CellposeSegmentatorPortable/image_utils.py at main · CellProfiling/CellposeSegmentatorPortable · GitHub

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import cv2
import numpy as np


def convert_bitdepth(image, bitdepth):
    # Float input is assumed normalized to [0, 1]; integer paths are unchanged
    if bitdepth == 8:
        if image.dtype == np.uint8:
            return np.uint8(image)
        elif np.issubdtype(image.dtype, np.floating):
            return (image * 255).astype(np.uint8)
        return (image / np.iinfo(image.dtype).max * 255).astype(np.uint8)
    elif bitdepth == 16:
        if image.dtype == np.uint16:
            return np.uint16(image)
        elif np.issubdtype(image.dtype, np.floating):
            return (image * 65535).astype(np.uint16)
        return (image / np.iinfo(image.dtype).max * 65535).astype(np.uint16)
    elif bitdepth == 32:
        if image.dtype == np.uint32:
            return np.uint32(image)
        elif np.issubdtype(image.dtype, np.floating):
            return (image * 4294967295).astype(np.uint32)
        return (image / np.iinfo(image.dtype).max * 4294967295).astype(np.uint32)
    return image


def read_grayscale_image(input_image, force_channel = -1, force_bit_depth = 0, minmax_norm = False):
    np_img = cv2.imread(input_image, cv2.IMREAD_UNCHANGED)
    if np_img is None:
        raise FileNotFoundError(f"Could not read image: {input_image}")
    if force_channel != -1:
        np_img = np_img[:, :, force_channel]
    elif np_img.ndim > 2:
        np_img = np.max(np_img, axis=2)
    if force_bit_depth != 0:
        np_img = convert_bitdepth(np_img, force_bit_depth)
    elif minmax_norm:
        np_img = (np_img - np.amin(np_img)) / (np.amax(np_img) - np.amin(np_img))

    return np_img