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3rdparty/opencv-4.5.4/modules/gapi/samples/semantic_segmentation.cpp 5.74 KB
f4334277   Hu Chunming   提交3rdparty
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  #include <opencv2/imgproc.hpp>
  #include <opencv2/gapi/infer/ie.hpp>
  #include <opencv2/gapi/cpu/gcpukernel.hpp>
  #include <opencv2/gapi/streaming/cap.hpp>
  #include <opencv2/highgui.hpp>
  
  const std::string keys =
      "{ h help |                                     | Print this help message }"
      "{ input  |                                     | Path to the input video file }"
      "{ output |                                     | Path to the output video file }"
      "{ ssm    | semantic-segmentation-adas-0001.xml | Path to OpenVINO IE semantic segmentation model (.xml) }";
  
  // 20 colors for 20 classes of semantic-segmentation-adas-0001
  const std::vector<cv::Vec3b> colors = {
      { 128, 64,  128 },
      { 232, 35,  244 },
      { 70,  70,  70 },
      { 156, 102, 102 },
      { 153, 153, 190 },
      { 153, 153, 153 },
      { 30,  170, 250 },
      { 0,   220, 220 },
      { 35,  142, 107 },
      { 152, 251, 152 },
      { 180, 130, 70 },
      { 60,  20,  220 },
      { 0,   0,   255 },
      { 142, 0,   0 },
      { 70,  0,   0 },
      { 100, 60,  0 },
      { 90,  0,   0 },
      { 230, 0,   0 },
      { 32,  11,  119 },
      { 0,   74,  111 },
  };
  
  namespace {
  std::string get_weights_path(const std::string &model_path) {
      const auto EXT_LEN = 4u;
      const auto sz = model_path.size();
      CV_Assert(sz > EXT_LEN);
  
      auto ext = model_path.substr(sz - EXT_LEN);
      std::transform(ext.begin(), ext.end(), ext.begin(), [](unsigned char c){
              return static_cast<unsigned char>(std::tolower(c));
          });
      CV_Assert(ext == ".xml");
      return model_path.substr(0u, sz - EXT_LEN) + ".bin";
  }
  
  void classesToColors(const cv::Mat &out_blob,
                             cv::Mat &mask_img) {
      const int H = out_blob.size[0];
      const int W = out_blob.size[1];
  
      mask_img.create(H, W, CV_8UC3);
      GAPI_Assert(out_blob.type() == CV_8UC1);
      const uint8_t* const classes = out_blob.ptr<uint8_t>();
  
      for (int rowId = 0; rowId < H; ++rowId) {
          for (int colId = 0; colId < W; ++colId) {
              uint8_t class_id = classes[rowId * W + colId];
              mask_img.at<cv::Vec3b>(rowId, colId) =
                  class_id < colors.size()
                  ? colors[class_id]
                  : cv::Vec3b{0, 0, 0}; // NB: sample supports 20 classes
          }
      }
  }
  
  void probsToClasses(const cv::Mat& probs, cv::Mat& classes) {
       const int C = probs.size[1];
       const int H = probs.size[2];
       const int W = probs.size[3];
  
       classes.create(H, W, CV_8UC1);
       GAPI_Assert(probs.depth() == CV_32F);
       float* out_p       = reinterpret_cast<float*>(probs.data);
       uint8_t* classes_p = reinterpret_cast<uint8_t*>(classes.data);
  
       for (int h = 0; h < H; ++h) {
           for (int w = 0; w < W; ++w) {
               double max = 0;
               int class_id = 0;
               for (int c = 0; c < C; ++c) {
                  int idx = c * H * W + h * W + w;
                      if (out_p[idx] > max) {
                          max = out_p[idx];
                          class_id = c;
                      }
               }
               classes_p[h * W + w] = static_cast<uint8_t>(class_id);
           }
       }
  }
  
  } // anonymous namespace
  
  namespace custom {
  G_API_OP(PostProcessing, <cv::GMat(cv::GMat, cv::GMat)>, "sample.custom.post_processing") {
      static cv::GMatDesc outMeta(const cv::GMatDesc &in, const cv::GMatDesc &) {
          return in;
      }
  };
  
  GAPI_OCV_KERNEL(OCVPostProcessing, PostProcessing) {
      static void run(const cv::Mat &in, const cv::Mat &out_blob, cv::Mat &out) {
          cv::Mat classes;
          // NB: If output has more than single plane, it contains probabilities
          // otherwise class id.
          if (out_blob.size[1] > 1) {
              probsToClasses(out_blob, classes);
          } else {
              out_blob.convertTo(classes, CV_8UC1);
              classes = classes.reshape(1, out_blob.size[2]);
          }
  
          cv::Mat mask_img;
          classesToColors(classes, mask_img);
  
          cv::resize(mask_img, out, in.size());
          const float blending = 0.3f;
          out = in * blending + out * (1 - blending);
      }
  };
  } // namespace custom
  
  int main(int argc, char *argv[]) {
      cv::CommandLineParser cmd(argc, argv, keys);
      if (cmd.has("help")) {
          cmd.printMessage();
          return 0;
      }
  
      // Prepare parameters first
      const std::string input  = cmd.get<std::string>("input");
      const std::string output = cmd.get<std::string>("output");
      const auto model_path    = cmd.get<std::string>("ssm");
      const auto weights_path  = get_weights_path(model_path);
      const auto device        = "CPU";
      G_API_NET(SemSegmNet, <cv::GMat(cv::GMat)>, "semantic-segmentation");
      const auto net = cv::gapi::ie::Params<SemSegmNet> {
          model_path, weights_path, device
      };
      const auto kernels = cv::gapi::kernels<custom::OCVPostProcessing>();
      const auto networks = cv::gapi::networks(net);
  
      // Now build the graph
      cv::GMat in;
      cv::GMat out_blob = cv::gapi::infer<SemSegmNet>(in);
      cv::GMat out = custom::PostProcessing::on(in, out_blob);
  
      cv::GStreamingCompiled pipeline = cv::GComputation(cv::GIn(in), cv::GOut(out))
          .compileStreaming(cv::compile_args(kernels, networks));
      auto inputs = cv::gin(cv::gapi::wip::make_src<cv::gapi::wip::GCaptureSource>(input));
  
      // The execution part
      pipeline.setSource(std::move(inputs));
      pipeline.start();
  
      cv::VideoWriter writer;
      cv::Mat outMat;
      while (pipeline.pull(cv::gout(outMat))) {
          cv::imshow("Out", outMat);
          cv::waitKey(1);
          if (!output.empty()) {
              if (!writer.isOpened()) {
                  const auto sz = cv::Size{outMat.cols, outMat.rows};
                  writer.open(output, cv::VideoWriter::fourcc('M','J','P','G'), 25.0, sz);
                  CV_Assert(writer.isOpened());
              }
              writer << outMat;
          }
      }
      return 0;
  }