate
NVR with realtime local object detection for IP cameras
Frigate - NVR With Realtime Object Detection for IP Cameras A complete and local NVR designed for Home Assistant with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras. Use of a Google Coral Accelerator is optional, but highly recommended. The Coral will outperform even the best CPUs and can process 100+ FPS with very little overhead. -
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Frigate NVR
NVR with realtime local object detection for IP cameras
Frigate is a local network video recorder designed for real-time object detection on IP camera streams using machine learning. It runs entirely on local hardware and integrates closely with Home Assistant to provide smart surveillance without relying on cloud processing. The system uses OpenCV and TensorFlow to analyze video feeds and detect objects such as people, vehicles, and animals in real time. Frigate is optimized for efficiency and supports hardware acceleration across a wide range of devices, including GPUs and specialized inference hardware. ... -
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Sherloq
An open source digital image forensic toolset
...Initially developed in C++ in 2015 and later transitioned to a Qt-based GUI in 2017, Sherloq has since been ported to Python with PySide2, Matplotlib, and OpenCV to improve accessibility and ease of development. Its interface allows users to inspect images with real-time zoom, metadata exploration, noise analysis, and specialized algorithms for detecting forgeries and manipulations. -
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AI YouTube Shorts Generator
A python tool that uses GPT-4, FFmpeg, and OpenCV
AI-YouTube-Shorts-Generator is a Python-based tool that automates the creation of short-form vertical video clips (“shorts”) from longer source videos — ideal for adapting content for platforms like YouTube Shorts, Instagram Reels, or TikTok. It analyzes input video (whether a local file or a YouTube URL), transcribes audio (with optional GPU-accelerated speech-to-text), uses an AI model to identify the most compelling or engaging segments, and then crops/resizes the video and applies... -
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VideoPipe
A cross-platform video structuring (video analysis) framework
VideoPipe is an open-source C++ framework designed for building modular video analysis pipelines that process and structure video data using computer vision models. It operates using a pipeline architecture where independent nodes can be combined flexibly to create customized workflows for tasks such as object detection, face recognition, and behavior analysis. The framework is designed to be lightweight and portable, with minimal dependencies compared to other video processing systems, making it easier to deploy across different environments. It supports multiple inference backends, including OpenCV DNN, TensorRT, PaddleInference, and ONNXRuntime, allowing developers to choose the most suitable runtime for their performance and hardware requirements. ... -
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JavaCV
Java interface to OpenCV, FFmpeg, and more
JavaCV uses wrappers from the JavaCPP Presets of commonly used libraries by researchers in the field of computer vision (OpenCV, FFmpeg, libdc1394, FlyCapture, Spinnaker, OpenKinect, librealsense, CL PS3 Eye Driver, videoInput, ARToolKitPlus, flandmark, Leptonica, and Tesseract) and provides utility classes to make their functionality easier to use on the Java platform, including Android. JavaCV also comes with hardware accelerated full-screen image display (CanvasFrame and GLCanvasFrame), easy-to-use methods to execute code in parallel on multiple cores (Parallel), user-friendly geometric and color calibration of cameras and projectors (GeometricCalibrator, ProCamGeometricCalibrator, ProCamColorCalibrator), detection and matching of feature points (ObjectFinder), a set of classes that implement direct image alignment of projector-camera systems (mainly GNImageAligner, ProjectiveTransformer, ProjectiveColorTransformer, ProCamTransformer, and ReflectanceInitializer), and more. -
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Computer Vision in Action
A computer vision closed-loop learning platform
Computer Vision in Action is a practical, example-rich repository that demonstrates real-world applications of computer vision techniques and algorithms in Python, often using OpenCV, deep learning models, and related tooling. It serves as a hands-on companion for learners and engineers who want to understand not just the theory, but how computer vision is actually implemented for tasks like object detection, image classification, feature tracking, optical flow, and image segmentation. The repository includes structured code examples, scripts, and notebooks that cover pipeline construction, preprocessing, model inference, and visual output rendering, making it easy for newcomers or intermediate practitioners to adapt patterns to their own projects. ... -
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Simd Library
C++ image processing and machine learning library with using of SIMD
The Simd Library is a free open-source image processing and machine learning library, designed for C and C++ programmers. It provides many useful high-performance algorithms for image processing such as pixel format conversion, image scaling and filtration, extraction of statistical information from images, motion detection, object detection and classification, neural networks. The algorithms are optimized with using of different SIMD CPU extensions. In particular, the library supports the... -
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LearnOpenCV
C++ and Python Examples
