#pragma once
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#include <memory>
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#include <vector>
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#include <deque>
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#include <algorithm>
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#ifdef OPENCV
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#include <opencv2/opencv.hpp> // C++
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#include "opencv2/highgui/highgui_c.h" // C
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#include "opencv2/imgproc/imgproc_c.h" // C
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#include <opencv2/cudaoptflow.hpp>
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#include <opencv2/cudaimgproc.hpp>
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#include <opencv2/cudaarithm.hpp>
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#include "opencv2/core/cuda.hpp"
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#endif // OPENCV
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#ifdef YOLODLL_EXPORTS
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#if defined(_MSC_VER)
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#define YOLODLL_API __declspec(dllexport)
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#else
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#define YOLODLL_API __attribute__((visibility("default")))
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#endif
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#else
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#if defined(_MSC_VER)
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#define YOLODLL_API __declspec(dllimport)
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#else
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#define YOLODLL_API
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#endif
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#endif
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struct bbox_t {
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unsigned int x, y, w, h; // (x,y) - top-left corner, (w, h) - width & height of bounded box
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float prob; // confidence - probability that the object was found correctly
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unsigned int obj_id; // class of object - from range [0, classes-1]
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unsigned int track_id; // tracking id for video (0 - untracked, 1 - inf - tracked object)
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};
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struct image_t {
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int h; // height
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int w; // width
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int c; // number of chanels (3 - for RGB)
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float *data; // pointer to the image data
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};
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class Detector {
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std::shared_ptr<void> detector_gpu_ptr;
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std::deque<std::vector<bbox_t>> prev_bbox_vec_deque;
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public:
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float nms = .4;
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YOLODLL_API Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0);
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YOLODLL_API ~Detector();
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YOLODLL_API std::vector<bbox_t> detect(std::string image_filename, float thresh = 0.2, bool use_mean = false);
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YOLODLL_API std::vector<bbox_t> detect(image_t img, float thresh = 0.2, bool use_mean = false);
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static YOLODLL_API image_t load_image(std::string image_filename);
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static YOLODLL_API void free_image(image_t m);
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YOLODLL_API int get_net_width() const;
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YOLODLL_API int get_net_height() const;
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YOLODLL_API std::vector<bbox_t> tracking(std::vector<bbox_t> cur_bbox_vec, int const frames_story = 6);
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#ifdef OPENCV
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std::vector<bbox_t> detect(cv::Mat mat, float thresh = 0.2, bool use_mean = false)
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{
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if(mat.data == NULL)
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throw std::runtime_error("Image is empty");
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auto image_ptr = mat_to_image_resize(mat);
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return detect_resized(*image_ptr, mat.size(), thresh, use_mean);
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}
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std::vector<bbox_t> detect_resized(image_t img, cv::Size init_size, float thresh = 0.2, bool use_mean = false)
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{
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if (img.data == NULL)
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throw std::runtime_error("Image is empty");
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auto detection_boxes = detect(img, thresh, use_mean);
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float wk = (float)init_size.width / img.w, hk = (float)init_size.height / img.h;
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for (auto &i : detection_boxes) i.x *= wk, i.w *= wk, i.y *= hk, i.h *= hk;
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return detection_boxes;
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}
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std::shared_ptr<image_t> mat_to_image_resize(cv::Mat mat) const
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{
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if (mat.data == NULL) return std::shared_ptr<image_t>(NULL);
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cv::Mat det_mat;
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cv::resize(mat, det_mat, cv::Size(get_net_width(), get_net_height()));
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return mat_to_image(det_mat);
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}
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static std::shared_ptr<image_t> mat_to_image(cv::Mat img)
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{
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std::shared_ptr<image_t> image_ptr(new image_t, [](image_t *img) { free_image(*img); delete img; });
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std::shared_ptr<IplImage> ipl_small = std::make_shared<IplImage>(img);
