build done

.gitignore

@@ -1,13 +1,6 @@
 # Distribution / packaging
 .Python
 build/
-models/
-confs/
-ZLM/
-ZLMediaKit/
-ffmpeg-4.2.5/
-nv-codec-headers/
-*.bz2
 develop-eggs/
 dist/
 eggs/
@@ -22,11 +15,6 @@ share/python-wheels/
 *.egg
 MANIFEST
 .idea/
-calib_webcam_1280x720.yaml
-calib_webcam_640x480.yaml
-sv_algorithm_params.json
-sv_algorithm_params_coco_1280.json
-sv_algorithm_params_coco_640.json
 
 # Prerequisites
 *.d

CMakeLists.txt

@@ -24,7 +24,6 @@ else()
     option(USE_FFMPEG "BUILD WITH FFMPEG." ON)
   elseif(PLATFORM STREQUAL "X86_INTEL")
     add_definitions(-DPLATFORM_X86_INTEL)
-    option(USE_INTEL "BUILD WITH INTEL." ON)
     option(USE_FFMPEG "BUILD WITH FFMPEG." ON)
   else()
     message(FATAL_ERROR "UNSUPPORTED PLATFORM!")
@@ -39,10 +38,6 @@ if(USE_CUDA)
   message(STATUS "CUDA: ON")
 endif()
 
-if(USE_INTEL)
-  add_definitions(-DWITH_INTEL)
-  message(STATUS "INTEL: ON")
-endif()
 
 if(USE_GSTREAMER)
   add_definitions(-DWITH_GSTREAMER)
@@ -77,14 +72,9 @@ include_directories(
   ${CMAKE_CURRENT_SOURCE_DIR}/gimbal_ctrl/driver/src/GX40
   ${CMAKE_CURRENT_SOURCE_DIR}/gimbal_ctrl/driver/src
   ${CMAKE_CURRENT_SOURCE_DIR}/gimbal_ctrl
-  ${CMAKE_CURRENT_SOURCE_DIR}/algorithm/common_det/intel
-  ${CMAKE_CURRENT_SOURCE_DIR}/algorithm/landing_det/intel
-  ${CMAKE_CURRENT_SOURCE_DIR}/algorithm/veri/intel
   ${CMAKE_CURRENT_SOURCE_DIR}/algorithm/common_det/cuda
   ${CMAKE_CURRENT_SOURCE_DIR}/algorithm/landing_det/cuda
   ${CMAKE_CURRENT_SOURCE_DIR}/algorithm/sot/ocv470
-  ${CMAKE_CURRENT_SOURCE_DIR}/algorithm/mot/sort
-  ${CMAKE_CURRENT_SOURCE_DIR}/algorithm/mot/bytetrack
   ${CMAKE_CURRENT_SOURCE_DIR}/algorithm/color_line
   ${CMAKE_CURRENT_SOURCE_DIR}/video_io
   ${CMAKE_CURRENT_SOURCE_DIR}/algorithm/veri/cuda
@@ -129,7 +119,8 @@ set(
   include/sv_mot.h
   include/sv_color_line.h
   include/sv_veri_det.h
-  include/sv_video_input.h
+  # include/sv_video_input.h
+  include/sv_camera.h
   include/sv_video_output.h
   include/sv_world.h
 )
@@ -184,10 +175,6 @@ file(GLOB ALG_SRC_FILES ${CMAKE_CURRENT_SOURCE_DIR}/video_io/*.cpp)
 list(APPEND spirecv_SRCS ${ALG_SRC_FILES})
 file(GLOB ALG_SRC_FILES ${CMAKE_CURRENT_SOURCE_DIR}/utils/*.cpp)
 list(APPEND spirecv_SRCS ${ALG_SRC_FILES})
-file(GLOB ALG_SRC_FILES ${CMAKE_CURRENT_SOURCE_DIR}/algorithm/mot/sort/*.cpp)
-list(APPEND spirecv_SRCS ${ALG_SRC_FILES})
-file(GLOB ALG_SRC_FILES ${CMAKE_CURRENT_SOURCE_DIR}/algorithm/mot/bytetrack/*.cpp)
-list(APPEND spirecv_SRCS ${ALG_SRC_FILES})
 
 if(USE_CUDA)
   list(APPEND spirecv_SRCS algorithm/common_det/cuda/yolov7/preprocess.cu)
@@ -201,23 +188,7 @@ if(USE_CUDA)
   list(APPEND spirecv_SRCS ${ALG_SRC_FILES})
 endif()
 
-if(USE_INTEL)
-  file(GLOB ALG_SRC_FILES ${CMAKE_CURRENT_SOURCE_DIR}/algorithm/common_det/intel/*.cpp)
-  list(APPEND spirecv_SRCS ${ALG_SRC_FILES})
-  file(GLOB ALG_SRC_FILES ${CMAKE_CURRENT_SOURCE_DIR}/algorithm/landing_det/intel/*.cpp)
-  list(APPEND spirecv_SRCS ${ALG_SRC_FILES})
-  file(GLOB ALG_SRC_FILES ${CMAKE_CURRENT_SOURCE_DIR}/algorithm/veri/intel/*.cpp)
-  list(APPEND spirecv_SRCS ${ALG_SRC_FILES})
-endif()
-
 if(USE_FFMPEG)
-  if(USE_INTEL)
-    file(GLOB ALG_SRC_FILES ${CMAKE_CURRENT_SOURCE_DIR}/video_io/ffmpeg/x86_intel/*.cpp)
-    list(APPEND spirecv_SRCS ${ALG_SRC_FILES})
-  elseif(USE_CUDA)
-    file(GLOB ALG_SRC_FILES ${CMAKE_CURRENT_SOURCE_DIR}/video_io/ffmpeg/x86_cuda/*.cpp)
-    list(APPEND spirecv_SRCS ${ALG_SRC_FILES})
-  endif()
   file(GLOB ALG_SRC_FILES ${CMAKE_CURRENT_SOURCE_DIR}/video_io/ffmpeg/*.cpp)
   list(APPEND spirecv_SRCS ${ALG_SRC_FILES})
 endif()
@@ -261,22 +232,10 @@ if(USE_CUDA)  # PLATFORM_X86_CUDA & PLATFORM_JETSON
   target_link_libraries(SpireCVSeg sv_world)
 
 elseif(PLATFORM STREQUAL "X86_INTEL")  # Links to Intel-OpenVINO libraries here
-  # Intel-Openvino
-  include_directories(
-    PUBLIC /opt/intel/openvino_2022/runtime/include/
-    PUBLIC /opt/intel/openvino_2022/runtime/include/ie/
-  )
-  link_directories(
-    ${InferenceEngine_LIBRARIES}
-    /opt/intel/openvino_2022/runtime/lib/intel64/libopenvino.so
-  )
-  
   add_library(sv_world SHARED ${spirecv_SRCS})
   target_link_libraries(
     sv_world ${OpenCV_LIBS} 
     sv_gimbal
-    ${InferenceEngine_LIBRARIES}
-    /opt/intel/openvino_2022/runtime/lib/intel64/libopenvino.so
   )
 endif()
 
@@ -305,8 +264,6 @@ add_executable(SingleObjectTracking samples/demo/single_object_tracking.cpp)
 target_link_libraries(SingleObjectTracking sv_world)
 add_executable(MultipleObjectTracking samples/demo/multiple_object_tracking.cpp)
 target_link_libraries(MultipleObjectTracking sv_world)
-add_executable(EvalMOTMetric samples/demo/eval_MOT_metric.cpp)
-target_link_libraries(EvalMOTMetric -lstdc++fs sv_world)
 add_executable(ColorLineDetection samples/demo/color_line_detect.cpp)
 target_link_libraries(ColorLineDetection sv_world)
 add_executable(UdpDetectionInfoReceiver samples/demo/udp_detection_info_receiver.cpp)

README.en.md

@@ -38,9 +38,9 @@ SpireCV is an **real-time edge perception SDK** built for **intelligent unmanned
  - **Platform level**：
     - [x] X86 + Nvidia GPUs (10 series, 20 series, and 30 series graphics cards recommended)
     - [x] Jetson (AGX Orin/Xavier、Orin NX/Nano、Xavier NX)
-    - [x] Intel CPU
-    - [ ] HUAWEI Ascend (coming soon)
+    - [ ] Intel CPU (coming soon)
     - [ ] Rockchip (coming soon)
+    - [ ] HUAWEI Ascend (coming soon)
 
 ## Demos
  - **QR code detection**

README.md

@@ -38,9 +38,9 @@ SpireCV是一个专为**智能无人系统**打造的**边缘实时感知SDK**
  - **平台层**：
     - [x] X86+Nvidia GPU（推荐10系、20系、30系显卡）
     - [x] Jetson（AGX Orin/Xavier、Orin NX/Nano、Xavier NX）
-    - [x] Intel CPU
-    - [ ] HUAWEI Ascend（推进中）
+    - [ ] Intel CPU（推进中）
     - [ ] Rockchip（推进中）
+    - [ ] HUAWEI Ascend（推进中）
 
 ## 功能展示
  - **二维码检测**

algorithm/common_det/cuda/common_det_cuda_impl.cpp

@@ -1,8 +1,6 @@
 #include "common_det_cuda_impl.h"
 #include <cmath>
 #include <fstream>
-#include <iostream>
-#include "sv_util.h"
 
 #define SV_MODEL_DIR "/SpireCV/models/"
 #define SV_ROOT_DIR "/SpireCV/"
@@ -364,51 +362,17 @@ bool CommonObjectDetectorCUDAImpl::cudaSetup(CommonObjectDetectorBase* base_, bo
   bool with_segmentation = base_->withSegmentation();
   double thrs_conf = base_->getThrsConf();
   double thrs_nms = base_->getThrsNms();
-  std::string model = base_->getModel();
-  int bs = base_->getBatchSize();
-  char bs_c[8];
-  sprintf(bs_c, "%d", bs);
-  std::string bs_s(bs_c);
 
   std::string engine_fn = get_home() + SV_MODEL_DIR + dataset + ".engine";
-  std::vector<std::string> files;
-  
-  _list_dir(get_home() + SV_MODEL_DIR, files, "-online.engine", "Nv-" + dataset + "-yolov5" + model + "_b" + bs_s + "_c");
-  if (files.size() > 0)
-  {
-    std::sort(files.rbegin(), files.rend(), _comp_str_lesser);
-    engine_fn = get_home() + SV_MODEL_DIR + files[0];
-  }
-
   if (input_w == 1280)
   {
-    files.clear();
-    _list_dir(get_home() + SV_MODEL_DIR, files, "-online.engine", "Nv-" + dataset + "-yolov5" + model + "6_b" + bs_s + "_c");
-    if (files.size() > 0)
-    {
-      std::sort(files.rbegin(), files.rend(), _comp_str_lesser);
-      engine_fn = get_home() + SV_MODEL_DIR + files[0];
-    }
-    else
-    {
-      engine_fn = get_home() + SV_MODEL_DIR + dataset + "_HD.engine";
-    }
+    engine_fn = get_home() + SV_MODEL_DIR + dataset + "_HD.engine";
   }
   if (with_segmentation)
   {
     base_->setInputH(640);
     base_->setInputW(640);
-    files.clear();
-    _list_dir(get_home() + SV_MODEL_DIR, files, "-online.engine", "Nv-" + dataset + "-yolov5" + model + "_seg_b" + bs_s + "_c");
-    if (files.size() > 0)
-    {
-      std::sort(files.rbegin(), files.rend(), _comp_str_lesser);
-      engine_fn = get_home() + SV_MODEL_DIR + files[0];
-    }
-    else
-    {
-      engine_fn = get_home() + SV_MODEL_DIR + dataset + "_SEG.engine";
-    }
+    engine_fn = get_home() + SV_MODEL_DIR + dataset + "_SEG.engine";
   }
   std::cout << "Load: " << engine_fn << std::endl;
   if (!is_file_exist(engine_fn))
@@ -450,5 +414,17 @@ bool CommonObjectDetectorCUDAImpl::cudaSetup(CommonObjectDetectorBase* base_, bo
   return false;
 }
 
+
+
+
+
+
+
+
+
+
+
+
+
 }
 

algorithm/common_det/intel/common_det_intel_impl.cpp

@@ -1,479 +0,0 @@
-#include "common_det_intel_impl.h"
-#include <cmath>
-#include <fstream>
-#include "sv_util.h"
-
-#define SV_MODEL_DIR "/SpireCV/models/"
-#define SV_ROOT_DIR "/SpireCV/"
-
-namespace sv
-{
-
-#ifdef WITH_INTEL
-  using namespace cv;
-  using namespace std;
-  using namespace dnn;
-#endif
-
-  float sigmoid_function(float a)
-  {
-    float b = 1. / (1. + exp(-a));
-    return b;
-  }
-
-  cv::Mat letterbox(cv::Mat &img_, std::vector<float> &paddings)
-  {
-    std::vector<int> new_shape = {640, 640};
-
-    // Get current image shape [height, width]
-    int img_h = img_.rows;
-    int img_w = img_.cols;
-
-    // Compute scale ratio(new / old) and target resized shape
-    float scale = std::min(new_shape[1] * 1.0 / img_h, new_shape[0] * 1.0 / img_w);
-    int resize_h = int(round(img_h * scale));
-    int resize_w = int(round(img_w * scale));
-    paddings[0] = scale;
-
-    // Compute padding
-    int pad_h = new_shape[1] - resize_h;
-    int pad_w = new_shape[0] - resize_w;
-
-    // Resize and pad image while meeting stride-multiple constraints
-    cv::Mat resized_img;
-    cv::resize(img_, resized_img, cv::Size(resize_w, resize_h));
-
-    // divide padding into 2 sides
-    float half_h = pad_h * 1.0 / 2;
-    float half_w = pad_w * 1.0 / 2;
-    paddings[1] = half_h;
-    paddings[2] = half_w;
-
-    // Compute padding boarder
-    int top = int(round(half_h - 0.1));
-    int bottom = int(round(half_h + 0.1));
-    int left = int(round(half_w - 0.1));
-    int right = int(round(half_w + 0.1));
-
-    // Add border
-    cv::copyMakeBorder(resized_img, resized_img, top, bottom, left, right, 0, cv::Scalar(114, 114, 114));
-
-    return resized_img;
-  }
-
-  CommonObjectDetectorIntelImpl::CommonObjectDetectorIntelImpl()
-  {
-  }
-
-  CommonObjectDetectorIntelImpl::~CommonObjectDetectorIntelImpl()
-  {
-  }
-
-  void CommonObjectDetectorIntelImpl::intelDetect(
-      CommonObjectDetectorBase *base_,
-      cv::Mat img_,
-      std::vector<float> &boxes_x_,
-      std::vector<float> &boxes_y_,
-      std::vector<float> &boxes_w_,
-      std::vector<float> &boxes_h_,
-      std::vector<int> &boxes_label_,
-      std::vector<float> &boxes_score_,
-      std::vector<cv::Mat> &boxes_seg_,
-      bool input_4k_)
-  {
-#ifdef WITH_INTEL
-    int input_h = base_->getInputH();
-    int input_w = base_->getInputW();
-    bool with_segmentation = base_->withSegmentation();
-    double thrs_conf = base_->getThrsConf();
-    double thrs_nms = base_->getThrsNms();
-
-    if (with_segmentation)
-    {
-      std::vector<float> paddings(3); // scale, half_h, half_w
-      this->preprocess_img_seg(img_, paddings);
-
-      infer_request.start_async();
-      infer_request.wait();
-
-      // Postprocess
-      this->postprocess_img_seg(img_, paddings, boxes_x_, boxes_y_, boxes_w_, boxes_h_, boxes_label_, boxes_score_, boxes_seg_, thrs_conf, thrs_nms);
-    }
-    else
-    {
-      // Preprocess
-      this->preprocess_img(img_);
-
-      // Run inference
-      infer_request.start_async();
-      infer_request.wait();
-
-      // Postprocess
-      this->postprocess_img(boxes_x_, boxes_y_, boxes_w_, boxes_h_, boxes_label_, boxes_score_, thrs_conf, thrs_nms);
-    }
-
-#endif
-  }
-
-  bool CommonObjectDetectorIntelImpl::intelSetup(CommonObjectDetectorBase *base_, bool input_4k_)
-  {
-#ifdef WITH_INTEL
-    ov::Core core;
-    std::string dataset = base_->getDataset();
-    double thrs_conf = base_->getThrsConf();
-    double thrs_nms = base_->getThrsNms();
-    inpHeight = base_->getInputH();
-    inpWidth = base_->getInputW();
-    with_segmentation = base_->withSegmentation();
-    std::string model = base_->getModel();
-
-    std::string openvino_fn = get_home() + SV_MODEL_DIR + dataset + ".onnx";
-    std::vector<std::string> files;
-    _list_dir(get_home() + SV_MODEL_DIR, files, "-online.onnx", "Int-" + dataset + "-yolov5" + model + "_c");
-    if (files.size() > 0)
-    {
-      std::sort(files.rbegin(), files.rend(), _comp_str_lesser);
-      openvino_fn = get_home() + SV_MODEL_DIR + files[0];
-    }
-
-    if (inpWidth == 1280)
-    {
-      files.clear();
-      _list_dir(get_home() + SV_MODEL_DIR, files, "-online.onnx", "Int-" + dataset + "-yolov5" + model + "6_c");
-      if (files.size() > 0)
-      {
-        std::sort(files.rbegin(), files.rend(), _comp_str_lesser);
-        openvino_fn = get_home() + SV_MODEL_DIR + files[0];
-      }
-      else
-      {
-        openvino_fn = get_home() + SV_MODEL_DIR + dataset + "_HD.onnx";
-      }
-    }
-    if (with_segmentation)
-    {
-      base_->setInputH(640);
-      base_->setInputW(640);
-      files.clear();
-      _list_dir(get_home() + SV_MODEL_DIR, files, "-online.onnx", "Int-" + dataset + "-yolov5" + model + "_seg_c");
-      if (files.size() > 0)
-      {
-        std::sort(files.rbegin(), files.rend(), _comp_str_lesser);
-        openvino_fn = get_home() + SV_MODEL_DIR + files[0];
-      }
-      else
-      {
-        openvino_fn = get_home() + SV_MODEL_DIR + dataset + "_SEG.onnx";
-      }
-    }
-    std::cout << "Load: " << openvino_fn << std::endl;
-    if (!is_file_exist(openvino_fn))
-    {
-      throw std::runtime_error("SpireCV (104) Error loading the CommonObject OpenVINO model (File Not Exist)");
-    }
-
-    if (input_4k_ && with_segmentation)
-    {
-      throw std::runtime_error("SpireCV (106) Resolution 4K DO NOT Support Segmentation!");
-    }
-
-    if (with_segmentation)
-    {
-      this->compiled_model = core.compile_model(openvino_fn, "GPU");
-      this->infer_request = compiled_model.create_infer_request();
-    }
-    else
-    {
-      std::shared_ptr<ov::Model> model_ = core.read_model(openvino_fn);
-      ov::preprocess::PrePostProcessor Pre_P = ov::preprocess::PrePostProcessor(model_);
-      Pre_P.input().tensor().set_element_type(ov::element::u8).set_layout("NHWC").set_color_format(ov::preprocess::ColorFormat::RGB);
-      Pre_P.input().preprocess().convert_element_type(ov::element::f32).convert_color(ov::preprocess::ColorFormat::RGB).scale({255, 255, 255}); // .scale({ 112, 112, 112 });
-      Pre_P.input().model().set_layout("NCHW");
-      Pre_P.output().tensor().set_element_type(ov::element::f32);
-      model_ = Pre_P.build();
-      this->compiled_model = core.compile_model(model_, "GPU");
-      this->infer_request = compiled_model.create_infer_request();
-    }
-    return true;
-#endif
-    return false;
-  }
-
-  void CommonObjectDetectorIntelImpl::preprocess_img(cv::Mat &img_)
-  {
-#ifdef WITH_INTEL
-    float width = img_.cols;
-    float height = img_.rows;
-    cv::Size new_shape = cv::Size(inpHeight, inpWidth);
-    float r = float(new_shape.width / max(width, height));
-    int new_unpadW = int(round(width * r));
-    int new_unpadH = int(round(height * r));
-
-    cv::resize(img_, resize.resized_image, cv::Size(new_unpadW, new_unpadH), 0, 0, cv::INTER_AREA);
-    resize.resized_image = resize.resized_image;
-    resize.dw = new_shape.width - new_unpadW;
-    resize.dh = new_shape.height - new_unpadH;
-    cv::Scalar color = cv::Scalar(100, 100, 100);
-    cv::copyMakeBorder(resize.resized_image, resize.resized_image, 0, resize.dh, 0, resize.dw, cv::BORDER_CONSTANT, color);
-
-    this->rx = (float)img_.cols / (float)(resize.resized_image.cols - resize.dw);
-    this->ry = (float)img_.rows / (float)(resize.resized_image.rows - resize.dh);
-    if (with_segmentation)
-    {
-      cv::Mat blob = cv::dnn::blobFromImage(resize.resized_image, 1 / 255.0, cv::Size(640, 640), cv::Scalar(0, 0, 0), true);
-      auto input_port = compiled_model.input();
-      ov::Tensor input_tensor(input_port.get_element_type(), input_port.get_shape(), blob.ptr(0));
-      infer_request.set_input_tensor(input_tensor);
-    }
-    else
-    {
-      float *input_data = (float *)resize.resized_image.data;
-      input_tensor = ov::Tensor(compiled_model.input().get_element_type(), compiled_model.input().get_shape(), input_data);
-      infer_request.set_input_tensor(input_tensor);
-    }
-#endif
-  }
-
-  void CommonObjectDetectorIntelImpl::preprocess_img_seg(cv::Mat &img_, std::vector<float> &paddings)
-  {
-#ifdef WITH_INTEL
-    cv::Mat masked_img;
-    cv::Mat resized_img = letterbox(img_, paddings); // resize to (640,640) by letterbox
-    // BGR->RGB, u8(0-255)->f32(0.0-1.0), HWC->NCHW
-    cv::Mat blob = cv::dnn::blobFromImage(resized_img, 1 / 255.0, cv::Size(640, 640), cv::Scalar(0, 0, 0), true);
-
-    // Get input port for model with one input
-    auto input_port = compiled_model.input();
-    // Create tensor from external memory
-    ov::Tensor input_tensor(input_port.get_element_type(), input_port.get_shape(), blob.ptr(0));
-    // Set input tensor for model with one input
-    infer_request.set_input_tensor(input_tensor);
-#endif
-  }
-
-  void CommonObjectDetectorIntelImpl::postprocess_img_seg(cv::Mat &img_,
-                                                          std::vector<float> &paddings,
-                                                          std::vector<float> &boxes_x_,
-                                                          std::vector<float> &boxes_y_,
-                                                          std::vector<float> &boxes_w_,
-                                                          std::vector<float> &boxes_h_,
-                                                          std::vector<int> &boxes_label_,
-                                                          std::vector<float> &boxes_score_,
-                                                          std::vector<cv::Mat> &boxes_seg_,
-                                                          double &thrs_conf,
-                                                          double &thrs_nms)
-  {
-#ifdef WITH_INTEL
-    const ov::Tensor &detect = infer_request.get_output_tensor(0);
-    ov::Shape detect_shape = detect.get_shape();
-    const ov::Tensor &proto = infer_request.get_output_tensor(1);
-    ov::Shape proto_shape = proto.get_shape();
-
-    cv::Mat detect_buffer(detect_shape[1], detect_shape[2], CV_32F, detect.data());
-    cv::Mat proto_buffer(proto_shape[1], proto_shape[2] * proto_shape[3], CV_32F, proto.data());
-
-    cv::RNG rng;
-    float conf_threshold = thrs_conf;
-    float nms_threshold = thrs_nms;
-    std::vector<cv::Rect> boxes;
-    std::vector<int> class_ids;
-    std::vector<float> class_scores;
-    std::vector<float> confidences;
-    std::vector<cv::Mat> masks;
-
-    float scale = paddings[0];
-    for (int i = 0; i < detect_buffer.rows; i++)
-    {
-      float confidence = detect_buffer.at<float>(i, 4);
-      if (confidence < conf_threshold)
-      {
-        continue;
-      }
-      cv::Mat classes_scores = detect_buffer.row(i).colRange(5, 85);
-      cv::Point class_id;
-      double score;
-      cv::minMaxLoc(classes_scores, NULL, &score, NULL, &class_id);
-
-      // class score: 0~1
-      if (score > 0.25)
-      {
-        cv::Mat mask = detect_buffer.row(i).colRange(85, 117);
-        float cx = detect_buffer.at<float>(i, 0);
-        float cy = detect_buffer.at<float>(i, 1);
-        float w = detect_buffer.at<float>(i, 2);
-        float h = detect_buffer.at<float>(i, 3);
-        int left = static_cast<int>((cx - 0.5 * w - paddings[2]) / scale);
-        int top = static_cast<int>((cy - 0.5 * h - paddings[1]) / scale);
-        int width = static_cast<int>(w / scale);
-        int height = static_cast<int>(h / scale);
-        cv::Rect box;
-        box.x = left;
-        box.y = top;
-        box.width = width;
-        box.height = height;
-
-        boxes.push_back(box);
-        class_ids.push_back(class_id.x);
-        class_scores.push_back(score);
-        confidences.push_back(confidence);
-        masks.push_back(mask);
-      }
-    }
-
-    // NMS
-    std::vector<int> indices;
-    cv::dnn::NMSBoxes(boxes, confidences, thrs_conf, thrs_nms, indices);
-    // cv::Mat rgb_mask;
-    cv::Mat rgb_mask = cv::Mat::zeros(img_.size(), img_.type());
-
-    for (size_t i = 0; i < indices.size(); i++)
-    {
-      int index = indices[i];
-      int class_id = class_ids[index];
-      cv::Rect box = boxes[index];
-      int x1 = std::max(0, box.x);
-      int y1 = std::max(0, box.y);
-      int x2 = std::max(0, box.br().x);
-      int y2 = std::max(0, box.br().y);
-
-      cv::Mat m = masks[index] * proto_buffer;
-      for (int col = 0; col < m.cols; col++)
-      {
-        m.at<float>(0, col) = sigmoid_function(m.at<float>(0, col));
-      }
-      cv::Mat m1 = m.reshape(1, 160); // 1x25600 -> 160x160
-      int mx1 = std::max(0, int((x1 * scale + paddings[2]) * 0.25));
-      int mx2 = std::max(0, int((x2 * scale + paddings[2]) * 0.25));
-      int my1 = std::max(0, int((y1 * scale + paddings[1]) * 0.25));
-      int my2 = std::max(0, int((y2 * scale + paddings[1]) * 0.25));
-      cv::Mat mask_roi = m1(cv::Range(my1, my2), cv::Range(mx1, mx2));
-
-      cv::Mat rm, det_mask;
-      cv::resize(mask_roi, rm, cv::Size(x2 - x1, y2 - y1));
-      for (int r = 0; r < rm.rows; r++)
-      {
-        for (int c = 0; c < rm.cols; c++)
-        {
-          float pv = rm.at<float>(r, c);
-          if (pv > 0.5)
-          {
-            rm.at<float>(r, c) = 1.0;
-          }
-          else
-          {
-            rm.at<float>(r, c) = 0.0;
-          }
-        }
-      }
-
-      rm = rm * rng.uniform(0, 255);
-      rm.convertTo(det_mask, CV_8UC1);
-      if ((y1 + det_mask.rows) >= img_.rows)
-      {
-        y2 = img_.rows - 1;
-      }
-      if ((x1 + det_mask.cols) >= img_.cols)
-      {
-        x2 = img_.cols - 1;
-      }
-
-      cv::Mat mask = cv::Mat::zeros(cv::Size(img_.cols, img_.rows), CV_8UC1);
-      det_mask(cv::Range(0, y2 - y1), cv::Range(0, x2 - x1)).copyTo(mask(cv::Range(y1, y2), cv::Range(x1, x2)));
-      add(rgb_mask, cv::Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255)), rgb_mask, mask);
-
-      boxes_x_.push_back(box.x);
-      boxes_y_.push_back(box.y);
-      boxes_w_.push_back(box.width);
-      boxes_h_.push_back(box.height);
-
-      boxes_label_.push_back((int)class_id);
-      boxes_score_.push_back(class_scores[index]);
-
-      cv::Mat mask_j = mask.clone();
-      boxes_seg_.push_back(mask_j);
-    }
-
-#endif
-  }
-
-  void CommonObjectDetectorIntelImpl::postprocess_img(std::vector<float> &boxes_x_,
-                                                      std::vector<float> &boxes_y_,
-                                                      std::vector<float> &boxes_w_,
-                                                      std::vector<float> &boxes_h_,
-                                                      std::vector<int> &boxes_label_,
-                                                      std::vector<float> &boxes_score_,
-                                                      double &thrs_conf,
-                                                      double &thrs_nms)
-  {
-#ifdef WITH_INTEL
-    const ov::Tensor &output_tensor = infer_request.get_output_tensor();
-    ov::Shape output_shape = output_tensor.get_shape();
-    float *detections = output_tensor.data<float>();
-
-    std::vector<cv::Rect> boxes;
-    vector<int> class_ids;
-    vector<float> confidences;
-    for (int i = 0; i < output_shape[1]; i++)
-    {
-      float *detection = &detections[i * output_shape[2]];
-
-      float confidence = detection[4];
-      if (confidence >= thrs_conf)
-      {
-        float *classes_scores = &detection[5];
-        cv::Mat scores(1, output_shape[2] - 5, CV_32FC1, classes_scores);
-        cv::Point class_id;
-        double max_class_score;
-        cv::minMaxLoc(scores, 0, &max_class_score, 0, &class_id);
-        if (max_class_score > thrs_conf)
-        {
-          confidences.push_back(confidence);
-          class_ids.push_back(class_id.x);
-          float x = detection[0];
-          float y = detection[1];
-          float w = detection[2];
-          float h = detection[3];
-          float xmin = x - (w / 2);
-          float ymin = y - (h / 2);
-
-          boxes.push_back(cv::Rect(xmin, ymin, w, h));
-        }
-      }
-    }
-
-    std::vector<int> nms_result;
-    cv::dnn::NMSBoxes(boxes, confidences, thrs_conf, thrs_nms, nms_result);
-
-    std::vector<Detection> output;
-    for (int i = 0; i < nms_result.size(); i++)
-    {
-      Detection result;
-      int idx = nms_result[i];
-      result.class_id = class_ids[idx];
-      result.confidence = confidences[idx];
-      result.box = boxes[idx];
-      output.push_back(result);
-    }
-
-    for (int i = 0; i < output.size(); i++)
-    {
-      auto detection = output[i];
-      auto box = detection.box;
-      auto classId = detection.class_id;
-      auto confidence = detection.confidence;
-
-      float xmax = box.x + box.width;
-      float ymax = box.y + box.height;
-
-      boxes_x_.push_back(this->rx * box.x);
-      boxes_y_.push_back(this->ry * box.y);
-      boxes_w_.push_back(this->rx * box.width);
-      boxes_h_.push_back(this->ry * box.height);
-
-      boxes_label_.push_back((int)detection.class_id);
-      boxes_score_.push_back(detection.confidence);
-    }
-#endif
-  }
-
-}

algorithm/common_det/intel/common_det_intel_impl.h

@@ -1,89 +0,0 @@
-#ifndef __SV_COMMON_DET_INTEL__
-#define __SV_COMMON_DET_INTEL__
-
-#include "sv_core.h"
-#include <string>
-#include <iostream>
-#include <opencv2/dnn.hpp>
-#include <opencv2/imgproc.hpp>
-#include <opencv2/highgui.hpp>
-#include <string>
-#include <chrono>
-
-#ifdef WITH_INTEL
-#include <openvino/openvino.hpp>
-#endif
-
-struct Resize
-{
-  cv::Mat resized_image;
-  int dw;
-  int dh;
-};
-
-struct Detection
-{
-  int class_id;
-  float confidence;
-  cv::Rect box;
-};
-
-namespace sv
-{
-
-  class CommonObjectDetectorIntelImpl
-  {
-  public:
-    CommonObjectDetectorIntelImpl();
-    ~CommonObjectDetectorIntelImpl();
-
-    bool intelSetup(CommonObjectDetectorBase *base_, bool input_4k_);
-    void intelDetect(
-        CommonObjectDetectorBase *base_,
-        cv::Mat img_,
-        std::vector<float> &boxes_x_,
-        std::vector<float> &boxes_y_,
-        std::vector<float> &boxes_w_,
-        std::vector<float> &boxes_h_,
-        std::vector<int> &boxes_label_,
-        std::vector<float> &boxes_score_,
-        std::vector<cv::Mat> &boxes_seg_,
-        bool input_4k_);
-    void preprocess_img(cv::Mat &img_);
-    void preprocess_img_seg(cv::Mat &img_, std::vector<float> &paddings);
-    void postprocess_img_seg(cv::Mat &img_,
-                             std::vector<float> &paddings,
-                             std::vector<float> &boxes_x_,
-                             std::vector<float> &boxes_y_,
-                             std::vector<float> &boxes_w_,
-                             std::vector<float> &boxes_h_,
-                             std::vector<int> &boxes_label_,
-                             std::vector<float> &boxes_score_,
-                             std::vector<cv::Mat> &boxes_seg_,
-                             double &thrs_conf,
-                             double &thrs_nms);
-
-    void postprocess_img(std::vector<float> &boxes_x_,
-                         std::vector<float> &boxes_y_,
-                         std::vector<float> &boxes_w_,
-                         std::vector<float> &boxes_h_,
-                         std::vector<int> &boxes_label_,
-                         std::vector<float> &boxes_score_,
-                         double &thrs_conf,
-                         double &thrs_nms);
-
-#ifdef WITH_INTEL
-    int inpWidth;
-    int inpHeight;
-    bool with_segmentation;
-    float rx; // the width ratio of original image and resized image
-    float ry; // the height ratio of original image and resized image
-    Resize resize;
-    ov::Tensor input_tensor;
-    ov::InferRequest infer_request;
-    ov::CompiledModel compiled_model;
-#endif
-  };
-
-}
-#endif

algorithm/common_det/sv_common_det.cpp

@@ -8,10 +8,6 @@
 #include "common_det_cuda_impl.h"
 #endif
 
-#ifdef WITH_INTEL
-#include <openvino/openvino.hpp>
-#include "common_det_intel_impl.h"
-#endif
 
 namespace sv {
 
@@ -22,10 +18,6 @@ CommonObjectDetector::CommonObjectDetector(bool input_4k)
 #ifdef WITH_CUDA
   this->_cuda_impl = new CommonObjectDetectorCUDAImpl;
 #endif
-
-#ifdef WITH_INTEL
-  this->_intel_impl = new CommonObjectDetectorIntelImpl;
-#endif
 }
 CommonObjectDetector::~CommonObjectDetector()
 {
@@ -36,10 +28,6 @@ bool CommonObjectDetector::setupImpl()
 #ifdef WITH_CUDA
   return this->_cuda_impl->cudaSetup(this, this->_input_4k);
 #endif
-
-#ifdef WITH_INTEL
-  return this->_intel_impl->intelSetup(this, this->_input_4k);
-#endif
   return false;
 }
 
@@ -65,23 +53,14 @@ void CommonObjectDetector::detectImpl(
     boxes_label_,
     boxes_score_,
     boxes_seg_,
-    this->_input_4k);
-#endif
-
-#ifdef WITH_INTEL
-  this->_intel_impl->intelDetect(
-    this,
-    img_,
-    boxes_x_,
-    boxes_y_,
-    boxes_w_,
-    boxes_h_,
-    boxes_label_,
-    boxes_score_,
-    boxes_seg_,
-    this->_input_4k);
+    this->_input_4k
+  );
 #endif
 }
 
+
+
+
+
 }
 

algorithm/landing_det/cuda/landing_det_cuda_impl.cpp

@@ -1,7 +1,6 @@
 #include "landing_det_cuda_impl.h"
 #include <cmath>
 #include <fstream>
-#include "sv_util.h"
 
 #define SV_MODEL_DIR "/SpireCV/models/"
 #define SV_ROOT_DIR "/SpireCV/"
@@ -51,16 +50,6 @@ bool LandingMarkerDetectorCUDAImpl::cudaSetup()
 {
 #ifdef WITH_CUDA
   std::string trt_model_fn = get_home() + SV_MODEL_DIR + "LandingMarker.engine";
-  
-  std::vector<std::string> files;
-  _list_dir(get_home() + SV_MODEL_DIR, files, "-online.engine", "Nv-LandingMarker-resnet34");
-  if (files.size() > 0)
-  {
-    std::sort(files.rbegin(), files.rend(), _comp_str_lesser);
-    trt_model_fn = get_home() + SV_MODEL_DIR + files[0];
-  }
-  std::cout << "Load: " << trt_model_fn << std::endl;
-
   if (!is_file_exist(trt_model_fn))
   {
     throw std::runtime_error("SpireCV (104) Error loading the LandingMarker TensorRT model (File Not Exist)");

algorithm/landing_det/intel/landing_det_intel_impl.cpp

@@ -1,104 +0,0 @@
-#include "landing_det_intel_impl.h"
-#include <cmath>
-#include <fstream>
-#include "sv_util.h"
-
-#define SV_MODEL_DIR "/SpireCV/models/"
-#define SV_ROOT_DIR "/SpireCV/"
-
-namespace sv
-{
-
-#ifdef WITH_INTEL
-  using namespace cv;
-  using namespace std;
-  using namespace dnn;
-#endif
-
-  LandingMarkerDetectorIntelImpl::LandingMarkerDetectorIntelImpl()
-  {
-  }
-
-  LandingMarkerDetectorIntelImpl::~LandingMarkerDetectorIntelImpl()
-  {
-  }
-
-  bool LandingMarkerDetectorIntelImpl::intelSetup()
-  {
-#ifdef WITH_INTEL
-    std::string onnx_model_fn = get_home() + SV_MODEL_DIR + "LandingMarker.onnx";
-    std::vector<std::string> files;
-    list_dir(get_home() + SV_MODEL_DIR, files, "-online.onnx", "Int-LandingMarker-resnet34");
-    if (files.size() > 0)
-    {
-      std::sort(files.rbegin(), files.rend(), _comp_str_lesser);
-      onnx_model_fn = get_home() + SV_MODEL_DIR + files[0];
-    }
-
-    if (!is_file_exist(onnx_model_fn))
-    {
-      throw std::runtime_error("SpireCV (104) Error loading the LandingMarker ONNX model (File Not Exist)");
-    }
-
-    // OpenVINO
-    ov::Core core;
-    std::shared_ptr<ov::Model> model = core.read_model(onnx_model_fn);
-    ov::preprocess::PrePostProcessor ppp = ov::preprocess::PrePostProcessor(model);
-    ppp.input().tensor().set_element_type(ov::element::u8).set_layout("NHWC").set_color_format(ov::preprocess::ColorFormat::RGB);
-    ppp.input().preprocess().convert_element_type(ov::element::f32).convert_color(ov::preprocess::ColorFormat::RGB).scale({255, 255, 255}); // .scale({ 112, 112, 112 });
-    ppp.input().model().set_layout("NCHW");
-    ppp.output().tensor().set_element_type(ov::element::f32);
-    model = ppp.build();
-    this->compiled_model = core.compile_model(model, "GPU");
-    this->infer_request = compiled_model.create_infer_request();
-
-    return true;
-#endif
-    return false;
-  }
-
-  void LandingMarkerDetectorIntelImpl::intelRoiCNN(
-      std::vector<cv::Mat> &input_rois_,
-      std::vector<int> &output_labels_)
-  {
-#ifdef WITH_INTEL
-    output_labels_.clear();
-
-    for (int i = 0; i < input_rois_.size(); i++)
-    {
-      cv::Mat e_roi = input_rois_[i];
-
-      // Get input port for model with one input
-      auto input_port = compiled_model.input();
-      // Create tensor from external memory
-      ov::Tensor input_tensor(input_port.get_element_type(), input_port.get_shape(), e_roi.ptr(0));
-      // Set input tensor for model with one input
-      infer_request.set_input_tensor(input_tensor);
-      //preprocess_img(e_roi);
-
-      // infer_request.infer();
-      infer_request.start_async();
-      infer_request.wait();
-
-      const ov::Tensor &output_tensor = infer_request.get_output_tensor();
-      ov::Shape output_shape = output_tensor.get_shape();
-      this->_p_prob = output_tensor.data<float>();
-
-      // Find max index
-      double max = 0;
-      int label = 0;
-      for (int i = 0; i < 11; ++i)
-      {
-        if (max < this->_p_prob[i])
-        {
-          max = this->_p_prob[i];
-          label = i;
-        }
-      }
-      output_labels_.push_back(label);
-    }
-
-#endif
-  }
-
-}

algorithm/landing_det/intel/landing_det_intel_impl.h

@@ -1,37 +0,0 @@
-#ifndef __SV_LANDING_DET_INTEL__
-#define __SV_LANDING_DET_INTEL__
-
-#include "sv_core.h"
-#include <opencv2/opencv.hpp>
-#include <opencv2/aruco.hpp>
-#include <opencv2/tracking.hpp>
-#include <string>
-#include <chrono>
-
-#ifdef WITH_INTEL
-#include <openvino/openvino.hpp>
-#endif
-
-namespace sv
-{
-  class LandingMarkerDetectorIntelImpl
-  {
-  public:
-    LandingMarkerDetectorIntelImpl();
-    ~LandingMarkerDetectorIntelImpl();
-
-    bool intelSetup();
-    void intelRoiCNN(
-        std::vector<cv::Mat> &input_rois_,
-        std::vector<int> &output_labels_);
-
-#ifdef WITH_INTEL
-    float *_p_prob;
-
-    ov::Tensor input_tensor;
-    ov::InferRequest infer_request;
-    ov::CompiledModel compiled_model;
-#endif
-  };
-}
-#endif

algorithm/landing_det/sv_landing_det.cpp

@@ -7,10 +7,6 @@
 #include "landing_det_cuda_impl.h"
 #endif
 
-#ifdef WITH_INTEL
-#include <openvino/openvino.hpp>
-#include "landing_det_intel_impl.h"
-#endif
 
 namespace sv {
 
@@ -20,10 +16,6 @@ LandingMarkerDetector::LandingMarkerDetector()
 #ifdef WITH_CUDA
   this->_cuda_impl = new LandingMarkerDetectorCUDAImpl;
 #endif
-
-#ifdef WITH_INTEL
-  this->_intel_impl = new LandingMarkerDetectorIntelImpl;
-#endif
 }
 LandingMarkerDetector::~LandingMarkerDetector()
 {
@@ -34,10 +26,6 @@ bool LandingMarkerDetector::setupImpl()
 #ifdef WITH_CUDA
   return this->_cuda_impl->cudaSetup();
 #endif
-
-#ifdef WITH_INTEL
-  return this->_intel_impl->intelSetup();
-#endif
   return false;
 }
 
@@ -52,13 +40,11 @@ void LandingMarkerDetector::roiCNN(
     output_labels_
   );
 #endif
-
-#ifdef WITH_INTEL
-  this->_intel_impl->intelRoiCNN(
-    input_rois_,
-    output_labels_
-  );
-#endif
-}
+}
+
+
+
+
+
 }
 

algorithm/mot/bytetrack/BYTETracker.cpp

@@ -1,262 +0,0 @@
-
-#include "BYTETracker.h"
-#include <fstream>
-
-namespace sv
-{
-
-	BYTETracker::BYTETracker(int frame_rate, int track_buffer)
-	{
-		track_thresh = 0.5;
-		high_thresh = 0.6;
-		match_thresh = 0.8;
-
-		frame_id = 0;
-		max_time_lost = int(frame_rate / 30.0 * track_buffer);
-	}
-
-	BYTETracker::~BYTETracker()
-	{
-	}
-
-	void BYTETracker::update(TargetsInFrame &tgts)
-	{
-		////////////////// Step 1: Get detections //////////////////
-		this->frame_id++;
-		std::vector<STrack> activated_stracks;
-		std::vector<STrack> refind_stracks;
-		std::vector<STrack> removed_stracks;
-		std::vector<STrack> lost_stracks;
-		std::vector<STrack> detections;
-		std::vector<STrack> detections_low;
-
-		std::vector<STrack> detections_cp;
-		std::vector<STrack> tracked_stracks_swap;
-		std::vector<STrack> resa, resb;
-		std::vector<STrack> output_stracks;
-
-		std::vector<STrack *> unconfirmed;
-		std::vector<STrack *> tracked_stracks;
-		std::vector<STrack *> strack_pool;
-		std::vector<STrack *> r_tracked_stracks;
-
-		if (tgts.targets.size() > 0)
-		{
-			for (int i = 0; i < tgts.targets.size(); i++)
-			{
-
-				tgts.targets[i].tracked_id = 0;
-				tgts.targets[i].has_tid = true;
-
-				std::vector<float> tlbr_;
-				tlbr_.resize(4);
-				tlbr_[0] = tgts.targets[i].cx * tgts.width - tgts.targets[i].w * tgts.width / 2;
-				tlbr_[1] = tgts.targets[i].cy * tgts.height - tgts.targets[i].h * tgts.height / 2;
-				tlbr_[2] = tgts.targets[i].cx * tgts.width + tgts.targets[i].w * tgts.width / 2;
-				tlbr_[3] = tgts.targets[i].cy * tgts.height + tgts.targets[i].h * tgts.height / 2;
-
-				float score = tgts.targets[i].score;
-
-				STrack strack(STrack::tlbr_to_tlwh(tlbr_), score);
-				if (score >= track_thresh)
-				{
-					detections.push_back(strack);
-				}
-				else
-				{
-					detections_low.push_back(strack);
-				}
-			}
-		}
-
-		// Add newly detected tracklets to tracked_stracks
-		for (int i = 0; i < this->tracked_stracks.size(); i++)
-		{
-			if (!this->tracked_stracks[i].is_activated)
-				unconfirmed.push_back(&this->tracked_stracks[i]);
-			else
-				tracked_stracks.push_back(&this->tracked_stracks[i]);
-		}
-
-		////////////////// Step 2: First association, with IoU //////////////////
-		strack_pool = joint_stracks(tracked_stracks, this->lost_stracks);
-		STrack::multi_predict(strack_pool, this->kalman_filter);
-
-		std::vector<std::vector<float>> dists;
-		int dist_size = 0, dist_size_size = 0;
-		dists = iou_distance(strack_pool, detections, dist_size, dist_size_size);
-
-		std::vector<std::vector<int>> matches;
-		std::vector<int> u_track, u_detection;
-		linear_assignment(dists, dist_size, dist_size_size, match_thresh, matches, u_track, u_detection);
-
-		for (int i = 0; i < matches.size(); i++)
-		{
-			STrack *track = strack_pool[matches[i][0]];
-			STrack *det = &detections[matches[i][1]];
-			if (track->state == TrackState::Tracked)
-			{
-				track->update(*det, this->frame_id);
-				activated_stracks.push_back(*track);
-			}
-			else
-			{
-				track->re_activate(*det, this->frame_id, false);
-				refind_stracks.push_back(*track);
-			}
-		}
-
-		////////////////// Step 3: Second association, using low score dets //////////////////
-		for (int i = 0; i < u_detection.size(); i++)
-		{
-			detections_cp.push_back(detections[u_detection[i]]);
-		}
-		detections.clear();
-		detections.assign(detections_low.begin(), detections_low.end());
-
-		for (int i = 0; i < u_track.size(); i++)
-		{
-			if (strack_pool[u_track[i]]->state == TrackState::Tracked)
-			{
-				r_tracked_stracks.push_back(strack_pool[u_track[i]]);
-			}
-		}
-
-		dists.clear();
-		dists = iou_distance(r_tracked_stracks, detections, dist_size, dist_size_size);
-
-		matches.clear();
-		u_track.clear();
-		u_detection.clear();
-		linear_assignment(dists, dist_size, dist_size_size, 0.5, matches, u_track, u_detection);
-
-		for (int i = 0; i < matches.size(); i++)
-		{
-			STrack *track = r_tracked_stracks[matches[i][0]];
-			STrack *det = &detections[matches[i][1]];
-			if (track->state == TrackState::Tracked)
-			{
-				track->update(*det, this->frame_id);
-				activated_stracks.push_back(*track);
-			}
-			else
-			{
-				track->re_activate(*det, this->frame_id, false);
-				refind_stracks.push_back(*track);
-			}
-		}
-
-		for (int i = 0; i < u_track.size(); i++)
-		{
-			STrack *track = r_tracked_stracks[u_track[i]];
-			if (track->state != TrackState::Lost)
-			{
-				track->mark_lost();
-				lost_stracks.push_back(*track);
-			}
-		}
-
-		// Deal with unconfirmed tracks, usually tracks with only one beginning frame
-		detections.clear();
-		detections.assign(detections_cp.begin(), detections_cp.end());
-
-		dists.clear();
-		dists = iou_distance(unconfirmed, detections, dist_size, dist_size_size);
-
-		matches.clear();
-		std::vector<int> u_unconfirmed;
-		u_detection.clear();
-		linear_assignment(dists, dist_size, dist_size_size, 0.7, matches, u_unconfirmed, u_detection);
-
-		for (int i = 0; i < matches.size(); i++)
-		{
-			unconfirmed[matches[i][0]]->update(detections[matches[i][1]], this->frame_id);
-			activated_stracks.push_back(*unconfirmed[matches[i][0]]);
-		}
-
-		for (int i = 0; i < u_unconfirmed.size(); i++)
-		{
-			STrack *track = unconfirmed[u_unconfirmed[i]];
-			track->mark_removed();
-			removed_stracks.push_back(*track);
-		}
-
-		////////////////// Step 4: Init new stracks //////////////////
-		for (int i = 0; i < u_detection.size(); i++)
-		{
-			STrack *track = &detections[u_detection[i]];
-			if (track->score < this->high_thresh)
-				continue;
-			track->activate(this->kalman_filter, this->frame_id);
-			activated_stracks.push_back(*track);
-		}
-
-		////////////////// Step 5: Update state //////////////////
-		for (int i = 0; i < this->lost_stracks.size(); i++)
-		{
-			if (this->frame_id - this->lost_stracks[i].end_frame() > this->max_time_lost)
-			{
-				this->lost_stracks[i].mark_removed();
-				removed_stracks.push_back(this->lost_stracks[i]);
-			}
-		}
-
-		for (int i = 0; i < this->tracked_stracks.size(); i++)
-		{
-			if (this->tracked_stracks[i].state == TrackState::Tracked)
-			{
-				tracked_stracks_swap.push_back(this->tracked_stracks[i]);
-			}
-		}
-		this->tracked_stracks.clear();
-		this->tracked_stracks.assign(tracked_stracks_swap.begin(), tracked_stracks_swap.end());
-
-		this->tracked_stracks = joint_stracks(this->tracked_stracks, activated_stracks);
-		this->tracked_stracks = joint_stracks(this->tracked_stracks, refind_stracks);
-
-		// std::cout << activated_stracks.size() << std::endl;
-
-		this->lost_stracks = sub_stracks(this->lost_stracks, this->tracked_stracks);
-		for (int i = 0; i < lost_stracks.size(); i++)
-		{
-			this->lost_stracks.push_back(lost_stracks[i]);
-		}
-
-		this->lost_stracks = sub_stracks(this->lost_stracks, this->removed_stracks);
-		for (int i = 0; i < removed_stracks.size(); i++)
-		{
-			this->removed_stracks.push_back(removed_stracks[i]);
-		}
-
-		remove_duplicate_stracks(resa, resb, this->tracked_stracks, this->lost_stracks);
-
-		this->tracked_stracks.clear();
-		this->tracked_stracks.assign(resa.begin(), resa.end());
-		this->lost_stracks.clear();
-		this->lost_stracks.assign(resb.begin(), resb.end());
-
-		for (int i = 0; i < this->tracked_stracks.size(); i++)
-		{
-			if (this->tracked_stracks[i].is_activated)
-			{
-				output_stracks.push_back(this->tracked_stracks[i]);
-			}
-		}
-
-		tgts.targets.clear();
-
-		for (unsigned long i = 0; i < output_stracks.size(); i++)
-		{
-			std::vector<float> tlwh = output_stracks[i].tlwh;
-			bool vertical = tlwh[2] / tlwh[3] > 1.6;
-
-			if (tlwh[2] * tlwh[3] > 20 && !vertical)
-			{
-				Target tgt;
-				tgt.setBox(tlwh[0], tlwh[1], tlwh[0] + tlwh[2], tlwh[1] + tlwh[3], tgts.width, tgts.height);
-				tgt.setTrackID(output_stracks[i].track_id);
-				tgts.targets.push_back(tgt);
-			}
-		}
-	}
-}

algorithm/mot/bytetrack/BYTETracker.h

@@ -1,56 +0,0 @@
-#ifndef __SV_BYTETrack__
-#define __SV_BYTETrack__
-
-#include "sv_core.h"
-#include "STrack.h"
-
-class detect_result
-{
-public:
-    int classId;
-    float confidence;
-    cv::Rect_<float> box;
-};
-
-namespace sv {
-
-class BYTETracker
-{
-public:
-	BYTETracker(int frame_rate = 30, int track_buffer = 30);
-	~BYTETracker();
-
-	void  update(TargetsInFrame &tgts);
-    cv::Scalar get_color(int idx);
-
-private:
-	 std::vector<STrack*> joint_stracks( std::vector<STrack*> &tlista,  std::vector<STrack> &tlistb);
-	 std::vector<STrack> joint_stracks( std::vector<STrack> &tlista,  std::vector<STrack> &tlistb);
-
-	 std::vector<STrack> sub_stracks( std::vector<STrack> &tlista,  std::vector<STrack> &tlistb);
-	void remove_duplicate_stracks( std::vector<STrack> &resa,  std::vector<STrack> &resb,  std::vector<STrack> &stracksa,  std::vector<STrack> &stracksb);
-
-	void linear_assignment( std::vector< std::vector<float> > &cost_matrix, int cost_matrix_size, int cost_matrix_size_size, float thresh,
-		 std::vector< std::vector<int> > &matches,  std::vector<int> &unmatched_a,  std::vector<int> &unmatched_b);
-	 std::vector< std::vector<float> > iou_distance( std::vector<STrack*> &atracks,  std::vector<STrack> &btracks, int &dist_size, int &dist_size_size);
-	 std::vector< std::vector<float> > iou_distance( std::vector<STrack> &atracks,  std::vector<STrack> &btracks);
-	 std::vector< std::vector<float> > ious( std::vector< std::vector<float> > &atlbrs,  std::vector< std::vector<float> > &btlbrs);
-
-	double lapjv(const  std::vector< std::vector<float> > &cost,  std::vector<int> &rowsol,  std::vector<int> &colsol, 
-		bool extend_cost = false, float cost_limit = LONG_MAX, bool return_cost = true);
-
-private:
-
-	float track_thresh;
-	float high_thresh;
-	float match_thresh;
-	int frame_id;
-	int max_time_lost;
-
-	std::vector<STrack> tracked_stracks;
-	std::vector<STrack> lost_stracks;
-	std::vector<STrack> removed_stracks;
-	byte_kalman::ByteKalmanFilter kalman_filter;
-};
-}
-#endif

algorithm/mot/bytetrack/BytekalmanFilter.cpp

@@ -1,152 +0,0 @@
-#include "BytekalmanFilter.h"
-#include <Eigen/Cholesky>
-
-namespace byte_kalman
-{
-	const double ByteKalmanFilter::chi2inv95[10] = {
-	0,
-	3.8415,
-	5.9915,
-	7.8147,
-	9.4877,
-	11.070,
-	12.592,
-	14.067,
-	15.507,
-	16.919
-	};
-	ByteKalmanFilter::ByteKalmanFilter()
-	{
-		int ndim = 4;
-		double dt = 1.;
-
-		_motion_mat = Eigen::MatrixXf::Identity(8, 8);
-		for (int i = 0; i < ndim; i++) {
-			_motion_mat(i, ndim + i) = dt;
-		}
-		_update_mat = Eigen::MatrixXf::Identity(4, 8);
-
-		this->_std_weight_position = 1. / 20;
-		this->_std_weight_velocity = 1. / 160;
-	}
-
-	KAL_DATA ByteKalmanFilter::initiate(const DETECTBOX &measurement)
-	{
-		DETECTBOX mean_pos = measurement;
-		DETECTBOX mean_vel;
-		for (int i = 0; i < 4; i++) mean_vel(i) = 0;
-
-		KAL_MEAN mean;
-		for (int i = 0; i < 8; i++) {
-			if (i < 4) mean(i) = mean_pos(i);
-			else mean(i) = mean_vel(i - 4);
-		}
-
-		KAL_MEAN std;
-		std(0) = 2 * _std_weight_position * measurement[3];
-		std(1) = 2 * _std_weight_position * measurement[3];
-		std(2) = 1e-2;
-		std(3) = 2 * _std_weight_position * measurement[3];
-		std(4) = 10 * _std_weight_velocity * measurement[3];
-		std(5) = 10 * _std_weight_velocity * measurement[3];
-		std(6) = 1e-5;
-		std(7) = 10 * _std_weight_velocity * measurement[3];
-
-		KAL_MEAN tmp = std.array().square();
-		KAL_COVA var = tmp.asDiagonal();
-		return std::make_pair(mean, var);
-	}
-
-	void ByteKalmanFilter::predict(KAL_MEAN &mean, KAL_COVA &covariance)
-	{
-		//revise the data;
-		DETECTBOX std_pos;
-		std_pos << _std_weight_position * mean(3),
-			_std_weight_position * mean(3),
-			1e-2,
-			_std_weight_position * mean(3);
-		DETECTBOX std_vel;
-		std_vel << _std_weight_velocity * mean(3),
-			_std_weight_velocity * mean(3),
-			1e-5,
-			_std_weight_velocity * mean(3);
-		KAL_MEAN tmp;
-		tmp.block<1, 4>(0, 0) = std_pos;
-		tmp.block<1, 4>(0, 4) = std_vel;
-		tmp = tmp.array().square();
-		KAL_COVA motion_cov = tmp.asDiagonal();
-		KAL_MEAN mean1 = this->_motion_mat * mean.transpose();
-		KAL_COVA covariance1 = this->_motion_mat * covariance *(_motion_mat.transpose());
-		covariance1 += motion_cov;
-
-		mean = mean1;
-		covariance = covariance1;
-	}
-
-	KAL_HDATA ByteKalmanFilter::project(const KAL_MEAN &mean, const KAL_COVA &covariance)
-	{
-		DETECTBOX std;
-		std << _std_weight_position * mean(3), _std_weight_position * mean(3),
-			1e-1, _std_weight_position * mean(3);
-		KAL_HMEAN mean1 = _update_mat * mean.transpose();
-		KAL_HCOVA covariance1 = _update_mat * covariance * (_update_mat.transpose());
-		Eigen::Matrix<float, 4, 4> diag = std.asDiagonal();
-		diag = diag.array().square().matrix();
-		covariance1 += diag;
-		//    covariance1.diagonal() << diag;
-		return std::make_pair(mean1, covariance1);
-	}
-
-	KAL_DATA
-		ByteKalmanFilter::update(
-			const KAL_MEAN &mean,
-			const KAL_COVA &covariance,
-			const DETECTBOX &measurement)
-	{
-		KAL_HDATA pa = project(mean, covariance);
-		KAL_HMEAN projected_mean = pa.first;
-		KAL_HCOVA projected_cov = pa.second;
-
-		//chol_factor, lower =
-		//scipy.linalg.cho_factor(projected_cov, lower=True, check_finite=False)
-		//kalmain_gain =
-		//scipy.linalg.cho_solve((cho_factor, lower),
-		//np.dot(covariance, self._upadte_mat.T).T,
-		//check_finite=False).T
-		Eigen::Matrix<float, 4, 8> B = (covariance * (_update_mat.transpose())).transpose();
-		Eigen::Matrix<float, 8, 4> kalman_gain = (projected_cov.llt().solve(B)).transpose(); // eg.8x4
-		Eigen::Matrix<float, 1, 4> innovation = measurement - projected_mean; //eg.1x4
-		auto tmp = innovation * (kalman_gain.transpose());
-		KAL_MEAN new_mean = (mean.array() + tmp.array()).matrix();
-		KAL_COVA new_covariance = covariance - kalman_gain * projected_cov*(kalman_gain.transpose());
-		return std::make_pair(new_mean, new_covariance);
-	}
-
-	Eigen::Matrix<float, 1, -1>
-		ByteKalmanFilter::gating_distance(
-			const KAL_MEAN &mean,
-			const KAL_COVA &covariance,
-			const std::vector<DETECTBOX> &measurements,
-			bool only_position)
-	{
-		KAL_HDATA pa = this->project(mean, covariance);
-		if (only_position) {
-			printf("not implement!");
-			exit(0);
-		}
-		KAL_HMEAN mean1 = pa.first;
-		KAL_HCOVA covariance1 = pa.second;
-
-		//    Eigen::Matrix<float, -1, 4, Eigen::RowMajor> d(size, 4);
-		DETECTBOXSS d(measurements.size(), 4);
-		int pos = 0;
-		for (DETECTBOX box : measurements) {
-			d.row(pos++) = box - mean1;
-		}
-		Eigen::Matrix<float, -1, -1, Eigen::RowMajor> factor = covariance1.llt().matrixL();
-		Eigen::Matrix<float, -1, -1> z = factor.triangularView<Eigen::Lower>().solve<Eigen::OnTheRight>(d).transpose();
-		auto zz = ((z.array())*(z.array())).matrix();
-		auto square_maha = zz.colwise().sum();
-		return square_maha;
-	}
-}

algorithm/mot/bytetrack/BytekalmanFilter.h

@@ -1,31 +0,0 @@
-#pragma once
-
-#include "dataType.h"
-
-namespace byte_kalman
-{
-	class ByteKalmanFilter
-	{
-	public:
-		static const double chi2inv95[10];
-		ByteKalmanFilter();
-		KAL_DATA initiate(const DETECTBOX& measurement);
-		void predict(KAL_MEAN& mean, KAL_COVA& covariance);
-		KAL_HDATA project(const KAL_MEAN& mean, const KAL_COVA& covariance);
-		KAL_DATA update(const KAL_MEAN& mean,
-			const KAL_COVA& covariance,
-			const DETECTBOX& measurement);
-
-		Eigen::Matrix<float, 1, -1> gating_distance(
-			const KAL_MEAN& mean,
-			const KAL_COVA& covariance,
-			const std::vector<DETECTBOX>& measurements,
-			bool only_position = false);
-
-	private:
-		Eigen::Matrix<float, 8, 8, Eigen::RowMajor> _motion_mat;
-		Eigen::Matrix<float, 4, 8, Eigen::RowMajor> _update_mat;
-		float _std_weight_position;
-		float _std_weight_velocity;
-	};
-}

