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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
- import os
- 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])
-
- def load_data(image_path, flip=False, is_test=False, image_size = 128):
- img = load_image(image_path)
- img = preprocess_img(img, img_size=image_size, flip=flip, is_test=is_test)
- img = img/127.5 - 1.
- if len(img.shape)<3:
- img = np.expand_dims(img, axis=2)
- return img
- def load_image(image_path):
- img = imread(image_path)
- return img
- def preprocess_img(img, img_size=128, flip=False, is_test=False):
- img = scipy.misc.imresize(img, [img_size, img_size])
- if (not is_test) and flip and np.random.random() > 0.5:
- img = np.fliplr(img)
- return img
- 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):
- dir = os.path.dirname(image_path)
- if not os.path.exists(dir):
- os.makedirs(dir)
- 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]
- if len(images.shape) < 4:
- img = np.zeros((h * size[0], w * size[1], 1))
- images = np.expand_dims(images, axis = 3)
- else:
- img = np.zeros((h * size[0], w * size[1], images.shape[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
- if images.shape[3] ==1:
- return np.concatenate([img,img,img],axis=2)
- else:
- return img.astype(np.uint8)
- 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.)*127.5)
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