Onnx interpreter Error with Custom detection with yolov8 and realsense frame grabbing

[shrutichakraborty]

Hello!

I am trying to adapt the tutorial-dnn-object-detection-live.cpp file in order to use a realsense to grab frames and apply my custom trained object detector. I am unfortunately facing some issues :

The trained detector weights are in onnx format. I only chaged the frame brabber part of the code to get images with realsense but the error seems to be frm the detection part.

Here is the adapted code :

#include <visp3/core/vpConfig.h>
#include <visp3/core/vpIoTools.h>
#include <visp3/detection/vpDetectorDNNOpenCV.h>
#include <visp3/gui/vpDisplayGDI.h>
#include <visp3/gui/vpDisplayOpenCV.h>
#include <visp3/gui/vpDisplayX.h>
#include <visp3/sensor/vpRealSense.h>
#include <visp3/sensor/vpRealSense2.h>

#if defined(HAVE_OPENCV_VIDEOIO)
#include <opencv2/videoio.hpp>
#endif

#ifdef VISP_HAVE_NLOHMANN_JSON
#include <nlohmann/json.hpp>
using json = nlohmann::json; //! json namespace shortcut
#endif

typedef enum
{
  DETECTION_CONTAINER_MAP = 0,
  DETECTION_CONTAINER_VECTOR = 1,
  DETECTION_CONTAINER_BOTH = 2,
  DETECTION_CONTAINER_COUNT = 3
} ChosenDetectionContainer;

std::string chosenDetectionContainerToString(const ChosenDetectionContainer &choice)
{
  switch (choice) {
  case DETECTION_CONTAINER_MAP:
    return "map";
  case DETECTION_CONTAINER_VECTOR:
    return "vector";
  case DETECTION_CONTAINER_BOTH:
    return "both";
  default:
    break;
  }
  return "unknown";
}

ChosenDetectionContainer chosenDetectionContainerFromString(const std::string &choiceStr)
{
  ChosenDetectionContainer choice(DETECTION_CONTAINER_COUNT);
  bool hasFoundMatch = false;
  for (unsigned int i = 0; i < DETECTION_CONTAINER_COUNT && !hasFoundMatch; i++) {
    ChosenDetectionContainer candidate = (ChosenDetectionContainer)i;
    hasFoundMatch = (chosenDetectionContainerToString(candidate) == vpIoTools::toLowerCase(choiceStr));
    if (hasFoundMatch) {
      choice = candidate;
    }
  }
  return choice;
}

std::string getAvailableDetectionContainer()
{
  std::string availableContainers("< ");
  for (unsigned int i = 0; i < DETECTION_CONTAINER_COUNT - 1; i++) {
    std::string name = chosenDetectionContainerToString((ChosenDetectionContainer)i);
    availableContainers += name + " , ";
  }
  availableContainers +=
    chosenDetectionContainerToString((ChosenDetectionContainer)(DETECTION_CONTAINER_COUNT - 1)) + " >";
  return availableContainers;
}

int main(int argc, const char *argv[])
{
  // Check if std:c++17 or higher
#if (defined(VISP_HAVE_REALSENSE) || defined(VISP_HAVE_REALSENSE2)) && defined(HAVE_OPENCV_DNN) && defined(HAVE_OPENCV_VIDEOIO) && \
    ((__cplusplus >= 201703L) || (defined(_MSVC_LANG) && (_MSVC_LANG >= 201703L)))
  try {
    std::string opt_device("0");
    //! [OpenCV DNN face detector]
    std::string opt_dnn_model = "opencv_face_detector_uint8.pb";
    std::string opt_dnn_config = "opencv_face_detector.pbtxt";
    std::string opt_dnn_framework = "none";
    std::string opt_dnn_label_file = "";
    vpDetectorDNNOpenCV::DNNResultsParsingType opt_dnn_type = vpDetectorDNNOpenCV::RESNET_10;
    //! [OpenCV DNN face detector]
    int opt_dnn_width = 300, opt_dnn_height = 300;
    double opt_dnn_meanR = 104.0, opt_dnn_meanG = 177.0, opt_dnn_meanB = 123.0;
    double opt_dnn_scale_factor = 1.0;
    bool opt_dnn_swapRB = false;
    bool opt_step_by_step = false;
    float opt_dnn_confThresh = 0.5f;
    float opt_dnn_nmsThresh = 0.4f;
    double opt_dnn_filterThresh = 0.25;
    ChosenDetectionContainer opt_dnn_containerType = DETECTION_CONTAINER_MAP;
    bool opt_verbose = false;
    std::string opt_input_json = "";
    std::string opt_output_json = "";
    unsigned int opt_width = 640;
    unsigned int opt_height = 480;
    int opt_fps = 30;