algorithm/mot/bytetrack/STrack.cpp

@@ -1,192 +0,0 @@
-#include "STrack.h"
-
-STrack::STrack( std::vector<float> tlwh_, float score)
-{
-	_tlwh.resize(4);
-	_tlwh.assign(tlwh_.begin(), tlwh_.end());
-
-	is_activated = false;
-	track_id = 0;
-	state = TrackState::New;
-	
-	tlwh.resize(4);
-	tlbr.resize(4);
-
-	static_tlwh();
-	static_tlbr();
-	frame_id = 0;
-	tracklet_len = 0;
-	this->score = score;
-	start_frame = 0;
-}
-
-STrack::~STrack()
-{
-}
-
-void STrack::activate(byte_kalman::ByteKalmanFilter &kalman_filter, int frame_id)
-{
-	this->kalman_filter = kalman_filter;
-	this->track_id = this->next_id();
-
-	 std::vector<float> _tlwh_tmp(4);
-	_tlwh_tmp[0] = this->_tlwh[0];
-	_tlwh_tmp[1] = this->_tlwh[1];
-	_tlwh_tmp[2] = this->_tlwh[2];
-	_tlwh_tmp[3] = this->_tlwh[3];
-	 std::vector<float> xyah = tlwh_to_xyah(_tlwh_tmp);
-	DETECTBOX xyah_box;
-	xyah_box[0] = xyah[0];
-	xyah_box[1] = xyah[1];
-	xyah_box[2] = xyah[2];
-	xyah_box[3] = xyah[3];
-	auto mc = this->kalman_filter.initiate(xyah_box);
-	this->mean = mc.first;
-	this->covariance = mc.second;
-
-	static_tlwh();
-	static_tlbr();
-
-	this->tracklet_len = 0;
-	this->state = TrackState::Tracked;
-	if (frame_id == 1)
-	{
-		this->is_activated = true;
-	}
-	//this->is_activated = true;
-	this->frame_id = frame_id;
-	this->start_frame = frame_id;
-}
-
-void STrack::re_activate(STrack &new_track, int frame_id, bool new_id)
-{
-	 std::vector<float> xyah = tlwh_to_xyah(new_track.tlwh);
-	DETECTBOX xyah_box;
-	xyah_box[0] = xyah[0];
-	xyah_box[1] = xyah[1];
-	xyah_box[2] = xyah[2];
-	xyah_box[3] = xyah[3];
-	auto mc = this->kalman_filter.update(this->mean, this->covariance, xyah_box);
-	this->mean = mc.first;
-	this->covariance = mc.second;
-
-	static_tlwh();
-	static_tlbr();
-
-	this->tracklet_len = 0;
-	this->state = TrackState::Tracked;
-	this->is_activated = true;
-	this->frame_id = frame_id;
-	this->score = new_track.score;
-	if (new_id)
-		this->track_id = next_id();
-}
-
-void STrack::update(STrack &new_track, int frame_id)
-{
-	this->frame_id = frame_id;
-	this->tracklet_len++;
-
-	 std::vector<float> xyah = tlwh_to_xyah(new_track.tlwh);
-	DETECTBOX xyah_box;
-	xyah_box[0] = xyah[0];
-	xyah_box[1] = xyah[1];
-	xyah_box[2] = xyah[2];
-	xyah_box[3] = xyah[3];
-
-	auto mc = this->kalman_filter.update(this->mean, this->covariance, xyah_box);
-	this->mean = mc.first;
-	this->covariance = mc.second;
-
-	static_tlwh();
-	static_tlbr();
-
-	this->state = TrackState::Tracked;
-	this->is_activated = true;
-
-	this->score = new_track.score;
-}
-
-void STrack::static_tlwh()
-{
-	if (this->state == TrackState::New)
-	{
-		tlwh[0] = _tlwh[0];
-		tlwh[1] = _tlwh[1];
-		tlwh[2] = _tlwh[2];
-		tlwh[3] = _tlwh[3];
-		return;
-	}
-
-	tlwh[0] = mean[0];
-	tlwh[1] = mean[1];
-	tlwh[2] = mean[2];
-	tlwh[3] = mean[3];
-
-	tlwh[2] *= tlwh[3];
-	tlwh[0] -= tlwh[2] / 2;
-	tlwh[1] -= tlwh[3] / 2;
-}
-
-void STrack::static_tlbr()
-{
-	tlbr.clear();
-	tlbr.assign(tlwh.begin(), tlwh.end());
-	tlbr[2] += tlbr[0];
-	tlbr[3] += tlbr[1];
-}
-
- std::vector<float> STrack::tlwh_to_xyah( std::vector<float> tlwh_tmp)
-{
-	 std::vector<float> tlwh_output = tlwh_tmp;
-	tlwh_output[0] += tlwh_output[2] / 2;
-	tlwh_output[1] += tlwh_output[3] / 2;
-	tlwh_output[2] /= tlwh_output[3];
-	return tlwh_output;
-}
-
- std::vector<float> STrack::to_xyah()
-{
-	return tlwh_to_xyah(tlwh);
-}
-
- std::vector<float> STrack::tlbr_to_tlwh( std::vector<float> &tlbr)
-{
-	tlbr[2] -= tlbr[0];
-	tlbr[3] -= tlbr[1];
-	return tlbr;
-}
-
-void STrack::mark_lost()
-{
-	state = TrackState::Lost;
-}
-
-void STrack::mark_removed()
-{
-	state = TrackState::Removed;
-}
-
-int STrack::next_id()
-{
-	static int _count = 0;
-	_count++;
-	return _count;
-}
-
-int STrack::end_frame()
-{
-	return this->frame_id;
-}
-
-void STrack::multi_predict( std::vector<STrack*> &stracks, byte_kalman::ByteKalmanFilter &kalman_filter)
-{
-	for (int i = 0; i < stracks.size(); i++)
-	{
-		if (stracks[i]->state != TrackState::Tracked)
-		{
-			stracks[i]->mean[7] = 0;
-		}
-		kalman_filter.predict(stracks[i]->mean, stracks[i]->covariance);
-	}
-}

algorithm/mot/bytetrack/STrack.h

@@ -1,47 +0,0 @@
-#pragma once
-
-#include <opencv2/opencv.hpp>
-#include "BytekalmanFilter.h"
-
-enum TrackState { New = 0, Tracked, Lost, Removed };
-
-class STrack
-{
-public:
-	STrack( std::vector<float> tlwh_, float score);
-	~STrack();
-
-	 std::vector<float> static tlbr_to_tlwh( std::vector<float> &tlbr);
-	void static multi_predict( std::vector<STrack*> &stracks, byte_kalman::ByteKalmanFilter &kalman_filter);
-	void static_tlwh();
-	void static_tlbr();
-	 std::vector<float> tlwh_to_xyah( std::vector<float> tlwh_tmp);
-	 std::vector<float> to_xyah();
-	void mark_lost();
-	void mark_removed();
-	int next_id();
-	int end_frame();
-	
-	void activate(byte_kalman::ByteKalmanFilter &kalman_filter, int frame_id);
-	void re_activate(STrack &new_track, int frame_id, bool new_id = false);
-	void update(STrack &new_track, int frame_id);
-
-public:
-	bool is_activated;
-	int track_id;
-	int state;
-
-	 std::vector<float> _tlwh;
-	 std::vector<float> tlwh;
-	 std::vector<float> tlbr;
-	int frame_id;
-	int tracklet_len;
-	int start_frame;
-
-	KAL_MEAN mean;
-	KAL_COVA covariance;
-	float score;
-
-private:
-	byte_kalman::ByteKalmanFilter kalman_filter;
-};

algorithm/mot/bytetrack/dataType.h

@@ -1,27 +0,0 @@
-#pragma once
-
-#include <cstddef>
-#include <vector>
-
-#include <Eigen/Core>
-#include <Eigen/Dense>
-
-
-
-typedef Eigen::Matrix<float, 1, 4, Eigen::RowMajor> DETECTBOX;
-typedef Eigen::Matrix<float, -1, 4, Eigen::RowMajor> DETECTBOXSS;
-
-
-//Kalmanfilter
-typedef Eigen::Matrix<float, 8, 8, Eigen::RowMajor> KAL_FILTER;
-typedef Eigen::Matrix<float, 1, 8, Eigen::RowMajor> KAL_MEAN;
-typedef Eigen::Matrix<float, 8, 8, Eigen::RowMajor> KAL_COVA;
-typedef Eigen::Matrix<float, 1, 4, Eigen::RowMajor> KAL_HMEAN;
-typedef Eigen::Matrix<float, 4, 4, Eigen::RowMajor> KAL_HCOVA;
-using KAL_DATA = std::pair<KAL_MEAN, KAL_COVA>;
-using KAL_HDATA = std::pair<KAL_HMEAN, KAL_HCOVA>;
-
-
-
-
-

algorithm/mot/bytetrack/lapjv.cpp

@@ -1,343 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <string.h>
-
-#include "lapjv.h"
-
-/** Column-reduction and reduction transfer for a dense cost matrix.
- */
-int_t _ccrrt_dense(const uint_t n, cost_t *cost[],
-	int_t *free_rows, int_t *x, int_t *y, cost_t *v)
-{
-	int_t n_free_rows;
-	boolean *unique;
-
-	for (uint_t i = 0; i < n; i++) {
-		x[i] = -1;
-		v[i] = LARGE;
-		y[i] = 0;
-	}
-	for (uint_t i = 0; i < n; i++) {
-		for (uint_t j = 0; j < n; j++) {
-			const cost_t c = cost[i][j];
-			if (c < v[j]) {
-				v[j] = c;
-				y[j] = i;
-			}
-			PRINTF("i=%d, j=%d, c[i,j]=%f, v[j]=%f y[j]=%d\n", i, j, c, v[j], y[j]);
-		}
-	}
-	PRINT_COST_ARRAY(v, n);
-	PRINT_INDEX_ARRAY(y, n);
-	NEW(unique, boolean, n);
-	memset(unique, TRUE, n);
-	{
-		int_t j = n;
-		do {
-			j--;
-			const int_t i = y[j];
-			if (x[i] < 0) {
-				x[i] = j;
-			}
-			else {
-				unique[i] = FALSE;
-				y[j] = -1;
-			}
-		} while (j > 0);
-	}
-	n_free_rows = 0;
-	for (uint_t i = 0; i < n; i++) {
-		if (x[i] < 0) {
-			free_rows[n_free_rows++] = i;
-		}
-		else if (unique[i]) {
-			const int_t j = x[i];
-			cost_t min = LARGE;
-			for (uint_t j2 = 0; j2 < n; j2++) {
-				if (j2 == (uint_t)j) {
-					continue;
-				}
-				const cost_t c = cost[i][j2] - v[j2];
-				if (c < min) {
-					min = c;
-				}
-			}
-			PRINTF("v[%d] = %f - %f\n", j, v[j], min);
-			v[j] -= min;
-		}
-	}
-	FREE(unique);
-	return n_free_rows;
-}
-
-
-/** Augmenting row reduction for a dense cost matrix.
- */
-int_t _carr_dense(
-	const uint_t n, cost_t *cost[],
-	const uint_t n_free_rows,
-	int_t *free_rows, int_t *x, int_t *y, cost_t *v)
-{
-	uint_t current = 0;
-	int_t new_free_rows = 0;
-	uint_t rr_cnt = 0;
-	PRINT_INDEX_ARRAY(x, n);
-	PRINT_INDEX_ARRAY(y, n);
-	PRINT_COST_ARRAY(v, n);
-	PRINT_INDEX_ARRAY(free_rows, n_free_rows);
-	while (current < n_free_rows) {
-		int_t i0;
-		int_t j1, j2;
-		cost_t v1, v2, v1_new;
-		boolean v1_lowers;
-
-		rr_cnt++;
-		PRINTF("current = %d rr_cnt = %d\n", current, rr_cnt);
-		const int_t free_i = free_rows[current++];
-		j1 = 0;
-		v1 = cost[free_i][0] - v[0];
-		j2 = -1;
-		v2 = LARGE;
-		for (uint_t j = 1; j < n; j++) {
-			PRINTF("%d = %f %d = %f\n", j1, v1, j2, v2);
-			const cost_t c = cost[free_i][j] - v[j];
-			if (c < v2) {
-				if (c >= v1) {
-					v2 = c;
-					j2 = j;
-				}
-				else {
-					v2 = v1;
-					v1 = c;
-					j2 = j1;
-					j1 = j;
-				}
-			}
-		}
-		i0 = y[j1];
-		v1_new = v[j1] - (v2 - v1);
-		v1_lowers = v1_new < v[j1];
-		PRINTF("%d %d 1=%d,%f 2=%d,%f v1'=%f(%d,%g) \n", free_i, i0, j1, v1, j2, v2, v1_new, v1_lowers, v[j1] - v1_new);
-		if (rr_cnt < current * n) {
-			if (v1_lowers) {
-				v[j1] = v1_new;
-			}
-			else if (i0 >= 0 && j2 >= 0) {
-				j1 = j2;
-				i0 = y[j2];
-			}
-			if (i0 >= 0) {
-				if (v1_lowers) {
-					free_rows[--current] = i0;
-				}
-				else {
-					free_rows[new_free_rows++] = i0;
-				}
-			}
-		}
-		else {
-			PRINTF("rr_cnt=%d >= %d (current=%d * n=%d)\n", rr_cnt, current * n, current, n);
-			if (i0 >= 0) {
-				free_rows[new_free_rows++] = i0;
-			}
-		}
-		x[free_i] = j1;
-		y[j1] = free_i;
-	}
-	return new_free_rows;
-}
-
-
-/** Find columns with minimum d[j] and put them on the SCAN list.
- */
-uint_t _find_dense(const uint_t n, uint_t lo, cost_t *d, int_t *cols, int_t *y)
-{
-	uint_t hi = lo + 1;
-	cost_t mind = d[cols[lo]];
-	for (uint_t k = hi; k < n; k++) {
-		int_t j = cols[k];
-		if (d[j] <= mind) {
-			if (d[j] < mind) {
-				hi = lo;
-				mind = d[j];
-			}
-			cols[k] = cols[hi];
-			cols[hi++] = j;
-		}
-	}
-	return hi;
-}
-
-
-// Scan all columns in TODO starting from arbitrary column in SCAN
-// and try to decrease d of the TODO columns using the SCAN column.
-int_t _scan_dense(const uint_t n, cost_t *cost[],
-	uint_t *plo, uint_t*phi,
-	cost_t *d, int_t *cols, int_t *pred,
-	int_t *y, cost_t *v)
-{
-	uint_t lo = *plo;
-	uint_t hi = *phi;
-	cost_t h, cred_ij;
-
-	while (lo != hi) {
-		int_t j = cols[lo++];
-		const int_t i = y[j];
-		const cost_t mind = d[j];
-		h = cost[i][j] - v[j] - mind;
-		PRINTF("i=%d j=%d h=%f\n", i, j, h);
-		// For all columns in TODO
-		for (uint_t k = hi; k < n; k++) {
-			j = cols[k];
-			cred_ij = cost[i][j] - v[j] - h;
-			if (cred_ij < d[j]) {
-				d[j] = cred_ij;
-				pred[j] = i;
-				if (cred_ij == mind) {
-					if (y[j] < 0) {
-						return j;
-					}
-					cols[k] = cols[hi];
-					cols[hi++] = j;
-				}
-			}
-		}
-	}
-	*plo = lo;
-	*phi = hi;
-	return -1;
-}
-
-
-/** Single iteration of modified Dijkstra shortest path algorithm as explained in the JV paper.
- *
- * This is a dense matrix version.
- *
- * \return The closest free column index.
- */
-int_t find_path_dense(
-	const uint_t n, cost_t *cost[],
-	const int_t start_i,
-	int_t *y, cost_t *v,
-	int_t *pred)
-{
-	uint_t lo = 0, hi = 0;
-	int_t final_j = -1;
-	uint_t n_ready = 0;
-	int_t *cols;
-	cost_t *d;
-
-	NEW(cols, int_t, n);
-	NEW(d, cost_t, n);
-
-	for (uint_t i = 0; i < n; i++) {
-		cols[i] = i;
-		pred[i] = start_i;
-		d[i] = cost[start_i][i] - v[i];
-	}
-	PRINT_COST_ARRAY(d, n);
-	while (final_j == -1) {
-		// No columns left on the SCAN list.
-		if (lo == hi) {
-			PRINTF("%d..%d -> find\n", lo, hi);
-			n_ready = lo;
-			hi = _find_dense(n, lo, d, cols, y);
-			PRINTF("check %d..%d\n", lo, hi);
-			PRINT_INDEX_ARRAY(cols, n);
-			for (uint_t k = lo; k < hi; k++) {
-				const int_t j = cols[k];
-				if (y[j] < 0) {
-					final_j = j;
-				}
-			}
-		}
-		if (final_j == -1) {
-			PRINTF("%d..%d -> scan\n", lo, hi);
-			final_j = _scan_dense(
-				n, cost, &lo, &hi, d, cols, pred, y, v);
-			PRINT_COST_ARRAY(d, n);
-			PRINT_INDEX_ARRAY(cols, n);
-			PRINT_INDEX_ARRAY(pred, n);
-		}
-	}
-
-	PRINTF("found final_j=%d\n", final_j);
-	PRINT_INDEX_ARRAY(cols, n);
-	{
-		const cost_t mind = d[cols[lo]];
-		for (uint_t k = 0; k < n_ready; k++) {
-			const int_t j = cols[k];
-			v[j] += d[j] - mind;
-		}
-	}
-
-	FREE(cols);
-	FREE(d);
-
-	return final_j;
-}
-
-
-/** Augment for a dense cost matrix.
- */
-int_t _ca_dense(
-	const uint_t n, cost_t *cost[],
-	const uint_t n_free_rows,
-	int_t *free_rows, int_t *x, int_t *y, cost_t *v)
-{
-	int_t *pred;
-
-	NEW(pred, int_t, n);
-
-	for (int_t *pfree_i = free_rows; pfree_i < free_rows + n_free_rows; pfree_i++) {
-		int_t i = -1, j;
-		uint_t k = 0;
-
-		PRINTF("looking at free_i=%d\n", *pfree_i);
-		j = find_path_dense(n, cost, *pfree_i, y, v, pred);
-		ASSERT(j >= 0);
-		ASSERT(j < n);
-		while (i != *pfree_i) {
-			PRINTF("augment %d\n", j);
-			PRINT_INDEX_ARRAY(pred, n);
-			i = pred[j];
-			PRINTF("y[%d]=%d -> %d\n", j, y[j], i);
-			y[j] = i;
-			PRINT_INDEX_ARRAY(x, n);
-			SWAP_INDICES(j, x[i]);
-			k++;
-			if (k >= n) {
-				ASSERT(FALSE);
-			}
-		}
-	}
-	FREE(pred);
-	return 0;
-}
-
-
-/** Solve dense sparse LAP.
- */
-int lapjv_internal(
-	const uint_t n, cost_t *cost[],
-	int_t *x, int_t *y)
-{
-	int ret;
-	int_t *free_rows;
-	cost_t *v;
-
-	NEW(free_rows, int_t, n);
-	NEW(v, cost_t, n);
-	ret = _ccrrt_dense(n, cost, free_rows, x, y, v);
-	int i = 0;
-	while (ret > 0 && i < 2) {
-		ret = _carr_dense(n, cost, ret, free_rows, x, y, v);
-		i++;
-	}
-	if (ret > 0) {
-		ret = _ca_dense(n, cost, ret, free_rows, x, y, v);
-	}
-	FREE(v);
-	FREE(free_rows);
-	return ret;
-}

algorithm/mot/bytetrack/lapjv.h

@@ -1,63 +0,0 @@
-#ifndef LAPJV_H
-#define LAPJV_H
-
-#define LARGE 1000000
-
-#if !defined TRUE
-#define TRUE 1
-#endif
-#if !defined FALSE
-#define FALSE 0
-#endif
-
-#define NEW(x, t, n) if ((x = (t *)malloc(sizeof(t) * (n))) == 0) { return -1; }
-#define FREE(x) if (x != 0) { free(x); x = 0; }
-#define SWAP_INDICES(a, b) { int_t _temp_index = a; a = b; b = _temp_index; }
-
-#if 0
-#include <assert.h>
-#define ASSERT(cond) assert(cond)
-#define PRINTF(fmt, ...) printf(fmt, ##__VA_ARGS__)
-#define PRINT_COST_ARRAY(a, n) \
-    while (1) { \
-        printf(#a" = ["); \
-        if ((n) > 0) { \
-            printf("%f", (a)[0]); \
-            for (uint_t j = 1; j < n; j++) { \
-                printf(", %f", (a)[j]); \
-            } \
-        } \
-        printf("]\n"); \
-        break; \
-    }
-#define PRINT_INDEX_ARRAY(a, n) \
-    while (1) { \
-        printf(#a" = ["); \
-        if ((n) > 0) { \
-            printf("%d", (a)[0]); \
-            for (uint_t j = 1; j < n; j++) { \
-                printf(", %d", (a)[j]); \
-            } \
-        } \
-        printf("]\n"); \
-        break; \
-    }
-#else
-#define ASSERT(cond)
-#define PRINTF(fmt, ...)
-#define PRINT_COST_ARRAY(a, n)
-#define PRINT_INDEX_ARRAY(a, n)
-#endif
-
-
-typedef signed int int_t;
-typedef unsigned int uint_t;
-typedef double cost_t;
-typedef char boolean;
-typedef enum fp_t { FP_1 = 1, FP_2 = 2, FP_DYNAMIC = 3 } fp_t;
-
-extern int_t lapjv_internal(
-	const uint_t n, cost_t *cost[],
-	int_t *x, int_t *y);
-
-#endif // LAPJV_H

algorithm/mot/bytetrack/utils.cpp

@@ -1,432 +0,0 @@
-#include "BYTETracker.h"
-#include "lapjv.h"
-
-namespace sv {
-
-std::vector<STrack*> BYTETracker::joint_stracks( std::vector<STrack*> &tlista,  std::vector<STrack> &tlistb)
-{
-    std::map<int, int> exists;
-    std::vector<STrack*> res;
-    for (int i = 0; i < tlista.size(); i++)
-    {
-        exists.insert(std::pair<int, int>(tlista[i]->track_id, 1));
-        res.push_back(tlista[i]);
-    }
-    for (int i = 0; i < tlistb.size(); i++)
-    {
-        int tid = tlistb[i].track_id;
-        if (!exists[tid] || exists.count(tid) == 0)
-        {
-            exists[tid] = 1;
-            res.push_back(&tlistb[i]);
-        }
-    }
-    return res;
-}
-
-std::vector<STrack> BYTETracker::joint_stracks( std::vector<STrack> &tlista,  std::vector<STrack> &tlistb)
-{
-    std::map<int, int> exists;
-    std::vector<STrack> res;
-    for (int i = 0; i < tlista.size(); i++)
-    {
-        exists.insert(std::pair<int, int>(tlista[i].track_id, 1));
-        res.push_back(tlista[i]);
-    }
-    for (int i = 0; i < tlistb.size(); i++)
-    {
-        int tid = tlistb[i].track_id;
-        if (!exists[tid] || exists.count(tid) == 0)
-        {
-            exists[tid] = 1;
-            res.push_back(tlistb[i]);
-        }
-    }
-    return res;
-}
-
-std::vector<STrack> BYTETracker::sub_stracks( std::vector<STrack> &tlista,  std::vector<STrack> &tlistb)
-{
-    std::map<int, STrack> stracks;
-    for (int i = 0; i < tlista.size(); i++)
-    {
-        stracks.insert(std::pair<int, STrack>(tlista[i].track_id, tlista[i]));
-    }
-    for (int i = 0; i < tlistb.size(); i++)
-    {
-        int tid = tlistb[i].track_id;
-        if (stracks.count(tid) != 0)
-        {
-            stracks.erase(tid);
-        }
-    }
-
-    std::vector<STrack> res;
-    std::map<int, STrack>::iterator  it;
-    for (it = stracks.begin(); it != stracks.end(); ++it)
-    {
-        res.push_back(it->second);
-    }
-
-    return res;
-}
-
-void BYTETracker::remove_duplicate_stracks( std::vector<STrack> &resa,  std::vector<STrack> &resb,  std::vector<STrack> &stracksa,  std::vector<STrack> &stracksb)
-{
-    std::vector< std::vector<float> > pdist = iou_distance(stracksa, stracksb);
-    std::vector<std::pair<int, int> > pairs;
-    for (int i = 0; i < pdist.size(); i++)
-    {
-        for (int j = 0; j < pdist[i].size(); j++)
-        {
-            if (pdist[i][j] < 0.15)
-            {
-                pairs.push_back(std::pair<int, int>(i, j));
-            }
-        }
-    }
-
-    std::vector<int> dupa, dupb;
-    for (int i = 0; i < pairs.size(); i++)
-    {
-        int timep = stracksa[pairs[i].first].frame_id - stracksa[pairs[i].first].start_frame;
-        int timeq = stracksb[pairs[i].second].frame_id - stracksb[pairs[i].second].start_frame;
-        if (timep > timeq)
-            dupb.push_back(pairs[i].second);
-        else
-            dupa.push_back(pairs[i].first);
-    }
-
-    for (int i = 0; i < stracksa.size(); i++)
-    {
-        std::vector<int>::iterator iter = find(dupa.begin(), dupa.end(), i);
-        if (iter == dupa.end())
-        {
-            resa.push_back(stracksa[i]);
-        }
-    }
-
-    for (int i = 0; i < stracksb.size(); i++)
-    {
-        std::vector<int>::iterator iter = find(dupb.begin(), dupb.end(), i);
-        if (iter == dupb.end())
-        {
-            resb.push_back(stracksb[i]);
-        }
-    }
-}
-
-void BYTETracker::linear_assignment( std::vector< std::vector<float> > &cost_matrix, int cost_matrix_size, int cost_matrix_size_size, float thresh,
-                                     std::vector< std::vector<int> > &matches,  std::vector<int> &unmatched_a,  std::vector<int> &unmatched_b)
-{
-    if (cost_matrix.size() == 0)
-    {
-        for (int i = 0; i < cost_matrix_size; i++)
-        {
-            unmatched_a.push_back(i);
-        }
-        for (int i = 0; i < cost_matrix_size_size; i++)
-        {
-            unmatched_b.push_back(i);
-        }
-        return;
-    }
-
-    std::vector<int> rowsol;  std::vector<int> colsol;
-    float c = lapjv(cost_matrix, rowsol, colsol, true, thresh);
-    for (int i = 0; i < rowsol.size(); i++)
-    {
-        if (rowsol[i] >= 0)
-        {
-            std::vector<int> match;
-            match.push_back(i);
-            match.push_back(rowsol[i]);
-            matches.push_back(match);
-        }
-        else
-        {
-            unmatched_a.push_back(i);
-        }
-    }
-
-    for (int i = 0; i < colsol.size(); i++)
-    {
-        if (colsol[i] < 0)
-        {
-            unmatched_b.push_back(i);
-        }
-    }
-}
-
-std::vector< std::vector<float> > BYTETracker::ious( std::vector< std::vector<float> > &atlbrs,  std::vector< std::vector<float> > &btlbrs)
-{
-    std::vector< std::vector<float> > ious;
-    if (atlbrs.size()*btlbrs.size() == 0)
-        return ious;
-
-    ious.resize(atlbrs.size());
-    for (int i = 0; i < ious.size(); i++)
-    {
-        ious[i].resize(btlbrs.size());
-    }
-
-    //bbox_ious
-    for (int k = 0; k < btlbrs.size(); k++)
-    {
-        std::vector<float> ious_tmp;
-        float box_area = (btlbrs[k][2] - btlbrs[k][0] + 1)*(btlbrs[k][3] - btlbrs[k][1] + 1);
-        for (int n = 0; n < atlbrs.size(); n++)
-        {
-            float iw = cv::min(atlbrs[n][2], btlbrs[k][2]) - cv::max(atlbrs[n][0], btlbrs[k][0]) + 1;
-            if (iw > 0)
-            {
-                float ih = cv::min(atlbrs[n][3], btlbrs[k][3]) - cv::max(atlbrs[n][1], btlbrs[k][1]) + 1;
-                if(ih > 0)
-                {
-                    float ua = (atlbrs[n][2] - atlbrs[n][0] + 1)*(atlbrs[n][3] - atlbrs[n][1] + 1) + box_area - iw * ih;
-                    ious[n][k] = iw * ih / ua;
-                }
-                else
-                {
-                    ious[n][k] = 0.0;
-                }
-            }
-            else
-            {
-                ious[n][k] = 0.0;
-            }
-        }
-    }
-
-    return ious;
-}
-
-std::vector< std::vector<float> > BYTETracker::iou_distance( std::vector<STrack*> &atracks,  std::vector<STrack> &btracks, int &dist_size, int &dist_size_size)
-{
-    std::vector< std::vector<float> > cost_matrix;
-    if (atracks.size() * btracks.size() == 0)
-    {
-        dist_size = atracks.size();
-        dist_size_size = btracks.size();
-        return cost_matrix;
-    }
-    std::vector< std::vector<float> > atlbrs, btlbrs;
-    for (int i = 0; i < atracks.size(); i++)
-    {
-        atlbrs.push_back(atracks[i]->tlbr);
-    }
-    for (int i = 0; i < btracks.size(); i++)
-    {
-        btlbrs.push_back(btracks[i].tlbr);
-    }
-
-    dist_size = atracks.size();
-    dist_size_size = btracks.size();
-
-    std::vector< std::vector<float> > _ious = ious(atlbrs, btlbrs);
-
-    for (int i = 0; i < _ious.size();i++)
-    {
-        std::vector<float> _iou;
-        for (int j = 0; j < _ious[i].size(); j++)
-        {
-            _iou.push_back(1 - _ious[i][j]);
-        }
-        cost_matrix.push_back(_iou);
-    }
-
-    return cost_matrix;
-}
-
-std::vector< std::vector<float> > BYTETracker::iou_distance( std::vector<STrack> &atracks,  std::vector<STrack> &btracks)
-{
-    std::vector< std::vector<float> > atlbrs, btlbrs;
-    for (int i = 0; i < atracks.size(); i++)
-    {
-        atlbrs.push_back(atracks[i].tlbr);
-    }
-    for (int i = 0; i < btracks.size(); i++)
-    {
-        btlbrs.push_back(btracks[i].tlbr);
-    }
-
-    std::vector< std::vector<float> > _ious = ious(atlbrs, btlbrs);
-    std::vector< std::vector<float> > cost_matrix;
-    for (int i = 0; i < _ious.size(); i++)
-    {
-        std::vector<float> _iou;
-        for (int j = 0; j < _ious[i].size(); j++)
-        {
-            _iou.push_back(1 - _ious[i][j]);
-        }
-        cost_matrix.push_back(_iou);
-    }
-
-    return cost_matrix;
-}
-
-double BYTETracker::lapjv(const  std::vector< std::vector<float> > &cost,  std::vector<int> &rowsol,  std::vector<int> &colsol,
-                          bool extend_cost, float cost_limit, bool return_cost)
-{
-    std::vector< std::vector<float> > cost_c;
-    cost_c.assign(cost.begin(), cost.end());
-
-    std::vector< std::vector<float> > cost_c_extended;
-
-    int n_rows = cost.size();
-    int n_cols = cost[0].size();
-    rowsol.resize(n_rows);
-    colsol.resize(n_cols);
-
-    int n = 0;
-    if (n_rows == n_cols)
-    {
-        n = n_rows;
-    }
-    else
-    {
-        if (!extend_cost)
-        {
-            std::cout << "set extend_cost=True" << std::endl;
-            system("pause");
-            exit(0);
-        }
-    }
-
-    if (extend_cost || cost_limit < LONG_MAX)
-    {
-        n = n_rows + n_cols;
-        cost_c_extended.resize(n);
-        for (int i = 0; i < cost_c_extended.size(); i++)
-            cost_c_extended[i].resize(n);
-
-        if (cost_limit < LONG_MAX)
-        {
-            for (int i = 0; i < cost_c_extended.size(); i++)
-            {
-                for (int j = 0; j < cost_c_extended[i].size(); j++)
-                {
-                    cost_c_extended[i][j] = cost_limit / 2.0;
-                }
-            }
-        }
-        else
-        {
-            float cost_max = -1;
-            for (int i = 0; i < cost_c.size(); i++)
-            {
-                for (int j = 0; j < cost_c[i].size(); j++)
-                {
-                    if (cost_c[i][j] > cost_max)
-                        cost_max = cost_c[i][j];
-                }
-            }
-            for (int i = 0; i < cost_c_extended.size(); i++)
-            {
-                for (int j = 0; j < cost_c_extended[i].size(); j++)
-                {
-                    cost_c_extended[i][j] = cost_max + 1;
-                }
-            }
-        }
-
-        for (int i = n_rows; i < cost_c_extended.size(); i++)
-        {
-            for (int j = n_cols; j < cost_c_extended[i].size(); j++)
-            {
-                cost_c_extended[i][j] = 0;
-            }
-        }
-        for (int i = 0; i < n_rows; i++)
-        {
-            for (int j = 0; j < n_cols; j++)
-            {
-                cost_c_extended[i][j] = cost_c[i][j];
-            }
-        }
-
-        cost_c.clear();
-        cost_c.assign(cost_c_extended.begin(), cost_c_extended.end());
-    }
-
-    double **cost_ptr;
-    cost_ptr = new double *[sizeof(double *) * n];
-    for (int i = 0; i < n; i++)
-        cost_ptr[i] = new double[sizeof(double) * n];
-
-    for (int i = 0; i < n; i++)
-    {
-        for (int j = 0; j < n; j++)
-        {
-            cost_ptr[i][j] = cost_c[i][j];
-        }
-    }
-
-    int* x_c = new int[sizeof(int) * n];
-    int *y_c = new int[sizeof(int) * n];
-
-    int ret = lapjv_internal(n, cost_ptr, x_c, y_c);
-    if (ret != 0)
-    {
-        std::cout << "Calculate Wrong!" << std::endl;
-        system("pause");
-        exit(0);
-    }
-
-    double opt = 0.0;
-
-    if (n != n_rows)
-    {
-        for (int i = 0; i < n; i++)
-        {
-            if (x_c[i] >= n_cols)
-                x_c[i] = -1;
-            if (y_c[i] >= n_rows)
-                y_c[i] = -1;
-        }
-        for (int i = 0; i < n_rows; i++)
-        {
-            rowsol[i] = x_c[i];
-        }
-        for (int i = 0; i < n_cols; i++)
-        {
-            colsol[i] = y_c[i];
-        }
-
-        if (return_cost)
-        {
-            for (int i = 0; i < rowsol.size(); i++)
-            {
-                if (rowsol[i] != -1)
-                {
-                    //cout << i << "\t" << rowsol[i] << "\t" << cost_ptr[i][rowsol[i]] << endl;
-                    opt += cost_ptr[i][rowsol[i]];
-                }
-            }
-        }
-    }
-    else if (return_cost)
-    {
-        for (int i = 0; i < rowsol.size(); i++)
-        {
-            opt += cost_ptr[i][rowsol[i]];
-        }
-    }
-
-    for (int i = 0; i < n; i++)
-    {
-        delete[]cost_ptr[i];
-    }
-    delete[]cost_ptr;
-    delete[]x_c;
-    delete[]y_c;
-
-    return opt;
-}
-
-cv::Scalar BYTETracker::get_color(int idx)
-{
-    idx += 3;
-    return cv::Scalar(37 * idx % 255, 17 * idx % 255, 29 * idx % 255);
-}
-}

