Gocv

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gocv is an Open Source & Models tool that provides Go language bindings for the OpenCV 4 computer vision library. It supports DNN, CUDA, OpenCV Contrib, and OpenVINO for advanced computer vision applications.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is gocv?

gocv offers Go language bindings for the OpenCV 4 computer vision library, extending its capabilities to Go developers. It supports the latest releases of Go and OpenCV (v4.12.0) across Linux, Docker, macOS, and Windows. The package includes support for Deep Neural Networks (DNN), CUDA for hardware acceleration using Nvidia GPUs, OpenCV Contrib modules, and Intel OpenVINO for optimized inference. This allows developers to build a wide range of computer vision applications, from basic video processing to complex face detection and object tracking, leveraging the power of OpenCV within the Go ecosystem. The project aims to make Go a first-class client for OpenCV developments.

Best used for

Ideal for developers who need to implement computer vision functionalities in Go applications, such as building face detection systems, creating object tracking solutions, and processing video streams. Especially valuable for those leveraging OpenCV's advanced features like DNN, CUDA, and OpenVINO within the Go programming language.

Common actions

build computer vision

process images

analyze video

integrate OpenCV

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Capabilities

Key features

Go language bindings
OpenCV 4 support
DNN integration
CUDA acceleration
OpenVINO support
Video capture processing
Face detection

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What versions of Go and OpenCV does gocv support?

gocv supports the latest releases of Go and OpenCV, specifically OpenCV v4.12.0 and beyond. It aims to keep pace with new developments in the OpenCV ecosystem, ensuring compatibility and access to the newest features for Go developers.

Does gocv support hardware acceleration?

Yes, gocv supports hardware acceleration through CUDA for Nvidia GPUs, allowing for faster processing of computer vision tasks. It also integrates with Intel OpenVINO, which optimizes inference for deep learning models on Intel hardware.

How can I detect memory leaks when using gocv?

gocv provides a Mat profiler to detect memory leaks, as Go's garbage collector doesn't manage C-based allocations. You can enable it with the `-tags matprofile` build flag and use `gocv.MatProfile.Count()` or `gocv.MatProfile.WriteTo()` to inspect unclosed Mat objects.

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