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darknet/include/yolo_v2_class.hpp

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#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
};
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>
#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);
class Detector {
std::shared_ptr<void> detector_gpu_ptr;
std::deque<std::vector<bbox_t>> prev_bbox_vec_deque;
const int cur_gpu_id;
public:
float nms = .4;
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);
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;
cv::cvtColor(img_src, img, cv::COLOR_RGB2BGR);
std::shared_ptr<image_t> image_ptr(new image_t, [](image_t *img) { free_image(*img); delete img; });
std::shared_ptr<IplImage> ipl_small = std::make_shared<IplImage>(img);
*image_ptr = ipl_to_image(ipl_small.get());
return image_ptr;
}
private:
static image_t ipl_to_image(IplImage* src)
{
unsigned char *data = (unsigned char *)src->imageData;
int h = src->height;
int w = src->width;
int c = src->nChannels;
int step = src->widthStep;
image_t out = make_image_custom(w, h, c);
int count = 0;
for (int k = 0; k < c; ++k) {
for (int i = 0; i < h; ++i) {
int i_step = i*step;
for (int j = 0; j < w; ++j) {
out.data[count++] = data[i_step + j*c + k] / 255.;
}
}
}
return out;
}
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
};
#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 = 9, 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() == 3) {
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);
}
update_cur_bbox_vec(_cur_bbox_vec);
//src_grey_gpu.upload(src_mat, stream); // use BGR
src_mat_gpu.upload(src_mat, stream);
cv::cuda::cvtColor(src_mat_gpu, src_grey_gpu, CV_BGR2GRAY, 1, stream);
}
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 = 9, 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() == 3) {
update_cur_bbox_vec(_cur_bbox_vec);
cv::cvtColor(new_src_mat, src_grey, CV_BGR2GRAY, 1);
}
}
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();
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);
}
}
}
}
};
#endif // OPENCV
//extern "C" {
#endif // __cplusplus
/*
// C - wrappers
LIB_API void create_detector(char const* cfg_filename, char const* weight_filename, int gpu_id);
LIB_API void delete_detector();
LIB_API bbox_t* detect_custom(image_t img, float thresh, bool use_mean, int *result_size);
LIB_API bbox_t* detect_resized(image_t img, int init_w, int init_h, float thresh, bool use_mean, int *result_size);
LIB_API bbox_t* detect(image_t img, int *result_size);
LIB_API image_t load_img(char *image_filename);
LIB_API void free_img(image_t m);
#ifdef __cplusplus
} // extern "C"
static std::shared_ptr<void> c_detector_ptr;
static std::vector<bbox_t> c_result_vec;
void create_detector(char const* cfg_filename, char const* weight_filename, int gpu_id) {
c_detector_ptr = std::make_shared<LIB_API Detector>(cfg_filename, weight_filename, gpu_id);
}
void delete_detector() { c_detector_ptr.reset(); }
bbox_t* detect_custom(image_t img, float thresh, bool use_mean, int *result_size) {
c_result_vec = static_cast<Detector*>(c_detector_ptr.get())->detect(img, thresh, use_mean);
*result_size = c_result_vec.size();
return c_result_vec.data();
}
bbox_t* detect_resized(image_t img, int init_w, int init_h, float thresh, bool use_mean, int *result_size) {
c_result_vec = static_cast<Detector*>(c_detector_ptr.get())->detect_resized(img, init_w, init_h, thresh, use_mean);
*result_size = c_result_vec.size();
return c_result_vec.data();
}
bbox_t* detect(image_t img, int *result_size) {
return detect_custom(img, 0.24, true, result_size);
}
image_t load_img(char *image_filename) {
return static_cast<Detector*>(c_detector_ptr.get())->load_image(image_filename);
}
void free_img(image_t m) {
static_cast<Detector*>(c_detector_ptr.get())->free_image(m);
}
#endif // __cplusplus
*/
#endif
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