CalvinBackup/neuralchen-SimSwap
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Feat: argument to skip frames existing in directory defined by temp_path #369

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StevenCyb wants to merge 1 commits from StevenCyb/feat-skip-existing-frames into main
pull from: StevenCyb/feat-skip-existing-frames
merge into: CalvinBackup:main
Conversation 0 Commits 1 Files Changed 9 +48 -41
9 changed files with 48 additions and 41 deletions
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25
docs/guidance/usage.md
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@@ -98,18 +98,19 @@ Difference between using mask and not using mask can be found [here](https://img
### Parameters
| Parameters | Function |
| :---- | :---- |
| --name | The SimSwap training logs name |
| --pic_a_path | Path of image with the target face |
| --pic_b_path | Path of image with the source face to swap |
| --pic_specific_path | Path of image with the specific face to be swapped |
|--multisepcific_dir |Path of image folder for multi specific face swapping|
| --video_path | Path of video with the source face to swap |
| --temp_path | Path to store intermediate files |
| --output_path | Path of directory to store the face swapping result |
| --no_simswaplogo |The hyper parameter to control whether to remove watermark |
| --use_mask |The hyper parameter to control whether to use face parsing for the better visual effects(I recommend to use)|
| Parameters | Function |
| :---- | :---- |
| --name | The SimSwap training logs name |
| --pic_a_path | Path of image with the target face |
| --pic_b_path | Path of image with the source face to swap |
| --pic_specific_path | Path of image with the specific face to be swapped |
| --multisepcific_dir |Path of image folder for multi specific face swapping |
| --video_path | Path of video with the source face to swap |
| --temp_path | Path to store intermediate files |
| --output_path | Path of directory to store the face swapping result |
| --no_simswaplogo | The hyper parameter to control whether to remove watermark |
| --use_mask | The hyper parameter to control whether to use face parsing for the better visual effects(I recommend to use) |
| --skip_existing_frames | Skip frame index if already exist in temp_path (will not compare if the same video) |
### Note
We expect users to have GPU with at least 3G memory.the For those who do not, we will provide Colab Notebook implementation in the future.

1
options/test_options.py
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@@ -34,5 +34,6 @@ class TestOptions(BaseOptions):
self.parser.add_argument('--no_simswaplogo', action='store_true', help='Remove the watermark')
self.parser.add_argument('--use_mask', action='store_true', help='Use mask for better result')
self.parser.add_argument('--crop_size', type=int, default=224, help='Crop of size of input image')
self.parser.add_argument('--skip_existing_frames', action='store_true', help='Skip frame index if already exist in temp_path')
self.isTrain = False

2
test_video_swap_multispecific.py
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@@ -95,5 +95,5 @@ if __name__ == '__main__':
video_swap(opt.video_path, target_id_norm_list,source_specific_id_nonorm_list, opt.id_thres, \
model, app, opt.output_path,temp_results_dir=opt.temp_path,no_simswaplogo=opt.no_simswaplogo,use_mask=opt.use_mask,crop_size=crop_size)
model, app, opt.output_path,temp_results_dir=opt.temp_path,no_simswaplogo=opt.no_simswaplogo,use_mask=opt.use_mask,crop_size=crop_size,skip_existing_frames=opt.skip_existing_frames)

2
test_video_swapmulti.py
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@@ -84,5 +84,5 @@ if __name__ == '__main__':
latend_id = F.normalize(latend_id, p=2, dim=1)
video_swap(opt.video_path, latend_id, model, app, opt.output_path,temp_results_dir=opt.temp_path,\
no_simswaplogo=opt.no_simswaplogo,use_mask=opt.use_mask,crop_size=crop_size)
no_simswaplogo=opt.no_simswaplogo,use_mask=opt.use_mask,crop_size=crop_size,skip_existing_frames=opt.skip_existing_frames)

2
test_video_swapsingle.py
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@@ -83,5 +83,5 @@ if __name__ == '__main__':
latend_id = F.normalize(latend_id, p=2, dim=1)
video_swap(opt.video_path, latend_id, model, app, opt.output_path,temp_results_dir=opt.temp_path,\
no_simswaplogo=opt.no_simswaplogo,use_mask=opt.use_mask,crop_size=crop_size)
no_simswaplogo=opt.no_simswaplogo,use_mask=opt.use_mask,crop_size=crop_size,skip_existing_frames=opt.skip_existing_frames)

