"""
===================================
Masked Normalized Cross-Correlation
===================================

In this example, we use the masked normalized cross-correlation to identify the relative shift
between two similar images containing invalid data.

In this case, the images cannot simply be masked before computing the cross-correlation,
as the masks will influence the computation. The influence of the masks must be removed from
the cross-correlation, as is described in [1]_.

In this example, we register the translation between two images. However, one of the
images has about 25% of the pixels which are corrupted.

.. [1] D. Padfield, "Masked object registration in the Fourier domain"
       IEEE Transactions on Image Processing (2012). :DOI:`10.1109/TIP.2011.2181402`

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

from skimage import data, draw
from skimage.feature import masked_register_translation
from scipy import ndimage as ndi

#############################################
# Define areas of the image which are invalid.
# Probability of an invalid pixel is 25%.
# This could be due to a faulty detector, or edges that
# are not affected by translation (e.g. moving object in a window).
# See reference paper for more examples
image = data.camera()
shift = (-22, 13)

corrupted_pixels = np.random.choice([False, True],
                                    size = image.shape,
                                    p = [0.25, 0.75])

# The shift corresponds to the pixel offset relative to the reference image
offset_image = ndi.shift(image, shift)
offset_image *= corrupted_pixels
print(f"Known offset (row, col): {shift}")

# Determine what the mask is based on which pixels are invalid
# In this case, we know what the mask should be since we corrupted
# the pixels ourselves
mask = corrupted_pixels

detected_shift = masked_register_translation(image, offset_image, mask)

print(f"Detected pixel offset (row, col): {-detected_shift}")

fig = plt.figure(figsize=(8, 3))
ax1 = plt.subplot(1, 3, 1)
ax2 = plt.subplot(1, 3, 2, sharex=ax1, sharey=ax1)
ax3 = plt.subplot(1, 3, 3, sharex=ax1, sharey=ax1)

ax1.imshow(image, cmap='gray')
ax1.set_axis_off()
ax1.set_title('Reference image')

ax2.imshow(offset_image.real, cmap='gray')
ax2.set_axis_off()
ax2.set_title('Corrupted, offset image')

ax3.imshow(mask, cmap='gray')
ax3.set_axis_off()
ax3.set_title('Masked pixels')


plt.show()

##################################################
# Solid masks are another illustrating example. In this case, we have a
# limited view of an image and an offset image. The masks for these images
# need not be the same. The `masked_register_translation` function will correctly identify
# which part of the images should be compared.
image = data.camera()
shift = (-22, 13)

rr1, cc1 = draw.ellipse(259, 248,
                      r_radius = 125, c_radius = 100,
                      shape = image.shape)

rr2, cc2 = draw.ellipse(300, 200,
                        r_radius = 110, c_radius = 180,
                        shape = image.shape)

mask1 = np.zeros_like(image, dtype = np.bool)
mask2 = np.zeros_like(image, dtype = np.bool)
mask1[rr1, cc1] = True
mask2[rr2, cc2] = True

offset_image = ndi.shift(image, shift)
image *= mask1
offset_image *= mask2

print(f"Known offset (row, col): {shift}")

detected_shift = masked_register_translation(image, offset_image, mask1, mask2)

print(f"Detected pixel offset (row, col): {-detected_shift}")

fig = plt.figure(figsize=(8,3))
ax1 = plt.subplot(1, 2, 1)
ax2 = plt.subplot(1, 2, 2, sharex=ax1, sharey=ax1)

ax1.imshow(image, cmap='gray')
ax1.set_axis_off()
ax1.set_title('Reference image')

ax2.imshow(offset_image.real, cmap='gray')
ax2.set_axis_off()
ax2.set_title('Masked, offset image')

plt.show()
