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"""
====================
Build image pyramids
====================
The ``pyramid_gaussian`` function takes an image and yields successive images
shrunk by a constant scale factor. Image pyramids are often used, e.g., to
implement algorithms for denoising, texture discrimination, and scale-invariant
detection.
"""
import numpy as np
import matplotlib.pyplot as plt
from skimage import data
from skimage.transform import pyramid_gaussian
image = data.astronaut()
rows, cols, dim = image.shape
pyramid = tuple(pyramid_gaussian(image, downscale=2, channel_axis=-1))
#####################################################################
# Generate a composite image for visualization
# ============================================
#
# For visualization, we generate a composite image with the same number of rows
# as the source image but with ``cols + pyramid[1].shape[1]`` columns. We then
# have space to stack all of the dowsampled images to the right of the
# original.
#
# Note: The sum of the number of rows in all dowsampled images in the pyramid
# may sometimes exceed the original image size in cases when image.shape[0] is
# not a power of two. We expand the number of rows in the composite slightly as
# necessary to account for this. Expansion beyond the number of rows in the
# original will also be necessary to cover cases where downscale < 2.
# determine the total number of rows and columns for the composite
composite_rows = max(rows, sum(p.shape[0] for p in pyramid[1:]))
composite_cols = cols + pyramid[1].shape[1]
composite_image = np.zeros((composite_rows, composite_cols, 3), dtype=np.double)
# store the original to the left
composite_image[:rows, :cols, :] = pyramid[0]
# stack all downsampled images in a column to the right of the original
i_row = 0
for p in pyramid[1:]:
n_rows, n_cols = p.shape[:2]
composite_image[i_row : i_row + n_rows, cols : cols + n_cols] = p
i_row += n_rows
fig, ax = plt.subplots()
ax.imshow(composite_image)
plt.show()
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