Davide Sapienza
176 lines
3.8 KiB
C++
176 lines
3.8 KiB
C++
#ifndef CENTERNETDETECTION3DTRACK_H
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#define CENTERNETDETECTION3DTRACK_H
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#include "kernels.h"
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#include "utils.h"
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#include "tkdnn.h"
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#include <opencv2/videoio.hpp>
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#include "opencv2/opencv.hpp"
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#include <time.h>
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#include <vector>
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#include <numeric> // std::iota
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#include <algorithm> // std::sort
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#include "DetectionNN3D.h"
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#include "kernelsThrust.h"
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namespace tk { namespace dnn {
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struct detectionRes
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{
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float score;
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int cl;
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cv::Mat ct, tr, bb0, bb1;
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float dep;
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float dim[3];
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float alpha;
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float x,y,z;
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float rot_y;
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detectionRes() : ct(cv::Mat(cv::Size(1,2), CV_32F)),
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tr(cv::Mat(cv::Size(1,2), CV_32F)),
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bb0(cv::Mat(cv::Size(1,2), CV_32F)),
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bb1(cv::Mat(cv::Size(1,2), CV_32F)) { }
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~detectionRes() {
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ct.release();
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tr.release();
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bb0.release();
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bb1.release();
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}
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};
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struct trackingRes
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{
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struct detectionRes det_res;
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int tracking_id;
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int age;
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int active;
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int color;
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};
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class CenternetDetection3DTrack : public DetectionNN3D
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{
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private:
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tk::dnn::dataDim_t dim;
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tk::dnn::dataDim_t dim2;
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tk::dnn::dataDim_t dim_hm;
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tk::dnn::dataDim_t dim_wh;
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tk::dnn::dataDim_t dim_reg;
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tk::dnn::dataDim_t dim_track;
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tk::dnn::dataDim_t dim_dep;
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tk::dnn::dataDim_t dim_rot;
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tk::dnn::dataDim_t dim_dim;
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tk::dnn::dataDim_t dim_amodel_offset;
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/* preprocessing */
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#ifdef OPENCV_CUDACONTRIB
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float *mean_d;
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float *stddev_d;
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#else
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cv::Vec<float, 3> mean;
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cv::Vec<float, 3> stddev;
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dnnType *input;
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#endif
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float *d_ptrs;
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cv::Mat src;
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cv::Mat dst;
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cv::Mat dst2;
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cv::Mat trans, trans2, trans_out;
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/* pre inf */
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bool iter0;
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dnnType *input_pre_inf_d;
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bool test_pre_inf = true;
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dnnType *img_d, *hm_d;
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tk::dnn::dataDim_t dim_in0;
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tk::dnn::dataDim_t dim_in1;
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dnnType *out_d;
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/* postprocessing */
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int K = 100;
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int width = 128;//56; // TODO
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// pointer used in the kernels
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float *src_out;
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int *ids_out;
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float *topk_scores;
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int *topk_inds_;
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float *topk_ys_;
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float *topk_xs_;
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int *ids_d, *ids_;
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float *ones;
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float *scores, *scores_d;
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int *clses, *clses_d;
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int *topk_inds_d;
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float *topk_ys_d;
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float *topk_xs_d;
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int *inttopk_xs_d, *inttopk_ys_d;
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float *bbx0, *bby0, *bbx1, *bby1;
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float *bbx0_d, *bby0_d, *bbx1_d, *bby1_d;
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int *intxs, *intys;
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float *track, *dep, *rot, *dim_, *wh, *amodel_offset;
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float *track_d, *dep_d, *rot_d, *dim_d, *wh_d, *amodel_offset_d;
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float *target_coords;
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/* visualization */
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cv::Mat r;
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cv::Mat calibs;
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cv::Mat corners, pts3DHomo;
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std::vector<std::vector<int>> face_id;
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cv::Scalar tr_colors[256];
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bool view2d = false;
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//processing
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struct threshold op;
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float out_thresh = 0.1;
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float new_thresh = 0.3;
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float vis_thresh = 0.3;
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float peakThreshold = 0.2;
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float centerThreshold = 0.3; //default 0.5
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//detections
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std::vector<struct detectionRes> det_res;
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int count_det;
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//tracks
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std::vector<struct trackingRes> tr_res;
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int count_tr;
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int track_id=0;
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bool init_preprocessing();
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bool init_pre_inf();
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bool init_postprocessing();
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bool init_visualization(const int n_classes);
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void pre_inf();
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void _get_additional_inputs();
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cv::Mat transform_preds_with_trans(float x1, float x2);
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void tracking();
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public:
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tk::dnn::Network *pre_phase_net = nullptr;
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CenternetDetection3DTrack() {};
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~CenternetDetection3DTrack() {};
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bool init(const std::string& tensor_path, const int n_classes=3);
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void preprocess(cv::Mat &frame);
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void postprocess();
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cv::Mat draw(cv::Mat &frame);
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};
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} // namespace dnn
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} // namespace tk
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#endif /*CENTERNETDETECTION3DTRACK_H*/ |