Fixed a bug caused by abnormal timestamps in the ROS environment

Signed-off-by: AiYangSky <1732570904@qq.com>

CMakeLists.txt

@@ -364,12 +364,14 @@ if(PLATFORM STREQUAL "JETSON")
 file(WRITE ${CMAKE_CURRENT_BINARY_DIR}/build/${PROJECT_NAME}Config.cmake.in [[
 @PACKAGE_INIT@
 find_package(OpenCV 4 REQUIRED)
+find_package(Eigen3 REQUIRED)
 link_directories(/usr/local/cuda/lib64)
 set(SV_INCLUDE_DIRS
   @SV_INSTALL_PREFIX@/include
   /usr/include/x86_64-linux-gnu
   /usr/local/cuda/include
   ${OpenCV_INCLUDE_DIRS}
+  ${EIGEN3_INCLUDE_DIRS}
   /usr/include/gstreamer-1.0
   /usr/local/include/gstreamer-1.0
   /usr/include/glib-2.0
@@ -388,6 +390,7 @@ elseif(PLATFORM STREQUAL "X86_CUDA")
 file(WRITE ${CMAKE_CURRENT_BINARY_DIR}/build/${PROJECT_NAME}Config.cmake.in [[
 @PACKAGE_INIT@
 find_package(OpenCV 4 REQUIRED)
+find_package(Eigen3 REQUIRED)
 find_package(fmt REQUIRED)
 link_directories(/usr/local/cuda/lib64)
 set(SV_INCLUDE_DIRS
@@ -395,6 +398,7 @@ set(SV_INCLUDE_DIRS
   /usr/include/x86_64-linux-gnu
   /usr/local/cuda/include
   ${OpenCV_INCLUDE_DIRS}
+  ${EIGEN3_INCLUDE_DIRS}
 )
 set(SV_LIBRARIES
   @SV_INSTALL_PREFIX@/lib/libsv_yoloplugins.so
@@ -409,11 +413,13 @@ elseif(PLATFORM STREQUAL "X86_INTEL")
 file(WRITE ${CMAKE_CURRENT_BINARY_DIR}/build/${PROJECT_NAME}Config.cmake.in [[
 @PACKAGE_INIT@
 find_package(OpenCV 4 REQUIRED)
+find_package(Eigen3 REQUIRED)
 find_package(fmt REQUIRED)
 set(SV_INCLUDE_DIRS
   @SV_INSTALL_PREFIX@/include
   /usr/include/x86_64-linux-gnu
   ${OpenCV_INCLUDE_DIRS}
+  ${EIGEN3_INCLUDE_DIRS}
 )
 set(SV_LIBRARIES
   @SV_INSTALL_PREFIX@/lib/libsv_world.so

algorithm/common_det/cuda/common_det_cuda_impl.cpp

@@ -1,6 +1,7 @@
 #include "common_det_cuda_impl.h"
 #include <cmath>
 #include <fstream>
+#include <iostream>
 #include "sv_util.h"
 
 #define SV_MODEL_DIR "/SpireCV/models/"
@@ -363,10 +364,16 @@ 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", dataset + "-yolov5s-");
+  
+  _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);
@@ -376,7 +383,7 @@ bool CommonObjectDetectorCUDAImpl::cudaSetup(CommonObjectDetectorBase* base_, bo
   if (input_w == 1280)
   {
     files.clear();
-    _list_dir(get_home() + SV_MODEL_DIR, files, "-online.engine", dataset + "-yolov5s6-");
+    _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);
@@ -392,7 +399,7 @@ bool CommonObjectDetectorCUDAImpl::cudaSetup(CommonObjectDetectorBase* base_, bo
     base_->setInputH(640);
     base_->setInputW(640);
     files.clear();
-    _list_dir(get_home() + SV_MODEL_DIR, files, "-online.engine", dataset + "-yolov5s-seg-");
+    _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);
@@ -443,17 +450,5 @@ bool CommonObjectDetectorCUDAImpl::cudaSetup(CommonObjectDetectorBase* base_, bo
   return false;
 }
 