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*image_ptr = ipl_to_image(ipl_small.get());
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rgbgr_image(*image_ptr);
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return image_ptr;
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}
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private:
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static image_t ipl_to_image(IplImage* src)
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{
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unsigned char *data = (unsigned char *)src->imageData;
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int h = src->height;
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int w = src->width;
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int c = src->nChannels;
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int step = src->widthStep;
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image_t out = make_image_custom(w, h, c);
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int i, j, k, count = 0;;
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for (k = 0; k < c; ++k) {
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for (i = 0; i < h; ++i) {
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for (j = 0; j < w; ++j) {
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out.data[count++] = data[i*step + j*c + k] / 255.;
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}
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}
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}
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return out;
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}
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static image_t make_empty_image(int w, int h, int c)
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{
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image_t out;
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out.data = 0;
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out.h = h;
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out.w = w;
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out.c = c;
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return out;
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}
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static image_t make_image_custom(int w, int h, int c)
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{
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image_t out = make_empty_image(w, h, c);
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out.data = (float *)calloc(h*w*c, sizeof(float));
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return out;
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}
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static void rgbgr_image(image_t im)
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{
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int i;
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for (i = 0; i < im.w*im.h; ++i) {
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float swap = im.data[i];
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im.data[i] = im.data[i + im.w*im.h * 2];
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im.data[i + im.w*im.h * 2] = swap;
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}
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}
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#endif // OPENCV
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};
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#if defined(TRACK_OPTFLOW) && defined(OPENCV)
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class Tracker_optflow {
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public:
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// just to avoid extra allocations
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cv::cuda::GpuMat src_mat_gpu;
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cv::cuda::GpuMat dst_mat_gpu, dst_grey_gpu;
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cv::cuda::GpuMat tmp_grey_gpu;
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cv::cuda::GpuMat prev_pts_flow_gpu, cur_pts_flow_gpu;
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cv::cuda::GpuMat status_gpu, err_gpu;
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cv::cuda::GpuMat src_grey_gpu; // used in both functions
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cv::Ptr<cv::cuda::SparsePyrLKOpticalFlow> sync_PyrLKOpticalFlow_gpu;
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void update_tracking_flow(cv::Mat src_mat, int gpu_id = 0)
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{
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int const old_gpu_id = cv::cuda::getDevice();
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static const int gpu_count = cv::cuda::getCudaEnabledDeviceCount();
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if (gpu_count > gpu_id)
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cv::cuda::setDevice(gpu_id);
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cv::cuda::Stream stream;
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if (sync_PyrLKOpticalFlow_gpu.empty()) {
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sync_PyrLKOpticalFlow_gpu = cv::cuda::SparsePyrLKOpticalFlow::create();
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//sync_PyrLKOpticalFlow_gpu->setWinSize(cv::Size(31, 31)); //sync_PyrLKOpticalFlow_gpu.winSize = cv::Size(31, 31);
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//sync_PyrLKOpticalFlow_gpu->setWinSize(cv::Size(15, 15)); //sync_PyrLKOpticalFlow_gpu.winSize = cv::Size(15, 15);
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sync_PyrLKOpticalFlow_gpu->setWinSize(cv::Size(21, 21));
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sync_PyrLKOpticalFlow_gpu->setMaxLevel(3); //sync_PyrLKOpticalFlow_gpu.maxLevel = 8; // +-32 points // def: 3
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sync_PyrLKOpticalFlow_gpu->setNumIters(6000); //sync_PyrLKOpticalFlow_gpu.iters = 8000; // def: 30
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//??? //sync_PyrLKOpticalFlow_gpu.getMinEigenVals = true;
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//std::cout << "sync_PyrLKOpticalFlow_gpu.maxLevel: " << sync_PyrLKOpticalFlow_gpu.maxLevel << std::endl;
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//std::cout << "sync_PyrLKOpticalFlow_gpu.iters: " << sync_PyrLKOpticalFlow_gpu.iters << std::endl;
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//std::cout << "sync_PyrLKOpticalFlow_gpu.winSize: " << sync_PyrLKOpticalFlow_gpu.winSize << std::endl;
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}
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if (src_mat.channels() == 3) {
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if (src_mat_gpu.cols == 0) {
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src_mat_gpu = cv::cuda::GpuMat(src_mat.size(), src_mat.type());
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src_grey_gpu = cv::cuda::GpuMat(src_mat.size(), CV_8UC1);
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}
