monochrome_detection/encode.py

import numpy as np

from image import load_image, ImageTyping

__all__ = [
    'rgb_encode',
]

_DEFAULT_ORDER = 'HWC'


def _get_hwc_map(order_: str):
    return tuple(_DEFAULT_ORDER.index(c) for c in order_.upper())


def rgb_encode(image: ImageTyping, order_: str = 'CHW', use_float: bool = True) -> np.ndarray:
    """
    Overview:
        Encode image as rgb channels.

    :param image: Image to be encoded.
    :param order_: Order of encoding, default is ``CHW``.
    :param use_float: Use float to represent the channels, default is ``True``. ``np.uint8`` will be used when false.
    :return: Encoded rgb image.

    Examples::
        >>> from PIL import Image
        >>> from encode import rgb_encode
        >>>
        >>> image = Image.open('custom_image.jpg')
        >>> image
        <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1606x1870 at 0x7F9EC37389D0>
        >>>
        >>> data = rgb_encode(image)
        >>> data.shape, data.dtype
        ((3, 1870, 1606), dtype('float32'))
        >>> data = rgb_encode(image, order_='CHW')
        >>> data.shape, data.dtype
        ((3, 1870, 1606), dtype('float32'))
        >>> data = rgb_encode(image, order_='WHC')
        >>> data.shape, data.dtype
        ((1606, 1870, 3), dtype('float32'))
        >>> data = rgb_encode(image, use_float=False)
        >>> data.shape, data.dtype
        ((3, 1870, 1606), dtype('uint8'))

    .. note::
        The function :func:`rgb_encode`'s result is the same as \
            ``torchvision.transforms.functional import to_tensor``'s result when the given ``image`` is in RGB mode.
    """
    image = load_image(image, mode='RGB')
    array = np.asarray(image)
    array = np.transpose(array, _get_hwc_map(order_))
    if use_float:
        array = (array / 255.0).astype(np.float32)
        assert array.dtype == np.float32
    else:
        assert array.dtype == np.uint8
    return array