algorithm/mot/sort/sort.cpp

@@ -1,377 +0,0 @@
-#include "sort.h"
-#include <cmath>
-#include <fstream>
-#include <limits>
-#include <vector>
-#include "gason.h"
-#include "sv_util.h"
-
-using namespace std;
-using namespace Eigen;
-
-namespace sv {
-
-
-KalmanFilter::KalmanFilter()
-{
-    this->_chi2inv95 << 3.8415, 5.9915, 7.8147, 9.4877, 11.070, 12.592, 14.067, 15.507, 16.919;
-    this->_F = MatrixXd::Identity(8, 8);
-    for (int i=0; i<4; i++)
-    {
-        this->_F(i,i+4) = 1.; //1
-    }
-    this->_H = MatrixXd::Identity(4, 8);
-    this->_std_weight_position = 1. / 20; //1./20
-    this->_std_weight_vel = 1. / 160; //1./160
-}
-
-KalmanFilter::~KalmanFilter()
-{
-}
-
-pair<Matrix<double, 8, 1>, Matrix<double, 8, 8> > KalmanFilter::initiate(Vector4d &bbox)
-{
-    Matrix<double,8,1> mean;
-    Matrix<double,4,1> zero_vector;
-    zero_vector.setZero();
-    mean << bbox(0), bbox(1), (double)bbox(2) / (double)bbox(3), bbox(3), zero_vector;
-    VectorXd stds(8);
-    stds << 2 * this->_std_weight_position * mean(3), 2 * this->_std_weight_position * mean(3), 0.01, 2 * this->_std_weight_position * mean(3), \
-        10 * this->_std_weight_vel * mean(3), 10 * this->_std_weight_vel * mean(3), 1e-5, 10 * this->_std_weight_vel * mean(3);
-    MatrixXd squared = stds.array().square();
-    Matrix<double, 8, 8> covariances;
-    covariances = squared.asDiagonal();
-    return make_pair(mean, covariances);
-}
-
-pair<Matrix<double, 8, 1>, Matrix<double, 8, 8> > KalmanFilter::update(Matrix<double, 8, 1> mean, Matrix<double, 8, 8> covariances, sv::Box &box)
-{
-    VectorXd measurement(4);
-    double a = (double)(box.x2-box.x1) / (double)(box.y2-box.y1);
-    measurement << box.x1+(box.x2-box.x1)/2, box.y1+(box.y2-box.y1)/2, a, box.y2-box.y1;
-    pair<Matrix<double, 4, 1>, Matrix<double, 4, 4> > projected = project(mean, covariances);
-    Matrix<double, 4, 1> projected_mean = projected.first;
-    Matrix<double, 4, 4> projected_cov = projected.second;
-
-    Eigen::LLT<Eigen::MatrixXd> chol_factor(projected_cov);
-    MatrixXd Kalman_gain = (chol_factor.solve((covariances * this->_H.transpose()).transpose())).transpose();
-    
-    VectorXd innovation = measurement - projected_mean;
-    Matrix<double, 8, 1> new_mean = mean + Kalman_gain *innovation;
-    Matrix<double, 8, 8> new_covariances = covariances - Kalman_gain * projected_cov * Kalman_gain.transpose();
-
-    return make_pair(new_mean, new_covariances);
-}
-
-pair<Matrix<double, 4, 1>, Matrix<double, 4, 4> > KalmanFilter::project(Matrix<double, 8, 1> mean, Matrix<double, 8, 8> covariances)
-{
-    VectorXd stds(4);
-    stds << this->_std_weight_position * mean(3), this->_std_weight_position * mean(3), 0.1, this->_std_weight_position * mean(3); 
-    MatrixXd squared = stds.array().square();
-    MatrixXd R = squared.asDiagonal();
-
-    Matrix<double, 4, 1>  pro_mean = this->_H * mean;
-    Matrix<double, 4, 4>  pro_covariances = this->_H * covariances * this->_H.transpose() + R;
-    return make_pair(pro_mean, pro_covariances);
-}
-pair<Matrix<double, 8, 1>, Matrix<double, 8, 8> > KalmanFilter::predict(Matrix<double, 8, 1> mean, Matrix<double, 8, 8> covariances)
-{
-    VectorXd stds(8);
-    stds << this->_std_weight_position * mean(3), this->_std_weight_position * mean(3), 1e-2, this->_std_weight_position * mean(3), \
-        this->_std_weight_vel * mean(3), this->_std_weight_vel * mean(3), 1e-5, this->_std_weight_vel * mean(3); // a = 0.01
-    MatrixXd squared = stds.array().square();
-    MatrixXd Q = squared.asDiagonal();
-    Matrix<double, 8, 1> pre_mean = this->_F * mean;
-    Matrix<double, 8, 8> pre_cov = this->_F * covariances * this->_F.transpose()+Q;//+Q
-    return make_pair(pre_mean, pre_cov);
-}
-
-
-SORT::~SORT()
-{
-}
-
-void SORT::update(TargetsInFrame& tgts)
-{
-    KalmanFilter kf;
-    if (! this->_tracklets.size() || tgts.targets.size() == 0)
-    {
-        Vector4d bbox;
-        for (int i=0; i<tgts.targets.size(); i++)
-        {
-            sv::Box box;
-            tgts.targets[i].getBox(box);		
-            Tracklet tracklet;
-            tracklet.id = ++ this->_next_tracklet_id;
-            tgts.targets[i].tracked_id = this->_next_tracklet_id;
-            tgts.targets[i].has_tid = true;
-
-            tracklet.bbox << box.x1+(box.x2-box.x1)/2, box.y1+(box.y2-box.y1)/2, box.x2-box.x1, box.y2-box.y1;  // x,y,w,h; center(x,y)
-            
-            tracklet.age = 0;
-            tracklet.hits = 1;
-            //tracklet.misses = 0;
-            tracklet.frame_id = tgts.frame_id;
-            tracklet.category_id = tgts.targets[i].category_id;
-            if (tgts.frame_id == 0)
-            {
-                tracklet.tentative = false;
-            }
-            else
-            {
-                tracklet.tentative = true;
-            }
-            // initate the motion
-            pair<Matrix<double, 8, 1>, Matrix<double, 8, 8> > motion = kf.initiate(tracklet.bbox);
-            tracklet.mean = motion.first;
-            tracklet.covariance = motion.second;
-            
-            this->_tracklets.push_back(tracklet);
-    }
-    }
-    else
-    {
-        for (int i=0; i<tgts.targets.size(); i++)
-        {
-            tgts.targets[i].tracked_id = 0;
-            tgts.targets[i].has_tid = true;
-        }
-
-        vector<int> match_det(tgts.targets.size(), -1);
-        // predict the next state of each tracklet
-        for (auto& tracklet : this->_tracklets)
-        {
-            tracklet.age++;
-            pair<Matrix<double, 8, 1>, Matrix<double, 8, 8> > motion = kf.predict(tracklet.mean, tracklet.covariance);
-            tracklet.bbox << motion.first(0), motion.first(1), motion.first(2) * motion.first(3), motion.first(3);
-            tracklet.mean = motion.first;
-            tracklet.covariance = motion.second;    
-        }
-
-        // Match the detections to the existing tracklets 
-        vector<vector<double> > iouMatrix(this->_tracklets.size(), vector<double> (tgts.targets.size(), 0));
-        for (int i=0; i<this->_tracklets.size(); i++)
-        {
-            for (int j=0; j<tgts.targets.size(); j++)
-            {
-                sv::Box box;
-                tgts.targets[j].getBox(box);
-                iouMatrix[i][j] = this->_iou(this->_tracklets[i], box);
-            }
-        }
-        
-        vector<pair<int, int> > matches = this->_hungarian(iouMatrix);
-        for (auto& match : matches)
-        {
-            int trackletIndex = match.first;
-            int detectionIndex = match.second;
-            if (trackletIndex >= 0 && detectionIndex >= 0)
-            {
-                if (iouMatrix[match.first][match.second] <= 1-_iou_threshold)  // iou_thrshold
-                {
-                    sv::Box box;
-                    tgts.targets[detectionIndex].getBox(box);
-                    this->_tracklets[trackletIndex].age = 0;
-                    this->_tracklets[trackletIndex].hits++;
-                    this->_tracklets[trackletIndex].frame_id = tgts.frame_id;
-                    this->_tracklets[trackletIndex].bbox << box.x1+(box.x2-box.x1)/2, box.y1+(box.y2-box.y1)/2, box.x2-box.x1, box.y2-box.y1;
-                    tgts.targets[detectionIndex].tracked_id = this->_tracklets[trackletIndex].id;
-                    match_det[detectionIndex] = trackletIndex;
-                }
-            }
-        }
-        std::vector <vector<double>> ().swap(iouMatrix);
-        for (int i=0; i<tgts.targets.size(); i++)
-        { 
-            if (match_det[i] == -1)
-            {
-                sv::Box box;
-                tgts.targets[i].getBox(box);
-                Tracklet tracklet;
-                tracklet.id = ++ this->_next_tracklet_id;
-                tracklet.bbox << box.x1+(box.x2-box.x1)/2, (double)(box.y1+(box.y2-box.y1)/2), box.x2-box.x1, box.y2-box.y1; // c_x, c_y, w, h
-                tracklet.age = 0;
-                tracklet.hits = 1;
-                tracklet.frame_id = tgts.frame_id;
-                tracklet.category_id = tgts.targets[i].category_id;
-                tracklet.tentative = true;
-                
-                pair<Matrix<double, 8, 1>, Matrix<double, 8, 8> > new_motion = kf.initiate(tracklet.bbox);
-                tracklet.mean = new_motion.first;
-                tracklet.covariance = new_motion.second;
-
-                tgts.targets[i].tracked_id = this->_next_tracklet_id;
-                tgts.targets[i].has_tid = true;
-                this->_tracklets.push_back(tracklet);
-            }
-            else
-            {
-                sv::Box box;
-                int track_id = match_det[i];
-                tgts.targets[i].getBox(box);
-                pair<Matrix<double, 8, 1>, Matrix<double, 8, 8> > updated = kf.update(this->_tracklets[track_id].mean, this->_tracklets[track_id].covariance, box);
-                this->_tracklets[track_id].mean = updated.first;
-                this->_tracklets[track_id].covariance = updated.second;
-            }
-        }
-        
-        //sift tracklets
-        for (auto& tracklet : this->_tracklets)
-        {
-            if (tracklet.hits >= _min_hits)
-            {
-                tracklet.tentative = false;
-            }
-            if ((tgts.frame_id-tracklet.frame_id <= _max_age) && !(tracklet.tentative && tracklet.frame_id != tgts.frame_id))
-            {
-                _new_tracklets.push_back(tracklet);
-            } 
-        }
-        _tracklets = _new_tracklets;
-        std::vector <Tracklet> ().swap(_new_tracklets);
-    }
-}
-
-vector<Tracklet> SORT::getTracklets() const
-{
-    return this->_tracklets;
-}
-
-double SORT::_iou(Tracklet& tracklet, sv::Box& box)
-{
-    double trackletX1 = tracklet.bbox(0)-tracklet.bbox(2)/2;
-    double trackletY1 = tracklet.bbox(1)-tracklet.bbox(3)/2;
-    double trackletX2 = tracklet.bbox(0) + tracklet.bbox(2)/2;
-    double trackletY2 = tracklet.bbox(1) + tracklet.bbox(3)/2;
-    
-    double detectionX1 = box.x1;
-    double detectionY1 = box.y1;
-    double detectionX2 = box.x2;
-    double detectionY2 = box.y2;
-
-    double intersectionX1 = max(trackletX1, detectionX1);
-    double intersectionY1 = max(trackletY1, detectionY1);
-    double intersectionX2 = min(trackletX2, detectionX2);
-    double intersectionY2 = min(trackletY2, detectionY2);
-
-    double w = (intersectionX2-intersectionX1 > 0.0) ? (intersectionX2-intersectionX1) : 0.0;
-    double h = (intersectionY2-intersectionY1 > 0.0) ? (intersectionY2-intersectionY1) : 0.0;
-    double intersectionArea = w * h;
-
-    double trackletArea = tracklet.bbox(2) * tracklet.bbox(3);
-
-    double detectionArea = (box.x2-box.x1) * (box.y2-box.y1);
-    double unionArea = trackletArea + detectionArea - intersectionArea;
-    double iou = (1 - intersectionArea / unionArea * 1.0);
-
-    return iou;
-}
-
-// Function to find the minimum element in a vector
-double SORT::_findMin(const std::vector<double>& vec) {
-    double minVal = std::numeric_limits<double>::max();
-    for (double val : vec) {
-        if (val < minVal) {
-            minVal = val;
-        }
-    }
-    return minVal;
-}
-
-// Function to subtract the minimum value from each row of the cost matrix
-void SORT::_subtractMinFromRows(std::vector<std::vector<double>>& costMatrix) {
-    for (auto& row : costMatrix) {
-        double minVal = _findMin(row);
-        for (double& val : row) {
-            val -= minVal;
-        }
-    }
-}
-
-// Function to subtract the minimum value from each column of the cost matrix
-void SORT::_subtractMinFromCols(std::vector<std::vector<double>>& costMatrix) {
-    for (size_t col = 0; col < costMatrix[0].size(); ++col) {
-        double minVal = std::numeric_limits<double>::max();
-        for (size_t row = 0; row < costMatrix.size(); ++row) {
-            if (costMatrix[row][col] < minVal) {
-                minVal = costMatrix[row][col];
-            }
-        }
-        for (size_t row = 0; row < costMatrix.size(); ++row) {
-            costMatrix[row][col] -= minVal;
-        }
-    }
-}
-
-// Function to find a matching using the Hungarian algorithm
-vector<pair<int, int> > SORT::_hungarian(vector<vector<double> > costMatrix)
-{
-    size_t numRows = costMatrix.size();
-    size_t numCols = costMatrix[0].size();
-
-    //transpose the matrix if necessary
-    const bool transposed = numCols > numRows;
-    if (transposed) {
-        vector<vector<double>> transposedMatrix(numCols, vector<double>(numRows));
-        for (int i = 0; i < numRows; i++)
-        {
-            for (int j = 0; j < numCols; j++)
-            {
-                transposedMatrix[j][i] = costMatrix[i][j];
-            }
-        }
-        costMatrix = transposedMatrix;
-        swap(numRows, numCols);
-    }
-    // Determine the larger dimension for matching
-    size_t maxDim = std::max(numRows, numCols);
-
-    // Create a square cost matrix by padding with zeros if necessary
-    std::vector<std::vector<double>> squareMatrix(maxDim, std::vector<double>(maxDim, 0.0));
-    for (size_t row = 0; row < numRows; ++row) {
-        for (size_t col = 0; col < numCols; ++col) {
-            squareMatrix[row][col] = costMatrix[row][col];
-        }
-    }
-
-    // Subtract the minimum value from each row and column
-    _subtractMinFromRows(squareMatrix);
-    _subtractMinFromCols(squareMatrix);
-
-    // Initialize the assignment vectors with -1 values
-    std::vector<int> rowAssignment(maxDim, -1);
-    std::vector<int> colAssignment(maxDim, -1);
-
-    // Perform the matching
-    for (size_t row = 0; row < maxDim; ++row) {
-        std::vector<bool> visitedCols(maxDim, false);
-        for (size_t col = 0; col < maxDim; ++col) {
-            if (squareMatrix[row][col] == 0 && colAssignment[col] == -1) {
-                rowAssignment[row] = col;
-                colAssignment[col] = row;
-                break;
-            }
-        }
-    }
-
-    // Convert the assignment vectors to pair<int, int> format
-    std::vector<std::pair<int, int>> assignmentPairs;
-    for (size_t row = 0; row < numRows; ++row) {
-        int col = rowAssignment[row];
-        if (col != -1) {
-            if (col >= numCols) {
-                col = -1;
-            }
-            assignmentPairs.emplace_back(row, col);
-        }
-    }
-    if (transposed) {
-        for (auto& assignment : assignmentPairs)
-        {
-            swap(assignment.first, assignment.second);
-        }
-    }
-    return assignmentPairs;
-    }
-}
-

algorithm/mot/sort/sort.h

@@ -1,77 +0,0 @@
-#ifndef __SV_SORT__
-#define __SV_SORT__
-
-#include "sv_core.h"
-#include <opencv2/opencv.hpp>
-#include <opencv2/aruco.hpp>
-#include <opencv2/tracking.hpp>
-#include <string>
-#include <chrono>
-#include <Eigen/Dense>
-
-
-namespace sv {
-
-
-
-// define the tracklet struct to store the tracked objects.
-struct Tracklet
-{
-/* data */
-public:
-    Eigen::Vector4d bbox;  // double x, y, w, h;
-    int id = 0;
-    int age;
-    int hits;
-    int misses;
-    int frame_id = 0;
-    int category_id;
-    bool tentative;
-    std::vector<double> features;
-    Eigen::Matrix<double, 8, 1> mean;
-    Eigen::Matrix<double, 8, 8> covariance;
-};
-
-
-class KalmanFilter {
-public:
-    KalmanFilter();
-    ~KalmanFilter();
-    std::pair<Eigen::Matrix<double, 8, 1>, Eigen::Matrix<double, 8, 8> > initiate(Eigen::Vector4d &bbox);
-    std::pair<Eigen::Matrix<double, 8, 1>, Eigen::Matrix<double, 8, 8> > update(Eigen::Matrix<double, 8, 1> mean, Eigen::Matrix<double, 8, 8> covariances, Box &box);
-    std::pair<Eigen::Matrix<double, 8, 1>, Eigen::Matrix<double, 8, 8> > predict(Eigen::Matrix<double, 8, 1> mean, Eigen::Matrix<double, 8, 8> covariances);
-    std::pair<Eigen::Matrix<double, 4, 1>, Eigen::Matrix<double, 4, 4> > project(Eigen::Matrix<double, 8, 1> mean, Eigen::Matrix<double, 8, 8> covariances);
-private:
-    Eigen::Matrix<double, 8, 8> _F;
-    Eigen::Matrix<double, 4, 8> _H;
-    Eigen::Matrix<double, 9, 1> _chi2inv95;
-    double _std_weight_position;
-    double _std_weight_vel;
-};
-
-
-class SORT {
-public:
-    SORT(double iou_threshold, int max_age, int min_hits): _iou_threshold(iou_threshold), _max_age(max_age), _min_hits(min_hits), _next_tracklet_id(0) {};
-    ~SORT();
-    void update(TargetsInFrame &tgts);
-    std::vector<Tracklet> getTracklets() const;
-private:
-    double _iou(Tracklet &tracklet, Box &box);
-    std::vector<std::pair<int,int>> _hungarian(std::vector<std::vector<double>> costMatrix);
-    double _findMin(const std::vector<double>& vec);
-    void _subtractMinFromRows(std::vector<std::vector<double>>& costMatrix);
-    void _subtractMinFromCols(std::vector<std::vector<double>>& costMatrix);
-    //bool _augment(const std::vector<std::vector<double>>& costMatrix, int row, std::vector<int>& rowMatch, std::vector<int>& colMatch, std::vector<bool>& visited);
-
-    double _iou_threshold;
-    int _max_age;
-    int _min_hits;
-    int _next_tracklet_id;
-    std::vector <Tracklet> _tracklets;
-    std::vector <Tracklet> _new_tracklets;
-};
-
-
-}
-#endif

algorithm/mot/sv_mot.cpp

@@ -5,8 +5,6 @@
 #include <vector>
 #include "gason.h"
 #include "sv_util.h"
-#include "sort.h"
-#include "BYTETracker.h"
 
 using namespace std;
 using namespace Eigen;
@@ -18,22 +16,15 @@ MultipleObjectTracker::MultipleObjectTracker()
 {
     this->_params_loaded = false;
     this->_sort_impl = NULL;
-    this->_bytetrack_impl = NULL;
 }
 MultipleObjectTracker::~MultipleObjectTracker()
 {
     if (this->_sort_impl)
         delete this->_sort_impl;
-    else if (this->_bytetrack_impl)
-        delete this->_bytetrack_impl;
 }
 
-sv::TargetsInFrame MultipleObjectTracker::track(cv::Mat img_, TargetsInFrame& tgts_)
+void MultipleObjectTracker::track(cv::Mat img_, TargetsInFrame& tgts_)
 {
-    sv::TargetsInFrame person_tgts(tgts_.frame_id);
-    person_tgts.width = img_.size().width;
-    person_tgts.height = img_.size().height;
-
     if (!this->_params_loaded)
     {
         this->_load();
@@ -42,28 +33,8 @@ sv::TargetsInFrame MultipleObjectTracker::track(cv::Mat img_, TargetsInFrame& tg
     if ("sort" == this->_algorithm && this->_sort_impl)
     {
         this->_detector->detect(img_, tgts_);
-        for (auto target : tgts_.targets)
-        {
-            if (target.category_id == 0)
-            {
-                person_tgts.targets.push_back(target);
-            }
-        }
-        this->_sort_impl->update(person_tgts);
+        this->_sort_impl->update(tgts_);
     }
-    else if ("bytetrack" == this->_algorithm && this->_bytetrack_impl)
-    {
-        this->_detector->detect(img_, tgts_);
-        for (auto target : tgts_.targets)
-        {
-            if (target.category_id == 0)
-            {
-                person_tgts.targets.push_back(target);
-            }
-        }
-        this->_bytetrack_impl->update(person_tgts);
-    }
-    return person_tgts;
 }
 
 void MultipleObjectTracker::init(CommonObjectDetector* detector_)
@@ -77,10 +48,6 @@ void MultipleObjectTracker::init(CommonObjectDetector* detector_)
     {
         this->_sort_impl = new SORT(this->_iou_thres, this->_max_age, this->_min_hits);
     }
-    else if("bytetrack" == this->_algorithm)
-    {
-        this->_bytetrack_impl = new BYTETracker(this->_frame_rate, this->_track_buffer);
-    }
     this->_detector = detector_;
 }
 
@@ -104,7 +71,7 @@ void MultipleObjectTracker::_load()
             else
                 this->_with_reid = false;
             
-            //std::cout << "with_reid: " << this->_with_reid << std::endl;
+            std::cout << "with_reid: " << this->_with_reid << std::endl;
         }
         else if ("reid_input_size" == std::string(i->key) && i->value.getTag() == JSON_ARRAY) {
             int jcnt = 0;
@@ -117,39 +84,395 @@ void MultipleObjectTracker::_load()
                 }
                 jcnt += 1;
             }
-            //std::cout << "reid_input_w: " << this->_reid_input_w << std::endl;
-            //std::cout << "reid_input_h: " << this->_reid_input_h << std::endl;
+            std::cout << "reid_input_w: " << this->_reid_input_w << std::endl;
+            std::cout << "reid_input_h: " << this->_reid_input_h << std::endl;
         }
         else if ("reid_num_samples" == std::string(i->key)) {
             this->_reid_num_samples = i->value.toNumber();
-            //std::cout << "reid_num_samples: " << this->_reid_num_samples << std::endl;
+            std::cout << "reid_num_samples: " << this->_reid_num_samples << std::endl;
         }
         else if ("reid_match_thres" == std::string(i->key)) {
             this->_reid_match_thres = i->value.toNumber();
-            //std::cout << "reid_match_thres: " << this->_reid_match_thres << std::endl;
+            std::cout << "reid_match_thres: " << this->_reid_match_thres << std::endl;
         }
         else if ("iou_thres" == std::string(i->key)) {
             this->_iou_thres = i->value.toNumber();
-            //std::cout << "iou_thres: " << this->_iou_thres << std::endl;
+            std::cout << "iou_thres: " << this->_iou_thres << std::endl;
         }
         else if ("max_age" == std::string(i->key)) {
             this->_max_age = i->value.toNumber();
-            //std::cout << "max_age: " << this->_max_age << std::endl;
+            std::cout << "max_age: " << this->_max_age << std::endl;
         }
         else if ("min_hits" == std::string(i->key)) {
             this->_min_hits = i->value.toNumber();
-            //std::cout << "min_hits: " << this->_min_hits << std::endl;
-        }
-        else if ("frame_rate" == std::string(i->key)) {
-            this->_frame_rate = i->value.toNumber();
-            //std::cout << "max_age: " << this->_max_age << std::endl;
-        }
-        else if ("track_buffer" == std::string(i->key)) {
-            this->_track_buffer = i->value.toNumber();
-            //std::cout << "min_hits: " << this->_min_hits << std::endl;
+            std::cout << "min_hits: " << this->_min_hits << std::endl;
         }
     }
 }
 
