c++ gpu推理darknet已训练好的模型，自动生成coco目标检测标注文件的完整代码
**yolo_v2_class.hpp**
#ifndef YOLO_V2_CLASS_HPP
#define YOLO_V2_CLASS_HPP
#ifndef LIB_API
#ifdef LIB_EXPORTS
#if defined(_MSC_VER)
#define LIB_API __declspec(dllexport)
#else
#define LIB_API __attribute__((visibility("default")))
#endif
#else
#if defined(_MSC_VER)
#define LIB_API
#else
#define LIB_API
#endif
#endif
#endif
#define C_SHARP_MAX_OBJECTS 1000
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
};
struct image_t {
int h; // height
int w; // width
int c; // number of chanels (3 - for RGB)
float *data; // pointer to the image data
};
struct bbox_t_container {
bbox_t candidates[C_SHARP_MAX_OBJECTS];
};
#ifdef __cplusplus
#include <memory>
#include <vector>
#include <deque>
#include <algorithm>
#include <chrono>
#include <string>
#include <sstream>
#include <iostream>
#include <cmath>
#ifdef OPENCV
#include <opencv2/opencv.hpp> // C++
#include <opencv2/highgui/highgui_c.h> // C
#include <opencv2/imgproc/imgproc_c.h> // C
#endif
extern "C" LIB_API int init(const char *configurationFilename, const char *weightsFilename, int gpu);
extern "C" LIB_API int detect_image(const char *filename, bbox_t_container &container);
extern "C" LIB_API int detect_mat(const uint8_t* data, const size_t data_length, bbox_t_container &container);
extern "C" LIB_API int dispose();
extern "C" LIB_API int get_device_count();
extern "C" LIB_API int get_device_name(int gpu, char* deviceName);
extern "C" LIB_API bool built_with_cuda();
extern "C" LIB_API bool built_with_cudnn();
extern "C" LIB_API bool built_with_opencv();
extern "C" LIB_API void send_json_custom(char const* send_buf, int port, int timeout);
extern "C" LIB_API int detect_mat2(const uint8_t* data, const int height, const int widtd, bbox_t_container &container);//自己添加
class Detector {
std::shared_ptr<void> detector_gpu_ptr;
std::deque<std::vector<bbox_t>> prev_bbox_vec_deque;
std::string _cfg_filename, _weight_filename;
public:
const int cur_gpu_id;
float nms = .45;
bool wait_stream;
LIB_API Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0);
LIB_API ~Detector();
LIB_API std::vector<bbox_t> detect(std::string image_filename, float thresh = 0.2, bool use_mean = false);
LIB_API std::vector<bbox_t> detect(image_t img, float thresh = 0.2, bool use_mean = false);
static LIB_API image_t load_image(std::string image_filename);
static LIB_API void free_image(image_t m);
LIB_API int get_net_width() const;
LIB_API int get_net_height() const;
LIB_API int get_net_color_depth() const;
LIB_API std::vector<bbox_t> tracking_id(std::vector<bbox_t> cur_bbox_vec, bool const change_history = true,
int const frames_story = 5, int const max_dist = 40);
LIB_API void *get_cuda_context();
//LIB_API bool send_json_http(std::vector<bbox_t> cur_bbox_vec, std::vector<std::string> obj_names, int frame_id,
// std::string filename = std::string(), int timeout = 400000, int port = 8070);
std::vector<bbox_t> detect_resized(image_t img, int init_w, int init_h, float thresh = 0.2, bool use_mean = false)
{
if (img.data == NULL)
throw std::runtime_error("Image is empty");
auto detection_boxes = detect(img, thresh, use_mean);
float wk = (float)init_w / img.w, hk = (float)init_h / img.h;
for (auto &i : detection_boxes) i.x *= wk, i.w *= wk, i.y *= hk, i.h *= hk;
return detection_boxes;
}
#ifdef OPENCV
std::vector<bbox_t> detect(cv::Mat mat, float thresh = 0.2, bool use_mean = false)
{
if(mat.data == NULL)
throw std::runtime_error("Image is empty");
auto image_ptr = mat_to_image_resize(mat);
return detect_resized(*image_ptr, mat.cols, mat.rows, thresh, use_mean);
}
std::shared_ptr<image_t> mat_to_image_resize(cv::Mat mat) const
{
if (mat.data == NULL) return std::shared_ptr<image_t>(NULL);
cv::Size network_size = cv::Size(get_net_width(), get_net_height());
cv::Mat det_mat;
if (mat.size() != network_size)
cv::resize(mat, det_mat, network_size);
else
det_mat = mat; // only reference is copied
return mat_to_image(det_mat);
}
static std::shared_ptr<image_t> mat_to_image(cv::Mat img_src)
{
cv::Mat img;
if (img_src.channels() == 4) cv::cvtColor(img_src, img, cv::COLOR_RGBA2BGR);
else if (img_src.channels() == 3) cv::cvtColor(img_src, img, cv::COLOR_RGB2BGR);
else if (img_src.channels() == 1) cv::cvtColor(img_src, img, cv::COLOR_GRAY2BGR);
else std::cerr << " Warning: img_src.channels() is not 1, 3 or 4. It is = " << img_src.channels() << std::endl;
std::shared_ptr<image_t> image_ptr(new image_t, [](image_t *img) {
free_image(*img); delete img; });
*image_ptr = mat_to_image_custom(img);
return image_ptr;
}
private:
static image_t mat_to_image_custom(cv::Mat mat)
{
int w = mat.cols;
int h = mat.rows;
int c = mat.channels();
image_t im = make_image_custom(w, h, c);
unsigned char *data = (unsigned char *)mat.data;
