sci.py

import numpy as np
import matplotlib.pyplot as plt
from scipy import ndimage as ndi
import cv2
from skimage import feature
np.set_printoptions(threshold='nan')
from skimage.morphology import convex_hull_image
from skimage import data, img_as_float

# Generate noisy image of a square
im = cv2.imread('IMG_8792.jpg',0)



im = ndi.gaussian_filter(im, 4)


# Compute the Canny filter for two values of sigma
edges1 = feature.canny(im)
edges2 = feature.canny(im, sigma=4)

# 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, $\sigma=12$', fontsize=20)

fig.tight_layout()                   
chull = convex_hull_image(edges2)

fig, axes = plt.subplots(1, 2, figsize=(8, 4))
ax = axes.ravel()

ax[0].set_title('Original picture')
ax[0].imshow(edges2, cmap=plt.cm.gray, interpolation='nearest')
ax[0].set_axis_off()

ax[1].set_title('Transformed picture')
ax[1].imshow(chull, cmap=plt.cm.gray, interpolation='nearest')
ax[1].set_axis_off()
plt.tight_layout()
plt.show()