input.py 文件源码

def __init__(self, hight, width, batch_size, folder_image, folder_label, format_image = '.jpg' , random = True):
    """
    Args:
      hight             :         hight of samples
      width             :         width of samples
      batch_size        :         batch size
      folder_image      :         the folder where the images are
      folder_label      :         the folder where the ground truth are
      format_image      :         format of images (usually jpg)
      random            :         is the queue shuffled (for training) or not (FIFO for test related tasks)
    """  

    self.hight           =       hight
    self.width           =       width
    self.batch_size      =       batch_size
    self.image           =       np.array([f for f in os.listdir(folder_image) if format_image in f])
    self.f1              =       folder_image
    self.f2              =       folder_label
    self.size_epoch      =       len(self.image)
    if random:
      self.queue           =       tf.RandomShuffleQueue(shapes=[(self.hight,self.width,3), (self.hight,self.width), []],dtypes=[tf.float32, tf.float32, tf.string],capacity=16*self.batch_size, min_after_dequeue=8*self.batch_size)
    else:
      self.queue           =       tf.FIFOQueue(shapes=[(self.hight,self.width,3), (self.hight,self.width), []],dtypes=[tf.float32, tf.float32, tf.string],capacity=16*self.batch_size)
    self.image_pl        =       tf.placeholder(tf.float32, shape=(batch_size,hight,width,3))
    self.label_pl        =       tf.placeholder(tf.float32, shape=(batch_size,hight,width))
    self.name_pl         =       tf.placeholder(tf.string, shape=(batch_size))
    self.enqueue_op      =       self.queue.enqueue_many([self.image_pl, self.label_pl, self.name_pl])