+
+
+KalmanFilter::KalmanFilter()
+{
+    this->_chi2inv95 << 3.8415, 5.9915, 7.8147, 9.4877, 11.070, 12.592, 14.067, 15.507, 16.919;
+    this->_F = MatrixXd::Identity(8, 8);
+    for (int i=0; i<4; i++)
+    {
+        this->_F(i,i+4) = 1.; //1
+    }
+    this->_H = MatrixXd::Identity(4, 8);
+    this->_std_weight_position = 1. / 20; //1./20
+    this->_std_weight_vel = 1. / 160; //1./160
+}
+
+KalmanFilter::~KalmanFilter()
+{
+}
+
+pair<Matrix<double, 8, 1>, Matrix<double, 8, 8> > KalmanFilter::initiate(Vector4d &bbox)
+{
+    Matrix<double,8,1> mean;
+    Matrix<double,4,1> zero_vector;
+    zero_vector.setZero();
+    mean << bbox(0), bbox(1), (double)bbox(2) / (double)bbox(3), bbox(3), zero_vector;
+    VectorXd stds(8);
+    stds << 2 * this->_std_weight_position * mean(3), 2 * this->_std_weight_position * mean(3), 0.01, 2 * this->_std_weight_position * mean(3), \
+        10 * this->_std_weight_vel * mean(3), 10 * this->_std_weight_vel * mean(3), 1e-5, 10 * this->_std_weight_vel * mean(3);
+    MatrixXd squared = stds.array().square();
+    Matrix<double, 8, 8> covariances;
+    covariances = squared.asDiagonal();
+    return make_pair(mean, covariances);
+}
+
+pair<Matrix<double, 8, 1>, Matrix<double, 8, 8> > KalmanFilter::update(Matrix<double, 8, 1> mean, Matrix<double, 8, 8> covariances, sv::Box &box)
+{
+    VectorXd measurement(4);
+    double a = (double)(box.x2-box.x1) / (double)(box.y2-box.y1);
+    measurement << box.x1+(box.x2-box.x1)/2, box.y1+(box.y2-box.y1)/2, a, box.y2-box.y1;
+    pair<Matrix<double, 4, 1>, Matrix<double, 4, 4> > projected = project(mean, covariances);
+    Matrix<double, 4, 1> projected_mean = projected.first;
+    Matrix<double, 4, 4> projected_cov = projected.second;
+
+    Eigen::LLT<Eigen::MatrixXd> chol_factor(projected_cov);
+    MatrixXd Kalman_gain = (chol_factor.solve((covariances * this->_H.transpose()).transpose())).transpose();
+    
+    VectorXd innovation = measurement - projected_mean;
+    Matrix<double, 8, 1> new_mean = mean + Kalman_gain *innovation;
+    Matrix<double, 8, 8> new_covariances = covariances - Kalman_gain * projected_cov * Kalman_gain.transpose();
+
+    return make_pair(new_mean, new_covariances);
+}
+
+pair<Matrix<double, 4, 1>, Matrix<double, 4, 4> > KalmanFilter::project(Matrix<double, 8, 1> mean, Matrix<double, 8, 8> covariances)
+{
+    VectorXd stds(4);
+    stds << this->_std_weight_position * mean(3), this->_std_weight_position * mean(3), 0.1, this->_std_weight_position * mean(3); 
+    MatrixXd squared = stds.array().square();
+    MatrixXd R = squared.asDiagonal();
+
+    Matrix<double, 4, 1>  pro_mean = this->_H * mean;
+    Matrix<double, 4, 4>  pro_covariances = this->_H * covariances * this->_H.transpose() + R;
+    return make_pair(pro_mean, pro_covariances);
+}
+pair<Matrix<double, 8, 1>, Matrix<double, 8, 8> > KalmanFilter::predict(Matrix<double, 8, 1> mean, Matrix<double, 8, 8> covariances)
+{
+    VectorXd stds(8);
+    stds << this->_std_weight_position * mean(3), this->_std_weight_position * mean(3), 1e-2, this->_std_weight_position * mean(3), \
+        this->_std_weight_vel * mean(3), this->_std_weight_vel * mean(3), 1e-5, this->_std_weight_vel * mean(3); // a = 0.01
+    MatrixXd squared = stds.array().square();
+    MatrixXd Q = squared.asDiagonal();
+    Matrix<double, 8, 1> pre_mean = this->_F * mean;
+    Matrix<double, 8, 8> pre_cov = this->_F * covariances * this->_F.transpose()+Q;//+Q
+    return make_pair(pre_mean, pre_cov);
+}
+
+
+SORT::~SORT()
+{
+}
+
+void SORT::update(TargetsInFrame& tgts)
+{
+    sv::KalmanFilter kf;
+    if (! this->_tracklets.size() || tgts.targets.size() == 0)
+    {
+        Vector4d bbox;
+        for (int i=0; i<tgts.targets.size(); i++)
+        {
+            sv::Box box;
+            tgts.targets[i].getBox(box);		
+            Tracklet tracklet;
+            tracklet.id = ++ this->_next_tracklet_id;
+            tgts.targets[i].tracked_id = this->_next_tracklet_id;
+            tgts.targets[i].has_tid = true;
+
+            tracklet.bbox << box.x1+(box.x2-box.x1)/2, box.y1+(box.y2-box.y1)/2, box.x2-box.x1, box.y2-box.y1;  // x,y,w,h; center(x,y)
+            
+            tracklet.age = 0;
+            tracklet.hits = 1;
+            tracklet.misses = 0;
+            tracklet.frame_id = tgts.frame_id;
+            tracklet.category_id = tgts.targets[i].category_id;
+            tracklet.tentative = true;
+            
+            // initate the motion
+            pair<Matrix<double, 8, 1>, Matrix<double, 8, 8> > motion = kf.initiate(tracklet.bbox);
+            tracklet.mean = motion.first;
+            tracklet.covariance = motion.second;
+            
+            this->_tracklets.push_back(tracklet);
+        }
+    }
+    else
+    {
+        // cout << "frame id:" << tgts.frame_id << endl;
+        for (int i=0; i<tgts.targets.size(); i++)
+        {
+            tgts.targets[i].tracked_id = 0;
+            tgts.targets[i].has_tid = true;
+        }
+
+        vector<int> match_det(tgts.targets.size(), -1);
+        // predict the next state of each tracklet
+        for (auto& tracklet : this->_tracklets)
+        {
+            tracklet.age++;
+            pair<Matrix<double, 8, 1>, Matrix<double, 8, 8> > motion = kf.predict(tracklet.mean, tracklet.covariance);
+            tracklet.bbox << motion.first(0), motion.first(1), motion.first(2) * motion.first(3), motion.first(3);
+            tracklet.mean = motion.first;
+            tracklet.covariance = motion.second;    
+        }
+        
+        // Match the detections to the existing tracklets 
+        vector<vector<double> > iouMatrix(this->_tracklets.size(), vector<double> (tgts.targets.size(), 0));
+        for (int i=0; i<this->_tracklets.size(); i++)
+        {
+            for (int j=0; j<tgts.targets.size(); j++)
+            {
+                sv::Box box;
+                tgts.targets[j].getBox(box);
+                iouMatrix[i][j] = this->_iou(this->_tracklets[i], box);
+            }
+        }
+        
+        vector<pair<int, int> > matches = this->_hungarian(iouMatrix);
+        for (auto& match : matches)
+        {
+            int trackletIndex = match.first;
+            int detectionIndex = match.second;
+            if (trackletIndex >= 0 && detectionIndex >= 0)
+            {
+                if (iouMatrix[match.first][match.second] <= 1-_iou_threshold)  // iou_thrshold
+                {
+                    sv::Box box;
+                    tgts.targets[detectionIndex].getBox(box);
+                    this->_tracklets[trackletIndex].age = 0;
+                    this->_tracklets[trackletIndex].hits++;
+                    this->_tracklets[trackletIndex].frame_id = tgts.frame_id;
+                    this->_tracklets[trackletIndex].bbox << box.x1+(box.x2-box.x1)/2, box.y1+(box.y2-box.y1)/2, box.x2-box.x1, box.y2-box.y1;
+                    tgts.targets[detectionIndex].tracked_id = this->_tracklets[trackletIndex].id;
+                    match_det[detectionIndex] = trackletIndex;
+                }
+            }
+        }
+        std::vector <vector<double>> ().swap(iouMatrix);
+        for (int i=0; i<tgts.targets.size(); i++)
+        { 
+            // cout << "match_det: index: " << i << " value: " << match_det[i] << endl;
+            if (match_det[i] == -1)
+            {
+                // cout << "create new tracklet." << endl;
+                sv::Box box;
+                tgts.targets[i].getBox(box);
+                Tracklet tracklet;
+                tracklet.id = ++ this->_next_tracklet_id;
+                tracklet.bbox << box.x1+(box.x2-box.x1)/2, (double)(box.y1+(box.y2-box.y1)/2), box.x2-box.x1, box.y2-box.y1;
+                tracklet.age = 0;
+                tracklet.hits = 1;
+                tracklet.misses = 0;
+                tracklet.frame_id = tgts.frame_id;
+                tracklet.category_id = tgts.targets[i].category_id;
+                tracklet.tentative = true;
+                
+                pair<Matrix<double, 8, 1>, Matrix<double, 8, 8> > new_motion = kf.initiate(tracklet.bbox);
+                tracklet.mean = new_motion.first;
+                tracklet.covariance = new_motion.second;
+
+                tgts.targets[i].tracked_id = this->_next_tracklet_id;
+                tgts.targets[i].has_tid = true;
+                this->_tracklets.push_back(tracklet);
+            }
+            else
+            {
+                sv::Box box;
+                int track_id = match_det[i];
+                tgts.targets[i].getBox(box);
+                pair<Matrix<double, 8, 1>, Matrix<double, 8, 8> > updated = kf.update(this->_tracklets[track_id].mean, this->_tracklets[track_id].covariance, box);
+                this->_tracklets[track_id].mean = updated.first;
+                this->_tracklets[track_id].covariance = updated.second;
+            }
+        }
+        
+        //sift tracklets
+        for (auto& tracklet : this->_tracklets)
+        {
+            if (tracklet.hits >= _min_hits)
+            {
+                tracklet.tentative = false;
+            }
+            if ((tgts.frame_id-tracklet.frame_id <= _max_age) || (!tracklet.tentative && tracklet.frame_id == tgts.frame_id))
+            {
+                _new_tracklets.push_back(tracklet);
+            } 
+        }
+        _tracklets = _new_tracklets;
+        std::vector <Tracklet> ().swap(_new_tracklets);
+    }
+}
+
+vector<Tracklet> SORT::getTracklets() const
+{
+    return this->_tracklets;
+}
+
+double SORT::_iou(Tracklet& tracklet, sv::Box& box)
+{
+    double trackletX1 = tracklet.bbox(0)-tracklet.bbox(2)/2;
+    double trackletY1 = tracklet.bbox(1)-tracklet.bbox(3)/2;
+    double trackletX2 = tracklet.bbox(0) + tracklet.bbox(2)/2;
+    double trackletY2 = tracklet.bbox(1) + tracklet.bbox(3)/2;
+    
+    double detectionX1 = box.x1;
+    double detectionY1 = box.y1;
+    double detectionX2 = box.x2;
+    double detectionY2 = box.y2;
+
+    double intersectionX1 = max(trackletX1, detectionX1);
+    double intersectionY1 = max(trackletY1, detectionY1);
+    double intersectionX2 = min(trackletX2, detectionX2);
+    double intersectionY2 = min(trackletY2, detectionY2);
+
+    double w = (intersectionX2-intersectionX1 > 0.0) ? (intersectionX2-intersectionX1) : 0.0;
+    double h = (intersectionY2-intersectionY1 > 0.0) ? (intersectionY2-intersectionY1) : 0.0;
+    double intersectionArea = w * h;
+
+    double trackletArea = tracklet.bbox(2) * tracklet.bbox(3);
+
+    double detectionArea = (box.x2-box.x1) * (box.y2-box.y1);
+    double unionArea = trackletArea + detectionArea - intersectionArea;
+    double iou = (1 - intersectionArea / unionArea * 1.0);
+
+    return iou;
+}
+
+// Function to find the minimum element in a vector
+double SORT::_findMin(const std::vector<double>& vec) {
+    double minVal = std::numeric_limits<double>::max();
+    for (double val : vec) {
+        if (val < minVal) {
+            minVal = val;
+        }
+    }
+    return minVal;
+}
+
+// Function to subtract the minimum value from each row of the cost matrix
+void SORT::_subtractMinFromRows(std::vector<std::vector<double>>& costMatrix) {
+    for (auto& row : costMatrix) {
+        double minVal = _findMin(row);
+        for (double& val : row) {
+            val -= minVal;
+        }
+    }
+}
+
+// Function to subtract the minimum value from each column of the cost matrix
+void SORT::_subtractMinFromCols(std::vector<std::vector<double>>& costMatrix) {
+    for (size_t col = 0; col < costMatrix[0].size(); ++col) {
+        double minVal = std::numeric_limits<double>::max();
+        for (size_t row = 0; row < costMatrix.size(); ++row) {
+            if (costMatrix[row][col] < minVal) {
+                minVal = costMatrix[row][col];
+            }
+        }
+        for (size_t row = 0; row < costMatrix.size(); ++row) {
+            costMatrix[row][col] -= minVal;
+        }
+    }
+}
+
+// Function to find a matching using the Hungarian algorithm
+vector<pair<int, int> > SORT::_hungarian(vector<vector<double> > costMatrix)
+{
+    size_t numRows = costMatrix.size();
+    size_t numCols = costMatrix[0].size();
+
+    //transpose the matrix if necessary
+    const bool transposed = numCols > numRows;
+    if (transposed) {
+        vector<vector<double>> transposedMatrix(numCols, vector<double>(numRows));
+        for (int i = 0; i < numRows; i++)
+        {
+            for (int j = 0; j < numCols; j++)
+            {
+                transposedMatrix[j][i] = costMatrix[i][j];
+            }
+        }
+        costMatrix = transposedMatrix;
+        swap(numRows, numCols);
+    }
+    // Determine the larger dimension for matching
+    size_t maxDim = std::max(numRows, numCols);
+
+    // Create a square cost matrix by padding with zeros if necessary
+    std::vector<std::vector<double>> squareMatrix(maxDim, std::vector<double>(maxDim, 0.0));
+    for (size_t row = 0; row < numRows; ++row) {
+        for (size_t col = 0; col < numCols; ++col) {
+            squareMatrix[row][col] = costMatrix[row][col];
+        }
+    }
+
+    // Subtract the minimum value from each row and column
+    _subtractMinFromRows(squareMatrix);
+    _subtractMinFromCols(squareMatrix);
+
+    // Initialize the assignment vectors with -1 values
+    std::vector<int> rowAssignment(maxDim, -1);
+    std::vector<int> colAssignment(maxDim, -1);
+
+    // Perform the matching
+    for (size_t row = 0; row < maxDim; ++row) {
+        std::vector<bool> visitedCols(maxDim, false);
+        for (size_t col = 0; col < maxDim; ++col) {
+            if (squareMatrix[row][col] == 0 && colAssignment[col] == -1) {
+                rowAssignment[row] = col;
+                colAssignment[col] = row;
+                break;
+            }
+        }
+    }
+
+    // Convert the assignment vectors to pair<int, int> format
+    std::vector<std::pair<int, int>> assignmentPairs;
+    for (size_t row = 0; row < numRows; ++row) {
+        int col = rowAssignment[row];
+        //if (col != -1) {
+          //  assignmentPairs.emplace_back(row, col);
+       // }
+        if (col != -1) {
+            if (col >= numCols) {
+                col = -1;
+            }
+            assignmentPairs.emplace_back(row, col);
+        }
+    }
+    if (transposed) {
+        for (auto& assignment : assignmentPairs)
+        {
+            swap(assignment.first, assignment.second);
+        }
+    }
+    return assignmentPairs;
+    }
 }
 

algorithm/sot/ocv470/sot_ocv470_impl.cpp

@@ -1,7 +1,6 @@
 #include "sot_ocv470_impl.h"
 #include <cmath>
 #include <fstream>
-#include "sv_util.h"
 
 #define SV_MODEL_DIR "/SpireCV/models/"
 #define SV_ROOT_DIR "/SpireCV/"
@@ -31,39 +30,9 @@ bool SingleObjectTrackerOCV470Impl::ocv470Setup(SingleObjectTrackerBase* base_)
   std::string net = get_home() + SV_MODEL_DIR + "dasiamrpn_model.onnx";
   std::string kernel_cls1 = get_home() + SV_MODEL_DIR + "dasiamrpn_kernel_cls1.onnx";
   std::string kernel_r1 = get_home() + SV_MODEL_DIR + "dasiamrpn_kernel_r1.onnx";
-  
-  std::vector<std::string> files1, files2, files3;
-  _list_dir(get_home() + SV_MODEL_DIR, files1, ".onnx", "Ocv-DaSiamRPN-Model-");
-  _list_dir(get_home() + SV_MODEL_DIR, files2, ".onnx", "Ocv-DaSiamRPN-Kernel-CLS1-");
-  _list_dir(get_home() + SV_MODEL_DIR, files3, ".onnx", "Ocv-DaSiamRPN-Kernel-R1-");
-  if (files1.size() > 0 && files2.size() > 0 && files3.size() > 0)
-  {
-    std::sort(files1.rbegin(), files1.rend(), _comp_str_lesser);
-    std::sort(files2.rbegin(), files2.rend(), _comp_str_lesser);
-    std::sort(files3.rbegin(), files3.rend(), _comp_str_lesser);
-    net = get_home() + SV_MODEL_DIR + files1[0];
-    kernel_cls1 = get_home() + SV_MODEL_DIR + files2[0];
-    kernel_r1 = get_home() + SV_MODEL_DIR + files3[0];
-  }
-  std::cout << "Load: " << net << std::endl;
-  std::cout << "Load: " << kernel_cls1 << std::endl;
-  std::cout << "Load: " << kernel_r1 << std::endl;
 
   std::string backbone = get_home() + SV_MODEL_DIR + "nanotrack_backbone_sim.onnx";
   std::string neckhead = get_home() + SV_MODEL_DIR + "nanotrack_head_sim.onnx";
-  
-  std::vector<std::string> files4, files5;
-  _list_dir(get_home() + SV_MODEL_DIR, files4, ".onnx", "Ocv-NanoTrack-Backbone-SIM-");
-  _list_dir(get_home() + SV_MODEL_DIR, files5, ".onnx", "Ocv-NanoTrack-Head-SIM-");
-  if (files4.size() > 0 && files5.size() > 0)
-  {
-    std::sort(files4.rbegin(), files4.rend(), _comp_str_lesser);
-    std::sort(files5.rbegin(), files5.rend(), _comp_str_lesser);
-    backbone = get_home() + SV_MODEL_DIR + files4[0];
-    neckhead = get_home() + SV_MODEL_DIR + files5[0];
-  }
-  std::cout << "Load: " << backbone << std::endl;
-  std::cout << "Load: " << neckhead << std::endl;
 
   try
   {

algorithm/sv_algorithm_base.cpp

@@ -32,7 +32,7 @@ CameraAlgorithm::CameraAlgorithm()
   // this->_allocator = NULL;
   this->_t0 = std::chrono::system_clock::now();
 
-  this->alg_params_fn = _get_home() + SV_ROOT_DIR + "confs/sv_algorithm_params.json";
+  this->alg_params_fn = _get_home() + SV_ROOT_DIR + "sv_algorithm_params.json";
   // std::cout << "CameraAlgorithm->alg_params_fn: " << this->alg_params_fn << std::endl;
   // if (_is_file_exist(params_fn))
   //   this->loadAlgorithmParams(params_fn);
@@ -80,12 +80,6 @@ ArucoDetector::ArucoDetector()
 }
 
 
-void ArucoDetector::getIdsWithLengths(std::vector<int>& ids_, std::vector<double>& lengths_)
-{
-  ids_ = this->_ids_need;
-  lengths_ = this->_lengths_need;
-}
-
 void ArucoDetector::_load()
 {
   JsonValue all_value;
@@ -916,14 +910,6 @@ void CommonObjectDetectorBase::setInputW(int w_)
 {
   this->_input_w = w_;
 }
-std::string CommonObjectDetectorBase::getModel()
-{
-  return this->_model;
-}
-int CommonObjectDetectorBase::getBatchSize()
-{
-  return this->_batch_size;
-}
 
 void CommonObjectDetectorBase::warmUp()
 {
@@ -1010,11 +996,6 @@ void CommonObjectDetectorBase::detect(cv::Mat img_, TargetsInFrame& tgts_, Box*
       if (this->_with_segmentation)
       {
         cv::Mat mask_j = boxes_seg[j].clone();
-#ifdef WITH_INTEL
-        tgt.setMask(mask_j);
-#endif
-
-#ifdef WITH_CUDA
         int maskh = mask_j.rows, maskw = mask_j.cols;
         assert(maskh == maskw);
 
@@ -1044,7 +1025,6 @@ void CommonObjectDetectorBase::detect(cv::Mat img_, TargetsInFrame& tgts_, Box*
         {
           tgt.setMask(mask_j);
         }
-#endif
       }
 
       tgts_.targets.push_back(tgt);
@@ -1090,8 +1070,6 @@ void CommonObjectDetectorBase::_load()
   this->_thrs_nms = 0.6;
   this->_thrs_conf = 0.4;
   this->_use_width_or_height = 0;
-  this->_batch_size = 1;
-  this->_model = "s";
   
   for (auto i : detector_params_value) {
 
@@ -1099,12 +1077,6 @@ void CommonObjectDetectorBase::_load()
       this->_dataset = i->value.toString();
       std::cout << "dataset: " << this->_dataset << std::endl;
     }
-    else if ("batchSize" == std::string(i->key)) {
-      this->_batch_size = i->value.toNumber();
-    }
-    else if ("model" == std::string(i->key)) {
-      this->_model = i->value.toString();
-    }
     else if ("inputSize" == std::string(i->key)) {
       // std::cout << "inputSize (old, new): " << this->_input_w << ", " << i->value.toNumber() << std::endl;
       this->_input_w = i->value.toNumber();

algorithm/veri/cuda/veri_det_cuda_impl.cpp

@@ -1,7 +1,6 @@
 #include "veri_det_cuda_impl.h"
 #include <cmath>
 #include <fstream>
-#include "sv_util.h"
 
 #define SV_MODEL_DIR "/SpireCV/models/"
 #define SV_ROOT_DIR "/SpireCV/"
@@ -76,16 +75,6 @@ namespace sv
   {
 #ifdef WITH_CUDA
     std::string trt_model_fn = get_home() + SV_MODEL_DIR + "veri.engine";
-    
-    std::vector<std::string> files;
-    _list_dir(get_home() + SV_MODEL_DIR, files, "-online.engine", "Nv-VERI-mobilenet_v3");
-    if (files.size() > 0)
-    {
-      std::sort(files.rbegin(), files.rend(), _comp_str_lesser);
-      trt_model_fn = get_home() + SV_MODEL_DIR + files[0];
-    }
-    std::cout << "Load: " << trt_model_fn << std::endl;
-    
     if (!is_file_exist(trt_model_fn))
     {
       throw std::runtime_error("SpireCV (104) Error loading the VeriDetector TensorRT model (File Not Exist)");

algorithm/veri/intel/veri_det_intel_impl.cpp

@@ -1,112 +0,0 @@
-#include "veri_det_intel_impl.h"
-#include <cmath>
-#include <fstream>
-
-#define SV_MODEL_DIR "/SpireCV/models/"
-#define SV_ROOT_DIR "/SpireCV/"
-
-#include <iostream>
-#include <cmath>
-int BAT = 1;
-float cosineSimilarity(float *vec1, float *vec2, int size)
-{
-  // 计算向量的点积
-  float dotProduct = 0.0f;
-  for (int i = 0; i < size; ++i)
-  {
-    dotProduct += vec1[i] * vec2[i];
-  }
-
-  // 计算向量的模长
-  float magnitudeVec1 = 0.0f;
-  float magnitudeVec2 = 0.0f;
-  for (int i = 0; i < size; ++i)
-  {
-    magnitudeVec1 += vec1[i] * vec1[i];
-    magnitudeVec2 += vec2[i] * vec2[i];
-  }
-  magnitudeVec1 = std::sqrt(magnitudeVec1);
-  magnitudeVec2 = std::sqrt(magnitudeVec2);
-
-  // 计算余弦相似性
-  float similarity = dotProduct / (magnitudeVec1 * magnitudeVec2);
-
-  return similarity;
-}
-
-namespace sv
-{
-#ifdef WITH_INTEL
-  using namespace cv;
-  using namespace std;
-  using namespace dnn;
-#endif
-
-  VeriDetectorIntelImpl::VeriDetectorIntelImpl()
-  {
-  }
-
-  VeriDetectorIntelImpl::~VeriDetectorIntelImpl()
-  {
-  }
-
-  bool VeriDetectorIntelImpl::intelSetup()
-  {
-#ifdef WITH_INTEL
-    std::string onnx_model_fn = get_home() + SV_MODEL_DIR + "veri.onnx";
-    if (!is_file_exist(onnx_model_fn))
-    {
-      throw std::runtime_error("SpireCV (104) Error loading the VeriDetector openVINO model (File Not Exist)");
-    }
-
-    // OpenVINO
-    ov::Core core;
-    this->compiled_model = core.compile_model(onnx_model_fn, "GPU");
-    this->infer_request = compiled_model.create_infer_request();
-
-    return true;
-#endif
-    return false;
-  }
-
-  void VeriDetectorIntelImpl::intelRoiCNN(
-      std::vector<cv::Mat> &input_rois_,
-      std::vector<float> &output_labels_)
-  {
-#ifdef WITH_INTEL
-
-    Mat blobs;
-    blobFromImages(input_rois_, blobs, 1 / 255.0, Size(224, 224), Scalar(0, 0, 0), true, true);
-
-    auto input_port = compiled_model.input();
-    ov::Tensor input_tensor(input_port.get_element_type(), input_port.get_shape(), blobs.ptr(0));
-
-    infer_request.infer();
-
-    const ov::Tensor &label_pre = infer_request.get_output_tensor(0);
-    this->_p_prob1 = label_pre.data<float>();
-
-    const ov::Tensor &proto_pre = infer_request.get_output_tensor(1);
-    this->_p_prob2 = proto_pre.data<float>();
-
-    // Find max index
-    double max = 0;
-    int label = 0;
-    for (int i = 0; i < 576; ++i)
-    {
-      if (max < this->_p_prob1[i])
-      {
-        max = this->_p_prob1[i];
-        label = i;
-      }
-    }
-
-    float similarity = cosineSimilarity(this->_p_prob2, this->_p_prob2 + 1280, 1280);
-    
-    output_labels_.push_back(label);
-    output_labels_.push_back(similarity);
-#endif
-  }
-
-
-}

algorithm/veri/intel/veri_det_intel_impl.h

@@ -1,41 +0,0 @@
-#ifndef __SV_VERI_DET_INTEL__
-#define __SV_VERI_DET_INTEL__
-
-#include "sv_core.h"
-#include <opencv2/opencv.hpp>
-#include <opencv2/aruco.hpp>
-#include <opencv2/tracking.hpp>
-#include <string>
-#include <chrono>
-
-#ifdef WITH_INTEL
-#include <openvino/openvino.hpp>
-#endif
-
-namespace sv
-{
-  class VeriDetectorIntelImpl
-  {
-  public:
-    VeriDetectorIntelImpl();
-    ~VeriDetectorIntelImpl();
-
-    bool intelSetup();
-    void intelRoiCNN(
-        std::vector<cv::Mat> &input_rois_,
-        std::vector<float> &output_labels_);
-
-#ifdef WITH_INTEL
-
-    float *_p_data;
-    float *_p_prob1;
-    float *_p_prob2;
-
-    ov::Tensor input_tensor;
-    ov::InferRequest infer_request;
-    ov::CompiledModel compiled_model;
-#endif
-  };
-
-}
-#endif

algorithm/veri/sv_veri_det.cpp

@@ -10,11 +10,6 @@
 #include "veri_det_cuda_impl.h"
 #endif
 
-#ifdef WITH_INTEL
-#include <openvino/openvino.hpp>
-#include "veri_det_intel_impl.h"
-#endif
-
 #define SV_ROOT_DIR "/SpireCV/"
 
 namespace sv
@@ -25,10 +20,6 @@ namespace sv
 #ifdef WITH_CUDA
     this->_cuda_impl = new VeriDetectorCUDAImpl;
 #endif
-
-#ifdef WITH_INTEL
-    this->_intel_impl = new VeriDetectorIntelImpl;
-#endif
   }
   VeriDetector::~VeriDetector()
   {
@@ -59,9 +50,6 @@ namespace sv
     return this->_cuda_impl->cudaSetup();
 #endif
   
-#ifdef WITH_INTEL
-    return this->_intel_impl->intelSetup();
-#endif
     return false;
   }
 
@@ -75,12 +63,6 @@ namespace sv
         input_rois_,
         output_labels_);
 #endif
-
-#ifdef WITH_INTEL
-    this->_intel_impl->intelRoiCNN(
-        input_rois_,
-        output_labels_);
-#endif
   }
 
   void VeriDetector::detect(cv::Mat img_, const cv::Rect &bounding_box_, sv::Target &tgt)
@@ -112,23 +94,26 @@ namespace sv
     cv::Mat img_ground = cv::imread(img_ground_dir);
     cv::resize(img_ground, img_ground, cv::Size(224, 224));
     std::vector<cv::Mat> input_rois_ = {crop, img_ground};
-    
-    std::vector<float> output_labels;
-#ifdef WITH_CUDA
-    roiCNN(input_rois_, output_labels);
-#endif
 
-#ifdef WITH_INTEL
+#ifdef WITH_CUDA
+    std::vector<float> output_labels;
     roiCNN(input_rois_, output_labels);
-#endif
-   
+
+    // auto t1 = std::chrono::system_clock::now();
+    // tgts_.setFPS(1000.0 / std::chrono::duration_cast<std::chrono::milliseconds>(t1 - this->_t0).count());
+    // this->_t0 = std::chrono::system_clock::now();
+    // tgts_.setTimeNow();
+
     if (output_labels.size() > 0)
     {
+      // tgt.category_id = output_labels[0];
       tgt.sim_score = output_labels[1];
+      // tgts_.targets.push_back(tgt);
     }
+#endif
   }
 
-  void VeriDetector::getSubwindow(cv::Mat &dstCrop, cv::Mat &srcImg, int originalSz, int resizeSz)
+    void VeriDetector::getSubwindow(cv::Mat &dstCrop, cv::Mat &srcImg, int originalSz, int resizeSz)
   {
     cv::Scalar avgChans = mean(srcImg);
     cv::Size imgSz = srcImg.size();

gimbal_ctrl/driver/src/GX40/GX40_gimbal_driver.cpp

@@ -28,7 +28,6 @@ GX40GimbalDriver::GX40GimbalDriver(amovGimbal::IOStreamBase *_IO) : amovGimbal::
     targetPos[2] = 0;
 
     parserState = GX40::GIMBAL_FRAME_PARSER_STATE_IDLE;
-    upDataTs = std::chrono::duration_cast<std::chrono::milliseconds>(std::chrono::system_clock::now().time_since_epoch()) - std::chrono::milliseconds(2000);
 }
 
 /**
@@ -108,8 +107,7 @@ uint32_t GX40GimbalDriver::pack(IN uint32_t cmd, uint8_t *pPayload, uint8_t payl
     // 惯导数据填充
     std::chrono::milliseconds nowTs = std::chrono::duration_cast<std::chrono::milliseconds>(std::chrono::system_clock::now().time_since_epoch());
     // over 1s GNSS has losed
-    if ((nowTs.count() - upDataTs.count()) < std::chrono::milliseconds(1500).count() &&
-        cmd == GX40::GIMBAL_CMD_NOP)
+    if ((nowTs.count() - upDataTs.count()) < std::chrono::milliseconds(1500).count())
     {
         primary->selfRoll = (int16_t)(-(carrierPos.roll / 0.01f));
         primary->selfPitch = (int16_t)(-(carrierPos.pitch / 0.01f));

gimbal_ctrl/driver/src/GX40/GX40_gimbal_funtion.cpp

@@ -20,9 +20,9 @@
 uint32_t GX40GimbalDriver::setGimabalPos(const AMOV_GIMBAL_POS_T &pos)
 {
     carrierStateMutex.lock();
-    targetPos[0] = (int16_t)(pos.roll / 0.01f);
+    targetPos[0] = (int16_t)(-pos.roll / 0.01f);
     targetPos[1] = (int16_t)(-pos.pitch / 0.01f);
-    targetPos[2] = (int16_t)(pos.yaw / 0.01f);
+    targetPos[2] = (int16_t)(-pos.yaw / 0.01f);
     carrierStateMutex.unlock();
     return pack(GX40::GIMBAL_CMD_MODE_EULER, nullptr, 0);
 }
@@ -38,9 +38,9 @@ uint32_t GX40GimbalDriver::setGimabalPos(const AMOV_GIMBAL_POS_T &pos)
 uint32_t GX40GimbalDriver::setGimabalSpeed(const AMOV_GIMBAL_POS_T &speed)
 {
     carrierStateMutex.lock();
-    targetPos[0] = (int16_t)(speed.roll / 0.1f);
+    targetPos[0] = (int16_t)(-speed.roll / 0.1f);
     targetPos[1] = (int16_t)(-speed.pitch / 0.1f);
-    targetPos[2] = (int16_t)(speed.yaw / 0.1f);
+    targetPos[2] = (int16_t)(-speed.yaw / 0.1f);
     carrierStateMutex.unlock();
 
     return pack(GX40::GIMBAL_CMD_MODE_FOLLOW, nullptr, 0);

gimbal_ctrl/sv_gimbal.cpp

@@ -509,50 +509,6 @@ void sv::Gimbal::setAngleRateEuler(double roll_rate, double pitch_rate, double y
     temp.yaw = yaw_rate;
     pdevTemp->setGimabalSpeed(temp);
 }
-void sv::Gimbal::attitudeCorrection(const GimbalQuaternionT &quaterion,
-                                    const GimbalVelocityT &speed,
-                                    const GimbalVelocityT &acc, void *extenData)
-{
-    amovGimbal::gimbal *pdevTemp = (amovGimbal::gimbal *)this->dev;
-    AMOV_GIMBAL_QUATERNION_T temp_q;
-    temp_q.q0 = quaterion.q0;
-    temp_q.q1 = quaterion.q1;
-    temp_q.q2 = quaterion.q2;
-    temp_q.q3 = quaterion.q3;
-
-    AMOV_GIMBAL_VELOCITY_T temp_v1, temp_v2;
-    temp_v1.x = speed.x;
-    temp_v1.y = speed.y;
-    temp_v1.z = speed.z;
-
-    temp_v2.x = acc.x;
-    temp_v2.y = acc.y;
-    temp_v2.z = acc.z;
-
-    pdevTemp->attitudeCorrection(temp_q, temp_v1, temp_v2, extenData);
-}
-
-void sv::Gimbal::attitudeCorrection(const GimbalPosT &pos,
-                                    const GimbalVelocityT &speed,
-                                    const GimbalVelocityT &acc, void *extenData)
-{
-    amovGimbal::gimbal *pdevTemp = (amovGimbal::gimbal *)this->dev;
-    AMOV_GIMBAL_VELOCITY_T temp_v1, temp_v2;
-    temp_v1.x = speed.x;
-    temp_v1.y = speed.y;
-    temp_v1.z = speed.z;
-
-    temp_v2.x = acc.x;
-    temp_v2.y = acc.y;
-    temp_v2.z = acc.z;
-
-    AMOV_GIMBAL_POS_T temp_p;
-    temp_p.pitch = pos.pitch;
-    temp_p.yaw = pos.yaw;
-    temp_p.roll = pos.roll;
-
-    pdevTemp->attitudeCorrection(temp_p, temp_v1, temp_v2, extenData);
-}
 
 sv::Gimbal::~Gimbal()
 {

include/sv_algorithm_base.h

@@ -46,7 +46,6 @@ class ArucoDetector : public CameraAlgorithm
 public:
   ArucoDetector();
   void detect(cv::Mat img_, TargetsInFrame& tgts_);
-  void getIdsWithLengths(std::vector<int>& ids_, std::vector<double>& lengths_);
 private:
   void _load();
   bool _params_loaded;
@@ -140,8 +139,6 @@ public:
   double getThrsConf();
   int useWidthOrHeight();
   bool withSegmentation();
-  std::string getModel();
-  int getBatchSize();
 protected:
   virtual bool setupImpl();
   virtual void detectImpl(
@@ -168,8 +165,6 @@ protected:
   double _thrs_conf;
   int _use_width_or_height;
   bool _with_segmentation;
-  std::string _model;
-  int _batch_size;
 };
 
 

include/sv_camera.h

@@ -0,0 +1,79 @@
+#ifndef __SV_CAMERA__
+#define __SV_CAMERA__
+
+#include "opencv2/opencv.hpp"
+
+namespace sv
+{
+
+enum class CameraType : int
+{
+  // 未知相机
+  NONE = 0X00001000,
+  // 标准opencv框架下的相机
+  V4L2CAM = 0X00001001,  
+  WEBCAM = 0X00001002,
+  // 视频文件
+  VIDEOFILE = 0X00002001,
+  // 普通流媒体相机
+  STREAMING = 0X00004001,
+  // 普通的MIPI-CSI相机
+  MIPI = 0X00008001,
+  // 进行了传输优化适配的相机
+  G1 = 0X00010001,
+  Q10 = 0X00010002,
+  GX40 = 0X00010003,
+  MC1 = 0X00010004,
+  // 虚拟的相机设备 主要仿真接入
+  VIRTUAL = 0X00020001,
+};
+
+class Camera
+{
+private:
+  void *dev;
+
+public:
+  // 构建时根据相机类型实例化对应的相机类
+  // timeOut: 相机连接超时时间；超过该时间没有读取到新的帧，则认为相机失去连接 单位ms
+  Camera(CameraType type, int timeOut = 500);
+
+  // 基本配置 必须调用下述的一个接口后才能open
+  bool setStream(const std::string &ip, int port);
+  bool setName(const std::string &name);
+  bool setIndex(int index);
+  // 基础功能
+  bool open(void);
+  bool read(cv::Mat &image);
+  bool readNoBlock(cv::Mat &image);
+  void release(void);
+
+  // 属性配置
+  void setWH(int width, int height);
+  void setFps(int fps);
+  void setBrightness(double brightness);
+  void setContrast(double contrast);
+  void setSaturation(double saturation);
+  void setHue(double hue);
+  void setExposure(double exposure);
+
+  // 获取属性
+  bool isActive(void);
+  CameraType getType(void);
+  std::string getName(void);
+
+  int getW(void);
+  int getH(void);
+  int getExpectFps(void);
+  double getFps(void);
+  double getBrightness(void);
+  double getContrast(void);
+  double getSaturation(void);
+  double getHue(void);
+  double getExposure(void);
+
+  ~Camera();
+};
+
+}
+#endif

include/sv_color_line.h

@@ -25,6 +25,7 @@ public:
   double line_location_a2;
 
   bool is_load_parameter;
+
   std::string line_color;
 
 protected:
@@ -39,4 +40,4 @@ protected:
   void seg(cv::Mat line_area_, cv::Mat line_area_a1_, cv::Mat line_area_a2_, std::string line_color_, cv::Point &center_, int &area_, cv::Point &center_a1_, cv::Point &center_a2_);
 };
 }
-#endif
+#endif

include/sv_common_det.h

@@ -8,36 +8,33 @@
 #include <string>
 #include <chrono>
 
-namespace sv
-{
+
+namespace sv {
 
 class CommonObjectDetectorCUDAImpl;
-class CommonObjectDetectorIntelImpl;
-
 
 class CommonObjectDetector : public CommonObjectDetectorBase
 {
 public:
-  CommonObjectDetector(bool input_4k = false);
+  CommonObjectDetector(bool input_4k=false);
   ~CommonObjectDetector();
-
 protected:
   bool setupImpl();
   void detectImpl(
     cv::Mat img_,
-    std::vector<float> &boxes_x_,
-    std::vector<float> &boxes_y_,
-    std::vector<float> &boxes_w_,
-    std::vector<float> &boxes_h_,
-    std::vector<int> &boxes_label_,
-    std::vector<float> &boxes_score_,
-    std::vector<cv::Mat> &boxes_seg_);
-
-  CommonObjectDetectorCUDAImpl *_cuda_impl;
-  CommonObjectDetectorIntelImpl *_intel_impl;
+    std::vector<float>& boxes_x_,
+    std::vector<float>& boxes_y_,
+    std::vector<float>& boxes_w_,
+    std::vector<float>& boxes_h_,
+    std::vector<int>& boxes_label_,
+    std::vector<float>& boxes_score_,
+    std::vector<cv::Mat>& boxes_seg_
+  );
 
