In [1]:

    

# contrast-stretching spatial filter
from __future__ import division # float/real division
from matplotlib import pyplot as plt
from matplotlib import image
import numpy as np
%matplotlib inline
img = image.imread('MP.tif')[...,0]
plt.figure(figsize=(12,12))
plt.imshow(img, cmap='gray')


    

    
Out[1]:





<matplotlib.image.AxesImage at 0x7f2cdc1c5bd0>






    

In [2]:

    

histogram = np.histogram(img.flatten(), bins=range(256))
plt.xlim((0,255))
plt.plot(histogram[0])


    

    
Out[2]:





[<matplotlib.lines.Line2D at 0x7f2cda8b0f50>]






    

In [3]:

    

# page 85 in gw - variables
# use ranges with data from histogram
r1 = 1
s1 = 1
c1 = s1/r1 # slope
# enhance 25-50
r2 = 150
s2 = 254
c2 = (s2-s1)/(r2-r1)
# r2 -> rmax
c3 = (255-s2)/(255-r2)


    

In [4]:

    

# mask for values below r1/above r2
below = img < r1
above = img > r2
contrast_img = np.zeros(img.shape)
# rmin -> r1
contrast_img[below] = img[below] * c1
# r1 -> r2, ~ means NOT
contrast_img[~below & ~above] = (img[~below & ~above] - r1) * c2
# r2 -> rmax
contrast_img[above] = (img[above] - r2) * c3 + s2


    

In [5]:

    

plt.figure(figsize=(12,12))
plt.imshow(contrast_img, cmap='gray')


    

    
Out[5]:





<matplotlib.image.AxesImage at 0x7f2cda868d50>






    

In [6]:

    

histogram = np.histogram(contrast_img.flatten(), bins=range(256))
plt.xlim((0,255))
plt.plot(histogram[0])


    

    
Out[6]:





[<matplotlib.lines.Line2D at 0x7f2cda7963d0>]






    

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