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2d69dd1215
Migrate G-API module from main repo to opencv_contrib #3827 Related to https://github.com/opencv/opencv/pull/26469 Required https://github.com/opencv/opencv/pull/26527 CI: https://github.com/opencv/ci-gha-workflow/pull/201 TODO: - [x] Python types generator fix - [x] CI update ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [ ] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake
54 lines
1.4 KiB
Markdown
54 lines
1.4 KiB
Markdown
# Graph API (gapi module) {#tutorial_table_of_content_gapi}
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In this section you will learn about graph-based image processing and
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how G-API module can be used for that.
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- @subpage tutorial_gapi_interactive_face_detection
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*Languages:* C++
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*Compatibility:* \> OpenCV 4.2
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*Author:* Dmitry Matveev
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This tutorial illustrates how to build a hybrid video processing
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pipeline with G-API where Deep Learning and image processing are
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combined effectively to maximize the overall throughput. This
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sample requires Intel® distribution of OpenVINO™ Toolkit version
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2019R2 or later.
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- @subpage tutorial_gapi_anisotropic_segmentation
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*Languages:* C++
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*Compatibility:* \> OpenCV 4.0
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*Author:* Dmitry Matveev
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This is an end-to-end tutorial where an existing sample algorithm
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is ported on G-API, covering the basic intuition behind this
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transition process, and examining benefits which a graph model
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brings there.
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- @subpage tutorial_gapi_face_beautification
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*Languages:* C++
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*Compatibility:* \> OpenCV 4.2
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*Author:* Orest Chura
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In this tutorial we build a complex hybrid Computer Vision/Deep
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Learning video processing pipeline with G-API.
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- @subpage tutorial_gapi_oak_devices
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*Languages:* C++
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*Compatibility:* \> OpenCV 4.6
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*Author:* Alessandro de Oliveira Faria (A.K.A. CABELO)
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In this tutorial we showed how to use the Luxonis DepthAI library with G-API.
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