From 23cb35e6c8eae8b59fab161036ae3f417a55c8db Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Fri, 30 Mar 2018 11:46:51 +0000
Subject: [PATCH] Changed small_object
---
src/yolo_v2_class.hpp | 493 ++++++++++++++++++++++++++++++++++++++++++++++--------
1 files changed, 414 insertions(+), 79 deletions(-)
diff --git a/src/yolo_v2_class.hpp b/src/yolo_v2_class.hpp
index 66eab73..68482e6 100644
--- a/src/yolo_v2_class.hpp
+++ b/src/yolo_v2_class.hpp
@@ -1,20 +1,4 @@
#pragma once
-#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
-
-#include <opencv2/cudaoptflow.hpp>
-#include <opencv2/cudaimgproc.hpp>
-#include <opencv2/cudaarithm.hpp>
-#include <opencv2/core/cuda.hpp>
-#endif // OPENCV
-
#ifdef YOLODLL_EXPORTS
#if defined(_MSC_VER)
#define YOLODLL_API __declspec(dllexport)
@@ -34,6 +18,7 @@
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 {
@@ -43,6 +28,17 @@
float *data; // pointer to the image data
};
+#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 // OPENCV
class Detector {
std::shared_ptr<void> detector_gpu_ptr;
@@ -62,7 +58,18 @@
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);
+ YOLODLL_API std::vector<bbox_t> tracking_id(std::vector<bbox_t> cur_bbox_vec, bool const change_history = true,
+ int const frames_story = 10, int const max_dist = 150);
+
+ 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)
@@ -70,17 +77,7 @@
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.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;
+ 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
@@ -147,15 +144,24 @@
};
-#if defined(TRACK_OPTFLOW) && defined(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) : gpu_count(cv::cuda::getCudaEnabledDeviceCount()), gpu_id(std::min(_gpu_id, gpu_count-1))
+ 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);
@@ -163,9 +169,9 @@
stream = cv::cuda::Stream();
sync_PyrLKOpticalFlow_gpu = cv::cuda::SparsePyrLKOpticalFlow::create();
- sync_PyrLKOpticalFlow_gpu->setWinSize(cv::Size(21, 21)); // 15, 21, 31
- sync_PyrLKOpticalFlow_gpu->setMaxLevel(3); // +- 3 pt
- sync_PyrLKOpticalFlow_gpu->setNumIters(2000); // def: 30
+ 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);
}
@@ -173,7 +179,6 @@
// just to avoid extra allocations
cv::cuda::GpuMat src_mat_gpu;
cv::cuda::GpuMat dst_mat_gpu, dst_grey_gpu;
- cv::cuda::GpuMat tmp_grey_gpu;
cv::cuda::GpuMat prev_pts_flow_gpu, cur_pts_flow_gpu;
cv::cuda::GpuMat status_gpu, err_gpu;
@@ -181,7 +186,40 @@
cv::Ptr<cv::cuda::SparsePyrLKOpticalFlow> sync_PyrLKOpticalFlow_gpu;
cv::cuda::Stream stream;
- void update_tracking_flow(cv::Mat src_mat)
+ 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)
@@ -193,15 +231,18 @@
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, 0, 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, std::vector<bbox_t> cur_bbox_vec)
+ 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";
@@ -215,13 +256,11 @@
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);
- tmp_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, 0, 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();
@@ -230,40 +269,13 @@
return cur_bbox_vec;
}
- cv::Mat prev_pts, prev_pts_flow_cpu, cur_pts_flow_cpu;
-
- for (auto &i : cur_bbox_vec) {
- float x_center = (i.x + i.w / 2);
- float y_center = (i.y + i.h / 2);
- 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);
-
-
- dst_grey_gpu.copyTo(tmp_grey_gpu, stream);
-
////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);
- tmp_grey_gpu.copyTo(src_grey_gpu, stream);
+ dst_grey_gpu.copyTo(src_grey_gpu, stream);
cv::Mat err_cpu, status_cpu;
err_gpu.download(err_cpu, stream);
@@ -273,24 +285,34 @@
std::vector<bbox_t> result_bbox_vec;
- for (size_t i = 0; i < cur_bbox_vec.size(); ++i)
+ if (err_cpu.cols == cur_bbox_vec.size() && status_cpu.cols == cur_bbox_vec.size())
{
- 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);
+ 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;
+ float moved_x = cur_key_pt.x - prev_key_pt.x;
+ float moved_y = cur_key_pt.y - prev_key_pt.y;
- if (err_cpu.cols > i && status_cpu.cols > i)
- if (abs(moved_x) < 100 && abs(moved_y) < 100)
- //if (err_cpu.at<float>(0, i) < 60 && status_cpu.at<unsigned char>(0, i) != 0)
+ 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);
@@ -298,9 +320,322 @@
}
};
+
+#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)
+#endif // defined(TRACK_OPTFLOW) && defined(OPENCV)
+
+#ifdef OPENCV
+
+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
+ YOLODLL_API void create_detector(char const* cfg_filename, char const* weight_filename, int gpu_id);
+ YOLODLL_API void delete_detector();
+ YOLODLL_API bbox_t* detect_custom(image_t img, float thresh, bool use_mean, int *result_size);
+ YOLODLL_API bbox_t* detect_resized(image_t img, int init_w, int init_h, float thresh, bool use_mean, int *result_size);
+ YOLODLL_API bbox_t* detect(image_t img, int *result_size);
+ YOLODLL_API image_t load_img(char *image_filename);
+ YOLODLL_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<YOLODLL_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
+*/
--
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