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DOC: Gather threshold algo in gallery
This commit is contained in:
François Boulogne
@@ -1,36 +0,0 @@
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"""
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==================================
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Minimum Algorithm For Thresholding
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==================================
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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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import matplotlib.pyplot as plt
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from skimage import data
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from skimage.filters.thresholding import threshold_minimum
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image = data.camera()
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threshold = threshold_minimum(image, bias='min')
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binarized = image > threshold
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fig, axes = plt.subplots(nrows=2, figsize=(7, 8))
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ax0, ax1 = axes
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plt.gray()
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ax0.imshow(image)
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ax0.set_title('Original image')
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ax1.imshow(binarized)
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ax1.set_title('Result')
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for ax in axes:
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ax.axis('off')
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plt.show()
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@@ -1,60 +0,0 @@
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"""
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====================
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Local Otsu Threshold
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====================
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This example shows how Otsu's threshold [1]_ 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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The example compares the local threshold with the global threshold.
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.. note: local is much slower than global thresholding
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.. [1] http://en.wikipedia.org/wiki/Otsu's_method
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"""
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from skimage import data
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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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import matplotlib
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import matplotlib.pyplot as plt
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matplotlib.rcParams['font.size'] = 9
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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, ax = plt.subplots(2, 2, figsize=(8, 5), sharex=True, sharey=True,
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subplot_kw={'adjustable': 'box-forced'})
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ax0, ax1, ax2, ax3 = ax.ravel()
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plt.tight_layout()
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fig.colorbar(ax0.imshow(img, cmap=plt.cm.gray),
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ax=ax0, orientation='horizontal')
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ax0.set_title('Original')
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ax0.axis('off')
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fig.colorbar(ax1.imshow(local_otsu, cmap=plt.cm.gray),
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ax=ax1, orientation='horizontal')
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ax1.set_title('Local Otsu (radius=%d)' % radius)
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ax1.axis('off')
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ax2.imshow(img >= local_otsu, cmap=plt.cm.gray)
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ax2.set_title('Original >= Local Otsu' % threshold_global_otsu)
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ax2.axis('off')
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ax3.imshow(global_otsu, cmap=plt.cm.gray)
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ax3.set_title('Global Otsu (threshold = %d)' % threshold_global_otsu)
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ax3.axis('off')
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plt.show()
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@@ -1,48 +0,0 @@
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"""
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=====================
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Adaptive Thresholding
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=====================
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Thresholding is the simplest way to segment objects from a background. If that
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background is relatively uniform, then you can use a global threshold value to
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binarize the image by pixel-intensity. If there's large variation in the
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background intensity, however, adaptive thresholding (a.k.a. local or dynamic
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thresholding) may produce better results.
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Here, we binarize an image using the `threshold_adaptive` function, which
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calculates thresholds in regions of size `block_size` surrounding each pixel
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(i.e. local neighborhoods). Each threshold value is the weighted mean of the
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local neighborhood minus an offset value.
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"""
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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 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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ax0, ax1, ax2 = axes
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plt.gray()
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ax0.imshow(image)
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ax0.set_title('Image')
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ax1.imshow(binary_global)
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ax1.set_title('Global thresholding')
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ax2.imshow(binary_adaptive)
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ax2.set_title('Adaptive thresholding')
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for ax in axes:
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ax.axis('off')
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plt.show()
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@@ -4,17 +4,10 @@ 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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Thresholding algorithms can be separated in two categories:
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- Histogram-based. The histogram of the pixel intensity is used and
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assumptions may be 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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Scikit-image includes a function to test thresholding algorithms provided
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in the library. Therefore, in a glance, you can select the best algorithm
|
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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 to know which algorithm will give the best
|
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results. Therefore, Scikit-image includes a function to test thresholding algorithms
|
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provided in the library. At a glance, you can select the best algorithm
|
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for you data, without a deep understanding of their mechanisms.
