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# import scipy.misc
from PIL import Image
import numpy as np
import copy
import os
class ImagePool(object):
def __init__(self, maxsize=50):
self.maxsize = maxsize
self.num_img = 0
self.images = []
def __call__(self, image):
if self.maxsize <= 0:
return image
if self.num_img < self.maxsize:
self.images.append(image)
self.num_img += 1
return image
if np.random.rand() > 0.5:
idx = int(np.random.rand() * self.maxsize)
tmp1 = copy.copy(self.images[idx])[0]
self.images[idx][0] = image[0]
idx = int(np.random.rand() * self.maxsize)
tmp2 = copy.copy(self.images[idx])[1]
self.images[idx][1] = image[1]
return [tmp1, tmp2]
else:
return image
def load_test_data(image_path, fine_size=256):
img = Image.open(image_path)
img = img.resize((fine_size * 2, fine_size))
img = np.array(img)
# Normalize image to the range [-1, 1]
img = img / 127.5 - 1
return img
def check_folder(path):
if not os.path.exists(path):
os.mkdir(path)
def load_train_data(image_path, load_size=286, fine_size=256, is_testing=False):
img_A = Image.open(image_path[0])
img_B = Image.open(image_path[1])
if not is_testing:
# Resize images using PIL
img_A = img_A.resize((load_size * 2, load_size))
img_B = img_B.resize((load_size * 2, load_size))
# Random crop
h1 = int(np.ceil(np.random.uniform(1e-2, load_size - fine_size)))
w1 = int(np.ceil(np.random.uniform(1e-2, (load_size - fine_size) * 2)))
img_A = np.array(img_A.crop((w1, h1, w1 + fine_size * 2, h1 + fine_size)))
img_B = np.array(img_B.crop((w1, h1, w1 + fine_size * 2, h1 + fine_size)))
# Random horizontal flip
if np.random.random() > 0.5:
img_A = np.fliplr(img_A)
img_B = np.fliplr(img_B)
else:
# Resize images using PIL for testing
img_A = img_A.resize((fine_size * 2, fine_size))
img_B = img_B.resize((fine_size * 2, fine_size))
# Normalize images to the range [-1, 1]
img_A = img_A / 127.5 - 1.0
img_B = img_B / 127.5 - 1.0
# Concatenate images along the channel axis
img_AB = np.concatenate((img_A, img_B), axis=2)
return img_AB
# -----------------------------
def get_image(image_path, image_size, is_crop=True, resize_w=64, is_grayscale=False):
return transform(
load_image(image_path, is_grayscale), image_size, is_crop, resize_w
)
def save_images(images, size, image_path):
return imsave(images, size, image_path)
def load_image(path, is_grayscale=False):
if is_grayscale:
return np.array(Image.open(path).convert("L")).astype(np.float)
else:
return np.array(Image.open(path).convert("RGB")).astype(np.float)
def merge_images(images, size):
return inverse_transform(images)
def merge(images, size):
h, w = images.shape[1], images.shape[2]
img = np.zeros((h * size[0], w * size[1], 3))
for idx, image in enumerate(images):
i = idx % size[1]
j = idx // size[1]
img[j * h : j * h + h, i * w : i * w + w, :] = image
return img
def imsave(image, size, path):
# Convert images to uint8 format and adjust the range
image = ((image + 1.0) * 127.5).astype(np.uint8)
# Merge images
# merged_image = merge(images, size).astype(np.uint8)
# Create a PIL Image from the numpy array
pil_image = Image.fromarray(image)
# Save the image using PIL
pil_image.save(path)
return None
def center_crop(x, crop_h, crop_w, resize_h=64, resize_w=64):
if crop_w is None:
crop_w = crop_h
h, w = x.shape[:2]
j = int(round((h - crop_h) / 2.0))
i = int(round((w - crop_w) / 2.0))
# Use PIL for resizing
cropped_image = Image.fromarray(x[j : j + crop_h, i : i + crop_w].astype(np.uint8))
cropped_image = cropped_image.resize((resize_w, resize_h))
return np.array(cropped_image) / 127.5 - 1.0
def transform(image, npx=64, is_crop=True, resize_w=64):
# npx: # of pixels width/height of image
if is_crop:
cropped_image = center_crop(image, npx, resize_w=resize_w)
else:
cropped_image = image
return np.array(cropped_image) / 127.5 - 1.0
def inverse_transform(images):
return (images + 1.0) / 2.0
def norm_img(img):
img = img / np.linalg.norm(img)
img = (img * 2.0) - 1.0
return img
def set_path(args, experiment_name):
args.checkpoint_dir = f"./check/{experiment_name}"
args.sample_dir = f"./check/{experiment_name}/sample"
if args.which_direction == "AtoB":
args.test_dir = f"./check/{experiment_name}/testa2b"
else:
args.test_dir = f"./check/{experiment_name}/testb2a"
args.conf_dir = f"./check/{experiment_name}/conf"
if not os.path.exists(args.checkpoint_dir):
os.makedirs(args.checkpoint_dir)
if not os.path.exists(args.sample_dir):
os.makedirs(args.sample_dir)
if not os.path.exists(args.test_dir):
os.makedirs(args.test_dir)
if not os.path.exists(args.conf_dir):
os.makedirs(args.conf_dir)
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