Davide Sapienza
516c1e6636
This commit changes the image input dimension, it updates the CenterNet detection class. Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>
116 lines
2.6 KiB
C++
116 lines
2.6 KiB
C++
#include <iostream>
|
|
#include <cstring>
|
|
#include <signal.h>
|
|
#include <stdlib.h> /* srand, rand */
|
|
#include <unistd.h>
|
|
#include <mutex>
|
|
#include "utils.h"
|
|
#include <time.h>
|
|
#include "kernels.h"
|
|
#include <vector>
|
|
#include <numeric> // std::iota
|
|
#include <algorithm> // std::sort
|
|
|
|
|
|
#include <opencv2/core/core.hpp>
|
|
#include <opencv2/highgui/highgui.hpp>
|
|
#include <opencv2/imgproc/imgproc.hpp>
|
|
|
|
#include "tkdnn.h"
|
|
#include "sorting.h"
|
|
|
|
namespace tk { namespace dnn {
|
|
|
|
/**
|
|
*
|
|
* @author Francesco Gatti
|
|
*/
|
|
class CenternetDetection {
|
|
|
|
private:
|
|
tk::dnn::NetworkRT *netRT = nullptr;
|
|
dnnType *input_h, *input, *input_d;
|
|
|
|
int ndets = 0;
|
|
// tk::dnn::Yolo::detection *dets = nullptr;
|
|
|
|
cv::Mat imageF;
|
|
cv::Mat bgr[3];
|
|
|
|
// variable to test cnet on dog pictures
|
|
tk::dnn::dataDim_t dim;
|
|
tk::dnn::dataDim_t dim2;
|
|
cv::Size sz;
|
|
const char *input_bin = "../tests/resnet101_cnet/debug/input.bin";
|
|
|
|
// 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;
|
|
|
|
cv::Vec<float, 3> mean;
|
|
cv::Vec<float, 3> stddev;
|
|
cv::Mat src;
|
|
cv::Mat dst;
|
|
//processing
|
|
float toll = 0.000001;
|
|
int K = 100;
|
|
int width = 128;//56; // TODO
|
|
|
|
|
|
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);
|
|
void testdog();
|
|
cv::Mat draw(cv::Mat &frame);
|
|
void update(cv::Mat &frame);
|
|
|
|
};
|
|
|
|
}}
|