In [1]:

    

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

from skimage import data
from skimage.morphology import disk
from skimage.filters.rank import median, maximum, minimum, gradient, autolevel, otsu
#from skimage.filters import threshold_otsu


    

In [2]:

    

def custom_plot(im):
    hist = np.histogram(im, bins=np.arange(0, 256))
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 3))
    ax1.imshow(im, interpolation='none', cmap=plt.cm.gray)
    ax1.axis('off')
    ax2.set_yscale('log')
    ax2.plot(hist[1][:-1], hist[0], lw=2)
    ax2.set_title('Histogram of grey values')


    

In [30]:

    

im = data.imread('Comp4.jpg',as_grey=True)
custom_plot(im)


    

In [22]:

    

im_open = minimum(maximum(im, disk(5)), disk(5))
custom_plot(im_open)


    

    




/Users/mviana/anaconda3/lib/python3.5/site-packages/skimage/util/dtype.py:111: UserWarning: Possible precision loss when converting from float64 to uint8
  "%s to %s" % (dtypeobj_in, dtypeobj))






    

In [34]:

    

plt.plot(im_text[np.round(0.5*im.shape[0])])


    

    




/Users/mviana/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:1: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
  if __name__ == '__main__':






    
Out[34]:





[<matplotlib.lines.Line2D at 0x11616b320>]






    

In [24]:

    

im_box = im_open < 200
im_box = minimum(maximum(im_box, disk(5)),disk(5))
custom_plot(im_box)


    

In [33]:

    

im_text = im
im_text[im_box==255] = 255

custom_plot(im_text)

##For some reason this block is not workin!


    

In [7]:

    

from matplotlib import cm
from skimage.feature import canny
from skimage.transform import (hough_line, hough_line_peaks, probabilistic_hough_line)


edges = canny(im_box, 2, 1, 25)
lines = probabilistic_hough_line(edges, threshold=0, line_length=(0.05*im.shape[0]), line_gap=3)


    

In [8]:

    

print('Number of lines = ',len(lines))


    

    




Number of lines =  16

In [9]:

    

fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(16, 6), sharex=True, sharey=True)
ax0.imshow(edges, cmap=cm.gray)
ax0.set_title('Canny edges')
ax0.set_axis_off()
ax0.set_adjustable('box-forced')

d = 0
ax1.imshow(edges * 0)
for line in lines:
    p0, p1 = line
    ax1.plot((p0[0], p1[0]), (p0[1], p1[1]))
    d = d + np.sqrt(np.power(p0[0]-p1[0],2)+np.power(p0[1]-p1[1],2))
    
plt.show()

print('Average lines length = ',d/len(lines))


    

    













    




Average lines length =  49.8044169638

In [10]:

    

edges = canny(im_text, 2, 1, 25)
lines = probabilistic_hough_line(edges, threshold=0, line_length=(0.05*im.shape[0]), line_gap=3)


    

In [11]:

    

print('Number of lines = ',len(lines))


    

    




Number of lines =  15

In [12]:

    

fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(16, 6), sharex=True, sharey=True)
ax0.imshow(edges, cmap=cm.gray)
ax0.set_title('Canny edges')
ax0.set_axis_off()
ax0.set_adjustable('box-forced')

d = 0
ax1.imshow(edges * 0)
for line in lines:
    p0, p1 = line
    ax1.plot((p0[0], p1[0]), (p0[1], p1[1]))
    d = d + np.sqrt(np.power(p0[0]-p1[0],2)+np.power(p0[1]-p1[1],2))
    
plt.show()

print('Average lines length = ',d/len(lines))


    

    













    




Average lines length =  51.6530290867

In [13]:

    

from skimage.feature import corner_harris, corner_subpix, corner_peaks
coords = corner_peaks(corner_harris(im_box), min_distance=50)
coords_subpix = corner_subpix(im_box, coords, window_size=13)
fig, ax = plt.subplots()
ax.imshow(im_box, interpolation='none', cmap=plt.cm.gray)
ax.plot(coords[:, 1], coords[:, 0], '.b', markersize=3)
ax.plot(coords_subpix[:, 1], coords_subpix[:, 0], '+r', markersize=15)
plt.show()


    

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