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fix formatting issues

This commit is contained in:
jario-jin 2023-08-17 15:44:18 +08:00
parent 8e1a62651d
commit 5867b339f4
5 changed files with 56 additions and 50 deletions
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12
algorithm/mot/sv_mot.cpp
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@ -195,7 +195,7 @@ void SORT::update(TargetsInFrame& tgts)
tracklet.age = 0;
tracklet.hits = 1;
tracklet.misses = 0;
tracklet.frame_id=tgts.frame_id;
tracklet.frame_id = tgts.frame_id;
tracklet.tentative = true;
// initate the motion
pair<Matrix<double, 8, 1>, Matrix<double, 8, 8> > motion = kf.initiate(tracklet.bbox);
@ -245,13 +245,13 @@ void SORT::update(TargetsInFrame& tgts)
int detectionIndex = match.second;
if (trackletIndex >= 0 && detectionIndex >= 0)
{
if (iouMatrix[match.first][match.second] >= _iou_threshold)//iou_thrshold
if (iouMatrix[match.first][match.second] >= _iou_threshold) // iou_thrshold
{
sv::Box box;
tgts.targets[detectionIndex].getBox(box);
this->_tracklets[trackletIndex].age = 0;
this->_tracklets[trackletIndex].hits++;
this->_tracklets[trackletIndex].frame_id=tgts.frame_id;
this->_tracklets[trackletIndex].frame_id = tgts.frame_id;
this->_tracklets[trackletIndex].bbox << box.x1, box.y1, box.x2-box.x1, box.y2-box.y1;
auto[mean, covariance] = kf.update(this->_tracklets[trackletIndex].mean, this->_tracklets[trackletIndex].covariance, box);
@ -264,7 +264,7 @@ void SORT::update(TargetsInFrame& tgts)
}
}
// create new tracklets for unmatched detections
for (int i = 0; i < tgts.targets.size(); i++)
for (int i=0; i<tgts.targets.size(); i++)
{
if (match_det[i] == -1)
{
@ -277,7 +277,7 @@ void SORT::update(TargetsInFrame& tgts)
tracklet.age = 0;
tracklet.hits = 1;
tracklet.misses = 0;
tracklet.frame_id=tgts.frame_id;
tracklet.frame_id = tgts.frame_id;
tracklet.tentative = true;
auto[new_mean, new_covariance] = kf.initiate(tracklet.bbox);
@ -295,7 +295,7 @@ void SORT::update(TargetsInFrame& tgts)
{
tracklet.tentative = false;
}
if ((tgts.frame_id-tracklet.frame_id <= _max_age) || (!tracklet.tentative && tracklet.frame_id==tgts.frame_id))
if ((tgts.frame_id-tracklet.frame_id <= _max_age) || (!tracklet.tentative && tracklet.frame_id == tgts.frame_id))
{
_new_tracklets.push_back(tracklet);
}

4
include/sv_mot.h
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@ -51,11 +51,11 @@ struct Tracklet
/* data */
public:
Eigen::Vector4d bbox; // double x, y, w, h;
int id=0;
int id = 0;
int age;
int hits;
int misses;
int frame_id=0;
int frame_id = 0;
bool tentative;
std::vector<double> features;
Eigen::Matrix<double, 8, 1> mean;

8
samples/test/eval_mAP_on_coco_val/coco_eval.py
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@ -11,19 +11,19 @@ if __name__ == '__main__':
# use COCO API to load forecast results and annotations
cocoGt = COCO(anno_json)
with open(pred_json,'r') as file:
data = json.load(file)
data = json.load(file)
# align anno_json with pred_json category_id
gtCatDicts = {}
for anns in range(len(cocoGt.getCatIds())):
gtCatDicts[anns] = cocoGt.getCatIds()[anns]
gtCatDicts[anns] = cocoGt.getCatIds()[anns]
pdCatIds=list(set([d['category_id'] for d in data]))
if not set(pdCatIds).issubset(set(cocoGt.getCatIds())):
for ins in data:
temp = int(gtCatDicts[ins['category_id']])
ins['category_id'] = temp
temp = int(gtCatDicts[ins['category_id']])
ins['category_id'] = temp
# load prediction results
cocoDt = cocoGt.loadRes(data)

39
samples/test/eval_mAP_on_coco_val/eval_mAP_on_coco_val.cpp
View File

@ -8,17 +8,21 @@ using namespace cv;
//extract name
std::string GetImageFileName(const std::string& imagePath) {
size_t lastSlash = imagePath.find_last_of("/\\");
if (lastSlash == std::string::npos) {
return imagePath;
} else {
std::string fileName = imagePath.substr(lastSlash + 1);
size_t lastDot = fileName.find_last_of(".");
if (lastDot != std::string::npos) {
return fileName.substr(0, lastDot);
}
return fileName;
size_t lastSlash = imagePath.find_last_of("/\\");
if (lastSlash == std::string::npos)
{
return imagePath;
}
else
{
std::string fileName = imagePath.substr(lastSlash + 1);
size_t lastDot = fileName.find_last_of(".");
if (lastDot != std::string::npos)
{
return fileName.substr(0, lastDot);
}
return fileName;
}
}
@ -60,8 +64,8 @@ int main(int argc, char *argv[])
}
for (int i = 0; i < val_image.size(); i++) {
for (int i = 0; i < val_image.size(); i++)
{
//create pred file
std::string val_image_name = GetImageFileName(val_image[i]);
std::string filename = folder+"/"+ val_image_name + ".txt";
@ -84,11 +88,12 @@ int main(int argc, char *argv[])
// reslusts
for (int j = 0; j < tgts.targets.size(); j++)
{
sv::Box b;
tgts.targets[j].getBox(b);
file<<tgts.targets[j].category_id<<" "<<(float)(b.x1+b.x2)/(2*cols)<<" "<<(float)(b.y1+b.y2)/(2*rows)<<" "<<(float)(b.x2-b.x1)/cols<<" "<<(float)(b.y2-b.y1)/rows<<" "<<(float)tgts.targets[j].score<<"\n";
}
file.close();
sv::Box b;
tgts.targets[j].getBox(b);
file << tgts.targets[j].category_id << " " << (float)(b.x1+b.x2) / (2*cols) << " " << (float)(b.y1+b.y2) / (2*rows) << \
" " << (float)(b.x2-b.x1) / cols << " " << (float)(b.y2-b.y1) / rows << " " << (float)tgts.targets[j].score << "\n";
}
file.close();
}
return 0;
}

43
samples/test/eval_mAP_on_coco_val/pd2cocojson.py
View File

@ -4,11 +4,11 @@ import os
import cv2
# revert prediction results to coco_json
path = os.path.abspath(os.path.join(os.getcwd(),"../../.."))
path = os.path.abspath(os.path.join(os.getcwd(), "../../.."))
# all files dir
images_path = path+'/val2017/val2017'
preds_path = path+'/val2017/preds'
images_path = path + '/val2017/val2017'
preds_path = path + '/val2017/preds'
coco_json_save ='pd_coco.json'
# config coco_json
@ -27,25 +27,26 @@ for image in images:
# read pred's txt
pred_path = preds_path + '/' + image_name + '.txt'
if not os.path.exists(pred_path):
continue
continue
with open(pred_path, 'r') as f:
preds = f.readlines()
preds = [l.strip() for l in preds]
for j,pred in enumerate(preds):
pred = pred.split(' ')
category_id = int(pred[0])
x = float(pred[1]) * width
y = float(pred[2]) * height
w = float(pred[3]) * width
h = float(pred[4]) * height
xmin = x - w / 2
ymin = y - h / 2
coco_json.append({
'image_id': int(image_name),
'category_id': int(category_id),
'bbox': [xmin, ymin, w, h],
'score': float(pred[5])})
preds = f.readlines()
preds = [l.strip() for l in preds]
for j,pred in enumerate(preds):
pred = pred.split(' ')
category_id = int(pred[0])
x = float(pred[1]) * width
y = float(pred[2]) * height
w = float(pred[3]) * width
h = float(pred[4]) * height
xmin = x - w / 2
ymin = y - h / 2
coco_json.append({
'image_id': int(image_name),
'category_id': int(category_id),
'bbox': [xmin, ymin, w, h],
'score': float(pred[5])
})
# save json
with open(os.path.join(coco_json_save), 'w') as f: