data_handler.py 文件源码

def preprocess_image_for_prediction(fixed_height: int=32, min_width: int=8):
    """
    Input function to use when exporting the model for making predictions (see estimator.export_savedmodel)
    :param fixed_height: height of the input image after resizing
    :param min_width: minimum width of image after resizing
    :return:
    """

    def serving_input_fn():
        # define placeholder for input image
        image = tf.placeholder(dtype=tf.float32, shape=[None, None, 1])

        shape = tf.shape(image)
        # Assert shape is h x w x c with c = 1

        ratio = tf.divide(shape[1], shape[0])
        increment = CONST.DIMENSION_REDUCTION_W_POOLING
        new_width = tf.cast(tf.round((ratio * fixed_height) / increment) * increment, tf.int32)

        resized_image = tf.cond(new_width < tf.constant(min_width, dtype=tf.int32),
                                true_fn=lambda: tf.image.resize_images(image, size=(fixed_height, min_width)),
                                false_fn=lambda: tf.image.resize_images(image, size=(fixed_height, new_width))
                                )

        # Features to serve
        features = {'images': resized_image[None],  # cast to 1 x h x w x c
                    'images_widths': new_width[None]  # cast to tensor
                    }

        # Inputs received
        receiver_inputs = {'images': image}

        return tf.estimator.export.ServingInputReceiver(features, receiver_inputs)

    return serving_input_fn