mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-27 19:48:43 +08:00
François Boulogne
254 lines
7.3 KiB
Python
254 lines
7.3 KiB
Python
"""
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============
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Thresholding
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============
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Thresholding is used to create a binary image from a grayscale image [1]_.
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It is the simplest way to segment objects from a background.
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Thresholding algorithms implemented in scikit-image can be separated in two
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categories:
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- Histogram-based. The histogram of the pixels' intensity is used and
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certain assumptions are made on the properties of this histogram (e.g. bimodal).
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- Local. To process a pixel, only the neighboring pixels are used.
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These algorithms often require more computation time.
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If you are not familiar with the details of the different algorithms and the
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underlying assumptions, it is often difficult to know which algorithm will give
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the best results. Therefore, Scikit-image includes a function to evaluate
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thresholding algorithms provided by the library. At a glance, you can select
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the best algorithm for you data without a deep understanding of their
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mechanisms.
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.. [1] https://en.wikipedia.org/wiki/Thresholding_%28image_processing%29
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.. seealso::
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Presentation on
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:ref:`sphx_glr_auto_examples_xx_applications_plot_rank_filters.py`.
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"""
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import matplotlib
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import matplotlib.pyplot as plt
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from skimage import data
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from skimage.filters import try_all_threshold
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img = data.page()
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fig, ax = try_all_threshold(img, figsize=(10, 8), verbose=False)
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plt.show()
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######################################################################
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# How to apply a threshold?
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# =========================
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#
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# Now, we illustrate how to apply one of these thresholding algorithms.
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# This example uses the mean value of pixel intensities. It is a simple
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# and naive threshold value, which is sometimes used as a guess value.
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#
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from skimage.filters import threshold_mean
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image = data.camera()
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thresh = threshold_mean(image)
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binary = image > thresh
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fig, axes = plt.subplots(ncols=2, figsize=(8, 3))
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ax = axes.ravel()
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ax[0].imshow(image, cmap=plt.cm.gray)
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ax[0].set_title('Original image')
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ax[1].imshow(binary, cmap=plt.cm.gray)
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ax[1].set_title('Result')
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for a in ax:
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a.axis('off')
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plt.show()
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######################################################################
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# Bimodal histogram
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# =================
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#
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# For pictures with a bimodal histogram, more specific algorithms can be used.
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# For instance, the minimum algorithm takes a histogram of the image and smooths it
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# repeatedly until there are only two peaks in the histogram. Then it
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# finds the minimum value between the two peaks. After smoothing the
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# histogram, there can be multiple pixel values with the minimum histogram
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# count, so you can pick the 'min', 'mid', or 'max' of these values.
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#
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from skimage.filters import threshold_minimum
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image = data.camera()
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thresh_min = threshold_minimum(image, bias='min')
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binary_min = image > thresh_min
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thresh_mid = threshold_minimum(image, bias='mid')
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binary_mid = image > thresh_mid
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thresh_max = threshold_minimum(image, bias='max')
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binary_max = image > thresh_max
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fig, axes = plt.subplots(4, 2, figsize=(10, 10))
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ax = axes.ravel()
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ax[0].imshow(image, cmap=plt.cm.gray)
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ax[0].set_title('Original')
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ax[0].axis('off')
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ax[1].hist(image.ravel(), bins=256)
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ax[1].set_title('Histogram')
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ax[2].imshow(binary_min, cmap=plt.cm.gray)
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ax[2].set_title('Thresholded (min)')
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ax[3].hist(image.ravel(), bins=256)
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ax[3].axvline(thresh_min, color='r')
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ax[4].imshow(binary_mid, cmap=plt.cm.gray)
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ax[4].set_title('Thresholded (mid)')
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ax[5].hist(image.ravel(), bins=256)
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ax[5].axvline(thresh_mid, color='r')
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ax[6].imshow(binary_max, cmap=plt.cm.gray)
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ax[6].set_title('Thresholded (max)')
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ax[7].hist(image.ravel(), bins=256)
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ax[7].axvline(thresh_max, color='r')
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for a in ax[::2]:
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a.axis('off')
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plt.show()
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######################################################################
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# Otsu's method [2]_ calculates an "optimal" threshold (marked by a red line in the
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# histogram below) by maximizing the variance between two classes of pixels,
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# which are separated by the threshold. Equivalently, this threshold minimizes
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# the intra-class variance.
