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hybridgroup/gocv

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7,463 stars·897 forks·Go·47 viewsgocv.io↗

Gocv

GoCV is a computer vision library and Go language binding for OpenCV. It serves as an image processing toolkit and deep learning inference engine, providing programmatic access to a wide range of algorithms for image manipulation, object detection, and video analysis.

The project differentiates itself through high-performance native bindings and hardware acceleration. It utilizes a foreign function interface to map Go calls to C++ functions and includes a hardware-agnostic backend dispatch to route neural network tasks to computation engines such as CUDA and OpenVINO.

The library covers a broad surface of visual analysis capabilities, including camera calibration and correction, feature detection, and marker recognition for QR codes and ArUco markers. It provides tools for object tracking, human pose estimation, and geometric shape analysis. Additionally, it handles fundamental image processing tasks like color space conversion, noise reduction, and matrix operations, alongside GUI window management for interactive visualization.

The project supports static binary linking and provides multi-architecture container images to simplify the installation of vision libraries and GPU-accelerated environments.

Features

  • Computer Vision Libraries - Provides a comprehensive Go library for processing, analyzing, and interpreting visual data from images and video.
  • Computer Vision Libraries - Provides a comprehensive Go library for building computer vision applications using OpenCV.
  • Bounding Box Detection - Identifies specific patterns or objects using classifier data and draws bounding boxes around them.
  • Real-Time Object Detection - Locates and follows specific objects or faces in real-time using deep neural networks.
  • Deep Learning Inference Engines - Provides a high-performance engine for executing pre-trained neural networks on CUDA and OpenVINO hardware.
  • Face Detection - Locates human faces in images or video streams using cascade classifiers or deep learning.
  • GPU Accelerated Computer Vision - Leverages GPU hardware to accelerate real-time image analysis and manipulation tasks.
  • GPU Acceleration - Configures deep learning backends to execute neural network computations on CUDA-enabled hardware.
  • Neural Networks - Provides the core ability to create and execute artificial neural networks across various hardware targets.
  • Pose Estimation - Detects and tracks the positions of human body joints and poses using neural networks.
  • Neural Network Tools - Provides utilities for processing input data through loaded networks to generate predictions and feature maps.
  • Camera Calibration - Calculates intrinsic and extrinsic camera parameters to correct lens distortion.
  • Image Processing - Provides a toolkit for loading, filtering, and performing geometric transformations on image matrices.
  • C-Shared Library Bindings - Provides a high-performance Go language binding for the native C++ OpenCV library.
  • C Function Bindings - Maps Go function calls to C++ OpenCV functions using a foreign function interface for high-performance operations.
  • Feature Descriptor Computation - Generates numerical representations of keypoints to enable comparison and matching of different images.
  • Homography Estimation - Calculates homography matrices to estimate perspective transformations between two 2D planes.
  • Pretrained Model Integrations - Implements utilities for importing pre-trained deep learning models from standard file formats.
  • Deep Learning Classifiers - Implements deep learning classifiers to identify and categorize objects within video streams.
  • Inference Compute Backends - Supports dynamic routing of inference tasks to specific hardware backends like CUDA and OpenVINO.
  • Motion Detection - Analyzes a video feed to identify the movement of humans or other objects.
  • Marker Detection - Identifies ArUco markers in images using configurable parameters for thresholding and corner refinement.
  • Video Object Tracking - Implements algorithms for maintaining object identity and motion analysis across sequential video frames.
  • Feature Detection - Identifies keypoints and descriptors within an image to find distinct visual landmarks.
  • Feature Point Localization - Locates corners and trackable points within an image to facilitate object tracking.
  • Image Inpainting - Implements inpainting techniques to fill missing pixel regions and restore image details.
  • Fisheye Lens Calibration - Computes camera parameters using chessboard patterns specifically to correct fisheye lens distortion.
  • Pattern Corner Detection - Locates interior corners of chessboard patterns for use in camera calibration.
  • Color Manipulation - Implements utilities for applying colormaps and creating grayscale versions of images.
  • Local Feature Matching - Compares descriptors from different images to find corresponding points using matching algorithms.
  • QR Codes - Locates and parses QR codes using the WeChat detector.
  • GPU Accelerated Image Operators - Executes arithmetic functions and morphology filters on the GPU to increase processing speed.
  • Manual Resource Closures - Manages memory for C++ objects using pointers in Go with explicit closure methods to prevent leaks.
  • Edge Detection Algorithms - Identifies structural boundaries within an image using Canny or Laplacian operators.
  • Geometric Shape Detection - Locates lines, circles, or corners within an image using the Hough transform.
  • Geometric Distortion Correction - Removes fisheye distortion from images using calibration matrices to produce rectilinear projections.
  • Image Decoding - Transforms memory buffers into pixel representations for computer vision analysis.
  • Image Denoising - Reduces image noise using Gaussian, Median, and Bilateral blurring techniques.
  • Morphological Operations - Provides morphological image operations like dilation and erosion to isolate or connect visual features.
  • Image Feature Engineering - Extracts keypoints, descriptors, and contours from images to perform pattern matching and similarity analysis.
  • Image File IO - Provides capabilities for reading image data from disk and writing processed images to various formats.
  • Image File Loading - Loads images from files into memory with options for size reduction and color conversion.
  • Image Format Encoding - Converts raw image matrices into compressed byte buffers for storage or transmission.
  • Image Graphics Rendering - Renders basic drawing primitives and graphical elements directly onto image data.
  • Contour Analysis - Extracts the boundaries of shapes to calculate area, arc length, and bounding rectangles.
  • Color Space Converters - Provides GPU-accelerated conversion between different image color models and manages alpha channels.
  • Real-Time Video Analytics - Processes live camera streams to track moving objects, count crossings, and analyze motion in real time.
  • Video Frame Capture - Provides utilities for reading and capturing individual frames from webcams or video files.
  • Input and Output Handling - Implements capabilities to capture video streams from hardware devices and write them to files.
  • C Source Embeddings - Bundles native vision libraries into standalone executables via static linking to eliminate shared library dependencies.
  • Memory Buffer Wrapping - Wraps multi-dimensional C arrays in Go types for direct image data manipulation without copying buffers.
  • Element-wise Array Operations - Performs high-performance mathematical operations across multi-dimensional image arrays.
  • Matrix Manipulations - Manipulates multi-dimensional arrays through reshaping, copying, and statistical analysis.
  • Pixel Connectivity Analysis - Labels and counts connected regions of pixels to separate distinct objects in binary images.
  • Image Primitive Drawing - Renders basic geometric shapes and lines directly onto image data.
  • Mouse Event Handlers - Captures user mouse interactions within a window and triggers associated callback functions.
  • User Interaction Handling - Captures keyboard and mouse events to enable interactive control of vision tasks.
  • Window Management - Manages GUI windows for image display and keyboard event handling.
  • AI and Machine Learning - Computer vision library using OpenCV.
  • Computer Vision - Package for computer vision and image processing.
  • Image Processing - Go bindings for OpenCV computer vision.

