mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 23:10:03 +08:00
Tony S Yu
149 lines
4.8 KiB
Python
149 lines
4.8 KiB
Python
"""
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===============================================================
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Comparing edge-based segmentation and region-based segmentation
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===============================================================
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In this example, we will see how to segment objects from a background.
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We use the ``coins`` image from ``skimage.data``. This image shows
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several coins outlined against a darker background. The segmentation of
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the coins cannot be done directly from the histogram of grey values,
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because the background shares enough grey levels with the coins that a
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thresholding segmentation is not sufficient. Simply thresholding the image
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leads either to missing significant parts of the coins, or to merging parts
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of the background with the coins.
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We first try an edge-based segmentation. We use the Canny detector to
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delineate the contours of the coins. These contours are filled using
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mathematical morphology (``scipy.ndimage.binary_fill_holes``). Small spurious
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objects are easily removed by applying a threshold on the size of
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unconnected objects. However, this method is not very robust, since contours
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that are not perfectly closed are not filled correctly. This happens for one
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of the coins.
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We therefore try a second method, that is region-based. Here we use the
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watershed transform. An elevation map is provided by the Sobel gradient
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of the image. Markers of the background and the coins are determined from
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the extreme parts of the histogram of grey values.
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This second method works even better, and the coins can be segmented and
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labeled individually.
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"""
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import numpy as np
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from scipy import ndimage
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import matplotlib.pyplot as plt
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import skimage
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from skimage.filter import canny, sobel
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from skimage.morphology import watershed
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#------------------ Loading data --------------------------------
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from skimage import data
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coins = data.coins()
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#------------ Histogram of grey values ---------------------------
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histo = np.histogram(coins, bins=np.arange(0, 256))
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plt.figure(figsize=(8, 3))
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plt.subplot(121)
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plt.imshow(coins, cmap=plt.cm.gray, interpolation='nearest')
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plt.axis('off')
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plt.subplot(122)
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plt.plot(histo[1][:-1], histo[0], lw=2)
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plt.title('histogram of grey values')
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#------------------ Tentative thresholding --------------------------------
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plt.figure(figsize=(6, 3))
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plt.subplot(121)
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plt.imshow(coins > 100, cmap=plt.cm.gray, interpolation='nearest')
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plt.title('coins > 100')
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plt.axis('off')
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plt.subplot(122)
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plt.imshow(coins > 150, cmap=plt.cm.gray, interpolation='nearest')
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plt.title('coins > 150')
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plt.axis('off')
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plt.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0,
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right=1)
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#------------------ Edge-based segmentation --------------------------------
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edges = canny(coins/255.)
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fill_coins = ndimage.binary_fill_holes(edges)
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label_objects, nb_labels = ndimage.label(fill_coins)
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sizes = np.bincount(label_objects.ravel())
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mask_sizes = sizes > 20
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mask_sizes[0] = 0
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coins_cleaned = mask_sizes[label_objects]
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plt.figure(figsize=(7, 3.))
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plt.subplot(131)
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plt.imshow(edges, cmap=plt.cm.gray, interpolation='nearest')
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plt.axis('off')
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plt.title('Canny detector')
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plt.subplot(132)
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plt.imshow(fill_coins, cmap=plt.cm.gray, interpolation='nearest')
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plt.axis('off')
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plt.title('Filling the holes')
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plt.subplot(133)
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plt.imshow(coins_cleaned, cmap=plt.cm.gray, interpolation='nearest')
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plt.axis('off')
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plt.title('Removing small objects')
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plt.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0,
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right=1)
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#------------------ Region-based segmentation --------------------------------
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markers = np.zeros_like(coins)
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markers[coins < 30] = 1
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markers[coins > 150] = 2
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elevation_map = sobel(coins)
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segmentation = watershed(elevation_map, markers)
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plt.figure(figsize=(7, 3))
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plt.subplot(131)
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plt.imshow(markers, cmap=plt.cm.spectral, interpolation='nearest')
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plt.axis('off')
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plt.title('markers')
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plt.subplot(132)
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plt.imshow(elevation_map, cmap=plt.cm.jet, interpolation='nearest')
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plt.axis('off')
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plt.title('elevation_map')
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plt.subplot(133)
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plt.imshow(segmentation, cmap=plt.cm.gray, interpolation='nearest')
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plt.axis('off')
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plt.title('segmentation')
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plt.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0,
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right=1)
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# ------------------- Removing a few small holes ---------------------
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segmentation = ndimage.binary_fill_holes(segmentation - 1)
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#------------------ Labeling the coins --------------------------------
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labeled_coins, _ = ndimage.label(segmentation)
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plt.figure(figsize=(6, 3))
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plt.subplot(121)
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plt.imshow(coins, cmap=plt.cm.gray, interpolation='nearest')
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plt.contour(segmentation, [0.5], linewidths=1.2, colors='y')
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plt.axis('off')
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plt.subplot(122)
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plt.imshow(labeled_coins, cmap=plt.cm.spectral, interpolation='nearest')
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plt.axis('off')
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plt.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0,
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right=1)
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plt.show()
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