Micaela Verucchi
86 lines
1.8 KiB
C++
86 lines
1.8 KiB
C++
#ifndef CENTERNETDETECTION_H
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#define CENTERNETDETECTION_H
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#include "kernels.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 "DetectionNN.h"
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#include "kernelsThrust.h"
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namespace tk { namespace dnn {
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class CenternetDetection : public DetectionNN
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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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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_, *ids_2, *ids_2d;
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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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float *target_coords;
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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;
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//processing
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float toll = 0.000001;
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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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struct threshold op;
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public:
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CenternetDetection() {};
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~CenternetDetection() {};
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bool init(const std::string& tensor_path, const int n_classes=80, const int n_batches=1, const float conf_thresh=0.3);
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void preprocess(cv::Mat &frame, const int bi=0);
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void postprocess(const int bi=0,const bool mAP=false);
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};
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} // namespace dnn
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} // namespace tk
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#endif /*CENTERNETDETECTION_H*/ |