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G-API: Refine Semantic Segmentation Demo #23766 ### Overview * Supported demo working with camera id (e.g `--input=0`) * Supported 3d output segmentation models (e.g `deeplabv3`) * Supported `desync` execution * Supported higher camera resolution * Changed the color map to pascal voc (https://cloud.githubusercontent.com/assets/4503207/17803328/1006ca80-65f6-11e6-9ff6-36b7ef5b9ac6.png) ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [ ] I agree to contribute to the project under Apache 2 License. - [ ] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [ ] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake
285 lines
9.2 KiB
C++
285 lines
9.2 KiB
C++
#include <opencv2/imgproc.hpp>
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#include <opencv2/gapi/infer/ie.hpp>
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#include <opencv2/gapi/cpu/gcpukernel.hpp>
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#include <opencv2/gapi/streaming/cap.hpp>
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#include <opencv2/gapi/operators.hpp>
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#include <opencv2/highgui.hpp>
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#include <opencv2/gapi/streaming/desync.hpp>
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#include <opencv2/gapi/streaming/format.hpp>
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#include <iomanip>
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const std::string keys =
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"{ h help | | Print this help message }"
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"{ desync | false | Desynchronize inference }"
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"{ input | | Path to the input video file }"
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"{ output | | Path to the output video file }"
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"{ ssm | semantic-segmentation-adas-0001.xml | Path to OpenVINO IE semantic segmentation model (.xml) }";
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// 20 colors for 20 classes of semantic-segmentation-adas-0001
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static std::vector<cv::Vec3b> colors = {
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{ 0, 0, 0 },
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{ 0, 0, 128 },
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{ 0, 128, 0 },
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{ 0, 128, 128 },
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{ 128, 0, 0 },
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{ 128, 0, 128 },
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{ 128, 128, 0 },
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{ 128, 128, 128 },
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{ 0, 0, 64 },
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{ 0, 0, 192 },
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{ 0, 128, 64 },
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{ 0, 128, 192 },
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{ 128, 0, 64 },
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{ 128, 0, 192 },
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{ 128, 128, 64 },
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{ 128, 128, 192 },
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{ 0, 64, 0 },
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{ 0, 64, 128 },
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{ 0, 192, 0 },
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{ 0, 192, 128 },
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{ 128, 64, 0 }
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};
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namespace {
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std::string get_weights_path(const std::string &model_path) {
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const auto EXT_LEN = 4u;
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const auto sz = model_path.size();
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CV_Assert(sz > EXT_LEN);
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auto ext = model_path.substr(sz - EXT_LEN);
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std::transform(ext.begin(), ext.end(), ext.begin(), [](unsigned char c){
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return static_cast<unsigned char>(std::tolower(c));
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});
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CV_Assert(ext == ".xml");
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return model_path.substr(0u, sz - EXT_LEN) + ".bin";
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}
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bool isNumber(const std::string &str) {
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return !str.empty() && std::all_of(str.begin(), str.end(),
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[](unsigned char ch) { return std::isdigit(ch); });
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}
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std::string toStr(double value) {
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std::stringstream ss;
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ss << std::fixed << std::setprecision(1) << value;
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return ss.str();
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}
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void classesToColors(const cv::Mat &out_blob,
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cv::Mat &mask_img) {
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const int H = out_blob.size[0];
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const int W = out_blob.size[1];
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mask_img.create(H, W, CV_8UC3);
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GAPI_Assert(out_blob.type() == CV_8UC1);
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const uint8_t* const classes = out_blob.ptr<uint8_t>();
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for (int rowId = 0; rowId < H; ++rowId) {
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for (int colId = 0; colId < W; ++colId) {
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uint8_t class_id = classes[rowId * W + colId];
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mask_img.at<cv::Vec3b>(rowId, colId) =
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class_id < colors.size()
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? colors[class_id]
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: cv::Vec3b{0, 0, 0}; // NB: sample supports 20 classes
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}
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}
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}
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void probsToClasses(const cv::Mat& probs, cv::Mat& classes) {
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const int C = probs.size[1];
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const int H = probs.size[2];
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const int W = probs.size[3];
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classes.create(H, W, CV_8UC1);
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GAPI_Assert(probs.depth() == CV_32F);
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float* out_p = reinterpret_cast<float*>(probs.data);
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uint8_t* classes_p = reinterpret_cast<uint8_t*>(classes.data);
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for (int h = 0; h < H; ++h) {
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for (int w = 0; w < W; ++w) {
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double max = 0;
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int class_id = 0;
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for (int c = 0; c < C; ++c) {
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int idx = c * H * W + h * W + w;
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if (out_p[idx] > max) {
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max = out_p[idx];
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class_id = c;
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}
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}
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classes_p[h * W + w] = static_cast<uint8_t>(class_id);
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}
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}
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}
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} // anonymous namespace
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namespace vis {
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static void putText(cv::Mat& mat, const cv::Point &position, const std::string &message) {
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auto fontFace = cv::FONT_HERSHEY_COMPLEX;
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int thickness = 2;
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cv::Scalar color = {200, 10, 10};
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double fontScale = 0.65;
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cv::putText(mat, message, position, fontFace,
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fontScale, cv::Scalar(255, 255, 255), thickness + 1);
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cv::putText(mat, message, position, fontFace, fontScale, color, thickness);
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}
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static void drawResults(cv::Mat &img, const cv::Mat &color_mask) {
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img = img / 2 + color_mask / 2;
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}
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} // namespace vis
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namespace custom {
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G_API_OP(PostProcessing, <cv::GMat(cv::GMat, cv::GMat)>, "sample.custom.post_processing") {
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static cv::GMatDesc outMeta(const cv::GMatDesc &in, const cv::GMatDesc &) {
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return in;
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}
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};
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GAPI_OCV_KERNEL(OCVPostProcessing, PostProcessing) {
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static void run(const cv::Mat &in, const cv::Mat &out_blob, cv::Mat &out) {
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int C = -1, H = -1, W = -1;
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if (out_blob.size.dims() == 4u) {
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C = 1; H = 2, W = 3;
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} else if (out_blob.size.dims() == 3u) {
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C = 0; H = 1, W = 2;
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} else {
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throw std::logic_error(
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"Number of dimmensions for model output must be 3 or 4!");
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}
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cv::Mat classes;
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// NB: If output has more than single plane, it contains probabilities
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// otherwise class id.
