"""
========================
Circular Hough Transform
========================

The Hough transform in its simplest form is a `method to detect
straight lines <http://en.wikipedia.org/wiki/Hough_transform>`__
but it can also be used to detect circles.

In the following example, the Hough transform is used to detect
coin positions and match their edges. We provide a range of
plausible radii. For each radius, two circles are extracted and
we finally keep the five most prominent candidates.
The result shows that coin positions are well-detected.


Algorithm overview
------------------

Given a black circle on a white background, we first guess its
radius (or a range of radii) to construct a new circle.
This circle is applied on each black pixel of the original picture
and the coordinates of this circle are voting in an accumulator.
From this geometrical construction, the original circle center
position receives the highest score.

Note that the accumulator size is built to be larger than the
original picture in order to detect centers outside the frame.
Its size is extended by two times the larger radius.

"""


import numpy as np
import matplotlib.pyplot as plt

from skimage import data, filter
from skimage.transform import hough_circle
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)

fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6))

# Detect two radii
hough_radii = np.arange(15, 30, 2)
hough_res = hough_circle(edges, hough_radii)

centers = []
accums = []
radii = []

for radius, h in zip(hough_radii, hough_res):
    # For each radius, extract two circles
    peaks = peak_local_max(h, num_peaks=2)
    centers.extend(peaks - hough_radii.max())
    accums.extend(h[peaks[:, 0], peaks[:, 1]])
    radii.extend([radius, radius])

# Draw the most prominent 5 circles
for idx in np.argsort(accums)[::-1][:5]:
    center_x, center_y = centers[idx]
    radius = radii[idx]
    cx, cy = circle_perimeter(center_y, center_x, radius)
    image[cy, cx] = 0

ax.imshow(image, cmap=plt.cm.gray)
plt.show()

@@ -1204,16 +1204,23 @@
 , filter
+, color
 %0Afrom sk
@@ -1975,16 +1975,46 @@
 circles%0A
+image = color.gray2rgb(image)%0A
 for idx 
@@ -2186,17 +2186,31 @@
 , cx%5D = 
-0
+(220, 250, 150)
 %0A%0Aax.ims

Use brighter color in circular Hough demo.