-
-
-
-
-
-
-
-
-
-
-
-
 }
 

algorithm/common_det/intel/common_det_intel_impl.cpp

@@ -1,6 +1,7 @@
 #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/"
@@ -124,17 +125,46 @@ namespace sv
     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)
     {
-      openvino_fn = get_home() + SV_MODEL_DIR + dataset + "_HD.onnx";
+      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);
-      openvino_fn = get_home() + SV_MODEL_DIR + dataset + "_SEG.onnx";
+      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))

algorithm/landing_det/cuda/landing_det_cuda_impl.cpp

@@ -53,7 +53,7 @@ bool LandingMarkerDetectorCUDAImpl::cudaSetup()
   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", "LandingMarker-");
+  _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);

algorithm/landing_det/intel/landing_det_intel_impl.cpp

@@ -1,6 +1,7 @@
 #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/"
@@ -26,6 +27,14 @@ namespace sv
   {
 #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)");

algorithm/sot/ocv470/sot_ocv470_impl.cpp

@@ -33,9 +33,9 @@ bool SingleObjectTrackerOCV470Impl::ocv470Setup(SingleObjectTrackerBase* base_)
   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, "-online.engine", "DaSiamRPN-Model-");
-  _list_dir(get_home() + SV_MODEL_DIR, files2, "-online.engine", "DaSiamRPN-Kernel-CLS1-");
-  _list_dir(get_home() + SV_MODEL_DIR, files3, "-online.engine", "DaSiamRPN-Kernel-R1-");
+  _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);
@@ -53,8 +53,8 @@ bool SingleObjectTrackerOCV470Impl::ocv470Setup(SingleObjectTrackerBase* base_)
   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, "-online.engine", "NanoTrack-Backbone-SIM-");
-  _list_dir(get_home() + SV_MODEL_DIR, files5, "-online.engine", "NanoTrack-Head-SIM-");
+  _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);

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 + "params/a-params/sv_algorithm_params.json";
+  this->alg_params_fn = _get_home() + SV_ROOT_DIR + "confs/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);
@@ -916,6 +916,14 @@ 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()
 {
@@ -1082,6 +1090,8 @@ 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) {
 
@@ -1089,6 +1099,12 @@ 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

@@ -78,7 +78,7 @@ namespace sv
     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", "VERI-");
+    _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);

gimbal_ctrl/driver/src/GX40/GX40_gimbal_driver.cpp

@@ -28,6 +28,7 @@ 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);
 }
 
 /**
@@ -107,7 +108,8 @@ 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())
+    if ((nowTs.count() - upDataTs.count()) < std::chrono::milliseconds(1500).count() &&
+        cmd == GX40::GIMBAL_CMD_NOP)
     {
         primary->selfRoll = (int16_t)(-(carrierPos.roll / 0.01f));
         primary->selfPitch = (int16_t)(-(carrierPos.pitch / 0.01f));

include/sv_algorithm_base.h

@@ -140,6 +140,8 @@ public:
   double getThrsConf();
   int useWidthOrHeight();
   bool withSegmentation();
+  std::string getModel();
+  int getBatchSize();
 protected:
   virtual bool setupImpl();
   virtual void detectImpl(
@@ -166,6 +168,8 @@ protected:
   double _thrs_conf;
   int _use_width_or_height;
   bool _with_segmentation;
+  std::string _model;
+  int _batch_size;
 };
 
 

include/sv_mot.h

@@ -8,8 +8,8 @@
 #include <opencv2/tracking.hpp>
 #include <string>
 #include <chrono>
-#include <Eigen/Dense>
-
+//#include <Eigen/Dense>
+#include <eigen3/Eigen/Dense>
 
 namespace sv {
 

params/a-params/sv_algorithm_params.json

@@ -1,11 +1,13 @@
 {
     "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],

params/a-params/sv_algorithm_params_1280_wo_mask.json

@@ -1,11 +1,13 @@
 {
     "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],

params/a-params/sv_algorithm_params_640_w_mask.json

@@ -1,11 +1,13 @@
 {
     "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],

params/a-params/sv_algorithm_params_640_wo_mask.json

@@ -1,11 +1,13 @@
 {
     "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],

params/a-params/sv_algorithm_params_coco_1280.json

@@ -1,11 +1,13 @@
 {
     "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],

params/a-params/sv_algorithm_params_coco_640.json

@@ -1,11 +1,13 @@
 {
     "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],

params/a-params/sv_algorithm_params_csrt.json

@@ -1,11 +1,13 @@
 {
     "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],

params/a-params/sv_algorithm_params_kcf.json

@@ -1,11 +1,13 @@
 {
     "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],

params/a-params/sv_algorithm_params_nano.json

@@ -1,11 +1,13 @@
 {
     "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],

params/a-params/sv_algorithm_params_siamrpn.json

@@ -1,11 +1,13 @@
 {
     "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],

