sort algorithms v0.2
This commit is contained in:
CZC-123
parent
0e539348d9
commit
8732e54980
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@ -8,7 +8,7 @@ add_definitions(-DAPI_EXPORTS)
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set(CMAKE_BUILD_TYPE "Release")
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## JETSON, X86_CUDA
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## JETSON, X86_CUDA, X86_INTEL
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message(STATUS "System:${CMAKE_HOST_SYSTEM_PROCESSOR}")
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if(NOT DEFINED PLATFORM)
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message(FATAL_ERROR "PLATFORM NOT SPECIFIED!")
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@ -23,6 +23,7 @@ else()
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option(USE_CUDA "BUILD WITH CUDA." ON)
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option(USE_FFMPEG "BUILD WITH FFMPEG." ON)
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elseif(PLATFORM STREQUAL "X86_INTEL")
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add_definitions(-DPLATFORM_X86_INTEL)
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option(USE_FFMPEG "BUILD WITH FFMPEG." ON)
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else()
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message(FATAL_ERROR "UNSUPPORTED PLATFORM!")
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@ -77,6 +78,7 @@ include_directories(
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${CMAKE_CURRENT_SOURCE_DIR}/video_io
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${CMAKE_CURRENT_SOURCE_DIR}/algorithm/ellipse_det
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${CMAKE_CURRENT_SOURCE_DIR}/utils
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${CMAKE_CURRENT_SOURCE_DIR}/algorithm/common_mot
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)
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if(USE_GSTREAMER)
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@ -111,6 +113,7 @@ set(
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include/sv_gimbal.h
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include/sv_algorithm_base.h
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include/sv_common_det.h
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include/sv_common_mot.h
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include/sv_landing_det.h
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include/sv_tracking.h
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include/sv_color_line.h
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@ -152,6 +155,7 @@ set(spirecv_SRCS
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algorithm/sv_algorithm_base.cpp
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algorithm/ellipse_det/ellipse_detector.cpp
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algorithm/common_det/sv_common_det.cpp
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algorithm/common_mot/sv_common_mot.cpp
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algorithm/landing_det/sv_landing_det.cpp
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algorithm/tracking/sv_tracking.cpp
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algorithm/color_line/sv_color_line.cpp
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@ -187,7 +191,7 @@ list(APPEND spirecv_SRCS ${ALG_SRC_FILES})
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endif()
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if(USE_CUDA)
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if(USE_CUDA) # PLATFORM_X86_CUDA & PLATFORM_JETSON
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# CUDA
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include_directories(/usr/local/cuda/include)
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link_directories(/usr/local/cuda/lib64)
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@ -199,24 +203,11 @@ if(USE_CUDA)
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target_link_libraries(sv_yoloplugins nvinfer cudart)
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cuda_add_library(sv_world SHARED ${spirecv_SRCS})
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if(USE_GSTREAMER)
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target_link_libraries(
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sv_world ${OpenCV_LIBS}
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sv_yoloplugins sv_gimbal
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nvinfer cudart
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gstrtspserver-1.0
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)
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else()
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target_link_libraries(
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sv_world ${OpenCV_LIBS}
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sv_yoloplugins sv_gimbal
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nvinfer cudart
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)
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endif()
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if(USE_FFMPEG)
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target_link_libraries(sv_world ${FFMPEG_LIBS} fmt)
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endif()
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target_link_libraries(
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sv_world ${OpenCV_LIBS}
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sv_yoloplugins sv_gimbal
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nvinfer cudart
