PEP8

doc/examples/plot_circular_elliptical_hough_transform.py

@@ -83,16 +83,16 @@ Ellipse detection
 
 In this second example, the aim is to detect the edge of a coffee cup.
 Basically, this is a projection of a circle, i.e. an ellipse.
-The problem to solve is much more difficult bacause five parameters have to be determined,
-instead of three for circles.
+The problem to solve is much more difficult bacause five parameters have to be
+determined, instead of three for circles.
 
 
 Algorithm overview
 ------------------
 
-The algorithm takes two different points belonging to the ellipse. It assumes that it is
-the main axis. A loop on all the other points determines how much an ellipse passes to
-them. A good match corresponds to high accumulator values.
+The algorithm takes two different points belonging to the ellipse. It assumes
+that it is the main axis. A loop on all the other points determines how much
+an ellipse passes to them. A good match corresponds to high accumulator values.
 
 A full description of the algorithm can be found in reference [1].
 
@@ -103,7 +103,6 @@ References
        method." Pattern Recognition, 2002. Proceedings. 16th International
        Conference on. Vol. 2. IEEE, 2002
 """
-import numpy as np
 import matplotlib.pyplot as plt
 
 from skimage import data, filter, color
@@ -113,7 +112,8 @@ from skimage.draw import ellipse_perimeter
 # Load picture, convert to grayscale and detect edges
 image_rgb = data.load('coffee.png')[100:240, 110:250]
 image_gray = color.rgb2gray(image_rgb)
-edges = filter.canny(image_gray, sigma=2.0, low_threshold=0.1, high_threshold=0.6)
+edges = filter.canny(image_gray, sigma=2.0,
+                     low_threshold=0.1, high_threshold=0.6)
 
 # Perform a Hough Transform
 # The accuracy corresponds to the bin size of a major axis.
@@ -128,17 +128,18 @@ yradius = int(accum[0][4])
 angle = accum[0][5]
 
 # Draw the ellipse on the original image
-cx, cy = ellipse_perimeter(center_y, center_x, yradius, xradius, orientation=angle)
+cx, cy = ellipse_perimeter(center_y, center_x,
+                           yradius, xradius, orientation=angle)
 image_rgb[cy, cx] = (0, 0, 220)
 # Draw the edge (white) and the resulting ellipse (red)
 edges = color.gray2rgb(edges)
 edges[cy, cx] = (250, 0, 0)
 
 fig = plt.subplots(figsize=(10, 6))
-plt.subplot(1,2,1)
+plt.subplot(1, 2, 1)
 plt.title('Original picture')
 plt.imshow(image_rgb)
-plt.subplot(1,2,2)
+plt.subplot(1, 2, 2)
 plt.title('Edge (white) and result (red)')
 plt.imshow(edges)