C++Yolov4目标检测实战
Introduction
今年2月份，Yolo之父Joseph Redmon由于Yolo被用于军事和隐私窥探退出CV界表示抗议，就当我们以为Yolo系列就此终结的时候，4月24日，Yolov4横空出世，新的接棒者出现，而一作正是赫赫有名的AB大神。
paper github
在本篇文章里，我们先不急去探究Yolov4的原理，而是从工程的角度来使用Yolov4。首先我们来看一下，Yolov4的性能有多么强劲，下面是使用不同显卡的时候，主流目标检测器的性能，从下图我们发现，Yolov4真的比自己的前辈Yolov3强劲了很多。
官方darknet
darknetAB
官方工程的使用在AB大神的github里面已经讲的非常清楚，可以实现Yolov4网络的训练，检测等功能。但是使用官方darknet项目我们很难直接进行定制化的项目开发，因此本文利用官方项目提供的C++接口进行目标检测实战。
Yolov4实战
首先我们从github下载yolov4.weights,不能上外网的同学下载应该很慢，我上传到百度云盘里，方便大家下载，链接如下：
链接: https://pan.baidu.com/s/14U9pkxJE3MHYj7KCWHnkNw 提取码: 54v4
工程包括两个文件main.cpp, yolo_v2_class.hpp.我把自己的整个工程上传到github上，同学们可以直接去git clone下来，觉得有用的同学麻烦star一下，谢谢。
main.cpp
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include // std::mutex, std::unique_lock
#include
// It makes sense only for video-Camera (not for video-File)
// To use - uncomment the following line. Optical-flow is supported only by OpenCV 3.x - 4.x
//#define TRACK_OPTFLOW
//#define GPU
// To use 3D-stereo camera ZED - uncomment the following line. ZED_SDK should be installed.
//#define ZED_STEREO
#include "yolo_v2_class.hpp" // imported functions from DLL
#ifdef OPENCV
#ifdef ZED_STEREO
#include
#if ZED_SDK_MAJOR_VERSION == 2
#define ZED_STEREO_2_COMPAT_MODE
#endif
#undef GPU // avoid conflict with sl::MEM::GPU
#ifdef ZED_STEREO_2_COMPAT_MODE
#pragma comment(lib, "sl_core64.lib")
#pragma comment(lib, "sl_input64.lib")
#endif
#pragma comment(lib, "sl_zed64.lib")
float getMedian(std::vector &v) {
size_t n = v.size() / 2;
std::nth_element(v.begin(), v.begin() + n, v.end());
return v[n];
}
std::vector get_3d_coordinates(std::vector bbox_vect, cv::Mat xyzrgba)
{
bool valid_measure;
int i, j;
const unsigned int R_max_global = 10;
std::vector bbox3d_vect;
for (auto &cur_box : bbox_vect) {
const unsigned int obj_size = std::min(cur_box.w, cur_box.h);
const unsigned int R_max = std::min(R_max_global, obj_size / 2);
int center_i = cur_box.x + cur_box.w * 0.5f, center_j = cur_box.y + cur_box.h * 0.5f;
std::vector x_vect, y_vect, z_vect;
for (int R = 0; R < R_max; R++) {
for (int y = -R; y <= R; y++) {
for (int x = -R; x <= R; x++) {
i = center_i + x;
j = center_j + y;
sl::float4 out(NAN, NAN, NAN, NAN);
if (i >= 0 && i < xyzrgba.cols && j >= 0 && j < xyzrgba.rows) {
cv::Vec4f &elem = xyzrgba.at(j, i); // x,y,z,w
out.x = elem[0];
out.y = elem[1];
out.z = elem[2];
out.w = elem[3];
}
valid_measure = std::isfinite(out.z);
if (valid_measure)
{
x_vect.push_back(out.x);
y_vect.push_back(out.y);
z_vect.push_back(out.z);
}
}
}
}
if (x_vect.size() * y_vect.size() * z_vect.size() > 0)
{
cur_box.x_3d = getMedian(x_vect);
cur_box.y_3d = getMedian(y_vect);
cur_box.z_3d = getMedian(z_vect);
}
else {
cur_box.x_3d = NAN;
cur_box.y_3d = NAN;
cur_box.z_3d = NAN;
}
bbox3d_vect.emplace_back(cur_box);
}
return bbox3d_vect;
}
cv::Mat slMat2cvMat(sl::Mat &input) {
int cv_type = -1; // Mapping between MAT_TYPE and CV_TYPE
if(input.getDataType() ==
#ifdef ZED_STEREO_2_COMPAT_MODE
sl::MAT_TYPE_32F_C4
#else
sl::MAT_TYPE::F32_C4
#endif
) {
cv_type = CV_32FC4;
} else cv_type = CV_8UC4; // sl::Mat used are either RGBA images or XYZ (4C) point clouds
return cv::Mat(input.getHeight(), input.getWidth(), cv_type, input.getPtr(
#ifdef ZED_STEREO_2_COMPAT_MODE
sl::MEM::MEM_CPU
#else
sl::MEM::CPU
#endif
));
}
cv::Mat zed_capture_rgb(sl::Camera &zed) {
sl::Mat left;
zed.retrieveImage(left);
cv::Mat left_rgb;
cv::cvtColor(slMat2cvMat(left), left_rgb, CV_RGBA2RGB);
return left_rgb;
}
cv::Mat zed_capture_3d(sl::Camera &zed) {
sl::Mat cur_cloud;
zed.retrieveMeasure(cur_cloud,
#ifdef ZED_STEREO_2_COMPAT_MODE
sl::MEASURE_XYZ
#else
sl::MEASURE::XYZ
#endif
);
return slMat2cvMat(cur_cloud).clone();
}
static sl::Camera zed; // ZED-camera
#else // ZED_STEREO
std::vector get_3d_coordinates(std::vector bbox_vect, cv::Mat xyzrgba) {
return bbox_vect;
}
#endif // ZED_STEREO
#include // C++
#include
#ifndef CV_VERSION_EPOCH // OpenCV 3.x and 4.x
#include
#define OPENCV_VERSION CVAUX_STR(CV_VERSION_MAJOR)"" CVAUX_STR(CV_VERSION_MINOR)"" CVAUX_STR(CV_VERSION_REVISION)
#ifndef USE_CMAKE_LIBS
#pragma comment(lib, "opencv_world" OPENCV_VERSION ".lib")
#ifdef TRACK_OPTFLOW
/*
#pragma comment(lib, "opencv_cudaoptflow" OPENCV_VERSION ".lib")
#pragma comment(lib, "opencv_cudaimgproc" OPENCV_VERSION ".lib")
#pragma comment(lib, "opencv_core" OPENCV_VERSION ".lib")
#pragma comment(lib, "opencv_imgproc" OPENCV_VERSION ".lib")
#pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib")