int step = mat.step;
for (int y = 0; y < h; ++y) {
for (int k = 0; k < c; ++k) {
for (int x = 0; x < w; ++x) {
im.data[k*w*h + y*w + x] = data[y*step + x*c + k] / 255.0f;
}
}
}
return im;
}
static image_t make_empty_image(int w, int h, int c)
{
image_t out;
out.data = 0;
out.h = h;
out.w = w;
out.c = c;
return out;
}
static image_t make_image_custom(int w, int h, int c)
{
image_t out = make_empty_image(w, h, c);
out.data = (float *)calloc(h*w*c, sizeof(float));
return out;
}
#endif // OPENCV
public:
bool send_json_http(std::vector<bbox_t> cur_bbox_vec, std::vector<std::string> obj_names, int frame_id,
std::string filename = std::string(), int timeout = 400000, int port = 8070)
{
std::string send_str;
char *tmp_buf = (char *)calloc(1024, sizeof(char));
if (!filename.empty()) {
sprintf(tmp_buf, "{\n \"frame_id\":%d, \n \"filename\":\"%s\", \n \"objects\": [ \n", frame_id, filename.c_str());
}
else {
sprintf(tmp_buf, "{\n \"frame_id\":%d, \n \"objects\": [ \n", frame_id);
}
send_str = tmp_buf;
free(tmp_buf);
for (auto & i : cur_bbox_vec) {
char *buf = (char *)calloc(2048, sizeof(char));
sprintf(buf, " {\"class_id\":%d, \"name\":\"%s\", \"absolute_coordinates\":{\"center_x\":%d, \"center_y\":%d, \"width\":%d, \"height\":%d}, \"confidence\":%f",
i.obj_id, obj_names[i.obj_id].c_str(), i.x, i.y, i.w, i.h, i.prob);
//sprintf(buf, " {\"class_id\":%d, \"name\":\"%s\", \"relative_coordinates\":{\"center_x\":%f, \"center_y\":%f, \"width\":%f, \"height\":%f}, \"confidence\":%f",
// i.obj_id, obj_names[i.obj_id], i.x, i.y, i.w, i.h, i.prob);
send_str += buf;
if (!std::isnan(i.z_3d)) {
sprintf(buf, "\n , \"coordinates_in_meters\":{\"x_3d\":%.2f, \"y_3d\":%.2f, \"z_3d\":%.2f}",
i.x_3d, i.y_3d, i.z_3d);
send_str += buf;
}
send_str += "}\n";
free(buf);
}
//send_str += "\n ] \n}, \n";
send_str += "\n ] \n}";
send_json_custom(send_str.c_str(), port, timeout);
return true;
}
};
// --------------------------------------------------------------------------------
#if defined(TRACK_OPTFLOW) && defined(OPENCV) && defined(GPU)
#include <opencv2/cudaoptflow.hpp>
#include <opencv2/cudaimgproc.hpp>
#include <opencv2/cudaarithm.hpp>
#include <opencv2/core/cuda.hpp>
class Tracker_optflow {
public:
const int gpu_count;
const int gpu_id;
const int flow_error;
Tracker_optflow(int _gpu_id = 0, int win_size = 15, int max_level = 3, int iterations = 8000, int _flow_error = -1) :
gpu_count(cv::cuda::getCudaEnabledDeviceCount()), gpu_id(std::min(_gpu_id, gpu_count-1)),
flow_error((_flow_error > 0)? _flow_error:(win_size*4))
{
int const old_gpu_id = cv::cuda::getDevice();
cv::cuda::setDevice(gpu_id);
stream = cv::cuda::Stream();
sync_PyrLKOpticalFlow_gpu = cv::cuda::SparsePyrLKOpticalFlow::create();
sync_PyrLKOpticalFlow_gpu->setWinSize(cv::Size(win_size, win_size)); // 9, 15, 21, 31
sync_PyrLKOpticalFlow_gpu->setMaxLevel(max_level); // +- 3 pt
sync_PyrLKOpticalFlow_gpu->setNumIters(iterations); // 2000, def: 30
cv::cuda::setDevice(old_gpu_id);
}
// just to avoid extra allocations
cv::cuda::GpuMat src_mat_gpu;
cv::cuda::GpuMat dst_mat_gpu, dst_grey_gpu;
cv::cuda::GpuMat prev_pts_flow_gpu, cur_pts_flow_gpu;
cv::cuda::GpuMat status_gpu, err_gpu;
cv::cuda::GpuMat src_grey_gpu; // used in both functions
cv::Ptr<cv::cuda::SparsePyrLKOpticalFlow> sync_PyrLKOpticalFlow_gpu;
cv::cuda::Stream stream;
std::vector<bbox_t> cur_bbox_vec;
std::vector<bool> good_bbox_vec_flags;
cv::Mat prev_pts_flow_cpu;
void update_cur_bbox_vec(std::vector<bbox_t> _cur_bbox_vec)
{
cur_bbox_vec = _cur_bbox_vec;
good_bbox_vec_flags = std::vector<bool>(cur_bbox_vec.size(), true);
cv::Mat prev_pts, cur_pts_flow_cpu;
for (auto &i : cur_bbox_vec) {
float x_center = (i.x + i.w / 2.0F);
float y_center = (i.y + i.h / 2.0F);
prev_pts.push_back(cv::Point2f(x_center, y_center));
}
if (prev_pts.rows == 0)
prev_pts_flow_cpu = cv::Mat();
else
cv::transpose(prev_pts, prev_pts_flow_cpu);
if (prev_pts_flow_gpu.cols < prev_pts_flow_cpu.cols) {
prev_pts_flow_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), prev_pts_flow_cpu.type());
cur_pts_flow_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), prev_pts_flow_cpu.type());
status_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), CV_8UC1);
err_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), CV_32FC1);
}
prev_pts_flow_gpu.upload(cv::Mat(prev_pts_flow_cpu), stream);
}
void update_tracking_flow(cv::Mat src_mat, std::vector<bbox_t> _cur_bbox_vec)
{
int const old_gpu_id = cv::cuda::getDevice();
if (old_gpu_id != gpu_id)
cv::cuda::setDevice(gpu_id);
if (src_mat.channels() == 1 || src_mat.channels() == 3 || src_mat.channels() == 4) {
if (src_mat_gpu.cols == 0) {
src_mat_gpu = cv::cuda::GpuMat(src_mat.size(), src_mat.type());
src_grey_gpu = cv::cuda::GpuMat(src_mat.size(), CV_8UC1);
}
