cityscapes.py 文件源码

def tf_ops(self, capacity=32):
        im_path, label_path = tf.train.slice_input_producer(
            [tf.constant(self.images), tf.constant(self.labels)],
            capacity=capacity,
            shuffle=self.shuffle)
        im_shape = [1024, 1024 + self.overlap, 3]
        label_shape = [1024, 1024 + self.overlap]
        queue = tf.FIFOQueue(capacity, [tf.float32, tf.int32],
                             shapes=[im_shape, label_shape])
        im = tf.read_file(im_path)
        im = tf.image.decode_image(im, channels=3)
        im = tf.cast(im, tf.float32)
        left_im = im[:, :1024 + self.overlap, :]
        right_im = im[:, 1024 - self.overlap:, :]
        left_im.set_shape(im_shape)
        right_im.set_shape(im_shape)
        label = tf.read_file(label_path)
        label = tf.image.decode_image(label, channels=1)
        label = label[:, :, 0]
        label = tf.cast(label, tf.int32)
        label_pad = tf.ones([1024, self.overlap], dtype=tf.int32) * 255
        left_label = tf.concat([label[:, :1024], label_pad], 1)
        right_label = tf.concat([label_pad, label[:, 1024:]], 1)
        left_label.set_shape(label_shape)
        right_label.set_shape(label_shape)
        ims = tf.stack([left_im, right_im], 0)
        labels = tf.stack([left_label, right_label], 0)
        enqueue_op = queue.enqueue_many([ims, labels])
        qr = tf.train.QueueRunner(queue, [enqueue_op])
        tf.train.add_queue_runner(qr)
        return queue.dequeue()