numpy uint8 pixel wrapping solution

Question

For an image processing class, I am doing point operations on monochrome images. Pixels are uint8 [0,255].

numpy uint8 will wrap. For example, 235+30 = 9. I need the pixels to saturate (max=255) or truncate (min=0) instead of wrapping.

My solution uses int32 pixels for the point math then converts to uint8 to save the image.

Is this the best way? Or is there a faster way?

#!/usr/bin/python

import sys
import numpy as np
import Image

def to_uint8( data ) :
    # maximum pixel
    latch = np.zeros_like( data )
    latch[:] = 255
    # minimum pixel
    zeros = np.zeros_like( data )

    # unrolled to illustrate steps
    d = np.maximum( zeros, data )
    d = np.minimum( latch, d )

    # cast to uint8
    return np.asarray( d, dtype="uint8" )

infilename=sys.argv[1]
img = Image.open(infilename)
data32 = np.asarray( img, dtype="int32")
data32 += 30
data_u8 = to_uint8( data32 )
outimg = Image.fromarray( data_u8, "L" )
outimg.save( "out.png" )

Input image:

Output image:
Output

Asked By: David Poole

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Source

Answer 1

Use numpy.clip:

import numpy as np
np.clip(data32, 0, 255, out=data32)
data_u8 = data32.astype('uint8')

Note that you can also brighten images without numpy this way:

import ImageEnhance
enhancer = ImageEnhance.Brightness(img)
outimg = enhancer.enhance(1.2)
outimg.save('out.png')

Answered By: unutbu

Answer 2

Basically, it comes down to checking before you add. For instance, you could define a function like this:

def clip_add(arr, amt):
    if amt > 0:
        cutoff = 255 - amt
        arr[arr > cutoff] = 255
        arr[arr <= cutoff] += amt
    else:
        cutoff = -amt
        arr[arr < cutoff] = 0
        arr[arr >= cutoff] += amt

Answered By: Justin Peel

Answer 3

You can use OpenCV add or subtract functions (additional explanation here).

>>> import numpy as np
>>> import cv2
>>> arr = np.array([100, 250, 255], dtype=np.uint8)
>>> arr
Out[1]: array([100, 250, 255], dtype=uint8)
>>> cv2.add(arr, 10, arr)  # Inplace
Out[2]: array([110, 255, 255], dtype=uint8)  # Saturated!
>>> cv2.subtract(arr, 150, arr)
Out[3]: array([  0, 105, 105], dtype=uint8)  # Truncated!

Unfortunately it’s impossible to use indexes for output array, so inplace calculations for each image channel may be performed in this, less efficient, way:

arr[..., channel] = cv2.add(arr[..., channel], 40)

Answered By: radioxoma

numpy uint8 pixel wrapping solution

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