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OpenCV usage with OpenVINO {#tutorial_dnn_openvino}
=====================
@prev_tutorial{tutorial_dnn_halide_scheduling}
@next_tutorial{tutorial_dnn_android}
| | |
| -: | :- |
| Original author | Aleksandr Voron |
| Compatibility | OpenCV == 4.x |
This tutorial provides OpenCV installation guidelines how to use OpenCV with OpenVINO.
Since 2021.1.1 release OpenVINO does not provide pre-built OpenCV.
The change does not affect you if you are using OpenVINO runtime directly or OpenVINO samples: it does not have a strong dependency to OpenCV.
However, if you are using Open Model Zoo demos or OpenVINO runtime as OpenCV DNN backend you need to get the OpenCV build.
There are 2 approaches how to get OpenCV:
- Install pre-built OpenCV from another sources: system repositories, pip, conda, homebrew. Generic pre-built OpenCV package may have several limitations:
- OpenCV version may be out-of-date
- OpenCV may not contain G-API module with enabled OpenVINO support (e.g. some OMZ demos use G-API functionality)
- OpenCV may not be optimized for modern hardware (default builds need to cover wide range of hardware)
- OpenCV may not support Intel TBB, Intel Media SDK
- OpenCV DNN module may not use OpenVINO as an inference backend
- Build OpenCV from source code against specific version of OpenVINO. This approach solves the limitations mentioned above.
The instruction how to follow both approaches is provided in [OpenCV wiki](https://github.com/opencv/opencv/wiki/BuildOpenCV4OpenVINO).
@ -418,10 +418,18 @@ homography from camera displacement:
The homography matrices are similar. If we compare the image 1 warped using both homography matrices:
Visually, it is hard to distinguish a difference between the result image from the homography computed from the camera displacement and the one estimated with @ref cv::findHomography function.
#### Exercise
This demo shows you how to compute the homography transformation from two camera poses. Try to perform the same operations, but by computing N inter homography this time. Instead of computing one homography to directly warp the source image to the desired camera viewpoint, perform N warping operations to see the different transformations operating.
You should get something similar to the following:
### Demo 4: Decompose the homography matrix {#tutorial_homography_Demo4}
OpenCV 3 contains the function @ref cv::decomposeHomographyMat which allows to decompose the homography matrix to a set of rotations, translations and plane normals.
Depth sensors compatible with Luxonis DepthAI library are supported through OpenCV Graph API (or G-API) module. RGB image and some other formats of output can be retrieved by using familiar interface of G-API module.
In order to use DepthAI sensor with OpenCV you should do the following preliminary steps:
-# Configure OpenCV with DepthAI library support by setting `WITH_OAK` flag in CMake. If DepthAI library is found in install folders OpenCV will be built with depthai-core (see a status `WITH_OAK` in CMake log).
-# Build OpenCV.
Source code
-----------
You can find source code how to process heterogeneous graphs in the `modules/gapi/samples/oak_basic_infer.cpp` of the OpenCV source code library.
Alexander Alekhin
2 years ago
