Try this version :
https://colab.research.google.com/drive/1zoRLaQvpqEBLpxk-Vp4MtNnkaku5Lb5t?usp=sharing

I think the face-alignment version seems to be particularly prone to errors.

Does anyone meet this problem? I've done everything before the final step and finally met this.

ValueError Traceback (most recent call last)
Cell In[5], line 1
----> 1 oup = model.run_single(src,tgt,crop_align=True,cat=True)
2 cv2.imshow("",oup)

Cell In[2], line 50
47 src_inp = self.preprocess(src_align)
48 tgt_inp = self.preprocess(tgt_align)
---> 50 tgt_params = self.get_params(cv2.resize(tgt_align,(256,256)),
51 info['rotated_lmk']/2.0).unsqueeze(0)
53 gen = self.forward(src_inp,tgt_inp,tgt_params)
55 gen = self.postprocess(gen[0])

File d:\HeadSwap\process\process_func.py:45, in Process.get_params(self, img, lmk)
43 def get_params(self,img,lmk):
---> 45 img,_,crop_param = self.preprocess_params(img,lmk)
46 with torch.no_grad():
47 coeff_3dmm = self.ParamsModel(img)

File d:\HeadSwap\process\process_func.py:73, in Process.preprocess_params(self, img, lm)
69 W,H = images.size
71 lm[:, -1] = H - 1 - lm[:, -1]
---> 73 trans_params, img, lm, _ = align_img(images, lm, self.lm3d_std)
74 img = torch.tensor(np.array(img)/255., dtype=torch.float32).permute(2, 0, 1)
...
76 img_new, lm_new, mask_new = resize_n_crop_img(img, lm, t, s, target_size=target_size, mask=mask)
---> 77 trans_params = np.array([w0, h0, s, t[0], t[1]],dtype=np.float32)
79 return trans_params, img_new, lm_new, mask_new

ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (5,) + inhomogeneous part.

Hello,

I tried your repo, but the results didn't work. What preprocessing do I need to do on the images before running them through inference.py?

Thanks!

I guess I'm not understanding the directions... i don't see this folder in any of the repos mentioned. Unless you mean for us to create it manually in this repo?

I tried to swap between a source image with blonde hair and pale skin, and a target with brown hair and tanned skin. The outcome has the shape of the source image but the color of the underlying target image (brown hair instead of blonde hair).

This is similar to one of your examples: https://github.com/LeslieZhoa/HeadSwap/blob/main/assets/1-e6de879a-f3c2-47f8-9588-feab95df7b9e.png
The source image has blonde hair but the output has black hair like the target.

I'm hoping for improvements that allows me to choose from keeping the color of the target or using the color from source. Would this require further model training? If this does, you might release improved code and models since I don't have the hardware to train anything yet.

I find that in those samples the result face light is affected by the source face but not the target one, any way to fix that?

After I set up /pretrained_models/BFM folder and run inference.py I got this error:

Traceback (most recent call last):
  File "C:\Users\User\anaconda3\envs\simswap\lib\site-packages\scipy\io\matlab\mio.py", line 39, in _open_file
    return open(file_like, mode), True
FileNotFoundError: [Errno 2] No such file or directory: 'pretrained_models/BFM\\similarity_Lm3D_all.mat'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "inference.py", line 172, in <module>
    'pretrained_models/BFM')
  File "inference.py", line 20, in __init__
    Process.__init__(self,params_path,bfm_folder)
  File "D:\HeadSwap\process\process_func.py", line 24, in __init__
    self.lm3d_std = load_lm3d(bfm_folder)
  File "D:\HeadSwap\model\third\Deep3dRec\preprocess.py", line 8, in load_lm3d
    Lm3D = loadmat(osp.join(bfm_folder, 'similarity_Lm3D_all.mat'))
  File "C:\Users\User\anaconda3\envs\simswap\lib\site-packages\scipy\io\matlab\mio.py", line 222, in loadmat
    with _open_file_context(file_name, appendmat) as f:
  File "C:\Users\User\anaconda3\envs\simswap\lib\contextlib.py", line 81, in __enter__
    return next(self.gen)
  File "C:\Users\User\anaconda3\envs\simswap\lib\site-packages\scipy\io\matlab\mio.py", line 17, in _open_file_context
    f, opened = _open_file(file_like, appendmat, mode)
  File "C:\Users\User\anaconda3\envs\simswap\lib\site-packages\scipy\io\matlab\mio.py", line 45, in _open_file
    return open(file_like, mode), True
FileNotFoundError: [Errno 2] No such file or directory: 'pretrained_models/BFM\\similarity_Lm3D_all.mat'

In the distribution of Basel Face Model (241,347 kB) there is no similarity_Lm3D_all.mat. Please specify which files are needed in BFM folder other than 01_MorphableModel.mat and Exp_Pca.bin from Deep3DFaceRecon_pytorch

Welcome

I run code in colab and when I executed this code
"model = Infer('pretrained_models/epoch_00190_iteration_000400000_checkpoint.pt',
'pretrained_models/Blender-401-00012900.pth',
'pretrained_models/parsing.pth',
'pretrained_models/epoch_20.pth',
'pretrained_models/BFM')"

There appeared an error below
" ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
in <cell line: 1>()
----> 1 model = Infer('pretrained_models/epoch_00190_iteration_000400000_checkpoint.pt',
2 'pretrained_models/Blender-401-00012900.pth',
3 'pretrained_models/parsing.pth',
4 'pretrained_models/epoch_20.pth',
5 'pretrained_models/BFM')

2 frames
/usr/lib/python3.10/enum.py in getattr(cls, name)
435 return cls.member_map[name]
436 except KeyError:
--> 437 raise AttributeError(name) from None
438
439 def getitem(cls, name):

AttributeError: _3D"

Please help me fix it

Hi, I tried to use your project to do head swapping, but I found the output result not very satisfying. For example, after the swap, the resulting face is completely distorted, especially the eyes. I wonder if there is any reason for this problem since from your demo pictures, the results look promising.

Hello, can you create a google colab version of this?