*/
#endif // TRACK_OPTFLOW
#endif // USE_CMAKE_LIBS
#else // OpenCV 2.x
#define OPENCV_VERSION CVAUX_STR(CV_VERSION_EPOCH)"" CVAUX_STR(CV_VERSION_MAJOR)"" CVAUX_STR(CV_VERSION_MINOR)
#ifndef USE_CMAKE_LIBS
#pragma comment(lib, "opencv_core" OPENCV_VERSION ".lib")
#pragma comment(lib, "opencv_imgproc" OPENCV_VERSION ".lib")
#pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib")
#pragma comment(lib, "opencv_video" OPENCV_VERSION ".lib")
#endif // USE_CMAKE_LIBS
#endif // CV_VERSION_EPOCH
using namespace std;
vector split(const string&s,char sepeartor)
{
vector split_vector;
int subinit=0;
for (int id=0;id!=s.length();id++)
{
if (s[id]==sepeartor)
{
split_vector.push_back(s.substr(subinit,id-subinit));
subinit=id+1;
}
}
split_vector.push_back(s.substr(subinit,s.length()-subinit));
return split_vector;
}
void draw_boxes(cv::Mat mat_img, std::vector result_vec, std::vector obj_names,
int current_det_fps = -1, int current_cap_fps = -1)
{
int const colors[6][3] = { { 1,0,1 },{ 0,0,1 },{ 0,1,1 },{ 0,1,0 },{ 1,1,0 },{ 1,0,0 } };
for (auto &i : result_vec) {
cv::Scalar color = obj_id_to_color(i.obj_id);
cv::rectangle(mat_img, cv::Rect(i.x, i.y, i.w, i.h), color, 2);
if (obj_names.size() > i.obj_id) {
std::string obj_name = obj_names[i.obj_id];
if (i.track_id > 0) obj_name += " - " + std::to_string(i.track_id);
cv::Size const text_size = getTextSize(obj_name, cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, 2, 0);
int max_width = (text_size.width > i.w + 2) ? text_size.width : (i.w + 2);
max_width = std::max(max_width, (int)i.w + 2);
//max_width = std::max(max_width, 283);
std::string coords_3d;
if (!std::isnan(i.z_3d)) {
std::stringstream ss;
ss << std::fixed << std::setprecision(2) << "x:" << i.x_3d << "m y:" << i.y_3d << "m z:" << i.z_3d << "m ";
coords_3d = ss.str();
cv::Size const text_size_3d = getTextSize(ss.str(), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, 1, 0);
int const max_width_3d = (text_size_3d.width > i.w + 2) ? text_size_3d.width : (i.w + 2);
if (max_width_3d > max_width) max_width = max_width_3d;
}
cv::rectangle(mat_img, cv::Point2f(std::max((int)i.x - 1, 0), std::max((int)i.y - 35, 0)),
cv::Point2f(std::min((int)i.x + max_width, mat_img.cols - 1), std::min((int)i.y, mat_img.rows - 1)),
color, CV_FILLED, 8, 0);
putText(mat_img, obj_name, cv::Point2f(i.x, i.y - 16), cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, cv::Scalar(0, 0, 0), 2);
if(!coords_3d.empty()) putText(mat_img, coords_3d, cv::Point2f(i.x, i.y-1), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, cv::Scalar(0, 0, 0), 1);
}
}
if (current_det_fps >= 0 && current_cap_fps >= 0) {
std::string fps_str = "FPS detection: " + std::to_string(current_det_fps) + " FPS capture: " + std::to_string(current_cap_fps);
putText(mat_img, fps_str, cv::Point2f(10, 20), cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, cv::Scalar(50, 255, 0), 2);
}
}
#endif // OPENCV
void show_console_result(std::vector const result_vec, std::vector const obj_names, int frame_id = -1) {
if (frame_id >= 0) std::cout << " Frame: " << frame_id << std::endl;
for (auto &i : result_vec) {
if (obj_names.size() > i.obj_id) std::cout << obj_names[i.obj_id] << " - ";
std::cout << "obj_id = " << i.obj_id << ", x = " << i.x << ", y = " << i.y
<< ", w = " << i.w << ", h = " << i.h
<< std::setprecision(3) << ", prob = " << i.prob << std::endl;
}
}
std::vector objects_names_from_file(std::string const filename) {
std::ifstream file(filename);
std::vector file_lines;
if (!file.is_open()) return file_lines;
for(std::string line; getline(file, line);) file_lines.push_back(line);
std::cout << "object names loaded \n";
return file_lines;
}
template
class send_one_replaceable_object_t {
const bool sync;
std::atomic a_ptr;
public:
void send(T const& _obj) {
T *new_ptr = new T;
*new_ptr = _obj;
if (sync) {
while (a_ptr.load()) std::this_thread::sleep_for(std::chrono::milliseconds(3));
}
std::unique_ptr old_ptr(a_ptr.exchange(new_ptr));
}
T receive() {
std::unique_ptr ptr;
do {
while(!a_ptr.load()) std::this_thread::sleep_for(std::chrono::milliseconds(3));
ptr.reset(a_ptr.exchange(NULL));
} while (!ptr);
T obj = *ptr;
return obj;
}
bool is_object_present() {
return (a_ptr.load() != NULL);
}
send_one_replaceable_object_t(bool _sync) : sync(_sync), a_ptr(NULL)
{}
};
int main(int argc, char *argv[])
{
std::string names_file = "coco.names";
std::string cfg_file = "cfg/yolov4.cfg";
