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Improved segmentation results, removed resize, code to reorder

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Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
This commit is contained in:
Micaela Verucchi
2020-07-22 19:21:46 +02:00
parent b4c8c2bbad
commit 286e777300
2 changed files with 191 additions and 59 deletions
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demo/demo/seg_demo.cpp
+38 -55
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@@ -14,20 +14,18 @@ void sig_handler(int signo) {
gRun = false;
}
void writePred(const std::string& images_names, const std::string& gt_folder, const std::string& out_folder, tk::dnn::SegmentationNN& segNN){
void writePred(const std::string& images_names, const std::string& gt_folder, const std::string& out_folder, tk::dnn::SegmentationNN& segNN, int& width, int& height, bool show=false){
std::ifstream all_gt(images_names);
std::string filename;
cv::Mat frame;
std::vector<cv::Mat> batch_frame;
std::vector<cv::Mat> batch_dnn_input;
for (; std::getline(all_gt, filename); ) {
std::cout<<filename<<std::endl;
frame = cv::imread(gt_folder + filename);
batch_dnn_input.clear();
batch_frame.clear();
batch_frame.push_back(frame);
batch_dnn_input.push_back(frame.clone());
segNN.update(batch_dnn_input, 1, false);
height = frame.rows;
width = frame.cols;
segNN.updateOriginal(frame, false);
if(show)
segNN.draw();
cv::imwrite(out_folder + filename, segNN.segmented[0]);
}
}
@@ -50,7 +48,7 @@ int main(int argc, char *argv[]) {
int n_classes = 19;
if(argc > 4)
n_classes = atoi(argv[4]);
bool show = false;
bool show = true;
if(argc > 5)
show = atoi(argv[5]);
bool write_pred = false;
@@ -64,80 +62,65 @@ int main(int argc, char *argv[]) {
tk::dnn::SegmentationNN segNN;
segNN.init(net, n_classes, n_batch);
int height = 0, width = 0;
if(write_pred){
std::string gt_folder = "../demo/CityScapes_val/images/";
std::string images_names = "../demo/CityScapes_val/all_images.txt";
std::string out_folder = "seg/";
writePred(images_names, gt_folder, out_folder, segNN);
return 0;
writePred(images_names, gt_folder, out_folder, segNN, width, height, show);
}
else{
if(!show)
SAVE_RESULT = true;
if(!show)
SAVE_RESULT = true;
gRun = true;
gRun = true;
cv::VideoCapture cap(input);
if(!cap.isOpened())
gRun = false;
else
std::cout<<"camera started\n";
cv::VideoCapture cap(input);
if(!cap.isOpened())
gRun = false;
else
std::cout<<"camera started\n";
cv::VideoWriter resultVideo;
if(SAVE_RESULT) {
int w = cap.get(cv::CAP_PROP_FRAME_WIDTH);
int h = cap.get(cv::CAP_PROP_FRAME_HEIGHT);
resultVideo.open("result.mp4", cv::VideoWriter::fourcc('M','P','4','V'), 30, cv::Size(1024, 1024));
}
cv::VideoWriter resultVideo;
if(SAVE_RESULT) {
int w = cap.get(cv::CAP_PROP_FRAME_WIDTH);
int h = cap.get(cv::CAP_PROP_FRAME_HEIGHT);
resultVideo.open("result.mp4", cv::VideoWriter::fourcc('M','P','4','V'), 30, cv::Size(1024, 1024));
}
cv::Mat frame;
std::vector<cv::Mat> batch_frame;
std::vector<cv::Mat> batch_dnn_input;
int height = 0, width = 0;
while(gRun) {
batch_dnn_input.clear();
batch_frame.clear();
for(int bi=0; bi< n_batch; ++bi){
cv::Mat frame;
while(gRun) {
cap >> frame;
if(!frame.data)
break;
height = frame.rows;
width = frame.cols;
batch_frame.push_back(frame);
// this will be resized to the net format
batch_dnn_input.push_back(frame.clone());
}
if(!frame.data)
break;
//inference
segNN.update(batch_dnn_input, n_batch);
frame = segNN.draw();
//inference
segNN.updateOriginal(frame);
if(show)
segNN.draw();
if(n_batch == 1 && SAVE_RESULT)
resultVideo << frame;
if(SAVE_RESULT)
resultVideo << segNN.segmented[0];
}
}
std::cout<<"segmentation end\n";
double mean = 0, mean_pre = 0, mean_post = 0;
std::cout<<COL_GREENB<<"\n\nTime stats for size ["<<width<<","<<height<<"] :\n";
// std::cout<<"Min: "<<*std::min_element(segNN.stats.begin(), segNN.stats.end())/n_batch<<" ms\n";
// std::cout<<"Max: "<<*std::max_element(segNN.stats.begin(), segNN.stats.end())/n_batch<<" ms\n";
for(int i=0; i<segNN.stats.size(); i++) mean += segNN.stats[i]; mean /= segNN.stats.size();
for(int i=0; i<segNN.stats_pre.size(); i++) mean_pre += segNN.stats_pre[i]; mean_pre /= segNN.stats_pre.size();
for(int i=0; i<segNN.stats_post.size(); i++) mean_post += segNN.stats_post[i]; mean_post /= segNN.stats_post.size();
std::cout<<"Avg pre:\t"<<mean_pre/n_batch<<" ms\t"<<1000/(mean_pre/n_batch)<<" FPS\n";
std::cout<<"Avg inf:\t"<<mean/n_batch<<" ms\t"<<1000/(mean/n_batch)<<" FPS\n";
std::cout<<"Avg post:\t"<<mean_post/n_batch<<" ms\t"<<1000/(mean_post/n_batch)<<" FPS\n\n";
std::cout<<"Avg tot:\t"<<(mean_pre + mean_post + mean) /n_batch<<" ms\t"<<1000/((mean_pre + mean_post + mean)/n_batch)<<" FPS\n"<<COL_END;
std::cout<<"Avg pre:\t"<<mean_pre<<" ms\t"<<1000/(mean_pre)<<" FPS\n";
std::cout<<"Avg inf:\t"<<mean<<" ms\t"<<1000/(mean)<<" FPS\n";
std::cout<<"Avg post:\t"<<mean_post<<" ms\t"<<1000/(mean_post)<<" FPS\n\n";
std::cout<<"Avg tot:\t"<<(mean_pre + mean_post + mean) <<" ms\t"<<1000/((mean_pre + mean_post + mean))<<" FPS\n"<<COL_END;
return 0;
}