184 lines
6 KiB
C++
184 lines
6 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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const std::string keys =
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"{ h help | | Print this help message }"
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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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const std::vector<cv::Vec3b> colors = {
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{ 128, 64, 128 },
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{ 232, 35, 244 },
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{ 70, 70, 70 },
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{ 156, 102, 102 },
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{ 153, 153, 190 },
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{ 153, 153, 153 },
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{ 30, 170, 250 },
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{ 0, 220, 220 },
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{ 35, 142, 107 },
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{ 152, 251, 152 },
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{ 180, 130, 70 },
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{ 60, 20, 220 },
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{ 0, 0, 255 },
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{ 142, 0, 0 },
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{ 70, 0, 0 },
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{ 100, 60, 0 },
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{ 90, 0, 0 },
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{ 230, 0, 0 },
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{ 32, 11, 119 },
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{ 0, 74, 111 },
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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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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 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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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[1] > 1) {
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probsToClasses(out_blob, classes);
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} else {
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out_blob.convertTo(classes, CV_8UC1);
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classes = classes.reshape(1, out_blob.size[2]);
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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());
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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 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 out_blob = cv::gapi::infer<SemSegmNet>(in);
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cv::GMat post_proc_out = custom::PostProcessing::on(in, out_blob);
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cv::GMat blending_in = in * 0.3f;
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cv::GMat blending_out = post_proc_out * 0.7f;
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cv::GMat out = blending_in + blending_out;
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cv::GStreamingCompiled pipeline = cv::GComputation(cv::GIn(in), cv::GOut(out))
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.compileStreaming(cv::compile_args(kernels, networks));
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auto inputs = cv::gin(cv::gapi::wip::make_src<cv::gapi::wip::GCaptureSource>(input));
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// The execution part
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pipeline.setSource(std::move(inputs));
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cv::VideoWriter writer;
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cv::TickMeter tm;
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cv::Mat outMat;
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std::size_t frames = 0u;
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tm.start();
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pipeline.start();
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while (pipeline.pull(cv::gout(outMat))) {
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++frames;
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cv::imshow("Out", outMat);
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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{outMat.cols, outMat.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 << outMat;
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}
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}
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tm.stop();
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std::cout << "Processed " << frames << " frames" << " (" << frames / tm.getTimeSec() << " FPS)" << std::endl;
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return 0;
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}
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