std::string weights_file = "yolov4.weights";
std::string filename;
if (argc > 4) { //voc.names yolo-voc.cfg yolo-voc.weights test.mp4
names_file = argv[1];
cfg_file = argv[2];
weights_file = argv[3];
filename = argv[4];
}
else if (argc > 1) filename = argv[1];
float const thresh = (argc > 5) ? std::stof(argv[5]) : 0.2;
Detector detector(cfg_file, weights_file);
auto obj_names = objects_names_from_file(names_file);
std::string out_videofile = "result.avi";
bool const save_output_videofile = false; // true - for history
bool const send_network = false; // true - for remote detection
bool const use_kalman_filter = false; // true - for stationary camera
bool detection_sync = true; // true - for video-file
#ifdef TRACK_OPTFLOW // for slow GPU
detection_sync = false;
Tracker_optflow tracker_flow;
//detector.wait_stream = true;
#endif // TRACK_OPTFLOW
while (true)
{
std::cout << "input image or video filename: ";
if(filename.size() == 0) std::cin >> filename;
if (filename.size() == 0) break;
try {
#ifdef OPENCV
preview_boxes_t large_preview(100, 150, false), small_preview(50, 50, true);
bool show_small_boxes = false;
std::string const file_ext = filename.substr(filename.find_last_of(".") + 1);
std::string const protocol = filename.substr(0, 7);
if (file_ext == "avi" || file_ext == "mp4" || file_ext == "mjpg" || file_ext == "mov" || // video file
protocol == "rtmp://" || protocol == "rtsp://" || protocol == "http://" || protocol == "https:/" || // video network stream
filename == "zed_camera" || file_ext == "svo" || filename == "web_camera") // ZED stereo camera
{
if (protocol == "rtsp://" || protocol == "http://" || protocol == "https:/" || filename == "zed_camera" || filename == "web_camera")
detection_sync = false;
cv::Mat cur_frame;
std::atomic fps_cap_counter(0), fps_det_counter(0);
std::atomic current_fps_cap(0), current_fps_det(0);
std::atomic exit_flag(false);
std::chrono::steady_clock::time_point steady_start, steady_end;
int video_fps = 25;
bool use_zed_camera = false;
track_kalman_t track_kalman;
#ifdef ZED_STEREO
sl::InitParameters init_params;
init_params.depth_minimum_distance = 0.5;
#ifdef ZED_STEREO_2_COMPAT_MODE
init_params.depth_mode = sl::DEPTH_MODE_ULTRA;
init_params.camera_resolution = sl::RESOLUTION_HD720;// sl::RESOLUTION_HD1080, sl::RESOLUTION_HD720
init_params.coordinate_units = sl::UNIT_METER;
init_params.camera_buffer_count_linux = 2;
if (file_ext == "svo") init_params.svo_input_filename.set(filename.c_str());
#else
init_params.depth_mode = sl::DEPTH_MODE::ULTRA;
init_params.camera_resolution = sl::RESOLUTION::HD720;// sl::RESOLUTION::HD1080, sl::RESOLUTION::HD720
init_params.coordinate_units = sl::UNIT::METER;
if (file_ext == "svo") init_params.input.setFromSVOFile(filename.c_str());
#endif
//init_params.sdk_cuda_ctx = (CUcontext)detector.get_cuda_context();
init_params.sdk_gpu_id = detector.cur_gpu_id;
if (filename == "zed_camera" || file_ext == "svo") {
std::cout << "ZED 3D Camera " << zed.open(init_params) << std::endl;
if (!zed.isOpened()) {
std::cout << " Error: ZED Camera should be connected to USB 3.0. And ZED_SDK should be installed. \n";
getchar();
return 0;
}
cur_frame = zed_capture_rgb(zed);
use_zed_camera = true;
}
#endif // ZED_STEREO
cv::VideoCapture cap;
if (filename == "web_camera") {
cap.open(0);
cap >> cur_frame;
} else if (!use_zed_camera) {
cap.open(filename);
cap >> cur_frame;
}
#ifdef CV_VERSION_EPOCH // OpenCV 2.x
video_fps = cap.get(CV_CAP_PROP_FPS);
#else
video_fps = cap.get(cv::CAP_PROP_FPS);
#endif
cv::Size const frame_size = cur_frame.size();
//cv::Size const frame_size(cap.get(CV_CAP_PROP_FRAME_WIDTH), cap.get(CV_CAP_PROP_FRAME_HEIGHT));
std::cout << "\n Video size: " << frame_size << std::endl;
cv::VideoWriter output_video;
if (save_output_videofile)
#ifdef CV_VERSION_EPOCH // OpenCV 2.x
output_video.open(out_videofile, CV_FOURCC('D', 'I', 'V', 'X'), std::max(35, video_fps), frame_size, true);
#else
output_video.open(out_videofile, cv::VideoWriter::fourcc('D', 'I', 'V', 'X'), std::max(35, video_fps), frame_size, true);
#endif
struct detection_data_t {
cv::Mat cap_frame;
std::shared_ptr det_image;
std::vector result_vec;
cv::Mat draw_frame;
bool new_detection;
uint64_t frame_id;
bool exit_flag;
cv::Mat zed_cloud;
std::queue track_optflow_queue;
detection_data_t() : exit_flag(false), new_detection(false) {}
};
const bool sync = detection_sync; // sync data exchange
