Davide Sapienza
be6ad27c11
This commit lets to use differtent batch size for 3D CenterNet and CenterTrack. Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>
521 lines
24 KiB
C++
521 lines
24 KiB
C++
#include "CenternetDetection3D.h"
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namespace tk { namespace dnn {
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bool CenternetDetection3D::init(const std::string& tensor_path, const int n_classes, const int n_batches, const float conf_thresh) {
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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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classes = n_classes;
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nBatches = n_batches;
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confThreshold = conf_thresh;
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dim = netRT->input_dim;
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const char *kitti_class_name[] = {
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"person", "car", "bicycle"};
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classesNames = std::vector<std::string>(kitti_class_name, std::end( kitti_class_name));
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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 = getColor(2, offset, classes);
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float g = getColor(1, offset, classes);
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float b = getColor(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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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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checkCuda(cudaMalloc(&input_d, sizeof(dnnType)*netRT->input_dim.tot() * nBatches));
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dim_hm = tk::dnn::dataDim_t(1, 3, 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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dim_dep = tk::dnn::dataDim_t(1, 1, 128, 128, 1);
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dim_rot = tk::dnn::dataDim_t(1, 8, 128, 128, 1);
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dim_dim = tk::dnn::dataDim_t(1, 3, 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( cudaMallocHost(&ids_, 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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checkCuda( cudaMalloc(&ones, dim_dep.c * dim_dep.h * dim_dep.w * sizeof(float)) );
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float *ones_h;
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checkCuda( cudaMallocHost(&ones_h, dim_dep.c * dim_dep.h * dim_dep.w * sizeof(float)) );
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for(int i=0; i<dim_dep.c * dim_dep.h * dim_dep.w; i++)
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ones_h[i]=1.0f;
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checkCuda( cudaMemcpy(ones, ones_h, dim_dep.c * dim_dep.h * dim_dep.w * sizeof(float), cudaMemcpyHostToDevice) );
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checkCuda( cudaFreeHost(ones_h) );
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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( 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(&inttopk_ys_d, K *sizeof(int)) );
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checkCuda( cudaMalloc(&inttopk_xs_d, K *sizeof(int)) );
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checkCuda( cudaMallocHost(&xs, K * sizeof(float)) );
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checkCuda( cudaMallocHost(&ys, K * sizeof(float)) );
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checkCuda( cudaMallocHost(&dep, K * dim_dep.c * sizeof(float)) );
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checkCuda( cudaMallocHost(&rot, K * dim_rot.c * sizeof(float)) );
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checkCuda( cudaMallocHost(&dim_, K * dim_dim.c * sizeof(float)) );
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checkCuda( cudaMallocHost(&wh, K * dim_wh.c * sizeof(float)) );
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checkCuda( cudaMalloc(&dep_d, K * dim_dep.c * sizeof(float)) );
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checkCuda( cudaMalloc(&rot_d, K * dim_rot.c * sizeof(float)) );
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checkCuda( cudaMalloc(&dim_d, K * dim_dim.c * sizeof(float)) );
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checkCuda( cudaMalloc(&wh_d, K * dim_wh.c * sizeof(float)) );
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checkCuda( cudaMallocHost(&target_coords, 4 * K *sizeof(float)) );
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#ifdef OPENCV_CUDACONTRIB
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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] = {0.485, 0.456, 0.406};
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float stddev[3] = {0.229, 0.224, 0.225};
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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() * nBatches));
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mean << 0.485, 0.456, 0.406;
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stddev << 0.229, 0.224, 0.225;
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#endif
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calibs = cv::Mat(cv::Size(4,3), CV_32F);
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calibs.at<float>(0,0) = 707.0493;
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calibs.at<float>(0,1) = 0.0;
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calibs.at<float>(0,2) = 604.0814;
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calibs.at<float>(0,3) = 45.75831;
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calibs.at<float>(1,0) = 0.0;
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calibs.at<float>(1,1) = 707.0493;
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calibs.at<float>(1,2) = 180.5066;
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calibs.at<float>(1,3) = -0.3454157;
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calibs.at<float>(2,0) = 0.0;
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calibs.at<float>(2,1) = 0.0;
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calibs.at<float>(2,2) = 1.0;
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calibs.at<float>(2,3) = 0.004981016;
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r = cv::Mat(cv::Size(3,3), CV_32F);
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r.at<float>(0,1) = 0.0;
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r.at<float>(1,0) = 0.0;
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r.at<float>(1,1) = 1.0;
