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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from __future__ import print_function
import os
import PIL
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
from torchvision.utils import make_grid
def array2image(ndarray):
return PIL.Image.fromarray(np.uint8(ndarray)).convert('RGB')
def tensor_to_ndarray(tensor, nrow=1, padding=0, normalize=True):
grid = make_grid(tensor, nrow, padding, normalize)
return grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()
def irregular_hole_synthesize(image, mask):
img_np = np.array(image).astype("uint8")
mask_np = np.array(mask).astype("uint8")
mask_np = mask_np / 255
img_new = img_np * (1 - mask_np) + mask_np * 255
return PIL.Image.fromarray(img_new.astype("uint8")).convert("RGB")
# Converts a Tensor into a Numpy array
# |imtype|: the desired type of the converted numpy array
def tensor2im(image_tensor, imtype=np.uint8, normalize=True):
if isinstance(image_tensor, list):
image_numpy = []
for i in range(len(image_tensor)):
image_numpy.append(tensor2im(image_tensor[i], imtype, normalize))
return image_numpy
image_numpy = image_tensor.cpu().float().numpy()
if normalize:
image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0
else:
image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0
image_numpy = np.clip(image_numpy, 0, 255)
if image_numpy.shape[2] == 1 or image_numpy.shape[2] > 3:
image_numpy = image_numpy[:, :, 0]
return image_numpy.astype(imtype)
# Converts a one-hot tensor into a colorful label map
def tensor2label(label_tensor, n_label, imtype=np.uint8):
if n_label == 0:
return tensor2im(label_tensor, imtype)
label_tensor = label_tensor.cpu().float()
if label_tensor.size()[0] > 1:
label_tensor = label_tensor.max(0, keepdim=True)[1]
label_tensor = Colorize(n_label)(label_tensor)
label_numpy = np.transpose(label_tensor.numpy(), (1, 2, 0))
return label_numpy.astype(imtype)
def scale_tensor(img_tensor, default_scale=256):
_, _, w, h = img_tensor.shape
if w < h:
ow = default_scale
oh = h / w * default_scale
else:
oh = default_scale
ow = w / h * default_scale
oh = int(round(oh / 16) * 16)
ow = int(round(ow / 16) * 16)
return F.interpolate(img_tensor, [ow, oh], mode="bilinear")
def data_transforms(img, size="full_size", method=Image.BICUBIC):
if size == "full_size":
ow, oh = img.size
h = int(round(oh / 16) * 16)
w = int(round(ow / 16) * 16)
if (h == oh) and (w == ow):
return img
return img.resize((w, h), method)
elif size == "scale_256":
ow, oh = img.size
pw, ph = ow, oh
if ow < oh:
ow = 256
oh = ph / pw * 256
else:
oh = 256
ow = pw / ph * 256
h = int(round(oh / 16) * 16)
w = int(round(ow / 16) * 16)
if (h == ph) and (w == pw):
return img
return img.resize((w, h), method)
def save_image(image_numpy, image_path):
image_pil = Image.fromarray(image_numpy)
image_pil.save(image_path)
def mkdirs(paths):
if isinstance(paths, list) and not isinstance(paths, str):
for path in paths:
mkdir(path)
else:
mkdir(paths)
def mkdir(path):
if not os.path.exists(path):
os.makedirs(path)
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