How to use color science and OpenCV in Python

Question

Here is my code using Colour to do color calibration. It uses numpy float64 type but how can I convert back to the format that is compatible in openCV, ideally uint8 because Canny only works with uint8?

import colour
import numpy as np
import cv2 

IMAGE = cv2.imread('/Users/kelsolaar/Downloads/EKcv1.jpeg')
IMAGE = cv2.cvtColor(IMAGE, cv2.COLOR_BGR2RGB)/255

# Reference values a likely non-linear 8-bit sRGB values.
# "colour.cctf_decoding" uses the sRGB EOTF by default.
REFERENCE_RGB = colour.cctf_decoding(
    np.array(
        [
            [240, 0, 22],
            [252, 222, 10],
            [30, 187, 22],
            [26, 0, 165],
        ]
    )
    / 255
)

colour.plotting.plot_multi_colour_swatches(colour.cctf_encoding(REFERENCE_RGB))

# Measured test values, the image is not properly decoded as it has a very specific ICC profile.
TEST_RGB = np.array(
    [
        [0.578, 0.0, 0.144],
        [0.895, 0.460, 0.0],
        [0.0, 0.183, 0.074],
        [0.067, 0.010, 0.070],
    ]
)
corrected = colour.colour_correction(IMAGE, REFERENCE_RGB, TEST_RGB)
colour.plotting.plot_image(
    corrected
)

This is some ways I found on stackoverflow but the image in uint8 doesn't look like the corrected image

#Method 1 which works but not uint8.....
img = cv2.cvtColor(corrected.astype(np.float32), cv2.COLOR_RGB2BGR)

# When I convert to unint8, it doesn't look like the original corrected image
# Method 2
corrected *= 255
corrected = corrected.astype(np.uint8)
img = cv2.cvtColor(corrected, cv2.COLOR_RGB2BGR)
# Method 3
img = cv2.normalize(img, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
img = img.astype(np.uint8)*255

Answer 1

One more step I need to do after applying as_8_bit_BGR_image(img) is to remove those pixels that are out of range.

def toOpenCVU8(img):
    out = img * 255
    out[out < 0] = 0
    out[out > 255] = 255
    out = cv2.cvtColor(out.astype(np.uint8), cv2.COLOR_RGB2BGR)