remove yolov5-trt-v2.0

src/detection/yolov5-trt-v2.0/calibrate_camera.py

@@ -1,163 +0,0 @@
-#!/usr/bin/python3
-"""
-~~~~~~~~~~~~~~~~~~
-Camera calibration
-~~~~~~~~~~~~~~~~~~
-
-Usage:
-    python calib.py \
-        -i /dev/video0 \
-        -grid 9x6 \
-        -out fisheye.yaml \
-        -framestep 20 \
-        -resolution 640x480
-        --fisheye
-"""
-import argparse
-import os
-import numpy as np
-import yaml
-import cv2
-import sys
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    # parser.add_argument("-i", "--input", type=int, default=0,
-                        # help="input camera device")
-    parser.add_argument("-grid", "--grid", default="10x7", help="size of the grid (rows x cols)")
-    parser.add_argument("-framestep", type=int, default=20, help="use every nth frame in the video")
-    parser.add_argument("-o", "--output", default="./yaml",
-                        help="path to output yaml file")
-    # parser.add_argument("-resolution", "--resolution", default="640x480",
-    #                     help="resolution of the camera")
-    parser.add_argument("-fisheye", "--fisheye", default=True,action="store_true",
-                        help="set ture if this is a fisheye camera")
-
-    args = parser.parse_args()
-
-   
-    cap = "v4l2src device=/dev/video0 ! video/x-raw, width=(int)1280, height=(int)720 ! videoconvert ! appsink"
-
-    if not os.path.exists(args.output):
-        os.mkdir(args.output)
-
-    
-    source = cv2.VideoCapture(cap)
-
-    W=1280
-    H=720
-    source.set(3, W)
-    source.set(4, H)
-
-    grid_size = tuple(int(x) for x in args.grid.split("x"))
-    grid_points = np.zeros((np.prod(grid_size), 3), np.float32)
-    grid_points[:, :2] = np.indices(grid_size).T.reshape(-1, 2)
-
-    objpoints = []  # 3d point in real world space
-    imgpoints = []  # 2d points in image plane
-
-    quit = False
-    do_calib = False
-    i = -1
-    while True:
-        i += 1
-        retcode, img = source.read()
-        W,H,_ = img.shape
-        if not retcode:
-            raise ValueError("cannot read frame from video")
-        if i % args.framestep != 0:
-            continue
-
-        print("searching for chessboard corners in frame " + str(i) + "...")
-        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-        found, corners = cv2.findChessboardCorners(
-            gray,
-            grid_size,
-            cv2.CALIB_CB_ADAPTIVE_THRESH +
-            cv2.CALIB_CB_NORMALIZE_IMAGE +
-            cv2.CALIB_CB_FILTER_QUADS
-        )
-        if found:
-            term = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.01)
-            cv2.cornerSubPix(gray, corners, (5, 5), (-1, -1), term)
-            print("OK")
-            imgpoints.append(corners.reshape(1, -1, 2))
-            objpoints.append(grid_points.reshape(1, -1, 3))
-            cv2.drawChessboardCorners(img, grid_size, corners, found)
-        text1 = "press c to calibrate"
-        text2 = "press q to quit"
-        text3 = "device: {}".format(cap)
-        fontscale = 0.6
-        cv2.putText(img, text1, (20, 70), cv2.FONT_HERSHEY_SIMPLEX, fontscale, (255, 200, 0), 2)
-        cv2.putText(img, text2, (20, 110), cv2.FONT_HERSHEY_SIMPLEX, fontscale, (255, 200, 0), 2)
-        cv2.putText(img, text3, (20, 30), cv2.FONT_HERSHEY_SIMPLEX, fontscale, (255, 200, 0), 2)
-        cv2.imshow("corners", img)
-        key = cv2.waitKey(1) & 0xFF
-        if key == ord("c"):
-            do_calib = True
-            break
-
-        elif key == ord("q"):
-            quit = True
-            break
-
-    if quit:
-        source.release()
-        cv2.destroyAllWindows()
-
-    if do_calib:
-        print("\nPerforming calibration...\n")
-        N_OK = len(objpoints)
-        if N_OK < 12:
-            print("Less than 12 corners detected, calibration failed")
-            return
-
-        K = np.zeros((3, 3))
-        D = np.zeros((4, 1))
-        rvecs = [np.zeros((1, 1, 3), dtype=np.float64) for _ in range(N_OK)]
-        tvecs = [np.zeros((1, 1, 3), dtype=np.float64) for _ in range(N_OK)]
-        calibration_flags = (cv2.fisheye.CALIB_RECOMPUTE_EXTRINSIC +
-                             cv2.fisheye.CALIB_CHECK_COND +
-                             cv2.fisheye.CALIB_FIX_SKEW)
-
-        # 求出内参矩阵和畸变系数
-        if True:
-            ret, K, D, rvecs, tvecs = cv2.fisheye.calibrate(
-                objpoints,
-                imgpoints,
-                (W, H),
-                K,
-                D,
-                rvecs,
-                tvecs,
-                calibration_flags,
-                (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 1e-6)
-            )
-        else:
-            ret, K, D, rvecs, tvecs = cv2.calibrateCamera(
-                objpoints,
-                imgpoints,
-                (W, H),
-                None,
-                None)
-        if ret:
-            print(ret)
-            print(K)
-            data = {"dim": np.array([W, H]).tolist(), "K": K.tolist(), "D": D.tolist()}
-            fname = "./yaml/camera147.yaml"
-            print(fname)
-            with open(fname, "w") as f:
-                yaml.safe_dump(data,f)
-            print("succesfully saved camera data")
-
-            cv2.putText(img, "Success!", (220 , 240), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 255), 2)
-        else:
-            cv2.putText(img, "Failed!", (220, 240), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 255), 2)
-
-        cv2.imshow("corners", img)
-        cv2.waitKey(0)
-
-
-if __name__ == "__main__":
-    main()

src/detection/yolov5-trt-v2.0/calibrate_camera_new.py

@@ -1,199 +0,0 @@
-#!/usr/bin/python3
-"""
-~~~~~~~~~~~~~~~~~~
-Camera calibration
-~~~~~~~~~~~~~~~~~~
-
-Usage:
-    python calib.py \
-        -i /dev/video0 \
-        -grid 9x6 \
-        -out fisheye.yaml \
-        -framestep 20 \
-        -resolution 640x480
-        --fisheye
-"""
-import argparse
-import os
-import numpy as np
-import yaml
-import cv2
-import sys
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    # parser.add_argument("-i", "--input", type=int, default=0,
-                        # help="input camera device")
-    parser.add_argument("-grid", "--grid", default="10x7", help="size of the grid (rows x cols)")
-    parser.add_argument("-framestep", type=int, default=20, help="use every nth frame in the video")
-    parser.add_argument("-o", "--output", default="./yaml",
-                        help="path to output yaml file")
-    # parser.add_argument("-resolution", "--resolution", default="640x480",
-    #                     help="resolution of the camera")
-    parser.add_argument("-fisheye", "--fisheye", default=True,action="store_true",
-                        help="set ture if this is a fisheye camera")
-
-    args = parser.parse_args()
-
-   
-    cap = "v4l2src device=/dev/video0 ! video/x-raw, width=(int)1280, height=(int)720 ! videoconvert ! appsink"
-
-    if not os.path.exists(args.output):
-        os.mkdir(args.output)
-
-    
-    source = cv2.VideoCapture(cap)
-
-    W=1280
-    H=720
-    source.set(3, W)
-    source.set(4, H)
-
-    grid_size = tuple(int(x) for x in args.grid.split("x"))
-    grid_points = np.zeros((np.prod(grid_size), 3), np.float32)
-    grid_points[:, :2] = np.indices(grid_size).T.reshape(-1, 2)
-
-    objpoints = []  # 3d point in real world space
-    imgpoints = []  # 2d points in image plane
-
-    quit = False
-    do_calib = False
-    i = -1
-    while True:
-        i += 1
-        retcode, img = source.read()
-        # img= img[120:480, 240:880]
-        # print(img.shape)
-        W,H,_ = img.shape
-        if not retcode:
-            raise ValueError("cannot read frame from video")
-        if i % args.framestep != 0:
-            continue
-
-        print("searching for chessboard corners in frame " + str(i) + "...")
-        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-        found, corners = cv2.findChessboardCorners(
-            gray,
-            grid_size,
-            cv2.CALIB_CB_ADAPTIVE_THRESH +
-            cv2.CALIB_CB_NORMALIZE_IMAGE +
-            cv2.CALIB_CB_FILTER_QUADS
-        )
-        if found:
-            term = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.01)
-            cv2.cornerSubPix(gray, corners, (5, 5), (-1, -1), term)
-            print("OK")
-            imgpoints.append(corners.reshape(1, -1, 2))
-            objpoints.append(grid_points.reshape(1, -1, 3))
-            cv2.drawChessboardCorners(img, grid_size, corners, found)
-        text1 = "press c to calibrate"
-        text2 = "press q to quit"
-        text3 = "device: {}".format(cap)
-        fontscale = 0.6
-        cv2.putText(img, text1, (20, 70), cv2.FONT_HERSHEY_SIMPLEX, fontscale, (255, 200, 0), 2)
-        cv2.putText(img, text2, (20, 110), cv2.FONT_HERSHEY_SIMPLEX, fontscale, (255, 200, 0), 2)
-        cv2.putText(img, text3, (20, 30), cv2.FONT_HERSHEY_SIMPLEX, fontscale, (255, 200, 0), 2)
-        cv2.imshow("corners", img)
-
-        print("\nPerforming calibration...\n")
-        N_OK = len(objpoints)
-        if N_OK > 12:
-            print("More than 12 corners detected")
-
-            K = np.zeros((3, 3))
-            D = np.zeros((4, 1))
-            rvecs = [np.zeros((1, 1, 3), dtype=np.float64) for _ in range(N_OK)]
-            tvecs = [np.zeros((1, 1, 3), dtype=np.float64) for _ in range(N_OK)]
-            calibration_flags = (cv2.fisheye.CALIB_RECOMPUTE_EXTRINSIC +
-                                cv2.fisheye.CALIB_CHECK_COND +
-                                cv2.fisheye.CALIB_FIX_SKEW)
-
-            try:
-                ret, K, D, rvecs, tvecs = cv2.fisheye.calibrate(
-                    objpoints,
-                    imgpoints,
-                    (W, H),
-                    K,
-                    D,
-                    rvecs,
-                    tvecs,
-                    calibration_flags,
-                    (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 1e-5)
-                )
-                break
-            except:
-                objpoints = []
-                imgpoints = []
-                print("Do not press C!!!!")
-
-
-        key = cv2.waitKey(1) & 0xFF
-        if key == ord("c"):
-            do_calib = True
-            break
-
-        elif key == ord("q"):
-            quit = True
-            break
-
-    if quit:
-        source.release()
-        cv2.destroyAllWindows()
-
-    if do_calib:
-        print("\nPerforming calibration...\n")
-        N_OK = len(objpoints)
-        if N_OK < 12:
-            print("Less than 12 corners detected, calibration failed")
-            return
-
-        K = np.zeros((3, 3))
-        D = np.zeros((4, 1))
-        rvecs = [np.zeros((1, 1, 3), dtype=np.float64) for _ in range(N_OK)]
-        tvecs = [np.zeros((1, 1, 3), dtype=np.float64) for _ in range(N_OK)]
-        calibration_flags = (cv2.fisheye.CALIB_RECOMPUTE_EXTRINSIC +
-                             cv2.fisheye.CALIB_CHECK_COND +
-                             cv2.fisheye.CALIB_FIX_SKEW)
-
-        # 求出内参矩阵和畸变系数
-        if True:
-            ret, K, D, rvecs, tvecs = cv2.fisheye.calibrate(
-                objpoints,
-                imgpoints,
-                (W, H),
-                K,
-                D,
-                rvecs,
-                tvecs,
-                calibration_flags,
-                (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 1e-5)
-            )
-
-        else:
-            ret, K, D, rvecs, tvecs = cv2.calibrateCamera(
-                objpoints,
-                imgpoints,
-                (W, H),
-                None,
-                None)
-        if ret:
-            print(ret)
-            print(K)
-            data = {"dim": np.array([W, H]).tolist(), "K": K.tolist(), "D": D.tolist()}
-            fname = "./yaml/camera147.yaml"
-            print(fname)
-            with open(fname, "w") as f:
-                yaml.safe_dump(data,f)
-            print("succesfully saved camera data")
-
-            cv2.putText(img, "Success!", (220 , 240), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 255), 2)
-        else:
-            cv2.putText(img, "Failed!", (220, 240), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 255), 2)
-
-        cv2.imshow("corners", img)
-        cv2.waitKey(0)
-
-
-if __name__ == "__main__":
-    main()

src/detection/yolov5-trt-v2.0/include/Hungarian.h

@@ -1,36 +0,0 @@
-//
-// Created by lqx on 20-4-23.
-//
-
-#ifndef TRACK_SORT_HUNGARIAN_H_H
-#define TRACK_SORT_HUNGARIAN_H_H
-
-#include <iostream>
-#include <vector>
-
-using namespace std;
-
-class HungarianAlgorithm
-{
-public:
-    HungarianAlgorithm();
-    ~HungarianAlgorithm();
-    double Solve(vector<vector<double>>& DistMatrix, vector<int>& Assignment);
-
-private:
-    void assignmentoptimal(int *assignment, double *cost, double *distMatrix, int nOfRows, int nOfColumns);
-    void buildassignmentvector(int *assignment, bool *starMatrix, int nOfRows, int nOfColumns);
-    void computeassignmentcost(int *assignment, double *cost, double *distMatrix, int nOfRows);
-    void step2a(int *assignment, double *distMatrix, bool *starMatrix, bool *newStarMatrix, bool *primeMatrix,
-                bool *coveredColumns, bool *coveredRows, int nOfRows, int nOfColumns, int minDim);
-    void step2b(int *assignment, double *distMatrix, bool *starMatrix, bool *newStarMatrix, bool *primeMatrix,
-                bool *coveredColumns, bool *coveredRows, int nOfRows, int nOfColumns, int minDim);
-    void step3(int *assignment, double *distMatrix, bool *starMatrix, bool *newStarMatrix, bool *primeMatrix,
-               bool *coveredColumns, bool *coveredRows, int nOfRows, int nOfColumns, int minDim);
-    void step4(int *assignment, double *distMatrix, bool *starMatrix, bool *newStarMatrix, bool *primeMatrix,
-               bool *coveredColumns, bool *coveredRows, int nOfRows, int nOfColumns, int minDim, int row, int col);
-    void step5(int *assignment, double *distMatrix, bool *starMatrix, bool *newStarMatrix, bool *primeMatrix,
-               bool *coveredColumns, bool *coveredRows, int nOfRows, int nOfColumns, int minDim);
-};
-
-#endif //TRACK_SORT_HUNGARIAN_H_H

