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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",
}
# Tracking Object In Video
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, duration=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"
# return io_args['output_video'], f"{io_args['tracking_result_dir']}/{video_name}_seg.mp4"
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, duration=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"
# return io_args['output_video'], f"{io_args['tracking_result_dir']}/{video_name}_seg.mp4" |