mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 02:30:57 +08:00
Johannes Schönberger
115 lines
3.2 KiB
Python
115 lines
3.2 KiB
Python
"""
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=========================
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Filtering regional maxima
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=========================
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Here, we use morphological reconstruction to create a background image, which
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we can subtract from the original image to isolate bright features (regional
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maxima).
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First we try reconstruction by dilation starting at the edges of the image. We
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initialize a seed image to the minimum intensity of the image, and set its
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border to be the pixel values in the original image. These maximal pixels will
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get dilated in order to reconstruct the background image.
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"""
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import numpy as np
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from scipy.ndimage import gaussian_filter
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import matplotlib.pyplot as plt
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from skimage import data
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from skimage import img_as_float
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from skimage.morphology import reconstruction
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# Convert to float: Important for subtraction later which won't work with uint8
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image = img_as_float(data.coins())
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image = gaussian_filter(image, 1)
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seed = np.copy(image)
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seed[1:-1, 1:-1] = image.min()
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mask = image
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dilated = reconstruction(seed, mask, method='dilation')
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"""
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Subtracting the dilated image leaves an image with just the coins and a flat,
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black background, as shown below.
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"""
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fig, (ax1, ax2, ax3) = plt.subplots(ncols=3, figsize=(8, 2.5))
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ax1.imshow(image)
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ax1.set_title('original image')
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ax1.axis('off')
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ax2.imshow(dilated, vmin=image.min(), vmax=image.max())
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ax2.set_title('dilated')
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ax2.axis('off')
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ax3.imshow(image - dilated)
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ax3.set_title('image - dilated')
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ax3.axis('off')
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plt.tight_layout()
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"""
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.. image:: PLOT2RST.current_figure
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Although the features (i.e. the coins) are clearly isolated, the coins
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surrounded by a bright background in the original image are dimmer in the
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subtracted image. We can attempt to correct this using a different seed image.
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Instead of creating a seed image with maxima along the image border, we can use
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the features of the image itself to seed the reconstruction process. Here, the
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seed image is the original image minus a fixed value, ``h``.
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"""
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h = 0.4
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seed = image - h
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dilated = reconstruction(seed, mask, method='dilation')
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hdome = image - dilated
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"""
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To get a feel for the reconstruction process, we plot the intensity of the
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mask, seed, and dilated images along a slice of the image (indicated by red
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line).
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"""
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fig, (ax1, ax2, ax3) = plt.subplots(ncols=3, figsize=(8, 2.5))
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yslice = 197
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ax1.plot(mask[yslice], '0.5', label='mask')
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ax1.plot(seed[yslice], 'k', label='seed')
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ax1.plot(dilated[yslice], 'r', label='dilated')
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ax1.set_ylim(-0.2, 2)
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ax1.set_title('image slice')
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ax1.set_xticks([])
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ax1.legend()
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ax2.imshow(dilated, vmin=image.min(), vmax=image.max())
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ax2.axhline(yslice, color='r', alpha=0.4)
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ax2.set_title('dilated')
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ax2.axis('off')
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ax3.imshow(hdome)
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ax3.axhline(yslice, color='r', alpha=0.4)
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ax3.set_title('image - dilated')
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ax3.axis('off')
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plt.tight_layout()
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plt.show()
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"""
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.. image:: PLOT2RST.current_figure
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As you can see in the image slice, each coin is given a different baseline
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intensity in the reconstructed image; this is because we used the local
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intensity (shifted by ``h``) as a seed value. As a result, the coins in the
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subtracted image have similar pixel intensities. The final result is known as
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the h-dome of an image since this tends to isolate regional maxima of height
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``h``. This operation is particularly useful when your images are unevenly
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illuminated.
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"""
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