Overview

All you need to do is include the VisionCpp.hpp header in your project and you are good to go! ( assuming that OpenCL and ComputeCPP is installed correctly. )

#include <visioncpp.hpp> //all that is needed

Below is a very simple application that will do the conversion RGB -> HSV. Full source code can be found in the examples folder. RGB is assumed to be a three-channel unsigned char storage with a reasonable channel order.

  // main, args, checks and all the boring stuff

  // ...

  // where VisionCpp will run.
  auto dev = visioncpp::make_device<visioncpp::backend::sycl,
                                    visioncpp::device::cpu>();

  // create a host container for input data
  std::shared_ptr<unsigned char> in_rgb(new unsigned char[3],
  [](unsigned char *dataMem) { delete[] dataMem;});

  in_rgb.get()[0] = atoi(argv[1]);
  in_rgb.get()[1] = atoi(argv[2]);
  in_rgb.get()[2] = atoi(argv[3]);

  // create a host container for output data
  std::shared_ptr<unsigned char> out_hsv(new unsigned char[3],
  [](unsigned char *dataMem) { delete[] dataMem;});

  // exiting this scope will sync data
  {
    // definition of the VisionCpp pipeline:

    // create terminal nodes - a leaf node ( data node ) of the expression tree.
    // terminal struct takes 4 arguments
    // 1st template parameter specifies the data U8 (unsigned char) C3 (three
    // channels)
    // 2nd number of columns in the storage
    // 3rd number of rows in the storage
    // 4th underlying storage type - currently only Buffer2D supported
    auto data =
        visioncpp::terminal<visioncpp::pixel::U8C3, 1, 1,
                            visioncpp::memory_type::Buffer2D>(in_rgb.get());
    auto data_out =
        visioncpp::terminal<visioncpp::pixel::U8C3, 1, 1,
                            visioncpp::memory_type::Buffer2D>(out_hsv.get());

    // unsigned char -> float RGB storage conversion
    auto node = visioncpp::point_operation<visioncpp::OP_U8C3ToF32C3>(data);
    // float RGB to float HSV conversion
    auto node2 = visioncpp::point_operation<visioncpp::OP_RGBToHSV>(node);
    // helper node that allows display of HSV
    // for unsigned char: V <- 255*V, S <- 255*S, H <- H/2 ( to fit in range of 0..255 )
    auto node3 = visioncpp::point_operation<visioncpp::OP_HSVToU8C3>(node2);

    // assign operation that writes output of the pipe to output terminal node
    auto pipe = visioncpp::assign(data_out, node3);
    // execute the pipeline
    // 1st template parameter defines if VisionCpp back-end fuses the expression
    // 2nd & 3rd shared memory sizes ( column, row )
    // 4th & 5th local work group size ( column , row )
    visioncpp::execute<visioncpp::policy::Fuse, 1, 1, 1, 1>(pipe, dev);
  }

  printf("RGB: %u %u %u \nHSV: %u %u %u \n", in_rgb.get()[0], in_rgb.get()[1],
         in_rgb.get()[2], out_hsv.get()[0], out_hsv.get()[1], out_hsv.get()[2]);

To successfully compile VisionCpp tests, you will need:

Assuming you are in the root of a git repo:

mkdir build
cd  build
cmake .. -DCOMPUTECPP_PACKAGE_ROOT_DIR={PATH_TO_COMPUTECPP_ROOT} -DCMAKE_CXX_COMPILER={FAVORITE_CXX_COMPILER}
make -j8
make test

The output binaries will be catalogued in bin folder.

| - build
  | - bin
    | - example
    | - test

Most of the examples are using camera.

• The Tuple class works only with clang++.