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)
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set(
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YOLO_SRCS
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@ -232,18 +223,20 @@ if(USE_CUDA)
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cuda_add_executable(SpireCVSeg samples/SpireCVSeg.cpp ${YOLO_SRCS})
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target_link_libraries(SpireCVSeg sv_world)
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elseif(PLATFORM STREQUAL "X86_INTEL")
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elseif(PLATFORM STREQUAL "X86_INTEL") # Links to Intel-OpenVINO libraries here
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add_library(sv_world SHARED ${spirecv_SRCS})
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target_link_libraries(
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sv_world ${OpenCV_LIBS}
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sv_gimbal
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)
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if(USE_GSTREAMER)
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target_link_libraries(sv_world gstrtspserver-1.0)
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endif()
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if(USE_FFMPEG)
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target_link_libraries(sv_world ${FFMPEG_LIBS} fmt)
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endif()
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endif()
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if(USE_GSTREAMER)
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target_link_libraries(sv_world gstrtspserver-1.0)
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endif()
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if(USE_FFMPEG)
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target_link_libraries(sv_world ${FFMPEG_LIBS} fmt)
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endif()
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#demo
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@ -278,12 +271,12 @@ target_link_libraries(GimbalLandingMarkerDetection sv_world)
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add_executable(GimbalUdpDetectionInfoSender samples/demo/gimbal_udp_detection_info_sender.cpp)
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target_link_libraries(GimbalUdpDetectionInfoSender sv_world)
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add_executable(SortTracker samples/demo/common_object_sort.cpp)
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target_link_libraries(SortTracker sv_world)
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add_executable(EvalFpsOnVideo samples/test/eval_fps_on_video.cpp)
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target_link_libraries(EvalFpsOnVideo sv_world)
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add_executable(SortTracker samples/demo/common_object_tracking_SORT.cpp samples/demo/SORT_2.cpp)
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target_link_libraries(SortTracker sv_world)
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include_directories(${CMAKE_CURRENT_SOURCE_DIR}/samples/calib)
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add_executable(CameraCalibrarion samples/calib/calibrate_camera_charuco.cpp)
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target_link_libraries(CameraCalibrarion ${OpenCV_LIBS})
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@ -1,104 +1,51 @@
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#include "SORT_2.h"
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#include "sv_common_mot.h"
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#include <cmath>
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#include "sv_util.h"
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using namespace std;
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using namespace Eigen;
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/*
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void SORT::update(vector<VectorXd> detections) {
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//allocate the memory for the vector in advance
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int numDetections = detections.size();
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detections.reserve(numDetections);
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if (! tracklets_.size() || ! detections.size()){
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for (int i = 0; i < detections.size(); i++)
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{
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//if(find(matches.begin(), matches.end(),make_pair(-1, i)) == matches.end())
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if (detections[i](4) == -1) {
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Tracklet tracklet;
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tracklet.id = nextTrackletId_++;//
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tracklet.bbox.segment<4>(0) = detections[i].segment<4>(0);
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tracklet.age = 0;
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tracklet.hits = 1;
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tracklet.misses = 0;
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//initate the motion
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//pair<VectorXd,MatrixXd> motion = kf.initiate(bbox_xywh_to_wyah(detect_mat[i]));