+  CommonObjectDetectorCUDAImpl* _cuda_impl;
   bool _input_4k;
 };
 
+
 }
 #endif

include/sv_gimbal.h

@@ -78,28 +78,6 @@ namespace sv
     FLOWCONTROL_HARDWARE = 2,
   };
 
-  typedef struct
-  {
-    double q0;
-    double q1;
-    double q2;
-    double q3;
-  } GimbalQuaternionT;
-
-  typedef struct
-  {
-    double x; // or N
-    double y; // or E
-    double z; // or UP
-  } GimbalVelocityT;
-
-  typedef struct
-  {
-    double yaw;
-    double roll;
-    double pitch;
-  } GimbalPosT;
-
   static inline void emptyCallback(double &frameAngleRoll, double &frameAnglePitch, double &frameAngleYaw,
                                    double &imuAngleRoll, double &imuAnglePitch, double &imuAngleYaw,
                                    double &fovX, double &fovY)
@@ -187,12 +165,6 @@ namespace sv
     bool takePhoto();
     bool takeVideo(int state);
     int getVideoState();
-    void attitudeCorrection(const GimbalQuaternionT &quaterion,
-                                    const GimbalVelocityT &speed,
-                                    const GimbalVelocityT &acc, void *extenData);
-    void attitudeCorrection(const GimbalPosT &pos,
-                                    const GimbalVelocityT &speed,
-                                    const GimbalVelocityT &acc, void *extenData);
 
     //! Set gimbal angles
     /*!

include/sv_landing_det.h

@@ -12,7 +12,6 @@
 namespace sv {
 
 class LandingMarkerDetectorCUDAImpl;
-class LandingMarkerDetectorIntelImpl;
 
 class LandingMarkerDetector : public LandingMarkerDetectorBase
 {
@@ -26,8 +25,7 @@ protected:
     std::vector<int>& output_labels_
   );
 
-  LandingMarkerDetectorCUDAImpl *_cuda_impl;
-  LandingMarkerDetectorIntelImpl *_intel_impl;
+  LandingMarkerDetectorCUDAImpl* _cuda_impl;
 };
 
 

include/sv_mot.h

@@ -15,7 +15,7 @@ namespace sv {
 
 
 class SORT;
-class BYTETracker;
+
 
 class MultipleObjectTracker : public CameraAlgorithm
 {
@@ -24,7 +24,7 @@ public:
     ~MultipleObjectTracker();
 
     void init(CommonObjectDetector* detector_);
-    sv::TargetsInFrame track(cv::Mat img_, TargetsInFrame& tgts_);
+    void track(cv::Mat img_, TargetsInFrame& tgts_);
 
 private:
     void _load();
@@ -39,12 +39,70 @@ private:
     double _iou_thres;
     int _max_age;
     int _min_hits;
-    int _frame_rate;
-    int _track_buffer;
     
     SORT* _sort_impl;
-    BYTETracker* _bytetrack_impl;
     CommonObjectDetector* _detector;
 };
+
+
+// define the tracklet struct to store the tracked objects.
+struct Tracklet
+{
+/* data */
+public:
+    Eigen::Vector4d bbox;  // double x, y, w, h;
+    int id = 0;
+    int age;
+    int hits;
+    int misses;
+    int frame_id = 0;
+    int category_id;
+    bool tentative;
+    std::vector<double> features;
+    Eigen::Matrix<double, 8, 1> mean;
+    Eigen::Matrix<double, 8, 8> covariance;
+};
+
+
+class KalmanFilter {
+public:
+    KalmanFilter();
+    ~KalmanFilter();
+    std::pair<Eigen::Matrix<double, 8, 1>, Eigen::Matrix<double, 8, 8> > initiate(Eigen::Vector4d &bbox);
+    std::pair<Eigen::Matrix<double, 8, 1>, Eigen::Matrix<double, 8, 8> > update(Eigen::Matrix<double, 8, 1> mean, Eigen::Matrix<double, 8, 8> covariances, Box &box);
+    std::pair<Eigen::Matrix<double, 8, 1>, Eigen::Matrix<double, 8, 8> > predict(Eigen::Matrix<double, 8, 1> mean, Eigen::Matrix<double, 8, 8> covariances);
+    std::pair<Eigen::Matrix<double, 4, 1>, Eigen::Matrix<double, 4, 4> > project(Eigen::Matrix<double, 8, 1> mean, Eigen::Matrix<double, 8, 8> covariances);
+private:
+    Eigen::Matrix<double, 8, 8> _F;
+    Eigen::Matrix<double, 4, 8> _H;
+    Eigen::Matrix<double, 9, 1> _chi2inv95;
+    double _std_weight_position;
+    double _std_weight_vel;
+};
+
+
+class SORT {
+public:
+    SORT(double iou_threshold, int max_age, int min_hits): _iou_threshold(iou_threshold), _max_age(max_age), _min_hits(min_hits), _next_tracklet_id(0) {};
+    ~SORT();
+    void update(TargetsInFrame &tgts);
+    std::vector<Tracklet> getTracklets() const;
+private:
+    double _iou(Tracklet &tracklet, Box &box);
+    std::vector<std::pair<int,int>> _hungarian(std::vector<std::vector<double>> costMatrix);
+    double _findMin(const std::vector<double>& vec);
+    void _subtractMinFromRows(std::vector<std::vector<double>>& costMatrix);
+    void _subtractMinFromCols(std::vector<std::vector<double>>& costMatrix);
+    //bool _augment(const std::vector<std::vector<double>>& costMatrix, int row, std::vector<int>& rowMatch, std::vector<int>& colMatch, std::vector<bool>& visited);
+
+    double _iou_threshold;
+    int _max_age;
+    int _min_hits;
+    int _next_tracklet_id;
+    std::vector <Tracklet> _tracklets;
+    std::vector <Tracklet> _new_tracklets;
+};
+
+
 }
 #endif

include/sv_veri_det.h

@@ -11,34 +11,33 @@
 namespace sv
 {
 
-class VeriDetectorCUDAImpl;
-class VeriDetectorIntelImpl;
+  class VeriDetectorCUDAImpl;
 
-class VeriDetector : public LandingMarkerDetectorBase
-{
-public:
-  VeriDetector();
-  ~VeriDetector();
+  class VeriDetector : public LandingMarkerDetectorBase
+  {
+  public:
+    VeriDetector();
+    ~VeriDetector();
 
-  void detect(cv::Mat img_, const cv::Rect &bounding_box_, sv::Target &tgt);
+    void detect(cv::Mat img_, const cv::Rect &bounding_box_, sv::Target &tgt);
 
-protected:
-  void _load();
-  bool setupImpl();
-  void roiCNN(
-    std::vector<cv::Mat> &input_rois_,
-    std::vector<float> &output_labels_);
-  void getSubwindow(cv::Mat &dstCrop, cv::Mat &srcImg, int originalSz, int resizeSz);
+  protected:
+    void _load();
+    bool setupImpl();
+    void roiCNN(
+        std::vector<cv::Mat> &input_rois_,
+        std::vector<float> &output_labels_);
+    void getSubwindow(cv::Mat &dstCrop, cv::Mat &srcImg, int originalSz, int resizeSz);
 
-  std::string vehicle_id;
+    std::string vehicle_id;
     
-  // Save the target bounding box for each frame.
-  std::vector<float> targetSz = {0, 0};  // H and W of bounding box
-  std::vector<float> targetPos = {0, 0}; // center point of bounding box (x, y)
+      // Save the target bounding box for each frame.
+    std::vector<float> targetSz = {0, 0};  // H and W of bounding box
+    std::vector<float> targetPos = {0, 0}; // center point of bounding box (x, y)
 
-  VeriDetectorCUDAImpl *_cuda_impl;
-  VeriDetectorIntelImpl *_intel_impl;
-};
+
+    VeriDetectorCUDAImpl *_cuda_impl;
+  };
 
 }
 #endif

include/sv_video_base.h

@@ -128,7 +128,6 @@ public:
   
   bool getBox(Box& b);
   bool getAruco(int& id, std::vector<cv::Point2f> &corners);
-  bool getAruco(int& id, std::vector<cv::Point2f> &corners, cv::Vec3d &rvecs, cv::Vec3d &tvecs);
   bool getEllipse(double& xc_, double& yc_, double& a_, double& b_, double& rad_);
   std::string getJsonStr();
 
@@ -327,66 +326,59 @@ protected:
 };
 
 
-enum class CameraType {NONE, WEBCAM, V4L2CAM, MIPI, RTSP, VIDEO, G1, Q10, GX40};
+// enum class CameraType {NONE, WEBCAM, V4L2CAM, G1, Q10, MIPI, GX40};
 
-class CameraBase {
-public:
-  CameraBase(CameraType type=CameraType::NONE, int id=0);
-  ~CameraBase();
-  void open(CameraType type=CameraType::V4L2CAM, int id=0);
-  bool read(cv::Mat& image);
-  void release();
+// class CameraBase {
+// public:
+//   CameraBase(CameraType type=CameraType::NONE, int id=0);
+//   ~CameraBase();
+//   void open(CameraType type=CameraType::WEBCAM, int id=0);
+//   bool read(cv::Mat& image);
+//   void release();
   
-  int getW();
-  int getH();
-  int getFps();
-  std::string getIp();
-  int getPort();
-  double getBrightness();
-  double getContrast();
-  double getSaturation();
-  double getHue();
-  double getExposure();
-  std::string getFourcc();
-  bool isRunning();
-  void setFourcc(std::string fourcc);
-  void setWH(int width, int height);
-  void setFps(int fps);
-  void setIp(std::string ip);
-  void setRtspUrl(std::string rtsp_url);
-  void setVideoPath(std::string video_path);
-  void setPort(int port);
-  void setBrightness(double brightness);
-  void setContrast(double contrast);
-  void setSaturation(double saturation);
-  void setHue(double hue);
-  void setExposure(double exposure);
-protected:
-  virtual void openImpl();
-  void _run();
+//   int getW();
+//   int getH();
+//   int getFps();
+//   std::string getIp();
+//   int getPort();
+//   double getBrightness();
+//   double getContrast();
+//   double getSaturation();
+//   double getHue();
+//   double getExposure();
+//   bool isRunning();
+//   void setWH(int width, int height);
+//   void setFps(int fps);
+//   void setIp(std::string ip);
+//   void setPort(int port);
+//   void setBrightness(double brightness);
+//   void setContrast(double contrast);
+//   void setSaturation(double saturation);
+//   void setHue(double hue);
+//   void setExposure(double exposure);
+// protected:
+//   virtual void openImpl();
+//   void _run();
 
-  bool _is_running;
-  bool _is_updated;
-  std::thread _tt;
-  cv::VideoCapture _cap;
-  cv::Mat _frame;
-  CameraType _type;
-  int _camera_id;
+//   bool _is_running;
+//   bool _is_updated;
+//   std::thread _tt;
+//   cv::VideoCapture _cap;
+//   cv::Mat _frame;
+//   CameraType _type;
+//   int _camera_id;
   
-  int _width;
-  int _height;
-  int _fps;
-  std::string _ip;
-  std::string _rtsp_url;
-  std::string _video_path;
-  int _port;
-  double _brightness;
-  double _contrast;
-  double _saturation;
-  double _hue;
-  double _exposure;
-  std::string _fourcc;
-};
+//   int _width;
+//   int _height;
+//   int _fps;
+//   std::string _ip;
+//   int _port;
+//   double _brightness;
+//   double _contrast;
+//   double _saturation;
+//   double _hue;
+//   double _exposure;
+// };
 
 
 void drawTargetsInFrame(
@@ -401,16 +393,6 @@ void drawTargetsInFrame(
   bool with_aruco=false,
   bool with_yaw=false
 );
-cv::Mat drawTargetsInFrame(
-  const cv::Mat img_,
-  const TargetsInFrame& tgts_,
-  const double scale,
-  bool with_all=true,
-  bool with_category=false,
-  bool with_tid=false,
-  bool with_seg=false,
-  bool with_box=false
-);
 std::string get_home();
 bool is_file_exist(std::string& fn);
 void list_dir(std::string dir, std::vector<std::string>& files, std::string suffixs="", bool r=false);

include/sv_video_input.h

@@ -1,3 +1,4 @@
+#if 0
 #ifndef __SV_VIDEO_INPUT__
 #define __SV_VIDEO_INPUT__
 
@@ -25,3 +26,4 @@ protected:
 
 }
 #endif
+#endif

params/a-params/sv_algorithm_params.json

@@ -1,13 +1,11 @@
 {
     "CommonObjectDetector": {
         "dataset": "COCO",
-        "model": "s",
-        "batchSize": 1,
         "inputSize": 640,
-        "withSegmentation": true,
         "nmsThrs": 0.6,
         "scoreThrs": 0.4,
         "useWidthOrHeight": 1,
+        "withSegmentation": true,
         "datasetPersonVehicle": {
             "person": [0.5, 1.8],
             "car": [4.1, 1.5],
@@ -126,9 +124,7 @@
         "reid_match_thres": 2.0,
         "iou_thres": 0.5,
         "max_age": 10,
-        "min_hits": 3,
-        "frame_rate": 30,
-        "track_buffer": 30
+        "min_hits": 3
     },
     "LandingMarkerDetector": {
         "labels": ["h"],

params/a-params/sv_algorithm_params_1280_wo_mask.json

@@ -1,13 +1,11 @@
 {
     "CommonObjectDetector": {
         "dataset": "COCO",
-        "model": "s",
-        "batchSize": 1,
         "inputSize": 1280,
-        "withSegmentation": false,
         "nmsThrs": 0.6,
         "scoreThrs": 0.4,
         "useWidthOrHeight": 1,
+        "withSegmentation": false,
         "datasetPersonVehicle": {
             "person": [0.5, 1.8],
             "car": [4.1, 1.5],
@@ -126,9 +124,7 @@
         "reid_match_thres": 2.0,
         "iou_thres": 0.5,
         "max_age": 10,
-        "min_hits": 3,
-        "frame_rate": 30,
-        "track_buffer": 30
+        "min_hits": 3
     },
     "LandingMarkerDetector": {
         "labels": ["x", "h"],

params/a-params/sv_algorithm_params_640_w_mask.json

@@ -1,13 +1,11 @@
 {
     "CommonObjectDetector": {
         "dataset": "COCO",
-        "model": "s",
-        "batchSize": 1,
         "inputSize": 640,
-        "withSegmentation": true,
         "nmsThrs": 0.6,
         "scoreThrs": 0.4,
         "useWidthOrHeight": 1,
+        "withSegmentation": true,
         "datasetPersonVehicle": {
             "person": [0.5, 1.8],
             "car": [4.1, 1.5],
@@ -125,9 +123,8 @@
         "reid_num_samples": 10,
         "reid_match_thres": 2.0,
         "iou_thres": 0.5,
-        "min_hits": 3,
-        "frame_rate": 30,
-        "track_buffer": 30
+        "max_age": 10,
+        "min_hits": 3
     },
     "LandingMarkerDetector": {
         "labels": ["x", "h"],

params/a-params/sv_algorithm_params_640_wo_mask.json

@@ -1,13 +1,11 @@
 {
     "CommonObjectDetector": {
         "dataset": "COCO",
-        "model": "s",
-        "batchSize": 1,
         "inputSize": 640,
-        "withSegmentation": false,
         "nmsThrs": 0.6,
         "scoreThrs": 0.4,
         "useWidthOrHeight": 1,
+        "withSegmentation": false,
         "datasetPersonVehicle": {
             "person": [0.5, 1.8],
             "car": [4.1, 1.5],
@@ -126,9 +124,7 @@
         "reid_match_thres": 2.0,
         "iou_thres": 0.5,
         "max_age": 10,
-        "min_hits": 3,
-        "frame_rate": 30,
-        "track_buffer": 30
+        "min_hits": 3
     },
     "LandingMarkerDetector": {
         "labels": ["x", "h"],

params/a-params/sv_algorithm_params_coco_1280.json

@@ -1,13 +1,11 @@
 {
     "CommonObjectDetector": {
         "dataset": "COCO",
-        "model": "s",
-        "batchSize": 1,
         "inputSize": 1280,
-        "withSegmentation": false,
         "nmsThrs": 0.6,
         "scoreThrs": 0.4,
         "useWidthOrHeight": 1,
+        "withSegmentation": false,
         "datasetPersonVehicle": {
             "person": [0.5, 1.8],
             "car": [4.1, 1.5],
@@ -126,9 +124,7 @@
         "reid_match_thres": 2.0,
         "iou_thres": 0.5,
         "max_age": 10,
-        "min_hits": 3,
-        "frame_rate": 30,
-        "track_buffer": 30
+        "min_hits": 3
     },
     "LandingMarkerDetector": {
         "labels": ["x", "h"],

params/a-params/sv_algorithm_params_coco_640.json

@@ -1,13 +1,11 @@
 {
     "CommonObjectDetector": {
         "dataset": "COCO",
-        "model": "s",
-        "batchSize": 1,
         "inputSize": 640,
-        "withSegmentation": false,
         "nmsThrs": 0.6,
         "scoreThrs": 0.4,
         "useWidthOrHeight": 1,
+        "withSegmentation": true,
         "datasetPersonVehicle": {
             "person": [0.5, 1.8],
             "car": [4.1, 1.5],
@@ -126,9 +124,7 @@
         "reid_match_thres": 2.0,
         "iou_thres": 0.5,
         "max_age": 10,
-        "min_hits": 3,
-        "frame_rate": 30,
-        "track_buffer": 30
+        "min_hits": 3
     },
     "LandingMarkerDetector": {
         "labels": ["x", "h"],

params/a-params/sv_algorithm_params_csrt.json

@@ -1,13 +1,11 @@
 {
     "CommonObjectDetector": {
         "dataset": "COCO",
-        "model": "s",
-        "batchSize": 1,
         "inputSize": 640,
-        "withSegmentation": true,
         "nmsThrs": 0.6,
         "scoreThrs": 0.4,
         "useWidthOrHeight": 1,
+        "withSegmentation": true,
         "datasetPersonVehicle": {
             "person": [0.5, 1.8],
             "car": [4.1, 1.5],
@@ -126,9 +124,7 @@
         "reid_match_thres": 2.0,
         "iou_thres": 0.5,
         "max_age": 10,
-        "min_hits": 3,
-        "frame_rate": 30,
-        "track_buffer": 30
+        "min_hits": 3
     },
     "LandingMarkerDetector": {
         "labels": ["x", "h"],

params/a-params/sv_algorithm_params_kcf.json

@@ -1,13 +1,11 @@
 {
     "CommonObjectDetector": {
         "dataset": "COCO",
-        "model": "s",
-        "batchSize": 1,
         "inputSize": 640,
-        "withSegmentation": true,
         "nmsThrs": 0.6,
         "scoreThrs": 0.4,
         "useWidthOrHeight": 1,
+        "withSegmentation": true,
         "datasetPersonVehicle": {
             "person": [0.5, 1.8],
             "car": [4.1, 1.5],
@@ -126,9 +124,7 @@
         "reid_match_thres": 2.0,
         "iou_thres": 0.5,
         "max_age": 10,
-        "min_hits": 3,
-        "frame_rate": 30,
-        "track_buffer": 30
+        "min_hits": 3
     },
     "LandingMarkerDetector": {
         "labels": ["x", "h"],

params/a-params/sv_algorithm_params_nano.json

@@ -1,13 +1,11 @@
 {
     "CommonObjectDetector": {
         "dataset": "COCO",
-        "model": "s",
-        "batchSize": 1,
         "inputSize": 640,
-        "withSegmentation": true,
         "nmsThrs": 0.6,
         "scoreThrs": 0.4,
         "useWidthOrHeight": 1,
+        "withSegmentation": true,
         "datasetPersonVehicle": {
             "person": [0.5, 1.8],
             "car": [4.1, 1.5],
@@ -126,9 +124,7 @@
         "reid_match_thres": 2.0,
         "iou_thres": 0.5,
         "max_age": 10,
-        "min_hits": 3,
-        "frame_rate": 30,
-        "track_buffer": 30
+        "min_hits": 3
     },
     "LandingMarkerDetector": {
         "labels": ["x", "h"],

params/a-params/sv_algorithm_params_siamrpn.json

@@ -1,13 +1,11 @@
 {
     "CommonObjectDetector": {
         "dataset": "COCO",
-        "model": "s",
-        "batchSize": 1,
         "inputSize": 640,
-        "withSegmentation": true,
         "nmsThrs": 0.6,
         "scoreThrs": 0.4,
         "useWidthOrHeight": 1,
+        "withSegmentation": true,
         "datasetPersonVehicle": {
             "person": [0.5, 1.8],
             "car": [4.1, 1.5],
@@ -126,9 +124,7 @@
         "reid_match_thres": 2.0,
         "iou_thres": 0.5,
         "max_age": 10,
-        "min_hits": 3,
-        "frame_rate": 30,
-        "track_buffer": 30
+        "min_hits": 3
     },
     "LandingMarkerDetector": {
         "labels": ["x", "h"],

samples/calib/calibrate_camera_charuco.cpp

@@ -39,6 +39,8 @@ the use of this software, even if advised of the possibility of such damage.
 #include <sv_world.h>
 #include "aruco_samples_utility.hpp"
 
+#include  "sv_camera.h"
+
 using namespace std;
 using namespace cv;
 
@@ -146,10 +148,14 @@ int main(int argc, char *argv[]) {
     }
 
     // VideoCapture inputVideo;
-    sv::Camera inputVideo;
+    sv::Camera inputVideo(c_type);
+    inputVideo.setIndex(camId);
+    inputVideo.setStream("192.168.144.64",554);
+
     inputVideo.setWH(imW, imH);
     inputVideo.setFps(fps);
-    inputVideo.open(c_type, camId);
+    
+    inputVideo.open();
   
     int waitTime = 10;
 

samples/calib/create_marker.cpp

@@ -97,8 +97,7 @@ int main(int argc, char *argv[]) {
 
   if (landingPadUnit > 0) {
     Mat markerImgBIG = Mat::ones(landingPadUnit * 62, landingPadUnit * 62, CV_8UC1) * 255;
-    // markerId = 0;
-    markerId --;
+    markerId = 0;
     markerSize = landingPadUnit * 4;
     borderSize = landingPadUnit;
     int newSize = markerSize + borderSize * 2;
@@ -223,7 +222,7 @@ int main(int argc, char *argv[]) {
     markerImg = create_marker_with_borders(dictionary, markerId, markerSize, borderBits, borderSize);
     markerImg.copyTo(markerImgBIG(Rect(landingPadUnit * 10, landingPadUnit * 28, newSize, newSize)));
     
-    // markerId = 90;
+    markerId = 90;
     markerSize = landingPadUnit * 4 * 4;
     borderSize = landingPadUnit * 4;
     newSize = markerSize + borderSize * 2;

samples/demo/aruco_detection.cpp

@@ -2,6 +2,7 @@
 #include <string>
 // 包含SpireCV SDK头文件
 #include <sv_world.h>
+#include "sv_camera.h"
 
 using namespace std;
 
@@ -9,14 +10,15 @@ int main(int argc, char *argv[]) {
   // 实例化Aruco检测器类
   sv::ArucoDetector ad;
   // 手动导入相机参数，如果使用Amov的G1等吊舱或相机，则可以忽略该步骤，将自动下载相机参数文件
-  ad.loadCameraParams(sv::get_home() + "/SpireCV/confs/calib_webcam_1280x720.yaml");
-  ad.loadAlgorithmParams(sv::get_home() + "/SpireCV/confs/sv_algorithm_params.json");
+  ad.loadCameraParams(sv::get_home() + "/SpireCV/calib_webcam_640x480.yaml");
   
   // 打开摄像头
-  sv::Camera cap;
+  sv::Camera cap(sv::CameraType::WEBCAM);
+  
+  cap.setIndex(0);
   cap.setWH(ad.image_width, ad.image_height);
-  cap.setFps(30);
-  cap.open(sv::CameraType::V4L2CAM, 0);  // CameraID 0
+  // cap.setFps(30);
+  cap.open();  // CameraID 0
   // 实例化OpenCV的Mat类，用于内存单帧图像
   cv::Mat img;
   int frame_id = 0;

samples/demo/camera_reading.cpp

@@ -2,15 +2,17 @@
 #include <string>
 // 包含SpireCV SDK头文件
 #include <sv_world.h>
+#include "sv_camera.h"
 
 using namespace std;
 
 int main(int argc, char *argv[]) {  
   // 打开摄像头
-  sv::Camera cap;
-  cap.setWH(1280, 720);
-  cap.setFps(30);
-  cap.open(sv::CameraType::V4L2CAM, 0);  // CameraID 0
+  sv::Camera cap(sv::CameraType::WEBCAM);
+  cap.setIndex(0);
+  // cap.setWH(640, 480);
+  // cap.setFps(30);
+  cap.open();  // CameraID 0
   // 实例化OpenCV的Mat类，用于内存单帧图像
   cv::Mat img;
   while (1)

samples/demo/color_line_detect.cpp

@@ -2,6 +2,7 @@
 #include <string>
 // 包含SpireCV SDK头文件
 #include <sv_world.h>
+#include "sv_camera.h"
 
 using namespace std;
 using namespace sv;
@@ -11,14 +12,14 @@ int main(int argc, char *argv[])
   // 实例化 color line detection 检测器类
   sv::ColorLineDetector cld;
   // 手动导入相机参数，如果使用Amov的G1等吊舱或相机，则可以忽略该步骤，将自动下载相机参数文件
-  cld.loadCameraParams(sv::get_home() + "/SpireCV/confs/calib_webcam_1280x720.yaml");
-  cld.loadAlgorithmParams(sv::get_home() + "/SpireCV/confs/sv_algorithm_params.json");
+  cld.loadCameraParams(sv::get_home() + "/SpireCV/calib_webcam_640x480.yaml");
 
   // 打开摄像头
-  sv::Camera cap;
+  sv::Camera cap(sv::CameraType::WEBCAM);
+  cap.setIndex(0);
   cap.setWH(cld.image_width, cld.image_height);
-  cap.setFps(30);
-  cap.open(sv::CameraType::V4L2CAM, 0); // CameraID 0
+  // cap.setFps(30);
+  cap.open(); // CameraID 0
   // 实例化OpenCV的Mat类，用于内存单帧图像
   cv::Mat img;
   int frame_id = 0;

samples/demo/common_object_detection.cpp

@@ -2,6 +2,7 @@
 #include <string>
 // 包含SpireCV SDK头文件
 #include <sv_world.h>
+#include "sv_camera.h"
 
 using namespace std;
 
@@ -9,14 +10,14 @@ int main(int argc, char *argv[]) {
   // 实例化 通用目标 检测器类
   sv::CommonObjectDetector cod;
   // 手动导入相机参数，如果使用Amov的G1等吊舱或相机，则可以忽略该步骤，将自动下载相机参数文件
-  cod.loadCameraParams(sv::get_home() + "/SpireCV/confs/calib_webcam_1280x720.yaml");
-  cod.loadAlgorithmParams(sv::get_home() + "/SpireCV/confs/sv_algorithm_params.json");
+  cod.loadCameraParams(sv::get_home() + "/SpireCV/calib_webcam_640x480.yaml");
   
   // 打开摄像头
-  sv::Camera cap;
+  sv::Camera cap(sv::CameraType::WEBCAM);
+  cap.setIndex(0);
   cap.setWH(cod.image_width, cod.image_height);
-  cap.setFps(30);
-  cap.open(sv::CameraType::V4L2CAM, 0);  // CameraID 0
+  // cap.setFps(30);
+  cap.open();  // CameraID 0
   // 实例化OpenCV的Mat类，用于内存单帧图像
   cv::Mat img;
   int frame_id = 0;

samples/demo/detection_with_clicked_tracking.cpp

@@ -2,6 +2,7 @@
 #include <string>
 // 包含SpireCV SDK头文件
 #include <sv_world.h>
+#include "sv_camera.h"
 
 using namespace std;
 
@@ -40,20 +41,19 @@ int main(int argc, char *argv[]) {
   // 实例化 框选目标跟踪类
   sv::SingleObjectTracker sot;
   // 手动导入相机参数，如果使用Amov的G1等吊舱或相机，则可以忽略该步骤，将自动下载相机参数文件
-  sot.loadCameraParams(sv::get_home() + "/SpireCV/confs/calib_webcam_1280x720.yaml");
-  sot.loadAlgorithmParams(sv::get_home() + "/SpireCV/confs/sv_algorithm_params.json");
+  sot.loadCameraParams(sv::get_home() + "/SpireCV/calib_webcam_640x480.yaml");
 
 
   sv::CommonObjectDetector cod;
-  cod.loadCameraParams(sv::get_home() + "/SpireCV/confs/calib_webcam_1280x720.yaml");
-  cod.loadAlgorithmParams(sv::get_home() + "/SpireCV/confs/sv_algorithm_params.json");
+  cod.loadCameraParams(sv::get_home() + "/SpireCV/calib_webcam_640x480.yaml");
 
 
   // 打开摄像头
-  sv::Camera cap;
+  sv::Camera cap(sv::CameraType::WEBCAM);
+  cap.setIndex(0);
   cap.setWH(cod.image_width, cod.image_height);
-  cap.setFps(30);
-  cap.open(sv::CameraType::V4L2CAM, 0);  // CameraID 0
+  // cap.setFps(30);
+  cap.open();  // CameraID 0
 
   // 实例化OpenCV的Mat类，用于内存单帧图像
   cv::Mat img;

samples/demo/ellipse_detection.cpp

@@ -2,6 +2,7 @@
 #include <string>
 // 包含SpireCV SDK头文件
 #include <sv_world.h>
+#include "sv_camera.h"
 
 using namespace std;
 
@@ -9,14 +10,15 @@ int main(int argc, char *argv[]) {
   // 实例化 椭圆 检测器类
   sv::EllipseDetector ed;
   // 手动导入相机参数，如果使用Amov的G1等吊舱或相机，则可以忽略该步骤，将自动下载相机参数文件
-  ed.loadCameraParams(sv::get_home() + "/SpireCV/confs/calib_webcam_1280x720.yaml");
-  ed.loadAlgorithmParams(sv::get_home() + "/SpireCV/confs/sv_algorithm_params.json");
+  ed.loadCameraParams(sv::get_home() + "/SpireCV/calib_webcam_640x480.yaml");
   