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.. [1] https://en.wikipedia.org/wiki/Thresholding_%28image_processing%29
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@@ -23,10 +16,10 @@ for you data, without a deep understanding of their mechanisms.
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import matplotlib
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import matplotlib.pyplot as plt
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from skimage.data import page
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from skimage import data
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from skimage.filters import thresholding
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img = page()
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img = data.page()
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# Here, we specify a radius for local thresholding algorithm.
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# If it is not specified, only global algorithms are called.
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@@ -35,52 +28,35 @@ fig, ax = thresholding.try_all_threshold(img, radius=20,
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plt.show()
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"""
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.. image:: PLOT2RST.current_figure
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How to apply a threshold?
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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 Otsu's method [2]_.
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|
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Otsu's method 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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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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import matplotlib
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import matplotlib.pyplot as plt
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from skimage.data import camera
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from skimage.filters import threshold_otsu
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matplotlib.rcParams['font.size'] = 9
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image = camera()
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thresh = threshold_otsu(image)
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binary = image > thresh
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fig = plt.figure(figsize=(8, 2.5))
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ax1 = plt.subplot(1, 3, 1, adjustable='box-forced')
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ax2 = plt.subplot(1, 3, 2)
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ax3 = plt.subplot(1, 3, 3, sharex=ax1, sharey=ax1, adjustable='box-forced')
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ax1.imshow(image, cmap=plt.cm.gray)
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ax1.set_title('Original')
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ax1.axis('off')
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ax2.hist(image)
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ax2.set_title('Histogram')
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ax2.axvline(thresh, color='r')
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ax3.imshow(binary, cmap=plt.cm.gray)
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ax3.set_title('Thresholded')
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ax3.axis('off')
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plt.show()
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#from skimage.filters.thresholding import threshold_mean
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#from skimage import data
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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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#
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#fig, axes = plt.subplots(nrows=2, figsize=(7, 8))
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#ax0, ax1 = axes
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#
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#ax0.imshow(image)
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#ax0.set_title('Original image')
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#
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#ax1.imshow(binary)
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#ax1.set_title('Result')
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#
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#for ax in axes:
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# ax.axis('off')
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#
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#plt.show()
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"""
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.. image:: PLOT2RST.current_figure
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@@ -0,0 +1,263 @@
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"""
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============
|
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Thresholding
|
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============
|
||||
|
||||
Thresholding is used to create a binary image from a grayscale image [1]_.
|
||||
It is the simplest way to segment objects from a background.
|
||||
|
||||
Thresholding algorithms implemented in scikit-image can be separated in two
|
||||
categories:
|
||||
|
||||
- Histogram-based. The histogram of the pixel intensity is used and
|
||||
assumptions may be made on the properties of this histogram (e.g. bimodal).
|
||||
- Local. To process a pixel, only the neighboring pixels are used.
|
||||
These algorithms often require more computation time.
|
||||
|
||||
|
||||
If you are not familiar with the details of the different algorithms and the
|
||||
underlying assumptions, it is often to know which algorithm will give the best
|
||||
results. Therefore, Scikit-image includes a function to test thresholding
|
||||
algorithms provided in the library. At a glance, you can select the best
|
||||
algorithm for you data, without a deep understanding of their mechanisms.
|
||||
|
||||
.. [1] https://en.wikipedia.org/wiki/Thresholding_%28image_processing%29
|
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|
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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 thresholding
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img = data.page()
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# Here, we specify a radius for local thresholding algorithm.
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# If it is not specified, only global algorithms are called.
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fig, ax = thresholding.try_all_threshold(img, radius=20,
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figsize=(10, 8), verbose=False)
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plt.show()
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"""
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.. image:: PLOT2RST.current_figure
|
||||
|
||||
How to apply a threshold?
|
||||
=========================
|
||||
|
||||
Now, we illustrate how to apply one of these thresholding algorithms
|
||||
This example uses the mean value of pixel intensities. It is a simple
|
||||
and naive threshold value, which is sometimes used as a guess value.
|
||||
"""
|
||||
|
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#from skimage.filters.thresholding import threshold_mean
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#from skimage import data
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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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#
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#fig, axes = plt.subplots(nrows=2, figsize=(7, 8))
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#ax0, ax1 = axes
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#
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#ax0.imshow(image)
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#ax0.set_title('Original image')
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#
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#ax1.imshow(binary)
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#ax1.set_title('Result')
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#
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#for ax in axes:
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# ax.axis('off')
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#
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#plt.show()
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"""
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.. image:: PLOT2RST.current_figure
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Bimodal histogram
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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
|
||||
repeatedly until there are only two peaks in the histogram. Then it
|
||||
finds the minimum value between the two peaks. After smoothing the
|
||||
histogram, there can be multiple pixel values with the minimum histogram
|
||||
count, so you can pick the 'min', 'mid', or 'max' of these values.
|
||||
|
||||
"""
|
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import matplotlib.pyplot as plt
|
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|
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from skimage import data
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from skimage.filters.thresholding import threshold_minimum
|
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|
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image = data.camera()
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thresh = threshold_minimum(image, bias='min')
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binary = image > thresh
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fig = plt.figure(figsize=(8, 2.5))
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ax1 = plt.subplot(1, 3, 1, adjustable='box-forced')
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ax2 = plt.subplot(1, 3, 2)
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ax3 = plt.subplot(1, 3, 3, sharex=ax1, sharey=ax1, adjustable='box-forced')
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ax1.imshow(image, cmap=plt.cm.gray)
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ax1.set_title('Original')
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ax1.axis('off')
|
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|
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ax2.hist(image)
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ax2.set_title('Histogram')
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ax2.axvline(thresh, color='r')
|
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|
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ax3.imshow(binary, cmap=plt.cm.gray)
|
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ax3.set_title('Thresholded')
|
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ax3.axis('off')
|
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|
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plt.show()
|
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|
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"""
|
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|
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.. image:: PLOT2RST.current_figure
|
||||
|
||||
Otsu's method [2]_ calculates an "optimal" threshold (marked by a red line in the
|
||||
histogram below) by maximizing the variance between two classes of pixels,
|
||||
which are separated by the threshold. Equivalently, this threshold minimizes
|
||||
the intra-class variance.
|
||||
|
||||
.. [2] http://en.wikipedia.org/wiki/Otsu's_method
|
||||
|
||||
"""
|
||||
import matplotlib
|
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import matplotlib.pyplot as plt
|
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|
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from skimage import data
|
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from skimage.filters import threshold_otsu
|
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|
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|
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matplotlib.rcParams['font.size'] = 9
|
||||
|
||||
|
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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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|
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fig = plt.figure(figsize=(8, 2.5))
|
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ax1 = plt.subplot(1, 3, 1, adjustable='box-forced')
|
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ax2 = plt.subplot(1, 3, 2)
|
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ax3 = plt.subplot(1, 3, 3, sharex=ax1, sharey=ax1, adjustable='box-forced')
|
||||
|
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ax1.imshow(image, cmap=plt.cm.gray)
|
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ax1.set_title('Original')
|
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ax1.axis('off')
|
||||
|
||||
ax2.hist(image)
|
||||
ax2.set_title('Histogram')
|
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ax2.axvline(thresh, color='r')
|
||||
|
||||
ax3.imshow(binary, cmap=plt.cm.gray)
|
||||
ax3.set_title('Thresholded')
|
||||
ax3.axis('off')
|
||||
|
||||
plt.show()
|
||||
|
||||
"""
|
||||
.. image:: PLOT2RST.current_figure
|
||||
|
||||
Local thresholding
|
||||
==================
|
||||
|
||||
If the image background is relatively uniform, then you can use a global
|
||||
threshold value as presented above. However, if there is large variation in the
|
||||
background intensity, adaptive thresholding (a.k.a. local or dynamic
|
||||
thresholding) may produce better results. Note that local is much slower than
|
||||
global thresholding
|
||||
|
||||
Here, we binarize an image using the `threshold_adaptive` function, which
|
||||
calculates thresholds in regions of size `block_size` surrounding each pixel
|
||||
(i.e. local neighborhoods). Each threshold value is the weighted mean of the
|
||||
local neighborhood minus an offset value.
