File size: 20,697 Bytes
320e465
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
87ca85b
 
 
320e465
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
# -*- coding: utf-8 -*-
import os
import cv2
import argparse
import imageio
import numpy as np
import scipy.ndimage
from PIL import Image
from tqdm import tqdm

import torch
import torchvision

from model.modules.flow_comp_raft import RAFT_bi
from model.recurrent_flow_completion import RecurrentFlowCompleteNet
from model.propainter import InpaintGenerator
from utils.download_util import load_file_from_url
from core.utils import to_tensors
from model.misc import get_device

import warnings
warnings.filterwarnings("ignore")

pretrain_model_url = 'https://github.com/sczhou/ProPainter/releases/download/v0.1.0/'

def imwrite(img, file_path, params=None, auto_mkdir=True):
    if auto_mkdir:
        dir_name = os.path.abspath(os.path.dirname(file_path))
        os.makedirs(dir_name, exist_ok=True)
    return cv2.imwrite(file_path, img, params)


# resize frames
def resize_frames(frames, size=None):    
    if size is not None:
        out_size = size
        process_size = (out_size[0]-out_size[0]%8, out_size[1]-out_size[1]%8)
        frames = [f.resize(process_size) for f in frames]
    else:
        out_size = frames[0].size
        process_size = (out_size[0]-out_size[0]%8, out_size[1]-out_size[1]%8)
        if not out_size == process_size:
            frames = [f.resize(process_size) for f in frames]
        
    return frames, process_size, out_size


#  read frames from video
def read_frame_from_videos(frame_root):
    if frame_root.endswith(('mp4', 'mov', 'avi', 'MP4', 'MOV', 'AVI')): # input video path
        video_name = os.path.basename(frame_root)[:-4]
        vframes, aframes, info = torchvision.io.read_video(filename=frame_root, pts_unit='sec') # RGB
        frames = list(vframes.numpy())
        frames = [Image.fromarray(f) for f in frames]
        fps = info['video_fps']
    else:
        video_name = os.path.basename(frame_root)
        frames = []
        fr_lst = sorted(os.listdir(frame_root))
        for fr in fr_lst:
            frame = cv2.imread(os.path.join(frame_root, fr))
            frame = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
            frames.append(frame)
        fps = None
    size = frames[0].size

    return frames, fps, size, video_name


def binary_mask(mask, th=0.1):
    mask[mask>th] = 1
    mask[mask<=th] = 0
    return mask
  
  
# read frame-wise masks
def read_mask(mpath, length, size, flow_mask_dilates=8, mask_dilates=5):
    masks_img = []
    masks_dilated = []
    flow_masks = []
    
    if mpath.endswith(('jpg', 'jpeg', 'png', 'JPG', 'JPEG', 'PNG')): # input single img path
       masks_img = [Image.open(mpath)]
    else:  
        mnames = sorted(os.listdir(mpath))
        for mp in mnames:
            masks_img.append(Image.open(os.path.join(mpath, mp)))
          
    for mask_img in masks_img:
        if size is not None:
            mask_img = mask_img.resize(size, Image.NEAREST)
        mask_img = np.array(mask_img.convert('L'))

        # Dilate 8 pixel so that all known pixel is trustworthy
        if flow_mask_dilates > 0:
            flow_mask_img = scipy.ndimage.binary_dilation(mask_img, iterations=flow_mask_dilates).astype(np.uint8)
        else:
            flow_mask_img = binary_mask(mask_img).astype(np.uint8)
        # Close the small holes inside the foreground objects
        # flow_mask_img = cv2.morphologyEx(flow_mask_img, cv2.MORPH_CLOSE, np.ones((21, 21),np.uint8)).astype(bool)
        # flow_mask_img = scipy.ndimage.binary_fill_holes(flow_mask_img).astype(np.uint8)
        flow_masks.append(Image.fromarray(flow_mask_img * 255))
        
        if mask_dilates > 0:
            mask_img = scipy.ndimage.binary_dilation(mask_img, iterations=mask_dilates).astype(np.uint8)
        else:
            mask_img = binary_mask(mask_img).astype(np.uint8)
        masks_dilated.append(Image.fromarray(mask_img * 255))
    
    if len(masks_img) == 1:
        flow_masks = flow_masks * length
        masks_dilated = masks_dilated * length

    return flow_masks, masks_dilated


def extrapolation(video_ori, scale):
    """Prepares the data for video outpainting.
    """
    nFrame = len(video_ori)
    imgW, imgH = video_ori[0].size