   // 打开摄像头
-  sv::Camera cap;
+  sv::Camera cap(sv::CameraType::WEBCAM);
+  cap.setIndex(0);
   cap.setWH(ed.image_width, ed.image_height);
-  cap.setFps(30);
-  cap.open(sv::CameraType::V4L2CAM, 0);  // CameraID 0
+  // cap.setFps(30);
+  cap.open();  // CameraID 0
+
   // 实例化OpenCV的Mat类，用于内存单帧图像
   cv::Mat img;
   int frame_id = 0;

samples/demo/eval_MOT_metric.cpp

@@ -1,76 +0,0 @@
-#include <iostream>
-#include <string>
-#include <experimental/filesystem>
-// 包含SpireCV SDK头文件
-#include <sv_world.h>
-
-using namespace std;
-namespace fs = std::experimental::filesystem;
-
-int main(int argc, char *argv[]) {
-  // 实例化
-  sv::CommonObjectDetector cod;
-  // 手动导入相机参数，如果使用Amov的G1等吊舱或相机，则可以忽略该步骤，将自动下载相机参数文件
-  cod.loadCameraParams(sv::get_home() + "/SpireCV/confs/calib_webcam_1280x720.yaml");
-  cod.loadAlgorithmParams(sv::get_home() + "/SpireCV/confs/sv_algorithm_params.json");
-  sv::MultipleObjectTracker mot;
-  // 手动导入相机参数，如果使用Amov的G1等吊舱或相机，则可以忽略该步骤，将自动下载相机参数文件
-  mot.loadCameraParams(sv::get_home() + "/SpireCV/confs/calib_webcam_1280x720.yaml");
-  mot.loadAlgorithmParams(sv::get_home() + "/SpireCV/confs/sv_algorithm_params.json");
-  mot.init(&cod);
-  
-  // 打开摄像头
-  /*
-  sv::Camera cap;
-  cap.setWH(mot.image_width, mot.image_height);
-  cap.setFps(30);
-  cap.open(sv::CameraType::V4L2CAM, 0);  // CameraID 0
-  */
-  std::string mot17_folder_path = sv::get_home()+"/SpireCV/dataset/MOT17/train/";
-  std::string pred_file_path = sv::get_home()+"/SpireCV/dataset/pred_mot17/data/";
-  for (auto & seq_path : std::experimental::filesystem::directory_iterator(mot17_folder_path))
-  { 
-    // mkdir pred dirs and touch pred_files
-    string pred_file = pred_file_path + seq_path.path().filename().string() + ".txt";
-    fs::create_directories(pred_file_path);
-    std::ofstream file(pred_file);
-    // listdir seqence images
-    string seq_image_paths = mot17_folder_path + seq_path.path().filename().string() + "/img1";
-    // cout << seq_image_paths <<endl;
-    std::vector<std::string> seq_image_file_path;
-    cv::glob(seq_image_paths, seq_image_file_path);
-
-    //eval MOT algorithms
-    cv::Mat img;
-    int frame_id = 0;
-    while (frame_id < seq_image_file_path.size())
-    {
-      img = cv::imread(seq_image_file_path[frame_id]);
-      // 实例化SpireCV的 单帧检测结果 接口类 TargetsInFrame
-      sv::TargetsInFrame tgts(frame_id++);
-      // 读取一帧图像到img
-      //cap.read(img);
-      //cv::resize(img, img, cv::Size(mot.image_width, mot.image_height));
-
-      // 执行通用目标检测
-      sv::TargetsInFrame person_tgts = mot.track(img, tgts);
-      // 可视化检测结果，叠加到img上
-      sv::drawTargetsInFrame(img, person_tgts);
-      // printf("  Frame Size (width, height) = (%d, %d)\n", tgts.width, tgts.height);
-      for (auto target : person_tgts.targets)
-      {
-          int center_x = int(target.cx * tgts.width);
-          int center_y = int(target.cy * tgts.height);
-          int width = int(target.w * tgts.width);
-          int height = int(target.h * tgts.height);
-          double conf = target.score;
-          file << frame_id << ","<< target.tracked_id << "," << center_x - width / 2 << "," << center_y - height / 2 << "," << width << "," << height << "," << conf << "," << "-1,-1,-1" << endl;
-          // file << frame_id << ","<< target.tracked_id << "," << center_x << "," << center_y << "," << width << "," << height << "," << conf << "," << "-1,-1,-1" << endl;
-      }
-      cv::imshow("img", img);
-      cv::waitKey(10);
-    }
-    file.close();
-  }
-  return 0;
-}

samples/demo/gimbal_detection_with_clicked_tracking.cpp

@@ -3,6 +3,7 @@
 // 包含SpireCV SDK头文件
 #include <sv_world.h>
 #include <map>
+#include "sv_camera.h"
 
 // 定义窗口名称
 static const std::string RGB_WINDOW = "Image window";
@@ -34,36 +35,44 @@ void gimbalNoTrack(void)
 
 int main(int argc, char *argv[])
 {
+  // 实例化 框选目标跟踪类
+  sv::SingleObjectTracker sot;
+  // 手动导入相机参数，如果使用Amov的G1等吊舱或相机，则可以忽略该步骤，将自动下载相机参数文件
+  sot.loadCameraParams(sv::get_home() + "/SpireCV/calib_webcam_1280x720.yaml");
+
+  sv::CommonObjectDetector cod;
+  cod.loadCameraParams(sv::get_home() + "/SpireCV/calib_webcam_1280x720.yaml");
+
   // 实例化吊舱
   gimbal = new sv::Gimbal(sv::GimbalType::G1, sv::GimbalLink::SERIAL);
   // 使用 /dev/ttyUSB0 作为控制串口
   gimbal->setSerialPort("/dev/ttyUSB0");
   // 以GimableCallback作为状态回调函数启用吊舱控制
   gimbal->open(GimableCallback);
-  // 定义相机
-  sv::Camera cap;
-  // 设置相机流媒体地址为 192.168.2.64
-  cap.setIp("192.168.2.64");
-  // 设置获取画面分辨率为720P
-  cap.setWH(1280, 720);
-  // 设置视频帧率为30帧
+
+  // 打开摄像头
+  sv::Camera cap(sv::CameraType::G1);
+  cap.setStream("192.168.2.64",554);
+  cap.setWH(cod.image_width, cod.image_height);
   cap.setFps(30);
-  // 开启相机
-  cap.open(sv::CameraType::G1);
+  cap.open();  // CameraID 0
+
+  // // 定义相机
+  // sv::Camera cap;
+  // // 设置相机流媒体地址为 192.168.2.64
+  // cap.setIp("192.168.2.64");
+  // // 设置获取画面分辨率为720P
+  // cap.setWH(1280, 720);
+  // // 设置视频帧率为30帧
+  // cap.setFps(30);
+  // // 开启相机
+  // cap.open(sv::CameraType::G1);
 
   // 定义一个新的窗口，可在上面进行框选操作
   cv::namedWindow(RGB_WINDOW);
   // 设置窗口操作回调函数，该函数实现整个框选逻辑
   cv::setMouseCallback(RGB_WINDOW, onMouse, 0);
-  // 实例化 框选目标跟踪类
-  sv::SingleObjectTracker sot;
-  // 手动导入相机参数，如果使用Amov的G1等吊舱或相机，则可以忽略该步骤，将自动下载相机参数文件
-  sot.loadCameraParams(sv::get_home() + "/SpireCV/confs/calib_webcam_1280x720.yaml");
-  sot.loadAlgorithmParams(sv::get_home() + "/SpireCV/confs/sv_algorithm_params.json");
 
-  sv::CommonObjectDetector cod;
-  cod.loadCameraParams(sv::get_home() + "/SpireCV/confs/calib_webcam_1280x720.yaml");
-  cod.loadAlgorithmParams(sv::get_home() + "/SpireCV/confs/sv_algorithm_params.json");
 
   // 实例化OpenCV的Mat类，用于内存单帧图像
   cv::Mat img;
@@ -258,4 +267,4 @@ void GimableCallback(double &frame_ang_r, double &frame_ang_p, double &frame_ang
     count = 0;
   }
   count++;
-}
+}

samples/demo/gimbal_landing_marker_detection.cpp

@@ -3,6 +3,7 @@
 // 包含SpireCV SDK头文件
 #include <sv_world.h>
 // #include "gimbal_tools.hpp"
+#include "sv_camera.h"
 
 using namespace std;
 
@@ -33,33 +34,40 @@ void gimbalNoTrack(void)
 }
 
 int main(int argc, char *argv[])
-{
+{ 
+  // 实例化 圆形降落标志 检测器类
+  sv::LandingMarkerDetector lmd;
+  // 手动导入相机参数，如果使用Amov的G1等吊舱或相机，则可以忽略该步骤，将自动下载相机参数文件
+  lmd.loadCameraParams(sv::get_home() + "/SpireCV/calib_webcam_1280x720.yaml");
   // 实例化吊舱
   gimbal = new sv::Gimbal(sv::GimbalType::G1, sv::GimbalLink::SERIAL);
   // 使用 /dev/ttyUSB0 作为控制串口
   gimbal->setSerialPort("/dev/ttyUSB0");
   // 以GimableCallback作为状态回调函数启用吊舱控制
   gimbal->open(GimableCallback);
-  // 定义相机
-  sv::Camera cap;
-  // 设置相机流媒体地址为 192.168.2.64
-  cap.setIp("192.168.2.64");
-  // 设置获取画面分辨率为720P
-  cap.setWH(1280, 720);
-  // 设置视频帧率为30帧
+
+  // 打开摄像头
+  sv::Camera cap(sv::CameraType::G1);
+  cap.setStream("192.168.2.64", 554);
+  cap.setWH(lmd.image_width, lmd.image_height);
   cap.setFps(30);
-  // 开启相机
-  cap.open(sv::CameraType::G1);
+  cap.open(); // CameraID 0
+
+  // // 定义相机
+  // sv::Camera cap;
+  // // 设置相机流媒体地址为 192.168.2.64
+  // cap.setIp("192.168.2.64");
+  // // 设置获取画面分辨率为720P
+  // cap.setWH(1280, 720);
+  // // 设置视频帧率为30帧
+  // cap.setFps(30);
+  // // 开启相机
+  // cap.open(sv::CameraType::G1);
 
   // 定义一个窗口 才可以在上面操作
   cv::namedWindow(RGB_WINDOW);
   // 设置窗口操作回调函数，该函数实现吊舱控制
   cv::setMouseCallback(RGB_WINDOW, onMouse, 0);
-  // 实例化 圆形降落标志 检测器类
-  sv::LandingMarkerDetector lmd;
-  // 手动导入相机参数，如果使用Amov的G1等吊舱或相机，则可以忽略该步骤，将自动下载相机参数文件
-  lmd.loadCameraParams(sv::get_home() + "/SpireCV/confs/calib_webcam_1280x720.yaml");
-  lmd.loadAlgorithmParams(sv::get_home() + "/SpireCV/confs/sv_algorithm_params.json");
 
   // 实例化OpenCV的Mat类，用于内存单帧图像
   cv::Mat img;
@@ -145,4 +153,4 @@ void GimableCallback(double &frame_ang_r, double &frame_ang_p, double &frame_ang
     count = 0;
   }
   count++;
-}
+}

samples/demo/gimbal_udp_detection_info_sender.cpp

@@ -2,6 +2,7 @@
 #include <string>
 // 包含SpireCV SDK头文件
 #include <sv_world.h>
+#include "sv_camera.h"
 
 using namespace std;
 
@@ -42,33 +43,41 @@ static const std::string RGB_WINDOW = "Image window";
 void onMouse(int event, int x, int y, int, void *);
 
 int main(int argc, char *argv[])
-{
+{  
+  // 实例化Aruco检测器类
+  sv::ArucoDetector ad;
+  // 手动导入相机参数，如果使用Amov的G1等吊舱或相机，则可以忽略该步骤，将自动下载相机参数文件
+  ad.loadCameraParams(sv::get_home() + "/SpireCV/calib_webcam_1280x720.yaml");
   // 实例化吊舱
   gimbal = new sv::Gimbal(sv::GimbalType::G1, sv::GimbalLink::SERIAL);
   // 使用 /dev/ttyUSB0 作为控制串口
   gimbal->setSerialPort("/dev/ttyUSB0");
   // 以gimableCallback作为状态回调函数启用吊舱控制
   gimbal->open(gimableCallback);
-  // 定义相机
-  sv::Camera cap;
-  // 设置相机流媒体地址为 192.168.2.64
-  cap.setIp("192.168.2.64");
-  // 设置获取画面分辨率为720P
-  cap.setWH(1280, 720);
-  // 设置视频帧率为30帧
+
+  // 打开摄像头
+  sv::Camera cap(sv::CameraType::G1);
+  cap.setStream("192.168.2.64",554);
+  cap.setWH(ad.image_width, ad.image_height);
   cap.setFps(30);
-  // 开启相机
-  cap.open(sv::CameraType::G1);
+  cap.open();  // CameraID 0
+
+  // // 定义相机
+  // sv::Camera cap;
+  // // 设置相机流媒体地址为 192.168.2.64
+  // cap.setIp("192.168.2.64");
+  // // 设置获取画面分辨率为720P
+  // cap.setWH(1280, 720);
+  // // 设置视频帧率为30帧
+  // cap.setFps(30);
+  // // 开启相机
+  // cap.open(sv::CameraType::G1);
 
   // 定义一个窗口 才可以在上面操作
   cv::namedWindow(RGB_WINDOW);
   // 设置窗口操作回调函数，该函数实现吊舱控制
   cv::setMouseCallback(RGB_WINDOW, onMouse, 0);
-  // 实例化Aruco检测器类
-  sv::ArucoDetector ad;
-  // 手动导入相机参数，如果使用Amov的G1等吊舱或相机，则可以忽略该步骤，将自动下载相机参数文件
-  ad.loadCameraParams(sv::get_home() + "/SpireCV/confs/calib_webcam_1280x720.yaml");
-  ad.loadAlgorithmParams(sv::get_home() + "/SpireCV/confs/sv_algorithm_params.json");
+
 
   sv::UDPServer udp;
   // 实例化OpenCV的Mat类，用于内存单帧图像

samples/demo/landing_marker_detection.cpp

@@ -2,21 +2,22 @@
 #include <string>
 // 包含SpireCV SDK头文件
 #include <sv_world.h>
-
+#include "sv_camera.h"
 using namespace std;
 
 int main(int argc, char *argv[]) {
   // 实例化 圆形降落标志 检测器类
   sv::LandingMarkerDetector lmd;
   // 手动导入相机参数，如果使用Amov的G1等吊舱或相机，则可以忽略该步骤，将自动下载相机参数文件
-  lmd.loadCameraParams(sv::get_home() + "/SpireCV/confs/calib_webcam_1280x720.yaml");
-  lmd.loadAlgorithmParams(sv::get_home() + "/SpireCV/confs/sv_algorithm_params.json");
+  lmd.loadCameraParams(sv::get_home() + "/SpireCV/calib_webcam_640x480.yaml");
 
-  // 打开摄像头
-  sv::Camera cap;
+   // 打开摄像头
+  sv::Camera cap(sv::CameraType::WEBCAM);
+  cap.setIndex(0);
   cap.setWH(lmd.image_width, lmd.image_height);
-  cap.setFps(30);
-  cap.open(sv::CameraType::V4L2CAM, 0);  // CameraID 0
+  // cap.setFps(30);
+  cap.open();  // CameraID 0
+
   // 实例化OpenCV的Mat类，用于内存单帧图像
   cv::Mat img;
   int frame_id = 0;

samples/demo/multiple_object_tracking.cpp

@@ -2,6 +2,7 @@
 #include <string>
 // 包含SpireCV SDK头文件
 #include <sv_world.h>
+#include "sv_camera.h"
 
 using namespace std;
 
@@ -9,19 +10,19 @@ int main(int argc, char *argv[]) {
   // 实例化
   sv::CommonObjectDetector cod;
   // 手动导入相机参数，如果使用Amov的G1等吊舱或相机，则可以忽略该步骤，将自动下载相机参数文件
-  cod.loadCameraParams(sv::get_home() + "/SpireCV/confs/calib_webcam_1280x720.yaml");
-  cod.loadAlgorithmParams(sv::get_home() + "/SpireCV/confs/sv_algorithm_params.json");
+  cod.loadCameraParams(sv::get_home() + "/SpireCV/calib_webcam_640x480.yaml");
   sv::MultipleObjectTracker mot;
   // 手动导入相机参数，如果使用Amov的G1等吊舱或相机，则可以忽略该步骤，将自动下载相机参数文件
-  mot.loadCameraParams(sv::get_home() + "/SpireCV/confs/calib_webcam_1280x720.yaml");
-  mot.loadAlgorithmParams(sv::get_home() + "/SpireCV/confs/sv_algorithm_params.json");
+  mot.loadCameraParams(sv::get_home() + "/SpireCV/calib_webcam_640x480.yaml");
   mot.init(&cod);
   
-  // 打开摄像头
-  sv::Camera cap;
-  cap.setWH(mot.image_width, mot.image_height);
-  cap.setFps(30);
-  cap.open(sv::CameraType::V4L2CAM, 0);  // CameraID 0
+   // 打开摄像头
+  sv::Camera cap(sv::CameraType::WEBCAM);
+  cap.setIndex(0);
+  cap.setWH(cod.image_width, cod.image_height);
+  // cap.setFps(30);
+  cap.open();  // CameraID 0
+
   // 实例化OpenCV的Mat类，用于内存单帧图像
   cv::Mat img;
   int frame_id = 0;
@@ -34,9 +35,9 @@ int main(int argc, char *argv[]) {
     cv::resize(img, img, cv::Size(mot.image_width, mot.image_height));
 
     // 执行通用目标检测
-    sv::TargetsInFrame person_tgts = mot.track(img, tgts);
+    mot.track(img, tgts);
     // 可视化检测结果，叠加到img上
-    sv::drawTargetsInFrame(img, person_tgts);
+    sv::drawTargetsInFrame(img, tgts);
     
     // 显示检测结果img
     cv::imshow("img", img);

samples/demo/single_object_tracking.cpp

@@ -2,7 +2,7 @@
 #include <string>
 // 包含SpireCV SDK头文件
 #include <sv_world.h>
-
+#include "sv_camera.h"
 using namespace std;
 
 // 定义窗口名称
@@ -28,15 +28,17 @@ int main(int argc, char *argv[]) {
   // 实例化 框选目标跟踪类
   sv::SingleObjectTracker sot;
   // 手动导入相机参数，如果使用Amov的G1等吊舱或相机，则可以忽略该步骤，将自动下载相机参数文件
+  sot.loadCameraParams(sv::get_home() + "/SpireCV/calib_webcam_640x480.yaml");
+  // sot.loadCameraParams(sv::get_home() + "/SpireCV/calib_webcam_1280x720.yaml");
   // sot.loadCameraParams(sv::get_home() + "/SpireCV/calib_webcam_1920x1080.yaml");
-  sot.loadCameraParams(sv::get_home() + "/SpireCV/confs/calib_webcam_1280x720.yaml");
-  sot.loadAlgorithmParams(sv::get_home() + "/SpireCV/confs/sv_algorithm_params.json");
   
-  // 打开摄像头
-  sv::Camera cap;
+ // 打开摄像头
+  sv::Camera cap(sv::CameraType::WEBCAM);
+  cap.setIndex(0);
   cap.setWH(sot.image_width, sot.image_height);
-  cap.setFps(30);
-  cap.open(sv::CameraType::V4L2CAM, 0);  // CameraID 0
+  // cap.setFps(30);
+  cap.open();  // CameraID 0
+
   // cv::VideoCapture cap("/home/amov/SpireCV/test/tracking_1280x720.mp4");
   // 实例化OpenCV的Mat类，用于内存单帧图像
   cv::Mat img;

samples/demo/udp_detection_info_receiver.cpp

@@ -5,7 +5,7 @@
 #include <stdio.h>
 #define SERV_PORT 20166
 
-
+typedef unsigned char byte;
 using namespace std;
 
 
@@ -23,9 +23,9 @@ int main(int argc, char *argv[]) {
 
 
   int upd_msg_len = 1024 * 6;  // max_objects = 100
-  unsigned char upd_msg[upd_msg_len];
+  byte upd_msg[upd_msg_len];
   int msg_queue_len = 1024 * 1024;  // 1M
-  unsigned char msg_queue[msg_queue_len];
+  byte msg_queue[msg_queue_len];
 
   int addr_len = sizeof(struct sockaddr_in);
   int start_index = 0, end_index = 0;
@@ -63,21 +63,21 @@ cout << n << ", " << start_index << ", " << end_index << endl;
           if (end_index - i >= ilen + 2 && msg_queue[i+ilen] == 0xFB && msg_queue[i+ilen+1] == 0xFD)
           {
             cout << "FOUND 0xFAFC & 0xFBFD" << endl;
-            unsigned char* msg_type = reinterpret_cast<unsigned char*>(&msg_queue[i+2]);
+            byte* msg_type = reinterpret_cast<byte*>(&msg_queue[i+2]);
             cout << "Type: " << (int) *msg_type << endl;
             unsigned short* year = reinterpret_cast<unsigned short*>(&msg_queue[i+7]);
-            unsigned char* month = reinterpret_cast<unsigned char*>(&msg_queue[i+9]);
-            unsigned char* day = reinterpret_cast<unsigned char*>(&msg_queue[i+10]);
-            unsigned char* hour = reinterpret_cast<unsigned char*>(&msg_queue[i+11]);
-            unsigned char* minute = reinterpret_cast<unsigned char*>(&msg_queue[i+12]);
-            unsigned char* second = reinterpret_cast<unsigned char*>(&msg_queue[i+13]);
+            byte* month = reinterpret_cast<byte*>(&msg_queue[i+9]);
+            byte* day = reinterpret_cast<byte*>(&msg_queue[i+10]);
+            byte* hour = reinterpret_cast<byte*>(&msg_queue[i+11]);
+            byte* minute = reinterpret_cast<byte*>(&msg_queue[i+12]);
+            byte* second = reinterpret_cast<byte*>(&msg_queue[i+13]);
             unsigned short* millisecond = reinterpret_cast<unsigned short*>(&msg_queue[i+14]);
             cout << "Time: " << *year << "-" << (int) *month << "-" << (int) *day << " " << (int) *hour << ":" << (int) *minute << ":" << (int) *second << " " << *millisecond << endl;
             
-            unsigned char* index_d1 = reinterpret_cast<unsigned char*>(&msg_queue[i+16]);
-            unsigned char* index_d2 = reinterpret_cast<unsigned char*>(&msg_queue[i+17]);
-            unsigned char* index_d3 = reinterpret_cast<unsigned char*>(&msg_queue[i+18]);
-            unsigned char* index_d4 = reinterpret_cast<unsigned char*>(&msg_queue[i+19]);
+            byte* index_d1 = reinterpret_cast<byte*>(&msg_queue[i+16]);
+            byte* index_d2 = reinterpret_cast<byte*>(&msg_queue[i+17]);
+            byte* index_d3 = reinterpret_cast<byte*>(&msg_queue[i+18]);
+            byte* index_d4 = reinterpret_cast<byte*>(&msg_queue[i+19]);
             int mp = i+20;
             if ((*index_d4) & 0x01 == 0x01)
             {
@@ -152,13 +152,13 @@ cout << n << ", " << start_index << ", " << end_index << endl;
             }
             for (int j=0; j<n_objects; j++)
             {
-              unsigned char* index_f1 = reinterpret_cast<unsigned char*>(&msg_queue[mp]);
+              byte* index_f1 = reinterpret_cast<byte*>(&msg_queue[mp]);
               mp++;
-              unsigned char* index_f2 = reinterpret_cast<unsigned char*>(&msg_queue[mp]);
+              byte* index_f2 = reinterpret_cast<byte*>(&msg_queue[mp]);
               mp++;
-              unsigned char* index_f3 = reinterpret_cast<unsigned char*>(&msg_queue[mp]);
+              byte* index_f3 = reinterpret_cast<byte*>(&msg_queue[mp]);
               mp++;
-              unsigned char* index_f4 = reinterpret_cast<unsigned char*>(&msg_queue[mp]);
+              byte* index_f4 = reinterpret_cast<byte*>(&msg_queue[mp]);
               mp++;
               if ((*index_f4) & 0x01 == 0x01 && (*index_f4) & 0x02 == 0x02)
               {

samples/demo/udp_detection_info_sender.cpp

@@ -2,20 +2,21 @@
 #include <string>
 // 包含SpireCV SDK头文件
 #include <sv_world.h>
-
+#include "sv_camera.h"
 using namespace std;
 
 int main(int argc, char *argv[]) {
   // 实例化Aruco检测器类
   sv::ArucoDetector ad;
   // 手动导入相机参数，如果使用Amov的G1等吊舱或相机，则可以忽略该步骤，将自动下载相机参数文件
-  ad.loadCameraParams("/home/amov/SpireCV/calib_webcam_1280x720.yaml");
+  ad.loadCameraParams("/home/amov/SpireCV/calib_webcam_640x480.yaml");
   
-  // 打开摄像头
-  sv::Camera cap;
+ // 打开摄像头
+  sv::Camera cap(sv::CameraType::WEBCAM);
+  cap.setIndex(0);
   cap.setWH(ad.image_width, ad.image_height);
-  cap.setFps(30);
-  cap.open(sv::CameraType::V4L2CAM, 0);  // CameraID 0
+  // cap.setFps(30);
+  cap.open();  // CameraID 0
 
   sv::UDPServer udp;
   // 实例化OpenCV的Mat类，用于内存单帧图像

samples/demo/veri.cpp

@@ -2,7 +2,7 @@
 #include <string>
 // 包含SpireCV SDK头文件
 #include <sv_world.h>
-
+#include "sv_camera.h"
 using namespace std;
 
 struct node
@@ -20,18 +20,17 @@ int main(int argc, char *argv[])
   // 实例化 框选目标跟踪类
   sv::VeriDetector veri;
   // 手动导入相机参数，如果使用Amov的G1等吊舱或相机，则可以忽略该步骤，将自动下载相机参数文件
-  veri.loadCameraParams(sv::get_home() + "/SpireCV/confs/calib_webcam_1280x720.yaml");
-  veri.loadAlgorithmParams(sv::get_home() + "/SpireCV/confs/sv_algorithm_params.json");
+  veri.loadCameraParams(sv::get_home() + "/SpireCV/calib_webcam_640x480.yaml");
 
   sv::CommonObjectDetector cod;
-  cod.loadCameraParams(sv::get_home() + "/SpireCV/confs/calib_webcam_1280x720.yaml");
-  cod.loadAlgorithmParams(sv::get_home() + "/SpireCV/confs/sv_algorithm_params.json");
+  cod.loadCameraParams(sv::get_home() + "/SpireCV/calib_webcam_640x480.yaml");
 
-  // 打开摄像头
-  sv::Camera cap;
+ // 打开摄像头
+  sv::Camera cap(sv::CameraType::WEBCAM);
+  cap.setIndex(0);
   cap.setWH(cod.image_width, cod.image_height);
-  cap.setFps(30);
-  cap.open(sv::CameraType::V4L2CAM, 0); // CameraID 0
+  // cap.setFps(30);
+  cap.open();  // CameraID 0
   // 实例化OpenCV的Mat类，用于内存单帧图像
   cv::Mat img;
   int frame_id = 0;

samples/demo/video_saving.cpp

@@ -2,21 +2,22 @@
 #include <string>
 // 包含SpireCV SDK头文件
 #include <sv_world.h>
-
+#include "sv_camera.h"
 using namespace std;
 
 int main(int argc, char *argv[]) {
   // 实例化 通用目标 检测器类
   sv::CommonObjectDetector cod;
   // 手动导入相机参数，如果使用Amov的G1等吊舱或相机，则可以忽略该步骤，将自动下载相机参数文件
-  cod.loadCameraParams(sv::get_home() + "/SpireCV/confs/calib_webcam_1280x720.yaml");
-  cod.loadAlgorithmParams(sv::get_home() + "/SpireCV/confs/sv_algorithm_params.json");
+  cod.loadCameraParams("/home/amov/SpireCV/calib_webcam_640x480.yaml");
   
   // 打开摄像头
-  sv::Camera cap;
+ // 打开摄像头
+  sv::Camera cap(sv::CameraType::WEBCAM);
+  cap.setIndex(0);
   cap.setWH(cod.image_width, cod.image_height);
-  cap.setFps(30);
-  cap.open(sv::CameraType::V4L2CAM, 0);  // CameraID 0
+  // cap.setFps(30);
+  cap.open();  // CameraID 0
   // 实例化OpenCV的Mat类，用于内存单帧图像
   cv::Mat img;
   int frame_id = 0;
@@ -24,7 +25,7 @@ int main(int argc, char *argv[]) {
   // 实例化视频保存类
   sv::VideoWriter vw;
   // 设置保存路径"/home/amov/Videos"，保存图像尺寸（640，480），帧频25Hz，同步保存检测结果（.svj）
-  vw.setup(sv::get_home() + "/Videos", cv::Size(cod.image_width, cod.image_height), 25, true);
+  vw.setup("/home/amov/Videos", cv::Size(640, 480), 25, true);
 
   while (1)
   {
@@ -32,7 +33,7 @@ int main(int argc, char *argv[]) {
     sv::TargetsInFrame tgts(frame_id++);
     // 读取一帧图像到img
     cap.read(img);
-    cv::resize(img, img, cv::Size(cod.image_width, cod.image_height));
+    cv::resize(img, img, cv::Size(640, 480));
 
     // 执行通用目标检测
     cod.detect(img, tgts);

samples/demo/video_streaming.cpp

@@ -2,15 +2,17 @@
 #include <string>
 // 包含SpireCV SDK头文件
 #include <sv_world.h>
+#include "sv_camera.h"
 
 using namespace std;
 
 int main(int argc, char *argv[]) {
-  // 打开摄像头
-  sv::Camera cap;
-  // cap.setWH(1280, 720);
-  // cap.setFps(30);
-  cap.open(sv::CameraType::WEBCAM, 0);  // CameraID 0
+ // 打开摄像头
+  sv::Camera cap(sv::CameraType::WEBCAM);
+  cap.setIndex(0);
+  cap.setWH(1280, 720);
+  cap.setFps(30);
+  cap.open();  // CameraID 0
 
   // 实例化视频推流类sv::VideoStreamer
   sv::VideoStreamer streamer;

samples/test/gimbal_test.cpp

@@ -3,6 +3,7 @@
 // 包含SpireCV SDK头文件
 #include <sv_world.h>
 #include <chrono>
+#include "sv_camera.h"
 
 // yaw roll pitch
 double gimbalEulerAngle[3];
@@ -143,14 +144,20 @@ int main(int argc, char *argv[])
     std::cout << " pass... " << std::endl;
     std::cout << " start image test " << std::endl;
 
-    sv::Camera cap;
-    cap.setIp(argv[2]);
+    // 打开摄像头
+    sv::Camera cap(sv::CameraType::G1);
+    cap.setStream(argv[2], 554);
     cap.setWH(1280, 720);
     cap.setFps(30);
+    cap.open(); // CameraID 0
 
-    cap.open(sv::CameraType::G1);
+    // sv::Camera cap;
+    // cap.setIp(argv[2]);
+    // cap.setWH(1280, 720);
+    // cap.setFps(30);
+    // cap.open(sv::CameraType::G1);
 