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tracklets_.push_back(tracklet);
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}
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}
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}
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else{
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// create new tracklets for unmatched detections
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for (int i = 0; i < detections.size(); i++)
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{
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if (detections[i](4)== -1) {
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Tracklet tracklet;
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tracklet.id = nextTrackletId_++;//
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tracklet.bbox.segment<4>(0) = detections[i].segment<4>(0);
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tracklet.age = 0;
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tracklet.hits = 1;
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tracklet.misses = 0;
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tracklets_.push_back(tracklet);
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}
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namespace sv {
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KalmanFilter::KalmanFilter(){
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chi2inv95_ << 3.8415, 5.9915, 7.8147, 9.4877, 11.070, 12.592, 14.067, 15.507, 16.919;
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F_=MatrixXd::Identity(8,8);
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for (int i=0;i<4;i++){
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F_(i,i+4)=1;
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}
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H_=MatrixXd::Identity(4,8);
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std_weight_position=1./20;
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std_weight_vel=1./160;
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}
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}*/
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KalmanFilter::KalmanFilter(){
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chi2inv95_ << 3.8415, 5.9915, 7.8147, 9.4877, 11.070, 12.592, 14.067, 15.507, 16.919;
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/*
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F_ << 1, 0, 0, 0, 1, 0, 0, 0,
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0, 1, 0, 0, 0, 1, 0, 0,
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0, 0, 1, 0, 0, 0, 1, 0,
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0, 0, 0, 1, 0, 0, 0, 1,
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0, 0, 0, 0, 1, 0, 0, 0,
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0, 0, 0, 0, 0, 1, 0, 0,
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0, 0, 0, 0, 0, 0, 1, 0,
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0, 0, 0, 0, 0, 0, 0, 1;
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H_ << 1, 0, 0, 0, 0, 0, 0, 0,
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0, 1, 0, 0, 0, 0, 0, 0,
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0, 0, 1, 0, 0, 0, 0, 0,
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0, 0, 0, 1, 0, 0, 0, 0;
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*/
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F_ = MatrixXd::Identity(8, 8);
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for (int i = 0; i < 4; i++) {
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F_(i, i + 4) = 1;
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}
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H_ = MatrixXd::Identity(4, 8);
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std_weight_position = 1. / 20;
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std_weight_vel = 1. / 160;
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}
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pair<Matrix<double,8,1>,Matrix<double,8,8>> KalmanFilter::initiate(Vector4d & bbox){
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Matrix<double,8,1> mean;
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mean << bbox(0), bbox(1), bbox(2)/bbox(3), bbox(3), 0, 0, 0, 0;
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//cout<<"the a is:"<<bbox(2)/bbox(3)<<endl;
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VectorXd stds(8);
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stds << 2 * std_weight_position * mean(3), 2 * std_weight_position * mean(3), 0.01, 2 * std_weight_position * mean(3), 10 * std_weight_vel * mean(3), 10 * std_weight_vel * mean(3), 1e-5, 10 * std_weight_vel * mean(3);
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MatrixXd squared = stds.array().square();
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Matrix<double, 8,8> covariances;
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covariances = squared.asDiagonal();
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return make_pair(mean, covariances);
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KalmanFilter::~KalmanFilter(){};
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pair<Matrix<double,8,1>,Matrix<double,8,8>> KalmanFilter::initiate(Vector4d &bbox){
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Matrix<double,8,1> mean;
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mean<<bbox(0), bbox(1), bbox(2)/bbox(3), bbox(3), 0, 0, 0, 0;
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VectorXd stds(8);