src/detection/yolov5-trt-v2.0/include/KalmanTracker.h

@@ -1,90 +0,0 @@
-//
-// Created by lqx on 20-4-23.
-//
-
-#ifndef TRACK_SORT_KALMANTRACKER_H
-#define TRACK_SORT_KALMANTRACKER_H
-
-///////////////////////////////////////////////////////////////////////////////
-// KalmanTracker.h: KalmanTracker Class Declaration
-
-#include "opencv2/video/tracking.hpp"
-#include "opencv2/highgui/highgui.hpp"
-
-using namespace std;
-using namespace cv;
-
-#define StateType Rect_<float>
-
-
-// This class represents the internel state of individual tracked objects observed as bounding box.
-class KalmanTracker
-{
-public:
-    KalmanTracker()
-    {
-        init_kf(StateType());
-        m_time_since_update = 0;
-        m_hits = 0;
-        m_hit_streak = 0;
-        m_age = 0;
-        m_id = kf_count;
-        //kf_count++;
-    }
-    KalmanTracker(StateType initRect)
-    {
-        init_kf(initRect);
-        m_time_since_update = 0;
-        m_hits = 0;
-        m_hit_streak = 0;
-        m_age = 0;
-        m_id = kf_count;
-        //kf_count++;
-    }
-
-    KalmanTracker(StateType initRect, int classId,float prob)
-    {
-        init_kf(initRect);
-        m_time_since_update = 0;
-        m_hits = 0;
-        m_hit_streak = 0;
-        m_age = 0;
-        m_id = kf_count;
-        kf_count++;
-        m_class_id = classId;
-        m_prob = prob;
-    }
-
-    ~KalmanTracker()
-    {
-        m_history.clear();
-        m_class_history.clear();
-    }
-
-    StateType predict();
-    void update(StateType stateMat,int classId, float prob);
-
-    StateType get_state();
-    StateType get_rect_xysr(float cx, float cy, float s, float r);
-
-    static int kf_count;
-
-    int m_time_since_update;
-    int m_hits;
-    int m_hit_streak;
-    int m_age;
-    int m_id;
-    int m_class_id;
-    std::vector<int> m_class_history;
-    float m_prob;
-
-private:
-    void init_kf(StateType stateMat);
-
-    cv::KalmanFilter kf;
-    cv::Mat measurement;
-
-    std::vector<StateType> m_history;
-};
-
-#endif //TRACK_SORT_KALMANTRACKER_H

src/detection/yolov5-trt-v2.0/include/detect_obstacle.h

@@ -1,54 +0,0 @@
-#ifndef DETECT_TURNSTILE_H
-#define DETECT_TURNSTILE_H
-
-#include "Hungarian.h"
-#include "KalmanTracker.h"
-
-namespace od{
-
-const int COLOR_MAP[3][3]={{0, 0, 255},{0, 255, 0},{0,255,255}};
-const int max_age = 3;
-const int min_hits = 3;
-const double iouThreshold = 0.5;
-
-struct bbox_t {
-    unsigned int x, y, w, h;       // (x,y) - top-left corner, (w, h) - width & height of bounded box
-    float prob;                    // confidence - probability that the object was found correctly
-    unsigned int obj_id;           // class of object - from range [0, classes-1]
-    unsigned int track_id;         // tracking id for video (0 - untracked, 1 - inf - tracked object)
-    unsigned int frames_counter;   // counter of frames on which the object was detected
-    float x_3d, y_3d, z_3d;        // center of object (in Meters) if ZED 3D Camera is used
-};
-
-typedef struct TrackingBox
-{
-    int frame;
-    int id;
-    int class_id;
-    float prob;
-    Rect_<float> box;
-    vector<int> class_history;
-}TrackingBox;
-
-//yolo data o DetectBox
-typedef struct DetectBox
-{
-    int class_id;
-    float prob;
-    Rect_<float> box;
-}DetectBox;
-
-//Computes IOU between two bounding boxes
-double GetIOU(Rect_<float> bb_test, Rect_<float> bb_gt);
-//画出检测框和相关信息
-void DrawBoxes(Mat &frame, vector<string> classes, int classId, int turnstileId,float conf, int left, int top, int right, int bottom);
-
-//画出检测结果,image
-void Drawer(Mat &frame, vector<bbox_t> outs, vector<string> classes);
-//画出检测结果,video
-void Drawer(Mat &frame, vector<od::TrackingBox> &track_result, vector<string> &classes);
-//tracking obstacle
-bool TrackObstacle(int frame_count,vector<KalmanTracker> &trackers,vector<bbox_t> &outs,vector<od::TrackingBox> &track_result);
-}
-
-#endif // DETECT_TURNSTILE_H

src/detection/yolov5-trt-v2.0/include/imageBuffer.h

@@ -1,82 +0,0 @@
-#ifndef IMAGEBUFFER_H
-#define IMAGEBUFFER_H
-
-#include <opencv2/opencv.hpp>
-#include <mutex>
-#include <condition_variable>
-#include <queue>
-template<typename T>
-class ConsumerProducerQueue
-{
-
-public:
-    ConsumerProducerQueue(int mxsz,bool dropFrame) :
-            maxSize(mxsz),dropFrame(dropFrame)
-    { }
-
-    bool add(T request)
-    {
-        std::unique_lock<std::mutex> lock(mutex);
-        if(dropFrame && isFull())
-        {
-            //lock.unlock();
-            //return false;
-            cpq.pop();
-            cpq.push(request);
-            cond.notify_all();
-            return true;
-        }
-        else {
-            cond.wait(lock, [this]() { return !isFull(); });
-            cpq.push(request);
-            //lock.unlock();
-            cond.notify_all();
-            return true;
-        }
-    }
-    void consume(T &request)
-    {
-        std::unique_lock<std::mutex> lock(mutex);
-        cond.wait(lock, [this]()
-        { return !isEmpty(); });
-        request = cpq.front();
-        cpq.pop();
-        //lock.unlock();
-        cond.notify_all();
-
-    }
-
-    bool isFull() const
-    {
-        return cpq.size() >= maxSize;
-    }
-
-    bool isEmpty() const
-    {
-        return cpq.size() == 0;
-    }
-
-    int length() const
-    {
-        return cpq.size();
-    }
-
-    void clear()
-    {
-        std::unique_lock<std::mutex> lock(mutex);
-        while (!isEmpty())
-        {
-            cpq.pop();
-        }
-        lock.unlock();
-        cond.notify_all();
-    }
-
-private:
-    std::condition_variable cond;  //条件变量允许通过通知进而实现线程同步
-    std::mutex mutex;     //提供了多种互斥操作，可以显式避免数据竞争
-    std::queue<T> cpq;    //容器适配器，它给予程序员队列的功能
-    int maxSize;
-    bool dropFrame;
-};
-#endif // IMAGEBUFFER_H

