Fix inconsistencies in examples and many more improvements

doc/examples/applications/plot_coins_segmentation.py

@@ -3,7 +3,7 @@
 Comparing edge-based segmentation and region-based segmentation
 ===============================================================
 
-In this example, we will see how to segment objects from a background.  We use
+In this example, we will see how to segment objects from a background. We use
 the ``coins`` image from ``skimage.data``, which shows several coins outlined
 against a darker background.
 """
@@ -108,9 +108,8 @@
 Region-based segmentation
 =========================
 
-We therefore try a region-based method using the
-watershed transform. First, we find an elevation map using the Sobel gradient of the
-image. 
+We therefore try a region-based method using the watershed transform. First, we
+find an elevation map using the Sobel gradient of the image.
 
 """
 
@@ -142,7 +141,8 @@
 """
 .. image:: PLOT2RST.current_figure
 
-Finally, we use the watershed transform to fill regions of the elevation map starting from the markers determined above:
+Finally, we use the watershed transform to fill regions of the elevation map
+starting from the markers determined above:
 
 """
 segmentation = morphology.watershed(elevation_map, markers)
@@ -155,8 +155,8 @@
 """
 .. image:: PLOT2RST.current_figure
 
-This last method works even better, and the coins can be segmented and 
-labeled individually.
+This last method works even better, and the coins can be segmented and labeled
+individually.
 
 """
 

doc/examples/plot_canny.py

@@ -13,12 +13,15 @@
 The Canny has three adjustable parameters: the width of the Gaussian (the
 noisier the image, the greater the width), and the low and high threshold for
 the hysteresis thresholding.
+
 """
 import numpy as np
 import matplotlib.pyplot as plt
 from scipy import ndimage
+
 from skimage import filter
 
+
 # Generate noisy image of a square
 im = np.zeros((128, 128))
 im[32:-32, 32:-32] = 1
@@ -52,6 +55,5 @@
 plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9,
                     bottom=0.02, left=0.02, right=0.98)
 
-
 plt.show()
 

doc/examples/plot_circular_hough_transform.py

@@ -29,8 +29,6 @@
 Its size is extended by two times the larger radius.
 
 """
-
-
 import numpy as np
 import matplotlib.pyplot as plt
 
@@ -39,6 +37,7 @@
 from skimage.feature import peak_local_max
 from skimage.draw import circle_perimeter
 
+
 # Load picture and detect edges
 image = data.coins()[0:95, 70:370]
 edges = filter.canny(image, sigma=3, low_threshold=10, high_threshold=50)

doc/examples/plot_contours.py

@@ -15,12 +15,12 @@
 (SIGGRAPH 87 Proceedings) 21(4) July 1987, p. 163-170).
 
 """
+import numpy as np
+import matplotlib.pyplot as plt
 
 from skimage import data
 from skimage import measure
 
-import numpy as np
-import matplotlib.pyplot as plt
 
 # Construct some test data
 x, y = np.ogrid[-np.pi:np.pi:100j, -np.pi:np.pi:100j]
@@ -39,4 +39,3 @@
 plt.xticks([])
 plt.yticks([])
 plt.show()
-

doc/examples/plot_convex_hull.py

@@ -13,12 +13,12 @@
 <http://blogs.mathworks.com/steve/2011/10/04/binary-image-convex-hull-algorithm-notes/>`__.
 
 """
-
 import numpy as np
 import matplotlib.pyplot as plt
 
 from skimage.morphology import convex_hull_image
 
+
 image = np.array(
     [[0, 0, 0, 0, 0, 0, 0, 0, 0],
      [0, 0, 0, 0, 1, 0, 0, 0, 0],

doc/examples/plot_corner.py

@@ -10,14 +10,14 @@
 .. [2] http://en.wikipedia.org/wiki/Interest_point_detection
 
 """
-
 from matplotlib import pyplot as plt
 
 from skimage import data
 from skimage.feature import corner_harris, corner_subpix, corner_peaks
 from skimage.transform import warp, AffineTransform
 from skimage.draw import ellipse
 
+
 tform = AffineTransform(scale=(1.3, 1.1), rotation=1, shear=0.7,
                         translation=(210, 50))
 image = warp(data.checkerboard(), tform.inverse, output_shape=(350, 350))

doc/examples/plot_daisy.py

@@ -11,7 +11,6 @@
 In this example a limited number of DAISY descriptors are extracted at a large
 scale for illustrative purposes.
 """
-
 from skimage.feature import daisy
 from skimage import data
 import matplotlib.pyplot as plt

doc/examples/plot_denoise.py

@@ -25,13 +25,13 @@
 pixels based on their spatial closeness and radiometric similarity.
 