2
test_video_swapspecific.py
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@@ -94,5 +94,5 @@ if __name__ == '__main__':
specific_person_id_nonorm = model.netArc(specific_person_downsample)
video_swap(opt.video_path, latend_id,specific_person_id_nonorm, opt.id_thres, \
model, app, opt.output_path,temp_results_dir=opt.temp_path,no_simswaplogo=opt.no_simswaplogo,use_mask=opt.use_mask,crop_size=crop_size)
model, app, opt.output_path,temp_results_dir=opt.temp_path,no_simswaplogo=opt.no_simswaplogo,use_mask=opt.use_mask,crop_size=crop_size,skip_existing_frames=opt.skip_existing_frames)

17
util/videoswap.py
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@@ -27,7 +27,7 @@ def _totensor(array):
img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
return img.float().div(255)
def video_swap(video_path, id_vetor, swap_model, detect_model, save_path, temp_results_dir='./temp_results', crop_size=224, no_simswaplogo = False,use_mask =False):
def video_swap(video_path, id_vetor, swap_model, detect_model, save_path, temp_results_dir='./temp_results', crop_size=224, no_simswaplogo = False, use_mask = False, skip_existing_frames = False):
video_forcheck = VideoFileClip(video_path)
if video_forcheck.audio is None:
no_audio = True
@@ -51,8 +51,8 @@ def video_swap(video_path, id_vetor, swap_model, detect_model, save_path, temp_r
# video_HEIGHT = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = video.get(cv2.CAP_PROP_FPS)
if os.path.exists(temp_results_dir):
shutil.rmtree(temp_results_dir)
if not skip_existing_frames and os.path.exists(temp_results_dir):
shutil.rmtree(temp_results_dir)
spNorm =SpecificNorm()
if use_mask:
@@ -64,17 +64,22 @@ def video_swap(video_path, id_vetor, swap_model, detect_model, save_path, temp_r
net.eval()
else:
net =None
if not os.path.exists(temp_results_dir):
os.mkdir(temp_results_dir)
# while ret:
for frame_index in tqdm(range(frame_count)):
ret, frame = video.read()
if skip_existing_frames and os.path.exists(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index))):
continue
if ret:
detect_results = detect_model.get(frame,crop_size)
if detect_results is not None:
# print(frame_index)
if not os.path.exists(temp_results_dir):
os.mkdir(temp_results_dir)
frame_align_crop_list = detect_results[0]
frame_mat_list = detect_results[1]
swap_result_list = []
@@ -97,8 +102,6 @@ def video_swap(video_path, id_vetor, swap_model, detect_model, save_path, temp_r
os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)),no_simswaplogo,pasring_model =net,use_mask=use_mask, norm = spNorm)
else:
if not os.path.exists(temp_results_dir):
os.mkdir(temp_results_dir)
frame = frame.astype(np.uint8)
if not no_simswaplogo:
frame = logoclass.apply_frames(frame)