    # Defines new FOV.
    imgH_extr = int(scale[0] * imgH)
    imgW_extr = int(scale[1] * imgW)
    imgH_extr = imgH_extr - imgH_extr % 8
    imgW_extr = imgW_extr - imgW_extr % 8
    H_start = int((imgH_extr - imgH) / 2)
    W_start = int((imgW_extr - imgW) / 2)

    # Extrapolates the FOV for video.
    frames = []
    for v in video_ori:
        frame = np.zeros(((imgH_extr, imgW_extr, 3)), dtype=np.uint8)
        frame[H_start: H_start + imgH, W_start: W_start + imgW, :] = v
        frames.append(Image.fromarray(frame))

    # Generates the mask for missing region.
    masks_dilated = []
    flow_masks = []
    
    dilate_h = 4 if H_start > 10 else 0
    dilate_w = 4 if W_start > 10 else 0
    mask = np.ones(((imgH_extr, imgW_extr)), dtype=np.uint8)
    
    mask[H_start+dilate_h: H_start+imgH-dilate_h, 
         W_start+dilate_w: W_start+imgW-dilate_w] = 0
    flow_masks.append(Image.fromarray(mask * 255))

    mask[H_start: H_start+imgH, W_start: W_start+imgW] = 0
    masks_dilated.append(Image.fromarray(mask * 255))
  
    flow_masks = flow_masks * nFrame
    masks_dilated = masks_dilated * nFrame
    
    return frames, flow_masks, masks_dilated, (imgW_extr, imgH_extr)


def get_ref_index(mid_neighbor_id, neighbor_ids, length, ref_stride=10, ref_num=-1):
    ref_index = []
    if ref_num == -1:
        for i in range(0, length, ref_stride):
            if i not in neighbor_ids:
                ref_index.append(i)
    else:
        start_idx = max(0, mid_neighbor_id - ref_stride * (ref_num // 2))
        end_idx = min(length, mid_neighbor_id + ref_stride * (ref_num // 2))
        for i in range(start_idx, end_idx, ref_stride):
            if i not in neighbor_ids:
                if len(ref_index) > ref_num:
                    break
                ref_index.append(i)
    return ref_index



if __name__ == '__main__':
    # device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    device = get_device()
    
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '-i', '--video', type=str, default='inputs/object_removal/bmx-trees', help='Path of the input video or image folder.')
    parser.add_argument(
        '-m', '--mask', type=str, default='inputs/object_removal/bmx-trees_mask', help='Path of the mask(s) or mask folder.')
    parser.add_argument(
        '-o', '--output', type=str, default='results', help='Output folder. Default: results')
    parser.add_argument(
        "--resize_ratio", type=float, default=1.0, help='Resize scale for processing video.')
    parser.add_argument(
        '--height', type=int, default=-1, help='Height of the processing video.')
    parser.add_argument(
        '--width', type=int, default=-1, help='Width of the processing video.')
    parser.add_argument(
        '--mask_dilation', type=int, default=4, help='Mask dilation for video and flow masking.')
    parser.add_argument(
        "--ref_stride", type=int, default=10, help='Stride of global reference frames.')
    parser.add_argument(
        "--neighbor_length", type=int, default=10, help='Length of local neighboring frames.')
    parser.add_argument(
        "--subvideo_length", type=int, default=80, help='Length of sub-video for long video inference.')
    parser.add_argument(
        "--raft_iter", type=int, default=20, help='Iterations for RAFT inference.')
    parser.add_argument(
        '--mode', default='video_inpainting', choices=['video_inpainting', 'video_outpainting'], help="Modes: video_inpainting / video_outpainting")
    parser.add_argument(
        '--scale_h', type=float, default=1.0, help='Outpainting scale of height for video_outpainting mode.')
    parser.add_argument(
        '--scale_w', type=float, default=1.2, help='Outpainting scale of width for video_outpainting mode.')
    parser.add_argument(
        '--save_fps', type=int, default=24, help='Frame per second. Default: 24')
    parser.add_argument(
        '--save_frames', action='store_true', help='Save output frames. Default: False')
    parser.add_argument(
        '--fp16', action='store_true', help='Use fp16 (half precision) during inference. Default: fp32 (single precision).')

