utils.py

# -*- coding: utf-8 -*-
"""
Scipy version > 0.18 is needed, due to 'mode' option from scipy.misc.imread function
"""

import os
import glob
import h5py
import random
import matplotlib.pyplot as plt

from PIL import Image  # for loading images as YCbCr format
import scipy.misc
import scipy.ndimage
import numpy as np

import tensorflow as tf
import cv2

FLAGS = tf.app.flags.FLAGS

def read_data(path):
  """
  Read h5 format data file
  
  Args:
    path: file path of desired file
    data: '.h5' file format that contains train data values
    label: '.h5' file format that contains train label values
  """
  with h5py.File(path, 'r') as hf:
    data = np.array(hf.get('data'))
    label = np.array(hf.get('label'))
    return data, label

def preprocess(path, scale=3):
  """
  Preprocess single image file 
    (1) Read original image as YCbCr format (and grayscale as default)
    (2) Normalize
    (3) Apply image file with bicubic interpolation

  Args:
    path: file path of desired file
    input_: image applied bicubic interpolation (low-resolution)
    label_: image with original resolution (high-resolution)
  """
  #读到图片
  image = imread(path, is_grayscale=True)
  #将图片label裁剪为scale的倍数
  label_ = modcrop(image, scale)

  # Must be normalized
  image = (image-127.5 )/ 127.5 
  label_ = (image-127.5 )/ 127.5 
  #下采样之后再插值
  input_ = scipy.ndimage.interpolation.zoom(label_, (1./scale), prefilter=False)
  input_ = scipy.ndimage.interpolation.zoom(input_, (scale/1.), prefilter=False)

  return input_, label_

def prepare_data(sess, dataset):
  """
  Args:
    dataset: choose train dataset or test dataset
    
    For train dataset, output data would be ['.../t1.bmp', '.../t2.bmp', ..., '.../t99.bmp']
  """
  if FLAGS.is_train:
    filenames = os.listdir(dataset)
    data_dir = os.path.join(os.getcwd(), dataset)
    data = glob.glob(os.path.join(data_dir, "*.bmp"))
    data.extend(glob.glob(os.path.join(data_dir, "*.tif")))
    #将图片按序号排序
    data.sort(key=lambda x:int(x[len(data_dir)+1:-4]))
  else:
    data_dir = os.path.join(os.sep, (os.path.join(os.getcwd(), dataset)))
    data = glob.glob(os.path.join(data_dir, "*.bmp"))
    data.extend(glob.glob(os.path.join(data_dir, "*.tif")))
    data.sort(key=lambda x:int(x[len(data_dir)+1:-4]))
  #print(data)

  return data

def make_data(sess, data, label,data_dir):
  """
  Make input data as h5 file format
  Depending on 'is_train' (flag value), savepath would be changed.
  """
  if FLAGS.is_train:
    #savepath = os.path.join(os.getcwd(), os.path.join('checkpoint',data_dir,'train.h5'))
    savepath = os.path.join('.', os.path.join('checkpoint_20',data_dir,'train.h5'))
    if not os.path.exists(os.path.join('.',os.path.join('checkpoint_20',data_dir))):
        os.makedirs(os.path.join('.',os.path.join('checkpoint_20',data_dir)))
  else:
    savepath = os.path.join('.', os.path.join('checkpoint_20',data_dir,'test.h5'))
    if not os.path.exists(os.path.join('.',os.path.join('checkpoint_20',data_dir))):
        os.makedirs(os.path.join('.',os.path.join('checkpoint_20',data_dir)))
  with h5py.File(savepath, 'w') as hf:
    hf.create_dataset('data', data=data)
    hf.create_dataset('label', data=label)

def imread(path, is_grayscale=True):
  """
  Read image using its path.
  Default value is gray-scale, and image is read by YCbCr format as the paper said.
  """
  if is_grayscale:
    #flatten=True 以灰度图的形式读取 
    return scipy.misc.imread(path, flatten=True, mode='YCbCr').astype(np.float)
  else:
    return scipy.misc.imread(path, mode='YCbCr').astype(np.float)

def modcrop(image, scale=3):
  """
  To scale down and up the original image, first thing to do is to have no remainder while scaling operation.
  