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stds << 2*std_weight_position*mean(3), 2*std_weight_position*mean(3), 0.01, 2*std_weight_position*mean(3), 10*std_weight_vel*mean(3), 10*std_weight_vel*mean(3), 1e-5, 10*std_weight_vel*mean(3);
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MatrixXd squared = stds.array().square();
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Matrix<double,8,8> covariances;
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covariances = squared.asDiagonal();
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return make_pair(mean, covariances);
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}
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pair<Matrix<double,8,1>,Matrix<double,8,8>> KalmanFilter::update(Matrix<double, 8, 1> mean, Matrix<double, 8, 8> covariances, sv::Box & box) {
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MatrixXd R_;
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Vector4d stds;
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pair<Matrix<double,8,1>,Matrix<double,8,8>> KalmanFilter::update(Matrix<double,8,1> mean, Matrix<double,8,8> covariances, sv::Box &box){
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MatrixXd R_;
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Vector4d stds;
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stds << std_weight_position * mean(3), std_weight_position* mean(3), 0.1, std_weight_position* mean(3);
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MatrixXd squared = stds.array().square();
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R_ = squared.asDiagonal();
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stds << std_weight_position * mean(3), std_weight_position* mean(3), 0.1, std_weight_position* mean(3);
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MatrixXd squared = stds.array().square();
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R_= squared.asDiagonal();
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MatrixXd S = H_ * covariances * H_.transpose()+R_;
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MatrixXd S = H_ * covariances * H_.transpose()+R_;
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MatrixXd Kalman_gain = covariances * H_.transpose() * S.inverse();
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MatrixXd Kalman_gain = covariances * H_.transpose() * S.inverse();
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VectorXd measurement(4);
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measurement << box.x1, box.y1, (box.x2-box.x1)/(box.y2-box.y1), box.y2-box.y1;
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Matrix<double, 8, 1> new_mean = mean + Kalman_gain * (measurement - H_ * mean);
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Matrix<double, 8, 8> new_covariances = (MatrixXd::Identity(8, 8) - Kalman_gain * H_) * covariances;
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return make_pair(new_mean, new_covariances);
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}
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}
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pair<Matrix<double, 8, 1>, Matrix<double, 8, 8>> KalmanFilter::predict(Matrix<double, 8, 1>mean, Matrix<double, 8, 8>covariances) {
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pair<Matrix<double, 8, 1>, Matrix<double, 8, 8>> KalmanFilter::predict(Matrix<double, 8, 1>mean, Matrix<double, 8, 8>covariances) {
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VectorXd stds(8);
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stds << std_weight_position * mean(3), std_weight_position* mean(3), 0.01, std_weight_position* mean(3), std_weight_vel* mean(3), std_weight_vel* mean(3), 1e-5, std_weight_vel* mean(3);
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@ -109,24 +56,25 @@ pair<Matrix<double, 8, 1>, Matrix<double, 8, 8>> KalmanFilter::predict(Matrix<do
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return make_pair(pre_mean, pre_cov);
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}
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SORT::~SORT(){
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}
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void SORT::update(sv::TargetsInFrame & tgts){
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KalmanFilter kf;
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sv::KalmanFilter kf;
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// if (! tracklets_.size() || ! tgts.targets.size())
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if (! tracklets_.size())
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{
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Vector4d bbox;
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for (int i = 0; i <tgts.targets.size(); i++)
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{
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//if(find(matches.begin(), matches.end(),make_pair(-1, i)) == matches.end())
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//if (tgts.targets[i].tracked_id == 0) {
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sv::Box box;
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tgts.targets[i].getBox(box);
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Tracklet tracklet;
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tracklet.id = ++nextTrackletId_;//
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tracklet.id = ++next_tracklet_id_;//
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cout<<tracklet.id<<endl;
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tgts.targets[i].tracked_id = nextTrackletId_;
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//bbox << box.x1,box.y1,box.x2-box.x1,box.y2-box.y1;