send_one_replaceable_object_t cap2prepare(sync), cap2draw(sync),
prepare2detect(sync), detect2draw(sync), draw2show(sync), draw2write(sync), draw2net(sync);
std::thread t_cap, t_prepare, t_detect, t_post, t_draw, t_write, t_network;
// capture new video-frame
if (t_cap.joinable()) t_cap.join();
t_cap = std::thread([&]()
{
uint64_t frame_id = 0;
detection_data_t detection_data;
do {
detection_data = detection_data_t();
#ifdef ZED_STEREO
if (use_zed_camera) {
while (zed.grab() !=
#ifdef ZED_STEREO_2_COMPAT_MODE
sl::SUCCESS
#else
sl::ERROR_CODE::SUCCESS
#endif
) std::this_thread::sleep_for(std::chrono::milliseconds(2));
detection_data.cap_frame = zed_capture_rgb(zed);
detection_data.zed_cloud = zed_capture_3d(zed);
}
else
#endif // ZED_STEREO
{
cap >> detection_data.cap_frame;
}
fps_cap_counter++;
detection_data.frame_id = frame_id++;
if (detection_data.cap_frame.empty() || exit_flag) {
std::cout << " exit_flag: detection_data.cap_frame.size = " << detection_data.cap_frame.size() << std::endl;
detection_data.exit_flag = true;
detection_data.cap_frame = cv::Mat(frame_size, CV_8UC3);
}
if (!detection_sync) {
cap2draw.send(detection_data); // skip detection
}
cap2prepare.send(detection_data);
} while (!detection_data.exit_flag);
std::cout << " t_cap exit \n";
});
// pre-processing video frame (resize, convertion)
t_prepare = std::thread([&]()
{
std::shared_ptr det_image;
detection_data_t detection_data;
do {
detection_data = cap2prepare.receive();
det_image = detector.mat_to_image_resize(detection_data.cap_frame);
detection_data.det_image = det_image;
prepare2detect.send(detection_data); // detection
} while (!detection_data.exit_flag);
std::cout << " t_prepare exit \n";
});
// detection by Yolo
if (t_detect.joinable()) t_detect.join();
t_detect = std::thread([&]()
{
std::shared_ptr det_image;
detection_data_t detection_data;
do {
detection_data = prepare2detect.receive();
det_image = detection_data.det_image;
std::vector result_vec;
if(det_image)
result_vec = detector.detect_resized(*det_image, frame_size.width, frame_size.height, thresh, true); // true
fps_det_counter++;
//std::this_thread::sleep_for(std::chrono::milliseconds(150));
detection_data.new_detection = true;
detection_data.result_vec = result_vec;
detect2draw.send(detection_data);
} while (!detection_data.exit_flag);
std::cout << " t_detect exit \n";
});
// draw rectangles (and track objects)
t_draw = std::thread([&]()
{
std::queue track_optflow_queue;
detection_data_t detection_data;
do {
// for Video-file
if (detection_sync) {
detection_data = detect2draw.receive();
}
// for Video-camera
else
{
// get new Detection result if present
if (detect2draw.is_object_present()) {
cv::Mat old_cap_frame = detection_data.cap_frame; // use old captured frame
detection_data = detect2draw.receive();
if (!old_cap_frame.empty()) detection_data.cap_frame = old_cap_frame;
}
// get new Captured frame
else {
std::vector old_result_vec = detection_data.result_vec; // use old detections
detection_data = cap2draw.receive();
detection_data.result_vec = old_result_vec;
}
}
cv::Mat cap_frame = detection_data.cap_frame;
cv::Mat draw_frame = detection_data.cap_frame.clone();
std::vector result_vec = detection_data.result_vec;
#ifdef TRACK_OPTFLOW
if (detection_data.new_detection) {
tracker_flow.update_tracking_flow(detection_data.cap_frame, detection_data.result_vec);
while (track_optflow_queue.size() > 0) {
draw_frame = track_optflow_queue.back();
result_vec = tracker_flow.tracking_flow(track_optflow_queue.front(), false);
track_optflow_queue.pop();
}
}
else {
track_optflow_queue.push(cap_frame);
result_vec = tracker_flow.tracking_flow(cap_frame, false);
}
detection_data.new_detection = true; // to correct kalman filter
#endif //TRACK_OPTFLOW
// track ID by using kalman filter
if (use_kalman_filter) {
if (detection_data.new_detection) {
result_vec = track_kalman.correct(result_vec);
}
else {
result_vec = track_kalman.predict();
}
}
// track ID by using custom function
else {
int frame_story = std::max(5, current_fps_cap.load());
result_vec = detector.tracking_id(result_vec, true, frame_story, 40);
}
if (use_zed_camera && !detection_data.zed_cloud.empty()) {
result_vec = get_3d_coordinates(result_vec, detection_data.zed_cloud);
}
//small_preview.set(draw_frame, result_vec);
//large_preview.set(draw_frame, result_vec);
draw_boxes(draw_frame, result_vec, obj_names, current_fps_det, current_fps_cap);
