Khronos OpenVX™ is an open, royalty-free standard for cross-platform acceleration of computer vision applications. OpenVX enables performance and power-optimized computer vision processing, especially important in embedded and real-time use cases such as face, body, and gesture tracking, smart video surveillance, advanced driver assistance systems (ADAS), object and scene reconstruction, augmented reality, visual inspection, robotics and more.
In this project, we provide OpenVX sample applications to use with any conformant implementation of OpenVX.
In this sample we will create an OpenVX graph to run VX Bubble Pop on a live camera. This sample application uses OpenCV to decode input image, draw bubbles/donuts and display the output.
- Step - 1: Build and install Conformant OpenVX Implementation. In this example we will use the OpenVX Sample Implementation available on GitHub
Build OpenVX on Linux
* Git Clone project with a recursive flag to get submodules
git clone --recursive https://github.com/KhronosGroup/OpenVX-sample-impl.git
* Use Build.py script
cd OpenVX-sample-impl/
python Build.py --os=Linux --arch=64 --conf=Debug --conf_vision --enh_vision --conf_nn
- Step - 2: Export OpenVX Directory Path
export OPENVX_DIR=$(pwd)/install/Linux/x64/Debug
- Step - 3: Clone the OpenVX Samples project and build the bubble pop application
cd ~/ && mkdir OpenVXSample-pop
cd OpenVXSample-pop/
git clone https://github.com/kiritigowda/openvx-samples.git
- Step - 4: CMake and Build the pop application
mkdir pop-build && cd pop-build
cmake -DOPENVX_INCLUDES=$OPENVX_DIR/include -DOPENVX_LIBRARIES=$OPENVX_DIR/bin/libopenvx.so ../openvx-samples/bubble-pop/
make
-
Step - 5: Run VX Pop application
- Bubbles
./vxPop --bubble
- Donuts
./vxPop --donut
In this sample we will create an OpenVX graph to run canny edge detection on an image or a live camera. This sample application uses OpenCV to decode input image and display the output.
- Step - 1: Build and install Conformant OpenVX Implementation. In this example we will use the OpenVX Sample Implementation available on GitHub
Build OpenVX on Linux
* Git Clone project with a recursive flag to get submodules
git clone --recursive https://github.com/KhronosGroup/OpenVX-sample-impl.git
* Use Build.py script
cd OpenVX-sample-impl/
python Build.py --os=Linux --arch=64 --conf=Debug --conf_vision --enh_vision --conf_nn
- Step - 2: Export OpenVX Directory Path
export OPENVX_DIR=$(pwd)/install/Linux/x64/Debug
- Step - 3: Clone the OpenVX Samples project and build the canny application
cd ~/ && mkdir OpenVXSample-canny
cd OpenVXSample-canny/
git clone https://github.com/kiritigowda/openvx-samples.git
- Step - 4: CMake and Build the canny application
mkdir canny-build && cd canny-build
cmake -DOPENVX_INCLUDES=$OPENVX_DIR/include -DOPENVX_LIBRARIES=$OPENVX_DIR/bin/libopenvx.so ../openvx-samples/canny-edge-detector/
make
-
Step - 5: Run Canny application
- Live
./cannyEdgeDetector --live
- Image
./cannyEdgeDetector --image ../openvx-samples/images/face.png
In this sample we will create an OpenVX graph to run skintone detection on an image or a live camera. This sample application uses OpenCV to decode input image and display the output.
- Step - 1: Build and install Conformant OpenVX Implementation. In this example we will use the OpenVX Sample Implementation available on GitHub
Build OpenVX on Linux
* Git Clone project with a recursive flag to get submodules
git clone --recursive https://github.com/KhronosGroup/OpenVX-sample-impl.git
* Use Build.py script
cd OpenVX-sample-impl/
python Build.py --os=Linux --arch=64 --conf=Debug --conf_vision --enh_vision --conf_nn
- Step - 2: Export OpenVX Directory Path
export OPENVX_DIR=$(pwd)/install/Linux/x64/Debug
- Step - 3: Clone the OpenVX Samples project and build the Skin Tone application
cd ~/ && mkdir OpenVXSample-skintone
cd OpenVXSample-skintone/
git clone https://github.com/kiritigowda/openvx-samples.git
- Step - 4: CMake and Build the Skin Tone application
mkdir skintone-build && cd skintone-build
cmake -DOPENVX_INCLUDES=$OPENVX_DIR/include -DOPENVX_LIBRARIES=$OPENVX_DIR/bin/libopenvx.so ../openvx-samples/skin-tone-detector/
make
-
Step - 5: Run Skin Tone Detector application
- Live
./skinToneDetector --live
- Image
./skinToneDetector --image ../openvx-samples/images/face.png
The samples VX Bubble Pop, VX Canny Edge Detector, & VX Skin Tone Detector are contributed by AMD from their MIVisionX Toolkit. We welcome contributions to this project from all developers. Please open a pull request with details of your sample application to be accepted into this project.