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r.at<float>(1,2) = 0.0;
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r.at<float>(2,1) = 0.0;
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corners = cv::Mat(cv::Size(8,3), CV_32F);
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corners.at<float>(1,0) = 0.0;
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corners.at<float>(1,1) = 0.0;
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corners.at<float>(1,2) = 0.0;
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corners.at<float>(1,3) = 0.0;
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pts3DHomo = cv::Mat(cv::Size(8,4), CV_32F);
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pts3DHomo.at<float>(3,0) = 1.0;
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pts3DHomo.at<float>(3,1) = 1.0;
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pts3DHomo.at<float>(3,2) = 1.0;
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pts3DHomo.at<float>(3,3) = 1.0;
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pts3DHomo.at<float>(3,4) = 1.0;
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pts3DHomo.at<float>(3,5) = 1.0;
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pts3DHomo.at<float>(3,6) = 1.0;
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pts3DHomo.at<float>(3,7) = 1.0;
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checkCuda( cudaMalloc(&d_ptrs, dim.c * dim.h*dim.w * sizeof(float)) );
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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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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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face_id.push_back({0,1,5,4});
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face_id.push_back({1,2,6, 5});
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face_id.push_back({2,3,7,6});
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face_id.push_back({3,0,4,7});
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// ([[0,1,5,4], [1,2,6, 5], [2,3,7,6], [3,0,4,7]]);
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}
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void CenternetDetection3D::preprocess(cv::Mat &frame, const int bi){
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// -----------------------------------pre-process ------------------------------------------
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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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cv::Size sz = originalSize[bi];
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// std::cout<<"image: "<<sz.width<<", "<<sz.height<<std::endl;
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cv::Size sz_old;
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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 / 2.0f, new_height /2.0f};
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float s[] = {new_width, new_height};
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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)=netRT->input_dim.w * 0.5;
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dst.at<float>(0,1)=netRT->input_dim.h * 0.5;
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dst.at<float>(1,0)=netRT->input_dim.w * 0.5;
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dst.at<float>(1,1)=netRT->input_dim.h * 0.5 + netRT->input_dim.w * -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_CUDACONTRIB
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std::cout<<"OPENCV CPMTROB\n";
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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(frame);
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// cv::cuda::resize (im_Orig, imageF1_d, cv::Size(new_width, new_height));
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imageF1_d = im_Orig;
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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(netRT->input_dim.w, netRT->input_dim.h), 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+ netRT->input_dim.tot()*bi, 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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std::cout<<"NO OPENCV CPMTROB\n";
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cv::Mat imageF;
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// resize(frame, imageF, cv::Size(new_width, new_height));
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imageF = frame;
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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(netRT->input_dim.w, netRT->input_dim.h), 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+ netRT->input_dim.tot()*bi], (void*)bgr[ch].data, imageF.rows*imageF.cols*sizeof(dnnType));
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}
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checkCuda(cudaMemcpyAsync(input_d+ netRT->input_dim.tot()*bi, input+ netRT->input_dim.tot()*bi, dim2.tot()*sizeof(dnnType), cudaMemcpyHostToDevice));
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#endif
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}
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void CenternetDetection3D::postprocess(const int bi, const bool mAP) {
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dnnType *rt_out[7];
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rt_out[0] = (dnnType *)netRT->buffersRT[1]+ netRT->buffersDIM[1].tot()*bi;
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rt_out[1] = (dnnType *)netRT->buffersRT[2]+ netRT->buffersDIM[2].tot()*bi;
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rt_out[2] = (dnnType *)netRT->buffersRT[3]+ netRT->buffersDIM[3].tot()*bi;
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rt_out[3] = (dnnType *)netRT->buffersRT[4]+ netRT->buffersDIM[4].tot()*bi;
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rt_out[4] = (dnnType *)netRT->buffersRT[5]+ netRT->buffersDIM[5].tot()*bi;
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rt_out[5] = (dnnType *)netRT->buffersRT[6]+ netRT->buffersDIM[6].tot()*bi;
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rt_out[6] = (dnnType *)netRT->buffersRT[7]+ netRT->buffersDIM[7].tot()*bi;
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// ------------------------------------ process --------------------------------------------
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activationSIGMOIDForward(rt_out[0], rt_out[0], dim_hm.tot());
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checkCuda( cudaDeviceSynchronize() );
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// output['dep'] = 1. / (output['dep'].sigmoid() + 1e-6) - 1.
