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[ENH] Fixed some PEP8 issues in example gallery.

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emmanuelle
2015-08-30 12:28:40 +02:00
parent 069c575955
commit 84ac53824d
20 changed files with 85 additions and 69 deletions
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doc/examples/plot_line_hough_transform.py
+17 -16
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@@ -16,16 +16,16 @@ 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,
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.
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
@@ -35,13 +35,13 @@ 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.
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.
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
----------
@@ -84,9 +84,9 @@ ax[0].set_title('Input image')
ax[0].axis('image')
ax[1].imshow(np.log(1 + h),
extent=[np.rad2deg(theta[-1]), np.rad2deg(theta[0]),
d[-1], d[0]],
cmap=plt.cm.gray, aspect=1/1.5)
extent=[np.rad2deg(theta[-1]), np.rad2deg(theta[0]),
d[-1], d[0]],
cmap=plt.cm.gray, aspect=1/1.5)
ax[1].set_title('Hough transform')
ax[1].set_xlabel('Angles (degrees)')
ax[1].set_ylabel('Distance (pixels)')
@@ -106,7 +106,8 @@ ax[2].axis('image')
image = data.camera()
edges = canny(image, 2, 1, 25)
lines = probabilistic_hough_line(edges, threshold=10, line_length=5, line_gap=3)
lines = probabilistic_hough_line(edges, threshold=10, line_length=5,
line_gap=3)
fig2, ax = plt.subplots(1, 3, figsize=(8, 3))