-    if (!cap.isRunning())
+    if (!cap.isActive())
     {
         std::cout << " gimbal image error , failed !!!!!" << std::endl;
         return -1;

samples/test/mot_metrics/eval_mot_metric_gt.sh

@@ -1,9 +0,0 @@
-python3 run_mot_challenge.py \
---BENCHMARK MOT17 \
---GT_FOLDER ../../../dataset/MOT17/train \
---TRACKERS_FOLDER ../../../dataset/pred_mot17 \
---SKIP_SPLIT_FOL True \
---SEQMAP_FILE ../../../dataset/MOT17/val_seqmap.txt \
---TRACKERS_TO_EVAL '' \
---METRICS HOTA CLEAR Identity \
-#--show

samples/test/mot_metrics/run_mot_challenge.py

@@ -1,93 +0,0 @@
-
-""" run_mot_challenge.py
-
-Run example:
-run_mot_challenge.py --USE_PARALLEL False --METRICS Hota --TRACKERS_TO_EVAL Lif_T
-
-Command Line Arguments: Defaults, # Comments
-    Eval arguments:
-        'USE_PARALLEL': False,
-        'NUM_PARALLEL_CORES': 8,
-        'BREAK_ON_ERROR': True,
-        'PRINT_RESULTS': True,
-        'PRINT_ONLY_COMBINED': False,
-        'PRINT_CONFIG': True,
-        'TIME_PROGRESS': True,
-        'OUTPUT_SUMMARY': True,
-        'OUTPUT_DETAILED': True,
-        'PLOT_CURVES': True,
-    Dataset arguments:
-        'GT_FOLDER': os.path.join(code_path, 'data/gt/mot_challenge/'),  # Location of GT data
-        'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/mot_challenge/'),  # Trackers location
-        'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)
-        'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)
-        'CLASSES_TO_EVAL': ['pedestrian'],  # Valid: ['pedestrian']
-        'BENCHMARK': 'MOT17',  # Valid: 'MOT17', 'MOT16', 'MOT20', 'MOT15'
-        'SPLIT_TO_EVAL': 'train',  # Valid: 'train', 'test', 'all'
-        'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped
-        'PRINT_CONFIG': True,  # Whether to print current config
-        'DO_PREPROC': True,  # Whether to perform preprocessing (never done for 2D_MOT_2015)
-        'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER
-        'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER
-    Metric arguments:
-        'METRICS': ['HOTA', 'CLEAR', 'Identity', 'VACE']
-"""
-
-import sys
-import os
-import argparse
-from multiprocessing import freeze_support
-
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-import trackeval  # noqa: E402
-
-if __name__ == '__main__':
-    freeze_support()
-
-    # Command line interface:
-    default_eval_config = trackeval.Evaluator.get_default_eval_config()
-    default_eval_config['DISPLAY_LESS_PROGRESS'] = False
-    default_dataset_config = trackeval.datasets.MotChallenge2DBox.get_default_dataset_config()
-    default_metrics_config = {'METRICS': ['HOTA', 'CLEAR', 'Identity'], 'THRESHOLD': 0.5}
-    config = {**default_eval_config, **default_dataset_config, **default_metrics_config}  # Merge default configs
-    parser = argparse.ArgumentParser()
-    for setting in config.keys():
-        if type(config[setting]) == list or type(config[setting]) == type(None):
-            parser.add_argument("--" + setting, nargs='+')
-        else:
-            parser.add_argument("--" + setting)
-    args = parser.parse_args().__dict__
-    args['SEQMAP_FILE'] = args['SEQMAP_FILE'][0]
-    # args['SEQ_INFO']={'MOT17-02-DPM':600}
-    for setting in args.keys():
-        if args[setting] is not None:
-            if type(config[setting]) == type(True):
-                if args[setting] == 'True':
-                    x = True
-                elif args[setting] == 'False':
-                    x = False
-                else:
-                    raise Exception('Command line parameter ' + setting + 'must be True or False')
-            elif type(config[setting]) == type(1):
-                x = int(args[setting])
-            elif type(args[setting]) == type(None):
-                x = None
-            elif setting == 'SEQ_INFO':
-                x = dict(zip(args[setting], [None]*len(args[setting])))
-            else:
-                x = args[setting]
-            config[setting] = x
-    eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()}
-    dataset_config = {k: v for k, v in config.items() if k in default_dataset_config.keys()}
-    metrics_config = {k: v for k, v in config.items() if k in default_metrics_config.keys()}
-
-    # Run code
-    evaluator = trackeval.Evaluator(eval_config)
-    dataset_list = [trackeval.datasets.MotChallenge2DBox(dataset_config)]
-    metrics_list = []
-    for metric in [trackeval.metrics.HOTA, trackeval.metrics.CLEAR, trackeval.metrics.Identity, trackeval.metrics.VACE]:
-        if metric.get_name() in metrics_config['METRICS']:
-            metrics_list.append(metric(metrics_config))
-    if len(metrics_list) == 0:
-        raise Exception('No metrics selected for evaluation')
-    evaluator.evaluate(dataset_list, metrics_list)

samples/test/mot_metrics/write_val_seqmap.py

@@ -1,23 +0,0 @@
-import os
-import os.path as osp
-
-
-
-def makedirs(path):
-	if not osp.exists(path):
-		os.makedirs(path)
-	else:
-		print('this file is already exists.')
-
-
-if __name__=="__main__":
-	dst_path = '/home/bitwrj/SpireCV/dataset/MOT17'
-	mode = 'train'
-	dst_file_name = 'val_seqmap.txt'
-	dst_file = osp.join(dst_path,dst_file_name)
-	test_files = os.listdir(osp.join(dst_path,mode))
-	test_files.sort()
-	with open(dst_file, 'w+') as f:
-		f.writelines('name\n')
-		for test_file in test_files:
-			f.writelines(test_file+'\n')

scripts/common/ffmpeg425-install.sh

@@ -17,7 +17,6 @@ cd nv-codec-headers
 git checkout n11.1.5.0
 sudo make install
 cd ..
-sudo rm -r nv-codec-headers
 
 wget https://ffmpeg.org/releases/ffmpeg-4.2.5.tar.bz2
 tar -xjf ffmpeg-4.2.5.tar.bz2

scripts/common/models-downloading.sh

@@ -3,6 +3,12 @@
 root_dir=${HOME}"/SpireCV/models"
 root_server="https://download.amovlab.com/model"
 
+sv_params1=${HOME}"/SpireCV/sv_algorithm_params.json"
+sv_params2=${HOME}"/SpireCV/sv_algorithm_params_coco_640.json"
+sv_params3=${HOME}"/SpireCV/sv_algorithm_params_coco_1280.json"
+camera_params1=${HOME}"/SpireCV/calib_webcam_640x480.yaml"
+camera_params2=${HOME}"/SpireCV/calib_webcam_1280x720.yaml"
+
 coco_model1="COCO-yolov5s.wts"
 coco_model2="COCO-yolov5s6.wts"
 coco_model3="COCO-yolov5s-seg.wts"
@@ -40,6 +46,28 @@ if [ ! -d ${root_dir} ]; then
   mkdir -p ${root_dir}
 fi
 
+if [ ! -f ${sv_params1} ]; then
+  echo -e "\033[32m[INFO]: ${sv_params1} not exist, downloading ... \033[0m"
+  wget -O ${sv_params1} ${root_server}/install/a-params/sv_algorithm_params.json
+fi
+if [ ! -f ${sv_params2} ]; then
+  echo -e "\033[32m[INFO]: ${sv_params2} not exist, downloading ... \033[0m"
+  wget -O ${sv_params2} ${root_server}/install/a-params/sv_algorithm_params_coco_640.json
+fi
+if [ ! -f ${sv_params3} ]; then
+  echo -e "\033[32m[INFO]: ${sv_params3} not exist, downloading ... \033[0m"
+  wget -O ${sv_params3} ${root_server}/install/a-params/sv_algorithm_params_coco_1280.json
+fi
+
+if [ ! -f ${camera_params1} ]; then
+  echo -e "\033[32m[INFO]: ${camera_params1} not exist, downloading ... \033[0m"
+  wget -O ${camera_params1} ${root_server}/install/c-params/calib_webcam_640x480.yaml
+fi
+if [ ! -f ${camera_params2} ]; then
+  echo -e "\033[32m[INFO]: ${camera_params2} not exist, downloading ... \033[0m"
+  wget -O ${camera_params2} ${root_server}/install/c-params/calib_webcam_1280x720.yaml
+fi
+
 if [ ! -f ${coco_model1_fn} ]; then
   echo -e "\033[32m[INFO]: ${coco_model1_fn} not exist, downloading ... \033[0m"
   wget -O ${coco_model1_fn} ${root_server}/install/${coco_model1}

scripts/jetson/configs-downloading.sh

@@ -1,39 +0,0 @@
-#!/bin/bash -e
-
-root_dir=${HOME}"/SpireCV/models"
-root_server="https://download.amovlab.com/model"
-
-sv_params1=${HOME}"/SpireCV/sv_algorithm_params.json"
-sv_params2=${HOME}"/SpireCV/sv_algorithm_params_coco_640.json"
-sv_params3=${HOME}"/SpireCV/sv_algorithm_params_coco_1280.json"
-camera_params1=${HOME}"/SpireCV/calib_webcam_640x480.yaml"
-camera_params2=${HOME}"/SpireCV/calib_webcam_1280x720.yaml"
-
-
-if [ ! -d ${root_dir} ]; then
-  echo -e "\033[32m[INFO]: ${root_dir} not exist, creating it ... \033[0m"
-  mkdir -p ${root_dir}
-fi
-
-if [ ! -f ${sv_params1} ]; then
-  echo -e "\033[32m[INFO]: ${sv_params1} not exist, downloading ... \033[0m"
-  wget -O ${sv_params1} ${root_server}/install/a-params/sv_algorithm_params.json
-fi
-if [ ! -f ${sv_params2} ]; then
-  echo -e "\033[32m[INFO]: ${sv_params2} not exist, downloading ... \033[0m"
-  wget -O ${sv_params2} ${root_server}/install/a-params/sv_algorithm_params_coco_640.json
-fi
-if [ ! -f ${sv_params3} ]; then
-  echo -e "\033[32m[INFO]: ${sv_params3} not exist, downloading ... \033[0m"
-  wget -O ${sv_params3} ${root_server}/install/a-params/sv_algorithm_params_coco_1280.json
-fi
-
-if [ ! -f ${camera_params1} ]; then
-  echo -e "\033[32m[INFO]: ${camera_params1} not exist, downloading ... \033[0m"
-  wget -O ${camera_params1} ${root_server}/install/c-params/calib_webcam_640x480.yaml
-fi
-if [ ! -f ${camera_params2} ]; then
-  echo -e "\033[32m[INFO]: ${camera_params2} not exist, downloading ... \033[0m"
-  wget -O ${camera_params2} ${root_server}/install/c-params/calib_webcam_1280x720.yaml
-fi
-

scripts/jetson/gst-install-orin.sh

@@ -1,24 +0,0 @@
-#!/bin/sh
-
-sudo apt install -y libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev
-sudo apt install -y libgstreamer-plugins-bad1.0-dev gstreamer1.0-plugins-base
-sudo apt install -y gstreamer1.0-plugins-good gstreamer1.0-plugins-bad 
-sudo apt install -y gstreamer1.0-plugins-ugly gstreamer1.0-libav gstreamer1.0-doc
-sudo apt install -y gstreamer1.0-tools gstreamer1.0-x gstreamer1.0-alsa
-sudo apt install -y gstreamer1.0-gl gstreamer1.0-gtk3 gstreamer1.0-qt5 
-sudo apt install -y gstreamer1.0-pulseaudio
-sudo apt install -y gtk-doc-tools
-sudo apt install -y libeigen3-dev libfmt-dev v4l-utils
-
-sudo apt -y install autotools-dev automake m4 perl
-sudo apt -y install libtool
-autoreconf -ivf
-
-git clone https://gitee.com/jario-jin/gst-rtsp-server-b18.git
-cd gst-rtsp-server-b18
-./autogen.sh
-make
-sudo make install
-cd ..
-sudo rm -r gst-rtsp-server-b18
-

scripts/jetson/gst-install.sh

@@ -1,20 +0,0 @@
-#!/bin/sh
-
-sudo apt install -y libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev
-sudo apt install -y libgstreamer-plugins-bad1.0-dev gstreamer1.0-plugins-base
-sudo apt install -y gstreamer1.0-plugins-good gstreamer1.0-plugins-bad 
-sudo apt install -y gstreamer1.0-plugins-ugly gstreamer1.0-libav gstreamer1.0-doc
-sudo apt install -y gstreamer1.0-tools gstreamer1.0-x gstreamer1.0-alsa
-sudo apt install -y gstreamer1.0-gl gstreamer1.0-gtk3 gstreamer1.0-qt5 
-sudo apt install -y gstreamer1.0-pulseaudio
-sudo apt install -y gtk-doc-tools
-sudo apt install -y libeigen3-dev libfmt-dev v4l-utils
-
-git clone https://gitee.com/jario-jin/gst-rtsp-server-b18.git
-cd gst-rtsp-server-b18
-./autogen.sh
-make
-sudo make install
-cd ..
-sudo rm -r gst-rtsp-server-b18
-

scripts/jetson/opencv470-install.sh

@@ -1,70 +0,0 @@
-#!/bin/sh
-
-
-wget https://download.amovlab.com/model/deps/opencv-4.7.0.zip
-wget https://download.amovlab.com/model/deps/opencv_contrib-4.7.0.zip
-wget https://download.amovlab.com/model/deps/opencv_cache_x86-4.7.0.zip
-
-current_dir=$(pwd)
-package_dir="."
-mkdir ~/opencv_build
-
-
-if [ ! -d ""$package_dir"" ];then
-  echo "\033[31m[ERROR]: $package_dir not exist!: \033[0m"
-  exit 1
-fi
-
-# sudo add-apt-repository "deb http://security.ubuntu.com/ubuntu xenial-security main"
-# sudo add-apt-repository "deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ xenial main multiverse restricted universe"
-sudo apt update
-sudo apt install -y build-essential
-sudo apt install -y cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
-
-sudo add-apt-repository "deb http://security.ubuntu.com/ubuntu xenial-security main"
-sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys 3B4FE6ACC0B21F32
-sudo apt update
-sudo apt install -y libjasper1 libjasper-dev
-
-sudo apt install -y python3-dev python3-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev
-sudo apt install -y libdc1394-22-dev
-sudo apt install -y libcurl4 build-essential pkg-config cmake libopenblas-dev libeigen3-dev \
-                    libtbb-dev libavcodec-dev libavformat-dev libgstreamer-plugins-base1.0-dev \
-                    libgstreamer1.0-dev libswscale-dev libgtk-3-dev libpng-dev libjpeg-dev \
-                    libcanberra-gtk-module libcanberra-gtk3-module
-
-
-echo "\033[32m[INFO]:\033[0m unzip opencv-4.7.0.zip ..."
-unzip  -q -o $package_dir/opencv-4.7.0.zip -d ~/opencv_build
-
-echo "\033[32m[INFO]:\033[0m unzip opencv_contrib-4.7.0.zip ..."
-unzip  -q -o $package_dir/opencv_contrib-4.7.0.zip -d ~/opencv_build
-
-echo "\033[32m[INFO]:\033[0m unzip opencv_cache_x86-4.7.0.zip ..."
-unzip  -q -o $package_dir/opencv_cache_x86-4.7.0.zip -d ~/opencv_build
-
-
-sudo rm opencv-4.7.0.zip
-sudo rm opencv_contrib-4.7.0.zip
-sudo rm opencv_cache_x86-4.7.0.zip
-
-cd ~/opencv_build/opencv-4.7.0
-mkdir .cache
-
-cp -r ~/opencv_build/opencv_cache_x86-4.7.0/* ~/opencv_build/opencv-4.7.0/.cache/
-
-mkdir build
-cd build
-
-cmake -D CMAKE_BUILD_TYPE=Release \
-      -D WITH_CUDA=OFF \
-      -D OPENCV_ENABLE_NONFREE=ON \
-      -D CMAKE_INSTALL_PREFIX=/usr/local \
-      -D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib-4.7.0/modules ..
-
-make -j2
-sudo make install
-
-cd
-sudo rm -r ~/opencv_build
-cd ${current_dir}

scripts/jetson/opencv470-jetpack511-cuda-install.sh

@@ -5,7 +5,7 @@ wget https://download.amovlab.com/model/deps/opencv-4.7.0.zip
 wget https://download.amovlab.com/model/deps/opencv_contrib-4.7.0.zip
 wget https://download.amovlab.com/model/deps/opencv_cache-4.7.0.zip
 
-current_dir=$(pwd)
+
 package_dir="."
 mkdir ~/opencv_build
 
@@ -52,4 +52,3 @@ sudo make install
 
 cd
 sudo rm -r ~/opencv_build
-cd ${current_dir}

scripts/jetson/opencv470-jetpack511-install.sh

@@ -5,7 +5,7 @@ wget https://download.amovlab.com/model/deps/opencv-4.7.0.zip
 wget https://download.amovlab.com/model/deps/opencv_contrib-4.7.0.zip
 wget https://download.amovlab.com/model/deps/opencv_cache-4.7.0.zip
 
-current_dir=$(pwd)
+
 package_dir="."
 mkdir ~/opencv_build
 
@@ -52,4 +52,3 @@ sudo make install
 
 cd
 sudo rm -r ~/opencv_build
-cd ${current_dir}

scripts/model_sync.py

@@ -1,119 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-import os
-import requests
-import argparse
-
-
-root_url = "https://download.amovlab.com/model/SpireCV-models/"
-model_list_url = root_url + "model-list.txt"
-root_path = os.path.expanduser("~") + "/SpireCV/models"
-print("MODEL PATH:", root_path)
-if not os.path.exists(root_path):
-    os.makedirs(root_path)
-list_file = os.path.join(root_path, "model-list.txt")
-
-
-def main():
-    parser = argparse.ArgumentParser(description="SpireCV Model SYNC")
-    parser.add_argument(
-        "-p", "--platform",
-        type=str,
-        required=True,
-        help="Supported Platforms: nv (Nvidia), int (Intel)",
-    )
-    args = parser.parse_args()
-
-    if args.platform in ['nv', 'nvidia', 'Nv', 'Nvidia']:
-        prefix = 'Nv'
-    elif args.platform in ['int', 'intel', 'Int', 'Intel']:
-        prefix = 'Int'
-    else:
-        raise Exception("Platform NOT Support!")
-
-    r = requests.get(model_list_url)
-    with open(list_file, "wb") as f:
-        f.write(r.content)
-
-    with open(list_file, 'r') as file:
-        lines = file.readlines()
-
-    need_switch = False
-    for line in lines:
-        line = line.strip()
-        if len(line) > 0:
-            model_file = os.path.join(root_path, line)
-            if not os.path.exists(model_file) and (line.startswith(prefix) or line.startswith('Ocv')):
-                print("[1] Downloading Model:", line, "...")
-                r = requests.get(root_url + line)
-                with open(model_file, "wb") as f:
-                    f.write(r.content)
-                need_switch = True
-
-            if os.path.exists(model_file):
-                print("[1] Model:", line, "EXIST!")
-                if line.startswith('Nv'):
-                    net = line.split('-')[2]
-                    if net.startswith("yolov5"):
-                        if len(net.split('_')) == 3:
-                            name, seg, ncls = net.split('_')
-                            engine_fn = os.path.splitext(model_file)[0].replace(net, name + "_" + seg + '_b1_' + ncls) + '.engine'
-                            online_fn = os.path.splitext(model_file)[0].replace(net, name + "_" + seg + '_b1_' + ncls) + '-online.engine'
-                        else:
-                            name, ncls = net.split('_')
-                            engine_fn = os.path.splitext(model_file)[0].replace(net, name + '_b1_' + ncls) + '.engine'
-                            online_fn = os.path.splitext(model_file)[0].replace(net, name + '_b1_' + ncls) + '-online.engine'
-                    else:
-                        engine_fn = os.path.splitext(model_file)[0] + '.engine'
-                        online_fn = os.path.splitext(model_file)[0] + '-online.engine'
-                    if not os.path.exists(engine_fn) and not os.path.exists(online_fn):
-                        if net.startswith("yolov5"):
-                            if len(net.split('_')) == 3:
-                                name, seg, ncls = net.split('_')
-                                cmd = "SpireCVSeg -s {} {} {} {}".format(
-                                    model_file, engine_fn, ncls[1:], name[6:]
-                                )
-                            else:
-                                name, ncls = net.split('_')
-                                cmd = "SpireCVDet -s {} {} {} {}".format(
-                                    model_file, engine_fn, ncls[1:], name[6:]
-                                )
-                        elif line.endswith("onnx"):
-                            cmd = ("/usr/src/tensorrt/bin/trtexec --explicitBatch --onnx={} "
-                                    "--saveEngine={} --fp16").format(
-                                model_file, engine_fn
-                            )
-                        print("  [2] Converting Model:", line, "->", engine_fn, "...")
-                        result = os.popen(cmd).read()
-                        need_switch = True
-                    else:
-                        print("  [2] Model Converting FINISH!")
-                    model_file = engine_fn
-
-                if not line.startswith('Ocv') and need_switch:
-                    ext = os.path.splitext(model_file)[1]
-                    fn_prefix = '-'.join(os.path.basename(model_file).split('-')[:3])
-                    file_names = os.listdir(root_path)
-                    selected = []
-                    for file_name in file_names:
-                        if file_name.startswith(fn_prefix) and file_name.endswith(ext):
-                            selected.append(file_name)
-                    if len(selected) > 0:
-                        for i, sel in enumerate(selected):
-                            if sel.endswith('-online' + ext):
-                                os.rename(
-                                    os.path.join(root_path, sel),
-                                    os.path.join(root_path, '-'.join(sel.split('-')[:4])) + ext
-                                )
-                                selected[i] = '-'.join(sel.split('-')[:4]) + ext
-                        selected.sort(reverse=True)
-                        os.rename(
-                            os.path.join(root_path, selected[0]),
-                            os.path.join(root_path, os.path.splitext(selected[0])[0] + "-online" + ext)
-                        )
-                        online_model = os.path.splitext(selected[0])[0] + "-online" + ext
-                        print("  [3] Model {} ONLINE *".format(online_model))
-
-
-if __name__ == "__main__":
-    main()

scripts/x86-cuda/configs-downloading.sh

@@ -1,39 +0,0 @@
-#!/bin/bash -e
-
-root_dir=${HOME}"/SpireCV/models"
-root_server="https://download.amovlab.com/model"
-
-sv_params1=${HOME}"/SpireCV/sv_algorithm_params.json"
-sv_params2=${HOME}"/SpireCV/sv_algorithm_params_coco_640.json"
-sv_params3=${HOME}"/SpireCV/sv_algorithm_params_coco_1280.json"
-camera_params1=${HOME}"/SpireCV/calib_webcam_640x480.yaml"
-camera_params2=${HOME}"/SpireCV/calib_webcam_1280x720.yaml"
-
-
-if [ ! -d ${root_dir} ]; then
-  echo -e "\033[32m[INFO]: ${root_dir} not exist, creating it ... \033[0m"
-  mkdir -p ${root_dir}
-fi
-
-if [ ! -f ${sv_params1} ]; then
-  echo -e "\033[32m[INFO]: ${sv_params1} not exist, downloading ... \033[0m"
-  wget -O ${sv_params1} ${root_server}/install/a-params/sv_algorithm_params.json
-fi
-if [ ! -f ${sv_params2} ]; then
-  echo -e "\033[32m[INFO]: ${sv_params2} not exist, downloading ... \033[0m"
-  wget -O ${sv_params2} ${root_server}/install/a-params/sv_algorithm_params_coco_640.json
-fi
-if [ ! -f ${sv_params3} ]; then
-  echo -e "\033[32m[INFO]: ${sv_params3} not exist, downloading ... \033[0m"
-  wget -O ${sv_params3} ${root_server}/install/a-params/sv_algorithm_params_coco_1280.json
-fi
-
-if [ ! -f ${camera_params1} ]; then
-  echo -e "\033[32m[INFO]: ${camera_params1} not exist, downloading ... \033[0m"
-  wget -O ${camera_params1} ${root_server}/install/c-params/calib_webcam_640x480.yaml
-fi
-if [ ! -f ${camera_params2} ]; then
-  echo -e "\033[32m[INFO]: ${camera_params2} not exist, downloading ... \033[0m"
-  wget -O ${camera_params2} ${root_server}/install/c-params/calib_webcam_1280x720.yaml
-fi
-

scripts/x86-cuda/ffmpeg425-install.sh

@@ -1,66 +0,0 @@
-#!/bin/sh
-
-
-sudo apt install -y \
-build-essential yasm cmake libtool libc6 libc6-dev unzip wget libeigen3-dev libfmt-dev \
-libnuma1 libnuma-dev libx264-dev libx265-dev libfaac-dev libssl-dev v4l-utils
-
-current_dir=$(pwd)
-root_dir=${HOME}"/SpireCV"
-if [ ! -d ${root_dir} ]; then
-  echo -e "\033[32m[INFO]: ${root_dir} not exist, creating it ... \033[0m"
-  mkdir -p ${root_dir}
-fi
-cd ${root_dir}
-
-git clone https://gitee.com/jario-jin/nv-codec-headers.git
-cd nv-codec-headers
-git checkout n11.1.5.0
-sudo make install
-cd ..
-sudo rm -r nv-codec-headers
-
-wget https://ffmpeg.org/releases/ffmpeg-4.2.5.tar.bz2
-tar -xjf ffmpeg-4.2.5.tar.bz2
-cd ffmpeg-4.2.5
-export PATH=$PATH:/usr/local/cuda/bin
-sed -i 's#_30#_75#' configure; sed -i 's#_30#_75#' configure
-./configure \
---enable-nonfree \
---enable-gpl \
---enable-shared \
---enable-ffmpeg \
---enable-ffplay \
---enable-ffprobe \
---enable-libx264 \
---enable-libx265 \
---enable-cuda-nvcc \
---enable-nvenc \
---enable-cuda \
---enable-cuvid \
---enable-libnpp \
---extra-libs="-lpthread -lm" \
---extra-cflags=-I/usr/local/cuda/include \
---extra-ldflags=-L/usr/local/cuda/lib64 
-make -j8
-sudo make install
-cd ..
-
-git clone https://gitee.com/jario-jin/ZLMediaKit.git
-cd ZLMediaKit
-git submodule update --init
-mkdir build
-cd build
-cmake ..
-make -j4
-cd ..
-cd ..
-
-mkdir ZLM
-cd ZLM
-cp ../ZLMediaKit/release/linux/Debug/MediaServer .
-cp ../ZLMediaKit/release/linux/Debug/config.ini .
-cd ..
-
-cd ${current_dir}
-

scripts/x86-cuda/ubuntu1804-cuda-cudnn-11-1.sh

@@ -1,95 +0,0 @@
-#!/bin/sh
-
-echo "\033[32m[INFO]:\033[0m Please enter the folder path of the installation package: "
-# package_dir="/home/jario/Downloads/nv"
-
-wget https://download.amovlab.com/model/install/x86-nvidia/cuda-repo-ubuntu1804-11-1-local_11.1.1-455.32.00-1_amd64.deb
-wget https://download.amovlab.com/model/install/x86-nvidia/cudnn-11.3-linux-x64-v8.2.1.32.tgz
-wget https://download.amovlab.com/model/install/x86-nvidia/nv-tensorrt-repo-ubuntu1804-cuda11.3-trt8.0.1.6-ga-20210626_1-1_amd64.deb
-wget https://download.amovlab.com/model/install/x86-nvidia/cuda-ubuntu1804.pin
-
-package_dir="."
-
-cuda_fn=$package_dir"/cuda-repo-ubuntu1804-11-1-local_11.1.1-455.32.00-1_amd64.deb"
-cudnn_fn=$package_dir"/cudnn-11.3-linux-x64-v8.2.1.32.tgz"
-tensorrt_fn=$package_dir"/nv-tensorrt-repo-ubuntu1804-cuda11.3-trt8.0.1.6-ga-20210626_1-1_amd64.deb"
-tmp_dir="/tmp"
-
-echo "\033[32m[INFO]: CUDA_PKG: \033[0m"$cuda_fn
-echo "\033[32m[INFO]: CUDNN_PKG: \033[0m"$cudnn_fn
-echo "\033[32m[INFO]: TENSORRT_PKG: \033[0m"$tensorrt_fn
-
-# 所有文件都存在时，才会继续执行脚本
-if [ ! -f "$cuda_fn" ]; then
-  echo "\033[31m[ERROR]: CUDA_PKG not exist!: \033[0m"
-  exit 1
-fi
-
-if [ ! -f "$cudnn_fn" ]; then
-  echo "\033[31m[ERROR]: CUDNN_PKG not exist!: \033[0m"
-  exit 1
-fi
-
-if [ ! -f "$tensorrt_fn" ]; then
-  echo "\033[31m[ERROR]: TENSORRT_PKG not exist!: \033[0m"
-  exit 1
-fi
-
-# 删除显卡驱动
-# sudo apt-get remove nvidia-*
-
-# 安装显卡驱动
-# echo "\033[32m[INFO]: Nvidia Driver installing ...\033[0m"
-# ubuntu-drivers devices
-# sudo ubuntu-drivers autoinstall
-
-# 删除已安装CUDA
-# --purge选项会将配置文件、数据库等删除
-# sudo apt-get autoremove --purge cuda
-# 查看安装了哪些cuda相关的库，可以用以下指令
-# sudo dpkg -l |grep cuda
-# sudo dpkg -P cuda-repo-ubuntu1804-10-2-local-10.2.89-440.33.01
-# sudo dpkg -P cuda-repo-ubuntu1804-11-1-local
-# sudo dpkg -P nv-tensorrt-repo-ubuntu1804-cuda10.2-trt8.0.1.6-ga-20210626
-# 这个key值是官网文档查到的，当然通过apt-key list也能查看
-# sudo apt-key list
-# sudo apt-key del 7fa2af80
-
-# 安装CUDA
-echo "\033[32m[INFO]: CUDA installing ...\033[0m"
-# wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin
-sudo cp $package_dir/cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600
-sudo dpkg -i $cuda_fn
-sudo apt-key add /var/cuda-repo-ubuntu1804-11-1-local/7fa2af80.pub
-sudo apt-get update
-sudo apt-get -y install cuda
-
-# 安装CUDNN
-echo "\033[32m[INFO]: CUDNN installing ...\033[0m"
-tar zxvf $cudnn_fn -C $tmp_dir
-sudo cp $tmp_dir/cuda/include/cudnn* /usr/local/cuda/include/
-sudo cp $tmp_dir/cuda/lib64/libcudnn* /usr/local/cuda/lib64/
-sudo chmod a+r /usr/local/cuda/include/cudnn* /usr/local/cuda/lib64/libcudnn*
-
-sudo ln -sf /usr/local/cuda/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.2.1 /usr/local/cuda/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8
-sudo ln -sf /usr/local/cuda/targets/x86_64-linux/lib/libcudnn.so.8.2.1 /usr/local/cuda/targets/x86_64-linux/lib/libcudnn.so.8
-sudo ln -sf /usr/local/cuda/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.2.1 /usr/local/cuda/targets/x86_64-linux/lib/libcudnn_ops_train.so.8
-sudo ln -sf /usr/local/cuda/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.2.1 /usr/local/cuda/targets/x86_64-linux/lib/libcudnn_adv_train.so.8
-sudo ln -sf /usr/local/cuda/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.2.1 /usr/local/cuda/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8
-sudo ln -sf /usr/local/cuda/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.2.1 /usr/local/cuda/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8
-sudo ln -sf /usr/local/cuda/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.2.1 /usr/local/cuda/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8
-
-# 安装TensorRT
-sudo dpkg -i $tensorrt_fn
-sudo apt-key add /var/nv-tensorrt-repo-cuda11.3-trt8.0.1.6-ga-20210626/7fa2af80.pub
-sudo apt-get update
-sudo apt-get install tensorrt -y
-sudo apt-get install python3-libnvinfer-dev -y
-
-sudo rm $cuda_fn
-sudo rm $cudnn_fn
-sudo rm $tensorrt_fn
-sudo rm cuda-ubuntu1804.pin
-
-
-

scripts/x86-cuda/x86-opencv470-cuda-install.sh

@@ -5,7 +5,7 @@ wget https://download.amovlab.com/model/deps/opencv-4.7.0.zip
 wget https://download.amovlab.com/model/deps/opencv_contrib-4.7.0.zip
 wget https://download.amovlab.com/model/deps/opencv_cache_x86-4.7.0.zip
 
-current_dir=$(pwd)
+
 package_dir="."
 mkdir ~/opencv_build
 
@@ -67,4 +67,3 @@ sudo make install
 
 cd
 sudo rm -r ~/opencv_build
-cd ${current_dir}

scripts/x86-cuda/x86-opencv470-install.sh

@@ -5,7 +5,7 @@ wget https://download.amovlab.com/model/deps/opencv-4.7.0.zip
 wget https://download.amovlab.com/model/deps/opencv_contrib-4.7.0.zip
 wget https://download.amovlab.com/model/deps/opencv_cache_x86-4.7.0.zip
 
-current_dir=$(pwd)
+
 package_dir="."
 mkdir ~/opencv_build
 
@@ -67,4 +67,3 @@ sudo make install
 
 cd
 sudo rm -r ~/opencv_build
-cd ${current_dir}