if (src_mat.channels() == 1) {
src_mat_gpu.upload(src_mat, stream);
src_mat_gpu.copyTo(src_grey_gpu);
}
else if (src_mat.channels() == 3) {
src_mat_gpu.upload(src_mat, stream);
cv::cuda::cvtColor(src_mat_gpu, src_grey_gpu, CV_BGR2GRAY, 1, stream);
}
else if (src_mat.channels() == 4) {
src_mat_gpu.upload(src_mat, stream);
cv::cuda::cvtColor(src_mat_gpu, src_grey_gpu, CV_BGRA2GRAY, 1, stream);
}
else {
std::cerr << " Warning: src_mat.channels() is not: 1, 3 or 4. It is = " << src_mat.channels() << " \n";
return;
}
}
update_cur_bbox_vec(_cur_bbox_vec);
if (old_gpu_id != gpu_id)
cv::cuda::setDevice(old_gpu_id);
}
std::vector<bbox_t> tracking_flow(cv::Mat dst_mat, bool check_error = true)
{
if (sync_PyrLKOpticalFlow_gpu.empty()) {
std::cout << "sync_PyrLKOpticalFlow_gpu isn't initialized \n";
return cur_bbox_vec;
}
int const old_gpu_id = cv::cuda::getDevice();
if(old_gpu_id != gpu_id)
cv::cuda::setDevice(gpu_id);
if (dst_mat_gpu.cols == 0) {
dst_mat_gpu = cv::cuda::GpuMat(dst_mat.size(), dst_mat.type());
dst_grey_gpu = cv::cuda::GpuMat(dst_mat.size(), CV_8UC1);
}
//dst_grey_gpu.upload(dst_mat, stream); // use BGR
dst_mat_gpu.upload(dst_mat, stream);
cv::cuda::cvtColor(dst_mat_gpu, dst_grey_gpu, CV_BGR2GRAY, 1, stream);
if (src_grey_gpu.rows != dst_grey_gpu.rows || src_grey_gpu.cols != dst_grey_gpu.cols) {
stream.waitForCompletion();
src_grey_gpu = dst_grey_gpu.clone();
cv::cuda::setDevice(old_gpu_id);
return cur_bbox_vec;
}
sync_PyrLKOpticalFlow_gpu.sparse(src_grey_gpu, dst_grey_gpu, prev_pts_flow_gpu, cur_pts_flow_gpu, status_gpu, &err_gpu); // OpenCV 2.4.x
sync_PyrLKOpticalFlow_gpu->calc(src_grey_gpu, dst_grey_gpu, prev_pts_flow_gpu, cur_pts_flow_gpu, status_gpu, err_gpu, stream); // OpenCV 3.x
cv::Mat cur_pts_flow_cpu;
cur_pts_flow_gpu.download(cur_pts_flow_cpu, stream);
dst_grey_gpu.copyTo(src_grey_gpu, stream);
cv::Mat err_cpu, status_cpu;
err_gpu.download(err_cpu, stream);
status_gpu.download(status_cpu, stream);
stream.waitForCompletion();
std::vector<bbox_t> result_bbox_vec;
if (err_cpu.cols == cur_bbox_vec.size() && status_cpu.cols == cur_bbox_vec.size())
{
for (size_t i = 0; i < cur_bbox_vec.size(); ++i)
{
cv::Point2f cur_key_pt = cur_pts_flow_cpu.at<cv::Point2f>(0, i);
cv::Point2f prev_key_pt = prev_pts_flow_cpu.at<cv::Point2f>(0, i);
float moved_x = cur_key_pt.x - prev_key_pt.x;
float moved_y = cur_key_pt.y - prev_key_pt.y;
if (abs(moved_x) < 100 && abs(moved_y) < 100 && good_bbox_vec_flags[i])
if (err_cpu.at<float>(0, i) < flow_error && status_cpu.at<unsigned char>(0, i) != 0 &&
((float)cur_bbox_vec[i].x + moved_x) > 0 && ((float)cur_bbox_vec[i].y + moved_y) > 0)
{
cur_bbox_vec[i].x += moved_x + 0.5;
cur_bbox_vec[i].y += moved_y + 0.5;
result_bbox_vec.push_back(cur_bbox_vec[i]);
}
else good_bbox_vec_flags[i] = false;
else good_bbox_vec_flags[i] = false;
//if(!check_error && !good_bbox_vec_flags[i]) result_bbox_vec.push_back(cur_bbox_vec[i]);
}
}
cur_pts_flow_gpu.swap(prev_pts_flow_gpu);
cur_pts_flow_cpu.copyTo(prev_pts_flow_cpu);
if (old_gpu_id != gpu_id)
cv::cuda::setDevice(old_gpu_id);
return result_bbox_vec;
}
};
#elif defined(TRACK_OPTFLOW) && defined(OPENCV)
//#include <opencv2/optflow.hpp>
#include <opencv2/video/tracking.hpp>
class Tracker_optflow {
public:
const int flow_error;
Tracker_optflow(int win_size = 15, int max_level = 3, int iterations = 8000, int _flow_error = -1) :
flow_error((_flow_error > 0)? _flow_error:(win_size*4))
{
sync_PyrLKOpticalFlow = cv::SparsePyrLKOpticalFlow::create();
sync_PyrLKOpticalFlow->setWinSize(cv::Size(win_size, win_size)); // 9, 15, 21, 31
sync_PyrLKOpticalFlow->setMaxLevel(max_level); // +- 3 pt
}
// just to avoid extra allocations
cv::Mat dst_grey;
cv::Mat prev_pts_flow, cur_pts_flow;
cv::Mat status, err;
cv::Mat src_grey; // used in both functions
cv::Ptr<cv::SparsePyrLKOpticalFlow> sync_PyrLKOpticalFlow;
std::vector<bbox_t> cur_bbox_vec;
std::vector<bool> good_bbox_vec_flags;
void update_cur_bbox_vec(std::vector<bbox_t> _cur_bbox_vec)
{
cur_bbox_vec = _cur_bbox_vec;
good_bbox_vec_flags = std::vector<bool>(cur_bbox_vec.size(), true);
cv::Mat prev_pts, cur_pts_flow;
for (auto &i : cur_bbox_vec) {
float x_center = (i.x + i.w / 2.0F);
float y_center = (i.y + i.h / 2.0F);
prev_pts.push_back(cv::Point2f(x_center, y_center));
}
if (prev_pts.rows == 0)
prev_pts_flow = cv::Mat();
else
cv::transpose(prev_pts, prev_pts_flow);
}
void update_tracking_flow(cv::Mat new_src_mat, std::vector<bbox_t> _cur_bbox_vec)
{
if (new_src_mat.channels() == 1) {
src_grey = new_src_mat.clone();
}
else if (new_src_mat.channels() == 3) {
cv::cvtColor(new_src_mat, src_grey, CV_BGR2GRAY, 1);
}
else if (new_src_mat.channels() == 4) {
cv::cvtColor(new_src_mat, src_grey, CV_BGRA2GRAY, 1);
}
else {
