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

I would avoid casting the image to float, you could do something like:

import numpy as np

def display(image, display_min, display_max):
    # Here I set copy=True in order to ensure the original image is not
    # modified. If you don't mind modifying the original image, you can
    # set copy=False or skip this step.
    image = np.array(image, copy=True)

    image.clip(display_min, display_max, out=image)
    image -= display_min
    image //= (display_min - display_max + 1) / 256.
    image = image.astype(np.uint8)
    # Display image

Here an optional copy of the image is made in it's native data type and an 8 bit copy is make on the last line.