//show_console_result(result_vec, obj_names, detection_data.frame_id);
//large_preview.draw(draw_frame);
//small_preview.draw(draw_frame, true);
detection_data.result_vec = result_vec;
detection_data.draw_frame = draw_frame;
draw2show.send(detection_data);
if (send_network) draw2net.send(detection_data);
if (output_video.isOpened()) draw2write.send(detection_data);
} while (!detection_data.exit_flag);
std::cout << " t_draw exit \n";
});
// write frame to videofile
t_write = std::thread([&]()
{
if (output_video.isOpened()) {
detection_data_t detection_data;
cv::Mat output_frame;
do {
detection_data = draw2write.receive();
if(detection_data.draw_frame.channels() == 4) cv::cvtColor(detection_data.draw_frame, output_frame, CV_RGBA2RGB);
else output_frame = detection_data.draw_frame;
output_video << output_frame;
} while (!detection_data.exit_flag);
output_video.release();
}
std::cout << " t_write exit \n";
});
// send detection to the network
t_network = std::thread([&]()
{
if (send_network) {
detection_data_t detection_data;
do {
detection_data = draw2net.receive();
detector.send_json_http(detection_data.result_vec, obj_names, detection_data.frame_id, filename);
} while (!detection_data.exit_flag);
}
std::cout << " t_network exit \n";
});
// show detection
detection_data_t detection_data;
do {
steady_end = std::chrono::steady_clock::now();
float time_sec = std::chrono::duration(steady_end - steady_start).count();
if (time_sec >= 1) {
current_fps_det = fps_det_counter.load() / time_sec;
current_fps_cap = fps_cap_counter.load() / time_sec;
steady_start = steady_end;
fps_det_counter = 0;
fps_cap_counter = 0;
}
detection_data = draw2show.receive();
cv::Mat draw_frame = detection_data.draw_frame;
//if (extrapolate_flag) {
// cv::putText(draw_frame, "extrapolate", cv::Point2f(10, 40), cv::FONT_HERSHEY_COMPLEX_SMALL, 1.0, cv::Scalar(50, 50, 0), 2);
//}
cv::imshow("window name", draw_frame);
filename.replace(filename.end()-4, filename.end(), "_yolov4_out.jpg");
int key = cv::waitKey(3); // 3 or 16ms
if (key == 'f') show_small_boxes = !show_small_boxes;
if (key == 'p') while (true) if (cv::waitKey(100) == 'p') break;
//if (key == 'e') extrapolate_flag = !extrapolate_flag;
if (key == 27) { exit_flag = true;}
//std::cout << " current_fps_det = " << current_fps_det << ", current_fps_cap = " << current_fps_cap << std::endl;
} while (!detection_data.exit_flag);
std::cout << " show detection exit \n";
cv::destroyWindow("window name");
// wait for all threads
if (t_cap.joinable()) t_cap.join();
if (t_prepare.joinable()) t_prepare.join();
if (t_detect.joinable()) t_detect.join();
if (t_post.joinable()) t_post.join();
if (t_draw.joinable()) t_draw.join();
if (t_write.joinable()) t_write.join();
if (t_network.joinable()) t_network.join();
break;
}
else if (file_ext == "txt") { // list of image files
std::ifstream file(filename);
if (!file.is_open()) std::cout << "File not found! \n";
else
for (std::string line; file >> line;) {
std::cout << line << std::endl;
cv::Mat mat_img = cv::imread(line);
std::vector result_vec = detector.detect(mat_img);
show_console_result(result_vec, obj_names);
//draw_boxes(mat_img, result_vec, obj_names);
//cv::imwrite("res_" + line, mat_img);
}
}
else { // image file
// to achive high performance for multiple images do these 2 lines in another thread
cv::Mat mat_img = cv::imread(filename);
auto det_image = detector.mat_to_image_resize(mat_img);
auto start = std::chrono::steady_clock::now();
std::vector result_vec = detector.detect_resized(*det_image, mat_img.size().width, mat_img.size().height);
auto end = std::chrono::steady_clock::now();
std::chrono::duration spent = end - start;
std::cout << " Time: " << spent.count() << " sec \n";
//result_vec = detector.tracking_id(result_vec); // comment it - if track_id is not required
draw_boxes(mat_img, result_vec, obj_names);
cv::imshow("window name", mat_img);
vector filenamesplit=split(filename,'/');
string endname=filenamesplit[filenamesplit.size()-1];
endname.replace(endname.end()-4,endname.end(),"_yolov4_out.jpg");
std::string outputfile="detect_result/"+endname;
imwrite(outputfile, mat_img);
show_console_result(result_vec, obj_names);
cv::waitKey(0);
}
#else // OPENCV
//std::vector result_vec = detector.detect(filename);
auto img = detector.load_image(filename);
std::vector result_vec = detector.detect(img);
detector.free_image(img);
show_console_result(result_vec, obj_names);
#endif // OPENCV
}
