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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 random
import cv2
import numpy as np
import os
from time import gmtime, strftime
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 = cv2.resize(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 cv2.imread(path, flatten = True)#.astype(np.float)
else:
return cv2.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 cv2.imwrite(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)