mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 13:02:29 +08:00
Johannes Schönberger
150 lines
5.0 KiB
Python
150 lines
5.0 KiB
Python
"""
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==================================================
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Comparing edge-based 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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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 3))
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ax1.imshow(coins, cmap=plt.cm.gray, interpolation='nearest')
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ax1.axis('off')
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ax2.plot(hist[1][:-1], hist[0], lw=2)
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ax2.set_title('histogram of grey values')
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######################################################################
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#
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# Thresholding
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# ============
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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
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# binary image that either misses significant parts of the coins or merges
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# parts of the background with the coins:
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3), sharex=True, sharey=True)
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ax1.imshow(coins > 100, cmap=plt.cm.gray, interpolation='nearest')
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ax1.set_title('coins > 100')
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ax1.axis('off')
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ax1.set_adjustable('box-forced')
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ax2.imshow(coins > 150, cmap=plt.cm.gray, interpolation='nearest')
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ax2.set_title('coins > 150')
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ax2.axis('off')
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ax2.set_adjustable('box-forced')
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margins = dict(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0, right=1)
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fig.subplots_adjust(**margins)
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######################################################################
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# Edge-based segmentation
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# =======================
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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
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# Canny edge-detector.
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from skimage.feature import canny
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edges = canny(coins/255.)
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fig, ax = plt.subplots(figsize=(4, 3))
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ax.imshow(edges, cmap=plt.cm.gray, interpolation='nearest')
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ax.axis('off')
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ax.set_title('Canny detector')
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######################################################################
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# These contours are then filled using mathematical morphology.
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from scipy import ndimage as ndi
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fill_coins = ndi.binary_fill_holes(edges)
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fig, ax = plt.subplots(figsize=(4, 3))
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ax.imshow(fill_coins, cmap=plt.cm.gray, interpolation='nearest')
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ax.axis('off')
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ax.set_title('Filling the holes')
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######################################################################
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# Small spurious objects are easily removed by setting a minimum size for
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# valid objects.
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from skimage import morphology
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coins_cleaned = morphology.remove_small_objects(fill_coins, 21)
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fig, ax = plt.subplots(figsize=(4, 3))
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ax.imshow(coins_cleaned, cmap=plt.cm.gray, interpolation='nearest')
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ax.axis('off')
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ax.set_title('Removing small objects')
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######################################################################
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# However, this method is not very robust, since contours that are not
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# perfectly closed are not filled correctly, as is the case for one unfilled
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# coin above.
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#
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#Region-based segmentation
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#=========================
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#
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#We therefore try a region-based method using the watershed transform.
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#First, we find an elevation map using the Sobel gradient of the image.
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from skimage.filters import sobel
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elevation_map = sobel(coins)
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fig, ax = plt.subplots(figsize=(4, 3))
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ax.imshow(elevation_map, cmap=plt.cm.gray, interpolation='nearest')
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ax.axis('off')
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ax.set_title('elevation_map')
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######################################################################
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# Next we find markers of the background and the coins based on the extreme
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# parts of the histogram of grey values.
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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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fig, ax = plt.subplots(figsize=(4, 3))
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ax.imshow(markers, cmap=plt.cm.spectral, interpolation='nearest')
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ax.axis('off')
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ax.set_title('markers')
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######################################################################
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# Finally, we use the watershed transform to fill regions of the elevation
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# map starting from the markers determined above:
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segmentation = morphology.watershed(elevation_map, markers)
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fig, ax = plt.subplots(figsize=(4, 3))
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ax.imshow(segmentation, cmap=plt.cm.gray, interpolation='nearest')
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ax.axis('off')
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ax.set_title('segmentation')
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######################################################################
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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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from skimage.color import label2rgb
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segmentation = ndi.binary_fill_holes(segmentation - 1)
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labeled_coins, _ = ndi.label(segmentation)
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image_label_overlay = label2rgb(labeled_coins, image=coins)
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3), sharex=True, sharey=True)
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ax1.imshow(coins, cmap=plt.cm.gray, interpolation='nearest')
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ax1.contour(segmentation, [0.5], linewidths=1.2, colors='y')
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ax1.axis('off')
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ax1.set_adjustable('box-forced')
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ax2.imshow(image_label_overlay, interpolation='nearest')
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ax2.axis('off')
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ax2.set_adjustable('box-forced')
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fig.subplots_adjust(**margins)
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