std::cerr << " Warning: new_src_mat.channels() is not: 1, 3 or 4. It is = " << new_src_mat.channels() << " \n";
return;
}
update_cur_bbox_vec(_cur_bbox_vec);
}
std::vector<bbox_t> tracking_flow(cv::Mat new_dst_mat, bool check_error = true)
{
if (sync_PyrLKOpticalFlow.empty()) {
std::cout << "sync_PyrLKOpticalFlow isn't initialized \n";
return cur_bbox_vec;
}
cv::cvtColor(new_dst_mat, dst_grey, CV_BGR2GRAY, 1);
if (src_grey.rows != dst_grey.rows || src_grey.cols != dst_grey.cols) {
src_grey = dst_grey.clone();
//std::cerr << " Warning: src_grey.rows != dst_grey.rows || src_grey.cols != dst_grey.cols \n";
return cur_bbox_vec;
}
if (prev_pts_flow.cols < 1) {
return cur_bbox_vec;
}
sync_PyrLKOpticalFlow_gpu.sparse(src_grey_gpu, dst_grey_gpu, prev_pts_flow_gpu, cur_pts_flow_gpu, status_gpu, &err_gpu); // OpenCV 2.4.x
sync_PyrLKOpticalFlow->calc(src_grey, dst_grey, prev_pts_flow, cur_pts_flow, status, err); // OpenCV 3.x
dst_grey.copyTo(src_grey);
std::vector<bbox_t> result_bbox_vec;
if (err.rows == cur_bbox_vec.size() && status.rows == cur_bbox_vec.size())
{
for (size_t i = 0; i < cur_bbox_vec.size(); ++i)
{
cv::Point2f cur_key_pt = cur_pts_flow.at<cv::Point2f>(0, i);
cv::Point2f prev_key_pt = prev_pts_flow.at<cv::Point2f>(0, i);
float moved_x = cur_key_pt.x - prev_key_pt.x;
float moved_y = cur_key_pt.y - prev_key_pt.y;
if (abs(moved_x) < 100 && abs(moved_y) < 100 && good_bbox_vec_flags[i])
if (err.at<float>(0, i) < flow_error && status.at<unsigned char>(0, i) != 0 &&
((float)cur_bbox_vec[i].x + moved_x) > 0 && ((float)cur_bbox_vec[i].y + moved_y) > 0)
{
cur_bbox_vec[i].x += moved_x + 0.5;
cur_bbox_vec[i].y += moved_y + 0.5;
result_bbox_vec.push_back(cur_bbox_vec[i]);
}
else good_bbox_vec_flags[i] = false;
else good_bbox_vec_flags[i] = false;
//if(!check_error && !good_bbox_vec_flags[i]) result_bbox_vec.push_back(cur_bbox_vec[i]);
}
}
prev_pts_flow = cur_pts_flow.clone();
return result_bbox_vec;
}
};
#else
class Tracker_optflow {
};
#endif // defined(TRACK_OPTFLOW) && defined(OPENCV)
#ifdef OPENCV
static cv::Scalar obj_id_to_color(int obj_id) {
int const colors[6][3] = {
{
1,0,1 },{
0,0,1 },{
0,1,1 },{
0,1,0 },{
1,1,0 },{
1,0,0 } };
int const offset = obj_id * 123457 % 6;
int const color_scale = 150 + (obj_id * 123457) % 100;
cv::Scalar color(colors[offset][0], colors[offset][1], colors[offset][2]);
color *= color_scale;
return color;
}
class preview_boxes_t {
enum {
frames_history = 30 }; // how long to keep the history saved
struct preview_box_track_t {
unsigned int track_id, obj_id, last_showed_frames_ago;
bool current_detection;
bbox_t bbox;
cv::Mat mat_obj, mat_resized_obj;
preview_box_track_t() : track_id(0), obj_id(0), last_showed_frames_ago(frames_history), current_detection(false) {
}
};
std::vector<preview_box_track_t> preview_box_track_id;
size_t const preview_box_size, bottom_offset;
bool const one_off_detections;
public:
preview_boxes_t(size_t _preview_box_size = 100, size_t _bottom_offset = 100, bool _one_off_detections = false) :
preview_box_size(_preview_box_size), bottom_offset(_bottom_offset), one_off_detections(_one_off_detections)
{
}
void set(cv::Mat src_mat, std::vector<bbox_t> result_vec)
{
size_t const count_preview_boxes = src_mat.cols / preview_box_size;
if (preview_box_track_id.size() != count_preview_boxes) preview_box_track_id.resize(count_preview_boxes);
// increment frames history
for (auto &i : preview_box_track_id)
i.last_showed_frames_ago = std::min((unsigned)frames_history, i.last_showed_frames_ago + 1);
// occupy empty boxes
for (auto &k : result_vec) {
bool found = false;
// find the same (track_id)
for (auto &i : preview_box_track_id) {
if (i.track_id == k.track_id) {
if (!one_off_detections) i.last_showed_frames_ago = 0; // for tracked objects
found = true;
break;
}
}
if (!found) {
// find empty box
for (auto &i : preview_box_track_id) {
if (i.last_showed_frames_ago == frames_history) {
if (!one_off_detections && k.frames_counter == 0) break; // don't show if obj isn't tracked yet
i.track_id = k.track_id;
i.obj_id = k.obj_id;
i.bbox = k;
i.last_showed_frames_ago = 0;
break;
}
}
}
}
// draw preview box (from old or current frame)
for (size_t i = 0; i < preview_box_track_id.size(); ++i)
{
// get object image
cv::Mat dst = preview_box_track_id[i].mat_resized_obj;
preview_box_track_id[i].current_detection = false;
for (auto &k : result_vec) {
if (preview_box_track_id[i].track_id == k.track_id) {
if (one_off_detections && preview_box_track_id[i].last_showed_frames_ago > 0) {
preview_box_track_id[i].last_showed_frames_ago = frames_history; break;
}
bbox_t b = k;
cv::Rect r(b.x, b.y, b.w, b.h);
cv::Rect img_rect(cv::Point2i(0, 0), src_mat.size());
cv::Rect rect_roi = r & img_rect;
if (rect_roi.width > 1 || rect_roi.height > 1) {
cv::Mat roi = src_mat(rect_roi);
cv::resize(roi, dst, cv::Size(preview_box_size, preview_box_size), cv::INTER_NEAREST);
preview_box_track_id[i].mat_obj = roi.clone();
preview_box_track_id[i].mat_resized_obj = dst.clone();
preview_box_track_id[i].current_detection = true;
preview_box_track_id[i].bbox = k;
}
break;
}
}
}
}
void draw(cv::Mat draw_mat, bool show_small_boxes = false)
{
// draw preview box (from old or current frame)
for (size_t i = 0; i < preview_box_track_id.size(); ++i)
{
auto &prev_box = preview_box_track_id[i];
// draw object image
cv::Mat dst = prev_box.mat_resized_obj;
if (prev_box.last_showed_frames_ago < frames_history &&
dst.size() == cv::Size(preview_box_size, preview_box_size))
{
cv::Rect dst_rect_roi(cv::Point2i(i * preview_box_size, draw_mat.rows - bottom_offset), dst.size());
cv::Mat dst_roi = draw_mat(dst_rect_roi);
dst.copyTo(dst_roi);
cv::Scalar color = obj_id_to_color(prev_box.obj_id);
int thickness = (prev_box.current_detection) ? 5 : 1;
cv::rectangle(draw_mat, dst_rect_roi, color, thickness);
unsigned int const track_id = prev_box.track_id;
std::string track_id_str = (track_id > 0) ? std::to_string(track_id) : "";
putText(draw_mat, track_id_str, dst_rect_roi.tl() - cv::Point2i(-4, 5), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.9, cv::Scalar(0, 0, 0), 2);
std::string size_str = std::to_string(prev_box.bbox.w) + "x" + std::to_string(prev_box.bbox.h);
putText(draw_mat, size_str, dst_rect_roi.tl() + cv::Point2i(0, 12), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, cv::Scalar(0, 0, 0), 1);
if (!one_off_detections && prev_box.current_detection) {
cv::line(draw_mat, dst_rect_roi.tl() + cv::Point2i(preview_box_size, 0),
cv::Point2i(prev_box.bbox.x, prev_box.bbox.y + prev_box.bbox.h),
color);
}
if (one_off_detections && show_small_boxes) {
cv::Rect src_rect_roi(cv::Point2i(prev_box.bbox.x, prev_box.bbox.y),
cv::Size(prev_box.bbox.w, prev_box.bbox.h));
unsigned int const color_history = (255 * prev_box.last_showed_frames_ago) / frames_history;
color = cv::Scalar(255 - 3 * color_history, 255 - 2 * color_history, 255 - 1 * color_history);
if (prev_box.mat_obj.size() == src_rect_roi.size()) {
prev_box.mat_obj.copyTo(draw_mat(src_rect_roi));
}
cv::rectangle(draw_mat, src_rect_roi, color, thickness);
putText(draw_mat, track_id_str, src_rect_roi.tl() - cv::Point2i(0, 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, cv::Scalar(0, 0, 0), 1);
}
}
}
}
};
class track_kalman_t
{
int track_id_counter;
std::chrono::steady_clock::time_point global_last_time;
float dT;
public:
int max_objects; // max objects for tracking
int min_frames; // min frames to consider an object as detected
const float max_dist; // max distance (in px) to track with the same ID
cv::Size img_size; // max value of x,y,w,h
struct tst_t {
int track_id;
int state_id;
std::chrono::steady_clock::time_point last_time;
int detection_count;
tst_t() : track_id(-1), state_id(-1) {
}
};
std::vector<tst_t> track_id_state_id_time;
std::vector<bbox_t> result_vec_pred;
struct one_kalman_t;
std::vector<one_kalman_t> kalman_vec;
struct one_kalman_t
{
cv::KalmanFilter kf;
cv::Mat state;
cv::Mat meas;
int measSize, stateSize, contrSize;
void set_delta_time(float dT) {
kf.transitionMatrix.at<float>(2) = dT;
kf.transitionMatrix.at<float>(9) = dT;
}
void set(bbox_t box)
{
initialize_kalman();
kf.errorCovPre.at<float>(0) = 1; // px
kf.errorCovPre.at<float>(7) = 1; // px
kf.errorCovPre.at<float>(14) = 1;
kf.errorCovPre.at<float>(21) = 1;
kf.errorCovPre.at<float>(28) = 1; // px
kf.errorCovPre.at<float>(35) = 1; // px
state.at<float>(0) = box.x;
state.at<float>(1) = box.y;
state.at<float>(2) = 0;
state.at<float>(3) = 0;
state.at<float>(4) = box.w;
state.at<float>(5) = box.h;
// <<<< Initialization
kf.statePost = state;
}
// Kalman.correct() calculates: statePost = statePre + gain * (z(k)-measurementMatrix*statePre);
// corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
void correct(bbox_t box) {
meas.at<float>(0) = box.x;
meas.at<float>(1) = box.y;
meas.at<float>(2) = box.w;
meas.at<float>(3) = box.h;
kf.correct(meas);
bbox_t new_box = predict();
if (new_box.w == 0 || new_box.h == 0) {
set(box);
//std::cerr << " force set(): track_id = " << box.track_id <<
// ", x = " << box.x << ", y = " << box.y << ", w = " << box.w << ", h = " << box.h << std::endl;
}
}
// Kalman.predict() calculates: statePre = TransitionMatrix * statePost;
// predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)
bbox_t predict() {
bbox_t box;
state = kf.predict();
box.x = state.at<float>(0);
box.y = state.at<float>(1);
box.w = state.at<float>(4);
box.h = state.at<float>(5);
return box;
}
void initialize_kalman()
{
kf = cv::KalmanFilter(stateSize, measSize, contrSize, CV_32F);
// Transition State Matrix A
// Note: set dT at each processing step!
