add examples - to be cont.

doc/examples/plot_lena_bilateral_denoise.py

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+"""
+====================================================
+Denoising the picture of Lena using total variation
+====================================================
+
+In this example, we denoise a noisy version of the picture of Lena
+using the total variation denoising filter. The result of this filter
+is an image that has a minimal total variation norm, while being as
+close to the initial image as possible. The total variation is the L1
+norm of the gradient of the image, and minimizing the total variation
+typically produces "posterized" images with flat domains separated by
+sharp edges.
+
+It is possible to change the degree of posterization by controlling
+the tradeoff between denoising and faithfulness to the original image.
+
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from skimage import data, color, img_as_ubyte
+from skimage.filter import tv_denoise
+from skimage.rank import bilateral_mean
+from skimage.morphology import disk
+
+l = img_as_ubyte(color.rgb2gray(data.lena()))
+l = l[230:290, 220:320]
+
+noisy = l + 0.4 * l.std() * np.random.random(l.shape)
+
+selem = disk(30)
+bilateral_denoised = bilateral_mean(noisy.astype(np.uint8), selem=selem,s0=10,s1=10)
+
+plt.figure(figsize=(8, 2))
+
+plt.subplot(131)
+plt.imshow(noisy, cmap=plt.cm.gray, vmin=40, vmax=220)
+plt.axis('off')
+plt.title('noisy', fontsize=20)
+plt.subplot(132)
+plt.imshow(bilateral_denoised, cmap=plt.cm.gray, vmin=40, vmax=220)
+plt.axis('off')
+plt.title('bilateral denoising', fontsize=20)
+
+selem = disk(30)
+bilateral_denoised = bilateral_mean(noisy.astype(np.uint8), selem=selem,s0=30,s1=30)
+plt.subplot(133)
+plt.imshow(bilateral_denoised, cmap=plt.cm.gray, vmin=40, vmax=220)
+plt.axis('off')
+plt.title('(more) bilateral denoising', fontsize=20)
+
+plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9, bottom=0, left=0,
+                    right=1)
+plt.show()

doc/examples/plot_local_equalize.py

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+"""
+===============================
+Local Histogram Equalization
+===============================
+
+This examples enhances an image with low contrast, using a method called
+*local histogram equalization*, which "spreads out the most frequent intensity
+values" in an image . The equalized image has a roughly linear cumulative
+distribution function for each pixel neigborhood.
+
+to be adjusted...
+
+.. [1] http://en.wikipedia.org/wiki/Histogram_equalization
+.. [2] http://homepages.inf.ed.ac.uk/rbf/HIPR2/stretch.htm
+
+"""
+
+from skimage import data
+from skimage.util.dtype import dtype_range
+from skimage import exposure
+from skimage.rank import egalise
+from skimage.morphology import disk
+
+
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+def plot_img_and_hist(img, axes, bins=256):
+    """Plot an image along with its histogram and cumulative histogram.
+
+    """
+    ax_img, ax_hist = axes
+    ax_cdf = ax_hist.twinx()
+
+    # Display image
+    ax_img.imshow(img, cmap=plt.cm.gray)
+    ax_img.set_axis_off()
+
+    # Display histogram
+    ax_hist.hist(img.ravel(), bins=bins)
+    ax_hist.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))
+    ax_hist.set_xlabel('Pixel intensity')
+
+    xmin, xmax = dtype_range[img.dtype.type]
+    ax_hist.set_xlim(xmin, xmax)
+
+    # Display cumulative distribution
+    img_cdf, bins = exposure.cumulative_distribution(img, bins)
+    ax_cdf.plot(bins, img_cdf, 'r')
+
+    return ax_img, ax_hist, ax_cdf
+
+
+# Load an example image
+img = data.moon()
+
+# Contrast stretching
+p2 = np.percentile(img, 2)
+p98 = np.percentile(img, 98)
+img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98))
+
+# Equalization
+selem = disk(30)
+img_eq = egalise(img,selem=selem)
+
+
+# Display results
+f, axes = plt.subplots(2, 3, figsize=(8, 4))
+
+ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0])
+ax_img.set_title('Low contrast image')
+ax_hist.set_ylabel('Number of pixels')
+
+ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale, axes[:, 1])
+ax_img.set_title('Contrast stretching')
+
+ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 2])
+ax_img.set_title('Local Histogram equalization')
+ax_cdf.set_ylabel('Fraction of total intensity')
+
+
+# prevent overlap of y-axis labels
+plt.subplots_adjust(wspace=0.4)
+plt.show()
+

doc/examples/plot_local_threshold.py

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+"""
+=====================
+Local Thresholding
+=====================
+
+Thresholding is the simplest way to segment objects from a background. If that
+background is relatively uniform, then you can use a global threshold value to
+binarize the image by pixel-intensity. If there's large variation in the
+background intensity, however, adaptive thresholding (a.k.a. local or dynamic
+thresholding) may produce better results.
+
+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.
+
+Added local threshold using rank filter
+
+to be adjusted ...
+
+"""
+import matplotlib.pyplot as plt
+
+from skimage import data
+from skimage.filter import threshold_otsu, threshold_adaptive
+
+from skimage.rank import threshold
+from skimage.morphology import disk
+
+
+image = data.page()
+
+global_thresh = threshold_otsu(image)
+binary_global = image > global_thresh
+
+block_size = 40
+binary_adaptive = threshold_adaptive(image, block_size, offset=10)
+
+selem = disk(10)
+loc_thresh = threshold(image,selem=selem)
+
+fig, axes = plt.subplots(nrows=4, figsize=(7, 8))
+ax0, ax1, ax2, ax3 = axes
+plt.gray()
+
+ax0.imshow(image)
+ax0.set_title('Image')
+
+ax1.imshow(binary_global)
+ax1.set_title('Global thresholding')
+
+ax2.imshow(binary_adaptive)
+ax2.set_title('Adaptive thresholding')
+
+ax3.imshow(loc_thresh)
+ax3.set_title('Local thresholding')
+
+
+for ax in axes:
+    ax.axis('off')
+
+plt.show()