Star history

Star history chart for hybridgroup/gocvStar history chart for hybridgroup/gocv

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Projects sharing features with Gocv

These projects share indexed features with Gocv. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
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  • peterbraden/node-opencvpeterbraden avatar

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    node-opencv is a high-performance C++ native addon and bridge that connects Node.js applications to the OpenCV library. It serves as an image processing toolkit and computer vision library, allowing JavaScript code to execute vision algorithms and image manipulation operations through native bindings. The project provides specialized capabilities for face and shape detection, as well as face identity recognition using trained models. It includes tools for object motion tracking through optical flow and background subtraction, along with the ability to identify specific patterns and analyze sh

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Frequently asked questions

What does hybridgroup/gocv do?

GoCV is a computer vision library and Go language binding for OpenCV. It serves as an image processing toolkit and deep learning inference engine, providing programmatic access to a wide range of algorithms for image manipulation, object detection, and video analysis.

What are the main features of hybridgroup/gocv?

The main features of hybridgroup/gocv are: Computer Vision Libraries, Bounding Box Detection, Real-Time Object Detection, Deep Learning Inference Engines, Face Detection, GPU Accelerated Computer Vision, GPU Acceleration, Neural Networks.

Which projects share features with hybridgroup/gocv?

Projects with overlapping indexed features include: dusty-nv/jetson-inference — jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU… shimat/opencvsharp — OpenCVSharp is a .NET library that wraps native OpenCV functions, providing C# developers with access to OpenCV's… kornia/kornia — Kornia is a differentiable computer vision library and cross-framework tensor vision toolset. It implements vision… peterbraden/node-opencv — node-opencv is a high-performance C++ native addon and bridge that connects Node.js applications to the OpenCV… opencv/opencv_contrib — This project is a collection of optional, community-contributed algorithms and specialized vision tools that extend… atduskgreg/opencv-processing — This project is a Java-based toolkit that integrates the OpenCV computer vision library into the Processing creative…