// [ 1 0 dT 0 0 0 ]
// [ 0 1 0 dT 0 0 ]
// [ 0 0 1 0 0 0 ]
// [ 0 0 0 1 0 0 ]
// [ 0 0 0 0 1 0 ]
// [ 0 0 0 0 0 1 ]
cv::setIdentity(kf.transitionMatrix);
// Measure Matrix H
// [ 1 0 0 0 0 0 ]
// [ 0 1 0 0 0 0 ]
// [ 0 0 0 0 1 0 ]
// [ 0 0 0 0 0 1 ]
kf.measurementMatrix = cv::Mat::zeros(measSize, stateSize, CV_32F);
kf.measurementMatrix.at<float>(0) = 1.0f;
kf.measurementMatrix.at<float>(7) = 1.0f;
kf.measurementMatrix.at<float>(16) = 1.0f;
kf.measurementMatrix.at<float>(23) = 1.0f;
// Process Noise Covariance Matrix Q - result smoother with lower values (1e-2)
// [ Ex 0 0 0 0 0 ]
// [ 0 Ey 0 0 0 0 ]
// [ 0 0 Ev_x 0 0 0 ]
// [ 0 0 0 Ev_y 0 0 ]
// [ 0 0 0 0 Ew 0 ]
// [ 0 0 0 0 0 Eh ]
//cv::setIdentity(kf.processNoiseCov, cv::Scalar(1e-3));
kf.processNoiseCov.at<float>(0) = 1e-2;
kf.processNoiseCov.at<float>(7) = 1e-2;
kf.processNoiseCov.at<float>(14) = 1e-2;// 5.0f;
kf.processNoiseCov.at<float>(21) = 1e-2;// 5.0f;
kf.processNoiseCov.at<float>(28) = 5e-3;
kf.processNoiseCov.at<float>(35) = 5e-3;
// Measures Noise Covariance Matrix R - result smoother with higher values (1e-1)
cv::setIdentity(kf.measurementNoiseCov, cv::Scalar(1e-1));
//cv::setIdentity(kf.errorCovPost, cv::Scalar::all(1e-2));
// <<<< Kalman Filter
set_delta_time(0);
}
one_kalman_t(int _stateSize = 6, int _measSize = 4, int _contrSize = 0) :
kf(_stateSize, _measSize, _contrSize, CV_32F), measSize(_measSize), stateSize(_stateSize), contrSize(_contrSize)
{
state = cv::Mat(stateSize, 1, CV_32F); // [x,y,v_x,v_y,w,h]
meas = cv::Mat(measSize, 1, CV_32F); // [z_x,z_y,z_w,z_h]
//cv::Mat procNoise(stateSize, 1, type)
// [E_x,E_y,E_v_x,E_v_y,E_w,E_h]
initialize_kalman();
}
};
// ------------------------------------------
track_kalman_t(int _max_objects = 1000, int _min_frames = 3, float _max_dist = 40, cv::Size _img_size = cv::Size(10000, 10000)) :
track_id_counter(0), max_objects(_max_objects), min_frames(_min_frames), max_dist(_max_dist), img_size(_img_size)
{
kalman_vec.resize(max_objects);
track_id_state_id_time.resize(max_objects);
result_vec_pred.resize(max_objects);
}
float calc_dt() {
dT = std::chrono::duration<double>(std::chrono::steady_clock::now() - global_last_time).count();
return dT;
}
static float get_distance(float src_x, float src_y, float dst_x, float dst_y) {
return sqrtf((src_x - dst_x)*(src_x - dst_x) + (src_y - dst_y)*(src_y - dst_y));
}
void clear_old_states() {
// clear old bboxes
for (size_t state_id = 0; state_id < track_id_state_id_time.size(); ++state_id)
{
float time_sec = std::chrono::duration<double>(std::chrono::steady_clock::now() - track_id_state_id_time[state_id].last_time).count();
float time_wait = 0.5; // 0.5 second
if (track_id_state_id_time[state_id].track_id > -1)
{
if ((result_vec_pred[state_id].x > img_size.width) ||
(result_vec_pred[state_id].y > img_size.height))
{
track_id_state_id_time[state_id].track_id = -1;
}
if (time_sec >= time_wait || track_id_state_id_time[state_id].detection_count < 0) {
//std::cerr << " remove track_id = " << track_id_state_id_time[state_id].track_id << ", state_id = " << state_id << std::endl;
track_id_state_id_time[state_id].track_id = -1; // remove bbox
}
}
}
}
tst_t get_state_id(bbox_t find_box, std::vector<bool> &busy_vec)
{
tst_t tst;
tst.state_id = -1;
float min_dist = std::numeric_limits<float>::max();
for (size_t i = 0; i < max_objects; ++i)
{
if (track_id_state_id_time[i].track_id > -1 && result_vec_pred[i].obj_id == find_box.obj_id && busy_vec[i] == false)
{
bbox_t pred_box = result_vec_pred[i];
float dist = get_distance(pred_box.x, pred_box.y, find_box.x, find_box.y);
float movement_dist = std::max(max_dist, static_cast<float>(std::max(pred_box.w, pred_box.h)) );
if ((dist < movement_dist) && (dist < min_dist)) {
min_dist = dist;
tst.state_id = i;
}
}
}
if (tst.state_id > -1) {
track_id_state_id_time[tst.state_id].last_time = std::chrono::steady_clock::now();
track_id_state_id_time[tst.state_id].detection_count = std::max(track_id_state_id_time[tst.state_id].detection_count + 2, 10);
tst = track_id_state_id_time[tst.state_id];
busy_vec[tst.state_id] = true;
}
else {
//std::cerr << " Didn't find: obj_id = " << find_box.obj_id << ", x = " << find_box.x << ", y = " << find_box.y <<
// ", track_id_counter = " << track_id_counter << std::endl;
}
return tst;
}
tst_t new_state_id(std::vector<bool> &busy_vec)
{
tst_t tst;
// find empty cell to add new track_id
auto it = std::find_if(track_id_state_id_time.begin(), track_id_state_id_time.end(), [&](tst_t &v) {
return v.track_id == -1; });
if (it != track_id_state_id_time.end()) {
it->state_id = it - track_id_state_id_time.begin();
//it->track_id = track_id_counter++;
it->track_id = 0;
it->last_time = std::chrono::steady_clock::now();
it->detection_count = 1;
tst = *it;
busy_vec[it->state_id] = true;
}
return tst;
}
std::vector<tst_t> find_state_ids(std::vector<bbox_t> result_vec)
{
std::vector<tst_t> tst_vec(result_vec.size());
std::vector<bool> busy_vec(max_objects, false);
for (size_t i = 0; i < result_vec.size(); ++i)
{
tst_t tst = get_state_id(result_vec[i], busy_vec);
int state_id = tst.state_id;
int track_id = tst.track_id;
// if new state_id
