CODEWITHSUNDEEP
python-3.xscikit-learnscikit-image
user8523104
user8523104

Reputation: 557

find best transformation matrix for two 2D data points

how to find best transformation matrix for aligning two 2D point set to get minimum Mean squared error value. this code is what I have done but this is not right: tform * src.

import numpy as np
from skimage import transform as tf
from sklearn.metrics import mean_squared_error
# estimate transformation parameters
src = np.array([0, 0, 10, 10]).reshape((2, 2))
dst = np.array([12, 14, 1, -20]).reshape((2, 2))
tform = tf.estimate_transform('similarity', src, dst)
print(src)
print(dst)
print(tform.params)
msq=mean_squared_error(tform*src,dst)

Upvotes: 2

Views: 1274

Answers (1)

user8523104
user8523104

Reputation: 557

finally I could find a right answer for my question 

import numpy as np
from skimage import transform as tf
from sklearn.metrics import mean_squared_error
# estimate transformation parameters
src = np.array([0,0 , 1,0 , 1,1 , 0,1]).reshape((4, 2))
dst = np.array([3,1 , 3,2 , 2,2 , 2,1]).reshape((4, 2))
tform = tf.estimate_transform('similarity', src, dst)
#tform is the transformation matrix for these data to align them
print(src)
print(dst)
print(tform.params)

mt = tf.matrix_transform(src, tform.params)#mt is the same dst
mean_squared_error(mt,dst) #should be zero
print( '{:.10f}'.format(mean_squared_error(mt,dst)) )

Upvotes: 2

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