catch (std::exception &e) { std::cerr << "exception: " << e.what() << "\n"; getchar(); }
catch (...) { std::cerr << "unknown exception \n"; getchar(); }
filename.clear();
}
return 0;
}
yolo_v2_class.hpp直接使用github的代码
Cmakelist.txt如下：
cmake_minimum_required(VERSION 3.5)
project(yolov4)
find_package( OpenCV 3 REQUIRED )
set(CMAKE_CXX_STANDARD 14)
include_directories(${OpenCV_INCLUDE_DIRS})
add_executable(yolov4 main.cpp yolo_v2_class.hpp)
target_link_libraries(yolov4 ${OpenCV_LIBS} libdarknet.so libpthread.so.0)
注意project的yolov4改成自己工程的名字，另外需要链接两个动态库，第一个库是darknet的所有网络层的定义，第二个库是linux的多线程库。两个库都放在/usr/lib目录下，因为这个目录是可以被C++自动检索到的。
libpthread.so.0默认情况下在/lib/x86_64-linux-gnu下，因此可以自动被检索到，只需要把它写在Cmakelist里面就可以。
/usr/bin/ld: CMakeFiles/yolov4.dir/main.cpp.o: undefined reference to symbol 'pthread_create@@GLIBC_2.2.5'
//lib/x86_64-linux-gnu/libpthread.so.0: error adding symbols: DSO missing from command line
collect2: error: ld returned 1 exit status
CMakeFiles/yolov4.dir/build.make:111: recipe for target 'yolov4' failed
make[2]: *** [yolov4] Error 1
CMakeFiles/Makefile2:67: recipe for target 'CMakeFiles/yolov4.dir/all' failed
make[1]: *** [CMakeFiles/yolov4.dir/all] Error 2
Makefile:83: recipe for target 'all' failed
make: *** [all] Error 2
libdarknet.so则可以通过编译AB大神的darknet工程得到，修改工程的makefile如下：
GPU=0
CUDNN=0
CUDNN_HALF=0
OPENCV=1
AVX=0
OPENMP=1
LIBSO=1
ZED_CAMERA=0 # ZED SDK 3.0 and above
ZED_CAMERA_v2_8=0 # ZED SDK 2.X
然后make，编译成功以后，就在目录里就出现了libdarknet.so动态库，将其copy至/usr/lib文件夹。
切换到我们的Yolov4文件夹，然后make，通过编译以后，可以运行可执行程序。
./yolov4 image/kite.jpg //检测图片
./yolov4 test.mp4 //检测视频
./yolov4 web_camera //检测摄像头
mini_batch = 1, batch = 1, time_steps = 1, train = 0
layer filters size/strd(dil) input output
0 conv 32 3 x 3/ 1 576 x 576 x 3 -> 576 x 576 x 32 0.573 BF
1 conv 64 3 x 3/ 2 576 x 576 x 32 -> 288 x 288 x 64 3.058 BF
2 conv 64 1 x 1/ 1 288 x 288 x 64 -> 288 x 288 x 64 0.679 BF
3 route 1 -> 288 x 288 x 64
4 conv 64 1 x 1/ 1 288 x 288 x 64 -> 288 x 288 x 64 0.679 BF
5 conv 32 1 x 1/ 1 288 x 288 x 64 -> 288 x 288 x 32 0.340 BF
6 conv 64 3 x 3/ 1 288 x 288 x 32 -> 288 x 288 x 64 3.058 BF
7 Shortcut Layer: 4, wt = 0, wn = 0, outputs: 288 x 288 x 64 0.005 BF
8 conv 64 1 x 1/ 1 288 x 288 x 64 -> 288 x 288 x 64 0.679 BF
9 route 8 2 -> 288 x 288 x 128
10 conv 64 1 x 1/ 1 288 x 288 x 128 -> 288 x 288 x 64 1.359 BF
11 conv 128 3 x 3/ 2 288 x 288 x 64 -> 144 x 144 x 128 3.058 BF
12 conv 64 1 x 1/ 1 144 x 144 x 128 -> 144 x 144 x 64 0.340 BF
13 route 11 -> 144 x 144 x 128
14 conv 64 1 x 1/ 1 144 x 144 x 128 -> 144 x 144 x 64 0.340 BF
15 conv 64 1 x 1/ 1 144 x 144 x 64 -> 144 x 144 x 64 0.170 BF
16 conv 64 3 x 3/ 1 144 x 144 x 64 -> 144 x 144 x 64 1.529 BF
17 Shortcut Layer: 14, wt = 0, wn = 0, outputs: 144 x 144 x 64 0.001 BF
18 conv 64 1 x 1/ 1 144 x 144 x 64 -> 144 x 144 x 64 0.170 BF
19 conv 64 3 x 3/ 1 144 x 144 x 64 -> 144 x 144 x 64 1.529 BF
20 Shortcut Layer: 17, wt = 0, wn = 0, outputs: 144 x 144 x 64 0.001 BF
21 conv 64 1 x 1/ 1 144 x 144 x 64 -> 144 x 144 x 64 0.170 BF
22 route 21 12 -> 144 x 144 x 128
23 conv 128 1 x 1/ 1 144 x 144 x 128 -> 144 x 144 x 128 0.679 BF
24 conv 256 3 x 3/ 2 144 x 144 x 128 -> 72 x 72 x 256 3.058 BF
25 conv 128 1 x 1/ 1 72 x 72 x 256 -> 72 x 72 x 128 0.340 BF
26 route 24 -> 72 x 72 x 256
27 conv 128 1 x 1/ 1 72 x 72 x 256 -> 72 x 72 x 128 0.340 BF
28 conv 128 1 x 1/ 1 72 x 72 x 128 -> 72 x 72 x 128 0.170 BF
29 conv 128 3 x 3/ 1 72 x 72 x 128 -> 72 x 72 x 128 1.529 BF
30 Shortcut Layer: 27, wt = 0, wn = 0, outputs: 72 x 72 x 128 0.001 BF
31 conv 128 1 x 1/ 1 72 x 72 x 128 -> 72 x 72 x 128 0.170 BF
32 conv 128 3 x 3/ 1 72 x 72 x 128 -> 72 x 72 x 128 1.529 BF
33 Shortcut Layer: 30, wt = 0, wn = 0, outputs: 72 x 72 x 128 0.001 BF
34 conv 128 1 x 1/ 1 72 x 72 x 128 -> 72 x 72 x 128 0.170 BF
35 conv 128 3 x 3/ 1 72 x 72 x 128 -> 72 x 72 x 128 1.529 BF
36 Shortcut Layer: 33, wt = 0, wn = 0, outputs: 72 x 72 x 128 0.001 BF
37 conv 128 1 x 1/ 1 72 x 72 x 128 -> 72 x 72 x 128 0.170 BF
38 conv 128 3 x 3/ 1 72 x 72 x 128 -> 72 x 72 x 128 1.529 BF
39 Shortcut Layer: 36, wt = 0, wn = 0, outputs: 72 x 72 x 128 0.001 BF
40 conv 128 1 x 1/ 1 72 x 72 x 128 -> 72 x 72 x 128 0.170 BF
41 conv 128 3 x 3/ 1 72 x 72 x 128 -> 72 x 72 x 128 1.529 BF
42 Shortcut Layer: 39, wt = 0, wn = 0, outputs: 72 x 72 x 128 0.001 BF
43 conv 128 1 x 1/ 1 72 x 72 x 128 -> 72 x 72 x 128 0.170 BF
44 conv 128 3 x 3/ 1 72 x 72 x 128 -> 72 x 72 x 128 1.529 BF
