from numpy import *
from math import sqrt


# Input: expects Nx3 matrix of points
# Returns R,t
# R = 3x3 rotation matrix
# t = 3x1 column vector

def rigid_transform_3D(A, B):
    assert len(A) == len(B)

    N = A.shape[0];  # total points

    centroid_A = mean(A, axis=0)
    centroid_B = mean(B, axis=0)

    # centre the points
    AA = A - tile(centroid_A, (N, 1))
    BB = B - tile(centroid_B, (N, 1))

    # dot is matrix multiplication for array
    H = transpose(AA) * BB

    U, S, Vt = linalg.svd(H)

    R = Vt.T * U.T

    # special reflection case
    if linalg.det(R) < 0:
        print "Reflection detected"
        Vt[2, :] *= -1
        R = Vt.T * U.T

    t = -R * centroid_A.T + centroid_B.T

    print t

    return R, t


# Test with random data

# Random rotation and translation
R = mat(random.rand(3, 3))
t = mat(random.rand(3, 1))

# make R a proper rotation matrix, force orthonormal
U, S, Vt = linalg.svd(R)
R = U * Vt

# remove reflection
if linalg.det(R) < 0:
    Vt[2, :] *= -1
    R = U * Vt

# number of points
n = 10

A = mat(random.rand(n, 3));
B = R * A.T + tile(t, (1, n))
B = B.T;

# recover the transformation
ret_R, ret_t = rigid_transform_3D(A, B)

A2 = (ret_R * A.T) + tile(ret_t, (1, n))
A2 = A2.T

# Find the error
err = A2 - B

err = multiply(err, err)
err = sum(err)
rmse = sqrt(err / n);

print "Points A"
print A
print ""

print "Points B"
print B
print ""

print "Rotation"
print R
print ""

print "Translation"
print t
print ""

print "RMSE:", rmse
print "If RMSE is near zero, the function is correct!"
from numpy import *
from math import sqrt


# Input: expects Nx3 matrix of points
# Returns R,t
# R = 3x3 rotation matrix
# t = 3x1 column vector

def rigid_transform_3D(A, B):
    assert len(A) == len(B)

    N = A.shape[0];  # total points

    centroid_A = mean(A, axis=0)
    centroid_B = mean(B, axis=0)

    # centre the points
    AA = A - tile(centroid_A, (N, 1))
    BB = B - tile(centroid_B, (N, 1))

    # dot is matrix multiplication for array
    H = transpose(AA) * BB

    U, S, Vt = linalg.svd(H)

    R = Vt.T * U.T

    # special reflection case
    if linalg.det(R) < 0:
        print "Reflection detected"
        Vt[2, :] *= -1
        R = Vt.T * U.T

    t = -R * centroid_A.T + centroid_B.T

    print t

    return R, t


# Test with random data

# Random rotation and translation
R = mat(random.rand(3, 3))
t = mat(random.rand(3, 1))

# make R a proper rotation matrix, force orthonormal
U, S, Vt = linalg.svd(R)
R = U * Vt

# remove reflection
if linalg.det(R) < 0:
    Vt[2, :] *= -1
    R = U * Vt

# number of points
n = 10

A = mat(random.rand(n, 3));
B = R * A.T + tile(t, (1, n))
B = B.T;

# recover the transformation
ret_R, ret_t = rigid_transform_3D(A, B)

A2 = (ret_R * A.T) + tile(ret_t, (1, n))
A2 = A2.T

# Find the error
err = A2 - B

err = multiply(err, err)
err = sum(err)
rmse = sqrt(err / n);

print "Points A"
print A
print ""

print "Points B"
print B
print ""

print "Rotation"
print R
print ""

print "Translation"
print t
print ""

print "RMSE:", rmse
print "If RMSE is near zero, the function is correct!"
# Input: expects Nx3 matrix of points # Returns R,t # R = 3x3 rotation matrix # t = 3x1 column vector # total points # centre the points # dot is matrix multiplication for array # special reflection case # Test with random data # Random rotation and translation # make R a proper rotation matrix, force orthonormal # remove reflection # number of points # recover the transformation # Find the error