utils.py

# import tensorflow as tf
# import tensorlayer as tl
# from tensorlayer.prepro import *
# # from config import config, log_config
# #
# # img_path = config.TRAIN.img_path
#
# import scipy
# import numpy as np
#
# def get_imgs_fn(file_name, path):
#     """ Input an image path and name, return an image array """
#     # return scipy.misc.imread(path + file_name).astype(np.float)
#     return scipy.misc.imread(path + file_name, mode='RGB')
#
# def crop_sub_imgs_fn(x, is_random=True):
#     # x = crop(x, wrg=384, hrg=384, is_random=is_random)
#     x = x / (255. / 2.)
#     x = x - 1.
#     # x = (x - 0.5)*2
#     return x
#
# def downsample_fn(x):
#     # We obtained the LR images by downsampling the HR images using bicubic kernel with downsampling factor r = 4.
#     x = imresize(x, size=[128, 128], interp='bicubic', mode=None)
#     x = x / (255. / 2.)
#     x = x - 1.
#     # x = (x - 0.5)*2
#     return x

import tensorflow as tf
import tensorlayer as tl
from tensorlayer.prepro import *
from skimage import color
# from config import config, log_config
#
# img_path = config.TRAIN.img_path

import scipy
import numpy as np

def get_imgs_fn(file_name, path):
    """ Input an image path and name, return an image array """
    # return scipy.misc.imread(path + file_name).astype(np.float)
    return scipy.misc.imread(path + file_name, mode='RGB')

def crop_sub_imgs_fn(x, is_random=True):
    # x = crop(x, wrg=384, hrg=384, is_random=is_random)
    if x.shape[2] == 4:
        x = color.rgba2rgb(x)
    if x.max() > 10:
        x = x / (255. / 2.)
        x = x - 1.
    else:
        x = x * 2 - 1
    # x = (x - 0.5)*2

    return x

def downsample_fn(x):
    # We obtained the LR images by downsampling the HR images using bicubic kernel with downsampling factor r = 4.
    x = imresize(x, size=[128, 128], interp='bicubic', mode=None)
    x = x / (255. / 2.)
    x = x - 1.
    # x = (x - 0.5)*2
    return x

def downsample_fn_3(x):
    # We obtained the LR images by downsampling the HR images using bicubic kernel with downsampling factor r = 4.
    x = imresize(x, size=[256, 256], interp='bicubic', mode=None)
    x = x / (255. / 2.)
    x = x - 1.
    # x = (x - 0.5)*2
    return x

def downsample_fn_2(x, size=128):
    # We obtained the LR images by downsampling the HR images using bicubic kernel with downsampling factor r = 4.
    x = imresize(x, size=[size, size], interp='bicubic', mode=None)
    x = x / (255. / 2.)
    x = x - 1.
    # x = (x - 0.5)*2
    return x