skin-tone-detector Directory Reference

Directories
	src

Detailed Description

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.

Prerequisites

• Conformant OpenVX Implementation
• OpenCV

Steps to run the skin tone sample

• 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