    args = parser.parse_args()

    # Use fp16 precision during inference to reduce running memory cost
    use_half = True if args.fp16 else False
    if device == torch.device('cpu'):
        use_half = False

    frames, fps, size, video_name = read_frame_from_videos(args.video)
    if not args.width == -1 and not args.height == -1:
        size = (args.width, args.height)
    if not args.resize_ratio == 1.0:
        size = (int(args.resize_ratio * size[0]), int(args.resize_ratio * size[1]))

    frames, size, out_size = resize_frames(frames, size)
    
    fps = args.save_fps if fps is None else fps
    save_root = os.path.join(args.output, video_name)
    if not os.path.exists(save_root):
        os.makedirs(save_root, exist_ok=True)

    if args.mode == 'video_inpainting':
        frames_len = len(frames)
        flow_masks, masks_dilated = read_mask(args.mask, frames_len, size, 
                                              flow_mask_dilates=args.mask_dilation,
                                              mask_dilates=args.mask_dilation)
        w, h = size
    elif args.mode == 'video_outpainting':
        assert args.scale_h is not None and args.scale_w is not None, 'Please provide a outpainting scale (s_h, s_w).'
        frames, flow_masks, masks_dilated, size = extrapolation(frames, (args.scale_h, args.scale_w))
        w, h = size
    else:
        raise NotImplementedError
    
    # for saving the masked frames or video
    masked_frame_for_save = []
    for i in range(len(frames)):
        mask_ = np.expand_dims(np.array(masks_dilated[i]),2).repeat(3, axis=2)/255.
        img = np.array(frames[i])
        green = np.zeros([h, w, 3]) 
        green[:,:,1] = 255
        alpha = 0.6
        # alpha = 1.0
        fuse_img = (1-alpha)*img + alpha*green
        fuse_img = mask_ * fuse_img + (1-mask_)*img
        masked_frame_for_save.append(fuse_img.astype(np.uint8))

    frames_inp = [np.array(f).astype(np.uint8) for f in frames]
    frames = to_tensors()(frames).unsqueeze(0) * 2 - 1    
    flow_masks = to_tensors()(flow_masks).unsqueeze(0)
    masks_dilated = to_tensors()(masks_dilated).unsqueeze(0)
    frames, flow_masks, masks_dilated = frames.to(device), flow_masks.to(device), masks_dilated.to(device)

    
    ##############################################
    # set up RAFT and flow competition model
    ##############################################
    ckpt_path = load_file_from_url(url=os.path.join(pretrain_model_url, 'raft-things.pth'), 
                                    model_dir='weights', progress=True, file_name=None)
    fix_raft = RAFT_bi(ckpt_path, device)
    
    ckpt_path = load_file_from_url(url=os.path.join(pretrain_model_url, 'recurrent_flow_completion.pth'), 
                                    model_dir='weights', progress=True, file_name=None)
    fix_flow_complete = RecurrentFlowCompleteNet(ckpt_path)
    for p in fix_flow_complete.parameters():
        p.requires_grad = False
    fix_flow_complete.to(device)
    fix_flow_complete.eval()


    ##############################################
    # set up ProPainter model
    ##############################################
    ckpt_path = load_file_from_url(url=os.path.join(pretrain_model_url, 'ProPainter.pth'), 
                                    model_dir='weights', progress=True, file_name=None)
    model = InpaintGenerator(model_path=ckpt_path).to(device)
    model.eval()

    
    ##############################################
    # ProPainter inference
    ##############################################
    video_length = frames.size(1)
    print(f'\nProcessing: {video_name} [{video_length} frames]...')
    with torch.no_grad():
        # ---- compute flow ----
        if frames.size(-1) <= 640: 
            short_clip_len = 12
        elif frames.size(-1) <= 720: 
            short_clip_len = 8
        elif frames.size(-1) <= 1280:
            short_clip_len = 4
        else:
            short_clip_len = 2
        
