mmr/tkDNN
Archived
1
0
Fork 0
Code Issues 46 Pull Requests 12 Releases 4 Activity

Add CenterTrack pre, post, visualization and demo.

Browse Source 
Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>
This commit is contained in:
Davide Sapienza
2020-12-07 18:45:29 +01:00
parent 9f10c3f6e2
commit 48ecebe6dd
3 changed files with 1043 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files
demo/demo/demo3D.cpp
+5
View File
@@ -5,6 +5,7 @@
#include <mutex>
#include "CenternetDetection3D.h"
#include "CenternetDetection3DTrack.h"
bool gRun;
bool SAVE_RESULT = false;
@@ -34,6 +35,7 @@ int main(int argc, char *argv[]) {
n_classes = atoi(argv[4]);
tk::dnn::CenternetDetection3D cnet;
tk::dnn::CenternetDetection3DTrack ctrack;
tk::dnn::DetectionNN3D *detNN;
@@ -42,6 +44,9 @@ int main(int argc, char *argv[]) {
case 'c':
detNN = &cnet;
break;
case 't':
detNN = &ctrack;
break;
default:
FatalError("Network type not allowed (3rd parameter)\n");
}
include/tkDNN/CenternetDetection3DTrack.h
+176
View File
@@ -0,0 +1,176 @@
#ifndef CENTERNETDETECTION3DTRACK_H
#define CENTERNETDETECTION3DTRACK_H
#include "kernels.h"
#include "utils.h"
#include "tkdnn.h"
#include <opencv2/videoio.hpp>
#include "opencv2/opencv.hpp"
#include <time.h>
#include <vector>
#include <numeric> // std::iota
#include <algorithm> // std::sort
#include "DetectionNN3D.h"
#include "kernelsThrust.h"
namespace tk { namespace dnn {
struct detectionRes
{
float score;
int cl;
cv::Mat ct, tr, bb0, bb1;
float dep;
float dim[3];
float alpha;
float x,y,z;
float rot_y;
detectionRes() : ct(cv::Mat(cv::Size(1,2), CV_32F)),
tr(cv::Mat(cv::Size(1,2), CV_32F)),
bb0(cv::Mat(cv::Size(1,2), CV_32F)),
bb1(cv::Mat(cv::Size(1,2), CV_32F)) { }
~detectionRes() {
ct.release();
tr.release();
bb0.release();
bb1.release();
}
};
struct trackingRes
{
struct detectionRes det_res;
int tracking_id;
int age;
int active;
int color;
};
class CenternetDetection3DTrack : public DetectionNN3D
{
private:
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;
tk::dnn::dataDim_t dim_track;
tk::dnn::dataDim_t dim_dep;
tk::dnn::dataDim_t dim_rot;
tk::dnn::dataDim_t dim_dim;
tk::dnn::dataDim_t dim_amodel_offset;
/* preprocessing */
#ifdef OPENCV_CUDACONTRIB
float *mean_d;
float *stddev_d;
#else
cv::Vec<float, 3> mean;
cv::Vec<float, 3> stddev;
dnnType *input;
#endif
float *d_ptrs;
cv::Mat src;
cv::Mat dst;
cv::Mat dst2;
cv::Mat trans, trans2, trans_out;
/* pre inf */
bool iter0;
dnnType *input_pre_inf_d;
bool test_pre_inf = true;
dnnType *img_d, *hm_d;
tk::dnn::dataDim_t dim_in0;
tk::dnn::dataDim_t dim_in1;
dnnType *out_d;
/* postprocessing */
int K = 100;
int width = 128;//56; // TODO
// pointer used in the kernels
float *src_out;
int *ids_out;
float *topk_scores;
int *topk_inds_;
float *topk_ys_;
float *topk_xs_;
int *ids_d, *ids_;
float *ones;
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;
int *intxs, *intys;
float *track, *dep, *rot, *dim_, *wh, *amodel_offset;
float *track_d, *dep_d, *rot_d, *dim_d, *wh_d, *amodel_offset_d;
float *target_coords;
/* visualization */
cv::Mat r;
cv::Mat calibs;
cv::Mat corners, pts3DHomo;
std::vector<std::vector<int>> face_id;
cv::Scalar tr_colors[256];
bool view2d = false;
//processing
struct threshold op;
float out_thresh = 0.1;
float new_thresh = 0.3;
float vis_thresh = 0.3;
float peakThreshold = 0.2;
float centerThreshold = 0.3; //default 0.5
//detections
std::vector<struct detectionRes> det_res;
int count_det;
//tracks
std::vector<struct trackingRes> tr_res;
int count_tr;
int track_id=0;
bool init_preprocessing();
bool init_pre_inf();
bool init_postprocessing();
bool init_visualization(const int n_classes);
void pre_inf();
void _get_additional_inputs();
cv::Mat transform_preds_with_trans(float x1, float x2);
void tracking();
public:
tk::dnn::Network *pre_phase_net = nullptr;
CenternetDetection3DTrack() {};
~CenternetDetection3DTrack() {};
bool init(const std::string& tensor_path, const int n_classes=3);
void preprocess(cv::Mat &frame);
void postprocess();
cv::Mat draw(cv::Mat &frame);
};
} // namespace dnn
} // namespace tk
#endif /*CENTERNETDETECTION3DTRACK_H*/
src/CenternetDetection3DTrack.cpp
+862
View File
@@ -0,0 +1,862 @@
#include "CenternetDetection3DTrack.h"
namespace tk { namespace dnn {
bool CenternetDetection3DTrack::init(const std::string& tensor_path, const int n_classes){
std::cout<<(tensor_path).c_str()<<"\n";
