Micaela Verucchi
496 lines
20 KiB
C++
496 lines
20 KiB
C++
#ifndef CENTERNETDETECTION_H
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#define CENTERNETDETECTION_H
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#include "CenternetDetection.h"
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#include "opencv2/imgproc/imgproc.hpp"
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// #include <opencv2/cudawarping.hpp>
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// #include <opencv2/cudaarithm.hpp>
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namespace tk { namespace dnn {
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float __colors[6][3] = { {1,0,1}, {0,0,1},{0,1,1},{0,1,0},{1,1,0},{1,0,0} };
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float get_color2(int c, int x, int max)
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{
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float ratio = ((float)x/max)*5;
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int i = floor(ratio);
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int j = ceil(ratio);
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ratio -= i;
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float r = (1-ratio) * __colors[i % 6][c % 3] + ratio*__colors[j % 6][c % 3];
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//printf("%f\n", r);
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return r;
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}
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bool CenternetDetection::init(std::string tensor_path) {
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std::cout<<(tensor_path).c_str()<<"\n";
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netRT = new tk::dnn::NetworkRT(NULL, (tensor_path).c_str() );
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dim = tk::dnn::dataDim_t(1, 3, 512, 512, 1);
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const char *coco_class_name_[] = {
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"person", "bicycle", "car", "motorcycle", "airplane",
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"bus", "train", "truck", "boat", "traffic light", "fire hydrant",
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"stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse",
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"sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack",
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"umbrella", "handbag", "tie", "suitcase", "frisbee", "skis",
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"snowboard", "sports ball", "kite", "baseball bat", "baseball glove",
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"skateboard", "surfboard", "tennis racket", "bottle", "wine glass",
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"cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich",
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"orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake",
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"chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv",
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"laptop", "mouse", "remote", "keyboard", "cell phone", "microwave",
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"oven", "toaster", "sink", "refrigerator", "book", "clock", "vase",
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"scissors", "teddy bear", "hair drier", "toothbrush"
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};
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coco_class_name = std::vector<std::string>(coco_class_name_, std::end( coco_class_name_ ));
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src = cv::Mat(cv::Size(2,3), CV_32F);
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dst = cv::Mat(cv::Size(2,3), CV_32F);
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dst2 = cv::Mat(cv::Size(2,3), CV_32F);
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trans = cv::Mat(cv::Size(3,2), CV_32F);
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trans2 = cv::Mat(cv::Size(3,2), CV_32F);
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// dets = tk::dnn::Yolo::allocateDetections(tk::dnn::Yolo::MAX_DETECTIONS, classes);
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checkCuda(cudaMalloc(&input_d, sizeof(dnnType)*netRT->input_dim.tot()));
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// dim_hm = tk::dnn::dataDim_t(1, 80, 56, 56, 1);
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// dim_wh = tk::dnn::dataDim_t(1, 2, 56, 56, 1);
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// dim_reg = tk::dnn::dataDim_t(1, 2, 56, 56, 1);
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dim_hm = tk::dnn::dataDim_t(1, 80, 128, 128, 1);
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dim_wh = tk::dnn::dataDim_t(1, 2, 128, 128, 1);
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dim_reg = tk::dnn::dataDim_t(1, 2, 128, 128, 1);
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checkCuda( cudaMalloc(&topk_scores, dim_hm.c * K *sizeof(float)) );
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checkCuda( cudaMalloc(&topk_inds_, dim_hm.c * K *sizeof(int)) );
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checkCuda( cudaMalloc(&topk_ys_, dim_hm.c * K *sizeof(float)) );
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checkCuda( cudaMalloc(&topk_xs_, dim_hm.c * K *sizeof(float)) );
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checkCuda( cudaMalloc(&ids_d, dim_hm.c * dim_hm.h * dim_hm.w*sizeof(int)) );