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tgts.targets[i].tracked_id = next_tracklet_id_;
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tracklet.bbox << box.x1,box.y1,box.x2-box.x1,box.y2-box.y1;//x,y,w,h
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tracklet.age = 0;
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@ -135,8 +83,6 @@ void SORT::update(sv::TargetsInFrame & tgts){
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//initate the motion
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pair<Matrix<double,8,1>,Matrix<double,8,8>> motion = kf.initiate(tracklet.bbox);
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//cout<<"mean:"<<motion.first<<endl;
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//cout<<"mean:"<<tracklet.mean<<endl;
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tracklet.mean=motion.first;
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tracklet.covariance = motion.second;
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@ -177,9 +123,6 @@ void SORT::update(sv::TargetsInFrame & tgts){
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for (auto& match : matches) {
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int trackletIndex = match.first;
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int detectionIndex = match.second;
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// cout<<"trackletIndex:"<<match.first<<endl;
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// cout<<"detectionIndex:"<<match.second<<endl;
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// cout<<"iou_eles:"<<iouMatrix[match.first][match.second]<<endl;
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if (trackletIndex>=0 && detectionIndex>=0) {
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if(iouMatrix[match.first][match.second]>=0) {
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sv::Box box;
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@ -204,10 +147,7 @@ void SORT::update(sv::TargetsInFrame & tgts){
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sv::Box box;
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tgts.targets[i].getBox(box);
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Tracklet tracklet;
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tracklet.id = ++nextTrackletId_;//
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//Vector4d bbox;
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//bbox << box.x1,box.y1,box.x2-box.x1,box.y2-box.y1;
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//tracklet.bboxes.push_back(bbox);
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tracklet.id = ++next_tracklet_id_;//
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tracklet.bbox << box.x1,box.y1,box.x2-box.x1,box.y2-box.y1;
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tracklet.age = 0;
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@ -218,7 +158,7 @@ void SORT::update(sv::TargetsInFrame & tgts){
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tracklet.mean = new_mean;
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tracklet.covariance = new_covariance;
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tgts.targets[i].tracked_id = nextTrackletId_;
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tgts.targets[i].tracked_id = next_tracklet_id_;
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tracklets_.push_back(tracklet);
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}
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}
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@ -233,6 +173,7 @@ void SORT::update(sv::TargetsInFrame & tgts){
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tracklets_ = newTracklets;
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*/
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}
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vector<Tracklet> SORT::getTracklets() const{
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return tracklets_;
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}
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@ -243,17 +184,7 @@ double SORT::iou(Tracklet & tracklet, sv::Box & box) {
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double trackletY1 = tracklet.bbox(1);
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double trackletX2 = tracklet.bbox(0)+tracklet.bbox(2);
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double trackletY2 = tracklet.bbox(1)+tracklet.bbox(3);
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/*
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double trackletX1 = tracklet.bboxes.back()(0);
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double trackletY1 = tracklet.bboxes.back()(1);
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double trackletX2 = tracklet.bboxes.back()(0)+tracklet.bboxes.back()(2);
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double trackletY2 = tracklet.bboxes.back()(1)+tracklet.bboxes.back()(3);
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double trackletX1 = tracklet.x;
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double trackletY1 = tracklet.y;
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double trackletX2 = tracklet.x+tracklet.w;
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double trackletY2 = tracklet.y+tracklet.h;
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*/
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double detectionX1 = box.x1;
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double detectionY1 = box.y1;
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double detectionX2 = box.x2;
