As in the title, I want to fit a cylinder to a group of 3D points with PYTHON. This is a nice solution with MATLAB. How can we do it with Python?

解決方案

Using scipy.optimize.leastsq, we can create an error function in which the difference between the observed cylinder radius and the modelled radius is minimized. The following is an example of fitting a vertical cylinder

import numpy as np

from scipy.optimize import leastsq

def cylinderFitting(xyz,p,th):

"""

This is a fitting for a vertical cylinder fitting

Reference:

http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXIX-B5/169/2012/isprsarchives-XXXIX-B5-169-2012.pdf

xyz is a matrix contain at least 5 rows, and each row stores x y z of a cylindrical surface

p is initial values of the parameter;

p[0] = Xc, x coordinate of the cylinder centre

P[1] = Yc, y coordinate of the cylinder centre

P[2] = alpha, rotation angle (radian) about the x-axis

P[3] = beta, rotation angle (radian) about the y-axis

P[4] = r, radius of the cylinder

th, threshold for the convergence of the least squares

"""

x = xyz[:,0]

y = xyz[:,1]

z = xyz[:,2]

fitfunc = lambda p, x, y, z: (- np.cos(p[3])*(p[0] - x) - z*np.cos(p[2])*np.sin(p[3]) - np.sin(p[2])*np.sin(p[3])*(p[1] - y))**2 + (z*np.sin(p[2]) - np.cos(p[2])*(p[1] - y))**2 #fit function

errfunc = lambda p, x, y, z: fitfunc(p, x, y, z) - p[4]**2 #error function

est_p , success = leastsq(errfunc, p, args=(x, y, z), maxfev=1000)

return est_p

if __name__=="__main__":

np.set_printoptions(suppress=True)

xyz = np.loadtxt('cylinder11.xyz')

#print xyz

print "Initial Parameters: "

p = np.array([-13.79,-8.45,0,0,0.3])

print p

print " "

print "Performing Cylinder Fitting ... "

est_p = cylinderFitting(xyz,p,0.00001)

print "Fitting Done!"

print " "

print "Estimated Parameters: "

print est_p

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