2つの2次元データ点の最良の変換行列を見つける

Question

最小二乗誤差値を得るために2つの2次元点集合を整列させるための最良の変換行列を見つける方法。このコードは私がやったことですが、これは正しくありません：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) 

Answer 1

最終的に私は正しい呼び出しは `TFORM（SRC）`である私の質問

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)))