eigenfaces

add option to display the eigenfaces, fisherfaces and training images

src/ofxFaceRecognizer.cpp

@@ -70,6 +70,9 @@ void ofxFaceRecognizer::setup(int method_used, int _maxFaces, bool bAlreadySaved
     }
 
 
+    if(methodId == 0 || methodId == 1) generateEigenFishFaces();
+
+
 }
 
 void ofxFaceRecognizer::update(ofImage _img){
@@ -144,6 +147,126 @@ void ofxFaceRecognizer::loadTrainingImages(string _folderName, int _maxFaces){
 
 }
 
+static Mat norm_0_255(InputArray _src) {
+    Mat src = _src.getMat();
+    // Create and return normalized image:
+    Mat dst;
+    switch(src.channels()) {
+        case 1:
+            cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
+            break;
+        case 3:
+            cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC3);
+            break;
+        default:
+            src.copyTo(dst);
+            break;
+    }
+    return dst;
+}
+
+void ofxFaceRecognizer::generateEigenFishFaces(){
+    int height = allTrainingMats[0].rows;
+
+    // Here is how to get the eigenvalues of this Eigenfaces model:
+    Mat eigenvalues = model->getMat("eigenvalues");
+    cout<<"eigenvalues.row "<<eigenvalues.rows<<endl;
+    cout<<"eigenvalues.cols "<<eigenvalues.cols<<endl;
+
+    // And we can do the same to display the Eigenvectors (read Eigenfaces):
+    Mat W = model->getMat("eigenvectors");
+    cout<<"W.row "<<W.rows<<endl;
+    cout<<"W.cols "<<W.cols<<endl;
+
+    // Get the sample mean from the training data
+    Mat mean = model->getMat("mean");
+    cout<<"mean.row "<<mean.rows<<endl;
+    cout<<"mean.cols "<<mean.cols<<endl;
+
+    // Display or save the first, at most 16 Fisherfaces:
+    cgrayscaleJET_array.clear();
+    cgrayscaleBONE_array.clear();
+
+    for (int a = 0; a < min(16, W.cols); a++) {
+        //for (int i = 0; i < 16; i++) {
+        string msg = format("Eigenvalue #%d = %.5f", a, eigenvalues.at<double>(a));
+        cout << msg << endl;
+        // get eigenvector #a
+        Mat ev = W.col(a).clone();
+        // Reshape to original size & normalize to [0...255] for imshow.
+        Mat grayscale = norm_0_255(ev.reshape(1, height));
+        // Show the image & apply a Bone colormap for better sensing.
+        Mat cgrayscale;
+        applyColorMap(grayscale, cgrayscale, COLORMAP_JET);
+
+        // Display or save:
+        ofImage ti;
+        toOf(cgrayscale,ti);
+        cgrayscaleJET_array.push_back(ti);
+
+        applyColorMap(grayscale, cgrayscale, COLORMAP_BONE);
+        toOf(cgrayscale,ti);
+        cgrayscaleBONE_array.push_back(ti);
+
+    }
+
+
+    reconstruction_array.clear();
+    // Display or save the image reconstruction at some predefined steps:
+    if(methodId == 0){
+        for(int num_component = min(W.cols, 10); num_component < min(W.cols, 300); num_component+=15) {
+            Mat evs = Mat(W, Range::all(), Range(0, num_component));
+            Mat projection = subspaceProject(evs, mean, allTrainingMats[0].reshape(1,1));
+            Mat reconstruction = subspaceReconstruct(evs, mean, projection);
+            // Normalize the result:
+            reconstruction = norm_0_255(reconstruction.reshape(1, allTrainingMats[0].rows));
+
+            // Normalize the result:
+            reconstruction = norm_0_255(reconstruction.reshape(1, allTrainingMats[0].rows));
+            // Display or save:
+
+            ofImage ti;
+            toOf(reconstruction,ti);
+            //reconstruction_array[num_component] = ti;
+            reconstruction_array.push_back(ti);
+        }
+    }
+    if(methodId == 1){
+        for(int num_component = 0; num_component < min(16, W.cols); num_component++) {
+            // Slice the Fisherface from the model:
+            Mat ev = W.col(num_component);
+            Mat projection = subspaceProject(ev, mean, allTrainingMats[0].reshape(1,1));
+            Mat reconstruction = subspaceReconstruct(ev, mean, projection);
+
+            // Normalize the result:
+            reconstruction = norm_0_255(reconstruction.reshape(1, allTrainingMats[0].rows));
+
+            // Display or save:
+            ofImage ti;
+            toOf(reconstruction,ti);
+            reconstruction_array.push_back(ti);
+        }
+    }
+
+}
+
+int ofxFaceRecognizer::getEigenfaceSize(){
+    return cgrayscaleJET_array.size();
+}
+int ofxFaceRecognizer::getReconstructionSize(){
+    return reconstruction_array.size();
+}
+void ofxFaceRecognizer::drawEigefaceJET(int _id, float _x, float _y, float _w, float _h){
+    cgrayscaleJET_array[_id].draw(_x, _y, _w, _h);
+}
+void ofxFaceRecognizer::drawEigenfaceBONE(int _id, float _x, float _y, float _w, float _h){
+    cgrayscaleBONE_array[_id].draw(_x, _y, _w, _h);
+}
+
+void ofxFaceRecognizer::drawReconstructionImage(int _id, float _x, float _y, float _w, float _h){
+   reconstruction_array[_id].draw(_x, _y, _w, _h);
+}
+
 int ofxFaceRecognizer::getPrediction(){
     return prediction;
 }

src/ofxFaceRecognizer.h

@@ -40,20 +40,21 @@ class ofxFaceRecognizer {
     int getMethodId();
     string getMethodName();
 
+    void generateEigenFishFaces();
+    void drawEigefaceJET(int _id, float _x, float _y, float _w, float _h);
+    void drawEigenfaceBONE(int _id, float _x, float _y, float _w, float _h);
+    void drawReconstructionImage(int _id, float _x, float _y, float _w, float _h);
+    int getEigenfaceSize();
+    int getReconstructionSize();
+
      vector<int> allTrainingLabels;
 protected:
 
     int databaseImage_width,databaseImage_height;
     Ptr<FaceRecognizer> model;
 
     int maxFaces;
-
-    /*
-    int method_used;
-    int maxFaces;
-    bool bAlreadySavedModel;
-    string folderName;
-    */
+
 
     vector<ofImage> allTrainingImages;
     vector<ofImage> oneImagePerPerson;
@@ -70,4 +71,9 @@ class ofxFaceRecognizer {
     string methodName;
     int methodId;
 
+    vector<ofImage>cgrayscaleJET_array;
+    vector<ofImage>cgrayscaleBONE_array;
+    vector<ofImage>reconstruction_array;
+
+
 };