netRT = new tk::dnn::NetworkRT(NULL, (tensor_path).c_str() );
dim = netRT->input_dim;
dim.c = 3;
init_preprocessing();
init_pre_inf();
init_postprocessing();
init_visualization(n_classes);
count_tr = 0;
}
bool CenternetDetection3DTrack::init_preprocessing(){
//image transformation
src = cv::Mat(cv::Size(2,3), CV_32F);
dst = cv::Mat(cv::Size(2,3), CV_32F);
dst2 = cv::Mat(cv::Size(2,3), CV_32F);
trans = cv::Mat(cv::Size(3,2), CV_32F);
trans2 = cv::Mat(cv::Size(3,2), CV_32F);
trans_out = cv::Mat(cv::Size(3,2), CV_32F);
dst2.at<float>(0,0)=width * 0.5;
dst2.at<float>(0,1)=width * 0.5;
dst2.at<float>(1,0)=width * 0.5;
dst2.at<float>(1,1)=width * 0.5 + width * -0.5;
dst2.at<float>(2,0)=dst2.at<float>(1,0) + (-dst2.at<float>(0,1)+dst2.at<float>(1,1) );
dst2.at<float>(2,1)=dst2.at<float>(1,1) + (dst2.at<float>(0,0)-dst2.at<float>(1,0) );
#ifdef OPENCV_CUDACONTRIB
checkCuda( cudaMalloc(&mean_d, 3 * sizeof(float)) );
checkCuda( cudaMalloc(&stddev_d, 3 * sizeof(float)) );
float mean[3] = {0.40789655, 0.44719303, 0.47026116};
float stddev[3] = {0.2886383, 0.27408165, 0.27809834};
checkCuda(cudaMemcpy(mean_d, mean, 3*sizeof(float), cudaMemcpyHostToDevice));
checkCuda(cudaMemcpy(stddev_d, stddev, 3*sizeof(float), cudaMemcpyHostToDevice));
#else
checkCuda(cudaMallocHost(&input, sizeof(dnnType)*dim.tot()));
mean << 0.40789655, 0.44719303, 0.47026116;
stddev << 0.2886383, 0.27408165, 0.27809834;
#endif
checkCuda(cudaMalloc(&input_d, sizeof(dnnType)*netRT->input_dim.tot()));
checkCuda(cudaMalloc(&input_pre_inf_d, sizeof(dnnType)*dim.tot()));
checkCuda( cudaMalloc(&d_ptrs, dim.tot() * sizeof(float)) );
}
bool CenternetDetection3DTrack::init_pre_inf(){
// initial steps: the first part of the network
const char *pre_img_conv1_bin = "/home/davide/Projects/repos/tkDNN/build/dla34_cnet3d_track/layers/base-pre_img_layer-0.bin";
const char *pre_hm_conv1_bin = "dla34_cnet3d_track/layers/base-pre_hm_layer-0.bin";
const char *conv1_bin = "dla34_cnet3d_track/layers/base-base_layer-0.bin";
const char *conv2_bin = "dla34_cnet3d_track/layers/base-level0-0.bin";
dim_in0 = tk::dnn::dataDim_t(1, 3, 512, 512, 1);
dim_in1 = tk::dnn::dataDim_t(1, 1, 512, 512, 1);
checkCuda( cudaMalloc(&out_d, netRT->input_dim.tot()*sizeof(dnnType)) );
checkCuda( cudaMalloc(&img_d, dim_in0.tot()*sizeof(dnnType)) );
checkCuda( cudaMalloc(&hm_d, dim_in1.tot()*sizeof(dnnType)) );
// init to zeros hm
dnnType *hm_h;
checkCuda( cudaMallocHost(&hm_h, 1 * dim.h * dim.w*sizeof(dnnType)) );
for(int i=0; i<1 * dim.h * dim.w; i++)
hm_h[i]=0.0f;
checkCuda( cudaMemcpy(hm_d, hm_h, 1 * dim.h * dim.w * sizeof(dnnType), cudaMemcpyHostToDevice) );
checkCuda( cudaFreeHost(hm_h) );
dnnType *i0_h, *i1_h, *i2_h;
// dnnType *i0_d, *i1_d, *i2_d;
// const char *input_bin = "dla34_cnet3d_track/debug/input.bin";
// const char *pre_img_bin = "dla34_cnet3d_track/debug/pre_imgages.bin";
// const char *pre_hm_bin = "dla34_cnet3d_track/debug/pre_hms.bin";
// readBinaryFile(pre_img_bin, dim_in0.tot(), &i0_h, &img_d);
// readBinaryFile(pre_hm_bin, dim_in1.tot(), &i1_h, &hm_d);
// readBinaryFile(input_bin, dim_in0.tot(), &i2_h, &input_pre_inf_d);
pre_phase_net = new tk::dnn::Network(dim_in0);
//pre-img
tk::dnn::Input *in_pre_img = new tk::dnn::Input(pre_phase_net, dim_in0, img_d);
tk::dnn::Conv2d *pre_img_conv1 = new tk::dnn::Conv2d(pre_phase_net, 16, 7, 7, 1, 1, 3, 3, pre_img_conv1_bin, true);
tk::dnn::Activation *pre_img_relu = new tk::dnn::Activation(pre_phase_net, CUDNN_ACTIVATION_RELU);
//pre-hm
tk::dnn::Input *in_pre_hm = new tk::dnn::Input(pre_phase_net, dim_in1, hm_d);
tk::dnn::Conv2d *pre_hm_conv1 = new tk::dnn::Conv2d(pre_phase_net, 16, 7, 7, 1, 1, 3, 3, pre_hm_conv1_bin, true);
tk::dnn::Activation *pre_hm_relu = new tk::dnn::Activation(pre_phase_net, CUDNN_ACTIVATION_RELU);
// image input
tk::dnn::Input *input_image = new tk::dnn::Input(pre_phase_net, dim_in0, input_pre_inf_d);
tk::dnn::Conv2d *conv1 = new tk::dnn::Conv2d(pre_phase_net, 16, 7, 7, 1, 1, 3, 3, conv1_bin, true);
tk::dnn::Activation *relu1 = new tk::dnn::Activation(pre_phase_net, CUDNN_ACTIVATION_RELU);
tk::dnn::Shortcut *s0_input = new tk::dnn::Shortcut(pre_phase_net, pre_img_relu);
tk::dnn::Shortcut *s1_input = new tk::dnn::Shortcut(pre_phase_net, pre_hm_relu);
// output data
out_d = s1_input->dstData;
//print network model
pre_phase_net->print();
iter0=true; // in the first iteration the last input is equal to the current input.
