Skip to content

fix: use PIL.Image instead of scipy.imresize in util.py #2

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

fix: use PIL.Image instead of scipy.imresize in util.py #2

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 2 additions & 2 deletions util.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
import matplotlib.pyplot as plt
import numpy as np
from torchvision import datasets
from scipy.misc import imresize
from PIL import Image

def show_result(G, x_, y_, num_epoch, show = False, save = False, path = 'result.png'):
# G.eval()
Expand Down Expand Up @@ -81,7 +81,7 @@ def data_load(path, subfolder, transform, batch_size, shuffle=True):
def imgs_resize(imgs, resize_scale = 286):
outputs = torch.FloatTensor(imgs.size()[0], imgs.size()[1], resize_scale, resize_scale)
for i in range(imgs.size()[0]):
img = imresize(imgs[i].numpy(), [resize_scale, resize_scale])
img = np.array(Image.fromarray(imgs[i].numpy(), mode="RGB").resize((resize_scale, resize_scale)))
outputs[i] = torch.FloatTensor((img.transpose(2, 0, 1).astype(np.float32).reshape(-1, imgs.size()[1], resize_scale, resize_scale) - 127.5) / 127.5)

return outputs
Expand Down