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@ -262,21 +193,21 @@ double SORT::iou(Tracklet & tracklet, sv::Box & box) {
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double intersectionY1 = max(trackletY1, detectionY1);
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double intersectionX2 = min(trackletX2, detectionX2);
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double intersectionY2 = min(trackletY2, detectionY2);
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//double intersectionArea = max(intersectionX2-intersectionX1, 0.0) * max(intersectionY2-intersectionY1, 0.0);
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double w = (intersectionX2-intersectionX1>0.0) ? (intersectionX2-intersectionX1):0.0;
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double h = (intersectionY2-intersectionY1>0.0) ? (intersectionY2-intersectionY1):0.0;
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double intersectionArea = w*h;
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double trackletArea = tracklet.bbox(2)*tracklet.bbox(3);
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//double trackletArea = tracklet.w*tracklet.h;
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double detectionArea = (box.x2-box.x1)*(box.y2-box.y1);
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double unionArea = trackletArea + detectionArea - intersectionArea;
|
||||
double iou = (1-intersectionArea / unionArea*1.0);
|
||||
|
||||
return iou;
|
||||
}
|
||||
|
||||
vector<pair<int, int>> SORT::hungarian(vector<vector<double>> costMatrix) {
|
||||
// vector<pair<int, int>> SORT::hungarian(array<array<double>> costMatrix) {
|
||||
int numRows = costMatrix.size();
|
||||
int numCols = costMatrix[0].size();
|
||||
|
||||
|
|
@ -352,3 +283,4 @@ bool SORT::Augment(const vector<vector<double>>& costMatrix, int row, vector<int
|
|||
}
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,60 @@
|
|||
#ifndef __SV_COMMON_MOT__
|
||||
#define __SV_COMMON_MOT__
|
||||
|
||||
#include <Eigen/Dense>
|
||||
#include <iostream>
|
||||
#include <vector>
|
||||
#include <opencv2/opencv.hpp>
|
||||
#include <array>
|
||||
#include "sv_core.h"
|
||||
|
||||
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;
|
||||
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);
|
||||
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);
|
||||
bool Augment(const std::vector<std::vector<double>>& costMatrix, int row, std::vector<int>& rowMatch, std::vector<int>& colMatch, std::vector<bool>& visited);
|
||||
private:
|
||||
double iou_threshold_;
|
||||
int max_age_;
|
||||
int min_hits_;
|
||||
int next_tracklet_id_;
|
||||
std::vector <Tracklet> tracklets_;
|
||||
};
|
||||
}
|
||||
#endif
|
||||
|
|
@ -8,6 +8,7 @@
|
|||
#include "sv_color_line.h"
|
||||
#include "sv_video_input.h"
|
||||
#include "sv_video_output.h"
|
||||
#include "sv_common_mot.h"
|
||||
|
||||
|
||||
#endif
|
||||
|
|
|
|||
|
|
@ -1,62 +0,0 @@
|
|||
#ifndef SORT_2_H
|
||||
#define SORT_2_H
|
||||
|
||||
#include <Eigen/Dense>
|
||||
#include <iostream>
|
||||
#include <vector>
|
||||
#include <opencv2/opencv.hpp>
|
||||
#include <sv_world.h>
|
||||
#include <array>
|
||||
|
||||
|
||||
|
||||
//define the tracklet struct to store the tracked objects
|
||||
|
||||
struct Tracklet {
|
||||
public:
|
||||
//Eigen::VectorXd bbox = Eigen::VectorXd::Zero(4);//x1,y1,w,h
|
||||
//std::vector<Eigen::Vector4d> bboxes;
|
||||
Eigen::Vector4d bbox;
|
||||
//double x, y, w, h;
|
||||
int id=0;
|
||||
int age;
|
||||
int hits;
|
||||
int misses;
|
||||
std::vector<double> features;
|
||||
Eigen::Matrix<double,8,1> mean;
|
||||
Eigen::Matrix<double,8,8> covariance;
|
||||
};
|
||||
|
||||
class KalmanFilter {
|
||||
public:
|
||||
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,sv::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);
|
||||
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 iouThreshold, int maxAge, int minHits): iouThreshold_(iouThreshold), maxAge_(maxAge), minHits_(minHits), nextTrackletId_(0) {};
|
||||
//void update(std::vector<Eigen::VectorXd> detections);
|
||||
void update(sv::TargetsInFrame & tgts);
|
||||
std::vector<Tracklet> getTracklets() const;
|
||||
private:
|
||||
double iou(Tracklet & tracklet, sv::Box & box);
|
||||
std::vector<std::pair<int, int>> hungarian(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);
|
||||
private:
|
||||
double iouThreshold_;
|
||||
int maxAge_;
|
||||
int minHits_;
|
||||
int nextTrackletId_;
|
||||
std::vector <Tracklet> tracklets_;
|
||||
};
|
||||
|
||||
#endif
|
||||
|
|
@ -7,7 +7,6 @@
|
|||
#include "logging.h"
|
||||
|
||||
// #include <opencv2/opencv.hpp>
|
||||
#include "SORT_2.h"
|
||||
|
||||
#define TRTCHECK(status) \
|
||||
do \
|
||||
|
|
@ -88,18 +87,18 @@ 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() + "/SpireVision/calib_webcam_640x480.yaml");
|
||||
cod.loadCameraParams(sv::get_home() + "/SpireCV/calib_webcam_640x480.yaml");
|
||||
|
||||
// 打开摄像头
|
||||
// sv::Camera cap;
|
||||
// cap.open(sv::CameraType::WEBCAM, 0);
|
||||
cv::VideoCapture cap;
|
||||
cap.open("/home/amov/SpireVision/video/predestrian/MOT17-01.mp4");
|
||||
sv::Camera cap;
|
||||
cap.open(sv::CameraType::WEBCAM, 0);
|
||||
// cv::VideoCapture cap;
|
||||
// cap.open("/home/nvidia/samples/video/MOT17-01.mp4");
|
||||
// cap.open("/home/amov/SpireVision/video/vehicle/vehicle_02.mp4");
|
||||
// 实例化OpenCV的Mat类，用于内存单帧图像
|
||||
cv::Mat img;
|
||||
int frame_id = 0;
|
||||
SORT tracker(0.5, 10, 3);
|
||||
sv::SORT tracker(0.5, 10, 3);
|
||||
while (1)
|
||||
{
|
||||
// 实例化SpireVision的 单帧检测结果 接口类 TargetsInFrame
|
||||
Loading…
Reference in New Issue