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#
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# .. [2] http://en.wikipedia.org/wiki/Otsu's_method
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#
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from skimage.filters import threshold_otsu
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image = data.camera()
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thresh = threshold_otsu(image)
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binary = image > thresh
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fig, axes = plt.subplots(ncols=3, figsize=(8, 2.5))
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ax = axes.ravel()
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ax[0] = plt.subplot(1, 3, 1, adjustable='box-forced')
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ax[1] = plt.subplot(1, 3, 2)
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ax[2] = plt.subplot(1, 3, 3, sharex=ax[0], sharey=ax[0], adjustable='box-forced')
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ax[0].imshow(image, cmap=plt.cm.gray)
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ax[0].set_title('Original')
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ax[0].axis('off')
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ax[1].hist(image.ravel(), bins=256)
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ax[1].set_title('Histogram')
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ax[1].axvline(thresh, color='r')
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ax[2].imshow(binary, cmap=plt.cm.gray)
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ax[2].set_title('Thresholded')
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ax[2].axis('off')
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plt.show()
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######################################################################
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# Local thresholding
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# ==================
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#
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# If the image background is relatively uniform, then you can use a global
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# threshold value as presented above. However, if there is large variation in the
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# background intensity, adaptive thresholding (a.k.a. local or dynamic
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# thresholding) may produce better results. Note that local is much slower than
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# global thresholding.
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#
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# Here, we binarize an image using the `threshold_adaptive` function, which
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# calculates thresholds in regions with a characteristic size `block_size` surrounding
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# each pixel (i.e. local neighborhoods). Each threshold value is the weighted mean
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# of the local neighborhood minus an offset value.
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#
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from skimage.filters import threshold_otsu, threshold_adaptive
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image = data.page()
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global_thresh = threshold_otsu(image)
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binary_global = image > global_thresh
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block_size = 35
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binary_adaptive = threshold_adaptive(image, block_size, offset=10)
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fig, axes = plt.subplots(nrows=3, figsize=(7, 8))
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ax = axes.ravel()
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plt.gray()
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ax[0].imshow(image)
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ax[0].set_title('Original')
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ax[1].imshow(binary_global)
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ax[1].set_title('Global thresholding')
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ax[2].imshow(binary_adaptive)
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ax[2].set_title('Adaptive thresholding')
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for a in ax:
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a.axis('off')
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plt.show()
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######################################################################
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# Now, we show how Otsu's threshold [2]_ method can be applied locally. For
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# each pixel, an "optimal" threshold is determined by maximizing the variance
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# between two classes of pixels of the local neighborhood defined by a
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# structuring element.
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#
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# The example compares the local threshold with the global threshold.
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#
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from skimage.morphology import disk
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from skimage.filters import threshold_otsu, rank
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from skimage.util import img_as_ubyte
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img = img_as_ubyte(data.page())
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radius = 15
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selem = disk(radius)
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local_otsu = rank.otsu(img, selem)
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threshold_global_otsu = threshold_otsu(img)
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global_otsu = img >= threshold_global_otsu
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fig, axes = plt.subplots(2, 2, figsize=(8, 5), sharex=True, sharey=True,
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subplot_kw={'adjustable': 'box-forced'})
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ax = axes.ravel()
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plt.tight_layout()
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fig.colorbar(ax[0].imshow(img, cmap=plt.cm.gray),
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ax=ax[0], orientation='horizontal')
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ax[0].set_title('Original')
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ax[0].axis('off')
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fig.colorbar(ax[1].imshow(local_otsu, cmap=plt.cm.gray),
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ax=ax[1], orientation='horizontal')
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ax[1].set_title('Local Otsu (radius=%d)' % radius)
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ax[1].axis('off')
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ax[2].imshow(img >= local_otsu, cmap=plt.cm.gray)
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ax[2].set_title('Original >= Local Otsu' % threshold_global_otsu)
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ax[2].axis('off')
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ax[3].imshow(global_otsu, cmap=plt.cm.gray)
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ax[3].set_title('Global Otsu (threshold = %d)' % threshold_global_otsu)
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ax[3].axis('off')
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plt.show()
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