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checkCuda( cudaMalloc(&ids_2d, dim_hm.c * dim_hm.h * dim_hm.w*sizeof(int)) );
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checkCuda( cudaMallocHost(&ids_, dim_hm.c * dim_hm.h * dim_hm.w*sizeof(int)) );
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checkCuda( cudaMallocHost(&ids_2, dim_hm.c * dim_hm.h * dim_hm.w*sizeof(int)) );
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for(int i =0; i<dim_hm.c * dim_hm.h * dim_hm.w; i++){
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ids_[i] = i;
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}
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int val = 0;
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for(int i =0; i <dim_hm.c * dim_hm.h * dim_hm.w; i++){
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ids_2[i] = val;
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if(i%dim_hm.c == 0)
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val = 0;
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}
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checkCuda( cudaMallocHost(&scores, K *sizeof(float)) );
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checkCuda( cudaMalloc(&scores_d, K *sizeof(float)) );
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checkCuda( cudaMallocHost(&clses, K *sizeof(int)) );
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checkCuda( cudaMalloc(&clses_d, K *sizeof(int)) );
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// checkCuda( cudaMallocHost(&topk_inds, K *sizeof(int)) );
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checkCuda( cudaMalloc(&topk_inds_d, K *sizeof(int)) );
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checkCuda( cudaMalloc(&topk_ys_d, K *sizeof(float)) );
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checkCuda( cudaMalloc(&topk_xs_d, K *sizeof(float)) );
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// checkCuda( cudaMalloc(&intid, K *sizeof(int)) );
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checkCuda( cudaMalloc(&inttopk_ys_d, K *sizeof(int)) );
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checkCuda( cudaMalloc(&inttopk_xs_d, K *sizeof(int)) );
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// checkCuda( cudaMalloc(&ids_d, dim_hm.c * K*sizeof(int)) );
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// checkCuda( cudaMallocHost(&wh_aus, dim_wh.tot()*sizeof(dnnType)) );
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checkCuda( cudaMallocHost(&bbx0, K * sizeof(float)) );
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checkCuda( cudaMallocHost(&bby0, K * sizeof(float)) );
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checkCuda( cudaMallocHost(&bbx1, K * sizeof(float)) );
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checkCuda( cudaMallocHost(&bby1, K * sizeof(float)) );
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checkCuda( cudaMalloc(&bbx0_d, K * sizeof(float)) );
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checkCuda( cudaMalloc(&bby0_d, K * sizeof(float)) );
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checkCuda( cudaMalloc(&bbx1_d, K * sizeof(float)) );
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checkCuda( cudaMalloc(&bby1_d, K * sizeof(float)) );
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checkCuda( cudaMallocHost(&target_coords, 4 * K *sizeof(float)) );
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#ifdef OPENCV_CUDA
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checkCuda( cudaMalloc(&mean_d, 3 * sizeof(float)) );
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checkCuda( cudaMalloc(&stddev_d, 3 * sizeof(float)) );
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float mean[3] = {0.408, 0.447, 0.47};
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float stddev[3] = {0.289, 0.274, 0.278};
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checkCuda(cudaMemcpy(mean_d, mean, 3*sizeof(float), cudaMemcpyHostToDevice));
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checkCuda(cudaMemcpy(stddev_d, stddev, 3*sizeof(float), cudaMemcpyHostToDevice));
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#else
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checkCuda(cudaMallocHost(&input, sizeof(dnnType)*netRT->input_dim.tot()));
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mean << 0.408, 0.447, 0.47;
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stddev << 0.289, 0.274, 0.278;
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#endif
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checkCuda( cudaMalloc(&d_ptrs, dim.c * dim.h*dim.w * sizeof(float)) );
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// mean << 0.408, 0.447, 0.47;
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// stddev << 0.289, 0.274, 0.278;
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// Alloc array used in the kernel
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checkCuda( cudaMalloc(&src_out, K *sizeof(float)) );
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checkCuda( cudaMalloc(&ids_out, K *sizeof(int)) );
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// checkCuda( cudaFree(src_out) );
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// checkCuda( cudaFree(ids_out) );
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dst2.at<float>(0,0)=width * 0.5;
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dst2.at<float>(0,1)=width * 0.5;
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dst2.at<float>(1,0)=width * 0.5;