        # use fp32 for RAFT
        if frames.size(1) > short_clip_len:
            gt_flows_f_list, gt_flows_b_list = [], []
            for f in range(0, video_length, short_clip_len):
                end_f = min(video_length, f + short_clip_len)
                if f == 0:
                    flows_f, flows_b = fix_raft(frames[:,f:end_f], iters=args.raft_iter)
                else:
                    flows_f, flows_b = fix_raft(frames[:,f-1:end_f], iters=args.raft_iter)
                
                gt_flows_f_list.append(flows_f)
                gt_flows_b_list.append(flows_b)
                torch.cuda.empty_cache()
                
            gt_flows_f = torch.cat(gt_flows_f_list, dim=1)
            gt_flows_b = torch.cat(gt_flows_b_list, dim=1)
            gt_flows_bi = (gt_flows_f, gt_flows_b)
        else:
            gt_flows_bi = fix_raft(frames, iters=args.raft_iter)
            torch.cuda.empty_cache()


        if use_half:
            frames, flow_masks, masks_dilated = frames.half(), flow_masks.half(), masks_dilated.half()
            gt_flows_bi = (gt_flows_bi[0].half(), gt_flows_bi[1].half())
            fix_flow_complete = fix_flow_complete.half()
            model = model.half()

        
        # ---- complete flow ----
        flow_length = gt_flows_bi[0].size(1)
        if flow_length > args.subvideo_length:
            pred_flows_f, pred_flows_b = [], []
            pad_len = 5
            for f in range(0, flow_length, args.subvideo_length):
                s_f = max(0, f - pad_len)
                e_f = min(flow_length, f + args.subvideo_length + pad_len)
                pad_len_s = max(0, f) - s_f
                pad_len_e = e_f - min(flow_length, f + args.subvideo_length)
                pred_flows_bi_sub, _ = fix_flow_complete.forward_bidirect_flow(
                    (gt_flows_bi[0][:, s_f:e_f], gt_flows_bi[1][:, s_f:e_f]), 
                    flow_masks[:, s_f:e_f+1])
                pred_flows_bi_sub = fix_flow_complete.combine_flow(
                    (gt_flows_bi[0][:, s_f:e_f], gt_flows_bi[1][:, s_f:e_f]), 
                    pred_flows_bi_sub, 
                    flow_masks[:, s_f:e_f+1])

                pred_flows_f.append(pred_flows_bi_sub[0][:, pad_len_s:e_f-s_f-pad_len_e])
                pred_flows_b.append(pred_flows_bi_sub[1][:, pad_len_s:e_f-s_f-pad_len_e])
                torch.cuda.empty_cache()
                
            pred_flows_f = torch.cat(pred_flows_f, dim=1)
            pred_flows_b = torch.cat(pred_flows_b, dim=1)
            pred_flows_bi = (pred_flows_f, pred_flows_b)
        else:
            pred_flows_bi, _ = fix_flow_complete.forward_bidirect_flow(gt_flows_bi, flow_masks)
            pred_flows_bi = fix_flow_complete.combine_flow(gt_flows_bi, pred_flows_bi, flow_masks)
            torch.cuda.empty_cache()
            

        # ---- image propagation ----
        masked_frames = frames * (1 - masks_dilated)
        subvideo_length_img_prop = min(100, args.subvideo_length) # ensure a minimum of 100 frames for image propagation
        if video_length > subvideo_length_img_prop:
            updated_frames, updated_masks = [], []
            pad_len = 10
            for f in range(0, video_length, subvideo_length_img_prop):
                s_f = max(0, f - pad_len)
                e_f = min(video_length, f + subvideo_length_img_prop + pad_len)
                pad_len_s = max(0, f) - s_f
                pad_len_e = e_f - min(video_length, f + subvideo_length_img_prop)

                b, t, _, _, _ = masks_dilated[:, s_f:e_f].size()
                pred_flows_bi_sub = (pred_flows_bi[0][:, s_f:e_f-1], pred_flows_bi[1][:, s_f:e_f-1])
                prop_imgs_sub, updated_local_masks_sub = model.img_propagation(masked_frames[:, s_f:e_f], 
                                                                       pred_flows_bi_sub, 
                                                                       masks_dilated[:, s_f:e_f], 
                                                                       'nearest')
                updated_frames_sub = frames[:, s_f:e_f] * (1 - masks_dilated[:, s_f:e_f]) + \
                                    prop_imgs_sub.view(b, t, 3, h, w) * masks_dilated[:, s_f:e_f]
                updated_masks_sub = updated_local_masks_sub.view(b, t, 1, h, w)
                