45 Shortcut Layer: 42, wt = 0, wn = 0, outputs: 72 x 72 x 128 0.001 BF
46 conv 128 1 x 1/ 1 72 x 72 x 128 -> 72 x 72 x 128 0.170 BF
47 conv 128 3 x 3/ 1 72 x 72 x 128 -> 72 x 72 x 128 1.529 BF
48 Shortcut Layer: 45, wt = 0, wn = 0, outputs: 72 x 72 x 128 0.001 BF
49 conv 128 1 x 1/ 1 72 x 72 x 128 -> 72 x 72 x 128 0.170 BF
50 conv 128 3 x 3/ 1 72 x 72 x 128 -> 72 x 72 x 128 1.529 BF
51 Shortcut Layer: 48, wt = 0, wn = 0, outputs: 72 x 72 x 128 0.001 BF
52 conv 128 1 x 1/ 1 72 x 72 x 128 -> 72 x 72 x 128 0.170 BF
53 route 52 25 -> 72 x 72 x 256
54 conv 256 1 x 1/ 1 72 x 72 x 256 -> 72 x 72 x 256 0.679 BF
55 conv 512 3 x 3/ 2 72 x 72 x 256 -> 36 x 36 x 512 3.058 BF
56 conv 256 1 x 1/ 1 36 x 36 x 512 -> 36 x 36 x 256 0.340 BF
57 route 55 -> 36 x 36 x 512
58 conv 256 1 x 1/ 1 36 x 36 x 512 -> 36 x 36 x 256 0.340 BF
59 conv 256 1 x 1/ 1 36 x 36 x 256 -> 36 x 36 x 256 0.170 BF
60 conv 256 3 x 3/ 1 36 x 36 x 256 -> 36 x 36 x 256 1.529 BF
61 Shortcut Layer: 58, wt = 0, wn = 0, outputs: 36 x 36 x 256 0.000 BF
62 conv 256 1 x 1/ 1 36 x 36 x 256 -> 36 x 36 x 256 0.170 BF
63 conv 256 3 x 3/ 1 36 x 36 x 256 -> 36 x 36 x 256 1.529 BF
64 Shortcut Layer: 61, wt = 0, wn = 0, outputs: 36 x 36 x 256 0.000 BF
65 conv 256 1 x 1/ 1 36 x 36 x 256 -> 36 x 36 x 256 0.170 BF
66 conv 256 3 x 3/ 1 36 x 36 x 256 -> 36 x 36 x 256 1.529 BF
67 Shortcut Layer: 64, wt = 0, wn = 0, outputs: 36 x 36 x 256 0.000 BF
68 conv 256 1 x 1/ 1 36 x 36 x 256 -> 36 x 36 x 256 0.170 BF
69 conv 256 3 x 3/ 1 36 x 36 x 256 -> 36 x 36 x 256 1.529 BF
70 Shortcut Layer: 67, wt = 0, wn = 0, outputs: 36 x 36 x 256 0.000 BF
71 conv 256 1 x 1/ 1 36 x 36 x 256 -> 36 x 36 x 256 0.170 BF
72 conv 256 3 x 3/ 1 36 x 36 x 256 -> 36 x 36 x 256 1.529 BF
73 Shortcut Layer: 70, wt = 0, wn = 0, outputs: 36 x 36 x 256 0.000 BF
74 conv 256 1 x 1/ 1 36 x 36 x 256 -> 36 x 36 x 256 0.170 BF
75 conv 256 3 x 3/ 1 36 x 36 x 256 -> 36 x 36 x 256 1.529 BF
76 Shortcut Layer: 73, wt = 0, wn = 0, outputs: 36 x 36 x 256 0.000 BF
77 conv 256 1 x 1/ 1 36 x 36 x 256 -> 36 x 36 x 256 0.170 BF
78 conv 256 3 x 3/ 1 36 x 36 x 256 -> 36 x 36 x 256 1.529 BF
79 Shortcut Layer: 76, wt = 0, wn = 0, outputs: 36 x 36 x 256 0.000 BF
80 conv 256 1 x 1/ 1 36 x 36 x 256 -> 36 x 36 x 256 0.170 BF
81 conv 256 3 x 3/ 1 36 x 36 x 256 -> 36 x 36 x 256 1.529 BF
82 Shortcut Layer: 79, wt = 0, wn = 0, outputs: 36 x 36 x 256 0.000 BF
83 conv 256 1 x 1/ 1 36 x 36 x 256 -> 36 x 36 x 256 0.170 BF
84 route 83 56 -> 36 x 36 x 512
85 conv 512 1 x 1/ 1 36 x 36 x 512 -> 36 x 36 x 512 0.679 BF
86 conv 1024 3 x 3/ 2 36 x 36 x 512 -> 18 x 18 x1024 3.058 BF
87 conv 512 1 x 1/ 1 18 x 18 x1024 -> 18 x 18 x 512 0.340 BF
88 route 86 -> 18 x 18 x1024
89 conv 512 1 x 1/ 1 18 x 18 x1024 -> 18 x 18 x 512 0.340 BF
90 conv 512 1 x 1/ 1 18 x 18 x 512 -> 18 x 18 x 512 0.170 BF
91 conv 512 3 x 3/ 1 18 x 18 x 512 -> 18 x 18 x 512 1.529 BF
92 Shortcut Layer: 89, wt = 0, wn = 0, outputs: 18 x 18 x 512 0.000 BF
93 conv 512 1 x 1/ 1 18 x 18 x 512 -> 18 x 18 x 512 0.170 BF
94 conv 512 3 x 3/ 1 18 x 18 x 512 -> 18 x 18 x 512 1.529 BF
95 Shortcut Layer: 92, wt = 0, wn = 0, outputs: 18 x 18 x 512 0.000 BF
96 conv 512 1 x 1/ 1 18 x 18 x 512 -> 18 x 18 x 512 0.170 BF
97 conv 512 3 x 3/ 1 18 x 18 x 512 -> 18 x 18 x 512 1.529 BF
98 Shortcut Layer: 95, wt = 0, wn = 0, outputs: 18 x 18 x 512 0.000 BF
99 conv 512 1 x 1/ 1 18 x 18 x 512 -> 18 x 18 x 512 0.170 BF
100 conv 512 3 x 3/ 1 18 x 18 x 512 -> 18 x 18 x 512 1.529 BF
101 Shortcut Layer: 98, wt = 0, wn = 0, outputs: 18 x 18 x 512 0.000 BF
102 conv 512 1 x 1/ 1 18 x 18 x 512 -> 18 x 18 x 512 0.170 BF
103 route 102 87 -> 18 x 18 x1024
104 conv 1024 1 x 1/ 1 18 x 18 x1024 -> 18 x 18 x1024 0.679 BF
105 conv 512 1 x 1/ 1 18 x 18 x1024 -> 18 x 18 x 512 0.340 BF
106 conv 1024 3 x 3/ 1 18 x 18 x 512 -> 18 x 18 x1024 3.058 BF
107 conv 512 1 x 1/ 1 18 x 18 x1024 -> 18 x 18 x 512 0.340 BF
108 max 5x 5/ 1 18 x 18 x 512 -> 18 x 18 x 512 0.004 BF
109 route 107 -> 18 x 18 x 512
110 max 9x 9/ 1 18 x 18 x 512 -> 18 x 18 x 512 0.013 BF
111 route 107 -> 18 x 18 x 512
112 max 13x13/ 1 18 x 18 x 512 -> 18 x 18 x 512 0.028 BF
113 route 112 110 108 107 -> 18 x 18 x2048
114 conv 512 1 x 1/ 1 18 x 18 x2048 -> 18 x 18 x 512 0.679 BF
115 conv 1024 3 x 3/ 1 18 x 18 x 512 -> 18 x 18 x1024 3.058 BF
116 conv 512 1 x 1/ 1 18 x 18 x1024 -> 18 x 18 x 512 0.340 BF
117 conv 256 1 x 1/ 1 18 x 18 x 512 -> 18 x 18 x 256 0.085 BF
118 upsample 2x 18 x 18 x 256 -> 36 x 36 x 256