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dst2.at<float>(1,1)=width * 0.5 + width * -0.5;
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dst2.at<float>(2,0)=dst2.at<float>(1,0) + (-dst2.at<float>(0,1)+dst2.at<float>(1,1) );
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dst2.at<float>(2,1)=dst2.at<float>(1,1) + (dst2.at<float>(0,0)-dst2.at<float>(1,0) );
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}
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cv::Mat CenternetDetection::draw(cv::Mat &imageOrig) {
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tk::dnn::box b;
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int x0, w, x1, y0, h, y1;
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int objClass;
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std::string det_class;
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int baseline = 0;
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float fontScale = 0.5;
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int thickness = 2;
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for(int c=0; c<classes; c++) {
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int offset = c*123457 % classes;
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float r = get_color2(2, offset, classes);
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float g = get_color2(1, offset, classes);
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float b = get_color2(0, offset, classes);
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colors[c] = cv::Scalar(int(255.0*b), int(255.0*g), int(255.0*r));
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}
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int num_detected = detected.size();
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for (int i = 0; i < num_detected; i++){
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b = detected[i];
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x0 = b.x;
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w = b.w;
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x1 = b.x + w;
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y0 = b.y;
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h = b.h;
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y1 = b.y + h;
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objClass = b.cl;
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det_class = coco_class_name[objClass];
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cv::rectangle(imageOrig, cv::Point(x0, y0), cv::Point(x1, y1), colors[objClass], 2);
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// draw label
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cv::Size textSize = getTextSize(det_class, cv::FONT_HERSHEY_SIMPLEX, fontScale, thickness, &baseline);
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cv::rectangle(imageOrig, cv::Point(x0, y0), cv::Point((x0 + textSize.width - 2), (y0 - textSize.height - 2)), colors[b.cl], -1);
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cv::putText(imageOrig, det_class, cv::Point(x0, (y0 - (baseline / 2))), cv::FONT_HERSHEY_SIMPLEX, fontScale, cv::Scalar(255, 255, 255), thickness);
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}
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return imageOrig;
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// cv::namedWindow("cnet", cv::WINDOW_NORMAL);
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// cv::imshow("cnet", imageOrig);
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// cv::waitKey(10000);
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}
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void CenternetDetection::preprocess()
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{
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auto start_t = std::chrono::steady_clock::now();
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auto step_t = std::chrono::steady_clock::now();
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auto end_t = std::chrono::steady_clock::now();
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// -----------------------------------pre-process ------------------------------------------
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// it will resize the images to `224 x 224` in GETTING_STARTED.md
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cv::Size sz = imageOrig.size();
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std::cout<<"image: "<<sz.width<<", "<<sz.height<<std::endl;
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float scale = 1.0;
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float new_height = sz.height * scale;
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float new_width = sz.width * scale;
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if(sz.height != sz_old.height && sz.width != sz_old.width){
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float c[] = {new_width / 2.0, new_height /2.0};
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float s[2];
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if(sz.width > sz.height){
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s[0] = sz.width * 1.0;
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s[1] = sz.width * 1.0;
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}
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else{
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s[0] = sz.height * 1.0;
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s[1] = sz.height * 1.0;
