forked from halfzm/ProPainter-Webui
-
Notifications
You must be signed in to change notification settings - Fork 0
/
seg_track_anything.py
204 lines (163 loc) · 7 KB
/
seg_track_anything.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
import os
import cv2
from PIL import Image
from aot_tracker import _palette
import numpy as np
import torch
import gc
from scipy.ndimage import binary_dilation
def save_prediction(pred_mask, output_dir, file_name):
save_mask = Image.fromarray(pred_mask.astype(np.uint8))
save_mask = save_mask.convert(mode='P')
save_mask.putpalette(_palette)
save_mask.save(os.path.join(output_dir, file_name))
def colorize_mask(pred_mask):
save_mask = Image.fromarray(pred_mask.astype(np.uint8))
save_mask = save_mask.convert(mode='P')
save_mask.putpalette(_palette)
save_mask = save_mask.convert(mode='RGB')
return np.array(save_mask)
def draw_mask(img, mask, alpha=0.5, id_countour=False):
img_mask = np.zeros_like(img)
img_mask = img
if id_countour:
# very slow ~ 1s per image
obj_ids = np.unique(mask)
obj_ids = obj_ids[obj_ids != 0]
for id in obj_ids:
# Overlay color on binary mask
if id <= 255:
color = _palette[id * 3:id * 3 + 3]
else:
color = [0, 0, 0]
foreground = img * (1 - alpha) + np.ones_like(img) * alpha * np.array(color)
binary_mask = (mask == id)
# Compose image
img_mask[binary_mask] = foreground[binary_mask]
countours = binary_dilation(binary_mask, iterations=1) ^ binary_mask
img_mask[countours, :] = 0
else:
binary_mask = (mask != 0)
countours = binary_dilation(binary_mask, iterations=1) ^ binary_mask
foreground = img * (1 - alpha) + colorize_mask(mask) * alpha
img_mask[binary_mask] = foreground[binary_mask]
img_mask[countours, :] = 0
return img_mask.astype(img.dtype)
def create_dir(dir_path):
if not os.path.isdir(dir_path):
os.makedirs(dir_path)
aot_model2ckpt = {
"deaotb": "./ckpt/DeAOTB_PRE_YTB_DAV.pth",
"deaotl": "./ckpt/DeAOTL_PRE_YTB_DAV",
"r50_deaotl": "./ckpt/R50_DeAOTL_PRE_YTB_DAV.pth",
}
def tracking_objects_in_video(SegTracker, input_video, input_img_seq=None, frame_num=0):
if input_video is not None:
video_name = os.path.basename(input_video).split('.')[0]
else:
return None, None
# create dir to save result
tracking_result_dir = f'{os.path.join(os.path.dirname(__file__), "output", f"{video_name}")}'
create_dir(tracking_result_dir)
io_args = {
'tracking_result_dir': tracking_result_dir,
'output_mask_dir': f'{tracking_result_dir}/{video_name}_masks',
'output_masked_frame_dir': f'{tracking_result_dir}/{video_name}_masked_frames',
'output_video': f'{tracking_result_dir}/{video_name}_seg.mp4', # keep same format as input video
# 'output_gif': f'{tracking_result_dir}/{video_name}_seg.gif',
}
return video_type_input_tracking(SegTracker, input_video, io_args, video_name, frame_num)
def video_type_input_tracking(SegTracker, input_video, io_args, video_name, frame_num=0):
pred_list = []
masked_pred_list = []
# source video to segment
cap = cv2.VideoCapture(input_video)
fps = cap.get(cv2.CAP_PROP_FPS)
if frame_num > 0:
output_mask_name = sorted([img_name for img_name in os.listdir(io_args['output_mask_dir'])])
output_masked_frame_name = sorted([img_name for img_name in os.listdir(io_args['output_masked_frame_dir'])])
for i in range(0, frame_num):
cap.read()
pred_list.append(
np.array(Image.open(os.path.join(io_args['output_mask_dir'], output_mask_name[i])).convert('P')))
masked_pred_list.append(
cv2.imread(os.path.join(io_args['output_masked_frame_dir'], output_masked_frame_name[i])))
# create dir to save predicted mask and masked frame
if frame_num == 0:
if os.path.isdir(io_args['output_mask_dir']):
# os.system(f"rm -r {io_args['output_mask_dir']}")
pass
if os.path.isdir(io_args['output_masked_frame_dir']):
# os.system(f"rm -r {io_args['output_masked_frame_dir']}")
pass
output_mask_dir = io_args['output_mask_dir']
create_dir(io_args['output_mask_dir'])
create_dir(io_args['output_masked_frame_dir'])
torch.cuda.empty_cache()
gc.collect()
sam_gap = SegTracker.sam_gap
frame_idx = 0
with torch.cuda.amp.autocast():
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
if frame_idx == 0:
pred_mask = SegTracker.first_frame_mask
torch.cuda.empty_cache()
gc.collect()
elif (frame_idx % sam_gap) == 0:
seg_mask = SegTracker.seg(frame)
torch.cuda.empty_cache()
gc.collect()
track_mask = SegTracker.track(frame)
# find new objects, and update tracker with new objects
new_obj_mask = SegTracker.find_new_objs(track_mask, seg_mask)
save_prediction(new_obj_mask, output_mask_dir, str(frame_idx + frame_num).zfill(5) + '_new.png')
pred_mask = track_mask + new_obj_mask
# segtracker.restart_tracker()
SegTracker.add_reference(frame, pred_mask)
else:
pred_mask = SegTracker.track(frame, update_memory=True)
torch.cuda.empty_cache()
gc.collect()
save_prediction(pred_mask, output_mask_dir, str(frame_idx + frame_num).zfill(5) + '.png')
pred_list.append(pred_mask)
print("processed frame {}, obj_num {}".format(frame_idx + frame_num, SegTracker.get_obj_num()), end='\r')
frame_idx += 1
cap.release()
print('\nfinished')
##################
# Visualization
##################
# draw pred mask on frame and save as a video
cap = cv2.VideoCapture(input_video)
fps = cap.get(cv2.CAP_PROP_FPS)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fourcc = cv2.VideoWriter_fourcc(*"mp4v")
out = cv2.VideoWriter(io_args['output_video'], fourcc, fps, (width, height))
frame_idx = 0
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
pred_mask = pred_list[frame_idx]
masked_frame = draw_mask(frame, pred_mask)
cv2.imwrite(f"{io_args['output_masked_frame_dir']}/{str(frame_idx).zfill(5)}.png", masked_frame[:, :, ::-1])
masked_pred_list.append(masked_frame)
masked_frame = cv2.cvtColor(masked_frame, cv2.COLOR_RGB2BGR)
out.write(masked_frame)
print('frame {} writed'.format(frame_idx), end='\r')
frame_idx += 1
out.release()
cap.release()
print("\n{} saved".format(io_args['output_video']))
print('\nfinished')
# manually release memory (after cuda out of memory)
del SegTracker
torch.cuda.empty_cache()
gc.collect()
return io_args['output_video']