In [2]:

    

import cv2
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline


    

In [3]:

    

afmdata = np.genfromtxt('../NaRWHAL/Data/BackgroundedTXTFiles/5umNetwork-SparseToDense')
afmdata= afmdata*(10**9)
height, width = afmdata.shape
afmimg=np.zeros((height, width, 3))


    

In [ ]:

    

factor=(255)/(afmdata.max()-afmdata.min())
for i in range(height):
    for j in range(width):
        intensity=np.int((afmdata[i][j]-afmdata.min())*factor)
        #afmimg[i][j]=np.array([np.int((afmdata[i][j]-afmdata.min())*factor),0,0])
        afmimg[i][j]=np.array([intensity, intensity, intensity])


    

In [4]:

    

afmimg = np.uint8(afmimg)
grayscaled = cv2.cvtColor(afmimg,cv2.COLOR_BGR2GRAY)
plt.matshow(grayscaled, origin = 'lower', cmap = 'viridis')


    

    
Out[4]:





<matplotlib.image.AxesImage at 0x10bebe898>






    

In [5]:

    

background = cv2.adaptiveThreshold(grayscaled, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 71, 3)
plt.matshow(background, origin = 'lower', cmap = 'viridis')


    

    
Out[5]:





<matplotlib.image.AxesImage at 0x10be3c390>






    

In [11]:

    

backgrounded = grayscaled - background
plt.matshow(backgrounded, origin = 'lower', cmap = 'viridis')


    

    
Out[11]:





<matplotlib.image.AxesImage at 0x11079cd30>






    

In [13]:

    

retval2,threshold2 = cv2.threshold(backgrounded, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
plt.matshow(threshold2, origin = 'lower', cmap = "viridis")


    

    
Out[13]:





<matplotlib.image.AxesImage at 0x110b085c0>






    

In [1]:

    

img = backgrounded
equ = cv2.equalizeHist(img)
res = np.hstack((img,equ)) #stacking images side-by-side
plt.matshow(res, cmap = 'viridis')
plt.title('Backgrounded Image with and without Histogram Equalization')


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-1-0e022987e9a9> in <module>()
----> 1 img = backgrounded
      2 equ = cv2.equalizeHist(img)
      3 res = np.hstack((img,equ)) #stacking images side-by-side
      4 plt.matshow(res, cmap = 'viridis')
      5 plt.title('Backgrounded Image with and without Histogram Equalization')

NameError: name 'backgrounded' is not defined

In [26]:

    

def histogram_equalization(source):
    """
    Takes in a uint8 image and equalizes the histogram
    """
    img = source
    equ = cv2.equalizeHist(img)
    
    return equ


    

In [27]:

    

test1 = histogram_equalization(backgrounded)


    

In [28]:

    

plt.matshow(test1)


    

    
Out[28]:





<matplotlib.image.AxesImage at 0x1121215f8>






    

In [ ]: