kears库中对样本图片resize的原理（target_size）

调用keras库去训练样本的时候，经常会用target_size吧图片resize自己想要的形状继续训练，可是需要预测的时候想单张把图片放进去预测，需要重复相同的resize步骤，于是看了源码，在image.py中有找到了方法

def load_img(path, grayscale=False, color_mode='rgb', target_size=None,
             interpolation='nearest'):
    """Loads an image into PIL format.

    # Arguments
        path: Path to image file.
        color_mode: One of "grayscale", "rbg", "rgba". Default: "rgb".
            The desired image format.
        target_size: Either `None` (default to original size)
            or tuple of ints `(img_height, img_width)`.
        interpolation: Interpolation method used to resample the image if the
            target size is different from that of the loaded image.
            Supported methods are "nearest", "bilinear", and "bicubic".
            If PIL version 1.1.3 or newer is installed, "lanczos" is also
            supported. If PIL version 3.4.0 or newer is installed, "box" and
            "hamming" are also supported. By default, "nearest" is used.

    # Returns
        A PIL Image instance.

    # Raises
        ImportError: if PIL is not available.
        ValueError: if interpolation method is not supported.
    """
    if grayscale is True:
        warnings.warn('grayscale is deprecated. Please use '
                      'color_mode = "grayscale"')
        color_mode = 'grayscale'
    if pil_image is None:
        raise ImportError('Could not import PIL.Image. '
                          'The use of `array_to_img` requires PIL.')
    img = pil_image.open(path)
    if color_mode == 'grayscale':
        if img.mode != 'L':
            img = img.convert('L')
    elif color_mode == 'rgba':
        if img.mode != 'RGBA':
            img = img.convert('RGBA')
    elif color_mode == 'rgb':
        if img.mode != 'RGB':
            img = img.convert('RGB')
    else:
        raise ValueError('color_mode must be "grayscale", "rbg", or "rgba"')
    if target_size is not None:
        width_height_tuple = (target_size[1], target_size[0])
        if img.size != width_height_tuple:
            if interpolation not in _PIL_INTERPOLATION_METHODS:
                raise ValueError(
                    'Invalid interpolation method {} specified. Supported '
                    'methods are {}'.format(
                        interpolation,
                        ", ".join(_PIL_INTERPOLATION_METHODS.keys())))
            resample = _PIL_INTERPOLATION_METHODS[interpolation]
            img = img.resize(width_height_tuple, resample)
    return img

可以看出，是用pil中nearest（默认）的模式resize的，所以只需要按照相同的方法resize就OK了