119 route 85 -> 36 x 36 x 512
120 conv 256 1 x 1/ 1 36 x 36 x 512 -> 36 x 36 x 256 0.340 BF
121 route 120 118 -> 36 x 36 x 512
122 conv 256 1 x 1/ 1 36 x 36 x 512 -> 36 x 36 x 256 0.340 BF
123 conv 512 3 x 3/ 1 36 x 36 x 256 -> 36 x 36 x 512 3.058 BF
124 conv 256 1 x 1/ 1 36 x 36 x 512 -> 36 x 36 x 256 0.340 BF
125 conv 512 3 x 3/ 1 36 x 36 x 256 -> 36 x 36 x 512 3.058 BF
126 conv 256 1 x 1/ 1 36 x 36 x 512 -> 36 x 36 x 256 0.340 BF
127 conv 128 1 x 1/ 1 36 x 36 x 256 -> 36 x 36 x 128 0.085 BF
128 upsample 2x 36 x 36 x 128 -> 72 x 72 x 128
129 route 54 -> 72 x 72 x 256
130 conv 128 1 x 1/ 1 72 x 72 x 256 -> 72 x 72 x 128 0.340 BF
131 route 130 128 -> 72 x 72 x 256
132 conv 128 1 x 1/ 1 72 x 72 x 256 -> 72 x 72 x 128 0.340 BF
133 conv 256 3 x 3/ 1 72 x 72 x 128 -> 72 x 72 x 256 3.058 BF
134 conv 128 1 x 1/ 1 72 x 72 x 256 -> 72 x 72 x 128 0.340 BF
135 conv 256 3 x 3/ 1 72 x 72 x 128 -> 72 x 72 x 256 3.058 BF
136 conv 128 1 x 1/ 1 72 x 72 x 256 -> 72 x 72 x 128 0.340 BF
137 conv 256 3 x 3/ 1 72 x 72 x 128 -> 72 x 72 x 256 3.058 BF
138 conv 255 1 x 1/ 1 72 x 72 x 256 -> 72 x 72 x 255 0.677 BF
139 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.20
nms_kind: greedynms (1), beta = 0.600000
140 route 136 -> 72 x 72 x 128
141 conv 256 3 x 3/ 2 72 x 72 x 128 -> 36 x 36 x 256 0.764 BF
142 route 141 126 -> 36 x 36 x 512
143 conv 256 1 x 1/ 1 36 x 36 x 512 -> 36 x 36 x 256 0.340 BF
144 conv 512 3 x 3/ 1 36 x 36 x 256 -> 36 x 36 x 512 3.058 BF
145 conv 256 1 x 1/ 1 36 x 36 x 512 -> 36 x 36 x 256 0.340 BF
146 conv 512 3 x 3/ 1 36 x 36 x 256 -> 36 x 36 x 512 3.058 BF
147 conv 256 1 x 1/ 1 36 x 36 x 512 -> 36 x 36 x 256 0.340 BF
148 conv 512 3 x 3/ 1 36 x 36 x 256 -> 36 x 36 x 512 3.058 BF
149 conv 255 1 x 1/ 1 36 x 36 x 512 -> 36 x 36 x 255 0.338 BF
150 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.10
nms_kind: greedynms (1), beta = 0.600000
151 route 147 -> 36 x 36 x 256
152 conv 512 3 x 3/ 2 36 x 36 x 256 -> 18 x 18 x 512 0.764 BF
153 route 152 116 -> 18 x 18 x1024
154 conv 512 1 x 1/ 1 18 x 18 x1024 -> 18 x 18 x 512 0.340 BF
155 conv 1024 3 x 3/ 1 18 x 18 x 512 -> 18 x 18 x1024 3.058 BF
156 conv 512 1 x 1/ 1 18 x 18 x1024 -> 18 x 18 x 512 0.340 BF
157 conv 1024 3 x 3/ 1 18 x 18 x 512 -> 18 x 18 x1024 3.058 BF
158 conv 512 1 x 1/ 1 18 x 18 x1024 -> 18 x 18 x 512 0.340 BF
159 conv 1024 3 x 3/ 1 18 x 18 x 512 -> 18 x 18 x1024 3.058 BF
160 conv 255 1 x 1/ 1 18 x 18 x1024 -> 18 x 18 x 255 0.169 BF
161 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.05
nms_kind: greedynms (1), beta = 0.600000
Total BFLOPS 115.293
avg_outputs = 958892
Loading weights from yolov4.weights...
seen 64, trained: 32032 K-images (500 Kilo-batches_64)
Done! Loaded 162 layers from weights-file
object names loaded
input image or video filename: Time: 7.29136 sec
surfboard - obj_id = 37, x = 521, y = 517, w = 32, h = 15, prob = 0.393
surfboard - obj_id = 37, x = 497, y = 521, w = 39, h = 10, prob = 0.255
surfboard - obj_id = 37, x = 814, y = 567, w = 30, h = 10, prob = 0.248
kite - obj_id = 33, x = 591, y = 79, w = 75, h = 71, prob = 0.994
kite - obj_id = 33, x = 279, y = 235, w = 23, h = 45, prob = 0.979
kite - obj_id = 33, x = 575, y = 343, w = 25, h = 25, prob = 0.951
kite - obj_id = 33, x = 1082, y = 393, w = 14, h = 28, prob = 0.943
kite - obj_id = 33, x = 464, y = 339, w = 16, h = 18, prob = 0.855
kite - obj_id = 33, x = 300, y = 375, w = 23, h = 32, prob = 0.68
kite - obj_id = 33, x = 760, y = 379, w = 7, h = 9, prob = 0.634
person - obj_id = 0, x = 110, y = 610, w = 51, h = 151, prob = 0.994
person - obj_id = 0, x = 213, y = 698, w = 53, h = 159, prob = 0.993
person - obj_id = 0, x = 1204, y = 450, w = 9, h = 12, prob = 0.872
person - obj_id = 0, x = 37, y = 509, w = 16, h = 51, prob = 0.871
person - obj_id = 0, x = 345, y = 487, w = 9, h = 14, prob = 0.866
person - obj_id = 0, x = 176, y = 539, w = 11, h = 32, prob = 0.832
person - obj_id = 0, x = 21, y = 529, w = 14, h = 26, prob = 0.801
person - obj_id = 0, x = 82, y = 506, w = 25, h = 57, prob = 0.697
person - obj_id = 0, x = 518, y = 506, w = 16, h = 18, prob = 0.606
person - obj_id = 0, x = 692, y = 462, w = 7, h = 6, prob = 0.552
person - obj_id = 0, x = 460, y = 471, w = 7, h = 6, prob = 0.394
person - obj_id = 0, x = 537, y = 514, w = 14, h = 17, prob = 0.381
测试图片结果
总结
在本文中，我们使用C++调用了作者在COCO数据集上的训练结果进行了图片测试，并且可以进行视频测试和网络摄像头测试（自己笔记本显卡太弱，视频跑不起来），如果我们想要自己训练数据集，仍然可以参考github，可以通过训练得到weights文件，然后按照本文所讲的进行测试。