    for (int i = 1; i < argc; i++) {
      if (std::string(argv[i]) == "--device" && i + 1 < argc) {
        opt_device = std::string(argv[++i]);
      }
      else if (std::string(argv[i]) == "--step-by-step") {
        opt_step_by_step = true;
      }
      else if (std::string(argv[i]) == "--model" && i + 1 < argc) {
        opt_dnn_model = std::string(argv[++i]);
      }
      else if (std::string(argv[i]) == "--type" && i + 1 < argc) {
        opt_dnn_type = vpDetectorDNNOpenCV::dnnResultsParsingTypeFromString(std::string(argv[++i]));
      }
      else if (std::string(argv[i]) == "--config" && i + 1 < argc) {
        opt_dnn_config = std::string(argv[++i]);
        if (opt_dnn_config.find("none") != std::string::npos) {
          opt_dnn_config = std::string();
        }
      }
      else if (std::string(argv[i]) == "--framework" && i + 1 < argc) {
        opt_dnn_framework = std::string(argv[++i]);
        if (opt_dnn_framework.find("none") != std::string::npos) {
          opt_dnn_framework = std::string();
        }
      }
      else if (std::string(argv[i]) == "--width" && i + 1 < argc) {
        opt_dnn_width = atoi(argv[++i]);
      }
      else if (std::string(argv[i]) == "--height" && i + 1 < argc) {
        opt_dnn_height = atoi(argv[++i]);
      }
      else if (std::string(argv[i]) == "--mean" && i + 3 < argc) {
        opt_dnn_meanR = atof(argv[++i]);
        opt_dnn_meanG = atof(argv[++i]);
        opt_dnn_meanB = atof(argv[++i]);
      }
      else if (std::string(argv[i]) == "--scale" && i + 1 < argc) {
        opt_dnn_scale_factor = atof(argv[++i]);
      }
      else if (std::string(argv[i]) == "--swapRB") {
        opt_dnn_swapRB = true;
      }
      else if (std::string(argv[i]) == "--confThresh" && i + 1 < argc) {
        opt_dnn_confThresh = (float)atof(argv[++i]);
      }
      else if (std::string(argv[i]) == "--nmsThresh" && i + 1 < argc) {
        opt_dnn_nmsThresh = (float)atof(argv[++i]);
      }
      else if (std::string(argv[i]) == "--filterThresh" && i + 1 < argc) {
        opt_dnn_filterThresh = atof(argv[++i]);
      }
      else if (std::string(argv[i]) == "--labels" && i + 1 < argc) {
        opt_dnn_label_file = std::string(argv[++i]);
      }
      else if (std::string(argv[i]) == "--container" && i + 1 < argc) {
        opt_dnn_containerType = chosenDetectionContainerFromString(std::string(argv[++i]));
      }
      else if (std::string(argv[i]) == "--input-json" && i + 1 < argc) {
        opt_input_json = std::string(std::string(argv[++i]));
      }
      else if (std::string(argv[i]) == "--output-json" && i + 1 < argc) {
        opt_output_json = std::string(std::string(argv[++i]));
      }
      else if (std::string(argv[i]) == "--verbose" || std::string(argv[i]) == "-v") {
        opt_verbose = true;
      }
      else if (std::string(argv[i]) == "--help" || std::string(argv[i]) == "-h") {
        std::cout << "\nSYNOPSIS " << std::endl
          << argv[0] << " [--device <video>]"
          << " [--model <dnn weights file>]"
          << " [--type <dnn type>]"
          << " [--config <dnn config file]"
          << " [--framework <name>]"
          << " [--width <blob width>] [--height <blob height>]"
          << " [--mean <meanR meanG meanB>]"
          << " [--scale <scale factor>]"
          << " [--swapRB]"
          << " [--confThresh <threshold>]"
          << " [--nmsThresh <threshold>]"
          << " [--filterThresh <threshold>]"
          << " [--labels <file>]"
          << " [--container <type>]"
          << " [--input-json <path_to_input_json>]"
          << " [--output-json <path_to_output_json>]"
          << " [--step-by-step]"
          << " [--verbose, -v]"
          << " [--help, -h]" << std::endl;
        std::cout << "\nOPTIONS " << std::endl
          << "  --device <video>" << std::endl
          << "      Camera device number or video name used to stream images." << std::endl
          << "      To use the first camera found on the bus set 0. On Ubuntu setting 0" << std::endl
          << "      will use /dev/video0 device. To use a video simply put the name of" << std::endl
          << "      the video, like \"path/my-video.mp4\" or \"path/image-%04d.png\"" << std::endl
          << "      if your video is a sequence of images." << std::endl
          << "      Default: " << opt_device << std::endl
          << std::endl
          << "  --model <dnn weights file>" << std::endl
          << "      Path to dnn network trained weights." << std::endl
          << "      Default: " << opt_dnn_model << std::endl
          << std::endl
          << "  --type <dnn type>" << std::endl
          << "      Type of dnn network. Admissible values are in " << std::endl
          << "      " << vpDetectorDNNOpenCV::getAvailableDnnResultsParsingTypes() << std::endl
          << "      Default: " << opt_dnn_type << std::endl
          << std::endl
          << "  --config <dnn config file>" << std::endl
          << "      Path to dnn network config file or \"none\" not to use one. " << std::endl
          << "      Default: " << opt_dnn_config << std::endl
          << std::endl
          << "  --framework <name>" << std::endl
          << "      Framework name or \"none\" not to specify one. " << std::endl
          << "      Default: " << opt_dnn_framework << std::endl
          << std::endl
          << "  --width <blob width>" << std::endl
          << "      Input images will be resized to this width. " << std::endl
          << "      Default: " << opt_dnn_width << std::endl
          << std::endl
          << "  --height <blob height>" << std::endl
          << "      Input images will be resized to this height. " << std::endl
          << "      Default: " << opt_dnn_height << std::endl
          << std::endl
          << "  --mean <meanR meanG meanB>" << std::endl
          << "      Mean RGB subtraction values. " << std::endl
          << "      Default: " << opt_dnn_meanR << " " << opt_dnn_meanG << " " << opt_dnn_meanB << std::endl
          << std::endl
          << "  --scale <scale factor>" << std::endl
          << "      Scale factor used to normalize the range of pixel values. " << std::endl
          << "      Default: " << opt_dnn_scale_factor << std::endl
          << std::endl
          << "  --swapRB" << std::endl
          << "      When used this option allows to swap Red and Blue channels. " << std::endl
          << std::endl
          << "  --confThresh <threshold>" << std::endl
          << "      Confidence threshold. " << std::endl
          << "      Default: " << opt_dnn_confThresh << std::endl
          << std::endl
          << "  --nmsThresh <threshold>" << std::endl
          << "      Non maximum suppression threshold. " << std::endl
          << "      Default: " << opt_dnn_nmsThresh << std::endl
          << std::endl
          << "  --filterThresh <threshold >" << std::endl
          << "      Filter threshold. Set 0. to disable." << std::endl
          << "      Default: " << opt_dnn_filterThresh << std::endl
          << std::endl
          << "  --labels <file>" << std::endl
          << "      Path to label file either in txt or yaml format. Keep empty if unknown." << std::endl
          << "      Default: \"" << opt_dnn_label_file << "\"" << std::endl
          << std::endl
          << "  --container <type>" << std::endl
          << "      Container type in " << getAvailableDetectionContainer() << std::endl
          << "      Default: " << chosenDetectionContainerToString(opt_dnn_containerType) << std::endl
          << std::endl
          << "  --input-json <path_to_input_json>" << std::endl
          << "      Input JSON file used to configure the DNN. If set, the other arguments will be used to override the values set in the json file." << std::endl
          << "      Default: empty" << std::endl
          << std::endl
          << "  --output-json <type>" << std::endl
          << "      Output JSON file where will be saved the DNN configuration. If empty, does not save the configuration." << std::endl
          << "      Default: empty" << std::endl
          << std::endl
          << "  --step-by-step" << std::endl
          << "      Enable step by step mode, waiting for a user click to process next image." << std::endl
          << std::endl
          << "  --verbose, -v" << std::endl
          << "      Enable verbose mode." << std::endl
          << std::endl
          << "  --help, -h" << std::endl
          << "      Display this helper message." << std::endl
          << std::endl;
        return EXIT_SUCCESS;
      }
    }

    // std::cout << "Video device         : " << opt_device << std::endl;
    std::cout << "Label file (optional): " << (opt_dnn_label_file.empty() ? "None" : opt_dnn_label_file) << std::endl;
    vpImage<vpRGBa> I;
#ifdef VISP_HAVE_REALSENSE2
    std::cout << "SDK        : Realsense 2" << std::endl;
    vpRealSense2 g;
    rs2::config config;
    config.disable_stream(RS2_STREAM_DEPTH);
    config.disable_stream(RS2_STREAM_INFRARED);
    config.enable_stream(RS2_STREAM_COLOR, opt_width, opt_height, RS2_FORMAT_RGBA8, opt_fps);
    g.open(config);
#else
    std::cout << "SDK        : Realsense 1" << std::endl;
    vpRealSense g;
    g.setStreamSettings(rs::stream::color, vpRealSense::vpRsStreamParams(opt_width, opt_height, rs::format::rgba8, 60));
    g.open();
#endif
    g.acquire(I);
    std::cout << "Image size : " << I.getWidth() << " " << I.getHeight() << std::endl;
    cv::VideoCapture capture;
    bool hasCaptureOpeningSucceeded;
    // if (vpMath::isNumber(opt_device)) {
    //   hasCaptureOpeningSucceeded = capture.open(std::atoi(opt_device.c_str()));
    // }
    // else {
    //   hasCaptureOpeningSucceeded = capture.open(opt_device);
    // }
    // if (!hasCaptureOpeningSucceeded) {
    //   std::cout << "Capture from camera: " << opt_device << " didn't work" << std::endl;
    //   return EXIT_FAILURE;
    // }

    // vpImage<vpRGBa> I;
#if defined(VISP_HAVE_X11)
    vpDisplayX d;
#elif defined(VISP_HAVE_GDI)
    vpDisplayGDI d;
#elif defined(HAVE_OPENCV_HIGHGUI)
    vpDisplayOpenCV d;
#endif
    d.setDownScalingFactor(vpDisplay::SCALE_AUTO);

    if (!opt_dnn_label_file.empty() && !vpIoTools::checkFilename(opt_dnn_label_file)) {
      throw(vpException(vpException::fatalError,
                        "The file containing the classes labels \"" + opt_dnn_label_file + "\" does not exist !"));
    }

    vpDetectorDNNOpenCV dnn;
#ifdef VISP_HAVE_NLOHMANN_JSON
    if (!opt_input_json.empty()) {
      //! [DNN json]
      dnn.initFromJSON(opt_input_json);
      //! [DNN json]
    }
#else
    if (!opt_input_json.empty()) {
      std::cerr << "Error: NLOHMANN JSON library is not installed, please install it following ViSP documentation to configure the vpDetectorDNNOpenCV from a JSON file." << std::endl;
      return EXIT_FAILURE;
    }
#endif
    else {
      //! [DNN params]
      vpDetectorDNNOpenCV::NetConfig netConfig(opt_dnn_confThresh, opt_dnn_nmsThresh, opt_dnn_label_file
        , cv::Size(opt_dnn_width, opt_dnn_height), opt_dnn_filterThresh, cv::Scalar(opt_dnn_meanR, opt_dnn_meanG, opt_dnn_meanB)
        , opt_dnn_scale_factor, opt_dnn_swapRB, opt_dnn_type
        , opt_dnn_model, opt_dnn_config, opt_dnn_framework
      );
      dnn.setNetConfig(netConfig);
      //! [DNN params]
    }

    std::cout << dnn.getNetConfig() << std::endl;

#ifdef VISP_HAVE_NLOHMANN_JSON
    if (!opt_output_json.empty()) {
      dnn.saveConfigurationInJSON(opt_output_json);
    }
#else
    if (!opt_output_json.empty()) {
      std::cerr << "Error: NLOHMANN JSON library is not installed, please install it following ViSP documentation to save the configuration in a JSON file." << std::endl;
    }
#endif

    cv::Mat frame;
    while (true) {
      g.acquire(I);
    //   if (frame.empty())
    //     break;

      if (I.getSize() == 0) {
        // vpImageConvert::convert(frame, I);
        d.init(I);
        vpDisplay::setTitle(I, "DNN object detection");
        if (opt_verbose) {
          std::cout << "Process image: " << I.getWidth() << " x " << I.getHeight() << std::endl;
        }
      }
    //   else {
    //     d.init(I);
    //     vpDisplay::setTitle(I, "DNN object detection");
    //     if (opt_verbose) {
    //       std::cout << "Process image: " << I.getWidth() << " x " << I.getHeight() << std::endl;
    //   }
      
      if (opt_verbose) {
        std::cout << "Process new image" << std::endl;
      }

      vpDisplay::display(I);

      if (opt_dnn_containerType == DETECTION_CONTAINER_MAP || opt_dnn_containerType == DETECTION_CONTAINER_BOTH) {
        double t = vpTime::measureTimeMs();
        //! [DNN object detection map mode]
        std::map<std::string, std::vector<vpDetectorDNNOpenCV::DetectedFeatures2D> > detections;
        dnn.detect(frame, detections);
        //! [DNN object detection map mode]
        t = vpTime::measureTimeMs() - t;

        //! [DNN class ids and confidences map mode]
        for (auto key_val : detections) {
          if (opt_verbose) {
            std::cout << "  Class name      : " << key_val.first << std::endl;
          }
          for (vpDetectorDNNOpenCV::DetectedFeatures2D detection : key_val.second) {
            if (opt_verbose) {
              std::cout << "  Bounding box    : " << detection.getBoundingBox() << std::endl;
              std::cout << "  Class Id        : " << detection.getClassId() << std::endl;
              if (detection.getClassName())
                std::cout << "  Class name      : " << detection.getClassName().value() << std::endl;
              std::cout << "  Confidence score: " << detection.getConfidenceScore() << std::endl;
            }
            detection.display(I);
          }
        }
        //! [DNN class ids and confidences map mode]

        std::ostringstream oss_map;
        oss_map << "Detection time (map): " << t << " ms";
        if (opt_verbose) {
          // Displaying timing result in console
          std::cout << "  " << oss_map.str() << std::endl;
        }
        // Displaying timing result on the image
        vpDisplay::displayText(I, 60, 20, oss_map.str(), vpColor::red);
      }

      if (opt_dnn_containerType == DETECTION_CONTAINER_VECTOR || opt_dnn_containerType == DETECTION_CONTAINER_BOTH) {
        double t_vector = vpTime::measureTimeMs();
        //! [DNN object detection vector mode]
        std::vector<vpDetectorDNNOpenCV::DetectedFeatures2D> detections_vec;
        dnn.detect(frame, detections_vec);
        //! [DNN object detection vector mode]
        t_vector = vpTime::measureTimeMs() - t_vector;

        //! [DNN class ids and confidences vector mode]
        for (auto detection : detections_vec) {
          if (opt_verbose) {
            std::cout << "  Bounding box    : " << detection.getBoundingBox() << std::endl;
            std::cout << "  Class Id        : " << detection.getClassId() << std::endl;
            std::optional<std::string> classname_opt = detection.getClassName();
            std::cout << "  Class name      : " << (classname_opt ? *classname_opt : "Not known") << std::endl;
            std::cout << "  Confidence score: " << detection.getConfidenceScore() << std::endl;
          }
          detection.display(I);
        }
        //! [DNN class ids and confidences vector mode]

        std::ostringstream oss_vec;
        oss_vec << "Detection time (vector): " << t_vector << " ms";
        if (opt_verbose) {
          // Displaying timing result in console
          std::cout << "  " << oss_vec.str() << std::endl;
        }
        // Displaying timing result on the image
        vpDisplay::displayText(I, 80, 20, oss_vec.str(), vpColor::red);
      }

      // // UI display
      if (opt_step_by_step) {
        vpDisplay::displayText(I, 20, 20, "Left click to display next image", vpColor::red);
      }
      vpDisplay::displayText(I, 40, 20, "Right click to quit", vpColor::red);

      vpDisplay::flush(I);
      vpMouseButton::vpMouseButtonType button;

      if (vpDisplay::getClick(I, button, opt_step_by_step)) {
        if (button == vpMouseButton::button1) {
          // Left click => next image
          continue;
        }
        else if (button == vpMouseButton::button3) {
          // Right click => stop the program
          break;
        }
      }
    }

  }
  catch (const vpException &e) {
    std::cout << e.what() << std::endl;
  }
#else
  (void)argc;
  (void)argv;
#endif
  return EXIT_SUCCESS;
}

Any help would be greatly appreciated!

[rolalaro]

Hi,

From the error message, the ONNX file contains a method that is not handled by OpenCV.
Have you tried the tutorial with the [YoloV8 onnx file mentionned in the documentation)(https://github.com/JustasBart/yolov8_CPP_Inference_OpenCV_ONNX/blob/minimalistic/source/models/yolov8s.onnx) ?