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if (out_blob.size[C] > 1) {
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probsToClasses(out_blob, classes);
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} else {
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if (out_blob.depth() != CV_32S) {
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throw std::logic_error(
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"Single channel output must have integer precision!");
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}
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cv::Mat view(out_blob.size[H], // cols
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out_blob.size[W], // rows
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CV_32SC1,
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out_blob.data);
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view.convertTo(classes, CV_8UC1);
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}
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cv::Mat mask_img;
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classesToColors(classes, mask_img);
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cv::resize(mask_img, out, in.size(), 0, 0, cv::INTER_NEAREST);
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}
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};
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} // namespace custom
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int main(int argc, char *argv[]) {
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cv::CommandLineParser cmd(argc, argv, keys);
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if (cmd.has("help")) {
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cmd.printMessage();
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return 0;
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}
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// Prepare parameters first
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const std::string input = cmd.get<std::string>("input");
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const std::string output = cmd.get<std::string>("output");
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const auto model_path = cmd.get<std::string>("ssm");
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const bool desync = cmd.get<bool>("desync");
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const auto weights_path = get_weights_path(model_path);
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const auto device = "CPU";
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G_API_NET(SemSegmNet, <cv::GMat(cv::GMat)>, "semantic-segmentation");
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const auto net = cv::gapi::ie::Params<SemSegmNet> {
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model_path, weights_path, device
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};
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const auto kernels = cv::gapi::kernels<custom::OCVPostProcessing>();
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const auto networks = cv::gapi::networks(net);
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// Now build the graph
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cv::GMat in;
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cv::GMat bgr = cv::gapi::copy(in);
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cv::GMat frame = desync ? cv::gapi::streaming::desync(bgr) : bgr;
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cv::GMat out_blob = cv::gapi::infer<SemSegmNet>(frame);
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cv::GMat out = custom::PostProcessing::on(frame, out_blob);
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cv::GStreamingCompiled pipeline = cv::GComputation(cv::GIn(in), cv::GOut(bgr, out))
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.compileStreaming(cv::compile_args(kernels, networks,
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cv::gapi::streaming::queue_capacity{1}));
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std::shared_ptr<cv::gapi::wip::GCaptureSource> source;
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if (isNumber(input)) {
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source = std::make_shared<cv::gapi::wip::GCaptureSource>(
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std::stoi(input),
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std::map<int, double> {
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{cv::CAP_PROP_FRAME_WIDTH, 1280},
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{cv::CAP_PROP_FRAME_HEIGHT, 720},
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{cv::CAP_PROP_BUFFERSIZE, 1},
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{cv::CAP_PROP_AUTOFOCUS, true}
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}
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);
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} else {
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source = std::make_shared<cv::gapi::wip::GCaptureSource>(input);
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}
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auto inputs = cv::gin(
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static_cast<cv::gapi::wip::IStreamSource::Ptr>(source));
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// The execution part
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pipeline.setSource(std::move(inputs));
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cv::TickMeter tm;
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cv::VideoWriter writer;
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cv::util::optional<cv::Mat> color_mask;
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cv::util::optional<cv::Mat> image;
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cv::Mat last_image;
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cv::Mat last_color_mask;
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pipeline.start();
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tm.start();
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std::size_t frames = 0u;
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std::size_t masks = 0u;
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while (pipeline.pull(cv::gout(image, color_mask))) {
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if (image.has_value()) {
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++frames;
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last_image = std::move(*image);
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}
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if (color_mask.has_value()) {
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++masks;
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last_color_mask = std::move(*color_mask);
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}
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if (!last_image.empty() && !last_color_mask.empty()) {
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tm.stop();
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std::string stream_fps = "Stream FPS: " + toStr(frames / tm.getTimeSec());
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std::string inference_fps = "Inference FPS: " + toStr(masks / tm.getTimeSec());
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cv::Mat tmp = last_image.clone();
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vis::drawResults(tmp, last_color_mask);
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vis::putText(tmp, {10, 22}, stream_fps);
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vis::putText(tmp, {10, 22 + 30}, inference_fps);
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cv::imshow("Out", tmp);
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cv::waitKey(1);
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if (!output.empty()) {
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if (!writer.isOpened()) {
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const auto sz = cv::Size{tmp.cols, tmp.rows};
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writer.open(output, cv::VideoWriter::fourcc('M','J','P','G'), 25.0, sz);
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CV_Assert(writer.isOpened());
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}
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writer << tmp;
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}
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tm.start();
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}
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}
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tm.stop();
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std::cout << "Processed " << frames << " frames" << " ("
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<< frames / tm.getTimeSec()<< " FPS)" << std::endl;
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return 0;
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}
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