Refactoring for detection NN
Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
This commit is contained in:
Micaela Verucchi
@@ -1,134 +1,90 @@
|
||||
#include <iostream>
|
||||
#include <cstring>
|
||||
#include <signal.h>
|
||||
#include <stdlib.h> /* srand, rand */
|
||||
#include <unistd.h>
|
||||
#include <mutex>
|
||||
#include "utils.h"
|
||||
#include <time.h>
|
||||
#ifndef CENTERNETDETECTION_H
|
||||
#define CENTERNETDETECTION_H
|
||||
|
||||
#include "kernels.h"
|
||||
#include <opencv2/videoio.hpp>
|
||||
#include "opencv2/opencv.hpp"
|
||||
#include <time.h>
|
||||
#include <vector>
|
||||
#include <numeric> // std::iota
|
||||
#include <algorithm> // std::sort
|
||||
|
||||
#include "DetectionNN.h"
|
||||
|
||||
#include <opencv2/core/core.hpp>
|
||||
#include <opencv2/highgui/highgui.hpp>
|
||||
#include <opencv2/imgproc/imgproc.hpp>
|
||||
#include "opencv2/opencv.hpp"
|
||||
|
||||
#include "tkdnn.h"
|
||||
#include "sorting.h"
|
||||
|
||||
namespace tk { namespace dnn {
|
||||
|
||||
/**
|
||||
*
|
||||
* @author Francesco Gatti
|
||||
*/
|
||||
class CenternetDetection {
|
||||
namespace tk { namespace dnn {
|
||||
|
||||
private:
|
||||
tk::dnn::NetworkRT *netRT = nullptr;
|
||||
dnnType *input_d;
|
||||
class CenternetDetection : public DetectionNN
|
||||
{
|
||||
private:
|
||||
std::vector<std::string> classesNames;
|
||||
|
||||
int ndets = 0;
|
||||
// tk::dnn::Yolo::detection *dets = nullptr;
|
||||
tk::dnn::dataDim_t dim;
|
||||
tk::dnn::dataDim_t dim2;
|
||||
tk::dnn::dataDim_t dim_hm;
|
||||
tk::dnn::dataDim_t dim_wh;
|
||||
tk::dnn::dataDim_t dim_reg;
|
||||
float *topk_scores;
|
||||
int *topk_inds_;
|
||||
float *topk_ys_;
|
||||
float *topk_xs_;
|
||||
int *ids_d, *ids_, *ids_2, *ids_2d;
|
||||
|
||||
cv::Mat imageOrig;
|
||||
// std::vector< cv::cuda::GpuMat > bgr;
|
||||
float *scores, *scores_d;
|
||||
int *clses, *clses_d;
|
||||
int *topk_inds_d;
|
||||
float *topk_ys_d;
|
||||
float *topk_xs_d;
|
||||
int *inttopk_xs_d, *inttopk_ys_d;
|
||||
|
||||
// variable to test cnet on dog pictures
|
||||
tk::dnn::dataDim_t dim;
|
||||
tk::dnn::dataDim_t dim2;
|
||||
cv::Size sz, sz_old;
|
||||
const char *input_bin = "../tests/resnet101_cnet/debug/input.bin";
|
||||
|
||||
cv::cuda::Stream stream;
|
||||
struct threshold op;
|
||||
// pre-process
|
||||
tk::dnn::dataDim_t dim_hm;
|
||||
tk::dnn::dataDim_t dim_wh;
|
||||
tk::dnn::dataDim_t dim_reg;
|
||||
float *topk_scores;
|
||||
int *topk_inds_;
|
||||
float *topk_ys_;
|
||||
float *topk_xs_;
|
||||
int *ids_d, *ids_, *ids_2, *ids_2d;
|
||||
|
||||
float *scores, *scores_d;
|
||||
int *clses, *clses_d;
|
||||
int *topk_inds_d;
|
||||
float *topk_ys_d;
|
||||
float *topk_xs_d;
|
||||
int *inttopk_xs_d, *inttopk_ys_d;
|
||||
float *bbx0, *bby0, *bbx1, *bby1;
|
||||
float *bbx0_d, *bby0_d, *bbx1_d, *bby1_d;
|
||||
|
||||
float *target_coords;
|
||||
|
||||
float *bbx0, *bby0, *bbx1, *bby1;
|
||||
float *bbx0_d, *bby0_d, *bbx1_d, *bby1_d;
|
||||
|
||||
float *target_coords;
|
||||
|
||||
|
||||
#ifdef OPENCV_CUDA
|
||||
float *mean_d;
|
||||
float *stddev_d;
|
||||
#else
|
||||
cv::Vec<float, 3> mean;
|
||||
cv::Vec<float, 3> stddev;
|
||||
dnnType *input;
|
||||
#endif
|
||||
|
||||
#ifdef OPENCV_CUDA
|
||||
float *mean_d;
|
||||
float *stddev_d;
|
||||
#else
|
||||
cv::Vec<float, 3> mean;
|
||||
cv::Vec<float, 3> stddev;
|
||||
dnnType *input;
|
||||
#endif
|
||||
float *d_ptrs;
|
||||
|
||||
float *d_ptrs;
|
||||
|
||||
cv::Mat src;
|
||||
cv::Mat dst;
|
||||
cv::Mat dst2;
|
||||
cv::Mat trans, trans2;
|
||||
//processing
|
||||
float toll = 0.000001;
|
||||
int K = 100;
|
||||
int width = 128;//56; // TODO
|
||||
cv::Mat src;
|
||||
cv::Mat dst;
|
||||
cv::Mat dst2;
|
||||
cv::Mat trans, trans2;
|
||||
//processing
|
||||
float toll = 0.000001;
|
||||
int K = 100;
|
||||
int width = 128;//56; // TODO
|
||||
|
||||
// pointer used in the kernels
|
||||
float *src_out;
|
||||
int *ids_out;
|
||||
|
||||
struct threshold op;
|
||||
|
||||
// pointer used in the kernels
|
||||
float *src_out;
|
||||
int *ids_out;
|
||||
|
||||
void preprocess();
|
||||
public:
|
||||
dnnType *rt_out[4];
|
||||
|
||||
float inp_height = 512;//224;//512;
|
||||
float inp_width = 512;//224;//512;
|
||||
|
||||
int classes = 80;
|
||||
int num = 0;
|
||||
int n_masks = 0;
|
||||
float thresh = 0.3;
|
||||
cv::Scalar colors[256];
|
||||
|
||||
// this is filled with results
|
||||
std::vector<tk::dnn::box> detected;
|
||||
// draw
|
||||
std::vector<std::string> coco_class_name;
|
||||
|
||||
// keep track of inference times (ms)
|
||||
std::vector<double> stats;
|
||||
|
||||
CenternetDetection() {}
|
||||
|
||||
virtual ~CenternetDetection() {}
|
||||
|
||||
/**
|
||||
* Method used for inizialize the class
|
||||
*
|
||||
* @return Success of the initialization
|
||||
*/
|
||||
bool init(std::string tensor_path);
|
||||
cv::Mat draw(cv::Mat &frame);
|
||||
void update(cv::Mat &frame);
|
||||
public:
|
||||
CenternetDetection() {};
|
||||
~CenternetDetection() {};
|
||||
|
||||
bool init(const std::string& tensor_path, const int n_classes=80);
|
||||
void preprocess(cv::Mat &frame);
|
||||
void update(cv::Mat &frame);
|
||||
void postprocess(dnnType **rt_out, const int n_out);
|
||||
cv::Mat draw(cv::Mat &frame);
|
||||
};
|
||||
|
||||
}}
|
||||
|
||||
} // namespace dnn
|
||||
} // namespace tk
|
||||
|
||||
|
||||
#endif /*CENTERNETDETECTION_H*/
|
||||
Reference in New Issue
Block a user