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}
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// ----------- get_affine_transform
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// rot_rad = pi * 0 / 100 --> 0
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src.at<float>(0,0)=c[0];
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src.at<float>(0,1)=c[1];
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src.at<float>(1,0)=c[0];
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src.at<float>(1,1)=c[1] + s[0] * -0.5;
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dst.at<float>(0,0)=inp_width * 0.5;
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dst.at<float>(0,1)=inp_height * 0.5;
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dst.at<float>(1,0)=inp_width * 0.5;
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dst.at<float>(1,1)=inp_height * 0.5 + inp_width * -0.5;
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src.at<float>(2,0)=src.at<float>(1,0) + (-src.at<float>(0,1)+src.at<float>(1,1) );
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src.at<float>(2,1)=src.at<float>(1,1) + (src.at<float>(0,0)-src.at<float>(1,0) );
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dst.at<float>(2,0)=dst.at<float>(1,0) + (-dst.at<float>(0,1)+dst.at<float>(1,1) );
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dst.at<float>(2,1)=dst.at<float>(1,1) + (dst.at<float>(0,0)-dst.at<float>(1,0) );
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trans = cv::getAffineTransform( src, dst );
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end_t = std::chrono::steady_clock::now();
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std::cout << " TIME gett affine trans: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
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step_t = end_t;
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trans2 = cv::getAffineTransform( dst2, src );
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end_t = std::chrono::steady_clock::now();
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std::cout << " TIME getAffineTrans 2: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
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step_t = end_t;
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}
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sz_old = sz;
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#ifdef OPENCV_CUDA
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cv::cuda::GpuMat im_Orig;
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cv::cuda::GpuMat imageF1_d, imageF2_d;
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im_Orig = cv::cuda::GpuMat(imageOrig);
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cv::cuda::resize (im_Orig, imageF1_d, cv::Size(new_width, new_height));
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checkCuda( cudaDeviceSynchronize() );
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sz = imageF1_d.size();
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std::cout<<"size: "<<sz.height<<" "<<sz.width<<" - "<<std::endl;
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end_t = std::chrono::steady_clock::now();
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std::cout << " TIME resize: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
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step_t = end_t;
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cv::cuda::warpAffine(imageF1_d, imageF2_d, trans, cv::Size(inp_width, inp_height), cv::INTER_LINEAR );
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checkCuda( cudaDeviceSynchronize() );
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imageF2_d.convertTo(imageF1_d, CV_32FC3, 1/255.0);
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checkCuda( cudaDeviceSynchronize() );
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end_t = std::chrono::steady_clock::now();
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std::cout << " TIME convert: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
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step_t = end_t;
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dim2 = dim;
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cv::cuda::GpuMat bgr[3];
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cv::cuda::split(imageF1_d,bgr);//split source
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end_t = std::chrono::steady_clock::now();
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std::cout << " TIME split: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
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step_t = end_t;
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for(int i=0; i<dim.c; i++)
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checkCuda( cudaMemcpy(d_ptrs + i*dim.h * dim.w, (float*)bgr[i].data, dim.h * dim.w * sizeof(float), cudaMemcpyDeviceToDevice) );
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normalize(d_ptrs, dim.c, dim.h, dim.w, mean_d, stddev_d);
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end_t = std::chrono::steady_clock::now();
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std::cout << " TIME normalize: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
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step_t = end_t;
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checkCuda(cudaMemcpy(input_d, d_ptrs, dim2.tot()*sizeof(dnnType), cudaMemcpyDeviceToDevice));
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end_t = std::chrono::steady_clock::now();