  We need to find modulo of height (and width) and scale factor.
  Then, subtract the modulo from height (and width) of original image size.
  There would be no remainder even after scaling operation.
  """
  if len(image.shape) == 3:
    h, w, _ = image.shape
    h = h - np.mod(h, scale)
    w = w - np.mod(w, scale)
    image = image[0:h, 0:w, :]
  else:
    h, w = image.shape
    h = h - np.mod(h, scale)
    w = w - np.mod(w, scale)
    image = image[0:h, 0:w]
  return image

def input_setup(sess,config,data_dir,index=0):
  """
  Read image files and make their sub-images and saved them as a h5 file format.
  """
  # Load data path
  if config.is_train:
    #取到所有的原始图片的地址
    data = prepare_data(sess, dataset=data_dir)
  else:
    data = prepare_data(sess, dataset=data_dir)

  sub_input_sequence = []
  sub_label_sequence = []
  padding = abs(config.image_size - config.label_size) / 2 # 6

  if config.is_train:
    for i in xrange(len(data)):
      #input_, label_ = preprocess(data[i], config.scale)
      input_=(imread(data[i])-127.5)/127.5
      label_=input_

      if len(input_.shape) == 3:
        h, w, _ = input_.shape
      else:
        h, w = input_.shape
      #按14步长采样小patch
      for x in range(0, h-config.image_size+1, config.stride):
        for y in range(0, w-config.image_size+1, config.stride):
          sub_input = input_[x:x+config.image_size, y:y+config.image_size] # [33 x 33]
          #注意这里的padding，前向传播时由于卷积是没有padding的，所以实际上预测的是测试patch的中间部分
          sub_label = label_[x+padding:x+padding+config.label_size, y+padding:y+padding+config.label_size] # [21 x 21]
          # Make channel value
          if data_dir == "Train":
            sub_input=cv2.resize(sub_input, (config.image_size/4,config.image_size/4),interpolation=cv2.INTER_CUBIC)
            sub_input = sub_input.reshape([config.image_size/4, config.image_size/4, 1])
            sub_label=cv2.resize(sub_label, (config.label_size/4,config.label_size/4),interpolation=cv2.INTER_CUBIC)
            sub_label = sub_label.reshape([config.label_size/4, config.label_size/4, 1])
            print('error')
          else:
            sub_input = sub_input.reshape([config.image_size, config.image_size, 1])  
            sub_label = sub_label.reshape([config.label_size, config.label_size, 1])
          
          sub_input_sequence.append(sub_input)
          sub_label_sequence.append(sub_label)

  else:
    #input_, label_ = preprocess(data[2], config.scale)
    #input_=np.lib.pad((imread(data[index])-127.5)/127.5,((padding,padding),(padding,padding)),'edge')
    #label_=input_
    input_=(imread(data[index])-127.5)/127.5
    if len(input_.shape) == 3:
      h_real, w_real, _ = input_.shape
    else:
      h_real, w_real = input_.shape
    padding_h=config.image_size-((h_real+padding)%config.label_size)
    padding_w=config.image_size-((w_real+padding)%config.label_size)
    input_=np.lib.pad(input_,((padding,padding_h),(padding,padding_w)),'edge')
    label_=input_
    h,w=input_.shape
    #print(input_.shape)
    # Numbers of sub-images in height and width of image are needed to compute merge operation.
    nx = ny = 0 
    for x in range(0, h-config.image_size+1, config.stride):
      nx += 1; ny = 0
      for y in range(0, w-config.image_size+1, config.stride):
        ny += 1
        sub_input = input_[x:x+config.image_size, y:y+config.image_size] # [33 x 33]
        sub_label = label_[x+padding:x+padding+config.label_size, y+padding:y+padding+config.label_size] # [21 x 21]
        
        sub_input = sub_input.reshape([config.image_size, config.image_size, 1])  
        sub_label = sub_label.reshape([config.label_size, config.label_size, 1])

        sub_input_sequence.append(sub_input)
        sub_label_sequence.append(sub_label)