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
-  byte upd_msg[upd_msg_len];
+  unsigned char upd_msg[upd_msg_len];
   int msg_queue_len = 1024 * 1024;  // 1M
-  byte msg_queue[msg_queue_len];
+  unsigned char 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;
-            byte* msg_type = reinterpret_cast<byte*>(&msg_queue[i+2]);
+            unsigned char* msg_type = reinterpret_cast<unsigned char*>(&msg_queue[i+2]);
             cout << "Type: " << (int) *msg_type << endl;
             unsigned short* year = reinterpret_cast<unsigned short*>(&msg_queue[i+7]);
-            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 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]);
             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;
             
-            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]);
+            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]);
             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++)
             {
-              byte* index_f1 = reinterpret_cast<byte*>(&msg_queue[mp]);
+              unsigned char* index_f1 = reinterpret_cast<unsigned char*>(&msg_queue[mp]);
               mp++;
-              byte* index_f2 = reinterpret_cast<byte*>(&msg_queue[mp]);
+              unsigned char* index_f2 = reinterpret_cast<unsigned char*>(&msg_queue[mp]);
               mp++;
-              byte* index_f3 = reinterpret_cast<byte*>(&msg_queue[mp]);
+              unsigned char* index_f3 = reinterpret_cast<unsigned char*>(&msg_queue[mp]);
               mp++;
-              byte* index_f4 = reinterpret_cast<byte*>(&msg_queue[mp]);
+              unsigned char* index_f4 = reinterpret_cast<unsigned char*>(&msg_queue[mp]);
               mp++;
               if ((*index_f4) & 0x01 == 0x01 && (*index_f4) & 0x02 == 0x02)
               {

scripts/model_sync.py

@@ -0,0 +1,119 @@
+#!/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-intel/ffmpeg-install.sh

@@ -1,6 +1,6 @@
 #!/bin/sh
 
-sudo apt install -y v4l-utils
+sudo apt install -y v4l-utils 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
 wget https://ffmpeg.org/releases/ffmpeg-4.2.5.tar.bz2
 tar -xjf ffmpeg-4.2.5.tar.bz2
 cd ffmpeg-4.2.5

scripts/x86-intel/libva-install.sh

@@ -2,5 +2,5 @@
 
 sudo apt-get install -y libmfx1 libmfx-tools libva-dev libmfx-dev intel-media-va-driver-non-free vainfo
 echo "export LIBVA_DRIVER_NAME=iHD" >> ~/.bashrc
-source ~/.bashrc
+export LIBVA_DRIVER_NAME=iHD
 

scripts/x86-intel/openvino-install.sh

@@ -16,5 +16,5 @@ cd /opt/intel
 sudo ln -s openvino_2022.3.1 openvino_2022
 
 echo "source /opt/intel/openvino_2022/setupvars.sh" >> ~/.bashrc
-source ~/.bashrc
+sh /opt/intel/openvino_2022/setupvars.sh
 cd ${current_dir}

video_io/sv_video_base.cpp

@@ -1173,6 +1173,7 @@ void CameraBase::openImpl()
 }
 void CameraBase::open(CameraType type, int id)
 {
+  this->release();
   this->_type = type;
   this->_camera_id = id;
 
@@ -1215,7 +1216,7 @@ bool CameraBase::read(cv::Mat& image)
         this->_is_updated = false;
         break;
       }
-      std::this_thread::sleep_for(std::chrono::milliseconds(20));
+      std::this_thread::sleep_for(std::chrono::milliseconds(2));
       n_try ++;
     }
   }
@@ -1227,7 +1228,10 @@ bool CameraBase::read(cv::Mat& image)
 }
 void CameraBase::release()
 {
-  _cap.release();
+  this->_is_running = false;
+  this->_is_updated = false;
+  if (this->_cap.isOpened())
+    this->_cap.release();
 }