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utils.py
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utils.py
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"""
Some codes from https://github.com/Newmu/dcgan_code
"""
from __future__ import division
import math
import json
import random
import pprint
import scipy.misc
import numpy as np
from time import gmtime, strftime
pp = pprint.PrettyPrinter()
get_stddev = lambda x, k_h, k_w: 1/math.sqrt(k_w*k_h*x.get_shape()[-1])
# -----------------------------
# new added functions for pix2pix
def load_data(image_path, flip=True, is_test=False):
img_A, img_B = load_image(image_path)
img_A, img_B = preprocess_A_and_B(img_A, img_B, flip=flip, is_test=is_test)
img_A = img_A/127.5 - 1.
img_B = img_B/127.5 - 1.
img_AB = np.concatenate((img_A, img_B), axis=2)
# img_AB shape: (fine_size, fine_size, input_c_dim + output_c_dim)
return img_AB
def load_image(image_path):
input_img = imread(image_path)
w = int(input_img.shape[1])
w2 = int(w/2)
img_A = input_img[:, 0:w2]
img_B = input_img[:, w2:w]
return img_A, img_B
def preprocess_A_and_B(img_A, img_B, load_size=286, fine_size=256, flip=True, is_test=False):
if is_test:
img_A = scipy.misc.imresize(img_A, [fine_size, fine_size])
img_B = scipy.misc.imresize(img_B, [fine_size, fine_size])
else:
img_A = scipy.misc.imresize(img_A, [load_size, load_size])
img_B = scipy.misc.imresize(img_B, [load_size, load_size])
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)))
img_A = img_A[h1:h1+fine_size, w1:w1+fine_size]
img_B = img_B[h1:h1+fine_size, w1:w1+fine_size]
if flip and np.random.random() > 0.5:
img_A = np.fliplr(img_A)
img_B = np.fliplr(img_B)
return img_A, img_B
# -----------------------------
def get_image(image_path, image_size, is_crop=True, resize_w=64, is_grayscale = False):
return transform(imread(image_path, is_grayscale), image_size, is_crop, resize_w)
def save_images(images, size, image_path):
return imsave(inverse_transform(images), size, image_path)
def imread(path, is_grayscale = False):
if (is_grayscale):
return scipy.misc.imread(path, flatten = True).astype(np.float)
else:
return scipy.misc.imread(path).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(images, size, path):
return scipy.misc.imsave(path, merge(images, size))
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.
def inverse_transform(images):
return (images+1.)/2.