if (state_id < 0) {
tst = new_state_id(busy_vec);
state_id = tst.state_id;
track_id = tst.track_id;
if (state_id > -1) {
kalman_vec[state_id].set(result_vec[i]);
//std::cerr << " post: ";
}
}
//std::cerr << " track_id = " << track_id << ", state_id = " << state_id <<
// ", x = " << result_vec[i].x << ", det_count = " << tst.detection_count << std::endl;
if (state_id > -1) {
tst_vec[i] = tst;
result_vec_pred[state_id] = result_vec[i];
result_vec_pred[state_id].track_id = track_id;
}
}
return tst_vec;
}
std::vector<bbox_t> predict()
{
clear_old_states();
std::vector<bbox_t> result_vec;
for (size_t i = 0; i < max_objects; ++i)
{
tst_t tst = track_id_state_id_time[i];
if (tst.track_id > -1) {
bbox_t box = kalman_vec[i].predict();
result_vec_pred[i].x = box.x;
result_vec_pred[i].y = box.y;
result_vec_pred[i].w = box.w;
result_vec_pred[i].h = box.h;
if (tst.detection_count >= min_frames)
{
if (track_id_state_id_time[i].track_id == 0) {
track_id_state_id_time[i].track_id = ++track_id_counter;
result_vec_pred[i].track_id = track_id_counter;
}
result_vec.push_back(result_vec_pred[i]);
}
}
}
//std::cerr << " result_vec.size() = " << result_vec.size() << std::endl;
//global_last_time = std::chrono::steady_clock::now();
return result_vec;
}
std::vector<bbox_t> correct(std::vector<bbox_t> result_vec)
{
calc_dt();
clear_old_states();
for (size_t i = 0; i < max_objects; ++i)
track_id_state_id_time[i].detection_count--;
std::vector<tst_t> tst_vec = find_state_ids(result_vec);
for (size_t i = 0; i < tst_vec.size(); ++i) {
tst_t tst = tst_vec[i];
int state_id = tst.state_id;
if (state_id > -1)
{
kalman_vec[state_id].set_delta_time(dT);
kalman_vec[state_id].correct(result_vec_pred[state_id]);
}
}
result_vec = predict();
global_last_time = std::chrono::steady_clock::now();
return result_vec;
}
};
// ----------------------------------------------
#endif // OPENCV
#endif // __cplusplus
#endif // YOLO_V2_CLASS_HPP
**yolov4.h**
#pragma once
#include <iostream>
#include<Windows.h>
#include <vector>
#ifdef _WIN32
#define OPENCV
#define GPU
#endif
#pragma comment(lib, "yolo_cpp_dll.lib") //引入YOLO动态链接库
#include "yolo_v2_class.hpp" //引用动态链接库中的头文件
#include <opencv2/opencv.hpp>
#include "opencv2/highgui/highgui.hpp"
using namespace std;
using namespace cv;
typedef struct {
Rect box;
float prob;//置信度
unsigned int obj_id;//类别序号
int index;
}MyBox;
//以下两段代码来自yolo_console_dll.sln，有修改
void draw_boxes(cv::Mat mat_img, std::vector<bbox_t> result_vec, std::vector<std::string> obj_names);
std::vector<std::string> objects_names_from_file(std::string const filename);
非极大值抑制后处理
bool compScore(MyBox a, MyBox b);
float get_boxs_iou(Rect rect1, Rect rect2);
//非极大值抑制
void nms(std::vector<bbox_t> result_vec, float confThreshold, float nmsThreshold, std::vector<MyBox> &result_vec1);
//画框
void my_draw_boxes(cv::Mat mat_img, std::vector<MyBox> result_vec, std::vector<std::string> obj_names);
//冒泡法排序，从上到下
//从小到大排序//y
void bubbleSort_MinToMax_y(vector<MyBox> &result_vec);
//从小到大排序//x
void bubbleSort_MinToMax_x(vector<MyBox> &result_vec);
**yolov4.cpp**
#include"yolov4.h"
//以下两段代码来自yolo_console_dll.sln，有修改
void draw_boxes(cv::Mat mat_img, std::vector<bbox_t> result_vec, std::vector<std::string> obj_names)
{
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];
cv::Size const text_size = getTextSize(obj_name, cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, 2, 0);
int const max_width = (text_size.width > i.w + 2) ? text_size.width : (i.w + 2);
cv::rectangle(mat_img, cv::Point2f(std::max((int)i.x - 1, 0), std::max((int)i.y - 30, 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 - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, cv::Scalar(0, 0, 0), 2);
}
}
}
std::vector<std::string> objects_names_from_file(std::string const filename) {
std::ifstream file(filename);
std::vector<std::string> 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;
}
bool compScore(MyBox a, MyBox b) {
if (a.prob>b.prob)
return true;
else
return false;
}
float get_boxs_iou(Rect rect1, Rect rect2)
{
int xx1, yy1, xx2, yy2;
xx1 = max(rect1.x, rect2.x);
yy1 = max(rect1.y, rect2.y);
xx2 = min(rect1.x + rect1.width - 1, rect2.x + rect2.width - 1);
yy2 = min(rect1.y + rect1.height - 1, rect2.y + rect2.height - 1);
int insection_width, insection_height;
insection_width = max(0, xx2 - xx1 + 1);
insection_height = max(0, yy2 - yy1 + 1);
float insection_area, union_area, iou;
insection_area = float(insection_width) * insection_height;
union_area = float(rect1.width*rect1.height + rect2.width*rect2.height - insection_area);
iou = insection_area / union_area;
return iou;
}
void nms(std::vector<bbox_t> result_vec, float confThreshold, float nmsThreshold, std::vector<MyBox> &result_vec1)
{
//std::vector<MyBox> result_vec1;
int sum = 0;
for (int i = 0; i<result_vec.size(); i++)
{
/*std::cout << "第" << i + 1 << std::endl;
std::cout << "box:" << result_vec[i].x << result_vec[i].y << result_vec[i].w << result_vec[i].h << std::endl;
std::cout << "置信度：" << result_vec[i].prob << std::endl;
std::cout << "类别序号：" << result_vec[i].obj_id<< std::endl;*/
if (result_vec[i].prob>confThreshold)
{
MyBox midle;
