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util.py
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util.py
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import scipy.misc
import numpy as np
def imread(path, is_grayscale=False):
if(is_grayscale):
return scipy.misc.imread(path, flatten=True).astype(np.float32)
else:
return scipy.misc.imread(path).astype(np.float32)
def transform(img, crop_size=64, is_crop=True, resize_w=64):
cropped_img = img
if is_crop:
cropped_img = center_crop(img, crop_size, resize_w=resize_w)
return np.array(cropped_img)/127.5 - 1.0
def center_crop(x, crop_h, crop_w=None, resize_w=64):
if crop_w is None:
crop_w = crop_h
h, w = x.shape[:2]
i, j = int(round((h - crop_h) / 2.0)), int(round((w - crop_w) / 2.0))
return scipy.misc.imresize(x[i:i+crop_h, j:j+crop_w], [resize_w, resize_w])
def get_img(path, crop_size, is_crop=True, resize_w=64, is_grayscale=False):
return transform(imread(path, is_grayscale), crop_size, is_crop, resize_w)
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 inverse_transform(images):
return (images+1.)/2.
def imsave(images, size, path):
return scipy.misc.imsave(path, merge(images, size))
def save_images(images, size, image_path):
return imsave(inverse_transform(images), size, image_path)