return true;
}
bool CenternetDetection3DTrack::init_postprocessing(){
srand(0); //seed = 0 for random colors
dim_hm = tk::dnn::dataDim_t(1, 10, 128, 128, 1);
dim_wh = tk::dnn::dataDim_t(1, 2, 128, 128, 1);
dim_reg = tk::dnn::dataDim_t(1, 2, 128, 128, 1);
dim_track = tk::dnn::dataDim_t(1, 2, 128, 128, 1);
dim_dep = tk::dnn::dataDim_t(1, 1, 128, 128, 1);
dim_rot = tk::dnn::dataDim_t(1, 8, 128, 128, 1);
dim_dim = tk::dnn::dataDim_t(1, 3, 128, 128, 1);
dim_amodel_offset = tk::dnn::dataDim_t(1, 2, 128, 128, 1);
checkCuda( cudaMalloc(&topk_scores, dim_hm.c * K *sizeof(float)) );
checkCuda( cudaMalloc(&topk_inds_, dim_hm.c * K *sizeof(int)) );
checkCuda( cudaMalloc(&topk_ys_, dim_hm.c * K *sizeof(float)) );
checkCuda( cudaMalloc(&topk_xs_, dim_hm.c * K *sizeof(float)) );
checkCuda( cudaMalloc(&ids_d, dim_hm.c * dim_hm.h * dim_hm.w*sizeof(int)) );
checkCuda( cudaMallocHost(&ids_, dim_hm.c * dim_hm.h * dim_hm.w*sizeof(int)) );
for(int i =0; i<dim_hm.c * dim_hm.h * dim_hm.w; i++){
ids_[i] = i;
}
checkCuda( cudaMalloc(&ones, dim_dep.c * dim_dep.h * dim_dep.w * sizeof(float)) );
float *ones_h;
checkCuda( cudaMallocHost(&ones_h, dim_dep.c * dim_dep.h * dim_dep.w * sizeof(float)) );
for(int i=0; i<dim_dep.c * dim_dep.h * dim_dep.w; i++)
ones_h[i]=1.0f;
checkCuda( cudaMemcpy(ones, ones_h, dim_dep.c * dim_dep.h * dim_dep.w * sizeof(float), cudaMemcpyHostToDevice) );
checkCuda( cudaFreeHost(ones_h) );
checkCuda( cudaMallocHost(&scores, K *sizeof(float)) );
checkCuda( cudaMalloc(&scores_d, K *sizeof(float)) );
checkCuda( cudaMallocHost(&clses, K *sizeof(int)) );
checkCuda( cudaMalloc(&clses_d, K *sizeof(int)) );
checkCuda( cudaMalloc(&topk_inds_d, K *sizeof(int)) );
checkCuda( cudaMalloc(&topk_ys_d, K *sizeof(float)) );
checkCuda( cudaMalloc(&topk_xs_d, K *sizeof(float)) );
checkCuda( cudaMalloc(&inttopk_ys_d, K *sizeof(int)) );
checkCuda( cudaMalloc(&inttopk_xs_d, K *sizeof(int)) );
checkCuda( cudaMallocHost(&bbx0, K * sizeof(float)) );
checkCuda( cudaMallocHost(&bby0, K * sizeof(float)) );
checkCuda( cudaMallocHost(&bbx1, K * sizeof(float)) );
checkCuda( cudaMallocHost(&bby1, K * sizeof(float)) );
checkCuda( cudaMalloc(&bbx0_d, K * sizeof(float)) );
checkCuda( cudaMalloc(&bby0_d, K * sizeof(float)) );
checkCuda( cudaMalloc(&bbx1_d, K * sizeof(float)) );
checkCuda( cudaMalloc(&bby1_d, K * sizeof(float)) );
checkCuda( cudaMallocHost(&intxs, K * sizeof(int)) );
checkCuda( cudaMallocHost(&intys, K * sizeof(int)) );
checkCuda( cudaMallocHost(&track, K * dim_track.c * sizeof(float)) );
checkCuda( cudaMallocHost(&dep, K * dim_dep.c * sizeof(float)) );
checkCuda( cudaMallocHost(&rot, K * dim_rot.c * sizeof(float)) );
checkCuda( cudaMallocHost(&dim_, K * dim_dim.c * sizeof(float)) );
checkCuda( cudaMallocHost(&wh, K * dim_wh.c * sizeof(float)) );
checkCuda( cudaMallocHost(&amodel_offset, K * dim_amodel_offset.c * sizeof(float)) );
checkCuda( cudaMalloc(&track_d, K * dim_track.c * sizeof(float)) );
checkCuda( cudaMalloc(&dep_d, K * dim_dep.c * sizeof(float)) );
checkCuda( cudaMalloc(&rot_d, K * dim_rot.c * sizeof(float)) );
checkCuda( cudaMalloc(&dim_d, K * dim_dim.c * sizeof(float)) );
checkCuda( cudaMalloc(&wh_d, K * dim_wh.c * sizeof(float)) );
checkCuda( cudaMalloc(&amodel_offset_d, K * dim_amodel_offset.c * sizeof(float)) );
checkCuda( cudaMallocHost(&target_coords, 4 * K *sizeof(float)) );
calibs = cv::Mat(cv::Size(4,3), CV_32F);
calibs.at<float>(0,0) = 633.0;
calibs.at<float>(0,1) = 0.0;
calibs.at<float>(0,2) = 0.0; //w/2
calibs.at<float>(0,3) = 0.0;
calibs.at<float>(1,0) = 0.0;
calibs.at<float>(1,1) = 633.0;
calibs.at<float>(1,2) = 0.0; //h/2
calibs.at<float>(1,3) = 0.0;
calibs.at<float>(2,0) = 0.0;
calibs.at<float>(2,1) = 0.0;
calibs.at<float>(2,2) = 1.0;
calibs.at<float>(2,3) = 0.0;
// Alloc array used in the kernel
checkCuda( cudaMalloc(&src_out, K *sizeof(float)) );
checkCuda( cudaMalloc(&ids_out, K *sizeof(int)) );
}
bool CenternetDetection3DTrack::init_visualization(const int n_classes){
classes = n_classes;
// const char *kitti_class_name[] = {
// "person", "car", "bicycle"};
// classesNames = std::vector<std::string>(kitti_class_name, std::end( kitti_class_name));
const char *class_name[] = {"car", "truck", "bus", "trailer", "construction_vehicle", "pedestrian",
"motorcycle", "bicycle", "traffic_cone", "barrier"};
classesNames = std::vector<std::string>(class_name, std::end( class_name));
// const char *coco_class_name[] = {
// "person", "bicycle", "car", "motorcycle", "airplane",
// "bus", "train", "truck", "boat", "traffic light", "fire hydrant",
// "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse",
// "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack",
