如何在opencv3中使用Python中的PCACompute函数？

Question

使用以下语法，cv2.PCACompute函数在OpenCV 2.4中运行良好：

import cv2
mean, eigvec = cv2.PCACompute(data)

该函数存在于OpenCV 3.1中，但引发以下异常：

^{pr2}$

C++ documentation在解释如何从Python调用它方面没有太大帮助。我猜InputOutputArray参数现在也是Python函数签名中的强制参数，但我无法找到使它们工作的方法。在

有没有办法让我恰当地称呼它？在

（注意：我知道有其他方法可以运行PCA，我可能会用其中一种。我只是好奇新的OpenCV绑定是如何工作的。）

Answer 1

简单回答：

mean, eigvec = cv2.PCACompute(data, mean=None)

---

详细信息：

1. 让搜索计算机计算来源首先。然后查找this：

   // [modules/core/src/pca.cpp](L351-L360)
   void cv::PCACompute(InputArray data, InputOutputArray mean,
                       OutputArray eigenvectors, int maxComponents)
   {
       CV_INSTRUMENT_REGION()
   
       PCA pca;
       pca(data, mean, 0, maxComponents);
       pca.mean.copyTo(mean);
       pca.eigenvectors.copyTo(eigenvectors);
   }
   

2. 好的，现在我们读一下document：

   C++: PCA& PCA::operator()(InputArray data, InputArray mean, int flags, int maxComponents=0)
   Python: cv2.PCACompute(data[, mean[, eigenvectors[, maxComponents]]]) → mean, eigenvectors
   
   Parameters: 
       data – input samples stored as the matrix rows or as the matrix columns.
       mean – optional mean value; if the matrix is empty (noArray()), the mean is computed from the data.
   flags –
       operation flags; currently the parameter is only used to specify the data layout.
   
       CV_PCA_DATA_AS_ROW indicates that the input samples are stored as matrix rows.
       CV_PCA_DATA_AS_COL indicates that the input samples are stored as matrix columns.
   maxComponents – maximum number of components that PCA should retain; by default, all the components are retained.
   

3. 这么说吧

   ## py
   mean, eigvec = cv2.PCACompute(data, mean=None)
   

   等于

   // cpp 
   PCA pca;
   pca(data, mean=noArray(), flags=CV_PCA_DATA_AS_ROW);
   ...