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import os
from PIL import Image
import torchvision.transforms as transforms
import numpy as np
import torch
IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP']
def is_image_file(filename):
"""if a given filename is a valid image
Parameters:
filename (str) -- image filename
"""
return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
def get_image_list(path):
"""read the paths of valid images from the given directory path
Parameters:
path (str) -- input directory path
"""
assert os.path.isdir(path), '{:s} is not a valid directory'.format(path)
images = []
for dirpath, _, fnames in sorted(os.walk(path)):
for fname in sorted(fnames):
if is_image_file(fname):
img_path = os.path.join(dirpath, fname)
images.append(img_path)
assert images, '{:s} has no valid image file'.format(path)
return images
def get_transform(load_size=0, grayscale=False, method=Image.BICUBIC, convert=True):
transform_list = []
if grayscale:
transform_list.append(transforms.Grayscale(1))
if load_size > 0:
osize = [load_size, load_size]
transform_list.append(transforms.Resize(osize, method))
if convert:
transform_list += [transforms.ToTensor()]
if grayscale:
transform_list += [transforms.Normalize((0.5,), (0.5,))]
else:
transform_list += [transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
return transforms.Compose(transform_list)
def read_img_path(path, load_size):
"""read tensors from a given image path
Parameters:
path (str) -- input image path
load_size(int) -- the input size. If <= 0, don't resize
"""
img = Image.open(path).convert('RGB')
aus_resize = None
if load_size > 0:
aus_resize = img.size
transform = get_transform(load_size=load_size)
image = transform(img)
return image.unsqueeze(0), aus_resize
def tensor_to_img(input_image, imtype=np.uint8):
""""Converts a Tensor array into a numpy image array.
Parameters:
input_image (tensor) -- the input image tensor array
imtype (type) -- the desired type of the converted numpy array
"""
if not isinstance(input_image, np.ndarray):
if isinstance(input_image, torch.Tensor): # get the data from a variable
image_tensor = input_image.data
else:
return input_image
image_numpy = image_tensor[0].cpu().float().numpy() # convert it into a numpy array
if image_numpy.shape[0] == 1: # grayscale to RGB
image_numpy = np.tile(image_numpy, (3, 1, 1))
image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 # post-processing: tranpose and scaling
else: # if it is a numpy array, do nothing
image_numpy = input_image
return image_numpy.astype(imtype)
def save_image(image_numpy, image_path, output_resize=None):
"""Save a numpy image to the disk
Parameters:
image_numpy (numpy array) -- input numpy array
image_path (str) -- the path of the image
output_resize(None or tuple) -- the output size. If None, don't resize
"""
image_pil = Image.fromarray(image_numpy)
if output_resize:
image_pil = image_pil.resize(output_resize, Image.BICUBIC)
image_pil.save(image_path) |