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src_mat_gpu.upload(src_mat, stream);
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cv::cuda::cvtColor(src_mat_gpu, src_grey_gpu, CV_BGR2GRAY, 0, stream);
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//std::cout << " \n\n OK !!! \n\n";
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}
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cv::cuda::setDevice(old_gpu_id);
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}
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std::vector<bbox_t> tracking_flow(cv::Mat dst_mat, std::vector<bbox_t> cur_bbox_vec, int gpu_id = 0)
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{
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if (sync_PyrLKOpticalFlow_gpu.empty()) {
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std::cout << "sync_PyrLKOpticalFlow_gpu isn't initialized \n";
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return cur_bbox_vec;
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}
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int const old_gpu_id = cv::cuda::getDevice();
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static const int gpu_count = cv::cuda::getCudaEnabledDeviceCount();
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if (gpu_count > gpu_id)
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cv::cuda::setDevice(gpu_id);
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cv::cuda::Stream stream;
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if (dst_mat_gpu.cols == 0) {
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dst_mat_gpu = cv::cuda::GpuMat(dst_mat.size(), dst_mat.type());
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dst_grey_gpu = cv::cuda::GpuMat(dst_mat.size(), CV_8UC1);
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tmp_grey_gpu = cv::cuda::GpuMat(dst_mat.size(), CV_8UC1);
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}
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dst_mat_gpu.upload(dst_mat, stream);
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cv::cuda::cvtColor(dst_mat_gpu, dst_grey_gpu, CV_BGR2GRAY, 0, stream);
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if (src_grey_gpu.rows != dst_grey_gpu.rows || src_grey_gpu.cols != dst_grey_gpu.cols) {
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stream.waitForCompletion();
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src_grey_gpu = dst_grey_gpu.clone();
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cv::cuda::setDevice(old_gpu_id);
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return cur_bbox_vec;
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}
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cv::Mat prev_pts, prev_pts_flow_cpu, cur_pts_flow_cpu;
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for (auto &i : cur_bbox_vec) {
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float x_center = (i.x + i.w / 2);
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float y_center = (i.y + i.h / 2);
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prev_pts.push_back(cv::Point2f(x_center, y_center));
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}
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if (prev_pts.rows == 0)
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prev_pts_flow_cpu = cv::Mat();
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else
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cv::transpose(prev_pts, prev_pts_flow_cpu);
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if (prev_pts_flow_gpu.cols < prev_pts_flow_cpu.cols) {
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prev_pts_flow_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), prev_pts_flow_cpu.type());
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cur_pts_flow_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), prev_pts_flow_cpu.type());
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status_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), CV_8UC1);
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err_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), CV_32FC1);
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}
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prev_pts_flow_gpu.upload(cv::Mat(prev_pts_flow_cpu), stream);
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dst_grey_gpu.copyTo(tmp_grey_gpu, stream);
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//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
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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
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//std::cout << "\n 1-e \n";
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cur_pts_flow_gpu.download(cur_pts_flow_cpu, stream);
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tmp_grey_gpu.copyTo(src_grey_gpu, stream);
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cv::Mat err_cpu, status_cpu;
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err_gpu.download(err_cpu, stream);
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status_gpu.download(status_cpu, stream);
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stream.waitForCompletion();
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std::vector<bbox_t> result_bbox_vec;
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for (size_t i = 0; i < cur_bbox_vec.size(); ++i)
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{
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cv::Point2f cur_key_pt = cur_pts_flow_cpu.at<cv::Point2f>(0, i);
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cv::Point2f prev_key_pt = prev_pts_flow_cpu.at<cv::Point2f>(0, i);
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float moved_x = cur_key_pt.x - prev_key_pt.x;
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float moved_y = cur_key_pt.y - prev_key_pt.y;
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if (err_cpu.cols > i && status_cpu.cols > i)
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if (abs(moved_x) < 100 && abs(moved_y) < 100)
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//if (err_cpu.at<float>(0, i) < 60 && status_cpu.at<unsigned char>(0, i) != 0)
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{
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cur_bbox_vec[i].x += moved_x + 0.5;
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cur_bbox_vec[i].y += moved_y + 0.5;
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result_bbox_vec.push_back(cur_bbox_vec[i]);
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}
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}
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cv::cuda::setDevice(old_gpu_id);
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return result_bbox_vec;
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}
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};
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#else
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class Tracker_optflow {};
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#endif // defined(TRACK_OPTFLOW) && defined(OPENCV)
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