In [2]:

    

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


    

In [8]:

    

afmdata = np.genfromtxt('../Data/BackgroundedTXTFiles/500nmGood-0')
afmdata= afmdata*(10**9)
height, width = afmdata.shape
afmimg=np.zeros((height, width, 3))


    

In [9]:

    

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 [16]:

    

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


    

    
Out[16]:





<matplotlib.image.AxesImage at 0x1150445c0>






    

In [56]:

    

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


    

    
Out[56]:





<matplotlib.image.AxesImage at 0x1250fdfd0>






    

In [21]:

    

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


    

    
Out[21]:





<matplotlib.image.AxesImage at 0x114417470>






    

In [61]:

    

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


    

    
Out[61]:





<matplotlib.image.AxesImage at 0x127354f98>






    

In [ ]: