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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 pprint
import scipy.misc
import numpy as np
import copy
from scipy.io import loadmat, savemat
pp = pprint.PrettyPrinter()
get_stddev = lambda x, k_h, k_w: 1/math.sqrt(k_w*k_h*x.get_shape()[-1])
D=1000
# -----------------------------
# new added functions for cyclegan
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_format='mat'):
if img_format=='mat':
img = matread(image_path)
img = np.resize(img, [fine_size, fine_size])
else:
img = imread(image_path)
img = scipy.misc.imresize(img, [fine_size, fine_size])
img = img/127.5 - 1
return img
def load_train_data(image_path, load_size=286, fine_size=256, is_testing=False, img_format='mat'):
if img_format=='mat':
img_A = matread(image_path[0])
img_B = matread(image_path[1])
'''if not is_testing:
img_A = np.resize(img_A, [load_size, load_size])
img_B = np.resize(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 np.random.random() > 0.5:
img_A = np.fliplr(img_A)
img_B = np.fliplr(img_B)
'''else:
img_A = scipy.misc.imresize(img_A, [fine_size, fine_size])
img_B = scipy.misc.imresize(img_B, [fine_size, fine_size])'''
halfmaxA = np.max(img_A)/2.0
halfmaxB = np.max(img_B)/2.0
img_A = img_A / halfmaxA - 1.
img_B = img_B / halfmaxB - 1.
img_A = np.expand_dims(img_A, axis=2)
img_B = np.expand_dims(img_B, axis=2)
img_AB = np.concatenate((img_A, img_B), axis=2)
# img_AB shape: (fine_size, fine_size, input_c_dim + output_c_dim)
else:
img_A = imread(image_path[0])
img_B = imread(image_path[1])
if not is_testing:
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 np.random.random() > 0.5:
img_A = np.fliplr(img_A)
img_B = np.fliplr(img_B)
else:
img_A = scipy.misc.imresize(img_A, [fine_size, fine_size])
img_B = scipy.misc.imresize(img_B, [fine_size, fine_size])
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 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, is_us=False):
img_mat = inverse_transform(images)
dict_mat={'env':img_mat}
savemat(image_path[:-3]+'mat',dict_mat)
return imsave(inverse_transform(images), size, image_path, is_us)
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, mode='RGB').astype(np.float)
def matread(path):
dict = loadmat(path)
if dict.has_key('p'):
return dict['p'].astype(np.float)
if dict.has_key('env'):
return dict['env'].astype(np.float)
def merge_images(images, size):
return inverse_transform(images)
def merge(images, size, is_grayscale=True, is_us=False):
h, w = images.shape[1], images.shape[2]
if is_grayscale:
img = np.zeros((h * size[0], w * size[1], 1))
else:
img = np.zeros((h * size[0], w * size[1], 3))
for idx, image in enumerate(images):
i = idx % size[1]
j = idx // size[1]
x_min = np.min(image)
x_max = np.max(image)
image-= x_min
image/= (x_max-x_min)
if is_us:
image = np.log(image*D+1)/np.log(D+1)
img[j*h:j*h+h, i*w:i*w+w, :] = image
return img
def imsave(images, size, path, is_us=False):
return scipy.misc.imsave(path, np.squeeze(merge(images, size,is_us=False)))
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.))
i = int(round((w - crop_w)/2.))
return scipy.misc.imresize(
x[j:j+crop_h, i:i+crop_w], [resize_h, resize_w])
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.