// "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis",
// "snowboard", "sports ball", "kite", "baseball bat", "baseball glove",
// "skateboard", "surfboard", "tennis racket", "bottle", "wine glass",
// "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich",
// "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake",
// "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv",
// "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave",
// "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase",
// "scissors", "teddy bear", "hair drier", "toothbrush"
// };
// classesNames = std::vector<std::string>(coco_class_name, std::end( coco_class_name));
for(int c=0; c<classes; c++) {
int offset = c*123457 % classes;
float r = getColor(2, offset, classes);
float g = getColor(1, offset, classes);
float b = getColor(0, offset, classes);
colors[c] = cv::Scalar(int(255.0*b), int(255.0*g), int(255.0*r));
}
for(int c=0; c<256; c++) {
int offset = c*123457 % 256;
float r = getColor(2, offset, 256);
float g = getColor(1, offset, 256);
float b = getColor(0, offset, 256);
tr_colors[c] = cv::Scalar(int(255.0*b), int(255.0*g), int(255.0*r));
}
r = cv::Mat(cv::Size(3,3), CV_32F);
r.at<float>(0,1) = 0.0;
r.at<float>(1,0) = 0.0;
r.at<float>(1,1) = 1.0;
r.at<float>(1,2) = 0.0;
r.at<float>(2,1) = 0.0;
corners = cv::Mat(cv::Size(8,3), CV_32F);
corners.at<float>(1,0) = 0.0;
corners.at<float>(1,1) = 0.0;
corners.at<float>(1,2) = 0.0;
corners.at<float>(1,3) = 0.0;
pts3DHomo = cv::Mat(cv::Size(8,4), CV_32F);
pts3DHomo.at<float>(3,0) = 1.0;
pts3DHomo.at<float>(3,1) = 1.0;
pts3DHomo.at<float>(3,2) = 1.0;
pts3DHomo.at<float>(3,3) = 1.0;
pts3DHomo.at<float>(3,4) = 1.0;
pts3DHomo.at<float>(3,5) = 1.0;
pts3DHomo.at<float>(3,6) = 1.0;
pts3DHomo.at<float>(3,7) = 1.0;
face_id.push_back({0,1,5,4});
face_id.push_back({1,2,6, 5});
face_id.push_back({2,3,7,6});
face_id.push_back({3,0,4,7});
// ([[0,1,5,4], [1,2,6, 5], [2,3,7,6], [3,0,4,7]]);
}
void CenternetDetection3DTrack::_get_additional_inputs(){
//None no additional input
}
void CenternetDetection3DTrack::pre_inf(){
TKDNN_TSTART
tk::dnn::dataDim_t dim_aus;
pre_phase_net->infer(dim_aus, nullptr);
TKDNN_TSTOP
checkCuda( cudaDeviceSynchronize() );
checkCuda( cudaMemcpy(input_d, pre_phase_net->layers[pre_phase_net->num_layers-1]->dstData, netRT->input_dim.tot()*sizeof(dnnType), cudaMemcpyDeviceToDevice) );
checkCuda( cudaDeviceSynchronize() );
}
void CenternetDetection3DTrack::preprocess(cv::Mat &frame){
// -----------------------------------pre-process ------------------------------------------
cv::Size sz = originalSize;
cv::Size sz_old;
float scale = 1.0;
float new_height = sz.height * scale;
float new_width = sz.width * scale;
if(sz.height != sz_old.height && sz.width != sz_old.width){
calibs.at<float>(0,2) = new_width / 2.0f;
calibs.at<float>(1,2) = new_height /2.0f;
float c[] = {new_width / 2.0f, new_height /2.0f};
float s[] = {dim.w, dim.h};
// float s = new_width >= new_height ? new_width : new_height;
// ----------- get_affine_transform
// rot_rad = pi * 0 / 100 --> 0
dim.print();
src.at<float>(0,0)=c[0];
src.at<float>(0,1)=c[1];
src.at<float>(1,0)=c[0];
src.at<float>(1,1)=c[1] + s[0] * -0.5;
dst.at<float>(0,0)=dim.w * 0.5;
dst.at<float>(0,1)=dim.h * 0.5;
dst.at<float>(1,0)=dim.w * 0.5;
dst.at<float>(1,1)=dim.h * 0.5 + dim.w * -0.5;
src.at<float>(2,0)=src.at<float>(1,0) + (-src.at<float>(0,1)+src.at<float>(1,1) );
src.at<float>(2,1)=src.at<float>(1,1) + (src.at<float>(0,0)-src.at<float>(1,0) );
dst.at<float>(2,0)=dst.at<float>(1,0) + (-dst.at<float>(0,1)+dst.at<float>(1,1) );
dst.at<float>(2,1)=dst.at<float>(1,1) + (dst.at<float>(0,0)-dst.at<float>(1,0) );
trans = cv::getAffineTransform( src, dst );
trans2 = cv::getAffineTransform( dst2, src );
trans2.convertTo(trans_out, CV_32F);
}
sz_old = sz;
#ifdef OPENCV_CUDACONTRIB
std::cout<<"OPENCV CPMTROB\n";
cv::cuda::GpuMat im_Orig;
cv::cuda::GpuMat imageF1_d, imageF2_d;
im_Orig = cv::cuda::GpuMat(frame);
// cv::cuda::resize (im_Orig, imageF1_d, cv::Size(new_width, new_height));
imageF1_d = im_Orig;
checkCuda( cudaDeviceSynchronize() );
sz = imageF1_d.size();
cv::cuda::warpAffine(imageF1_d, imageF2_d, trans, cv::Size(dim.w, dim.h), cv::INTER_LINEAR );
checkCuda( cudaDeviceSynchronize() );
imageF2_d.convertTo(imageF1_d, CV_32FC3, 1/255.0);
checkCuda( cudaDeviceSynchronize() );
dim2 = dim;
cv::cuda::GpuMat bgr[3];
cv::cuda::split(imageF1_d,bgr);//split source
for(int i=0; i<dim.c; i++)
checkCuda( cudaMemcpy(d_ptrs + i*dim.h * dim.w, (float*)bgr[i].data, dim.h * dim.w * sizeof(float), cudaMemcpyDeviceToDevice) );
normalize(d_ptrs, dim.c, dim.h, dim.w, mean_d, stddev_d);