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activationSIGMOIDForward(rt_out[4], rt_out[4], dim_dep.tot());
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checkCuda( cudaDeviceSynchronize() );
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transformDep(ones, ones + dim_dep.tot(), rt_out[4], rt_out[4] + dim_dep.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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// ----------- nms end
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// ----------- topk
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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;
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}
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checkCuda( cudaMemcpy(ids_d, ids_, dim_hm.c * dim_hm.h * dim_hm.w*sizeof(int), cudaMemcpyHostToDevice) );
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sort(rt_out[0],rt_out[0]+dim_hm.tot(),ids_d);
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checkCuda( cudaDeviceSynchronize() );
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topk(rt_out[0], ids_d, K, scores_d, topk_inds_d, topk_ys_d, topk_xs_d);
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checkCuda( cudaDeviceSynchronize() );
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checkCuda( cudaMemcpy(scores, scores_d, K *sizeof(float), cudaMemcpyDeviceToHost) );
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topKxyclasses(topk_inds_d, topk_inds_d+K, K, width, dim_hm.w*dim_hm.h, clses_d, inttopk_xs_d, inttopk_ys_d);
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checkCuda( cudaMemcpy(topk_xs_d, (float *)inttopk_xs_d, K*sizeof(float), cudaMemcpyDeviceToDevice) );
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checkCuda( cudaMemcpy(topk_ys_d, (float *)inttopk_ys_d, K*sizeof(float), cudaMemcpyDeviceToDevice) );
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checkCuda( cudaMemcpy(clses, clses_d, K*sizeof(int), cudaMemcpyDeviceToHost) );
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// ----------- topk end
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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);
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// checkCuda( cudaDeviceSynchronize() );
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getRecordsFromTopKId(topk_inds_d, K, dim_dep.c, dim_dep.h * dim_dep.w, rt_out[4], dep_d, ids_out);
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checkCuda( cudaMemcpy(dep, dep_d, K * dim_dep.c * sizeof(float), cudaMemcpyDeviceToHost) );
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getRecordsFromTopKId(topk_inds_d, K, dim_rot.c, dim_rot.h * dim_rot.w, rt_out[5], rot_d, ids_out);
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checkCuda( cudaMemcpy(rot, rot_d, K * dim_rot.c * sizeof(float), cudaMemcpyDeviceToHost) );
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getRecordsFromTopKId(topk_inds_d, K, dim_dim.c, dim_dim.h * dim_dim.w, rt_out[6], dim_d, ids_out);
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checkCuda( cudaMemcpy(dim_, dim_d, K * dim_dim.c * sizeof(float), cudaMemcpyDeviceToHost) );
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getRecordsFromTopKId(topk_inds_d, K, dim_wh.c, dim_wh.h * dim_wh.w, rt_out[2], wh_d, ids_out);
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checkCuda( cudaMemcpy(wh, wh_d, K * dim_wh.c * sizeof(float), cudaMemcpyDeviceToHost) );
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checkCuda( cudaMemcpy(xs, topk_xs_d, K * sizeof(float), cudaMemcpyDeviceToHost) );
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checkCuda( cudaMemcpy(ys, topk_ys_d, K * sizeof(float), cudaMemcpyDeviceToHost) );