  """
  len(sub_input_sequence) : the number of sub_input (33 x 33 x ch) in one image
  (sub_input_sequence[0]).shape : (33, 33, 1)
  """
  # Make list to numpy array. With this transform
  arrdata = np.asarray(sub_input_sequence) # [?, 33, 33, 1]
  arrlabel = np.asarray(sub_label_sequence) # [?, 21, 21, 1]
  #print(arrdata.shape)
  make_data(sess, arrdata, arrlabel,data_dir)

  if not config.is_train:
    print(nx,ny)
    print(h_real,w_real)
    return nx, ny,h_real,w_real
    
def imsave(image, path):
  return scipy.misc.imsave(path, image)

def merge(images, size):
  h, w = images.shape[1], images.shape[2]
  img = np.zeros((h*size[0], w*size[1], 1))
  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*127.5+127.5)
  
def gradient(input):
    #filter_x=tf.reshape(tf.constant([[-1.,0.,1.],[-1.,0.,1.],[-1.,0.,1.]]),[3,3,1,1])
    #filter_y=tf.reshape(tf.constant([[-1.,-1.,-1],[0,0,0],[1,1,1]]),[3,3,1,1])
    #d_x=tf.nn.conv2d(input,filter_x,strides=[1,1,1,1], padding='SAME')
    #d_y=tf.nn.conv2d(input,filter_y,strides=[1,1,1,1], padding='SAME')
    #d=tf.sqrt(tf.square(d_x)+tf.square(d_y))
    filter=tf.reshape(tf.constant([[0.,1.,0.],[1.,-4.,1.],[0.,1.,0.]]),[3,3,1,1])
    d=tf.nn.conv2d(input,filter,strides=[1,1,1,1], padding='SAME')
    #print(d)
    return d
    
def weights_spectral_norm(weights, u=None, iteration=1, update_collection=None, reuse=False, name='weights_SN'):
    with tf.variable_scope(name) as scope:
        if reuse:
            scope.reuse_variables()

        w_shape = weights.get_shape().as_list()
        w_mat = tf.reshape(weights, [-1, w_shape[-1]])
        if u is None:
            u = tf.get_variable('u', shape=[1, w_shape[-1]], initializer=tf.truncated_normal_initializer(), trainable=False)

        def power_iteration(u, ite):
            v_ = tf.matmul(u, tf.transpose(w_mat))
            v_hat = l2_norm(v_)
            u_ = tf.matmul(v_hat, w_mat)
            u_hat = l2_norm(u_)
            return u_hat, v_hat, ite+1
        
        u_hat, v_hat,_ = power_iteration(u,iteration)
        
        sigma = tf.matmul(tf.matmul(v_hat, w_mat), tf.transpose(u_hat))
        
        w_mat = w_mat/sigma
        
        if update_collection is None:
            with tf.control_dependencies([u.assign(u_hat)]):
                w_norm = tf.reshape(w_mat, w_shape)
        else:
            if not(update_collection == 'NO_OPS'):
                print(update_collection)
                tf.add_to_collection(update_collection, u.assign(u_hat))
            
            w_norm = tf.reshape(w_mat, w_shape)
        return w_norm
    
def lrelu(x, leak=0.2):
    return tf.maximum(x, leak * x)
    
def l2_norm(input_x, epsilon=1e-12):
    input_x_norm = input_x/(tf.reduce_sum(input_x**2)**0.5 + epsilon)
    return input_x_norm