<p>几件小事:</p>
<ol>
<li>使用np.数组如果可以的话</li>
<li>不导入*</li>
</ol>
<p>我已经把密码改了np.数组演示user333700的含义。另外,我将投影矩阵转换为12维向量,因为大多数优化器都希望变量以向量形式进行优化。在</p>
<p>运行下面编辑的代码时会遇到的错误是TypeError:输入参数不正确。我相信这是因为你试图用线性最小二乘法来寻找12个参数,但你只有8个约束条件。在</p>
<pre><code>import numpy as np
import pylab as p
from scipy.optimize import leastsq
Projected_x = np.array([[ -69.69 , 255.3825, 1. ],
[ -69.69 , 224.6175, 1. ],
[-110.71 , 224.6175, 1. ],
[-110.71 , 255.3825, 1. ],
[ 709.69 , 224.6175, 1. ],
[ 709.69 , 255.3825, 1. ],
[ 750.71 , 255.3825, 1. ],
[ 750.71 , 224.6175, 1. ]])
Projected_x = Projected_x.transpose()
Pmat = np.array( [ 5.79746167e+02, 0.00000000e+00, 3.20000000e+02, 0.00000000e+00,
0.00000000e+00, 4.34809625e+02, 2.40000000e+02, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 1.00000000e+00, 0.00000000e+00] )
reconst_X = np.array([[-0.95238194, -0.58146697, 0.61506506, 0.00539229],
[-0.99566105, -0.76178453, 0.72451719, 0.00502341],
[-1.15401215, -0.81736486, 0.79417098, 0.00546999],
[-1.11073304, -0.6370473 , 0.68471885, 0.00583888],
[ 2.71283058, 2.34190758, -1.80448545, -0.00612243],
[ 2.7561097 , 2.52222514, -1.91393758, -0.00575354],
[ 2.9144608 , 2.57780547, -1.98359137, -0.00620013],
[ 2.87118168, 2.39748791, -1.87413925, -0.00656901]])
def residuals(p, y, x):
err = y - np.dot(p.reshape(3,4),x.T)
print p
return np.sum(err**2, axis=0)
p0 = Pmat
plsq = leastsq(residuals, p0, args=(Projected_x, reconst_X ) )
print plsq[0]
</code></pre>