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// ---------------------------------- post-process -----------------------------------------
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// ddd_post_process_2d
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cv::Mat new_pt1(cv::Size(1,2), CV_32F);
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cv::Mat new_pt2(cv::Size(1,2), CV_32F);
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for(int i = 0; i<K; i++){
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new_pt1.at<float>(0,0)=static_cast<float>(trans2.at<double>(0,0))*xs[i] +
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static_cast<float>(trans2.at<double>(0,1))*ys[i] +
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static_cast<float>(trans2.at<double>(0,2))*1.0;
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new_pt1.at<float>(0,1)=static_cast<float>(trans2.at<double>(1,0))*xs[i] +
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static_cast<float>(trans2.at<double>(1,1))*ys[i] +
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static_cast<float>(trans2.at<double>(1,2))*1.0;
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new_pt2.at<float>(0,0)=static_cast<float>(trans2.at<double>(0,0))*wh[i] +
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static_cast<float>(trans2.at<double>(0,1))*wh[K+i] +
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static_cast<float>(trans2.at<double>(0,2))*1.0;
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new_pt2.at<float>(0,1)=static_cast<float>(trans2.at<double>(1,0))*wh[i] +
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static_cast<float>(trans2.at<double>(1,1))*wh[K+i] +
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static_cast<float>(trans2.at<double>(1,2))*1.0;
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|
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target_coords[i*4] = new_pt1.at<float>(0,0);
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target_coords[i*4+1] = new_pt1.at<float>(0,1);
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target_coords[i*4+2] = new_pt2.at<float>(0,0);
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target_coords[i*4+3] = new_pt2.at<float>(0,1);
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|
}
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|
|
|
float alpha;
|
|
float x, y, z, rot_y;
|
|
detected3D.clear();
|
|
for(int i = 0; i<classes; i++){
|
|
for(int j=0; j<K; j++){
|
|
if(clses[j] == i){
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|
//get alpha
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|
if(rot[1*K + j] > rot[5*K + j])
|
|
alpha = std::atan2(rot[2*K + j], rot[3*K + j]) -0.5 * M_PI;
|
|
else
|
|
alpha = std::atan2(rot[6*K + j], rot[7*K + j]) +0.5 * M_PI;
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|
|
|
// unproject_2d_to_3d
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|
z = dep[j] - calibs.at<float>(2,3);// z = depth - P[2, 3]
|
|
x = (target_coords[j*4] * dep[j] - calibs.at<float>(0,3) - calibs.at<float>(0,2) * z) / calibs.at<float>(0,0);
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|
y = (target_coords[j*4+1] * dep[j] - calibs.at<float>(1,3) - calibs.at<float>(1,2) * z) / calibs.at<float>(1,1) + (dim_[j] / 2);
|
|
// alpha2rot_y
|
|
rot_y = (alpha + std::atan2(target_coords[j*4] - calibs.at<float>(0,2), calibs.at<float>(0,0)));
|
|
if(rot_y>M_PI)
|
|
rot_y -= 2*M_PI;
|
|
if(rot_y<M_PI)
|
|
rot_y += 2*M_PI;
|
|
|
|
if(scores[j] > confThreshold) {
|
|
if(z>0) {
|
|
// compute_box_3d
|
|
r.at<float>(0,0) = std::cos(rot_y);
|
|
r.at<float>(0,2) = std::sin(rot_y);
|
|
