[python] Convert np.array of type float64 to type uint8 scaling values

I have a particular np.array data which represents a particular grayscale image. I need to use SimpleBlobDetector() that unfortunately only accepts 8bit images, so I need to convert this image, obviously having a quality-loss.

I've already tried:

import numpy as np
import cv2
[...]
data = data / data.max() #normalizes data in range 0 - 255
data = 255 * data
img = data.astype(np.uint8)
cv2.imshow("Window", img)

But cv2.imshow is not giving the image as expected, but with strange distortion...

In the end, I only need to convert a np.float64 to np.uint8 scaling all the values and truncating the rest, eg. 65535 becomes 255, 65534 becomes 254 and so on.... Any help?

Thanks.

you can use skimage.img_as_ubyte(yourdata) it will make you numpy array ranges from 0->255

from skimage import img_as_ubyte

img = img_as_ubyte(data)
cv2.imshow("Window", img)

Considering that you are using OpenCV, the best way to convert between data types is to use normalize function.

img_n = cv2.normalize(src=img, dst=None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U)

However, if you don't want to use OpenCV, you can do this in numpy

def convert(img, target_type_min, target_type_max, target_type):
    imin = img.min()
    imax = img.max()

    a = (target_type_max - target_type_min) / (imax - imin)
    b = target_type_max - a * imax
    new_img = (a * img + b).astype(target_type)
    return new_img

And then use it like this

imgu8 = convert(img16u, 0, 255, np.uint8)

This is based on the answer that I found on crossvalidated board in comments under this solution https://stats.stackexchange.com/a/70808/277040