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// This file is part of VisionCpp, a lightweight C++ template library
// for computer vision and image processing.
//
// Copyright (C) 2016 Codeplay Software Limited. All Rights Reserved.
//
// Contact: [email protected]
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
/// \file simple_conv.cc
///
/// ---------Example---------
/// Simple Convolution
///
/// \brief This example implements a simple separable convolution using mean
/// filter
///
#ifdef NOT_COMPATIBLE_WITH_COMPUTECPP_050
// include OpenCV for camera display
#include <opencv2/opencv.hpp>
// include VisionCpp
#include <visioncpp.hpp>
// main program
int main(int argc, char** argv) {
// open video or camera
cv::VideoCapture cap;
if (argc == 1) {
cap.open(0);
std::cout << "To use video" << std::endl;
std::cout << "example>: ./example path/to/video.avi" << std::endl;
} else if (argc > 1) {
cap.open(argv[1]);
}
// check if we succeeded
if (!cap.isOpened()) {
std::cout << "Opening Camera/Video Failed." << std::endl;
return -1;
}
// set fixed resolution on the camera
constexpr size_t COLS = 640;
constexpr size_t ROWS = 480;
// where VisionCpp will run?
auto dev = visioncpp::make_device<visioncpp::backend::sycl,
visioncpp::device::cpu>();
std::shared_ptr<unsigned char> vc_buffer(
new unsigned char[COLS * ROWS * 3],
[](unsigned char* dataMem) { delete[] dataMem; });
// creating a 1x9 filter mean filter
float filter_array[9] = {1.0f / 9.0f, 1.0f / 9.0f, 1.0f / 9.0f,
1.0f / 9.0f, 1.0f / 9.0f, 1.0f / 9.0f,
1.0f / 9.0f, 1.0f / 9.0f, 1.0f / 9.0f};
// create opencv mat
cv::Mat frame;
cv::Mat output;
for (;;) {
// read frame
cap.read(frame);
// check if image was loaded
if (!frame.data) {
break;
}
// resize image to the desirable size
cv::resize(frame, frame, cv::Size(COLS, ROWS), 0, 0, cv::INTER_CUBIC);
// column-wise filter for separable convolution
auto filter_col =
visioncpp::terminal<float, 9, 1, visioncpp::memory_type::Buffer2D,
visioncpp::scope::Constant>(filter_array);
// row wise filter for separable convolution
auto filter_row =
visioncpp::terminal<float, 1, 9, visioncpp::memory_type::Buffer2D,
visioncpp::scope::Constant>(filter_array);
// wrap OpenCV frame data in our leaf node
auto data =
visioncpp::terminal<visioncpp::pixel::U8C3, COLS, ROWS,
visioncpp::memory_type::Buffer2D>(frame.data);
auto data_out =
visioncpp::terminal<visioncpp::pixel::U8C3, COLS, ROWS,
visioncpp::memory_type::Buffer2D>(vc_buffer.get());
auto node2 = visioncpp::neighbour_operation<visioncpp::OP_SepFilterCol>(
data, filter_col);
auto node3 = visioncpp::neighbour_operation<visioncpp::OP_SepFilterRow>(
node2, filter_row);
// writing back to Mat
auto k = visioncpp::assign(data_out, node3);
// execute the pipe
visioncpp::execute<visioncpp::policy::Fuse, 32, 32, 16, 16>(k, dev);
// show image
output = cv::Mat(ROWS, COLS, CV_8UC3, vc_buffer.get());
cv::imshow("Reference Image", frame);
cv::imshow("Mean Filter Convolution", output);
// loop
if (cv::waitKey(30) >= 0) break;
}
// release video/camera
cap.release();
return 0;
}
#endif