checkCuda(cudaMemcpy(input_pre_inf_d, d_ptrs, dim2.tot()*sizeof(dnnType), cudaMemcpyDeviceToDevice));
checkCuda( cudaDeviceSynchronize() );
#else
std::cout<<"NO OPENCV CPMTROB\n";
cv::Mat imageF;
// resize(frame, imageF, cv::Size(new_width, new_height));
imageF = frame;
sz = imageF.size();
cv::warpAffine(imageF, imageF, trans, cv::Size(dim.w, dim.h), cv::INTER_LINEAR );
sz = imageF.size();
imageF.convertTo(imageF, CV_32FC3, 1/255.0);
dim2 = dim;
//split channels
cv::Mat bgr[3];
cv::split(imageF,bgr);//split source
for(int i=0; i<3; i++){
bgr[i] = bgr[i] - mean[i];
bgr[i] = bgr[i] / stddev[i];
}
for(int i=0; i<dim2.c; i++) {
int idx = i*imageF.rows*imageF.cols;
int ch =i;//dim2.c-3 +i;//i;//
memcpy((void*)&input[idx], (void*)bgr[ch].data, imageF.rows*imageF.cols*sizeof(dnnType));
}
checkCuda(cudaMemcpyAsync(input_pre_inf_d, input, dim2.tot()*sizeof(dnnType), cudaMemcpyHostToDevice));
checkCuda( cudaDeviceSynchronize() );
#endif
if(iter0) {
checkCuda( cudaMemcpy(img_d, input_pre_inf_d, dim.tot()*sizeof(dnnType), cudaMemcpyDeviceToDevice) );
checkCuda( cudaDeviceSynchronize() );
iter0=false;
}
pre_inf();
checkCuda( cudaMemcpy(img_d, input_pre_inf_d, dim.tot()*sizeof(dnnType), cudaMemcpyDeviceToDevice) );
checkCuda( cudaDeviceSynchronize() );
}
cv::Mat CenternetDetection3DTrack::transform_preds_with_trans(float x1, float x2){
cv::Mat target_coords(cv::Size(1,3), CV_32F);
target_coords.at<float>(0,0) = x1;
target_coords.at<float>(0,1) = x2;
target_coords.at<float>(0,2) = 1.0;
return trans_out * target_coords;
}
void CenternetDetection3DTrack::tracking(){
float item_size[count_det];
int item_cl[count_det];
float dets[2*count_det];
for(int i=0; i<count_det; i++){
item_size[i] = (det_res[i].bb1.at<float>(0,0) - det_res[i].bb0.at<float>(0,0)) *
(det_res[i].bb1.at<float>(0,1) - det_res[i].bb0.at<float>(0,1));
item_cl[i] = det_res[i].cl;
dets[i*2] = det_res[i].ct.at<float>(0,0);
dets[i*2+1] = det_res[i].ct.at<float>(0,1);
}
float track_size[count_tr];
int track_cl[count_tr];
float tracks[2*count_tr];
for(int i=0; i<count_tr; i++){
track_size[i] = (tr_res[i].det_res.bb1.at<float>(0,0) - tr_res[i].det_res.bb0.at<float>(0,0)) *
(tr_res[i].det_res.bb1.at<float>(0,1) - tr_res[i].det_res.bb0.at<float>(0,1));
track_cl[i] = tr_res[i].det_res.cl;
tracks[i*2] = tr_res[i].det_res.ct.at<float>(0,0);
tracks[i*2+1] = tr_res[i].det_res.ct.at<float>(0,1);
}
float dist[count_tr*count_det];
bool invalid;
for(int i=0; i<count_tr; i++){
for(int j=0; j<count_det; j++){
dist[j*count_tr+i] = pow((tracks[i*2] - dets[j*2]), 2) +
pow((tracks[i*2+1] - dets[j*2+1]), 2);
invalid = dist[j*count_tr+i] > track_size[i] || dist[j*count_tr+i] > item_size[j] || item_cl[j] != track_cl[i];
dist[j*count_tr+i] = dist[j*count_tr+i] + invalid * (1 << 18);
}
}
int matched_indices[2*count_tr];
float min_tr;
int min_idtr=-1;
for(int i=0; i<count_tr; i++) {
matched_indices[i*2] = -1;
matched_indices[i*2+1] = -1;
}
for(int i=0; i<count_det; i++){
min_tr=(1 << 18);
for(int j=0; j<count_tr; j++){
if(dist[i*count_tr+j]<min_tr) {
min_tr = dist[i*count_tr+j];
min_idtr = j;
}
}
if(min_tr < (1<<16)) {
for(int j=0; j<count_det; j++){
dist[j*count_tr+min_idtr] = (1 << 18);
}
matched_indices[2*min_idtr] = min_idtr;
matched_indices[2*min_idtr+1] = i;
}
}
bool unmatched_dets[count_det];
for(int i=0; i<count_det; i++)
unmatched_dets[i] = false;
bool unmatched_tracks[count_tr];
for(int i=0; i<count_tr; i++)
unmatched_tracks[i] = false;
for(int i=0; i<count_tr; i++) {
if(matched_indices[2*i] != -1)
unmatched_tracks[matched_indices[2*i]]=true;
if(matched_indices[2*i+1] != -1)
unmatched_dets[matched_indices[2*i+1]]=true;
}
//match
for(int i=0; i<count_tr; i++) {
if(matched_indices[2*i+1] != -1 && matched_indices[2*i] != -1) { //second condition is optional
int tr_id = matched_indices[2*i];
int d_id = matched_indices[2*i+1];
// tr_res[tr_id].det_res = det_res[d_id];
tr_res[tr_id].det_res.score = det_res[d_id].score;
tr_res[tr_id].det_res.cl = det_res[d_id].cl;
tr_res[tr_id].det_res.ct = det_res[d_id].ct;
tr_res[tr_id].det_res.tr = det_res[d_id].tr;
tr_res[tr_id].det_res.bb0 = det_res[d_id].bb0;
tr_res[tr_id].det_res.bb1 = det_res[d_id].bb1;
tr_res[tr_id].det_res.dep = det_res[d_id].dep;
tr_res[tr_id].det_res.dim[0] = det_res[d_id].dim[0];
tr_res[tr_id].det_res.dim[1] = det_res[d_id].dim[1];
tr_res[tr_id].det_res.dim[2] = det_res[d_id].dim[2];
tr_res[tr_id].det_res.alpha = det_res[d_id].alpha;
tr_res[tr_id].det_res.x = det_res[d_id].x;
tr_res[tr_id].det_res.y = det_res[d_id].y;
tr_res[tr_id].det_res.z = det_res[d_id].z;
tr_res[tr_id].det_res.rot_y = det_res[d_id].rot_y;
