reader.py 文件源码

def preprocess(image, size, max_length):
    shape = tf.shape(image)
    size_t = tf.constant(size, tf.float64)
    height = tf.cast(shape[0], tf.float64)
    width = tf.cast(shape[1], tf.float64)

    cond_op = tf.less(width, height) if max_length else tf.less(height, width)

    new_height, new_width = tf.cond(
        cond_op, lambda: (size_t, (width * size_t) / height),
        lambda: ((height * size_t) / width, size_t))
    new_size = [tf.to_int32(new_height), tf.to_int32(new_width)]
    resized_image = tf.image.resize_images(image, new_size)
    normalised_image = resized_image - mean_pixel
    return normalised_image


# max_length: Wether size dictates longest or shortest side. Default longest