src/detection/yolov5-trt-v2.0/main.cpp

@@ -1,701 +0,0 @@
-#include "NvInfer.h"
-#include "cuda_runtime_api.h"
-#include <fstream>
-#include <iostream>
-#include <map>
-#include <sstream>
-#include <vector>
-#include <chrono>
-#include <cmath>
-#include <cassert>
-
-
-#include<opencv2/core/core.hpp>
-#include<opencv2/highgui/highgui.hpp>
-#include <opencv2/opencv.hpp>
-
-#include "modulecomm.h"
-#include "xmlparam.h"
-#include "ivfault.h"
-#include "ivlog.h"
-#include "ivexit.h"
-#include "ivversion.h"
-#include "rawpic.pb.cc"
-#include "lightarray.pb.h"
-#include "brainstate.pb.h"
-#include <QCoreApplication>
-#include <thread>
-#include "opencv2/imgcodecs/legacy/constants_c.h"
-#include "qmutex.h"
-#include "condition_variable"
-
-// onnx转换头文件
-#include "NvOnnxParser.h"
-using namespace nvonnxparser;
-
-const std::string engine_path = "/home/nvidia/code/modularization/src/detection/yolov5-trt-v2.0/best-640.engine"; // engine path
-
-cv::Mat camera_matrix,dist_coe,map1,map2;
-float time_read_img = 0.0;
-float time_infer = 0.0;
-cv::Mat res_img; // result image
-int time_num = 0;
-
-float* prob;
-using namespace nvinfer1;
-
-IExecutionContext* context;
-ICudaEngine* engine;
-IRuntime* runtime;
-
-
-bool gbstate = true;
-void * gp;
-void * gpdetect;
-iv::Ivfault *gfault = nullptr;
-iv::Ivlog *givlog = nullptr;
-
-
-using namespace std;
-
-
-#define CHECK(status) \
-    do\
-    {\
-        auto ret = (status);\
-        if (ret != 0)\
-        {\
-            std::cerr << "Cuda failure: " << ret << std::endl;\
-            abort();\
-        }\
-    } while (0)
-struct  Detection {
-    //center_x center_y  w h
-    float bbox[4];
-    float conf;  // bbox_conf * cls_conf
-    int class_id;
-    int index;
-};
-
-vector<Detection> results;
-/*-----------------------protobuf-------------------------*/
-iv::vision::Lightarray light_array;
-
-// stuff we know about the network and the input/output blobs
-static const int INPUT_H = 640;
-static const int INPUT_W = 640;
-static const int cls_num = 4;
-//不同输入尺寸anchor:960-->56700 | 640-->25200 | 416-->10647 | 320-->6300
-static const int anchor_output_num = 25200;
-static const int OUTPUT_SIZE = 1* anchor_output_num *(cls_num+5); //1000 * sizeof(Detection) / sizeof(float) + 1;
-
-
-const char* INPUT_BLOB_NAME = "images";
-const char* OUTPUT_BLOB_NAME = "output";
-
-
-
-//static Logger gLogger;
-
-//构建Logger
-class Logger : public ILogger
-{
-    void log(Severity severity, const char* msg) noexcept override
-    {
-        // suppress info-level messages
-        if (severity <= Severity::kWARNING)
-            std::cout << msg << std::endl;
-    }
-} gLogger;
-
-
-
-void doInference(IExecutionContext& context, float* input, float* output, int batchSize)
-{
-    const ICudaEngine& engine = context.getEngine();
-    // Pointers to input and output device buffers to pass to engine.
-    // Engine requires exactly IEngine::getNbBindings() number of buffers.
-    assert(engine.getNbBindings() == 2);
-    void* buffers[2];
-    // In order to bind the buffers, we need to know the names of the input and output tensors.
-    // Note that indices are guaranteed to be less than IEngine::getNbBindings()
-    const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);
-    const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);
-
-    //std::cout<<inputIndex<<" "<<outputIndex<<std::endl;
-    //const int inputIndex = 0;
-    //const int outputIndex = 1;
-    // Create GPU buffers on device
-    cudaMalloc(&buffers[inputIndex], batchSize * 3 * INPUT_H * INPUT_W * sizeof(float));
-    cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float));
-    // Create stream
-    cudaStream_t stream;
-    CHECK(cudaStreamCreate(&stream));
-    // DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
-    CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream));
-    context.enqueue(batchSize, buffers, stream, nullptr);
-
-    //std::cout<<buffers[outputIndex+1]<<std::endl;
-    CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));
-    cudaStreamSynchronize(stream);
-    // Release stream and buffers
-    cudaStreamDestroy(stream);
-    CHECK(cudaFree(buffers[inputIndex]));
-    CHECK(cudaFree(buffers[outputIndex]));
-
-}
-
-
-
-//加工图片变成拥有batch的输入， tensorrt输入需要的格式，为一个维度
-void ProcessImage(cv::Mat image, float input_data[]) {
-    //只处理一张图片,总之结果为一维[batch*3*INPUT_W*INPUT_H]
-    //以下代码为投机取巧了
-
-    cv::resize(image, image, cv::Size(INPUT_W, INPUT_H), 0, 0, cv::INTER_LINEAR);
-    std::vector<cv::Mat> InputImage;
-
-    InputImage.push_back(image);
-
-
-
-    int ImgCount = InputImage.size();
-
-    //float input_data[BatchSize * 3 * INPUT_H * INPUT_W];
-    for (int b = 0; b < ImgCount; b++) {
-        cv::Mat img = InputImage.at(b);
-        int w = img.cols;
-        int h = img.rows;
-        int i = 0;
-        for (int row = 0; row < h; ++row) {
-            uchar* uc_pixel = img.data + row * img.step;
-            for (int col = 0; col < INPUT_W; ++col) {
-                input_data[b * 3 * INPUT_H * INPUT_W + i] = (float)uc_pixel[2] / 255.0;
-                input_data[b * 3 * INPUT_H * INPUT_W + i + INPUT_H * INPUT_W] = (float)uc_pixel[1] / 255.0;
-                input_data[b * 3 * INPUT_H * INPUT_W + i + 2 * INPUT_H * INPUT_W] = (float)uc_pixel[0] / 255.0;
-                uc_pixel += 3;
-                ++i;
-            }
-        }
-
-    }
-
-}
-
-
-
-
-//********************************************** NMS code **********************************//
-
-
-struct Bbox {
-    int x;
-    int y;
-    int w;
-    int h;
-};
-
-float iou(Bbox box1, Bbox box2) {
-
-    int x1 = max(box1.x, box2.x);
-    int y1 = max(box1.y, box2.y);
-    int x2 = min(box1.x + box1.w, box2.x + box2.w);
-    int y2 = min(box1.y + box1.h, box2.y + box2.h);
-    int w = max(0, x2 - x1);
-    int h = max(0, y2 - y1);
-    float over_area = w * h;
-    return over_area / (box1.w * box1.h + box2.w * box2.h - over_area);
-}
-
-int get_max_index(vector<Detection> pre_detection) {
-    //获得最佳置信度的值，并返回对应的索引值
-    int index;
-    float conf;
-    if (pre_detection.size() > 0) {
-        index = 0;
-        conf = pre_detection.at(0).conf;
-        for (int i = 0; i < pre_detection.size(); i++) {
-            if (conf < pre_detection.at(i).conf) {
-                index = i;
-                conf = pre_detection.at(i).conf;
-            }
-        }
-        return index;
-    }
-    else {
-        return -1;
-    }
-
-
-}
-bool judge_in_lst(int index, vector<int> index_lst) {
-    //若index在列表index_lst中则返回true，否则返回false
-    if (index_lst.size() > 0) {
-        for (int i = 0; i < index_lst.size(); i++) {
-            if (index == index_lst.at(i)) {
-                return true;
-            }
-        }
-    }
-    return false;
-}
-vector<int> nms(vector<Detection> pre_detection, float iou_thr)
-{
-    /*
-    返回需保存box的pre_detection对应位置索引值
-
-    */
-    int index;
-    vector<Detection> pre_detection_new;
-    //Detection det_best;
-    Bbox box_best, box;
-    float iou_value;
-    vector<int> keep_index;
-    vector<int> del_index;
-    bool keep_bool;
-    bool del_bool;
-    int rr = 0;
-    int zz = 0;
-
-    if (pre_detection.size() > 0) {
-
-        pre_detection_new.clear();
-        // 循环将预测结果建立索引
-        for (int i = 0; i < pre_detection.size(); i++) {
-            pre_detection.at(i).index = i;
-            pre_detection_new.push_back(pre_detection.at(i));
-        }
-        //循环便利获得保留box位置索引-相对输入pre_detection位置
-        while (pre_detection_new.size() > 0) {
-            index = get_max_index(pre_detection_new);
-            if (index >= 0) {
-                keep_index.push_back(pre_detection_new.at(index).index); //保留索引位置
-
-                // 更新最佳保留box
-                box_best.x = pre_detection_new.at(index).bbox[0];
-                box_best.y = pre_detection_new.at(index).bbox[1];
-                box_best.w = pre_detection_new.at(index).bbox[2];
-                box_best.h = pre_detection_new.at(index).bbox[3];
-
-                for (int j = 0; j < pre_detection.size(); j++) {
-                    keep_bool = judge_in_lst(pre_detection.at(j).index, keep_index);
-                    del_bool = judge_in_lst(pre_detection.at(j).index, del_index);
-                    if ((!keep_bool) && (!del_bool)) { //不在keep_index与del_index才计算iou
-                        box.x = pre_detection.at(j).bbox[0];
-                        box.y = pre_detection.at(j).bbox[1];
-                        box.w = pre_detection.at(j).bbox[2];
-                        box.h = pre_detection.at(j).bbox[3];
-                        iou_value = iou(box_best, box);
-                        if (iou_value > iou_thr) {
-                            del_index.push_back(j); //记录大于阈值将删除对应的位置
-                        }
-                    }
-
-                }
-                //更新pre_detection_new
-                pre_detection_new.clear();
-                for (int j = 0; j < pre_detection.size(); j++) {
-                    keep_bool = judge_in_lst(pre_detection.at(j).index, keep_index);
-                    del_bool = judge_in_lst(pre_detection.at(j).index, del_index);
-                    if ((!keep_bool) && (!del_bool)) {
-                        pre_detection_new.push_back(pre_detection.at(j));
-                    }
-                }
-            }
-        }
-    }
-
-    del_index.clear();
-    del_index.shrink_to_fit();
-    pre_detection_new.clear();
-    pre_detection_new.shrink_to_fit();
-
-    return  keep_index;
-
-}
-
-
-
-
-
-vector<Detection>  postprocess(float* prob, float conf_thr = 0.2, float nms_thr = 0.4) {
-    /*
-    #####################此函数处理一张图预测结果#########################
-    prob为[x y w h  score  multi-pre] 如80类-->(1,anchor_num,85)
-
-    */
-
-    vector<Detection> pre_results;
-    vector<int> nms_keep_index;
-    vector<Detection> results;
-    bool keep_bool;
-    Detection pre_res;
-    float conf;
-    int tmp_idx;
-    float tmp_cls_score;
-    for (int i = 0; i < anchor_output_num; i++) {
-        tmp_idx = i * (cls_num + 5);
-        pre_res.bbox[0] = prob[tmp_idx + 0];
-        pre_res.bbox[1] = prob[tmp_idx + 1];
-        pre_res.bbox[2] = prob[tmp_idx + 2];
-        pre_res.bbox[3] = prob[tmp_idx + 3];
-        conf = prob[tmp_idx + 4];  //是为目标的置信度
-        tmp_cls_score = prob[tmp_idx + 5] * conf;
-        pre_res.class_id = 0;
-        pre_res.conf = tmp_cls_score;
-        for (int j = 1; j < cls_num; j++) {
-            tmp_idx = i * (cls_num + 5) + 5 + j; //获得对应类别索引
-            if (tmp_cls_score < prob[tmp_idx] * conf)
-            {
-                tmp_cls_score = prob[tmp_idx] * conf;
-                pre_res.class_id = j;
-                pre_res.conf = tmp_cls_score;
-            }
-        }
-        if (conf >= conf_thr) {
-
-            pre_results.push_back(pre_res);
-        }
-
-    }
-
-    //使用nms
-    nms_keep_index=nms(pre_results,nms_thr);
-
-    for (int i = 0; i < pre_results.size(); i++) {
-        keep_bool = judge_in_lst(i, nms_keep_index);
-        if (keep_bool) {
-            results.push_back(pre_results.at(i));
-        }
-
-    }
-
-
-    pre_results.clear();
-    pre_results.shrink_to_fit();
-    nms_keep_index.clear();
-    nms_keep_index.shrink_to_fit();
-
-
-    return results;
-
-}
-
-cv::Mat draw_rect(cv::Mat image, vector<Detection> results) {
-    /*
-    image 为图像
-
-    struct  Detection {
-    float bbox[4];  //center_x center_y  w h
-    float conf;  // 置信度
-    int class_id; //类别id
-    int index;    //可忽略
-    };
-
-    */
-
-    float x;
-    float y;
-    float w;
-    float h;
-    string info;
-
-    cv::Rect rect;
-    for (int i = 0; i < results.size(); i++) {
-
-        x = results.at(i).bbox[0];
-        y= results.at(i).bbox[1];
-        w= results.at(i).bbox[2];
-        h=results.at(i).bbox[3];
-
-        /*---------------protobuf----------------*/
-        iv::vision::Light *light = light_array.add_light();
-        light->set_index(i+1);
-        light->set_type(results.at(i).class_id+1);
-
-        /*----------------------------------------*/
-
-
-        x = (int)(x - w / 2);
-        y = (int)(y - h / 2);
-        w = (int)w;
-        h = (int)h;
-        info = "id:";
-        info.append(to_string(results.at(i).class_id));
-        info.append(" s:");
-        info.append(to_string((int)(results.at(i).conf*100)  )   );
-        info.append("%");
-        rect= cv::Rect(x, y, w, h);
-        if(results.at(i).class_id == 0){ // red light
-            cv::rectangle(image, rect, cv::Scalar(0, 0, 255), 2, 1, 0);//矩形的两个顶点，两个顶点都包括在矩形内部
-            cv::putText(image, info, cv::Point(x, y), cv::FONT_HERSHEY_SIMPLEX, 0.4, cv::Scalar(0, 0, 255), 0.6, 1, false);
-        }else if(results.at(i).class_id == 1){ // green light
-            cv::rectangle(image, rect, cv::Scalar(0, 255, 0), 2, 1, 0);//矩形的两个顶点，两个顶点都包括在矩形内部
-            cv::putText(image, info, cv::Point(x, y), cv::FONT_HERSHEY_SIMPLEX, 0.4, cv::Scalar(0, 255, 0), 0.6, 1, false);
-        }else if(results.at(i).class_id == 2){ // yellow light
-            cv::rectangle(image, rect, cv::Scalar(0, 255, 255), 2, 1, 0);//矩形的两个顶点，两个顶点都包括在矩形内部
-            cv::putText(image, info, cv::Point(x, y), cv::FONT_HERSHEY_SIMPLEX, 0.4, cv::Scalar(0, 255, 255), 0.6, 1, false);
-        }else{
-            cv::rectangle(image, rect, cv::Scalar(255, 255, 255), 2, 1, 0);//矩形的两个顶点，两个顶点都包括在矩形内部
-            cv::putText(image, info, cv::Point(x, y), cv::FONT_HERSHEY_SIMPLEX, 0.4, cv::Scalar(255, 255, 255), 0.6, 1, false);
-        }
-//        cv::rectangle(image, rect, cv::Scalar(0, 255, 0), 1, 1, 0);//矩形的两个顶点，两个顶点都包括在矩形内部
-//        cv::putText(image, info, cv::Point(x, y), cv::FONT_HERSHEY_SIMPLEX, 0.4, cv::Scalar(0, 255, 0), 0.4, 1, false);
-
-    }
-
-
-    return image;
-
-}
-
-void LoadEngine(const std::string engine_path){
-    //加载engine引擎
-    char* trtModelStream{ nullptr };
-    size_t size{ 0 };
-    std::ifstream file(engine_path, std::ios::binary);
-    if (file.good()) {
-        file.seekg(0, file.end);
-        size = file.tellg();
-        file.seekg(0, file.beg);
-        trtModelStream = new char[size];
-        assert(trtModelStream);
-        file.read(trtModelStream, size);
-        file.close();
-    }
-
-    //反序列为engine，创建context
-
-    runtime = createInferRuntime(gLogger);
-    assert(runtime != nullptr);
-    engine = runtime->deserializeCudaEngine(trtModelStream, size, nullptr);
-    assert(engine != nullptr);
-    context = engine->createExecutionContext();
-    assert(context != nullptr);
-    delete[] trtModelStream;
-
-    //在主机上分配页锁定内存
-    CHECK(cudaHostAlloc((void **)&prob, OUTPUT_SIZE * sizeof(float), cudaHostAllocDefault));
-}
-
-void shareMsg(){
-    int size = light_array.ByteSize();
-    char * strdata = new char[light_array.ByteSize()];
-    if(light_array.SerializeToArray(strdata, size))
-    {
-        iv::modulecomm::ModuleSendMsg(gpdetect, strdata, size);
-    }
-    else
-    {
-        std::cout<<"light_array serialize error."<<std::endl;
-    }
-    light_array.Clear();
-    delete strdata;
-
-    /*--------------------test ParseFromArray-------------------*/
-//    iv::vision::Lightarray light_array1;
-//    light_array1.ParseFromArray(strdata,size);
-//    cout<<"parsefromarray:"<<std::endl;
-//    cout<<"light_size:"<<light_array1.light_size()<<endl;
-//    for (int i=0;i<light_array1.light_size();i++) {
-//        std::cout<<"index:"<<light_array1.light(i).index()<<" type:"
-//                <<light_array1.light(i).type()
-//                <<std::endl;
-
-//    }
-}
-
-void infer(cv::Mat img) {
-
-   // 处理图片为固定输出
-
-    auto start = std::chrono::system_clock::now();  //时间函数
-    static float data[3 * INPUT_H * INPUT_W];
-
-    ProcessImage(img, data);
-    auto end = std::chrono::system_clock::now();
-    time_read_img = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() + time_read_img;
-
-//    cout<<"read img time: "<<time_read_img<<"ms"<<endl;
-    //Run inference
-    start = std::chrono::system_clock::now();  //时间函数
-    doInference(*context, data, prob, 1);
-    cout<<"doinference"<<endl;
-    end = std::chrono::system_clock::now();
-    time_infer = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() + time_infer;
-    std::cout << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
-
-    //输出后处理
-    //std::cout <<"prob="<<prob << std::endl;
-    results.clear();
-    results=postprocess(prob, 0.3,  0.4);
-
-    cv::resize(img, img, cv::Size(INPUT_W, INPUT_H), 0, 0, cv::INTER_LINEAR);
-    res_img=draw_rect(img,results);
-
-//    cv::imshow("streetLight", res_img);
-//    cv::waitKey(1);
-
-    cout << "ok" << endl;
-    time_num++;
-    shareMsg();
-
-}
-
-
-
-void exitfunc()
-{
-    return;
-}
-
-void Listenpic(const char * strdata,const unsigned int nSize,const unsigned int index,const QDateTime * dt,const char * strmemname)
-{
-
-    std::cout<<"one time"<<std::endl;
-    if(nSize<1000)return;
-    iv::vision::rawpic pic;
-
-    if(false == pic.ParseFromArray(strdata,nSize))
-    {
-        std::cout<<"picview Listenpic fail."<<std::endl;
-        return;
-    }
-
-    cv::Mat mat(pic.height(),pic.width(),pic.mattype());
-    if(pic.type() == 1)
-        memcpy(mat.data,pic.picdata().data(),mat.rows*mat.cols*mat.elemSize());
-    else
-    {
-       mat.release();
-       std::vector<unsigned char> buff(pic.picdata().data(),pic.picdata().data() + pic.picdata().size());
-       mat = cv::imdecode(buff,CV_LOAD_IMAGE_COLOR);
-    }
-
-    cv::Mat img_camera=mat;
-
-//    img_camera = img_camera(cv::Range(0,640), cv::Range(320,960));//height width
-//    img_camera = img_camera(cv::Range(240,600), cv::Range(320,960));
-    img_camera = img_camera(cv::Range(240,600), cv::Range(460,820));
-//    cv::remap(img_camera,img_camera,map1,map2,cv::INTER_LINEAR,cv::BORDER_CONSTANT);
-
-    infer(img_camera);
-
-    mat.release();
-}
-
-void ListenLight(const char * strdata,const unsigned int nSize,const unsigned int index,const QDateTime * dt,const char * strmemname)
-{
-
-    std::cout<<"one time"<<std::endl;
-    if(nSize<1000)return;
-    iv::vision::rawpic pic;
-    iv::brain::brainstate brain_state;
-
-    if(false == brain_state.ParseFromArray(strdata,nSize))
-    {
-        std::cout<<"brain_state Listen fail."<<std::endl;
-        return;
-    }
-
-    cv::Mat mat(pic.height(),pic.width(),pic.mattype());
-    if(pic.type() == 1)
-        memcpy(mat.data,pic.picdata().data(),mat.rows*mat.cols*mat.elemSize());
-    else
-    {
-       mat.release();
-       std::vector<unsigned char> buff(pic.picdata().data(),pic.picdata().data() + pic.picdata().size());
-       mat = cv::imdecode(buff,CV_LOAD_IMAGE_COLOR);
-    }
-
-    cv::Mat img_camera=mat;
-
-//    img_camera = img_camera(cv::Range(0,640), cv::Range(320,960));//height width
-//    img_camera = img_camera(cv::Range(240,600), cv::Range(320,960));
-    img_camera = img_camera(cv::Range(240,600), cv::Range(460,820));
-//    cv::remap(img_camera,img_camera,map1,map2,cv::INTER_LINEAR,cv::BORDER_CONSTANT);
-
-    infer(img_camera);
-
-    mat.release();
-}
-
-int main(int argc, char** argv)
-{
-    showversion("yolov5s");
-    QCoreApplication a(argc, argv);
-
-    gfault = new iv::Ivfault("lidar_cnn_segmentation");
-    givlog = new iv::Ivlog("lidar_cnn_segmentation");
-    gfault->SetFaultState(0,0,"cnn initialize.");
-
-
-//================================== camera calib init ==========================
-
-    cv::FileStorage calib_file("/home/nvidia/code/modularization/src/detection/yolov5-trt-v2.0/yaml/camera_middle_640_360.yaml", cv::FileStorage::READ);
-    calib_file["cameraMatrix"]>>camera_matrix;
-    calib_file["distCoeffs"]>>dist_coe;
-    cv::Mat R = cv::Mat::eye(3, 3, CV_64F);
-    cv::Size imgsize=cv::Size(640,360);
-    cv::initUndistortRectifyMap(camera_matrix, dist_coe, R, camera_matrix,imgsize,CV_16SC2,map1,map2);
-
-//==============================================================================
-
-    bool light_start = false;
-    while(light_start == false){
-        light_start = iv::modulecomm::RegisterRecv("lightStart",ListenLight);
-    }
-
-
-    LoadEngine(engine_path);
-    gp = iv::modulecomm::RegisterRecv("image00",Listenpic);
-    gpdetect = iv::modulecomm::RegisterSend("streetLight",10000000,1);
-
-//==============================================================================================
-//    cv::Mat img_test = cv::imread("/home/nvidia/modularization/src/detection/yolov5-trt/data/00002.jpg");
-//    infer(img_test);
-//    while(true){
-//        cv::imshow("streetLight", res_img);
-//        cv::waitKey(10);
-//    }
-//==========================================================================================
-    cv::VideoCapture cap("/home/nvidia/code/modularization/src/detection/yolov5-trt-v2.0/data/phone_light1.mp4");
-    if(!cap.isOpened()){
-        std::cout<<"open failed"<<std::endl;
-        return -1;
-    }
-    cv::Mat frame;
-    cv::waitKey(1000);
-    while(true){
-        cap>>frame;
-        if(frame.empty()){
-            break;
-        }
-        //frame = frame(cv::Range(240,480), cv::Range(520,760));//height width
-        //frame = frame(cv::Range(0,640), cv::Range(320,960));//height width
-        //frame = frame(cv::Range(240,600), cv::Range(320,960));//height width
-
-        frame = frame(cv::Range(0,640), cv::Range(320,980));//height width
- //        cv::remap(frame,frame,map1,map2,cv::INTER_LINEAR,cv::BORDER_CONSTANT);
-        //infer(frame);
-        cv::imshow("streetLight", frame);
-        if (cv::waitKey(10) == 'q') break;
-    }
-    cv::destroyAllWindows();
-
-//========================================================================================
-//    cv::waitKey(5000);
-//    while(true){
-//        cv::imshow("streetLight", res_img);
-//        cv::waitKey(10);
-//    }
-    std::cout << "C++ 2engine" << " mean read img time =" << time_read_img / time_num << "ms\t" << " mean infer img time =" << time_infer / time_num << "ms" << std::endl;
-
-    // Destroy the engine
-    context->destroy();
-    engine->destroy();
-    runtime->destroy();
-
-    return 0;
-}
-

