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fix: replace Image.BICUBIC with Image.Resampling.BICUBIC #134

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Merged
merged 1 commit into from
Apr 4, 2023
Merged

fix: replace Image.BICUBIC with Image.Resampling.BICUBIC #134

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4 changes: 2 additions & 2 deletions data/base_dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -95,8 +95,8 @@ def get_affine_mat(opt, size):
affine_inv = np.linalg.inv(affine)
return affine, affine_inv, flip

def apply_img_affine(img, affine_inv, method=Image.BICUBIC):
return img.transform(img.size, Image.AFFINE, data=affine_inv.flatten()[:6], resample=Image.BICUBIC)
def apply_img_affine(img, affine_inv, method=Image.Resampling.BICUBIC):
return img.transform(img.size, Image.AFFINE, data=affine_inv.flatten()[:6], resample=Image.Resampling.BICUBIC)

def apply_lm_affine(landmark, affine, flip, size):
_, h = size
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4 changes: 2 additions & 2 deletions util/preprocess.py
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Expand Up @@ -142,11 +142,11 @@ def resize_n_crop_img(img, lm, t, s, target_size=224., mask=None):
up = (h/2 - target_size/2 + float((h0/2 - t[1])*s)).astype(np.int32)
below = up + target_size

img = img.resize((w, h), resample=Image.BICUBIC)
img = img.resize((w, h), resample=Image.Resampling.BICUBIC)
img = img.crop((left, up, right, below))

if mask is not None:
mask = mask.resize((w, h), resample=Image.BICUBIC)
mask = mask.resize((w, h), resample=Image.Resampling.BICUBIC)
mask = mask.crop((left, up, right, below))

lm = np.stack([lm[:, 0] - t[0] + w0/2, lm[:, 1] -
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8 changes: 4 additions & 4 deletions util/util.py
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Original file line number Diff line number Diff line change
Expand Up @@ -107,9 +107,9 @@ def save_image(image_numpy, image_path, aspect_ratio=1.0):
if aspect_ratio is None:
pass
elif aspect_ratio > 1.0:
image_pil = image_pil.resize((h, int(w * aspect_ratio)), Image.BICUBIC)
image_pil = image_pil.resize((h, int(w * aspect_ratio)), Image.Resampling.BICUBIC)
elif aspect_ratio < 1.0:
image_pil = image_pil.resize((int(h / aspect_ratio), w), Image.BICUBIC)
image_pil = image_pil.resize((int(h / aspect_ratio), w), Image.Resampling.BICUBIC)
image_pil.save(image_path)


Expand Down Expand Up @@ -166,13 +166,13 @@ def correct_resize_label(t, size):
return torch.stack(resized, dim=0).to(device)


def correct_resize(t, size, mode=Image.BICUBIC):
def correct_resize(t, size, mode=Image.Resampling.BICUBIC):
device = t.device
t = t.detach().cpu()
resized = []
for i in range(t.size(0)):
one_t = t[i:i + 1]
one_image = Image.fromarray(tensor2im(one_t)).resize(size, Image.BICUBIC)
one_image = Image.fromarray(tensor2im(one_t)).resize(size, Image.Resampling.BICUBIC)
resized_t = torchvision.transforms.functional.to_tensor(one_image) * 2 - 1.0
resized.append(resized_t)
return torch.stack(resized, dim=0).to(device)
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