import tensorflow as tf
from tensorflow.contrib import slim
from scipy import misc
import os, random
import numpy as np
class ImageData:
def __init__(self, load_size, channels, augment_flag):
self.load_size = load_size
self.channels = channels
self.augment_flag = augment_flag
def image_processing(self, filename):
x = tf.read_file(filename)
x_decode = tf.image.decode_jpeg(x, channels=self.channels)
img = tf.image.resize_images(x_decode, [self.load_size, self.load_size])
img = tf.cast(img, tf.float32) / 127.5 - 1
if self.augment_flag :
augment_size = self.load_size + (30 if self.load_size == 256 else 15)
p = random.random()
if p > 0.5:
img = augmentation(img, augment_size)
return img
def load_test_data(image_path, size=256):
img = misc.imread(image_path, mode='RGB')
img = misc.imresize(img, [size, size])
img = np.expand_dims(img, axis=0)
img = preprocessing(img)
return img
def preprocessing(x):
x = x/127.5 - 1 # -1 ~ 1
return x
def augmentation(image, augment_size):
seed = random.randint(0, 2 ** 31 - 1)
ori_image_shape = tf.shape(image)
image = tf.image.random_flip_left_right(image, seed=seed)
image = tf.image.resize_images(image, [augment_size, augment_size])
image = tf.random_crop(image, ori_image_shape, seed=seed)
return image
def save_images(images, size, image_path):
return imsave(inverse_transform(images), size, image_path)
def inverse_transform(images):
return (images+1.) / 2
def imsave(images, size, path):
return misc.imsave(path, merge(images, size))
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[h*j:h*(j+1), w*i:w*(i+1), :] = image
return img
def show_all_variables():
model_vars = tf.trainable_variables()
slim.model_analyzer.analyze_vars(model_vars, print_info=True)
def check_folder(log_dir):
if not os.path.exists(log_dir):
os.makedirs(log_dir)
return log_dir
def str2bool(x):
return x.lower() in ('true')
