%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
from scipy.ndimage import gaussian_filter
from skimage import img_as_float
from skimage.morphology import reconstruction

# Convert to float: Important for subtraction later which won't work with uint8
im = Image.open('kabuto.jpg').convert('L')
image = np.array(im)
image = img_as_float(image)
image = gaussian_filter(image, 1)

seed = np.copy(image)
seed[1:-1, 1:-1] = image.min()
mask = image

dilated = reconstruction(seed, mask, method='dilation')

fig, (ax0, ax1, ax2) = plt.subplots(nrows=1,
                                    ncols=3,
                                    figsize=(8, 2.5),
                                    sharex=True,
                                    sharey=True)

ax0.imshow(image, cmap='gray')
ax0.set_title('original image')
ax0.axis('off')

ax1.imshow(dilated, vmin=image.min(), vmax=image.max(), cmap='gray')
ax1.set_title('dilated')
ax1.axis('off')

ax2.imshow(image - dilated, cmap='gray')
ax2.set_title('image - dilated')
ax2.axis('off')
fig.tight_layout()
#plt.savefig('kabuto_dilated.jpg',dpi=150)

fig, (ax0, ax1, ax2) = plt.subplots(nrows=1, ncols=3, figsize=(8, 2.5))
yslice = 147

ax0.plot(mask[yslice], 'm', label='mask')
ax0.plot(seed[yslice], 'g', label='seed')
ax0.plot(dilated[yslice], 'r', label='dilated')
#
ax0.set_title('image slice')
ax0.set_xticks([])
ax0.legend()

ax1.imshow(dilated, vmin=image.min(), vmax=image.max(), cmap='gray')
ax1.axhline(yslice, color='r', alpha=0.4)
ax1.set_title('dilated')
ax1.axis('off')

ax2.imshow(image - dilated, cmap='gray')
ax2.axhline(yslice, color='r', alpha=0.4)
ax2.set_title('image - dilated')
ax2.axis('off')

fig.tight_layout()
#plt.savefig('kabuto_dilated_withline.jpg',dpi=150)
plt.show()