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Update cnet branch.

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This commit splits the demo3D in two demo: one for the 3D object
detection and one for the tracking.

It renames the files related to CenterTrack.

It adds a new parameter to select the tracker mode (2D or 3D).

Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>
This commit is contained in:
Davide Sapienza
2021-05-11 16:17:23 +02:00
parent 0dc96d2a9e
commit 34c1c3d577
7 changed files with 469 additions and 153 deletions
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src/CenternetDetection3DTrack.cpp → src/CenterTrack.cpp
+24 -23
View File
@@ -1,16 +1,17 @@
#include "CenternetDetection3DTrack.h"
#include "CenterTrack.h"
namespace tk { namespace dnn {
bool CenternetDetection3DTrack::init(const std::string& tensor_path, const int n_classes, const int n_batches,
const float conf_thresh, const std::vector<cv::Mat>& k_calibs) {
bool CenterTrack::init(const std::string& tensor_path, const int n_classes, const int n_batches,
const float conf_thresh, const bool mode_3d, const std::vector<cv::Mat>& k_calibs) {
netRT = new tk::dnn::NetworkRT(NULL, (tensor_path).c_str() );
dim = netRT->input_dim;
dim.c = 3;
nBatches = n_batches;
confThreshold = conf_thresh;
mode3D = mode_3d;
inputCalibs = k_calibs;
init_preprocessing();
init_pre_inf();
@@ -18,7 +19,7 @@ bool CenternetDetection3DTrack::init(const std::string& tensor_path, const int n
init_visualization(n_classes);
}
bool CenternetDetection3DTrack::init_preprocessing(){
bool CenterTrack::init_preprocessing(){
//image transformation
src = cv::Mat(cv::Size(2,3), CV_32F);
dst = cv::Mat(cv::Size(2,3), CV_32F);
@@ -60,12 +61,12 @@ bool CenternetDetection3DTrack::init_preprocessing(){
checkCuda( cudaMalloc(&d_ptrs, dim.tot() * sizeof(float)) );
}
bool CenternetDetection3DTrack::init_pre_inf(){
bool CenterTrack::init_pre_inf(){
// initial steps: the first part of the network
const char *pre_img_conv1_bin = "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";
const char *pre_img_conv1_bin = "dla34_ctrack/layers/base-pre_img_layer-0.bin";
const char *pre_hm_conv1_bin = "dla34_ctrack/layers/base-pre_hm_layer-0.bin";
const char *conv1_bin = "dla34_ctrack/layers/base-base_layer-0.bin";
const char *conv2_bin = "dla34_ctrack/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);
@@ -82,9 +83,9 @@ bool CenternetDetection3DTrack::init_pre_inf(){
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";
// const char *input_bin = "dla34_ctrack/debug/input.bin";
// const char *pre_img_bin = "dla34_ctrack/debug/pre_imgages.bin";
// const char *pre_hm_bin = "dla34_ctrack/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);
@@ -114,7 +115,7 @@ bool CenternetDetection3DTrack::init_pre_inf(){
return true;
}
bool CenternetDetection3DTrack::init_postprocessing(){
bool CenterTrack::init_postprocessing(){
srand(0); //seed = 0 for random colors
dim_hm = tk::dnn::dataDim_t(1, 10, 128, 128, 1);
@@ -203,7 +204,7 @@ bool CenternetDetection3DTrack::init_postprocessing(){
trackId.resize(nBatches, 0);
}
bool CenternetDetection3DTrack::init_visualization(const int n_classes){
bool CenterTrack::init_visualization(const int n_classes){
classes = n_classes;
// const char *kitti_class_name[] = {
// "person", "car", "bicycle"};
@@ -275,11 +276,11 @@ bool CenternetDetection3DTrack::init_visualization(const int n_classes){
// ([[0,1,5,4], [1,2,6, 5], [2,3,7,6], [3,0,4,7]]);
}
void CenternetDetection3DTrack::_get_additional_inputs(){
void CenterTrack::_get_additional_inputs(){
//None no additional input
}
void CenternetDetection3DTrack::pre_inf(const int bi){
void CenterTrack::pre_inf(const int bi){
TKDNN_TSTART
tk::dnn::dataDim_t dim_aus;
pre_phase_net->infer(dim_aus, nullptr);
@@ -289,7 +290,7 @@ void CenternetDetection3DTrack::pre_inf(const int bi){
checkCuda( cudaDeviceSynchronize() );
}
void CenternetDetection3DTrack::preprocess(cv::Mat &frame, const int bi){
void CenterTrack::preprocess(cv::Mat &frame, const int bi){
cv::Size sz = originalSize[bi];
// float scale = 1.0;
float new_height = dim.h;//sz.height * scale;
@@ -403,7 +404,7 @@ void CenternetDetection3DTrack::preprocess(cv::Mat &frame, const int bi){
checkCuda( cudaDeviceSynchronize() );
}
cv::Mat CenternetDetection3DTrack::transform_preds_with_trans(float x1, float x2){
cv::Mat CenterTrack::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;
@@ -411,7 +412,7 @@ cv::Mat CenternetDetection3DTrack::transform_preds_with_trans(float x1, float x2
return transOut * target_coords;
}
void CenternetDetection3DTrack::tracking(const int bi) {
void CenterTrack::tracking(const int bi) {
float item_size[countDet];
int item_cl[countDet];
float dets[2*countDet];
@@ -600,7 +601,7 @@ void CenternetDetection3DTrack::tracking(const int bi) {
}
void CenternetDetection3DTrack::postprocess(const int bi, const bool mAP) {
void CenterTrack::postprocess(const int bi, const bool mAP) {
dnnType *rt_out[9];
rt_out[0] = (dnnType *)netRT->buffersRT[1]+ netRT->buffersDIM[1].tot()*bi;
rt_out[1] = (dnnType *)netRT->buffersRT[2]+ netRT->buffersDIM[2].tot()*bi;
@@ -734,7 +735,7 @@ void CenternetDetection3DTrack::postprocess(const int bi, const bool mAP) {
tracking(bi);
}
void CenternetDetection3DTrack::draw(std::vector<cv::Mat>& frames) {
void CenterTrack::draw(std::vector<cv::Mat>& frames) {
struct trackingRes t;
float sc;
int id;
@@ -755,7 +756,7 @@ void CenternetDetection3DTrack::draw(std::vector<cv::Mat>& frames) {
cv::Size text_size = getTextSize(txt, cv::FONT_HERSHEY_SIMPLEX, font_scale, thickness, &baseline);
if(t.det_res.score > confThreshold){// && t.active!=0) {
if(view2d) {
if(!mode3D) {
cv::rectangle(frames[bi],
cv::Point(t.det_res.bb0.at<float>(0,0) * scale_x, t.det_res.bb0.at<float>(0,1) * scale_y),
cv::Point(t.det_res.bb1.at<float>(0,0) * scale_x, t.det_res.bb1.at<float>(0,1) * scale_y),
@@ -776,7 +777,7 @@ void CenternetDetection3DTrack::draw(std::vector<cv::Mat>& frames) {
cv::Scalar(255, 0, 255), 2);
}
//3d
if(!view2d && t.det_res.z > 1){
if(mode3D && t.det_res.z > 1){
r.at<float>(0,0) = std::cos(t.det_res.rot_y);
r.at<float>(0,2) = std::sin(t.det_res.rot_y);
r.at<float>(2,0) = -std::sin(t.det_res.rot_y);