DataHandeling.py 文件源码

def _get_image(self):

        im_filename = tf.sparse_tensor_to_dense(tf.string_split(tf.expand_dims(self.raw_queue.dequeue(), 0), ':'), '')
        im_filename.set_shape([1, 2])
        im_raw = tf.read_file(self.base_folder+im_filename[0][0])
        seg_raw = tf.read_file(self.base_folder+im_filename[0][1])

        image = tf.reshape(tf.cast(tf.image.decode_png(im_raw, channels=1, dtype=tf.uint16), tf.float32),
                           self.image_size, name='input_image')
        seg = tf.reshape(tf.cast(tf.image.decode_png(seg_raw, channels=1, dtype=tf.uint8), tf.float32), self.image_size,
                         name='input_seg')
        if self.partial_frame:
            crop_y_start = int(((1-self.partial_frame) * self.image_size[0])/2)
            crop_y_end = int(((1+self.partial_frame) * self.image_size[0])/2)
            crop_x_start = int(((1-self.partial_frame) * self.image_size[1])/2)
            crop_x_end = int(((1+self.partial_frame) * self.image_size[1])/2)
            image = tf.slice(image, [crop_y_start, crop_x_start, 0], [crop_y_end, crop_x_end, -1])
            seg = tf.slice(seg, [crop_y_start, crop_x_start, 0], [crop_y_end, crop_x_end, -1])

        return image, seg, im_filename[0][0], im_filename[0][1]