src/detection/yolov5-trt-v2.0/main_new.cpp

@@ -1,877 +0,0 @@
-#include "NvInfer.h"
-#include "cuda_runtime_api.h"
-#include <fstream>
-#include <iostream>
-#include <map>
-#include <sstream>
-#include <vector>
-#include <chrono>
-#include <cmath>
-#include <cassert>
-#include <algorithm>
-
-
-#include<opencv2/core/core.hpp>
-#include<opencv2/highgui/highgui.hpp>
-#include <opencv2/opencv.hpp>
-
-#include "modulecomm.h"
-#include "xmlparam.h"
-#include "ivfault.h"
-#include "ivlog.h"
-#include "ivexit.h"
-#include "ivversion.h"
-#include "rawpic.pb.h"
-#include "lightarray.pb.h"
-#include "brainstate.pb.h"
-#include <QCoreApplication>
-#include <thread>
-#include "opencv2/imgcodecs/legacy/constants_c.h"
-#include "qmutex.h"
-#include "condition_variable"
-#include "imageBuffer.h"
-#include "detect_obstacle.h"
-
-// onnx转换头文件
-#include "NvOnnxParser.h"
-using namespace nvonnxparser;
-using namespace nvinfer1;
-using namespace std;
-
-//全局变量
-struct  Detection {
-    //center_x center_y  w h
-    float bbox[4];
-    float conf;  // bbox_conf * cls_conf
-    int class_id;
-    int index;
-};
-
-struct Bbox {
-    int x;
-    int y;
-    int w;
-    int h;
-};
-
-
-float conf_thr = 0.5;
-float nms_thr = 0.4;
-
-const bool calibrationstate = false;
-const bool cropstate = false;
-cv::Range crop_height = cv::Range(40,680);
-cv::Range crop_width = cv::Range(320,960);
-const bool trackstate = true;
-
-// stuff we know about the network and the input/output blobs
-static const int INPUT_H = 640;
-static const int INPUT_W = 640;
-static const int cls_num = 3;
-//不同输入尺寸anchor:960-->56700 | 640-->25200 | 416-->10647 | 320-->6300
-static const int anchor_output_num = 25200;
-static const int OUTPUT_SIZE = 1* anchor_output_num *(cls_num+5); //1000 * sizeof(Detection) / sizeof(float) + 1;
-
-const char* INPUT_BLOB_NAME = "images";
-const char* OUTPUT_BLOB_NAME = "output";
-
-const char* onnx_path = "/home/nvidia/code/modularization/src/detection/yolov5-trt-v2.0/model/yolov5s_640_old+new.onnx"; //onnx path
-const std::string engine_path = "/home/nvidia/code/modularization/src/detection/yolov5-trt-v2.0/model/yolov5s_640_old+new.engine"; // engine path
-IExecutionContext* context;
-ICudaEngine* engine;
-IRuntime* runtime;
-float* prob;
-
-bool test_video = true;
-//string video_path = "v4l2src device=/dev/video0 ! video/x-raw, width=(int)1280, height=(int)720 ! videoconvert ! appsink";
-string video_path = "/home/nvidia/code/modularization/src/detection/yolov5-trt-v2.0/data/camera_test4.mp4";
-
-void * gpcamera;
-string cameraname="image00";
-ConsumerProducerQueue<cv::Mat> * imageBuffer =  new ConsumerProducerQueue<cv::Mat>(3,true);
-
-void * gpbrain;  //获取红绿灯开启状态
-string brainname = "brainstate";
-bool lightstart = true;
-
-void * gpdetect;
-string detectname = "lightarray";  //检测结果
-
-
-iv::Ivfault *gfault = nullptr;
-iv::Ivlog *givlog = nullptr;
-
-cv::Mat camera_matrix,dist_coe,map1,map2;  //标定参数
-float time_read_img = 0.0;
-float time_infer = 0.0;
-int time_num = 0;
-
-#define CHECK(status) \
-    do\
-    {\
-        auto ret = (status);\
-        if (ret != 0)\
-        {\
-            std::cerr << "Cuda failure: " << ret << std::endl;\
-            abort();\
-        }\
-    } while (0)
-
-//static Logger gLogger;
-//构建Logger
-class Logger : public ILogger
-{
-    void log(Severity severity, const char* msg) noexcept override
-    {
-        // suppress info-level messages
-        if (severity <= Severity::kWARNING)
-            std::cout << msg << std::endl;
-    }
-} gLogger;
-
-
-// Creat the engine using only the API and not any parser.
-ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config)
-{
-
-    INetworkDefinition* network = builder->createNetworkV2(1U); //此处重点1U为OU就有问题
-
-    IParser* parser = createParser(*network, gLogger);
-    parser->parseFromFile(onnx_path, static_cast<int32_t>(ILogger::Severity::kWARNING));
-    //解析有错误将返回
-    for (int32_t i = 0; i < parser->getNbErrors(); ++i) { std::cout << parser->getError(i)->desc() << std::endl; }
-    std::cout << "successfully parse the onnx model" << std::endl;
-
-    // Build engine
-    builder->setMaxBatchSize(maxBatchSize);
-    config->setMaxWorkspaceSize(1 << 20);
-    //config->setFlag(nvinfer1::BuilderFlag::kFP16); // 设置精度计算
-    //config->setFlag(nvinfer1::BuilderFlag::kINT8);
-    ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
-    std::cout << "successfully  convert onnx to  engine！！！ " << std::endl;
-
-    //销毁
-    network->destroy();
-    //parser->destroy();
-
-    return engine;
-}
-
-void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream)
-{
-
-    // Create builder
-    IBuilder* builder = createInferBuilder(gLogger);
-    IBuilderConfig* config = builder->createBuilderConfig();
-
-    // Create model to populate the network, then set the outputs and create an engine
-    ICudaEngine* engine = createEngine(maxBatchSize, builder, config);
-
-    assert(engine != nullptr);
-
-
-
-    // Serialize the engine
-    (*modelStream) = engine->serialize();
-    // Close everything down
-    engine->destroy();
-    builder->destroy();
-    config->destroy();
-
-}
-
-
-int get_trtengine() {
-
-    IHostMemory* modelStream{ nullptr };
-    APIToModel(1, &modelStream);
-    assert(modelStream != nullptr);
-
-    std::ofstream p(engine_path, std::ios::binary);
-    if (!p)
-    {
-        std::cerr << "could not open plan output file" << std::endl;
-        return -1;
-    }
-    p.write(reinterpret_cast<const char*>(modelStream->data()), modelStream->size());
-    modelStream->destroy();
-
-    return 0;
-
-}
-
-
-void doInference(IExecutionContext& context, float* input, float* output, int batchSize)
-{
-    const ICudaEngine& engine = context.getEngine();
-    // Pointers to input and output device buffers to pass to engine.
-    // Engine requires exactly IEngine::getNbBindings() number of buffers.
-    assert(engine.getNbBindings() == 2);
-    void* buffers[2];
-    // In order to bind the buffers, we need to know the names of the input and output tensors.
-    // Note that indices are guaranteed to be less than IEngine::getNbBindings()
-    const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);
-    const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);
-
-    //std::cout<<inputIndex<<" "<<outputIndex<<std::endl;
-    //const int inputIndex = 0;
-    //const int outputIndex = 1;
-    // Create GPU buffers on device
-    cudaMalloc(&buffers[inputIndex], batchSize * 3 * INPUT_H * INPUT_W * sizeof(float));
-    cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float));
-    // Create stream
-    cudaStream_t stream;
-    CHECK(cudaStreamCreate(&stream));
-    // DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
-    CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream));
-    context.enqueue(batchSize, buffers, stream, nullptr);
-
-    //std::cout<<buffers[outputIndex+1]<<std::endl;
-    CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));
-    cudaStreamSynchronize(stream);
-    // Release stream and buffers
-    cudaStreamDestroy(stream);
-    CHECK(cudaFree(buffers[inputIndex]));
-    CHECK(cudaFree(buffers[outputIndex]));
-
-}
-
-//加工图片变成拥有batch的输入， tensorrt输入需要的格式，为一个维度
-void ProcessImage(cv::Mat image, float input_data[]) {
-    //只处理一张图片,总之结果为一维[batch*3*INPUT_W*INPUT_H]
-    //以下代码为投机取巧了
-
-    cv::Mat resize_img ;
-    cv::resize(image, resize_img, cv::Size(INPUT_W, INPUT_H), 0, 0, cv::INTER_LINEAR);
-    std::vector<cv::Mat> InputImage;
-
-    InputImage.push_back(resize_img);
-    int ImgCount = InputImage.size();
-
-    //float input_data[BatchSize * 3 * INPUT_H * INPUT_W];
-    for (int b = 0; b < ImgCount; b++) {
-        cv::Mat img = InputImage.at(b);
-        int w = img.cols;
-        int h = img.rows;
-        int i = 0;
-        for (int row = 0; row < h; ++row) {
-            uchar* uc_pixel = img.data + row * img.step;
-            for (int col = 0; col < INPUT_W; ++col) {
-                input_data[b * 3 * INPUT_H * INPUT_W + i] = (float)uc_pixel[2] / 255.0;
-                input_data[b * 3 * INPUT_H * INPUT_W + i + INPUT_H * INPUT_W] = (float)uc_pixel[1] / 255.0;
-                input_data[b * 3 * INPUT_H * INPUT_W + i + 2 * INPUT_H * INPUT_W] = (float)uc_pixel[0] / 255.0;
-                uc_pixel += 3;
-                ++i;
-            }
-        }
-
-    }
-
-}
-
-//********************************************** NMS code **********************************//
-float iou(Bbox box1, Bbox box2) {
-
-    int x1 = max(box1.x, box2.x);
-    int y1 = max(box1.y, box2.y);
-    int x2 = min(box1.x + box1.w, box2.x + box2.w);
-    int y2 = min(box1.y + box1.h, box2.y + box2.h);
-    int w = max(0, x2 - x1);
-    int h = max(0, y2 - y1);
-    float over_area = w * h;
-    return over_area / (box1.w * box1.h + box2.w * box2.h - over_area);
-}
-
-int get_max_index(vector<Detection> pre_detection) {
-    //获得最佳置信度的值，并返回对应的索引值
-    int index;
-    float conf;
-    if (pre_detection.size() > 0) {
-        index = 0;
-        conf = pre_detection.at(0).conf;
-        for (int i = 0; i < pre_detection.size(); i++) {
-            if (conf < pre_detection.at(i).conf) {
-                index = i;
-                conf = pre_detection.at(i).conf;
-            }
-        }
-        return index;
-    }
-    else {
-        return -1;
-    }
-
-
-}
-bool judge_in_lst(int index, vector<int> index_lst) {
-    //若index在列表index_lst中则返回true，否则返回false
-    if (index_lst.size() > 0) {
-        for (int i = 0; i < index_lst.size(); i++) {
-            if (index == index_lst.at(i)) {
-                return true;
-            }
-        }
-    }
-    return false;
-}
-vector<int> nms(vector<Detection> pre_detection, float iou_thr)
-{
-    /*
-    返回需保存box的pre_detection对应位置索引值
-
-    */
-    int index;
-    vector<Detection> pre_detection_new;
-    //Detection det_best;
-    Bbox box_best, box;
-    float iou_value;
-    vector<int> keep_index;
-    vector<int> del_index;
-    bool keep_bool;
-    bool del_bool;
-    int rr = 0;
-    int zz = 0;
-
-    if (pre_detection.size() > 0) {
-
-        pre_detection_new.clear();
-        // 循环将预测结果建立索引
-        for (int i = 0; i < pre_detection.size(); i++) {
-            pre_detection.at(i).index = i;
-            pre_detection_new.push_back(pre_detection.at(i));
-        }
-        //循环遍历获得保留box位置索引-相对输入pre_detection位置
-        while (pre_detection_new.size() > 0) {
-            index = get_max_index(pre_detection_new);
-            if (index >= 0) {
-                keep_index.push_back(pre_detection_new.at(index).index); //保留索引位置
-
-                // 更新最佳保留box
-                box_best.x = pre_detection_new.at(index).bbox[0];
-                box_best.y = pre_detection_new.at(index).bbox[1];
-                box_best.w = pre_detection_new.at(index).bbox[2];
-                box_best.h = pre_detection_new.at(index).bbox[3];
-
-                for (int j = 0; j < pre_detection.size(); j++) {
-                    keep_bool = judge_in_lst(pre_detection.at(j).index, keep_index);
-                    del_bool = judge_in_lst(pre_detection.at(j).index, del_index);
-                    if ((!keep_bool) && (!del_bool)) { //不在keep_index与del_index才计算iou
-                        box.x = pre_detection.at(j).bbox[0];
-                        box.y = pre_detection.at(j).bbox[1];
-                        box.w = pre_detection.at(j).bbox[2];
-                        box.h = pre_detection.at(j).bbox[3];
-                        iou_value = iou(box_best, box);
-                        if (iou_value > iou_thr) {
-                            del_index.push_back(j); //记录大于阈值将删除对应的位置
-                        }
-                    }
-
-                }
-                //更新pre_detection_new
-                pre_detection_new.clear();
-                for (int j = 0; j < pre_detection.size(); j++) {
-                    keep_bool = judge_in_lst(pre_detection.at(j).index, keep_index);
-                    del_bool = judge_in_lst(pre_detection.at(j).index, del_index);
-                    if ((!keep_bool) && (!del_bool)) {
-                        pre_detection_new.push_back(pre_detection.at(j));
-                    }
-                }
-            }
-        }
-    }
-
-    del_index.clear();
-    del_index.shrink_to_fit();
-    pre_detection_new.clear();
-    pre_detection_new.shrink_to_fit();
-
-    return  keep_index;
-
-}
-void postprocess(float* prob,vector<Detection> &results,float conf_thr = 0.2, float nms_thr = 0.4) {
-    /*
-    #####################此函数处理一张图预测结果#########################
-    prob为[x y w h  score  multi-pre] 如80类-->(1,anchor_num,85)
-
-    */
-
-    vector<Detection> pre_results;
-    vector<int> nms_keep_index;
-    bool keep_bool;
-    Detection pre_res;
-    float conf;
-    int tmp_idx;
-    float tmp_cls_score;
-    for (int i = 0; i < anchor_output_num; i++) {
-        tmp_idx = i * (cls_num + 5);
-        pre_res.bbox[0] = prob[tmp_idx + 0];
-        pre_res.bbox[1] = prob[tmp_idx + 1];
-        pre_res.bbox[2] = prob[tmp_idx + 2];
-        pre_res.bbox[3] = prob[tmp_idx + 3];
-        conf = prob[tmp_idx + 4];  //是为目标的置信度
-        tmp_cls_score = prob[tmp_idx + 5] * conf;
-        pre_res.class_id = 0;
-        pre_res.conf = tmp_cls_score;
-        for (int j = 1; j < cls_num; j++) {
-            tmp_idx = i * (cls_num + 5) + 5 + j; //获得对应类别索引
-            if (tmp_cls_score < prob[tmp_idx] * conf)
-            {
-                tmp_cls_score = prob[tmp_idx] * conf;
-                pre_res.class_id = j;
-                pre_res.conf = tmp_cls_score;
-            }
-        }
-        if (conf >= conf_thr) {
-
-            pre_results.push_back(pre_res);
-        }
-
-    }
-
-    //使用nms
-    nms_keep_index=nms(pre_results,nms_thr);
-
-    for (int i = 0; i < pre_results.size(); i++) {
-        keep_bool = judge_in_lst(i, nms_keep_index);
-        if (keep_bool) {
-            results.push_back(pre_results.at(i));
-        }
-
-    }
-
-    pre_results.clear();
-    pre_results.shrink_to_fit();
-    nms_keep_index.clear();
-    nms_keep_index.shrink_to_fit();
-
-}
-
-cv::Mat draw_rect(cv::Mat image, vector<Detection> results) {
-    /*
-    image 为图像
-
-    struct  Detection {
-    float bbox[4];  //center_x center_y  w h
-    float conf;  // 置信度
-    int class_id; //类别id
-    int index;    //可忽略
-    };
-
-    */
-
-    float x;
-    float y;
-    float w;
-    float h;
-    string info;
-
-    cv::Rect rect;
-    for (int i = 0; i < results.size(); i++) {
-
-        x = results.at(i).bbox[0];
-        y= results.at(i).bbox[1];
-        w= results.at(i).bbox[2];
-        h=results.at(i).bbox[3];
-
-        x = (int)(x - w / 2);
-        y = (int)(y - h / 2);
-        w = (int)w;
-        h = (int)h;
-        info = "id:";
-        info.append(to_string(results.at(i).class_id));
-        info.append(" s:");
-        info.append(to_string((int)(results.at(i).conf*100)  )   );
-        info.append("%");
-        rect= cv::Rect(x, y, w, h);
