Using numpy to efficiently convert 16-bit image data to 8 bit for display, with intensity scaling

Question

I frequently convert 16-bit grayscale image data to 8-bit image data for display. It\'s almost always useful to adjust the minimum and maximum display intensity to highlight

Answer 1

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

Basically for converting from uint16 to uint8 algorithm looks like this

a = (255 - 0) / (65535 - 0)
b = 255 - a * 65535
newvalue = (a * img + b).astype(np.uint8)

A generalized version would look like this

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

e.g.

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