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perf_ann_tool copy.py
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perf_ann_tool copy.py
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#author: Laurids Radtke
#date: 12.01.2022
#This tool is written to help manually remove errors the auto annotation did and make the annotation perfect so it can be used for machine learning
#Navigation: p = forward, ö = backward, ä = next annotation, l = previous annotation, ü = exit, k = insert new track id
import json
import cv2
import keyboard
if __name__ == '__main__':
total_track_ids = 0
highest_track_id = 0
individual_track_ids = 0
pre_track_id_list = []
post_track_id_list = []
added_track_id_list = []
corrected_track_id_list = []
total_track_ids_corrected = 0
total_track_ids_added = 0
individual_track_ids_added = 0
individual_track_ids_corrected = 0
total_track_ids_post = 0
individual_track_ids_post = 0
new_track_id = 1
json_file = 'D:\\UserData\\z004d1kc\\OneDrive - Siemens AG\\BA\\Datasets\\WittstockBerlin\\annotations\\Iteration 1\\6109_imperf_track_ids_2cats_perf_bbox_vid_2 copy.json'
images_directory = 'D:\\UserData\\z004d1kc\\OneDrive - Siemens AG\\BA\\Datasets\\WittstockBerlin\\images\\'
with open(json_file) as f:
data = json.load(f)
file_name_arr = []
for image in data['images']:
file_name = image['file_name']
id = image['id']
file_name_arr.append((file_name,id))
img_index = 0
aux = 0
forward_flag = 0
backward_flag = 0
exit_flag = 0
####### 30,31,35
for anno in data['annotations']:
if anno['track_id'] >= 0:
total_track_ids += 1
if anno['track_id'] > highest_track_id:
highest_track_id = anno['track_id']
for i in range(highest_track_id + 1):
pre_track_id_list.append(0)
for anno in data['annotations']:
if anno['track_id'] >= 0:
track_id = anno['track_id']
pre_track_id_list[track_id] += 1
for i in pre_track_id_list:
if i > 0:
individual_track_ids += 1
with open('D:\\UserData\\z004d1kc\\OneDrive - Siemens AG\\BA\\Datasets\\WittstockBerlin\\annotations\\Iteration 1\\stats_iteration_1 copy.txt', 'r+') as f:
stat_file = f.readlines()
stat_file[29] = str(total_track_ids) + "\n"
stat_file[30] = str(individual_track_ids) + "\n"
stat_file[34] = str(pre_track_id_list) + "\n"
f.seek(0)
f.writelines(stat_file)
f.truncate()
#######
while True:
if aux == 0:
frame = cv2.imread(images_directory + file_name_arr[0][0])
else:
if cv2.waitKey(0) == ord('p') or forward_flag == 1:
if forward_flag == 1:
forward_flag = 0
img_index += 1
frame = cv2.imread(images_directory + file_name_arr[img_index][0])
elif cv2.waitKey(0) == ord('ö') or backward_flag == 1:
if backward_flag == 1:
backward_flag = 0
img_index -= 1
frame = cv2.imread(images_directory + file_name_arr[img_index][0])
elif cv2.waitKey(0) == ord('ü') or exit_flag == 1:
if exit_flag == 1:
exit_flag = 0
break
else:
continue
annotations_current_img = list(filter(lambda annotation: annotation['image_id'] == file_name_arr[img_index][1], data['annotations']))
selected_ann = 0
if annotations_current_img:
while True:
selected_ann = selected_ann % len(annotations_current_img)
for ind,anno in enumerate(annotations_current_img): #show selected annotation to user
bbox = anno['bbox']
if 'track_id' in anno:
track_id = anno['track_id']
p1 = (int(bbox[0]), int(bbox[1]))
p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))
if ind == selected_ann: #change color
color = (255, 0, 0)
cv2.rectangle(frame, p1, p2, color=color, thickness=2)
cv2.putText(frame, 'track_id: ' + str(track_id), (int(bbox[0]), int(bbox[1]-10)), cv2.FONT_HERSHEY_SIMPLEX, 0.9, color=color, thickness=2)
