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Code
def reduce_circles(circle_indices, distMin, param_votes):
"""Helper function for hough_circle
Averages clusters of circles:param circle_indices: numpy array of circles detected. Stored as a = [[radii], [rows], [cols]]
wherea[0][i]is the radius of the ith circle
a[1][i] is the row where the center of the ith circle is locateda[2][i]is the col where the center of the ith circle is located
:param distMin: int, minimum distance between circles detected. (The function averages circles clustered withina distMin x distMin square)
:param votesMin: int, minimum threshold for accumulator value. The higher this value is,the less false circles detected
:return: numpy array of circles"""
rad_indices = circle_indices[0]r_indices = circle_indices[1]
c_indices = circle_indices[2]print rad_indices
print r_indicesprint c_indices
total_circles = len(rad_indices)print "total circles ", total_circles
new_circle_indices = [[],[],[]]radtemp = 0 # accumulator value for radii
rtemp = 0 # accumulator value for rowsctemp = 0 # accumulator value for columns
weightcount = 0 # accumulator value for weight of each circle.# A proposed circle with n votes contributes n^2 weight
count = 0 # total number of circles detected in a cluster.minThresh = total_circles / 5 # Each cluster has a count that records number of circles in that cluster.
# If count is below minThresh, the cluster is disregarded as a false positive.# Remove an (radius, center) once it's been determined part of a cluster. Iterate until no more proposed circles to
# iterate through. Iterate backwards so deleting things doesn't mess up indexing.while np.size(rad_indices)!= 0:
# get centerr0 = r_indices[0]
c0 = c_indices[0]for i in reversed(range(0, total_circles)):
# If iterate through all other proposed circles, and find ones with center within a# distMin x distMin square.
if abs(r0 - r_indices[i])<distMin and abs(c0 - c_indices[i]<distMin):
# (radius, center) values of proposed circle wwe've iterated torad = rad_indices[i]
r = r_indices[i]c = c_indices[i]
# increase accumulator values, weighted by weightweight = param_votes[rad][r][c]*param_votes[rad][r][c]radtemp += rad * weight
rtemp += r * weightctemp += c * weight
# delete proposed circle from rad_indicesrad_indices = np.delete(rad_indices, i)
r_indices = np.delete(r_indices, i)c_indices = np.delete(c_indices, i)
weightcount += weightcount += 1
total_circles = len(rad_indices)# if more circles in cluster than minThresh, average the radii and center values to combine the cluster into
# one circleif count>= minThresh:
new_circle_indices[0].append(radtemp / weightcount)
new_circle_indices[1].append(rtemp / weightcount)
new_circle_indices[2].append(ctemp /weightcount)
# Optional code. If you know you won't have circles within circles, uncomment this code.# This removes circles with centers inside of other circles.
# for i in reversed(range(0, total_circles)):# if abs(rtemp / weightcount - r_indices[i])<radtemp / weightcount and abs(ctemp /weightcount - c_indices[i])<radtemp / weightcount:
# rad_indices = np.delete(rad_indices, i)# r_indices = np.delete(r_indices, i)
# c_indices = np.delete(c_indices, i)# total_circles = len(rad_indices)
# reset accumulators to 0 for the next clusterradtemp = 0
rtemp = 0ctemp = 0
count = 0weightcount = 0
return np.array(new_circle_indices)
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