// tr_res[matched_indices[2*i]].tracking_id = ; is the same
// tr_res[matched_indices[2*i]].color = ; is the same
tr_res[tr_id].age = 1;
tr_res[tr_id].active = tr_res[tr_id].active+1;
}
}
//delete target umatched track
int new_count_tr = 0;
for(int i=0; i<count_tr; i++) {
if(unmatched_tracks[i])
new_count_tr++;
}
if(new_count_tr == 0 && count_tr != 0) { //reset
tr_res.clear();
count_tr = 0;
}
int old_count_tr = count_tr;
if(count_tr != 0 && new_count_tr != count_tr) {
std::vector<struct trackingRes> new_tr_res;
int id_new_tr=0;
for(int i=0; i<count_tr; i++) {
if(unmatched_tracks[i]) {
struct trackingRes new_tr_res_;
// new_tr_res_new_det_res.det_res = tr_res[i].det_res;
new_tr_res_.det_res.score = tr_res[i].det_res.score;
new_tr_res_.det_res.cl = tr_res[i].det_res.cl;
new_tr_res_.det_res.ct = tr_res[i].det_res.ct;
new_tr_res_.det_res.tr = tr_res[i].det_res.tr;
new_tr_res_.det_res.bb0 = tr_res[i].det_res.bb0;
new_tr_res_.det_res.bb1 = tr_res[i].det_res.bb1;
new_tr_res_.det_res.dep = tr_res[i].det_res.dep;
new_tr_res_.det_res.dim[0] = tr_res[i].det_res.dim[0];
new_tr_res_.det_res.dim[1] = tr_res[i].det_res.dim[1];
new_tr_res_.det_res.dim[2] = tr_res[i].det_res.dim[2];
new_tr_res_.det_res.alpha = tr_res[i].det_res.alpha;
new_tr_res_.det_res.x = tr_res[i].det_res.x;
new_tr_res_.det_res.y = tr_res[i].det_res.y;
new_tr_res_.det_res.z = tr_res[i].det_res.z;
new_tr_res_.det_res.rot_y = tr_res[i].det_res.rot_y;
new_tr_res_.tracking_id = tr_res[i].tracking_id;
new_tr_res_.age = tr_res[i].age;
new_tr_res_.active = tr_res[i].active;
new_tr_res_.color = tr_res[i].color;
id_new_tr++;
new_tr_res.push_back(new_tr_res_);
}
}
if(count_tr) {
tr_res.clear();
}
count_tr = new_count_tr;
tr_res=new_tr_res;
}
int count_tr_ = count_tr;
for(int i=0; i<count_det; i++) {
if((!unmatched_dets[i]) && det_res[i].score > new_thresh) {
count_tr_ ++;
struct trackingRes new_tr_res_;
new_tr_res_.det_res.score = det_res[i].score;
new_tr_res_.det_res.cl = det_res[i].cl;
new_tr_res_.det_res.ct = det_res[i].ct;
new_tr_res_.det_res.tr = det_res[i].tr;
new_tr_res_.det_res.bb0 = det_res[i].bb0;
new_tr_res_.det_res.bb1 = det_res[i].bb1;
new_tr_res_.det_res.dep = det_res[i].dep;
new_tr_res_.det_res.dim[0] = det_res[i].dim[0];
new_tr_res_.det_res.dim[1] = det_res[i].dim[1];
new_tr_res_.det_res.dim[2] = det_res[i].dim[2];
new_tr_res_.det_res.alpha = det_res[i].alpha;
new_tr_res_.det_res.x = det_res[i].x;
new_tr_res_.det_res.y = det_res[i].y;
new_tr_res_.det_res.z = det_res[i].z;
new_tr_res_.det_res.rot_y = det_res[i].rot_y;
new_tr_res_.tracking_id = track_id++;
new_tr_res_.age = 1;
new_tr_res_.active = 1;
new_tr_res_.color = rand() % 256;
tr_res.push_back(new_tr_res_);
}
}
count_tr = count_tr_;
if(track_id==1000)
track_id=0;
det_res.clear();
}
void CenternetDetection3DTrack::postprocess(){
dnnType *rt_out[9];
rt_out[0] = (dnnType *)netRT->buffersRT[1];
rt_out[1] = (dnnType *)netRT->buffersRT[2];
rt_out[2] = (dnnType *)netRT->buffersRT[3];
rt_out[3] = (dnnType *)netRT->buffersRT[4];
rt_out[4] = (dnnType *)netRT->buffersRT[5];
rt_out[5] = (dnnType *)netRT->buffersRT[6];
rt_out[6] = (dnnType *)netRT->buffersRT[7];
rt_out[7] = (dnnType *)netRT->buffersRT[8];
rt_out[8] = (dnnType *)netRT->buffersRT[9];
// ------------------------------------ process --------------------------------------------
activationSIGMOIDForward(rt_out[0], rt_out[0], dim_hm.tot());
checkCuda( cudaDeviceSynchronize() );
// output['dep'] = 1. / (output['dep'].sigmoid() + 1e-6) - 1.
activationSIGMOIDForward(rt_out[5], rt_out[5], dim_dep.tot());
checkCuda( cudaDeviceSynchronize() );
transformDep(ones, ones + dim_dep.tot(), rt_out[5], rt_out[5] + dim_dep.tot());
checkCuda( cudaDeviceSynchronize() );
// nms
subtractWithThreshold(rt_out[0], rt_out[0] + dim_hm.tot(), rt_out[1], rt_out[0], op);
// ----------- nms end
// ----------- topk
if(K > dim_hm.h * dim_hm.w){
printf ("Error topk (K is too large)\n");
return;
}
checkCuda( cudaMemcpy(ids_d, ids_, dim_hm.c * dim_hm.h * dim_hm.w*sizeof(int), cudaMemcpyHostToDevice) );
sort(rt_out[0],rt_out[0]+dim_hm.tot(),ids_d);
checkCuda( cudaDeviceSynchronize() );
topk(rt_out[0], ids_d, K, scores_d, topk_inds_d, topk_ys_d, topk_xs_d);
checkCuda( cudaDeviceSynchronize() );
checkCuda( cudaMemcpy(scores, scores_d, K *sizeof(float), cudaMemcpyDeviceToHost) );
topKxyclasses(topk_inds_d, topk_inds_d+K, K, width, dim_hm.w*dim_hm.h, clses_d, inttopk_xs_d, inttopk_ys_d);
checkCuda( cudaDeviceSynchronize() );
checkCuda( cudaMemcpy(topk_xs_d, (float *)inttopk_xs_d, K*sizeof(float), cudaMemcpyDeviceToDevice) );
checkCuda( cudaMemcpy(topk_ys_d, (float *)inttopk_ys_d, K*sizeof(float), cudaMemcpyDeviceToDevice) );