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std::cout << " TIME Memcpy to input_d: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
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step_t = end_t;
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#else
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cv::Mat imageF;
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resize(imageOrig, imageF, cv::Size(new_width, new_height));
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sz = imageF.size();
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std::cout<<"size: "<<sz.height<<" "<<sz.width<<" - "<<std::endl;
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end_t = std::chrono::steady_clock::now();
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std::cout << " TIME resize: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
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step_t = end_t;
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cv::Mat trans = cv::getAffineTransform( src, dst );
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cv::warpAffine(imageF, imageF, trans, cv::Size(inp_width, inp_height), cv::INTER_LINEAR );
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end_t = std::chrono::steady_clock::now();
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std::cout << " TIME warpAffine: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
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step_t = end_t;
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sz = imageF.size();
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std::cout<<"size: "<<sz.height<<" "<<sz.width<<" - "<<std::endl;
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imageF.convertTo(imageF, CV_32FC3, 1/255.0);
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end_t = std::chrono::steady_clock::now();
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std::cout << " TIME convertto: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
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step_t = end_t;
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dim2 = dim;
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//split channels
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cv::Mat bgr[3];
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cv::split(imageF,bgr);//split source
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for(int i=0; i<3; i++){
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bgr[i] = bgr[i] - mean[i];
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bgr[i] = bgr[i] / stddev[i];
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}
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//write channels
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for(int i=0; i<dim2.c; i++) {
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int idx = i*imageF.rows*imageF.cols;
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int ch = dim2.c-3 +i;
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// std::cout<<"i: "<<i<<", idx: "<<idx<<", ch: "<<ch<<std::endl;
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memcpy((void*)&input[idx], (void*)bgr[ch].data, imageF.rows*imageF.cols*sizeof(dnnType));
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}
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checkCuda(cudaMemcpyAsync(input_d, input, dim2.tot()*sizeof(dnnType), cudaMemcpyHostToDevice));
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#endif
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}
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void CenternetDetection::update(cv::Mat &image_orig) {
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imageOrig = image_orig;
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if(!imageOrig.data) {
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std::cout<<"CENTERNET: NO IMAGE DATA\n";
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return;
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}
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TIMER_START
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auto start_t = std::chrono::steady_clock::now();
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auto step_t = std::chrono::steady_clock::now();
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auto end_t = std::chrono::steady_clock::now();
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preprocess();
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printCenteredTitle(" TENSORRT inference ", '=', 30); {
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dim2.print();
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TIMER_START
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netRT->infer(dim2, input_d);
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TIMER_STOP
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dim2.print();
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}
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step_t = std::chrono::steady_clock::now();
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// ------------------------------------ process --------------------------------------------
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rt_out[0] = (dnnType *)netRT->buffersRT[1];
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rt_out[1] = (dnnType *)netRT->buffersRT[2];
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rt_out[2] = (dnnType *)netRT->buffersRT[3];
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rt_out[3] = (dnnType *)netRT->buffersRT[4];
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activationSIGMOIDForward(rt_out[0], rt_out[0], dim_hm.tot());
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checkCuda( cudaDeviceSynchronize() );
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subtractWithThreshold(rt_out[0], rt_out[0] + dim_hm.tot(), rt_out[1], rt_out[0], op);
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end_t = std::chrono::steady_clock::now();
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std::cout << " TIME threshold: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
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step_t = end_t;
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// ----------- nms end
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|
// ----------- topk
|
|
|
|
if(K > dim_hm.h * dim_hm.w){
|
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printf ("Error topk (K is too large)\n");
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|
return;
|
|
}
|
|
|
|
checkCuda( cudaMemcpy(ids_d, ids_, dim_hm.c * dim_hm.h * dim_hm.w*sizeof(int), cudaMemcpyHostToDevice) );
|
|
|
|
sort(rt_out[0],
|
|
rt_out[0]+dim_hm.tot(),
|
|
ids_d);
|
|
checkCuda( cudaDeviceSynchronize() );
|
|
end_t = std::chrono::steady_clock::now();
|
|
std::cout << " TIME sort: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
|
|
step_t = end_t;
|
|
|
|
topk(rt_out[0], ids_d, K, scores_d,
|
|
topk_inds_d, topk_ys_d, topk_xs_d);
|
|
checkCuda( cudaDeviceSynchronize() );
|
|
|
|
end_t = std::chrono::steady_clock::now();
|
|
std::cout << " TIME topk: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
|
|
step_t = end_t;
|
|
|
|
checkCuda( cudaMemcpy(scores, scores_d, K *sizeof(float), cudaMemcpyDeviceToHost) );
|
|
|
|
topKxyclasses(topk_inds_d, topk_inds_d+K, K, width, dim_hm.w*dim_hm.h, clses_d, inttopk_xs_d, inttopk_ys_d);
|
|
end_t = std::chrono::steady_clock::now();
|
|
std::cout << " TIME topk x y clses 2: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
|
|
step_t = end_t;
|
|
|
|
checkCuda( cudaMemcpy(topk_xs_d, (float *)inttopk_xs_d, K*sizeof(float), cudaMemcpyDeviceToDevice) );
|
|
checkCuda( cudaMemcpy(topk_ys_d, (float *)inttopk_ys_d, K*sizeof(float), cudaMemcpyDeviceToDevice) );
|
|
|
|
checkCuda( cudaMemcpy(clses, clses_d, K*sizeof(int), cudaMemcpyDeviceToHost) );
|
|
|
|
// ----------- topk end
|
|
|
|
topKxyAddOffset(topk_inds_d, K, dim_reg.h*dim_reg.w, inttopk_xs_d, inttopk_ys_d, topk_xs_d, topk_ys_d, rt_out[3], src_out, ids_out);
|
|
// checkCuda( cudaDeviceSynchronize() );
|
|
|
|
end_t = std::chrono::steady_clock::now();
|
|
std::cout << " TIME add offset: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
|
|
step_t = end_t;
|
|
|
|
bboxes(topk_inds_d, K, dim_wh.h*dim_wh.w, topk_xs_d, topk_ys_d, rt_out[2], bbx0_d, bbx1_d, bby0_d, bby1_d, src_out, ids_out);
|
|
// checkCuda( cudaDeviceSynchronize() );
|
|
|
|
checkCuda( cudaMemcpy(bbx0, bbx0_d, K * sizeof(float), cudaMemcpyDeviceToHost) );
|
|
checkCuda( cudaMemcpy(bby0, bby0_d, K * sizeof(float), cudaMemcpyDeviceToHost) );
|
|
checkCuda( cudaMemcpy(bbx1, bbx1_d, K * sizeof(float), cudaMemcpyDeviceToHost) );
|
|
checkCuda( cudaMemcpy(bby1, bby1_d, K * sizeof(float), cudaMemcpyDeviceToHost) );
|
|
|
|
end_t = std::chrono::steady_clock::now();
|
|
std::cout << " TIME bboxes: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
|
|
step_t = end_t;
|
|
|
|
// ---------------------------------- post-process -----------------------------------------
|
|
|
|
// --------- ctdet_post_process
|
|
// --------- transform_preds
|
|
cv::Mat new_pt1(cv::Size(1,2), CV_32F);
|
|
cv::Mat new_pt2(cv::Size(1,2), CV_32F);
|
|
|
|
for(int i = 0; i<K; i++){
|
|
new_pt1.at<float>(0,0)=static_cast<float>(trans2.at<double>(0,0))*bbx0[i] +
|
|
static_cast<float>(trans2.at<double>(0,1))*bby0[i] +
|
|
static_cast<float>(trans2.at<double>(0,2))*1.0;
|
|
new_pt1.at<float>(0,1)=static_cast<float>(trans2.at<double>(1,0))*bbx0[i] +
|
|
static_cast<float>(trans2.at<double>(1,1))*bby0[i] +
|
|
static_cast<float>(trans2.at<double>(1,2))*1.0;
|
|
|
|
new_pt2.at<float>(0,0)=static_cast<float>(trans2.at<double>(0,0))*bbx1[i] +
|
|
static_cast<float>(trans2.at<double>(0,1))*bby1[i] +
|
|
static_cast<float>(trans2.at<double>(0,2))*1.0;
|
|
new_pt2.at<float>(0,1)=static_cast<float>(trans2.at<double>(1,0))*bbx1[i] +
|
|
static_cast<float>(trans2.at<double>(1,1))*bby1[i] +
|
|
static_cast<float>(trans2.at<double>(1,2))*1.0;
|
|
|
|
target_coords[i*4] = new_pt1.at<float>(0,0);
|
|
target_coords[i*4+1] = new_pt1.at<float>(0,1);
|
|
target_coords[i*4+2] = new_pt2.at<float>(0,0);
|
|
target_coords[i*4+3] = new_pt2.at<float>(0,1);
|
|
}
|
|
|
|
detected.clear();
|
|
for(int i = 0; i<classes; i++){
|
|
for(int j=0; j<K; j++)
|
|
if(clses[j] == i){
|
|
if(scores[j] > thresh){
|
|
// std::cout<<"th: "<<scores[j]<<" - cl: "<<clses[j]<<" i: "<<i<<std::endl;
|
|
//add coco bbox
|
|
//det[0:4], i, det[4]
|
|
int x0 = target_coords[j*4];
|
|
int y0 = target_coords[j*4+1];
|
|
int x1 = target_coords[j*4+2];
|
|
int y1 = target_coords[j*4+3];
|
|
int obj_class = clses[j];
|
|
float prob = scores[j];
|
|
// std::cout<<"("<<x0<<", "<<y0<<"),("<<x1<<", "<<y1<<")"<<std::endl;
|
|
tk::dnn::box res;
|
|
res.cl = obj_class;
|
|
res.prob = prob;
|
|
res.x = x0;
|
|
res.y = y0;
|
|
res.w = x1 - x0;
|
|
res.h = y1 - y0;
|
|
detected.push_back(res);
|
|
}
|
|
}
|
|
}
|
|
|
|
end_t = std::chrono::steady_clock::now();
|
|
std::cout << " TIME detections: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << " us" << std::endl;
|
|
step_t = end_t;
|
|
|
|
std::cout<<"TOTAL: \n";
|
|
TIMER_STOP
|
|
stats.push_back(t_ns);
|
|
}
|
|
}}
|
|
|
|
#endif /*CENTERNETDETECTION_H*/
|