LearnOpenCV is a large educational repository that provides practical computer vision and deep learning examples in both Python and C++. The project accompanies the LearnOpenCV blog and contains hundreds of hands-on tutorials covering topics such as object detection, image processing, pose estimation, and neural networks. It is structured as a learning resource where each directory corresponds to a specific article or technical walkthrough. The repository supports beginners and advanced practitioners by offering reproducible code that demonstrates real-world computer vision techniques. Many examples integrate popular frameworks like PyTorch, OpenCV, and ONNX to reflect modern AI workflows. ... -
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ArUco
Augmented reality library based on OpenCV
ArUco is a minimal library for Augmented Reality applications based exclusively on OpenCV. Relies on b/w markers with codes that are detected by calling a single function. Trivial integration of your augmented reality applications with OpenGL and OGRE. See documentation at https://drive.google.com/file/d/1OiavRVYVJ-WH88sQg1LUsh8CuJZUQyrX/view?usp=sharing -
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OpenFace Face Recognition
Face recognition with deep neural networks
OpenFace is a Python and Torch implementation of face recognition with deep neural networks and is based on the CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering by Florian Schroff, Dmitry Kalenichenko, and James Philbin at Google. Torch allows the network to be executed on a CPU or with CUDA. This research was supported by the National Science Foundation (NSF) under grant number CNS-1518865. Additional support was provided by the Intel Corporation, Google,... -
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Open Model Zoo
Pre-trained Deep Learning models and demos
Open Model Zoo is a large repository of high-quality pre-trained deep learning models and demonstration applications designed to work with the OpenVINO™ toolkit, offering a comprehensive starting point for a wide range of AI and computer vision workloads. It includes hundreds of models covering object detection, classification, segmentation, pose estimation, speech recognition, text-to-speech, and more, many of which are already converted into formats optimized for inference on CPUs, GPUs, VPUs, and other accelerators supported by OpenVINO. In addition to model files, Open Model Zoo provides demo applications that show realistic usage patterns and help developers quickly prototype and understand inference pipelines in C++, Python, or via the OpenCV Graph API. ... -
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photoStacker
Aligns and stacks astronomical images using point-set registration.
PhotoStacker aligns and stacks astronomical night sky images using Coherent Point Drift (CPD) for precise star alignment. This project leverages external tools like SExtractor for source extraction and OpenCV ORB for feature detection and matching. It includes custom binaries for detection, registration, and transformation, ensuring accurate image alignment. The workflow script automates the process, making it easy to stack images for enhanced astronomical photography. The project is ideal for astrophotographers seeking to improve image quality through stacking, utilizing robust algorithms and open-source tools. -
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MarkerMapper
A cost-effective localization system using planar squared markers
...This project analyzes the images and returns the 3D location of the markers: the marker map. The map generated can be used by ArUCO library for localizing your camera. The library depends on Aruco for the marker detection: https://www.uco.es/investiga/grupos/ava/portfolio/aruco/ It also depends on OpenCv and eigen3 (the later is included in the 3rdparty directory). -
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Imaging Instruments Lite
Image processing App for Windows Desktop
Imaging Instruments lite is a comprehensive image processing application developed following the Model-View-Controller (MVC) design pattern, utilizing Python, Tkinter, and OpenCV. It provides users with image manipulation capabilities, leveraging multi-threading with OpenMP and GPU acceleration using CUDA-C. Fueled by yerba mate and a passion for coding. Created by Agustin Tortolero. website: https://agustintortolero.pythonanywhere.com/ Source code:... -
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clickmymouse
Automation tool designed to perform mouse clicks
...Timing Settings: Users can set the minimum and maximum time for the click interval. There is also a setting for inactive time, which is the time without movement before click operations start. Motion Detection: The program monitors a specified area of the screen for movement using OpenCV for image processing. If motion is detected, the inactive timer is reset. Random Click Intervals: Click intervals can be random within the defined minimum and maximum limits, making the click patterns less predictable. In summary, "ClickMyMouse" is a powerful tool for automating mouse clicks. -
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Imaging Instruments
High-performance image processing C++ application for Windows Desktop
Imaging Instruments is a high-performance image processing application crafted for efficiency and designed to deliver powerful and reliable image manipulation capabilities. Developed using the Model-View-Controller (MVC) architecture, Imaging Instruments harnesses the full potential of modern technologies, combining C++, Qt6, CUDA-C, and OpenCV to deliver a powerful and seamless experience for both professionals and enthusiasts. With multi-threading support via OpenMP and GPU acceleration... -
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scanExtract
scanExtract: CLI tool to split and rotate scanned photos precisely.