checkCuda( cudaMemcpy(intxs, inttopk_xs_d, K * sizeof(int), cudaMemcpyDeviceToHost) );
checkCuda( cudaMemcpy(intys, inttopk_ys_d, K * sizeof(int), cudaMemcpyDeviceToHost) );
checkCuda( cudaMemcpy(clses, clses_d, K*sizeof(int), cudaMemcpyDeviceToHost) );
// ----------- topk end
topKxyAddOffset(topk_inds_d, K, dim_reg.h*dim_reg.w, inttopk_xs_d, inttopk_ys_d, topk_xs_d, topk_ys_d, rt_out[3], src_out, ids_out);
checkCuda( cudaDeviceSynchronize() );
bboxes(topk_inds_d, K, dim_wh.h*dim_wh.w, topk_xs_d, topk_ys_d, rt_out[2], bbx0_d, bbx1_d, bby0_d, bby1_d, src_out, ids_out);
checkCuda( cudaDeviceSynchronize() );
checkCuda( cudaMemcpy(bbx0, bbx0_d, K * sizeof(float), cudaMemcpyDeviceToHost) );
checkCuda( cudaMemcpy(bby0, bby0_d, K * sizeof(float), cudaMemcpyDeviceToHost) );
checkCuda( cudaMemcpy(bbx1, bbx1_d, K * sizeof(float), cudaMemcpyDeviceToHost) );
checkCuda( cudaMemcpy(bby1, bby1_d, K * sizeof(float), cudaMemcpyDeviceToHost) );
//regression heads
// ['tracking', 'dep', 'rot', 'dim', 'amodel_offset',
// 'nuscenes_att', 'velocity']
getRecordsFromTopKId(topk_inds_d, K, dim_track.c, dim_track.h * dim_track.w, rt_out[4], track_d, ids_out);
checkCuda( cudaMemcpy(track, track_d, K * dim_track.c * sizeof(float), cudaMemcpyDeviceToHost) );
getRecordsFromTopKId(topk_inds_d, K, dim_dep.c, dim_dep.h * dim_dep.w, rt_out[5], dep_d, ids_out);
checkCuda( cudaMemcpy(dep, dep_d, K * dim_dep.c * sizeof(float), cudaMemcpyDeviceToHost) );
getRecordsFromTopKId(topk_inds_d, K, dim_rot.c, dim_rot.h * dim_rot.w, rt_out[6], rot_d, ids_out);
checkCuda( cudaMemcpy(rot, rot_d, K * dim_rot.c * sizeof(float), cudaMemcpyDeviceToHost) );
getRecordsFromTopKId(topk_inds_d, K, dim_dim.c, dim_dim.h * dim_dim.w, rt_out[7], dim_d, ids_out);
checkCuda( cudaMemcpy(dim_, dim_d, K * dim_dim.c * sizeof(float), cudaMemcpyDeviceToHost) );
getRecordsFromTopKId(topk_inds_d, K, dim_amodel_offset.c, dim_amodel_offset.h * dim_amodel_offset.w, rt_out[8], amodel_offset_d, ids_out);
checkCuda( cudaMemcpy(amodel_offset, amodel_offset_d, K * dim_amodel_offset.c * sizeof(float), cudaMemcpyDeviceToHost) );
// ---------------------------------- post-process -----------------------------------------
count_det = 0;
det_res.clear();
for(int i = 0; i<K; i++){
if(scores[i] < out_thresh)
break;
count_det ++;
struct detectionRes new_det_res;
new_det_res.score = scores[i];
new_det_res.cl = clses[i]+1;
// ret_s=scores[i];
// ret_c=clses[i]+1;
new_det_res.ct = transform_preds_with_trans(intxs[i], intys[i]);
new_det_res.tr = transform_preds_with_trans(intxs[i] + track[i], intys[i] + track[i+K]);
new_det_res.tr = new_det_res.tr -new_det_res.ct;
new_det_res.bb0 = transform_preds_with_trans(bbx0[i], bby0[i]);
new_det_res.bb1 = transform_preds_with_trans(bbx1[i], bby1[i]);
new_det_res.ct = transform_preds_with_trans(((bbx0[i]+bbx1[i])/2 + amodel_offset[i]),
((bby0[i]+bby1[i])/2 + amodel_offset[i+K]));
new_det_res.dep = dep[i];
new_det_res.dim[0] = dim_[i];
new_det_res.dim[1] = dim_[i+K];
new_det_res.dim[2] = dim_[i+2*K];
// unproject_2d_to_3d
new_det_res.z = dep[i] - calibs.at<float>(2,3);
new_det_res.x = ((float)new_det_res.ct.at<float>(0,0) * dep[i] - calibs.at<float>(0,3) - calibs.at<float>(0,2) * new_det_res.z) / calibs.at<float>(0,0);
new_det_res.y = ((float)new_det_res.ct.at<float>(0,1) * dep[i] - calibs.at<float>(1,3) - calibs.at<float>(1,2) * new_det_res.z) / calibs.at<float>(1,1) + (dim_[i] / 2);
// alpha2rot_y
// idx = rot[:, 1] > rot[:, 5]
// alpha1 = np.arctan2(rot[:, 2], rot[:, 3]) + (-0.5 * np.pi)
// alpha2 = np.arctan2(rot[:, 6], rot[:, 7]) + ( 0.5 * np.pi)
// return alpha1 * idx + alpha2 * (1 - idx)
if(rot[1*K + i] > rot[5*K + i])
new_det_res.alpha = std::atan2(rot[2*K + i], rot[3*K + i]) -0.5 * M_PI;
else
new_det_res.alpha = std::atan2(rot[6*K + i], rot[7*K + i]) +0.5 * M_PI;
new_det_res.rot_y = (new_det_res.alpha + std::atan2((float)new_det_res.ct.at<float>(0,0) - calibs.at<float>(0,2), calibs.at<float>(0,0)));
new_det_res.ct = new_det_res.ct + new_det_res.tr; //dest
det_res.push_back(new_det_res);
}
// track step
tracking();
}
cv::Mat CenternetDetection3DTrack::draw(cv::Mat &frame) {
float sc;
int id;
std::string txt;
int baseline = 0;
float font_scale = 0.8;
int thickness = 2;
for(int i=0; i<count_tr; i++) {
id = tr_res[i].tracking_id;
txt = classesNames[tr_res[i].det_res.cl-1]+'-'+std::to_string(id); //forse ha bisogno di cl-1
cv::Size text_size = getTextSize(txt, cv::FONT_HERSHEY_SIMPLEX, font_scale, thickness, &baseline);
if(tr_res[i].det_res.score > vis_thresh){// && tr_res[i].active!=0) {
if(view2d) {
cv::rectangle(frame, cv::Point(tr_res[i].det_res.bb0.at<float>(0,0), tr_res[i].det_res.bb0.at<float>(0,1)),
