From f762e6adb50d0a7eb72825b322e7d4192ae29ef3 Mon Sep 17 00:00:00 2001
From: Alexey <AlexeyAB@users.noreply.github.com>
Date: Sat, 03 Feb 2018 12:38:31 +0000
Subject: [PATCH] Merge pull request #357 from rajendraarora16/new-changes-darknet
---
src/yolo_v2_class.hpp | 301 ++++++++++++++++++++++++++++++++++++++++++--------
1 files changed, 253 insertions(+), 48 deletions(-)
diff --git a/src/yolo_v2_class.hpp b/src/yolo_v2_class.hpp
index 0116cce..a9bd7b2 100644
--- a/src/yolo_v2_class.hpp
+++ b/src/yolo_v2_class.hpp
@@ -1,6 +1,8 @@
#pragma once
#include <memory>
#include <vector>
+#include <deque>
+#include <algorithm>
#ifdef OPENCV
#include <opencv2/opencv.hpp> // C++
@@ -8,57 +10,95 @@
#include "opencv2/imgproc/imgproc_c.h" // C
#endif // OPENCV
-//extern "C" {
-//#include "image.h"
-//}
-
#ifdef YOLODLL_EXPORTS
+#if defined(_MSC_VER)
#define YOLODLL_API __declspec(dllexport)
#else
+#define YOLODLL_API __attribute__((visibility("default")))
+#endif
+#else
+#if defined(_MSC_VER)
#define YOLODLL_API __declspec(dllimport)
+#else
+#define YOLODLL_API
+#endif
#endif
struct bbox_t {
- float x, y, w, h;
- float prob;
- unsigned int obj_id;
+ 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
};
-typedef struct {
- int h;
- int w;
- int c;
- float *data;
-} image_t;
+struct image_t {
+ int h; // height
+ int w; // width
+ int c; // number of chanels (3 - for RGB)
+ float *data; // pointer to the image data
+};
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;
YOLODLL_API Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0);
YOLODLL_API ~Detector();
- YOLODLL_API std::vector<bbox_t> Detector::detect(std::string image_filename, float thresh = 0.2);
+ YOLODLL_API std::vector<bbox_t> detect(std::string image_filename, float thresh = 0.2, bool use_mean = false);
+ 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() const;
+ YOLODLL_API int get_net_height() const;
- YOLODLL_API std::vector<bbox_t> detect(image_t img, float thresh = 0.2);
-
+ YOLODLL_API std::vector<bbox_t> tracking_id(std::vector<bbox_t> cur_bbox_vec, bool const change_history = true,
+ int const frames_story = 6, int const max_dist = 150);
#ifdef OPENCV
- std::vector<bbox_t> detect(cv::Mat mat, float thresh = 0.2) {
- std::shared_ptr<image_t> image_ptr(new image_t, [](image_t *img) { free_image(*img); } );
- *image_ptr = mat_to_image(mat);
- return detect(*image_ptr, thresh);
+ 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.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()));
+ 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 mat_to_image(cv::Mat img)
- {
- std::shared_ptr<IplImage> ipl_small = std::make_shared<IplImage>(img);
- image_t im_small = ipl_to_image(ipl_small.get());
- rgbgr_image(im_small);
- return im_small;
- }
static image_t ipl_to_image(IplImage* src)
{
@@ -68,15 +108,17 @@
int c = src->nChannels;
int step = src->widthStep;
image_t out = make_image_custom(w, h, c);
- int i, j, k, count = 0;;
+ int count = 0;
- for (k = 0; k < c; ++k) {
- for (i = 0; i < h; ++i) {
- for (j = 0; j < w; ++j) {
- out.data[count++] = data[i*step + j*c + k] / 255.;
+ 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;
}
@@ -97,24 +139,187 @@
return out;
}
- static void rgbgr_image(image_t im)
- {
- int i;
- for (i = 0; i < im.w*im.h; ++i) {
- float swap = im.data[i];
- im.data[i] = im.data[i + im.w*im.h * 2];
- im.data[i + im.w*im.h * 2] = swap;
- }
- }
-
- static void free_image(image_t m)
- {
- if (m.data) {
- free(m.data);
- }
- }
#endif // OPENCV
+
};
+#if defined(TRACK_OPTFLOW) && defined(OPENCV)
+
+#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 = 2000, 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.resize(cur_bbox_vec.size());
+ for (auto &i : good_bbox_vec_flags) i = 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 (!check_error || (err_cpu.at<float>(0, i) < flow_error && status_cpu.at<unsigned char>(0, i) != 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;
+ }
+ }
+
+ 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;
+ }
+
+};
+#else
+
+class Tracker_optflow {};
+
+#endif // defined(TRACK_OPTFLOW) && defined(OPENCV)
--
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