scanExtract is a command-line utility designed for quick and precise digitization of photo prints. Simply place multiple photos on a flatbed scanner, scan them, and let scanExtract automatically detect, split, and individually rotate each image to achieve perfectly aligned borders. Using high-precision algorithms, the tool handles each photo separately, eliminating the need for careful placement on the scanner. It saves images with an overflow border to ensure no details are lost. After... -
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This program broken The program is a real-time face detection application built using OpenCV and Tkinter. It allows you to detect faces using your webcam and displays the video feed with rectangles drawn around the detected faces. When you run the program, a GUI window will appear. The window has a title "Real-time Face Detection" and a description below it. The description guides you to adjust the settings and click the "Start" button to begin face detection. ...
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Pigo
Fast face detection, pupil/eyes localization
Fast face detection, pupil/eyes localization and facial landmark points detection library in pure Go. Pigo is a pure Go face detection, pupil/eyes localization and facial landmark points detection library based on the Pixel Intensity Comparison-based Object detection paper. The reason why Pigo has been developed is because almost all of the currently existing solutions for face detection in the Go ecosystem are purely bindings to some C/C++ libraries like OpenCV or dlib, but calling a C program through cgo introduces huge latencies and implies a significant trade-off in terms of performance. ... -
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Darknet
Convolutional Neural Networks
Darknet is an open source neural network framework written in C and CUDA, developed by Joseph Redmon. It is best known as the original implementation of the YOLO (You Only Look Once) real-time object detection system. Darknet is lightweight, fast, and easy to compile, making it suitable for research and production use. The repository provides pre-trained models, configuration files, and tools for training custom object detection models. With GPU acceleration via CUDA and OpenCV integration, it achieves high performance in image recognition tasks. ... -
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Face Mask Detection
Face Mask Detection system based on computer vision and deep learning
Face Mask Detection system based on computer vision and deep learning using OpenCV and Tensorflow/Keras. Face Mask Detection System built with OpenCV, Keras/TensorFlow using Deep Learning and Computer Vision concepts in order to detect face masks in static images as well as in real-time video streams. Amid the ongoing COVID-19 pandemic, there are no efficient face mask detection applications which are now in high demand for transportation means, densely populated areas, residential districts, large-scale manufacturers and other enterprises to ensure safety. ... -
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qiji-font
Typeface from Ming Dynasty woodblock printed books
...Download high-resolution PDFs and split pages into images. Manually lay a grid on top of each page to generate bounding boxes for characters (potentially replaceable by an automatic corner-detection algorithm). Generate a low-poly mask for each character on the grid, and save the thumbnails (using OpenCV). First, red channel is subtracted from the grayscale, in order to clean the annotations printed in red ink. Next, the image is thresholded and fed into the contour-tracing algorithm. A metric is then used to discard shapes that are unlikely to be part of the character in interest.