 """
-
 import numpy as np
 import matplotlib.pyplot as plt
 
 from skimage import data, color, img_as_float
 from skimage.filter import denoise_tv_chambolle, denoise_bilateral
 
+
 lena = img_as_float(data.lena())
 lena = lena[220:300, 220:320]
 

doc/examples/plot_edge_filter.py

@@ -13,6 +13,7 @@
 from skimage.data import camera
 from skimage.filter import roberts, sobel
 
+
 image = camera()
 edge_roberts = roberts(image)
 edge_sobel = sobel(image)

doc/examples/plot_entropy.py

@@ -1,22 +1,23 @@
 """
-===================
+=======
 Entropy
-===================
-
+=======
 
 """
+import numpy as np
+import matplotlib.pyplot as plt
+
 from skimage import data
 from skimage.filter.rank import entropy
 from skimage.morphology import disk
-import numpy as np
-import matplotlib.pyplot as plt
+
 
 # defining a 8- and a 16-bit test images
 a8 = data.camera()
-a16 = data.camera().astype(np.uint16)*4
+a16 = a8.astype(np.uint16) * 4
 
-ent8 = entropy(a8,disk(5)) # pixel value contain 10x the local entropy
-ent16 = entropy(a16,disk(5)) # pixel value contain 1000x the local entropy
+ent8 = entropy(a8, disk(5)) # pixel value contain 10x the local entropy
+ent16 = entropy(a16, disk(5)) # pixel value contain 1000x the local entropy
 
 # display results
 plt.figure(figsize=(10, 10))
@@ -41,4 +42,3 @@
 plt.xlabel('entropy*1000')
 plt.colorbar()
 plt.show()
-

doc/examples/plot_equalize.py

@@ -17,13 +17,12 @@
 .. [2] http://homepages.inf.ed.ac.uk/rbf/HIPR2/stretch.htm
 
 """
+import matplotlib.pyplot as plt
+import numpy as np
 
 from skimage import data, img_as_float
 from skimage import exposure
 
-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.

doc/examples/plot_gabor.py

@@ -12,11 +12,11 @@
 for classification, which is based on the least squared error for simplicity.
 
 """
-
 import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
 from scipy import ndimage as nd
+
 from skimage import data
 from skimage.util import img_as_float
 from skimage.filter import gabor_kernel

doc/examples/plot_gabors_from_lena.py

@@ -35,7 +35,6 @@
        Interaction, and Functional Architecture in the Cat's Visual Cortex,
        J. Physiol. 160 pp.  106-154 1962
 """
-
 import numpy as np
 from scipy.cluster.vq import kmeans2
 from scipy import ndimage as ndi

doc/examples/plot_glcm.py

@@ -19,10 +19,11 @@
 regression, to label image patches from new images.
 
 """
+import matplotlib.pyplot as plt
 
 from skimage.feature import greycomatrix, greycoprops
 from skimage import data
-import matplotlib.pyplot as plt
+
 
 PATCH_SIZE = 21
 

doc/examples/plot_hog.py

@@ -1,4 +1,4 @@
-r'''
+"""
 ===============================
 Histogram of Oriented Gradients
 ===============================
@@ -77,12 +77,13 @@
 .. [2] David G. Lowe, "Distinctive image features from scale-invariant
        keypoints," International Journal of Computer Vision, 60, 2 (2004),
        pp. 91-110.
-'''
+
+"""
+import matplotlib.pyplot as plt
 
 from skimage.feature import hog
 from skimage import data, color, exposure
 
-import matplotlib.pyplot as plt
 
 image = color.rgb2gray(data.lena())
 

doc/examples/plot_holes_and_peaks.py

@@ -10,6 +10,7 @@
 image.
 
 We start with an image containing both peaks and holes:
+
 """
 import matplotlib.pyplot as plt
 

doc/examples/plot_hough_transform.py

@@ -1,50 +1,47 @@
-'''
+"""
 ===============
 Hough transform
 ===============
 
-The Hough transform in its simplest form is a `method to detect
-straight lines <http://en.wikipedia.org/wiki/Hough_transform>`__.
+The Hough transform in its simplest form is a `method to detect straight lines
+<http://en.wikipedia.org/wiki/Hough_transform>`__.
 
-In the following example, we construct an image with a line
-intersection.  We then use the Hough transform to explore a parameter
-space for straight lines that may run through the image.
+In the following example, we construct an image with a line intersection.  We
+then use the Hough transform to explore a parameter space for straight lines
+that may run through the image.
 
 Algorithm overview
 ------------------
 
-Usually, lines are parameterised as :math:`y = mx + c`, with a
-gradient :math:`m` and y-intercept `c`.  However, this would mean that
-:math:`m` goes to infinity for vertical lines.  Instead, we therefore
-construct a segment perpendicular to the line, leading to the origin.
-The line is represented by the length of that segment, :math:`r`, and
-the angle it makes with the x-axis, :math:`\theta`.
-
-The Hough transform constructs a histogram array representing the
-parameter space (i.e., an :math:`M \times N` matrix, for :math:`M`
-different values of the radius and :math:`N` different values of
-:math:`\theta`).  For each parameter combination, :math:`r` and
-:math:`\theta`, we then find the number of non-zero pixels in the
-input image that would fall close to the corresponding line, and
+Usually, lines are parameterised as :math:`y = mx + c`, with a gradient
+:math:`m` and y-intercept `c`. However, this would mean that :math:`m` goes to
+infinity for vertical lines. Instead, we therefore construct a segment
+perpendicular to the line, leading to the origin. The line is represented by the
+length of that segment, :math:`r`, and the angle it makes with the x-axis,
+:math:`\theta`.
+
+The Hough transform constructs a histogram array representing the parameter
+space (i.e., an :math:`M \times N` matrix, for :math:`M` different values of the
+radius and :math:`N` different values of :math:`\theta`).  For each parameter
+combination, :math:`r` and :math:`\theta`, we then find the number of non-zero
+pixels in the input image that would fall close to the corresponding line, and
 increment the array at position :math:`(r, \theta)` appropriately.
 
-We can think of each non-zero pixel "voting" for potential line
-candidates.  The local maxima in the resulting histogram indicates the
-parameters of the most probably lines.  In our example, the maxima
-occur at 45 and 135 degrees, corresponding to the normal vector
-angles of each line.
+We can think of each non-zero pixel "voting" for potential line candidates. The
+local maxima in the resulting histogram indicates the parameters of the most
+probably lines. In our example, the maxima occur at 45 and 135 degrees,
+corresponding to the normal vector angles of each line.
 
-Another approach is the Progressive Probabilistic Hough Transform
-[1]_.  It is based on the assumption that using a random subset of
-voting points give a good approximation to the actual result, and that
-lines can be extracted during the voting process by walking along
-connected components.  This returns the beginning and end of each
-line segment, which is useful.
+Another approach is the Progressive Probabilistic Hough Transform [1]_. It is
+based on the assumption that using a random subset of voting points give a good
+approximation to the actual result, and that lines can be extracted during the
+voting process by walking along connected components. This returns the beginning
+and end of each line segment, which is useful.
 
-The function `probabilistic_hough` has three parameters: a general
-threshold that is applied to the Hough accumulator, a minimum line
-length and the line gap that influences line merging.  In the example
-below, we find lines longer than 10 with a gap less than 3 pixels.
+The function `probabilistic_hough` has three parameters: a general threshold
+that is applied to the Hough accumulator, a minimum line length and the line gap
+that influences line merging. In the example below, we find lines longer than 10
+with a gap less than 3 pixels.
 
 References
 ----------
@@ -57,7 +54,7 @@
        Detect Lines and Curves in Pictures," Comm. ACM, Vol. 15,
        pp. 11-15 (January, 1972)
 
-'''
+"""
 
 from skimage.transform import (hough_line, hough_line_peaks,
                                probabilistic_hough_line)

doc/examples/plot_ihc_color_separation.py

@@ -3,9 +3,9 @@
 Immunohistochemical staining colors separation
 ==============================================
 
-In this example we separate the immunohistochemical (IHC) staining
-from the hematoxylin counterstaining. The separation is achieved with the
-method described in [1]_, known as "color deconvolution".
+In this example we separate the immunohistochemical (IHC) staining from the
+hematoxylin counterstaining. The separation is achieved with the method
+described in [1]_, known as "color deconvolution".
 
 The IHC staining expression of the FHL2 protein is here revealed with
 Diaminobenzidine (DAB) which gives a brown color.
@@ -15,6 +15,7 @@
        staining by color deconvolution.," Analytical and quantitative
        cytology and histology / the International Academy of Cytology [and]
        American Society of Cytology, vol. 23, no. 4, pp. 291-9, Aug. 2001.
+
 """
 import matplotlib.pyplot as plt