mirror/darknet
1
0
Fork 0
mirror of https://github.com/pjreddie/darknet.git synced 2023-08-10 21:13:14 +03:00
Code Issues Projects Releases Wiki Activity

Improved speed of yolo_console_dll.cpp - 40 FPS on 4K using GeForce GTX 960

Browse Source
This commit is contained in:
AlexeyAB
2017-09-12 17:16:21 +03:00
parent 6f1ce1f3e8
commit 8fa9f44211
3 changed files with 31 additions and 16 deletions
Download Patch File Download Diff File Expand all files Collapse all files

14
src/yolo_console_dll.cpp
View File

@ -101,6 +101,7 @@ int main(int argc, char *argv[])
protocol == "rtsp://" || protocol == "http://" || protocol == "https:/") // video network stream
{
cv::Mat cap_frame, cur_frame, det_frame, write_frame;
std::shared_ptr<image_t> det_image;
std::vector<bbox_t> result_vec, thread_result_vec;
detector.nms = 0.02; // comment it - if track_id is not required
std::atomic<bool> consumed, videowrite_ready;
@ -116,9 +117,10 @@ int main(int argc, char *argv[])
std::chrono::steady_clock::time_point steady_start, steady_end;
cv::VideoCapture cap(filename); cap >> cur_frame;
int const video_fps = cap.get(CV_CAP_PROP_FPS);
cv::Size const frame_size = cur_frame.size();
cv::VideoWriter output_video;
if (save_output_videofile)
output_video.open(out_videofile, CV_FOURCC('D', 'I', 'V', 'X'), std::max(35, video_fps), cur_frame.size(), true);
output_video.open(out_videofile, CV_FOURCC('D', 'I', 'V', 'X'), std::max(35, video_fps), frame_size, true);
while (!cur_frame.empty()) {
if (t_cap.joinable()) {
@ -132,7 +134,7 @@ int main(int argc, char *argv[])
if(consumed)
{
std::unique_lock<std::mutex> lock(mtx);
cur_frame.copyTo(det_frame);
det_image = detector.mat_to_image_resize(cur_frame);
result_vec = thread_result_vec;
result_vec = detector.tracking(result_vec); // comment it - if track_id is not required
consumed = false;
@ -140,14 +142,14 @@ int main(int argc, char *argv[])
// launch thread once
if (!t_detect.joinable()) {
t_detect = std::thread([&]() {
cv::Mat current_mat = det_frame.clone();
auto current_image = det_image;
consumed = true;
while (!current_mat.empty()) {
auto result = detector.detect(current_mat, 0.24, true);
while (current_image.use_count() > 0) {
auto result = detector.detect_resized(*current_image, frame_size, 0.24, true);
++fps_det_counter;
std::unique_lock<std::mutex> lock(mtx);
thread_result_vec = result;
current_mat = det_frame.clone();
current_image = det_image;
consumed = true;
cv.notify_all();
}

4
src/yolo_v2_class.cpp
View File

@ -109,11 +109,11 @@ YOLODLL_API Detector::~Detector()
#endif
}
YOLODLL_API int Detector::get_net_width() {
YOLODLL_API int Detector::get_net_width() const {
detector_gpu_t &detector_gpu = *reinterpret_cast<detector_gpu_t *>(detector_gpu_ptr.get());
return detector_gpu.net.w;
}
YOLODLL_API int Detector::get_net_height() {
YOLODLL_API int Detector::get_net_height() const {
detector_gpu_t &detector_gpu = *reinterpret_cast<detector_gpu_t *>(detector_gpu_ptr.get());
return detector_gpu.net.h;
}

29
src/yolo_v2_class.hpp
View File

@ -51,8 +51,8 @@ public:
YOLODLL_API std::vector<bbox_t> detect(image_t img, float thresh = 0.2, bool use_mean = false);
static YOLODLL_API image_t load_image(std::string image_filename);
static YOLODLL_API void free_image(image_t m);
YOLODLL_API int get_net_width();
YOLODLL_API int get_net_height();
YOLODLL_API int get_net_width() const;
YOLODLL_API int get_net_height() const;
YOLODLL_API std::vector<bbox_t> tracking(std::vector<bbox_t> cur_bbox_vec, int const frames_story = 6);
@ -60,14 +60,27 @@ public:
std::vector<bbox_t> detect(cv::Mat mat, float thresh = 0.2, bool use_mean = false)
{
if(mat.data == NULL)
throw std::runtime_error("file not found");
throw std::runtime_error("Image is empty");
auto image_ptr = mat_to_image_resize(mat);
return detect_resized(*image_ptr, mat.size(), thresh, use_mean);
}
std::vector<bbox_t> detect_resized(image_t img, cv::Size init_size, 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_size.width / img.w, hk = (float)init_size.height / img.h;
for (auto &i : detection_boxes) i.x *= wk, i.w *= wk, i.y *= hk, i.h *= hk;
return detection_boxes;
}
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::Mat det_mat;
cv::resize(mat, det_mat, cv::Size(get_net_width(), get_net_height()));
auto image_ptr = mat_to_image(det_mat);
auto detection_boxes = detect(*image_ptr, thresh, use_mean);
float wk = (float)mat.cols / det_mat.cols, hk = (float)mat.rows / det_mat.rows;
for (auto &i : detection_boxes) i.x*=wk, i.w*= wk, i.y*=hk, i.h*=hk;
return detection_boxes;
return mat_to_image(det_mat);
}
static std::shared_ptr<image_t> mat_to_image(cv::Mat img)