Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,570 Bytes
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import torch
from PIL import Image
import numpy as np
def scale_image(img, scale):
w, h = img.size
new_w = int(w * scale)
new_h = int(h * scale)
# Adjust to nearest multiple of 32
new_w = (new_w // 32) * 32
new_h = (new_h // 32) * 32
return img.resize((new_w, new_h), Image.LANCZOS), new_w, new_h
def padding_image(images, new_width, new_height):
new_image = Image.new('RGB', (new_width, new_height), (255, 255, 255))
aspect_ratio = images.width / images.height
if new_width / new_height > 1:
if aspect_ratio > new_width / new_height:
new_img_width = new_width
new_img_height = int(new_img_width / aspect_ratio)
else:
new_img_height = new_height
new_img_width = int(new_img_height * aspect_ratio)
else:
if aspect_ratio > new_width / new_height:
new_img_width = new_width
new_img_height = int(new_img_width / aspect_ratio)
else:
new_img_height = new_height
new_img_width = int(new_img_height * aspect_ratio)
resized_img = images.resize((new_img_width, new_img_height))
paste_x = (new_width - new_img_width) // 2
paste_y = (new_height - new_img_height) // 2
new_image.paste(resized_img, (paste_x, paste_y))
return new_image
def get_image_latent(ref_image=None, sample_size=None, padding=False):
if ref_image is not None:
if isinstance(ref_image, str):
ref_image = Image.open(ref_image).convert("RGB")
if padding:
ref_image = padding_image(
ref_image, sample_size[1], sample_size[0])
ref_image = ref_image.resize((sample_size[1], sample_size[0]))
ref_image = torch.from_numpy(np.array(ref_image))
ref_image = ref_image.unsqueeze(0).permute(
[3, 0, 1, 2]).unsqueeze(0) / 255
elif isinstance(ref_image, Image.Image):
ref_image = ref_image.convert("RGB")
if padding:
ref_image = padding_image(
ref_image, sample_size[1], sample_size[0])
ref_image = ref_image.resize((sample_size[1], sample_size[0]))
ref_image = torch.from_numpy(np.array(ref_image))
ref_image = ref_image.unsqueeze(0).permute(
[3, 0, 1, 2]).unsqueeze(0) / 255
else:
ref_image = torch.from_numpy(np.array(ref_image))
ref_image = ref_image.unsqueeze(0).permute(
[3, 0, 1, 2]).unsqueeze(0) / 255
return ref_image |