19
util/videoswap_multispecific.py
View File

@@ -20,7 +20,7 @@ def _totensor(array):
img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
return img.float().div(255)
def video_swap(video_path, target_id_norm_list,source_specific_id_nonorm_list,id_thres, swap_model, detect_model, save_path, temp_results_dir='./temp_results', crop_size=224, no_simswaplogo = False,use_mask =False):
def video_swap(video_path, target_id_norm_list,source_specific_id_nonorm_list,id_thres, swap_model, detect_model, save_path, temp_results_dir='./temp_results', crop_size=224, no_simswaplogo = False, use_mask =False, skip_existing_frames = False):
video_forcheck = VideoFileClip(video_path)
if video_forcheck.audio is None:
no_audio = True
@@ -44,8 +44,8 @@ def video_swap(video_path, target_id_norm_list,source_specific_id_nonorm_list,id
# video_HEIGHT = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = video.get(cv2.CAP_PROP_FPS)
if os.path.exists(temp_results_dir):
shutil.rmtree(temp_results_dir)
if not skip_existing_frames and os.path.exists(temp_results_dir):
shutil.rmtree(temp_results_dir)
spNorm =SpecificNorm()
mse = torch.nn.MSELoss().cuda()
@@ -60,16 +60,21 @@ def video_swap(video_path, target_id_norm_list,source_specific_id_nonorm_list,id
else:
net =None
if not os.path.exists(temp_results_dir):
os.mkdir(temp_results_dir)
# while ret:
for frame_index in tqdm(range(frame_count)):
ret, frame = video.read()
if skip_existing_frames and os.path.exists(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index))):
continue
if ret:
detect_results = detect_model.get(frame,crop_size)
if detect_results is not None:
# print(frame_index)
if not os.path.exists(temp_results_dir):
os.mkdir(temp_results_dir)
frame_align_crop_list = detect_results[0]
frame_mat_list = detect_results[1]
@@ -113,16 +118,12 @@ def video_swap(video_path, target_id_norm_list,source_specific_id_nonorm_list,id
reverse2wholeimage(swap_result_ori_pic_list,swap_result_list, swap_result_matrix_list, crop_size, frame, logoclass,\
os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)),no_simswaplogo,pasring_model =net,use_mask=use_mask, norm = spNorm)
else:
if not os.path.exists(temp_results_dir):
os.mkdir(temp_results_dir)
frame = frame.astype(np.uint8)
if not no_simswaplogo:
frame = logoclass.apply_frames(frame)
cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
else:
if not os.path.exists(temp_results_dir):
os.mkdir(temp_results_dir)
frame = frame.astype(np.uint8)
if not no_simswaplogo:
frame = logoclass.apply_frames(frame)

19
util/videoswap_specific.py
View File

@@ -20,7 +20,7 @@ def _totensor(array):
img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
return img.float().div(255)
def video_swap(video_path, id_vetor,specific_person_id_nonorm,id_thres, swap_model, detect_model, save_path, temp_results_dir='./temp_results', crop_size=224, no_simswaplogo = False,use_mask =False):
def video_swap(video_path, id_vetor,specific_person_id_nonorm,id_thres, swap_model, detect_model, save_path, temp_results_dir='./temp_results', crop_size=224, no_simswaplogo = False, use_mask =False, skip_existing_frames = False):
video_forcheck = VideoFileClip(video_path)
if video_forcheck.audio is None:
no_audio = True
@@ -44,8 +44,8 @@ def video_swap(video_path, id_vetor,specific_person_id_nonorm,id_thres, swap_mod
# video_HEIGHT = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = video.get(cv2.CAP_PROP_FPS)
if os.path.exists(temp_results_dir):
shutil.rmtree(temp_results_dir)
if not skip_existing_frames and os.path.exists(temp_results_dir):
shutil.rmtree(temp_results_dir)
spNorm =SpecificNorm()
mse = torch.nn.MSELoss().cuda()
@@ -60,16 +60,21 @@ def video_swap(video_path, id_vetor,specific_person_id_nonorm,id_thres, swap_mod
else:
net =None
if not os.path.exists(temp_results_dir):
os.mkdir(temp_results_dir)
# while ret:
for frame_index in tqdm(range(frame_count)):
ret, frame = video.read()
if skip_existing_frames and os.path.exists(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index))):
continue
if ret:
detect_results = detect_model.get(frame,crop_size)
if detect_results is not None:
# print(frame_index)
if not os.path.exists(temp_results_dir):
os.mkdir(temp_results_dir)
frame_align_crop_list = detect_results[0]
frame_mat_list = detect_results[1]
@@ -97,16 +102,12 @@ def video_swap(video_path, id_vetor,specific_person_id_nonorm,id_thres, swap_mod
reverse2wholeimage([frame_align_crop_tenor_list[min_index]], [swap_result], [frame_mat_list[min_index]], crop_size, frame, logoclass,\
os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)),no_simswaplogo,pasring_model =net,use_mask= use_mask, norm = spNorm)
else:
if not os.path.exists(temp_results_dir):
os.mkdir(temp_results_dir)
frame = frame.astype(np.uint8)
if not no_simswaplogo:
frame = logoclass.apply_frames(frame)
cv2.imwrite(os.path.join(temp_results_dir, 'frame_{:0>7d}.jpg'.format(frame_index)), frame)
else:
if not os.path.exists(temp_results_dir):
os.mkdir(temp_results_dir)
frame = frame.astype(np.uint8)
if not no_simswaplogo:
frame = logoclass.apply_frames(frame)