Edge detection

Canny filter

Morphological operator

Tasks

Try different thresholds for binarization (lena)
Apply skeletonize, erosion to bin images (lena,camera)
Apply canny filter (lena, camera)

Binarization



In [1]:

    
import numpy as np
from skimage import data
import matplotlib.pyplot as plt
image = data.camera()
tt = np.median(image)
binary = image > tt

fig, axes = plt.subplots(ncols=2, figsize=(8, 3))
ax = axes.ravel()

ax[0].imshow(image, cmap=plt.cm.gray)
ax[0].set_title('Original image')

ax[1].imshow(binary, cmap=plt.cm.gray)
ax[1].set_title('Result')

for a in ax:
    a.axis('off')

plt.show()









    



---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-1-db6933e9092b> in <module>()
      5 binary = image > tt
      6 
----> 7 fig, axes = plt.subplots(ncols=2, figsize=(8, 3))
      8 ax = axes.ravel()
      9 

NameError: name 'plt' is not defined

Example of canny filter



In [ ]:

    
import numpy as np
import matplotlib.pyplot as plt
from scipy import ndimage as ndi

from skimage import feature


# Generate noisy image of a square
im = np.zeros((128, 128))
im[32:-32, 32:-32] = 1

im = ndi.rotate(im, 15, mode='constant')
im = ndi.gaussian_filter(im, 4)
im += 0.2 * np.random.random(im.shape)

# Compute the Canny filter for two values of sigma
edges1 = feature.canny(im)
edges2 = feature.canny(im, sigma=3)

# display results
fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(8, 3),
                                    sharex=True, sharey=True)

ax1.imshow(im, cmap=plt.cm.gray)
ax1.axis('off')
ax1.set_title('noisy image', fontsize=20)

ax2.imshow(edges1, cmap=plt.cm.gray)
ax2.axis('off')
ax2.set_title('Canny filter, $\sigma=1$', fontsize=20)

ax3.imshow(edges2, cmap=plt.cm.gray)
ax3.axis('off')
ax3.set_title('Canny filter, $\sigma=3$', fontsize=20)

fig.tight_layout()

plt.show()



In [4]:

    
import os
import matplotlib.pyplot as plt
from skimage.data import data_dir
from skimage.util import img_as_ubyte
from skimage import io


def plot_comparison(original, filtered, filter_name):

    fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(8, 4), sharex=True,
                                   sharey=True)
    ax1.imshow(original, cmap=plt.cm.gray)
    ax1.set_title('original')
    ax1.axis('off')
    ax1.set_adjustable('box-forced')
    ax2.imshow(filtered, cmap=plt.cm.gray)
    ax2.set_title(filter_name)
    ax2.axis('off')
    ax2.set_adjustable('box-forced')
    plt.show()



In [ ]:



In [ ]:

    
import os
from skimage import io
import matplotlib.pyplot as plt

from skimage.morphology import disk
from skimage.morphology import erosion, dilation, opening, closing
horse = io.imread(os.path.join(data_dir, "horse.png"), as_grey=True)
selem = disk(6)
eroded = erosion(horse, selem)
plot_comparison(horse, eroded, 'erosion')



In [5]:

    
import os



from skimage import io
from skimage.data import data_dir
import matplotlib.pyplot as plt
from skimage.morphology import disk
from skimage.morphology import erosion, skeletonize, opening, closing
horse = io.imread(os.path.join(data_dir, "horse.png"), as_grey=True)
selem = disk(6)
sk = skeletonize(horse == 0)
plot_comparison(horse, sk, 'skeletonize')



In [ ]: