File size: 10,916 Bytes
c985ba4 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 |
import os
import cv2
from model_args import segtracker_args,sam_args,aot_args
from PIL import Image
from aot_tracker import _palette
import numpy as np
import torch
import gc
import imageio
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 os.path.isdir(dir_path):
os.system(f"rm -r {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, fps):
if input_video is not None:
video_name = os.path.basename(input_video).split('.')[0]
elif input_img_seq is not None:
file_name = input_img_seq.name.split('/')[-1].split('.')[0]
file_path = f'./assets/{file_name}'
imgs_path = sorted([os.path.join(file_path, img_name) for img_name in os.listdir(file_path)])
video_name = file_name
else:
return None, None
# create dir to save result
tracking_result_dir = f'{os.path.join(os.path.dirname(__file__), "tracking_results", 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',
}
if input_video is not None:
return video_type_input_tracking(SegTracker, input_video, io_args, video_name)
elif input_img_seq is not None:
return img_seq_type_input_tracking(SegTracker, io_args, video_name, imgs_path, fps)
def video_type_input_tracking(SegTracker, input_video, io_args, video_name):
# source video to segment
cap = cv2.VideoCapture(input_video)
fps = cap.get(cv2.CAP_PROP_FPS)
# create dir to save predicted mask and masked frame
output_mask_dir = io_args['output_mask_dir']
create_dir(io_args['output_mask_dir'])
create_dir(io_args['output_masked_frame_dir'])
pred_list = []
masked_pred_list = []
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).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).zfill(5) + '.png')
pred_list.append(pred_mask)
print("processed frame {}, obj_num {}".format(frame_idx, 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))
num_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
fourcc = cv2.VideoWriter_fourcc(*"mp4v")
# if input_video[-3:]=='mp4':
# fourcc = cv2.VideoWriter_fourcc(*"mp4v")
# elif input_video[-3:] == 'avi':
# fourcc = cv2.VideoWriter_fourcc(*"MJPG")
# # fourcc = cv2.VideoWriter_fourcc(*"XVID")
# else:
# fourcc = int(cap.get(cv2.CAP_PROP_FOURCC))
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')
# save colorized masks as a gif
imageio.mimsave(io_args['output_gif'], masked_pred_list, fps=fps)
print("{} saved".format(io_args['output_gif']))
# zip predicted mask
os.system(f"zip -r {io_args['tracking_result_dir']}/{video_name}_pred_mask.zip {io_args['output_mask_dir']}")
# manually release memory (after cuda out of memory)
del SegTracker
torch.cuda.empty_cache()
gc.collect()
return io_args['output_video'], f"{io_args['tracking_result_dir']}/{video_name}_pred_mask.zip"
def img_seq_type_input_tracking(SegTracker, io_args, video_name, imgs_path, fps):
# create dir to save predicted mask and masked frame
output_mask_dir = io_args['output_mask_dir']
create_dir(io_args['output_mask_dir'])
create_dir(io_args['output_masked_frame_dir'])
pred_list = []
masked_pred_list = []
torch.cuda.empty_cache()
gc.collect()
sam_gap = SegTracker.sam_gap
frame_idx = 0
with torch.cuda.amp.autocast():
for img_path in imgs_path:
frame_name = os.path.basename(img_path).split('.')[0]
frame = cv2.imread(img_path)
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, f'{frame_name}_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, f'{frame_name}.png')
pred_list.append(pred_mask)
print("processed frame {}, obj_num {}".format(frame_idx, SegTracker.get_obj_num()),end='\r')
frame_idx += 1
print('\nfinished')
##################
# Visualization
##################
# draw pred mask on frame and save as a video
height, width = pred_list[0].shape
fourcc = cv2.VideoWriter_fourcc(*"mp4v")
out = cv2.VideoWriter(io_args['output_video'], fourcc, fps, (width, height))
frame_idx = 0
for img_path in imgs_path:
frame_name = os.path.basename(img_path).split('.')[0]
frame = cv2.imread(img_path)
frame = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)
pred_mask = pred_list[frame_idx]
masked_frame = draw_mask(frame, pred_mask)
masked_pred_list.append(masked_frame)
cv2.imwrite(f"{io_args['output_masked_frame_dir']}/{frame_name}.png", masked_frame[:, :, ::-1])
masked_frame = cv2.cvtColor(masked_frame,cv2.COLOR_RGB2BGR)
out.write(masked_frame)
print('frame {} writed'.format(frame_name),end='\r')
frame_idx += 1
out.release()
print("\n{} saved".format(io_args['output_video']))
print('\nfinished')
# save colorized masks as a gif
imageio.mimsave(io_args['output_gif'], masked_pred_list, fps=fps)
print("{} saved".format(io_args['output_gif']))
# zip predicted mask
os.system(f"zip -r {io_args['tracking_result_dir']}/{video_name}_pred_mask.zip {io_args['output_mask_dir']}")
# manually release memory (after cuda out of memory)
del SegTracker
torch.cuda.empty_cache()
gc.collect()
return io_args['output_video'], f"{io_args['tracking_result_dir']}/{video_name}_pred_mask.zip" |