In [1]:

    

import numpy as np
import matplotlib
import matplotlib.pyplot as plt

from skimage import data
from skimage.util.dtype import dtype_range
from skimage.util import img_as_ubyte
from skimage import exposure
from skimage.morphology import disk
from skimage.filters import rank


    

In [3]:

    

matplotlib.rcParams['font.size'] = 9


def plot_img_and_hist(img, axes, bins=256):
    """Plot an image along with its histogram and cumulative histogram.

    """
    ax_img, ax_hist = axes
    ax_cdf = ax_hist.twinx()

    # Display image
    ax_img.imshow(img, cmap=plt.cm.gray)
    ax_img.set_axis_off()

    # Display histogram
    ax_hist.hist(img.ravel(), bins=bins)
    ax_hist.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))
    ax_hist.set_xlabel('Pixel intensity')

    xmin, xmax = dtype_range[img.dtype.type]
    ax_hist.set_xlim(xmin, xmax)

    # Display cumulative distribution
    img_cdf, bins = exposure.cumulative_distribution(img, bins)
    ax_cdf.plot(bins, img_cdf, 'r')

    return ax_img, ax_hist, ax_cdf


    

In [4]:

    

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


    

In [12]:

    

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 [9]:

    

afmimg = np.array([0, 0.5, 1], dtype=float)
afmimg.shape


    

    




/Users/wesleytatum/miniconda3/lib/python3.5/site-packages/skimage/util/dtype.py:110: UserWarning: Possible precision loss when converting from float64 to uint8
  "%s to %s" % (dtypeobj_in, dtypeobj))






    
Out[9]:





(3,)

In [5]:

    

# Global equalize
img_rescale = exposure.equalize_hist(afmdata)

# Equalization
selem = disk(30)
img_eq = rank.equalize(afmdata, selem=selem)


# Display results
fig = plt.figure(figsize=(8, 5))
axes = np.zeros((2, 3), dtype=np.object)
axes[0, 0] = plt.subplot(2, 3, 1, adjustable='box-forced')
axes[0, 1] = plt.subplot(2, 3, 2, sharex=axes[0, 0], sharey=axes[0, 0],
                         adjustable='box-forced')
axes[0, 2] = plt.subplot(2, 3, 3, sharex=axes[0, 0], sharey=axes[0, 0],
                         adjustable='box-forced')
axes[1, 0] = plt.subplot(2, 3, 4)
axes[1, 1] = plt.subplot(2, 3, 5)
axes[1, 2] = plt.subplot(2, 3, 6)

ax_img, ax_hist, ax_cdf = plot_img_and_hist(afmdata, axes[:, 0])
ax_img.set_title('Low contrast image')
ax_hist.set_ylabel('Number of pixels')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale, axes[:, 1])
ax_img.set_title('Global equalise')

ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 2])
ax_img.set_title('Local equalize')
ax_cdf.set_ylabel('Fraction of total intensity')


# prevent overlap of y-axis labels
fig.tight_layout()
plt.show()


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-5-4243b01c0c95> in <module>()
      4 # Equalization
      5 selem = disk(30)
----> 6 img_eq = rank.equalize(img, selem=selem)
      7 
      8 

NameError: name 'img' is not defined

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