mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 00:39:44 +08:00
Tony S Yu
188 lines
4.6 KiB
Python
188 lines
4.6 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. We use
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the ``coins`` image from ``skimage.data``, which shows several coins outlined
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against a darker background.
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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from skimage import data
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coins = data.coins()
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hist = 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(hist[1][:-1], hist[0], lw=2)
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plt.title('histogram of grey values')
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"""
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.. image:: PLOT2RST.current_figure
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Thresholding
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============
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A simple way to segment the coins is to choose a threshold based on the
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histogram of grey values. Unfortunately, thresholding this image gives a binary
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image that either misses significant parts of the coins or merges parts of the
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background with the coins:
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"""
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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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margins = dict(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0, right=1)
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plt.subplots_adjust(**margins)
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"""
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.. image:: PLOT2RST.current_figure
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Edge-based segmentation
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=======================
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Next, we try to delineate the contours of the coins using edge-based
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segmentation. To do this, we first get the edges of features using the Canny
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edge-detector.
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"""
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from skimage.filter import canny
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edges = canny(coins/255.)
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plt.figure(figsize=(4, 3))
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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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"""
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.. image:: PLOT2RST.current_figure
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These contours are then filled using mathematical morphology.
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"""
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from scipy import ndimage
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fill_coins = ndimage.binary_fill_holes(edges)
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plt.figure(figsize=(4, 3))
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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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"""
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.. image:: PLOT2RST.current_figure
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Small spurious objects are easily removed by setting a minimum size for valid
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objects.
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"""
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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=(4, 3))
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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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"""
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.. image:: PLOT2RST.current_figure
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However, this method is not very robust, since contours that are not perfectly
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closed are not filled correctly, as is the case for one unfilled coin above.
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Region-based segmentation
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=========================
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We therefore try a region-based method using the
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watershed transform. First, we find an elevation map using the Sobel gradient of the
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image.
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"""
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from skimage.filter import sobel
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elevation_map = sobel(coins)
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plt.figure(figsize=(4, 3))
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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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"""
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.. image:: PLOT2RST.current_figure
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Next we find markers of the background and the coins based on the extreme parts
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of the histogram of grey values.
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"""
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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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plt.figure(figsize=(4, 3))
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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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"""
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.. image:: PLOT2RST.current_figure
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Finally, we use the watershed transform to fill regions of the elevation map starting from the markers determined above:
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"""
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from skimage.morphology import watershed
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segmentation = watershed(elevation_map, markers)
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plt.figure(figsize=(4, 3))
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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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"""
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.. image:: PLOT2RST.current_figure
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This last 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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segmentation = ndimage.binary_fill_holes(segmentation - 1)
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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(**margins)
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"""
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.. image:: PLOT2RST.current_figure
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"""
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plt.show()
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