-        if(results.at(i).class_id == 0){ // red light
-            cv::rectangle(image, rect, cv::Scalar(0, 0, 255), 2, 1, 0);//矩形的两个顶点，两个顶点都包括在矩形内部
-            cv::putText(image, info, cv::Point(x, y), cv::FONT_HERSHEY_SIMPLEX, 0.4, cv::Scalar(0, 0, 255), 0.6, 1, false);
-        }else if(results.at(i).class_id == 1){ // green light
-            cv::rectangle(image, rect, cv::Scalar(0, 255, 0), 2, 1, 0);//矩形的两个顶点，两个顶点都包括在矩形内部
-            cv::putText(image, info, cv::Point(x, y), cv::FONT_HERSHEY_SIMPLEX, 0.4, cv::Scalar(0, 255, 0), 0.6, 1, false);
-        }else if(results.at(i).class_id == 2){ // yellow light
-            cv::rectangle(image, rect, cv::Scalar(0, 255, 255), 2, 1, 0);//矩形的两个顶点，两个顶点都包括在矩形内部
-            cv::putText(image, info, cv::Point(x, y), cv::FONT_HERSHEY_SIMPLEX, 0.4, cv::Scalar(0, 255, 255), 0.6, 1, false);
-        }else{
-            cv::rectangle(image, rect, cv::Scalar(255, 255, 255), 2, 1, 0);//矩形的两个顶点，两个顶点都包括在矩形内部
-            cv::putText(image, info, cv::Point(x, y), cv::FONT_HERSHEY_SIMPLEX, 0.4, cv::Scalar(255, 255, 255), 0.6, 1, false);
-        }
-
-    }
-    return image;
-}
-
-bool LoadEngine(const std::string engine_path){
-    //加载engine引擎
-    char* trtModelStream{ nullptr };
-    size_t size{ 0 };
-    std::ifstream file(engine_path, std::ios::binary);
-    if(!file)
-    {
-        cout<<engine_path<<" not found!"<<endl;
-        return false;
-    }
-    if (file.good()) {
-        file.seekg(0, file.end);
-        size = file.tellg();
-        file.seekg(0, file.beg);
-        trtModelStream = new char[size];
-        assert(trtModelStream);
-        file.read(trtModelStream, size);
-        file.close();
-    }
-    //反序列为engine，创建context
-    runtime = createInferRuntime(gLogger);
-    assert(runtime != nullptr);
-    engine = runtime->deserializeCudaEngine(trtModelStream, size, nullptr);
-    //assert(engine != nullptr);
-    if(engine == nullptr)
-        return false;
-    context = engine->createExecutionContext();
-    assert(context != nullptr);
-    delete[] trtModelStream;
-
-    //在主机上分配页锁定内存
-    CHECK(cudaHostAlloc((void **)&prob, OUTPUT_SIZE * sizeof(float), cudaHostAllocDefault));
-    return true;
-}
-
-void shareLightMsg(vector<Detection> results)
-{
-    iv::vision::Lightarray light_array;   //向共享内存传结果
-    for (int i = 0; i < results.size(); i++)
-    {
-        float x = results.at(i).bbox[0];
-        float y = results.at(i).bbox[1];
-        float w = results.at(i).bbox[2];
-        float h = results.at(i).bbox[3];
-        /*---------------protobuf----------------*/
-        iv::vision::Light *light = light_array.add_light();
-        iv::vision::Center light_center;
-        light_center.set_x(x);
-        light_center.set_y(y);
-        light->mutable_center()->CopyFrom(light_center);
-        light->set_index(i+1);
-        light->set_type(results.at(i).class_id+1);
-    }
-
-    int size = light_array.ByteSize();
-    char * strdata = new char[light_array.ByteSize()];
-    if(light_array.SerializeToArray(strdata, size))
-    {
-        iv::modulecomm::ModuleSendMsg(gpdetect, strdata, size);
-    }
-    else
-    {
-        std::cout<<"light_array serialize error."<<std::endl;
-    }
-    light_array.Clear();
-    delete strdata;
-
-    /*--------------------test ParseFromArray-------------------*/
-//    iv::vision::Lightarray light_array1;
-//    light_array1.ParseFromArray(strdata,size);
-//    cout<<"parsefromarray:"<<std::endl;
-//    cout<<"light_size:"<<light_array1.light_size()<<endl;
-//    for (int i=0;i<light_array1.light_size();i++) {
-//        std::cout<<"index:"<<light_array1.light(i).index()<<" type:"
-//                <<light_array1.light(i).type()
-//                <<std::endl;
-
-//    }
-
-}
-
-void infer(cv::Mat img,vector<Detection> &results) {
-
-   // 处理图片为固定输出
-    auto start = std::chrono::system_clock::now();  //时间函数
-    static float data[3 * INPUT_H * INPUT_W];
-    ProcessImage(img, data);
-    auto end = std::chrono::system_clock::now();
-    //time_read_img = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() + time_read_img;
-
-    //cout<<"read img time: "<<std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count()<<"ms"<<endl;
-
-    //Run inference
-    start = std::chrono::system_clock::now();  //时间函数
-    //cout<<"doinference"<<endl;
-    doInference(*context, data, prob, 1);
-    end = std::chrono::system_clock::now();
-    //time_infer = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() + time_infer;
-    std::cout <<"doinference: "<< std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
-    postprocess(prob, results, conf_thr,  nms_thr);
-    //cout << "ok" << endl;
-    //time_num++;
-}
-
-
-void exitfunc()
-{
-    return;
-}
-
-//读取视频数据
-void ReadFunc(int n)
-{
-    cv::VideoCapture cap(video_path);
-    if(!cap.isOpened())
-    {
-        cout<<"camera failed to open"<<endl;
-    }
-    while(1)
-    {
-        cv::Mat frame;
-        //读视频的时候加上,读摄像头去掉
-        if(imageBuffer->isFull())
-        {
-            continue;
-        }
-        if(cap.read(frame))
-        {
-            if(calibrationstate)
-                cv::remap(frame,frame,map1,map2,cv::INTER_LINEAR,cv::BORDER_CONSTANT);
-            if(cropstate)
-                frame = frame(crop_height,crop_width);
-            imageBuffer->add(frame);
-        }
-        else
-        {
-            std::this_thread::sleep_for(std::chrono::milliseconds(1));
-        }
-    }
-}
-void Listenpic(const char * strdata,const unsigned int nSize,const unsigned int index,const QDateTime * dt,const char * strmemname)
-{
-    if(nSize<1000)return;
-    iv::vision::rawpic pic;
-    if(false == pic.ParseFromArray(strdata,nSize))
-    {
-        std::cout<<"picview Listenpic fail."<<std::endl;
-        return;
-    }
-    cv::Mat mat(pic.height(),pic.width(),pic.mattype());
-    if(pic.type() == 1)
-        memcpy(mat.data,pic.picdata().data(),mat.rows*mat.cols*mat.elemSize());
-    else
-    {
-        //     mat.release();
-        std::vector<unsigned char> buff(pic.picdata().data(),pic.picdata().data() + pic.picdata().size());
-        mat = cv::imdecode(buff,cv::IMREAD_COLOR);
-    }
-    if(calibrationstate)
-        cv::remap(mat,mat,map1,map2,cv::INTER_LINEAR,cv::BORDER_CONSTANT);
-    if(cropstate)
-        mat = mat(crop_height,crop_width);
-    imageBuffer->add(mat);
-    mat.release();
-}
-
-//从共享内存中获取signal
-void Listensignal(const char * strdata,const unsigned int nSize,const unsigned int index,const QDateTime * dt,const char * strmemname)
-{
-    if(nSize<1)return;
-    iv::brain::brainstate brain_state;
-    if(false == brain_state.ParseFromArray(strdata,nSize))
-    {
-        std::cout<<"Listen signal fail."<<std::endl;
-        return;
-    }
-
-    cout<<"brain_state traficlightstart: "<<brain_state.mbtraficlightstart()<<endl;
-
-    lightstart = brain_state.mbtraficlightstart();
-}
-
-
-bool  comp(const od::TrackingBox &a, const od::TrackingBox &b) {
-    return a.id < b.id;
-}
-
-
-int main(int argc, char** argv)
-{
-    showversion("yolov5s");
-    QCoreApplication a(argc, argv);
-
-    gfault = new iv::Ivfault("traffic_light_detection");
-    givlog = new iv::Ivlog("traffic_light_detection");
-    gfault->SetFaultState(0,0,"yolov5 initialize.");
-
-    if(argc==3)
-    {
-        test_video = (strcmp(argv[1], "true") == 0)?true:false;
-        video_path = argv[2];
-    }
-    if(argc==2)
-    {
-        test_video = (strcmp(argv[1], "true") == 0)?true:false;
-    }
-
-    if(test_video)
-        std::thread * readthread = new std::thread(ReadFunc,1);
-    else
-        gpcamera= iv::modulecomm::RegisterRecv(&cameraname[0],Listenpic);
-
-//================================== camera calib init ==========================
-    if (calibrationstate)
-    {
-        cv::FileStorage calib_file("/home/nvidia/code/modularization/src/detection/yolov5-trt-v2.0/yaml/camera_middle_640_360.yaml", cv::FileStorage::READ);
-        calib_file["cameraMatrix"]>>camera_matrix;
-        calib_file["distCoeffs"]>>dist_coe;
-        cv::Mat R = cv::Mat::eye(3, 3, CV_64F);
-        cv::Size imgsize=cv::Size(1280,720);
-        cv::initUndistortRectifyMap(camera_matrix, dist_coe, R, camera_matrix,imgsize,CV_16SC2,map1,map2);
-    }
-//==============================================================================
-    if(!LoadEngine(engine_path))
-    {
-        cout<<"Build engine to "<< engine_path <<endl;
-        get_trtengine();
-        cout << "Build engine done!"<<endl;
-        cout<<"Reload engine from "<< engine_path <<endl;
-        LoadEngine(engine_path);
-    }
-    gpbrain = iv::modulecomm::RegisterRecv(&brainname[0],Listensignal);
-    gpdetect = iv::modulecomm::RegisterSend(&detectname[0],10000,1);
-    while(1)
-    {
-        if(lightstart)
-        {
-            std::cout<<"------start program------"<<std::endl;
-            vector<KalmanTracker> trackers_90;
-            KalmanTracker::kf_count = 0; // tracking id relies on this, so we have to reset it in each seq.
-            int frame_count = 0;
-            double waittime = (double)cv::getTickCount();
-            while (1)
-            {
-                if(imageBuffer->isEmpty())
-                {
-                    double waittotal = (double)cv::getTickCount() - waittime;
-                    double totaltime = waittotal/cv::getTickFrequency();
-                    if(totaltime>10.0)
-                    {
-                        cout<<"Cant't get frame and quit"<<endl;
-                        lightstart = false;
-                        cv::destroyAllWindows();
-                        std::cout<<"------end program------"<<std::endl;
-                        break;
-                    }
-                    cout<<"Wait for frame "<<totaltime<<"s"<<endl;
-                    continue;
-                }
-                auto start = std::chrono::system_clock::now();  //时间函数
-                cv::Mat frame;
-                imageBuffer->consume(frame);
-                frame_count++;
-                cv::Mat res_img; // result image
-                vector<Detection> results;
-                vector<Detection>results_track;
-
-//================================== infer ==========================
-                infer(frame,results);
-
-//================================== track ==========================
-                if (trackstate)
-                {
-                    auto start_track = std::chrono::system_clock::now();  //时间函数
-                    od::bbox_t bbox_t_90;   //转成跟踪格式
-                    vector<od::bbox_t> outs_90;
-                    for (int i = 0; i < results.size(); i++)
-                    {
-                        bbox_t_90.x = results.at(i).bbox[0];
-                        bbox_t_90.y = results.at(i).bbox[1];
-                        bbox_t_90.w = results.at(i).bbox[2];
-                        bbox_t_90.h = results.at(i).bbox[3];
-                        bbox_t_90.prob = results.at(i).conf;
-                        bbox_t_90.obj_id = results.at(i).class_id;
-                        outs_90.push_back(bbox_t_90);
-                    }
-                    vector<od::TrackingBox>track_result_90;
-                    bool track_flag_90 = od::TrackObstacle(frame_count,trackers_90,outs_90,track_result_90);
-
-                    //sort(track_result_90.begin(), track_result_90.end(), comp);  //track id 本来就是由大到小
-
-                    for(unsigned int i=0;i < track_result_90.size(); i++)
-                    {
-                        Detection obstacle;
-                        obstacle.bbox[0] = track_result_90[i].box.x;
-                        obstacle.bbox[1] = track_result_90[i].box.y;
-                        obstacle.bbox[2] = track_result_90[i].box.width;
-                        obstacle.bbox[3] = track_result_90[i].box.height;
-
-                        //cout<<"11111: "<<track_result_90[i].id<<endl;
-
-                        //通过判断5帧数输出颜色
-                        vector<int> class_history;
-                        class_history = track_result_90[i].class_history;
-                        if(class_history.size()>0)
-                        {
-                            vector<int> color_num(3);
-                            for(int j=0;j<class_history.size();j++)
-                            {
-                                int class_id = class_history[j];
-                                color_num[class_id] += 1;
-                            }
-                            std::vector<int>::iterator biggest = std::max_element(std::begin(color_num),std::end(color_num));
-                            int maxindex = std::distance(std::begin(color_num),biggest);
-                            obstacle.class_id = maxindex;
-                        }
-                        else {obstacle.class_id = track_result_90[i].class_id;}
-                        obstacle.conf = track_result_90[i].prob;
-                        results_track.push_back(obstacle);
-                    }
-                    auto end_track = std::chrono::system_clock::now();  //时间函数
-                    //std::cout <<"track: "<< std::chrono::duration_cast<std::chrono::milliseconds>(end_track - start_track).count() << "ms" << std::endl;
-
-                }
-//================================== track ==========================
-
-                vector<Detection>results_final;
-                results_final = (trackstate)?results_track:results;
-                shareLightMsg(results_final);
-                auto end = std::chrono::system_clock::now();  //时间函数
-                std::cout <<"total time traffic: "<< std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
-
-                //显示检测检测结果
-                cv::resize(frame, frame, cv::Size(INPUT_W, INPUT_H), 0, 0, cv::INTER_LINEAR);
-                res_img=draw_rect(frame,results_final);
-
-                results.clear();
-                results_track.clear();
-                results_final.clear();
-
-                cv::namedWindow("Result",cv::WINDOW_NORMAL);
-                cv::imshow("Result",frame);
-                if(cv::waitKey(10) == 'q')
-                {
-                    cv::destroyAllWindows();
-                    //yolo_context->destroy();
-                    //start_up = false;
-                    break;
-                }
-                if(cv::waitKey(1) == 's')
-                    cv::waitKey(0);
-                //writer << frame;
-                //waittime = (double)cv::getTickCount();
-                if(!lightstart)
-                {
-                    cv::destroyAllWindows();
-                    //yolo_context->destroy();
-                    //start_up = false;
-                    std::cout<<"------end program------"<<std::endl;
-                    break;
-                }
-            }
-        }
-        //else cout<<"Wait for lightstart==true"<<endl;
-    }
-
-    // Destroy the engine
-    context->destroy();
-    engine->destroy();
-    runtime->destroy();
-
-    return 0;
-}
-

src/detection/yolov5-trt-v2.0/saveVideo.py

@@ -1,40 +0,0 @@
-import cv2
-import os
-import argparse
-import time
-
-def arg_parser():
-    parser = argparse.ArgumentParser(description='Save Image')
-    parser.add_argument('--savedir', type=str, default='./Image',help='图片保存的路径')
-    return parser.parse_args()
-
-if __name__ == '__main__':
-	args = arg_parser()
-	savedir = args.savedir
-	if not os.path.exists(savedir):
-		os.makedirs(savedir)
-	#米文读取森云摄像头
-	cap = cv2.VideoCapture("v4l2src device=/dev/video0 ! video/x-raw, width=(int)1280, height=(int)720 ! videoconvert ! appsink")
-	#cap = cv2.VideoCapture('/home/vanessa/lqx/PycharmProjects/fire-detect-yolov4-master/yolov4_turnstile/20201231145638.avi')
-	image_nums_start = 0
-	while True:
-		ret,frame = cap.read()
-		if not ret:
-			print("Camera read failed !!!")
-			break
-		#image_name = '{:s}.jpg'.format('{:d}'.format(image_nums_start).zfill(4))
-		image_name = str(time.time()) + '.jpg'
-		save_path = os.path.join(savedir,image_name)
-		###抽帧
-		#frame = cv2.flip(frame,0)
-		#frame = cv2.flip(frame,1)
-		if image_nums_start % 10 == 0:
-			cv2.imwrite(save_path,frame)
-		image_nums_start += 1
-		image1=frame[40:680,320:960]
-		print("shape: ",image1.shape)
-		cv2.imshow('frame',image1)
-		if cv2.waitKey(1) & 0xFF==ord('q'):
-			break
-	cap.release()
-	cv2.destroyWindow('frame')

src/detection/yolov5-trt-v2.0/src/Hungarian.cpp

@@ -1,398 +0,0 @@
-//
-// Created by lqx on 20-4-23.
-//
-///////////////////////////////////////////////////////////////////////////////
-// Hungarian.cpp: Implementation file for Class HungarianAlgorithm.
-//
-// This is a C++ wrapper with slight modification of a hungarian algorithm implementation by Markus Buehren.
-// The original implementation is a few mex-functions for use in MATLAB, found here:
-// http://www.mathworks.com/matlabcentral/fileexchange/6543-functions-for-the-rectangular-assignment-problem
-//
-// Both this code and the orignal code are published under the BSD license.
-// by Cong Ma, 2016
-//
-
-#include <math.h>
-#include <cfloat>
-#include "Hungarian.h"
-
-
-HungarianAlgorithm::HungarianAlgorithm(){}
-HungarianAlgorithm::~HungarianAlgorithm(){}
-
-
-//********************************************************//
-// A single function wrapper for solving assignment problem.
-//********************************************************//
-double HungarianAlgorithm::Solve(vector<vector<double>>& DistMatrix, vector<int>& Assignment)
-{
-    unsigned int nRows = DistMatrix.size();
-    unsigned int nCols = DistMatrix[0].size();
-
-    double *distMatrixIn = new double[nRows * nCols];
-    int *assignment = new int[nRows];
-    double cost = 0.0;
-
-    // Fill in the distMatrixIn. Mind the index is "i + nRows * j".
-    // Here the cost matrix of size MxN is defined as a double precision array of N*M elements.
-    // In the solving functions matrices are seen to be saved MATLAB-internally in row-order.
-    // (i.e. the matrix [1 2; 3 4] will be stored as a vector [1 3 2 4], NOT [1 2 3 4]).
-    for (unsigned int i = 0; i < nRows; i++)
-        for (unsigned int j = 0; j < nCols; j++)
-            distMatrixIn[i + nRows * j] = DistMatrix[i][j];
-
-    // call solving function
-    assignmentoptimal(assignment, &cost, distMatrixIn, nRows, nCols);
-
-    Assignment.clear();
-    for (unsigned int r = 0; r < nRows; r++)
-        Assignment.push_back(assignment[r]);
-
-    delete[] distMatrixIn;
-    delete[] assignment;
-    return cost;
-}
-
-
-//********************************************************//
-// Solve optimal solution for assignment problem using Munkres algorithm, also known as Hungarian Algorithm.
-//********************************************************//
-void HungarianAlgorithm::assignmentoptimal(int *assignment, double *cost, double *distMatrixIn, int nOfRows, int nOfColumns)
-{
-    double *distMatrix, *distMatrixTemp, *distMatrixEnd, *columnEnd, value, minValue;
-    bool *coveredColumns, *coveredRows, *starMatrix, *newStarMatrix, *primeMatrix;
-    int nOfElements, minDim, row, col;
-
-    /* initialization */
-    *cost = 0;
-    for (row = 0; row<nOfRows; row++)
-        assignment[row] = -1;
-
-    /* generate working copy of distance Matrix */
-    /* check if all matrix elements are positive */
-    nOfElements = nOfRows * nOfColumns;
-    distMatrix = (double *)malloc(nOfElements * sizeof(double));
-    distMatrixEnd = distMatrix + nOfElements;
-
-    for (row = 0; row<nOfElements; row++)
-    {
-        value = distMatrixIn[row];
-        if (value < 0)
-            cerr << "All matrix elements have to be non-negative." << endl;
-        distMatrix[row] = value;
-    }
-
-
-    /* memory allocation */
-    coveredColumns = (bool *)calloc(nOfColumns, sizeof(bool));
-    coveredRows = (bool *)calloc(nOfRows, sizeof(bool));
-    starMatrix = (bool *)calloc(nOfElements, sizeof(bool));
-    primeMatrix = (bool *)calloc(nOfElements, sizeof(bool));
-    newStarMatrix = (bool *)calloc(nOfElements, sizeof(bool)); /* used in step4 */
-
-    /* preliminary steps */
-    if (nOfRows <= nOfColumns)
-    {
-        minDim = nOfRows;
-
-        for (row = 0; row<nOfRows; row++)
-        {
-            /* find the smallest element in the row */
-            distMatrixTemp = distMatrix + row;
-            minValue = *distMatrixTemp;
-            distMatrixTemp += nOfRows;
-            while (distMatrixTemp < distMatrixEnd)
-            {
-                value = *distMatrixTemp;
-                if (value < minValue)
-                    minValue = value;
-                distMatrixTemp += nOfRows;
-            }
-
-            /* subtract the smallest element from each element of the row */
-            distMatrixTemp = distMatrix + row;
-            while (distMatrixTemp < distMatrixEnd)
-            {
-                *distMatrixTemp -= minValue;
-                distMatrixTemp += nOfRows;
-            }
-        }
-
-        /* Steps 1 and 2a */
-        for (row = 0; row<nOfRows; row++)
-            for (col = 0; col<nOfColumns; col++)
-                if (fabs(distMatrix[row + nOfRows*col]) < DBL_EPSILON)
-                    if (!coveredColumns[col])
-                    {
-                        starMatrix[row + nOfRows*col] = true;
-                        coveredColumns[col] = true;
-                        break;
-                    }
-    }
-    else /* if(nOfRows > nOfColumns) */
-    {
-        minDim = nOfColumns;
-
-        for (col = 0; col<nOfColumns; col++)
-        {
-            /* find the smallest element in the column */
-            distMatrixTemp = distMatrix + nOfRows*col;
-            columnEnd = distMatrixTemp + nOfRows;
-
-            minValue = *distMatrixTemp++;
-            while (distMatrixTemp < columnEnd)
-            {
-                value = *distMatrixTemp++;
-                if (value < minValue)
-                    minValue = value;
-            }
-
-            /* subtract the smallest element from each element of the column */
-            distMatrixTemp = distMatrix + nOfRows*col;
-            while (distMatrixTemp < columnEnd)
-                *distMatrixTemp++ -= minValue;
-        }
-
-        /* Steps 1 and 2a */
-        for (col = 0; col<nOfColumns; col++)
-            for (row = 0; row<nOfRows; row++)
-                if (fabs(distMatrix[row + nOfRows*col]) < DBL_EPSILON)
-                    if (!coveredRows[row])
-                    {
-                        starMatrix[row + nOfRows*col] = true;
-                        coveredColumns[col] = true;
-                        coveredRows[row] = true;
-                        break;
-                    }
-        for (row = 0; row<nOfRows; row++)
-            coveredRows[row] = false;
-
-    }
-
-    /* move to step 2b */
-    step2b(assignment, distMatrix, starMatrix, newStarMatrix, primeMatrix, coveredColumns, coveredRows, nOfRows, nOfColumns, minDim);
-
-    /* compute cost and remove invalid assignments */
-    computeassignmentcost(assignment, cost, distMatrixIn, nOfRows);
-
-    /* free allocated memory */
-    free(distMatrix);
-    free(coveredColumns);
-    free(coveredRows);
-    free(starMatrix);
-    free(primeMatrix);
-    free(newStarMatrix);
-
-    return;
-}
-
-/********************************************************/
-void HungarianAlgorithm::buildassignmentvector(int *assignment, bool *starMatrix, int nOfRows, int nOfColumns)
-{
-    int row, col;
-
-    for (row = 0; row<nOfRows; row++)
-        for (col = 0; col<nOfColumns; col++)
-            if (starMatrix[row + nOfRows*col])
-            {
-#ifdef ONE_INDEXING
-                assignment[row] = col + 1; /* MATLAB-Indexing */
-#else
-                assignment[row] = col;
-#endif
-                break;
-            }
-}
-
-/********************************************************/
-void HungarianAlgorithm::computeassignmentcost(int *assignment, double *cost, double *distMatrix, int nOfRows)
-{
-    int row, col;
-
-    for (row = 0; row<nOfRows; row++)
-    {
-        col = assignment[row];
-        if (col >= 0)
-            *cost += distMatrix[row + nOfRows*col];
-    }
-}
-
-/********************************************************/
-void HungarianAlgorithm::step2a(int *assignment, double *distMatrix, bool *starMatrix, bool *newStarMatrix, bool *primeMatrix, bool *coveredColumns, bool *coveredRows, int nOfRows, int nOfColumns, int minDim)
-{
-    bool *starMatrixTemp, *columnEnd;
-    int col;
-
-    /* cover every column containing a starred zero */
-    for (col = 0; col<nOfColumns; col++)
-    {
-        starMatrixTemp = starMatrix + nOfRows*col;
-        columnEnd = starMatrixTemp + nOfRows;
-        while (starMatrixTemp < columnEnd){
-            if (*starMatrixTemp++)
-            {
-                coveredColumns[col] = true;
-                break;
-            }
-        }
-    }
-
-    /* move to step 3 */
-    step2b(assignment, distMatrix, starMatrix, newStarMatrix, primeMatrix, coveredColumns, coveredRows, nOfRows, nOfColumns, minDim);
-}
-
-/********************************************************/
-void HungarianAlgorithm::step2b(int *assignment, double *distMatrix, bool *starMatrix, bool *newStarMatrix, bool *primeMatrix, bool *coveredColumns, bool *coveredRows, int nOfRows, int nOfColumns, int minDim)
-{
-    int col, nOfCoveredColumns;
-
-    /* count covered columns */
-    nOfCoveredColumns = 0;
-    for (col = 0; col<nOfColumns; col++)
-        if (coveredColumns[col])
-            nOfCoveredColumns++;
-
-    if (nOfCoveredColumns == minDim)
-    {
-        /* algorithm finished */
-        buildassignmentvector(assignment, starMatrix, nOfRows, nOfColumns);
-    }
-    else
-    {
-        /* move to step 3 */
-        step3(assignment, distMatrix, starMatrix, newStarMatrix, primeMatrix, coveredColumns, coveredRows, nOfRows, nOfColumns, minDim);
-    }
-
-}
-
-/********************************************************/
-void HungarianAlgorithm::step3(int *assignment, double *distMatrix, bool *starMatrix, bool *newStarMatrix, bool *primeMatrix, bool *coveredColumns, bool *coveredRows, int nOfRows, int nOfColumns, int minDim)
-{
-    bool zerosFound;
-    int row, col, starCol;
-
-    zerosFound = true;
-    while (zerosFound)
-    {
-        zerosFound = false;
-        for (col = 0; col<nOfColumns; col++)
-            if (!coveredColumns[col])
-                for (row = 0; row<nOfRows; row++)
-                    if ((!coveredRows[row]) && (fabs(distMatrix[row + nOfRows*col]) < DBL_EPSILON))
-                    {
-                        /* prime zero */
-                        primeMatrix[row + nOfRows*col] = true;
-
-                        /* find starred zero in current row */
-                        for (starCol = 0; starCol<nOfColumns; starCol++)
-                            if (starMatrix[row + nOfRows*starCol])
-                                break;
-
-                        if (starCol == nOfColumns) /* no starred zero found */
-                        {
-                            /* move to step 4 */
-                            step4(assignment, distMatrix, starMatrix, newStarMatrix, primeMatrix, coveredColumns, coveredRows, nOfRows, nOfColumns, minDim, row, col);
-                            return;
-                        }
-                        else
-                        {
-                            coveredRows[row] = true;
-                            coveredColumns[starCol] = false;
-                            zerosFound = true;
-                            break;
-                        }
-                    }
-    }
-
-    /* move to step 5 */
-    step5(assignment, distMatrix, starMatrix, newStarMatrix, primeMatrix, coveredColumns, coveredRows, nOfRows, nOfColumns, minDim);
-}
-
-/********************************************************/
-void HungarianAlgorithm::step4(int *assignment, double *distMatrix, bool *starMatrix, bool *newStarMatrix, bool *primeMatrix, bool *coveredColumns, bool *coveredRows, int nOfRows, int nOfColumns, int minDim, int row, int col)
-{
-    int n, starRow, starCol, primeRow, primeCol;
-    int nOfElements = nOfRows*nOfColumns;
-
-    /* generate temporary copy of starMatrix */
-    for (n = 0; n<nOfElements; n++)
-        newStarMatrix[n] = starMatrix[n];
-
-    /* star current zero */
-    newStarMatrix[row + nOfRows*col] = true;
-
-    /* find starred zero in current column */
-    starCol = col;
-    for (starRow = 0; starRow<nOfRows; starRow++)
-        if (starMatrix[starRow + nOfRows*starCol])
-            break;
-
-    while (starRow<nOfRows)
-    {
-        /* unstar the starred zero */
-        newStarMatrix[starRow + nOfRows*starCol] = false;
-
-        /* find primed zero in current row */
-        primeRow = starRow;
-        for (primeCol = 0; primeCol<nOfColumns; primeCol++)
-            if (primeMatrix[primeRow + nOfRows*primeCol])
-                break;
-
-        /* star the primed zero */
-        newStarMatrix[primeRow + nOfRows*primeCol] = true;
-
-        /* find starred zero in current column */
-        starCol = primeCol;
-        for (starRow = 0; starRow<nOfRows; starRow++)
-            if (starMatrix[starRow + nOfRows*starCol])
-                break;
-    }
-
-    /* use temporary copy as new starMatrix */
-    /* delete all primes, uncover all rows */
-    for (n = 0; n<nOfElements; n++)
-    {
-        primeMatrix[n] = false;
-        starMatrix[n] = newStarMatrix[n];
-    }
-    for (n = 0; n<nOfRows; n++)
-        coveredRows[n] = false;
-
-    /* move to step 2a */
-    step2a(assignment, distMatrix, starMatrix, newStarMatrix, primeMatrix, coveredColumns, coveredRows, nOfRows, nOfColumns, minDim);
-}
-
-/********************************************************/
-void HungarianAlgorithm::step5(int *assignment, double *distMatrix, bool *starMatrix, bool *newStarMatrix, bool *primeMatrix, bool *coveredColumns, bool *coveredRows, int nOfRows, int nOfColumns, int minDim)
-{
-    double h, value;
-    int row, col;
-
-    /* find smallest uncovered element h */
-    h = DBL_MAX;
-    for (row = 0; row<nOfRows; row++)
-        if (!coveredRows[row])
-            for (col = 0; col<nOfColumns; col++)
-                if (!coveredColumns[col])
-                {
-                    value = distMatrix[row + nOfRows*col];
-                    if (value < h)
-                        h = value;
-                }
-
-    /* add h to each covered row */
-    for (row = 0; row<nOfRows; row++)
-        if (coveredRows[row])
-            for (col = 0; col<nOfColumns; col++)
-                distMatrix[row + nOfRows*col] += h;
-
-    /* subtract h from each uncovered column */
-    for (col = 0; col<nOfColumns; col++)
-        if (!coveredColumns[col])
-            for (row = 0; row<nOfRows; row++)
-                distMatrix[row + nOfRows*col] -= h;
-
-    /* move to step 3 */
-    step3(assignment, distMatrix, starMatrix, newStarMatrix, primeMatrix, coveredColumns, coveredRows, nOfRows, nOfColumns, minDim);
-}
-

src/detection/yolov5-trt-v2.0/src/KalmanTracker.cpp

@@ -1,106 +0,0 @@
-//
-// Created by lqx on 20-4-23.
-//
-
-///////////////////////////////////////////////////////////////////////////////
-// KalmanTracker.cpp: KalmanTracker Class Implementation Declaration
-
-#include "KalmanTracker.h"
-#include "detect_obstacle.h"
-#include <iostream>
-
-int KalmanTracker::kf_count = 0;
-
-// initialize Kalman filter
-void KalmanTracker::init_kf(StateType stateMat)
-{
-    int stateNum = 7;
-    int measureNum = 4;
-    kf = KalmanFilter(stateNum, measureNum, 0);
-
-    measurement = Mat::zeros(measureNum, 1, CV_32F);
-
-    kf.transitionMatrix = (Mat_<float>(stateNum, stateNum) <<
-                                                            1, 0, 0, 0, 1, 0, 0,
-            0, 1, 0, 0, 0, 1, 0,
-            0, 0, 1, 0, 0, 0, 1,
-            0, 0, 0, 1, 0, 0, 0,
-            0, 0, 0, 0, 1, 0, 0,
-            0, 0, 0, 0, 0, 1, 0,
-            0, 0, 0, 0, 0, 0, 1);
-
-    setIdentity(kf.measurementMatrix);
-    setIdentity(kf.processNoiseCov, Scalar::all(1e-2));
-    setIdentity(kf.measurementNoiseCov, Scalar::all(1e-1));
-    setIdentity(kf.errorCovPost, Scalar::all(1));
-
-    // initialize state vector with bounding box in [cx,cy,s,r] style
-    kf.statePost.at<float>(0, 0) = stateMat.x + stateMat.width / 2;
-    kf.statePost.at<float>(1, 0) = stateMat.y + stateMat.height / 2;
-    kf.statePost.at<float>(2, 0) = stateMat.area();
-    kf.statePost.at<float>(3, 0) = stateMat.width / stateMat.height;
-}
-
-// Predict the estimated bounding box.
-StateType KalmanTracker::predict()
-{
-    // predict
-    Mat p = kf.predict();
-    m_age += 1;
-
-    if (m_time_since_update > od::max_age)
-        m_hit_streak = 0;
-    m_time_since_update += 1;
-    StateType predictBox = get_rect_xysr(p.at<float>(0, 0), p.at<float>(1, 0), p.at<float>(2, 0), p.at<float>(3, 0));
-    m_history.push_back(predictBox);
-    return m_history.back();
-}
-
-// Update the state vector with observed bounding box.
-void KalmanTracker::update(StateType stateMat, int classId, float prob)
-{
-    m_time_since_update = 0;
-    m_history.clear();
-    m_hits += 1;
-    m_hit_streak += 1;
-    m_class_id = classId;
-
-    //只保留最近的5帧检测类别
-    if (m_class_history.size() >= 5)
-        m_class_history.erase(m_class_history.begin());
-    m_class_history.push_back(classId);
-
-    m_prob = prob;
-    // measurement
-    measurement.at<float>(0, 0) = stateMat.x + stateMat.width / 2;
-    measurement.at<float>(1, 0) = stateMat.y + stateMat.height / 2;
-    measurement.at<float>(2, 0) = stateMat.area();
-    measurement.at<float>(3, 0) = stateMat.width / stateMat.height;
-
-    // update
-    kf.correct(measurement);
-}
-
-// Return the current state vector
-StateType KalmanTracker::get_state()
-{
-    Mat s = kf.statePost;
-    return get_rect_xysr(s.at<float>(0, 0), s.at<float>(1, 0), s.at<float>(2, 0), s.at<float>(3, 0));
-}
-
-
-// Convert bounding box from [cx,cy,s,r] to [x,y,w,h] style.
-StateType KalmanTracker::get_rect_xysr(float cx, float cy, float s, float r)
-{
-    float w = sqrt(s * r);
-    float h = s / w;
-    float x = (cx - w / 2);
-    float y = (cy - h / 2);
-
-    if (x < 0 && cx > 0)
-        x = 0;
-    if (y < 0 && cy > 0)
-        y = 0;
-
-    return StateType(x, y, w, h);
-}

src/detection/yolov5-trt-v2.0/src/detect_obstacle.cpp

@@ -1,260 +0,0 @@
-
-#include <set>
-#include "detect_obstacle.h"
-
-namespace od{
-// Computes IOU between two bounding boxes
-double GetIOU(Rect_<float> bb_test, Rect_<float> bb_gt)
-{
-    float in = (bb_test & bb_gt).area();
-    float un = bb_test.area() + bb_gt.area() - in;
-
-    if (un < DBL_EPSILON)
-        return 0;
-
-    return (double)(in / un);
-}
-
-
-//tracking obstacle
-bool TrackObstacle(int frame_count,vector<KalmanTracker> &trackers,vector<bbox_t> &outs,vector<od::TrackingBox> &track_result)
-{
-    // variables used in the for-loop
-    vector<Rect_<float>> predictedBoxes;
-    vector<vector<double>> iouMatrix;
-    vector<int> assignment;
-    set<int> unmatchedDetections;
-    set<int> unmatchedTrajectories;
-    set<int> allItems;
-    set<int> matchedItems;
-    vector<cv::Point> matchedPairs;
-    unsigned int trkNum = 0;
-    unsigned int detNum = 0;
-    vector<od::DetectBox> detect_outs;
-    //bbox_t to Detect_box
-    for(unsigned int i=0;i<outs.size();i++)
-    {
-        od::DetectBox detect_temp;
-        detect_temp.class_id = outs[i].obj_id;
-        detect_temp.prob = outs[i].prob;
-        float tpx = outs[i].x;
-        float tpy = outs[i].y;
-        float tpw = outs[i].w;
-        float tph = outs[i].h;
-        //detect_temp.box = Rect_<float>(Point_<float>(tpx, tpy),Point_<float>(tpx + tpw, tpy + tph));
-        detect_temp.box = Rect_<float>(tpx,tpy,tpw,tph);
-        detect_outs.push_back(detect_temp);
-    }
-    //tracking
-    if (trackers.size() == 0) // the first frame met
-    {
-        // initialize kalman trackers using first detections.
-        for (unsigned int i = 0; i < outs.size(); i++)
-        {
-            KalmanTracker trk = KalmanTracker(detect_outs[i].box,
-                                              detect_outs[i].class_id,
-                                              detect_outs[i].prob);
-            trackers.push_back(trk);
-        }
-        return false;
-    }
-    ///////////////////////////////////////
-    // 3.1. get predicted locations from existing trackers.
-    predictedBoxes.clear();
-
-    for (auto it = trackers.begin(); it != trackers.end();)
-    {
-        Rect_<float> pBox = (*it).predict();
-        if (pBox.x >= 0 && pBox.y >= 0)
-        {
-            predictedBoxes.push_back(pBox);
-            it++;
-        }
-        else
-        {
-
-            cerr << "Box invalid at frame: " << frame_count <<" id "<<(*it).m_id+1<<endl;
-            it = trackers.erase(it);
-
-        }
-    }
-
-    if (trackers.size() == 0 || detect_outs.size() == 0) return false;
-
-    ///////////////////////////////////////
-    // 3.2. associate detections to tracked object (both represented as bounding boxes)
-    // dets : detFrameData[fi]
-    trkNum = predictedBoxes.size();
-    detNum = outs.size();
-
-    iouMatrix.clear();
-    iouMatrix.resize(trkNum, vector<double>(detNum, 0));
-
-    for (unsigned int i = 0; i < trkNum; i++) // compute iou matrix as a distance matrix
-    {
-        for (unsigned int j = 0; j < detNum; j++)
-        {
-            // use 1-iou because the hungarian algorithm computes a minimum-cost assignment.
-            iouMatrix[i][j] = 1 - GetIOU(predictedBoxes[i], detect_outs[j].box);
-        }
-    }
-    // solve the assignment problem using hungarian algorithm.
-    // the resulting assignment is [track(prediction) : detection], with len=preNum
-    HungarianAlgorithm HungAlgo;
-    assignment.clear();
-    HungAlgo.Solve(iouMatrix, assignment);
-
-    // find matches, unmatched_detections and unmatched_predictions
-    unmatchedTrajectories.clear();
-    unmatchedDetections.clear();
-    allItems.clear();
-    matchedItems.clear();
-    if (detNum > trkNum) //	there are unmatched detections
-    {
-        for (unsigned int n = 0; n < detNum; n++)
-            allItems.insert(n);
-
-        for (unsigned int i = 0; i < trkNum; ++i)
-            matchedItems.insert(assignment[i]);
-
-        set_difference(allItems.begin(), allItems.end(),
-                       matchedItems.begin(), matchedItems.end(),
-                       insert_iterator<set<int>>(unmatchedDetections, unmatchedDetections.begin()));
-    }
-    else if (detNum < trkNum) // there are unmatched trajectory/predictions
-    {
-        for (unsigned int i = 0; i < trkNum; ++i)
-            if (assignment[i] == -1) // unassigned label will be set as -1 in the assignment algorithm
-                unmatchedTrajectories.insert(i);
-    }
-    // filter out matched with low IOU
-    matchedPairs.clear();
-    for (unsigned int i = 0; i < trkNum; ++i)
-    {
-        if (assignment[i] == -1) // pass over invalid values
-            continue;
-        if (1 - iouMatrix[i][assignment[i]] < od::iouThreshold)
-        {
-            unmatchedTrajectories.insert(i);
-            unmatchedDetections.insert(assignment[i]);
-        }
-        else
-            matchedPairs.push_back(cv::Point(i, assignment[i]));
-    }
-    ///////////////////////////////////////
-    // 3.3. updating trackers
-    // update matched trackers with assigned detections.
-    // each prediction is corresponding to a tracker
-    int detIdx, trkIdx;
-    for (unsigned int i = 0; i < matchedPairs.size(); i++)
-    {
-        trkIdx = matchedPairs[i].x;
-        detIdx = matchedPairs[i].y;
-        trackers[trkIdx].update(detect_outs[detIdx].box,
-                                detect_outs[detIdx].class_id,
-                                detect_outs[detIdx].prob);
-    }
-    // create and initialise new trackers for unmatched detections
-    for (auto umd : unmatchedDetections)
-    {
-        KalmanTracker tracker = KalmanTracker(detect_outs[umd].box,
-                                              detect_outs[umd].class_id,
-                                              detect_outs[umd].prob);
-        trackers.push_back(tracker);
-    }
-
-#if 0
-    //get unique trackers,merg same trackers
-    unsigned int trackers_num = trackers.size();
-    iouMatrix.clear();
-    iouMatrix.resize(trackers_num, vector<double>(trackers_num, 0));
-    for (unsigned int i = 0; i < trackers_num; i++) // compute iou matrix as a distance matrix
-    {
-        for (unsigned int j = 0; j < trackers_num; j++)
-        {
-            // use 1-iou because the hungarian algorithm computes a minimum-cost assignment.
-            if(j==i)
-                iouMatrix[i][j] = 1;
-            else
-                iouMatrix[i][j] = 1 - GetIOU(trackers[i].get_state(), trackers[j].get_state());
-        }
-    }
-    // solve the assignment problem using hungarian algorithm.
-    // the resulting assignment is [track(prediction) : detection], with len=preNum
-    assignment.clear();
-    HungAlgo.Solve(iouMatrix, assignment);
-    // filter out matched with low IOU
-    matchedPairs.clear();
-    for (unsigned int i = 0; i < trackers_num; ++i)
-    {
-        if (assignment[i] == -1) // pass over invalid values
-            continue;
-        if (iouMatrix[i][assignment[i]] < od::iouThreshold)
-        {
-            matchedPairs.push_back(cv::Point(i, assignment[i]));
-        }
-    }
-    int index1,index2;
-    vector<int> delete_index;
-    for (unsigned int i = 0; i < matchedPairs.size(); i++)
-    {
-        index1 = matchedPairs[i].x;
-        index2 = matchedPairs[i].y;
-        if(index1 >= index2)
-            continue;
-        if((trackers[index1].m_id > trackers[index2].m_id) && (trackers[index1].m_class_history.size()>0))
-        {
-            trackers[index1].m_id = trackers[index2].m_id;
-            trackers[index1].m_class_history.insert(trackers[index1].m_class_history.begin(),
-                                                    trackers[index2].m_class_history.begin(),trackers[index2].m_class_history.end());
-            delete_index.push_back(index2);
-        }
-        else if((trackers[index2].m_id > trackers[index1].m_id) && (trackers[index2].m_class_history.size()>0))
-        {
-            trackers[index2].m_id = trackers[index1].m_id;
-            trackers[index2].m_class_history.insert(trackers[index2].m_class_history.begin(),
-                                                    trackers[index1].m_class_history.begin(),trackers[index1].m_class_history.end());
-            delete_index.push_back(index1);
-
-        }
-    }
-    for(unsigned int i = 0; i < delete_index.size(); i++)
-    {
-        int idx = delete_index[i] - i;
-        trackers.erase(trackers.begin() + idx);
-    }
-#endif
-
-    // get trackers' output
-    track_result.clear();
-    for (auto it = trackers.begin(); it != trackers.end();)
-    {
-        if (((*it).m_time_since_update <= od::max_age) &&
-                ((*it).m_hit_streak >= od::min_hits || frame_count <= od::min_hits))
-        {
-            od::TrackingBox res;
-            res.box = (*it).get_state();
-            res.id = (*it).m_id + 1;
-            res.frame = frame_count;
-            res.class_id = (*it).m_class_id;
-            res.prob = (*it).m_prob;
-            res.class_history = (*it).m_class_history;
-            track_result.push_back(res);
-            it++;
-        }
-        else
-            it ++;
-        //remove dead tracklet
-        if(it != trackers.end() && (*it).m_time_since_update > od::max_age)
-        {
-            it = trackers.erase(it);
-        }
-    }
-    if(track_result.size()>0)
-        return true;
-    else return false;
-}
-}
-
-
-

src/detection/yolov5-trt-v2.0/yaml/camera.yaml

@@ -1,12 +0,0 @@
-%YAML:1.0
-
-cameraMatrix:
-    rows: 3
-    cols: 3
-    dt: f
-    data: [329.8960137714725, 0.0, 306.98655702722937, 0.0, 329.8476481330821, 147.92102146061933, 0.0, 0.0, 1.0]
-distCoeffs: 
-    rows: 1
-    cols: 4
-    dt: f
-    data: [0.06540082256039308, 0.003679070986049775, 0.02795206257493852, 0.019310323874426313]

src/detection/yolov5-trt-v2.0/yaml/camera147.yaml

@@ -1,12 +0,0 @@
-%YAML:1.0
-
-cameraMatrix:
-    rows: 3
-    cols: 3
-    dt: f
-    data: [329.8960137714725, 0.0, 306.98655702722937, 0.0, 329.8476481330821, 147.92102146061933, 0.0, 0.0, 1.0]
-distCoeffs: 
-    rows: 1
-    cols: 4
-    dt: f
-    data: [0.06540082256039308, 0.003679070986049775, 0.02795206257493852, 0.019310323874426313]

src/detection/yolov5-trt-v2.0/yaml/camera_middle_640_360.yaml

@@ -1,12 +0,0 @@
-%YAML:1.0
-
-cameraMatrix:
-    rows: 3
-    cols: 3
-    dt: f
-    data: [380.0899898274016, 0.0, 312.50422695333214,0.0, 378.9971350621845, 158.00203854195047, 0.0, 0.0, 1.0]
-distCoeffs: 
-    rows: 1
-    cols: 5
-    dt: f
-    data: [-0.31123247928669645, 0.14133965413035393, -0.0030934980146547883, -0.0005549080413969106,-0.03447171176210059]

src/detection/yolov5-trt-v2.0/yaml/init.yaml

@@ -1,16 +0,0 @@
-source:
-- 0
-
-src:
-- [140, 370]
-- [220, 298]
-- [410, 292]
-- [478, 360]
-dst:
-- [125, 371]
-- [125, 218]
-- [484, 218]
-- [484, 370]
-
-meters: 2.0
-pixels: 640

src/detection/yolov5-trt-v2.0/yaml/pers_ori.yaml

@@ -1,10 +0,0 @@
-%YAML:1.0
-
-
-board_left_top: [267,188]
-
-board_left_bottom: [174,251]
-
-board_right_top: [347,189]
-
-board_right_bottom: [447,257]

src/detection/yolov5-trt-v2.0/yaml_bak_zhengke/camera.yaml

@@ -1,12 +0,0 @@
-%YAML:1.0
-
-cameraMatrix:
-    rows: 3
-    cols: 3
-    dt: f
-    data: [329.8960137714725, 0.0, 306.98655702722937, 0.0, 329.8476481330821, 147.92102146061933, 0.0, 0.0, 1.0]
-distCoeffs: 
-    rows: 1
-    cols: 4
-    dt: f
-    data: [0.06540082256039308, 0.003679070986049775, 0.02795206257493852, 0.019310323874426313]

src/detection/yolov5-trt-v2.0/yaml_bak_zhengke/camera_ori.yaml

@@ -1,12 +0,0 @@
-%YAML:1.0
-
-cameraMatrix:
-    rows: 3
-    cols: 3
-    dt: f
-    data: [283.2175041932327, 0.0, 618.5248476440728, 0.0, 282.8089524514217, 386.63584727262344, 0.0, 0.0, 1.0]
-distCoeffs: 
-    rows: 1
-    cols: 4
-    dt: f
-    data: [0.1362060349662609,-0.027120543730038277,0.16273833878800492,-0.16242892318961671]

src/detection/yolov5-trt-v2.0/yaml_bak_zhengke/init.yaml

@@ -1,16 +0,0 @@
-source:
-- 0
-
-src:
-- [140, 370]
-- [220, 298]
-- [410, 292]
-- [478, 360]
-dst:
-- [125, 371]
-- [125, 218]
-- [484, 218]
-- [484, 370]
-
-meters: 2.0
-pixels: 640

src/detection/yolov5-trt-v2.0/yaml_bak_zhengke/pers.yaml

@@ -1,19 +0,0 @@
-%YAML:1.0
-
-
-board_left_top: [255,189]
-
-board_left_bottom: [193,232]
-
-board_right_top: [342,189]
-
-board_right_bottom: [403,232]
-
-
-board_left_top_dst: [569,409]
-
-board_left_bottom_dst: [569,504]
-
-board_right_top_dst: [635,409]
-
-board_right_bottom_dst: [635,504]

src/detection/yolov5-trt-v2.0/yaml_bak_zhengke/pers3.yaml

@@ -1,19 +0,0 @@
-%YAML:1.0
-
-
-board_left_top: [569,459]
-
-board_left_bottom: [509,504]
-
-board_right_top: [635,459]
-
-board_right_bottom: [696,504]
-
-
-board_left_top_dst: [569,409]
-
-board_left_bottom_dst: [569,504]
-
-board_right_top_dst: [635,409]
-
-board_right_bottom_dst: [635,504]

src/detection/yolov5-trt-v2.0/yaml_bak_zhengke/pers_1.yaml

@@ -1,19 +0,0 @@
-%YAML:1.0
-
-
-board_left_top: [569,459]
-
-board_left_bottom: [509,504]
-
-board_right_top: [635,459]
-
-board_right_bottom: [696,504]
-
-
-board_left_top_dst: [509,459]
-
-board_left_bottom_dst: [509,550]
-
-board_right_top_dst: [635,459]
-
-board_right_bottom_dst: [635,550]

src/detection/yolov5-trt-v2.0/yaml_bak_zhengke/pers_2.yaml

@@ -1,19 +0,0 @@
-%YAML:1.0
-
-
-board_left_top: [126,276]
-
-board_left_bottom: [33,312]
-
-board_right_top: [206,276]
-
-board_right_bottom: [273,312]
-
-
-board_left_top_dst: [200,100]
-
-board_left_bottom_dst: [200,800]
-
-board_right_top_dst: [900,100]
-
-board_right_bottom_dst: [900,800]

src/detection/yolov5-trt-v2.0/yaml_bak_zhengke/pers_ori.yaml

@@ -1,10 +0,0 @@
-%YAML:1.0
-
-
-board_left_top: [283,352]
-
-board_left_bottom: [269,406]
-
-board_right_top: [353,352]
-
-board_right_bottom: [363,406]

src/detection/yolov5-trt-v2.0/yolov5-trt-v2.0.pro

@@ -1,83 +0,0 @@
-QT -= gui
-
-CONFIG += c++11 console
-CONFIG -= app_bundle
-
-# The following define makes your compiler emit warnings if you use
-# any Qt feature that has been marked deprecated (the exact warnings
-# depend on your compiler). Please consult the documentation of the
-# deprecated API in order to know how to port your code away from it.
-DEFINES += QT_DEPRECATED_WARNINGS
-
-# You can also make your code fail to compile if it uses deprecated APIs.
-# In order to do so, uncomment the following line.
-# You can also select to disable deprecated APIs only up to a certain version of Qt.
-#DEFINES += QT_DISABLE_DEPRECATED_BEFORE=0x060000    # disables all the APIs deprecated before Qt 6.0.0
-
-# Default rules for deployment.
-qnx: target.path = /tmp/$${TARGET}/bin
-else: unix:!android: target.path = /opt/$${TARGET}/bin
-!isEmpty(target.path): INSTALLS += target
-
-
-INCLUDEPATH += yolov5-trt-v2.0/include \
-               include
-
-SOURCES += main_new.cpp \
-           src/Hungarian.cpp \
-           src/KalmanTracker.cpp \
-           src/detect_obstacle.cpp\
-           ../../include/msgtype/rawpic.pb.cc \
-           ../../include/msgtype/lightarray.pb.cc \
-           ../../include/msgtype/brainstate.pb.cc
-
-
-INCLUDEPATH += /usr/src/tensorrt/include \
-               /usr/include/aarch64-linux-gnu \
-               /usr/include/opencv4 \
-               /usr/local/cuda/include \
-               /usr/src/tensorrt/samples/common \
-               /usr/local/include \
-               /usr/include/opencv2 \
-               /usr/include/opencv \
-
-
-LIBS += /usr/lib/aarch64-linux-gnu/libopencv_highgui.so \
-        /usr/lib/aarch64-linux-gnu/libopencv_core.so    \
-        /usr/lib/aarch64-linux-gnu/libopencv_imgproc.so \
-        /usr/lib/aarch64-linux-gnu/libopencv_imgcodecs.so \
-        /usr/lib/aarch64-linux-gnu/libopencv_videoio.so \
-        /usr/lib/aarch64-linux-gnu/libopencv_video.so \
-        /usr/lib/aarch64-linux-gnu/libopencv_calib3d.so \
-
-LIBS += /usr/local/cuda/lib64/libcudart.so \
-        /usr/lib/aarch64-linux-gnu/libnvparsers.so \
-        /usr/lib/aarch64-linux-gnu/libnvinfer.so \
-        /usr/lib/aarch64-linux-gnu/libnvonnxparser.so \
-        /usr/lib/aarch64-linux-gnu/libnvinfer_plugin.so
-
-LIBS += -L"/usr/local/lib" -lprotobuf
-LIBS += -L/usr/lib/aarch64-linux-gnu -lstdc++fs
-
-
-
-INCLUDEPATH += ../../../include
-LIBS +=  ../../../bin/libxmlparam.so \
-         ../../../bin/libivlog.so \
-         ../../../bin/libivfault.so \
-         ../../../bin/libmodulecomm.so
-
-
-!include(../../../include/common.pri ) {
-    error( "Couldn't find the common.pri file!" )
-}
-
-!include(../../../include/ivprotobuf.pri ) {
-    error( "Couldn't find the ivprotobuf.pri file!" )
-}
-!include(../../../include/ivopencv.pri ) {
-    error( "Couldn't find the ivopencv.pri file!" )
-}
-INCLUDEPATH += $$PWD/../../include/msgtype
-LIBS += -L/usr/lib/aarch64-linux-gnu/ -lglog
-