|
||||
|
||||
"""
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
from skimage import data
|
||||
from skimage.filters import threshold_otsu, threshold_adaptive
|
||||
|
||||
|
||||
image = data.page()
|
||||
|
||||
global_thresh = threshold_otsu(image)
|
||||
binary_global = image > global_thresh
|
||||
|
||||
block_size = 35
|
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binary_adaptive = threshold_adaptive(image, block_size, offset=10)
|
||||
|
||||
fig, axes = plt.subplots(nrows=3, figsize=(7, 8))
|
||||
ax0, ax1, ax2 = axes
|
||||
plt.gray()
|
||||
|
||||
ax0.imshow(image)
|
||||
ax0.set_title('Original')
|
||||
|
||||
ax1.imshow(binary_global)
|
||||
ax1.set_title('Global thresholding')
|
||||
|
||||
ax2.imshow(binary_adaptive)
|
||||
ax2.set_title('Adaptive thresholding')
|
||||
|
||||
for ax in axes:
|
||||
ax.axis('off')
|
||||
|
||||
plt.show()
|
||||
|
||||
"""
|
||||
.. image:: PLOT2RST.current_figure
|
||||
|
||||
Now, we show how Otsu's threshold [2]_ method can be applied locally. For
|
||||
each pixel, an "optimal" threshold is determined by maximizing the variance
|
||||
between two classes of pixels of the local neighborhood defined by a
|
||||
structuring element.
|
||||
|
||||
The example compares the local threshold with the global threshold.
|
||||
|
||||
"""
|
||||
|
||||
from skimage import data
|
||||
from skimage.morphology import disk
|
||||
from skimage.filters import threshold_otsu, rank
|
||||
from skimage.util import img_as_ubyte
|
||||
|
||||
import matplotlib
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
matplotlib.rcParams['font.size'] = 9
|
||||
img = img_as_ubyte(data.page())
|
||||
|
||||
radius = 15
|
||||
selem = disk(radius)
|
||||
|
||||
local_otsu = rank.otsu(img, selem)
|
||||
threshold_global_otsu = threshold_otsu(img)
|
||||
global_otsu = img >= threshold_global_otsu
|
||||
|
||||
fig, ax = plt.subplots(2, 2, figsize=(8, 5), sharex=True, sharey=True,
|
||||
subplot_kw={'adjustable': 'box-forced'})
|
||||
ax0, ax1, ax2, ax3 = ax.ravel()
|
||||
plt.tight_layout()
|
||||
|
||||
fig.colorbar(ax0.imshow(img, cmap=plt.cm.gray),
|
||||
ax=ax0, orientation='horizontal')
|
||||
ax0.set_title('Original')
|
||||
ax0.axis('off')
|
||||
|
||||
fig.colorbar(ax1.imshow(local_otsu, cmap=plt.cm.gray),
|
||||
ax=ax1, orientation='horizontal')
|
||||
ax1.set_title('Local Otsu (radius=%d)' % radius)
|
||||
ax1.axis('off')
|
||||
|
||||
ax2.imshow(img >= local_otsu, cmap=plt.cm.gray)
|
||||
ax2.set_title('Original >= Local Otsu' % threshold_global_otsu)
|
||||
ax2.axis('off')
|
||||
|
||||
ax3.imshow(global_otsu, cmap=plt.cm.gray)
|
||||
ax3.set_title('Global Otsu (threshold = %d)' % threshold_global_otsu)
|
||||
ax3.axis('off')
|
||||
|
||||
plt.show()
|
||||
Reference in New Issue
Block a user