'Stretching' histograms (levels) in Numpy, Python

Question

I have grayscale image whose background is, on a 0-255 color scale, a mid-white color with an average pixel color value of 246; the foreground is mid-grey with an average pixel-color value of 186.

I would like to 'shift' every pixel above 246 to 255, every pixel below 186 to zero, and 'stretch' everything between. Is there any ready-made algorithm/process to do this in numpy or python, or must the new levels/histogram be calculated 'manually' (as I have done thus far)?

This is the equivalent of, in Gimp or Photoshop, opening the levels window and selecting, with the white and black eyedropper respectively, a light region we want to make white and a darker region we want to make black: the application modifies the levels/histogram ('stretches' the values between the points selected) accordingly.

Some images of what I'm attempting:

There are quite a few things going on, but I tried to sum it all up into a few screenshots. — Josef M. Schomburg, Apr 08 '19 at 06:46
Nota: the reason I'm doing this is because existing algorithms result in text that, although black, has missing patches and gaps. OpenCv manages to find countours quite well, though, and I use these to create the mask that defines what regions should be averaged, and it is this that gives the two values to be knocked up and down.. — Josef M. Schomburg, Apr 08 '19 at 06:55
Also see: [Histogram equalization of grayscale images with NumPy](https://stackoverflow.com/questions/28518684/histogram-equalization-of-grayscale-images-with-numpy) — NoDataDumpNoContribution, Apr 09 '19 at 07:51
You could do just what you described: (1) Threshold all pixels above 246 to 255 (2) Threshold all pixels below 186 to zero (3) Normalize all pixels in between to a maximum of 244. I don't think there is any ready-made algorithm for this, as all 3 steps to achieve your desired result are trivial. — T A, Apr 10 '19 at 12:41

Divakar · Accepted Answer · 2019-04-07T18:37:49.193

Here's one way -

def stretch(a, lower_thresh, upper_thresh):
    r = 255.0/(upper_thresh-lower_thresh+2) # unit of stretching
    out = np.round(r*(a-lower_thresh+1)).astype(a.dtype) # stretched values
    out[a<lower_thresh] = 0
    out[a>upper_thresh] = 255
    return out

As per OP, the criteria set was :

'shift' every pixel above 246 to 255, hence 247 and above should become 255.
every pixel below 186 to zero, hence 185 and below should become 0.
Hence, based on above mentioned two requirements, 186 should become something greater than 0 and so on, until 246 which should be lesser than 255.

Alternatively, we can also use np.where to make it a bit more compact -

def stretch(a, lower_thresh, upper_thresh):
    r = 255.0/(upper_thresh-lower_thresh+2) # unit of stretching
    out = np.round(r*np.where(a>=lower_thresh,a-lower_thresh+1,0)).clip(max=255)
    return out.astype(a.dtype)

Sample run -

# check out first row input, output for variations
In [216]: a
Out[216]: 
array([[186, 187, 188, 246, 247],
       [251, 195, 103,   9, 211],
       [ 21, 242,  36,  87,  70]], dtype=uint8)

In [217]: stretch(a, lower_thresh=186, upper_thresh=246)
Out[217]: 
array([[  4,   8,  12, 251, 255], 
       [255,  41,   0,   0, 107],
       [  0, 234,   0,   0,   0]], dtype=uint8)

Both answers are great, but choosing this one for its explanative qualities (for those learning python). — Josef M. Schomburg, Apr 14 '19 at 12:03

Paul Panzer · Answer 2 · 2019-04-08T02:30:16.570

If your picture is uint8 and typical picture size, one efficient method is setting up a lookup table:

L, H = 186, 246
lut = np.r_[0:0:(L-1)*1j, 0.5:255.5:(H-L+3)*1j, 255:255:(255-H-1)*1j].astype('u1')

# example
from scipy.misc import face
f = face()

rescaled = lut[f]

For smaller images it is faster (on my setup it crosses over at around 100,000 gray scale pixels) to transform directly:

fsmall = (f[::16, ::16].sum(2)//3).astype('u1')

slope = 255/(H-L+2)
rescaled = ((1-L+0.5/slope+fsmall)*slope).clip(0, 255).astype('u1')

'Stretching' histograms (levels) in Numpy, Python

2 Answers2