cv::Point(tr_res[i].det_res.bb1.at<float>(0,0), tr_res[i].det_res.bb1.at<float>(0,1)), tr_colors[tr_res[i].color], thickness);
cv::rectangle(frame, cv::Point(tr_res[i].det_res.bb0.at<float>(0,0),
tr_res[i].det_res.bb0.at<float>(0,1) - text_size.height - thickness),
cv::Point(tr_res[i].det_res.bb0.at<float>(0,0) + text_size.width,
tr_res[i].det_res.bb0.at<float>(0,1)), tr_colors[tr_res[i].color], -1);
cv::putText(frame, txt, cv::Point(tr_res[i].det_res.bb0.at<float>(0,0),
tr_res[i].det_res.bb0.at<float>(0,1) - thickness -1),
cv::FONT_HERSHEY_SIMPLEX, font_scale, cv::Scalar(255, 255, 255), 1);
cv::arrowedLine(frame, cv::Point((int)tr_res[i].det_res.ct.at<float>(0,0),
(int)tr_res[i].det_res.ct.at<float>(0,1)),
cv::Point((int)(tr_res[i].det_res.ct.at<float>(0,0) + tr_res[i].det_res.tr.at<float>(0,0)),
(int)(tr_res[i].det_res.ct.at<float>(0,1) + tr_res[i].det_res.tr.at<float>(0,1))),
cv::Scalar(255, 0, 255), 2);
}
//3d
if(!view2d && tr_res[i].det_res.z > 1){
r.at<float>(0,0) = std::cos(tr_res[i].det_res.rot_y);
r.at<float>(0,2) = std::sin(tr_res[i].det_res.rot_y);
r.at<float>(2,0) = -std::sin(tr_res[i].det_res.rot_y);
r.at<float>(2,2) = std::cos(tr_res[i].det_res.rot_y);
corners.at<float>(0,0) = tr_res[i].det_res.dim[2]/2;
corners.at<float>(0,1) = tr_res[i].det_res.dim[2]/2;
corners.at<float>(0,2) = -tr_res[i].det_res.dim[2]/2;
corners.at<float>(0,3) = -tr_res[i].det_res.dim[2]/2;
corners.at<float>(0,4) = tr_res[i].det_res.dim[2]/2;
corners.at<float>(0,5) = tr_res[i].det_res.dim[2]/2;
corners.at<float>(0,6) = -tr_res[i].det_res.dim[2]/2;
corners.at<float>(0,7) = -tr_res[i].det_res.dim[2]/2;
corners.at<float>(1,4) = -tr_res[i].det_res.dim[0];
corners.at<float>(1,5) = -tr_res[i].det_res.dim[0];
corners.at<float>(1,6) = -tr_res[i].det_res.dim[0];
corners.at<float>(1,7) = -tr_res[i].det_res.dim[0];
corners.at<float>(2,0) = tr_res[i].det_res.dim[1]/2;
corners.at<float>(2,1) = -tr_res[i].det_res.dim[1]/2;
corners.at<float>(2,2) = -tr_res[i].det_res.dim[1]/2;
corners.at<float>(2,3) = tr_res[i].det_res.dim[1]/2;
corners.at<float>(2,4) = tr_res[i].det_res.dim[1]/2;
corners.at<float>(2,5) = -tr_res[i].det_res.dim[1]/2;
corners.at<float>(2,6) = -tr_res[i].det_res.dim[1]/2;
corners.at<float>(2,7) = tr_res[i].det_res.dim[1]/2;
cv::Mat aus = r * corners;
for(int k=0; k<8; k++) {
aus.at<float>(0,k) += tr_res[i].det_res.x;
aus.at<float>(1,k) += tr_res[i].det_res.y;
aus.at<float>(2,k) += tr_res[i].det_res.z;
}
// corners.copyTo(pts3DHomo(cv::Rect(0, 0, 8, 3)));
for(int k1=0; k1<3; k1++) {
for(int k2=0; k2<8; k2++)
pts3DHomo.at<float>(k1,k2) = aus.at<float>(k1,k2);
}
aus.release();
aus = calibs * pts3DHomo;
std::vector<float> res_corners;
for(int k=0; k<8; k++) {
res_corners.push_back(aus.at<float>(0,k) / aus.at<float>(2,k));
res_corners.push_back(aus.at<float>(1,k) / aus.at<float>(2,k));
}
aus.release();
for(int ind_f = 3; ind_f>=0; ind_f--) {
for(int j=0; j<4; j++) {
cv::line(frame, cv::Point(res_corners.at(face_id.at(ind_f).at(j) * 2),
res_corners.at(face_id.at(ind_f).at(j) * 2 + 1)),
cv::Point(res_corners.at(face_id.at(ind_f).at((j+1)%4) * 2),
res_corners.at(face_id.at(ind_f).at((j+1)%4) * 2 + 1)),
tr_colors[tr_res[i].color], 2);
if(ind_f == 0) {
cv::line(frame, cv::Point(res_corners.at(face_id.at(ind_f).at(0) * 2),
res_corners.at(face_id.at(ind_f).at(0) * 2 + 1)),
cv::Point(res_corners.at(face_id.at(ind_f).at(2) * 2),
res_corners.at(face_id.at(ind_f).at(2) * 2 + 1)), tr_colors[tr_res[i].color], 2);
cv::line(frame, cv::Point(res_corners.at(face_id.at(ind_f).at(1) * 2),
res_corners.at(face_id.at(ind_f).at(1) * 2 + 1)),
cv::Point(res_corners.at(face_id.at(ind_f).at(3) * 2),
res_corners.at(face_id.at(ind_f).at(3) * 2 + 1)), tr_colors[tr_res[i].color], 2);
}
}
}
float bb0=(1 << 10), bb1=0, bb2=(1 << 10), bb3=0;
for(int k=0; k<8; k++) {
if(res_corners[2*k]<bb0)
bb0=res_corners[2*k];
if(res_corners[2*k]>bb1)
bb1=res_corners[2*k];
if(res_corners[2*k+1]<bb2)
bb2=res_corners[2*k+1];
if(res_corners[2*k+1]>bb3)
bb3=res_corners[2*k+1];
}
cv::rectangle(frame, cv::Point(bb0, bb2), cv::Point(bb1, bb3),
tr_colors[tr_res[i].color], thickness);
cv::rectangle(frame, cv::Point(bb0, bb2 - text_size.height - thickness),
cv::Point(bb0 + text_size.width, bb2), tr_colors[tr_res[i].color], -1);
cv::putText(frame, txt, cv::Point(bb0, bb2 - thickness -1), cv::FONT_HERSHEY_SIMPLEX,
font_scale, cv::Scalar(255, 255, 255), 1);
cv::arrowedLine(frame, cv::Point((int)((bb0 + bb1)/2), (int)((bb2 + bb3)/2)),
cv::Point((int)((bb0 + bb1)/2 + tr_res[i].det_res.tr.at<float>(0,0)),
(int)((bb2 + bb3)/2 + tr_res[i].det_res.tr.at<float>(0,1))),
cv::Scalar(255, 0, 255), 2);
}
}
}
return frame;
}
}}