                updated_frames.append(updated_frames_sub[:, pad_len_s:e_f-s_f-pad_len_e])
                updated_masks.append(updated_masks_sub[:, pad_len_s:e_f-s_f-pad_len_e])
                torch.cuda.empty_cache()
                
            updated_frames = torch.cat(updated_frames, dim=1)
            updated_masks = torch.cat(updated_masks, dim=1)
        else:
            b, t, _, _, _ = masks_dilated.size()
            prop_imgs, updated_local_masks = model.img_propagation(masked_frames, pred_flows_bi, masks_dilated, 'nearest')
            updated_frames = frames * (1 - masks_dilated) + prop_imgs.view(b, t, 3, h, w) * masks_dilated
            updated_masks = updated_local_masks.view(b, t, 1, h, w)
            torch.cuda.empty_cache()
            
    
    ori_frames = frames_inp
    comp_frames = [None] * video_length

    neighbor_stride = args.neighbor_length // 2
    if video_length > args.subvideo_length:
        ref_num = args.subvideo_length // args.ref_stride
    else:
        ref_num = -1
    
    # ---- feature propagation + transformer ----
    for f in tqdm(range(0, video_length, neighbor_stride)):
        neighbor_ids = [
            i for i in range(max(0, f - neighbor_stride),
                                min(video_length, f + neighbor_stride + 1))
        ]
        ref_ids = get_ref_index(f, neighbor_ids, video_length, args.ref_stride, ref_num)
        selected_imgs = updated_frames[:, neighbor_ids + ref_ids, :, :, :]
        selected_masks = masks_dilated[:, neighbor_ids + ref_ids, :, :, :]
        selected_update_masks = updated_masks[:, neighbor_ids + ref_ids, :, :, :]
        selected_pred_flows_bi = (pred_flows_bi[0][:, neighbor_ids[:-1], :, :, :], pred_flows_bi[1][:, neighbor_ids[:-1], :, :, :])
        
        with torch.no_grad():
            # 1.0 indicates mask
            l_t = len(neighbor_ids)
            
            # pred_img = selected_imgs # results of image propagation
            pred_img = model(selected_imgs, selected_pred_flows_bi, selected_masks, selected_update_masks, l_t)
            
            pred_img = pred_img.view(-1, 3, h, w)

            pred_img = (pred_img + 1) / 2
            pred_img = pred_img.cpu().permute(0, 2, 3, 1).numpy() * 255
            binary_masks = masks_dilated[0, neighbor_ids, :, :, :].cpu().permute(
                0, 2, 3, 1).numpy().astype(np.uint8)
            for i in range(len(neighbor_ids)):
                idx = neighbor_ids[i]
                img = np.array(pred_img[i]).astype(np.uint8) * binary_masks[i] \
                    + ori_frames[idx] * (1 - binary_masks[i])
                if comp_frames[idx] is None:
                    comp_frames[idx] = img
                else: 
                    comp_frames[idx] = comp_frames[idx].astype(np.float32) * 0.5 + img.astype(np.float32) * 0.5
                    
                comp_frames[idx] = comp_frames[idx].astype(np.uint8)
        
        torch.cuda.empty_cache()
                
    # save each frame
    if args.save_frames:
        for idx in range(video_length):
            f = comp_frames[idx]
            f = cv2.resize(f, out_size, interpolation = cv2.INTER_CUBIC)
            f = cv2.cvtColor(f, cv2.COLOR_BGR2RGB)
            img_save_root = os.path.join(save_root, 'frames', str(idx).zfill(4)+'.png')
            imwrite(f, img_save_root)
                    

    # if args.mode == 'video_outpainting':
    #     comp_frames = [i[10:-10,10:-10] for i in comp_frames]
    #     masked_frame_for_save = [i[10:-10,10:-10] for i in masked_frame_for_save]
    
    # save videos frame
    masked_frame_for_save = [cv2.resize(f, out_size) for f in masked_frame_for_save]
    comp_frames = [cv2.resize(f, out_size) for f in comp_frames]
    imageio.mimwrite(os.path.join(save_root, 'masked_in.mp4'), masked_frame_for_save, fps=fps, quality=7)
    imageio.mimwrite(os.path.join(save_root, 'inpaint_out.mp4'), comp_frames, fps=fps, quality=7)
    
    print(f'\nAll results are saved in {save_root}')
    
    torch.cuda.empty_cache()