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Comment convertir une image 16 bits en une image 8 bits dans OpenCV?

J'ai une image en niveaux de gris 16 bits et je veux la convertir en une image en niveaux de gris 8 bits dans OpenCV pour python pour l'utiliser avec diverses fonctions (comme findContours, etc.). Est-il possible de le faire dans python ou je dois passer en C++?

pythonopencvimage-processing
20
pap-x

Vous pouvez utiliser des méthodes de conversion numpy car un tapis OpenCV est un tableau numpy.

Cela marche:

img8 = (img16/256).astype('uint8')
13
Vasanth

Code de scipy (désormais obsolète):

def bytescaling(data, cmin=None, cmax=None, high=255, low=0):
    """
    Converting the input image to uint8 dtype and scaling
    the range to ``(low, high)`` (default 0-255). If the input image already has 
    dtype uint8, no scaling is done.
    :param data: 16-bit image data array
    :param cmin: bias scaling of small values (def: data.min())
    :param cmax: bias scaling of large values (def: data.max())
    :param high: scale max value to high. (def: 255)
    :param low: scale min value to low. (def: 0)
    :return: 8-bit image data array
    """
    if data.dtype == np.uint8:
        return data

    if high > 255:
        high = 255
    if low < 0:
        low = 0
    if high < low:
        raise ValueError("`high` should be greater than or equal to `low`.")

    if cmin is None:
        cmin = data.min()
    if cmax is None:
        cmax = data.max()

    cscale = cmax - cmin
    if cscale == 0:
        cscale = 1

    scale = float(high - low) / cscale
    bytedata = (data - cmin) * scale + low
    return (bytedata.clip(low, high) + 0.5).astype(np.uint8)
2
A. Attia

Il est vraiment facile de convertir en 8 bits en utilisant scipy.misc.bytescale. La matrice OpenCV est un tableau numpy, donc l'octet fera exactement ce que vous voulez.

from scipy.misc import bytescale
img8 = bytescale(img16)
2
Thomas Van Der Weide

Vous pouvez le faire dans Python en utilisant NumPy en mappant l'image via une table de recherche.

import numpy as np


def map_uint16_to_uint8(img, lower_bound=None, upper_bound=None):
    '''
    Map a 16-bit image trough a lookup table to convert it to 8-bit.

    Parameters
    ----------
    img: numpy.ndarray[np.uint16]
        image that should be mapped
    lower_bound: int, optional
        lower bound of the range that should be mapped to ``[0, 255]``,
        value must be in the range ``[0, 65535]`` and smaller than `upper_bound`
        (defaults to ``numpy.min(img)``)
    upper_bound: int, optional
       upper bound of the range that should be mapped to ``[0, 255]``,
       value must be in the range ``[0, 65535]`` and larger than `lower_bound`
       (defaults to ``numpy.max(img)``)

    Returns
    -------
    numpy.ndarray[uint8]
    '''
    if not(0 <= lower_bound < 2**16) and lower_bound is not None:
        raise ValueError(
            '"lower_bound" must be in the range [0, 65535]')
    if not(0 <= upper_bound < 2**16) and upper_bound is not None:
        raise ValueError(
            '"upper_bound" must be in the range [0, 65535]')
    if lower_bound is None:
        lower_bound = np.min(img)
    if upper_bound is None:
        upper_bound = np.max(img)
    if lower_bound >= upper_bound:
        raise ValueError(
            '"lower_bound" must be smaller than "upper_bound"')
    lut = np.concatenate([
        np.zeros(lower_bound, dtype=np.uint16),
        np.linspace(0, 255, upper_bound - lower_bound).astype(np.uint16),
        np.ones(2**16 - upper_bound, dtype=np.uint16) * 255
    ])
    return lut[img].astype(np.uint8)


# Let's generate an example image (normally you would load the 16-bit image: cv2.imread(filename, cv2.IMREAD_UNCHANGED))
img = (np.random.random((100, 100)) * 2**16).astype(np.uint16)

# Convert it to 8-bit
map_uint16_to_uint8(img)
2
mdh