else:
color = (0, 255, 0)
cv2.rectangle(frame, p1, p2, color=color, thickness=2)
cv2.putText(frame, 'track_id: ' + str(track_id), (int(bbox[0]), int(bbox[1]-10)), cv2.FONT_HERSHEY_SIMPLEX, 0.9, color=color, thickness=2)
cv2.imshow('frame', cv2.resize(frame, (800, 800)))
if cv2.waitKey(20) == ord('q'):
break
if cv2.waitKey(0) == ord('k'):
new_track_id = int(input("enter new track id: "))
for ind,anno in enumerate(annotations_current_img): #edit track_id
if ind == selected_ann:
current_ann = annotations_current_img[selected_ann]
correction = True
if current_ann['track_id'] < 0:
correction = False
#new_track_id = int(input("enter new track id: "))
anno['track_id'] = new_track_id
####### 26,27,37,38,28,29
if correction:
total_track_ids_corrected += 1
if new_track_id >= len(corrected_track_id_list):
for i in range(1 + new_track_id - len(corrected_track_id_list)):
corrected_track_id_list.append(0)
corrected_track_id_list[new_track_id] += 1
indiv_track_id = 0
for j in corrected_track_id_list:
if j > 0:
indiv_track_id += 1
if indiv_track_id > individual_track_ids_corrected:
individual_track_ids_corrected = indiv_track_id
if not correction:
total_track_ids_added += 1
if new_track_id >= len(added_track_id_list):
for i in range(1 + new_track_id - len(added_track_id_list)):
added_track_id_list.append(0)
added_track_id_list[new_track_id] += 1
indiv_track_id = 0
for j in added_track_id_list:
if j > 0:
indiv_track_id += 1
if indiv_track_id > individual_track_ids_added:
individual_track_ids_added = indiv_track_id
with open('D:\\UserData\\z004d1kc\\OneDrive - Siemens AG\\BA\\Datasets\\WittstockBerlin\\annotations\\Iteration 1\\stats_iteration_1 copy.txt', 'r+') as f:
stat_file = f.readlines()
stat_file[25] = str(total_track_ids_added) + "\n"
stat_file[26] = str(total_track_ids_corrected) + "\n"
stat_file[36] = str(added_track_id_list) + "\n"
stat_file[37] = str(corrected_track_id_list) + "\n"
stat_file[27] = str(individual_track_ids_added) + "\n"
stat_file[28] = str(individual_track_ids_corrected) + "\n"
f.seek(0)
f.writelines(stat_file)
f.truncate()
#######
with open(json_file, 'w') as f:
json.dump(data, f, indent=4)
if cv2.waitKey(0) == ord('ä'):
selected_ann += 1
continue
elif cv2.waitKey(0) == ord('l'):
selected_ann -= 1
continue
elif cv2.waitKey(0) == ord('p'):
forward_flag = 1
break
elif cv2.waitKey(0) == ord('ö'):
backward_flag = 1
break
elif cv2.waitKey(0) == ord('ü'):
exit_flag = 1
break
else:
continue
else:
print(img_index)
cv2.imshow('frame', cv2.resize(frame, (800, 800)))
if cv2.waitKey(20) == ord('q'):
break
aux += 1
print("x")
cv2.destroyAllWindows()
with open(json_file, 'w') as f:
json.dump(data, f, indent=4)
######## 36,39,40
for anno in data['annotations']:
if anno['track_id'] >= 0:
total_track_ids_post += 1
if anno['track_id'] > highest_track_id:
highest_track_id = anno['track_id']
for i in range(highest_track_id + 1):
post_track_id_list.append(0)
for anno in data['annotations']:
if anno['track_id'] >= 0:
track_id = anno['track_id']
post_track_id_list[track_id] += 1
for i in pre_track_id_list:
if i > 0:
individual_track_ids_post += 1
with open('D:\\UserData\\z004d1kc\\OneDrive - Siemens AG\\BA\\Datasets\\WittstockBerlin\\annotations\\Iteration 1\\stats_iteration_1 copy.txt', 'r+') as f:
stat_file = f.readlines()
stat_file[38] = str(total_track_ids_post) + "\n"
stat_file[39] = str(individual_track_ids_post) + "\n"
stat_file[35] = str(post_track_id_list) + "\n"
f.seek(0)
f.writelines(stat_file)
f.truncate()
#######