r.at<float>(2,0) = -std::sin(rot_y);
|
|
r.at<float>(2,2) = std::cos(rot_y);
|
|
|
|
corners.at<float>(0,0) = dim_[2*K+j]/2;
|
|
corners.at<float>(0,1) = dim_[2*K+j]/2;
|
|
corners.at<float>(0,2) = -dim_[2*K+j]/2;
|
|
corners.at<float>(0,3) = -dim_[2*K+j]/2;
|
|
corners.at<float>(0,4) = dim_[2*K+j]/2;
|
|
corners.at<float>(0,5) = dim_[2*K+j]/2;
|
|
corners.at<float>(0,6) = -dim_[2*K+j]/2;
|
|
corners.at<float>(0,7) = -dim_[2*K+j]/2;
|
|
|
|
corners.at<float>(1,4) = -dim_[j];
|
|
corners.at<float>(1,5) = -dim_[j];
|
|
corners.at<float>(1,6) = -dim_[j];
|
|
corners.at<float>(1,7) = -dim_[j];
|
|
|
|
corners.at<float>(2,0) = dim_[K+j]/2;
|
|
corners.at<float>(2,1) = -dim_[K+j]/2;
|
|
corners.at<float>(2,2) = -dim_[K+j]/2;
|
|
corners.at<float>(2,3) = dim_[K+j]/2;
|
|
corners.at<float>(2,4) = dim_[K+j]/2;
|
|
corners.at<float>(2,5) = -dim_[K+j]/2;
|
|
corners.at<float>(2,6) = -dim_[K+j]/2;
|
|
corners.at<float>(2,7) = dim_[K+j]/2;
|
|
cv::Mat aus = r * corners;
|
|
|
|
for(int k=0; k<8; k++) {
|
|
aus.at<float>(0,k) += x;
|
|
aus.at<float>(1,k) += y;
|
|
aus.at<float>(2,k) += z;
|
|
}
|
|
// corners.copyTo(pts3DHomo(cv::Rect(0, 0, 8, 3)));
|
|
for(int k1=0; k1<3; k1++) {
|
|
for(int k2=0; k2<8; k2++)
|
|
pts3DHomo.at<float>(k1,k2) = aus.at<float>(k1,k2);
|
|
}
|
|
aus.release();
|
|
aus = calibs * pts3DHomo;
|
|
|
|
tk::dnn::box3D res;
|
|
for(int k=0; k<8; k++) {
|
|
res.corners.push_back(aus.at<float>(0,k) / aus.at<float>(2,k));
|
|
res.corners.push_back(aus.at<float>(1,k) / aus.at<float>(2,k));
|
|
}
|
|
res.cl = i;
|
|
res.prob = scores[j];
|
|
//res.print();
|
|
detected3D.push_back(res);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
batchDetected.push_back(detected3D);
|
|
}
|
|
|
|
void CenternetDetection3D::draw(std::vector<cv::Mat>& frames) {
|
|
tk::dnn::box3D b;
|
|
int x0, w, x1, y0, h, y1;
|
|
int objClass;
|
|
std::string det_class;
|
|
|
|
int baseline = 0;
|
|
float font_scale = 0.5;
|
|
int thickness = 2;
|
|
|
|
for(int bi=0; bi<frames.size(); ++bi){
|
|
// draw dets
|
|
for(int i=0; i<batchDetected[bi].size(); i++) {
|
|
b = batchDetected[bi][i];
|
|
|
|
for(int ind_f = 3; ind_f>=0; ind_f--) {
|
|
for(int j=0; j<4; j++) {
|
|
cv::line(frames[bi], cv::Point(b.corners.at(face_id.at(ind_f).at(j) * 2),
|
|
b.corners.at(face_id.at(ind_f).at(j) * 2 + 1)),
|
|
cv::Point(b.corners.at(face_id.at(ind_f).at((j+1)%4) * 2),
|
|
b.corners.at(face_id.at(ind_f).at((j+1)%4) * 2 + 1)),
|
|
colors[b.cl], 2);
|
|
if(ind_f == 0) {
|
|
cv::line(frames[bi], cv::Point(b.corners.at(face_id.at(ind_f).at(0) * 2),
|
|
b.corners.at(face_id.at(ind_f).at(0) * 2 + 1)),
|
|
cv::Point(b.corners.at(face_id.at(ind_f).at(2) * 2),
|
|
b.corners.at(face_id.at(ind_f).at(2) * 2 + 1)), colors[b.cl], 2);
|
|
cv::line(frames[bi], cv::Point(b.corners.at(face_id.at(ind_f).at(1) * 2),
|
|
b.corners.at(face_id.at(ind_f).at(1) * 2 + 1)),
|
|
cv::Point(b.corners.at(face_id.at(ind_f).at(3) * 2),
|
|
b.corners.at(face_id.at(ind_f).at(3) * 2 + 1)), colors[b.cl], 2);
|
|
}
|
|
}
|
|
}
|
|
// draw label
|
|
cv::Size text_size = getTextSize(classesNames[b.cl], cv::FONT_HERSHEY_SIMPLEX, font_scale, thickness, &baseline);
|
|
cv::rectangle(frames[bi], cv::Point(b.corners.at(face_id.at(0).at(0) * 2),
|
|
b.corners.at(face_id.at(0).at(0) * 2 + 1)),
|
|
cv::Point((b.corners.at(face_id.at(0).at(0) * 2) + text_size.width - 2),
|
|
(b.corners.at(face_id.at(0).at(0) * 2 + 1)) - text_size.height - 2), colors[b.cl], -1);
|
|
cv::putText(frames[bi], classesNames[b.cl], cv::Point(b.corners.at(face_id.at(0).at(0) * 2),
|
|
b.corners.at(face_id.at(0).at(0) * 2 + 1) - (baseline / 2)),
|
|
cv::FONT_HERSHEY_SIMPLEX, font_scale, cv::Scalar(255, 255, 255), thickness);
|
|
}
|
|
}
|
|
}
|
|
|
|
}}
|
|
|
|
|