midle.box.x = result_vec[i].x;
midle.box.y = result_vec[i].y;
midle.box.width = result_vec[i].w;
midle.box.height = result_vec[i].h;
midle.prob = result_vec[i].prob;
midle.obj_id = result_vec[i].obj_id;
midle.index = sum;
result_vec1.push_back(midle);
sum++;
}
}
std::cout << "置信度符合总数：" << sum << std::endl;
if (result_vec1.empty())
{
return;
}
sort(result_vec1.begin(), result_vec1.end(), compScore);//将候选框按照置信度排序
for (int i = 0; i < result_vec1.size(); i++)//总的
{
if (result_vec1[i].prob > 0)
{
for (int j = i + 1; j < result_vec1.size(); j++)
{
if (result_vec1[j].prob > 0)//判断当前候选框是否被抑制
{
float iou = get_boxs_iou(result_vec1[i].box, result_vec1[j].box);//计算正交比
if (iou > nmsThreshold)//nms_thresh为阈值
{
result_vec1[j].prob = 0;//将该候选框设置为抑制
}
}
}
}
}
for (auto it = result_vec1.begin(); it != result_vec1.end();)
{
if ((*it).prob == 0)
{
result_vec1.erase(it);//清除被抑制的候选框
}
else
{
it++;
}
}
}
void my_draw_boxes(cv::Mat mat_img, std::vector<MyBox> result_vec, std::vector<std::string> obj_names)
{
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.box.x, i.box.y, i.box.width, i.box.height), color, 2);
if (obj_names.size() > i.obj_id)
{
std::string obj_name = obj_names[i.obj_id] +std::to_string(i.box.height);
cv::Size const text_size = getTextSize(obj_name, cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, 2, 0);
int const max_width = (text_size.width > i.box.width + 2) ? text_size.width : (i.box.width + 2);
cv::rectangle(mat_img, cv::Point2f(std::max((int)i.box.x - 1, 0), std::max((int)i.box.y - 30, 0)),
cv::Point2f(std::min((int)i.box.x + max_width, mat_img.cols - 1), std::min((int)i.box.y, mat_img.rows - 1)),
color, CV_FILLED, 8, 0);
putText(mat_img, obj_name, cv::Point2f(i.box.x, i.box.y - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.7, cv::Scalar(0, 0, 0), 2);
}
}
}
//冒泡法排序，从上到下
//从小到大排序//y
void bubbleSort_MinToMax_y(vector<MyBox> &result_vec)
{
int N = result_vec.size();
for (int i = 1; i < N; i++)
{
for (int j = 0; j < N - i; j++)
{
if (result_vec[j].box.y > result_vec[j + 1].box.y)
{
//交换
MyBox buffer = result_vec[j];
result_vec[j] = result_vec[j + 1];
result_vec[j + 1] = buffer;
}
}
}
for (int i = 0; i < N;i++)
{
std::cout << "xuhao y:" << i + 1 << std::endl;
std::cout << "y:" << result_vec[i].box.y << std::endl;
}
}
//从小到大排序//x
void bubbleSort_MinToMax_x(vector<MyBox> &result_vec)
{
int N = result_vec.size();
for (int i = 1; i < N; i++)
{
for (int j = 0; j < N - i; j++)
{
if (result_vec[j].box.x > result_vec[j + 1].box.x)
{
//交换
MyBox buffer = result_vec[j];
result_vec[j] = result_vec[j + 1];
result_vec[j + 1] = buffer;
}
}
}
for (int i = 0; i < N; i++)
{
std::cout << "xuhao x:" << i + 1 << std::endl;
std::cout << "x:" << result_vec[i].box.x << std::endl;
}
}
**detect.cpp**
#include"yolov4.h"
std::string names_file = "yolov4.names";
std::string cfg_file = "yolov4.cfg";
std::string weights_file = "yolov4_last.weights";
Detector detector(cfg_file, weights_file, 0); //初始化检测器
char * left(char *dst, char *src)
{
char *p = src;
char *q = dst;
int len = strlen(src);
//if (n>len) n = len;
int n = len - 4;
while (n--) *(q++) = *(p++);
*(q++) = '\0';
return dst;
}
int main()
{
std::vector<std::string> obj_names;
obj_names = objects_names_from_file(names_file);
vector<String> image_files;
string DataDir = "./test01.16/";
string DataType = "*jpg";
string DataPath = DataDir + DataType;
glob(DataPath, image_files);
int correct_num = 0;
int count = 0;
for (int i = 0; i < image_files.size(); i++)
{
count++;
cout << count << " -----> " << image_files[i] << endl;
//---------- 图像读取成Mat ----------//
char *ImagePath = new char[image_files[i].length() + 1];
strcpy(ImagePath, image_files[i].c_str());
Mat image = imread(ImagePath);
生成txt标注文件//
double ws = 1.0/double(image.cols);
double hs = 1.0/double(image.rows);
std::vector<bbox_t> result_vec11 = detector.detect(image, 0.15);//检测
std::vector<MyBox> result_vec12;
//非极大值抑制
nms(result_vec11, 0.2, 0.2, result_vec12);
std::cout << "符合条件总数：" << result_vec12.size() << std::endl;
char* dst = new char[image_files[i].length() + 1];
left(dst, ImagePath);//路径
char buffer[256] = {
0 };
sprintf(buffer, "%s.txt", dst);
ofstream file(buffer, ios::out);//打开一个文件,等同于file.open("test.txt", ios::out);
int sum = 0;
for (int i = 0; i < result_vec12.size(); i++)
{
std::cout << "第" << i + 1 << std::endl;
std::cout << "box:" << result_vec12[i].box.x << result_vec12[i].box.y << result_vec12[i].box.width << result_vec12[i].box.height << std::endl;
std::cout << "置信度：" << result_vec12[i].prob << std::endl;
std::cout << "类别序号：" << result_vec12[i].obj_id << std::endl;
double dx = (double(result_vec12[i].box.x) +1+ double(result_vec12[i].box.width)/2.0)*ws;
double dy = (double(result_vec12[i].box.y)+1+ double(result_vec12[i].box.height)/2.0)*hs;
double dw = double(result_vec12[i].box.width)*ws;
double dh = double(result_vec12[i].box.height)*hs;
char rr[120] = {
0 };
sprintf(rr, "%d %lf %lf %lf %lf\n", result_vec12[i].obj_id, dx,dy,dw,dh);
string str0 = rr;
cout << "string: " << str0.c_str() << endl;
file << str0.c_str(); //将输入的内容放入txt文件中
}
file.close();
}
std::cout << "图像总数：" << count << std::endl;
cv::waitKey(0);
return 0;
}