Davide Sapienza
127 lines
2.9 KiB
C++
127 lines
2.9 KiB
C++
#include <iostream>
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#include <cstring>
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#include <signal.h>
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#include <stdlib.h> /* srand, rand */
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#include <unistd.h>
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#include <mutex>
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#include "utils.h"
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#include <time.h>
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#include "kernels.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 <opencv2/core/core.hpp>
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#include <opencv2/highgui/highgui.hpp>
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#include <opencv2/imgproc/imgproc.hpp>
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#include "opencv2/opencv.hpp"
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#include "tkdnn.h"
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#include "sorting.h"
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namespace tk { namespace dnn {
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/**
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*
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* @author Francesco Gatti
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*/
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class CenternetDetection {
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private:
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tk::dnn::NetworkRT *netRT = nullptr;
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dnnType *input_d;
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int ndets = 0;
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// tk::dnn::Yolo::detection *dets = nullptr;
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cv::Mat imageF;
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cv::cuda::GpuMat imageF1_d, imageF2_d;
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cv::cuda::GpuMat bgr[3];
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// std::vector< cv::cuda::GpuMat > bgr;
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// variable to test cnet on dog pictures
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tk::dnn::dataDim_t dim;
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tk::dnn::dataDim_t dim2;
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cv::Size sz, sz_old;
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const char *input_bin = "../tests/resnet101_cnet/debug/input.bin";
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cv::cuda::Stream stream;
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struct threshold op;
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// pre-process
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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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float *mean_d;
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float *stddev_d;
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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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public:
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dnnType *rt_out[4];
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float inp_height = 512;//224;//512;
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float inp_width = 512;//224;//512;
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int classes = 80;
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int num = 0;
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int n_masks = 0;
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float thresh = 0.3;
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cv::Scalar colors[256];
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// this is filled with results
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std::vector<tk::dnn::box> detected;
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// draw
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std::vector<std::string> coco_class_name;
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// keep track of inference times (ms)
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std::vector<double> stats;
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CenternetDetection() {}
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virtual ~CenternetDetection() {}
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/**
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* Method used for inizialize the class
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*
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* @return Success of the initialization
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*/
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bool init(std::string tensor_path);
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cv::Mat draw(cv::Mat &frame);
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void update(cv::Mat &frame);
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};
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}}
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