Adds support for PCACompute

This PR adds support for PCACompute via wrapping one of the opencv
versions with this function signature. A smoke test has been added that
calls the wrapped function and verifies that the parameters are being
passed correctly.

In opencv there are four versions with this function signature, I have
chosen to wrap (what I think is) the most common with the most
parameters.

Link to supported functionality now: https://docs.opencv.org/4.x/d2/de8/group__core__array.html#ga4e2073c7311f292a0648f04c37b73781

ROADMAP.md

@@ -18,7 +18,7 @@ Your pull requests will be greatly appreciated!
  - [ ] [Mahalanobis](https://docs.opencv.org/master/d2/de8/group__core__array.html#ga4493aee129179459cbfc6064f051aa7d)
  - [ ] [mulTransposed](https://docs.opencv.org/master/d2/de8/group__core__array.html#gadc4e49f8f7a155044e3be1b9e3b270ab)
  - [ ] [PCABackProject](https://docs.opencv.org/master/d2/de8/group__core__array.html#gab26049f30ee8e94f7d69d82c124faafc)
- - [ ] [PCACompute](https://docs.opencv.org/master/d2/de8/group__core__array.html#ga4e2073c7311f292a0648f04c37b73781)
+ - [X] [PCACompute](https://docs.opencv.org/master/d2/de8/group__core__array.html#ga4e2073c7311f292a0648f04c37b73781)
  - [ ] [PCAProject](https://docs.opencv.org/master/d2/de8/group__core__array.html#ga6b9fbc7b3a99ebfd441bbec0a6bc4f88)
  - [ ] [PSNR](https://docs.opencv.org/master/d2/de8/group__core__array.html#ga07aaf34ae31d226b1b847d8bcff3698f)
  - [ ] [randn](https://docs.opencv.org/master/d2/de8/group__core__array.html#gaeff1f61e972d133a04ce3a5f81cf6808)

core.cpp

@@ -504,6 +504,10 @@ void Mat_EigenNonSymmetric(Mat src, Mat eigenvalues, Mat eigenvectors) {
  cv::eigenNonSymmetric(*src, *eigenvalues, *eigenvectors);
 }
 
+void Mat_PCACompute(Mat src, Mat mean, Mat eigenvalues, Mat eigenvectors, int maxComponents) {
+ cv::PCACompute(*src, *mean, *eigenvalues, *eigenvectors, maxComponents);
+}
+
 void Mat_Exp(Mat src, Mat dst) {
  cv::exp(*src, *dst);
 }

core.go

@@ -1219,6 +1219,20 @@ func EigenNonSymmetric(src Mat, eigenvalues *Mat, eigenvectors *Mat) {
  C.Mat_EigenNonSymmetric(src.p, eigenvalues.p, eigenvectors.p)
 }
 
+// PCACompute performs PCA.
+//
+// The computed eigenvalues are sorted from the largest to the smallest and the corresponding
+// eigenvectors are stored as eigenvectors rows.
+//
+// Note: Calling with maxComponents == 0 (opencv default) will cause all components to be retained.
+//
+// For further details, please see:
+// https://docs.opencv.org/4.x/d2/de8/group__core__array.html#ga27a565b31d820b05dcbcd47112176b6e
+//
+func PCACompute(src Mat, mean *Mat, eigenvalues *Mat, eigenvectors *Mat, maxComponents int) {
+ C.Mat_PCACompute(src.p, mean.p, eigenvalues.p, eigenvectors.p, C.int(maxComponents))
+}
+
 // Exp calculates the exponent of every array element.
 //
 // For further details, please see:

core.h

@@ -379,6 +379,7 @@ void Mat_DFT(Mat m, Mat dst, int flags);
 void Mat_Divide(Mat src1, Mat src2, Mat dst);
 bool Mat_Eigen(Mat src, Mat eigenvalues, Mat eigenvectors);
 void Mat_EigenNonSymmetric(Mat src, Mat eigenvalues, Mat eigenvectors);
+void Mat_PCACompute(Mat src, Mat mean, Mat eigenvalues, Mat eigenvectors, int maxComponents);
 void Mat_Exp(Mat src, Mat dst);
 void Mat_ExtractChannel(Mat src, Mat dst, int coi);
 void Mat_FindNonZero(Mat src, Mat idx);

core_test.go

@@ -2070,6 +2070,29 @@ func TestMatEigenNonSymmetric(t *testing.T) {
  eigenvalues.Close()
 }
 
+func TestPCACompute(t *testing.T) {
+ src := NewMatWithSize(10, 10, MatTypeCV32F)
+ // Set some source data so the PCA is done on a non-zero matrix.
+ src.SetFloatAt(0, 0, 17)
+ src.SetFloatAt(2, 1, 5)
+ src.SetFloatAt(9, 9, 25)
+ mean := NewMat()
+ eigenvalues := NewMat()
+ eigenvectors := NewMat()
+ maxComponents := 2
+ PCACompute(src, &mean, &eigenvalues, &eigenvectors, maxComponents)
+ if mean.Empty() || eigenvectors.Empty() || eigenvalues.Empty() {
+ t.Error("TestPCACompute should not have empty eigenvectors or eigenvalues.")
+ }
+ if eigenvectors.Rows() > maxComponents {
+ t.Errorf("TestPCACompute unexpected numComponents, got=%d, want<=%d", eigenvectors.Rows(), maxComponents)
+ }
+ src.Close()
+ mean.Close()
+ eigenvectors.Close()
+ eigenvalues.Close()
+}
+
 func TestMatExp(t *testing.T) {
  src := NewMatWithSize(10, 10, MatTypeCV32F)
  dst := NewMat()