From 64d8cf1e2e09bb0a51bcccaffce5bb7723791404 Mon Sep 17 00:00:00 2001
From: Andrey Pavlenko <andrey.pavlenko@itseez.com>
Date: Sat, 12 Jul 2014 19:28:43 +0400
Subject: [PATCH] improving face-rec sample: * CSV can contain dir-s and
 wildcards * save trained model

---
 samples/cpp/facerec_demo.cpp | 135 +++++++++++++++++++----------------
 1 file changed, 75 insertions(+), 60 deletions(-)

diff --git a/samples/cpp/facerec_demo.cpp b/samples/cpp/facerec_demo.cpp
index b92308e898..f3837d4d3f 100644
--- a/samples/cpp/facerec_demo.cpp
+++ b/samples/cpp/facerec_demo.cpp
@@ -27,35 +27,38 @@
 using namespace cv;
 using namespace std;
 
-static Mat toGrayscale(InputArray _src) {
-    Mat src = _src.getMat();
-    // only allow one channel
-    if(src.channels() != 1) {
-        CV_Error(CV_StsBadArg, "Only Matrices with one channel are supported");
-    }
-    // create and return normalized image
-    Mat dst;
-    cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
-    return dst;
-}
-
 static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, std::map<int, string>& labelsInfo, char separator = ';') {
-    std::ifstream file(filename.c_str(), ifstream::in);
-    if (!file) {
-        string error_message = "No valid input file was given, please check the given filename.";
-        CV_Error(CV_StsBadArg, error_message);
-    }
+    ifstream csv(filename.c_str());
+    if (!csv) CV_Error(CV_StsBadArg, "No valid input file was given, please check the given filename.");
     string line, path, classlabel, info;
-    while (getline(file, line)) {
+    while (getline(csv, line)) {
         stringstream liness(line);
+        path.clear(); classlabel.clear(); info.clear();
         getline(liness, path, separator);
         getline(liness, classlabel, separator);
         getline(liness, info, separator);
         if(!path.empty() && !classlabel.empty()) {
-            images.push_back(imread(path, 0));
-            labels.push_back(atoi(classlabel.c_str()));
+            cout << "Processing " << path << endl;
+            int label = atoi(classlabel.c_str());
             if(!info.empty())
-                labelsInfo.insert(std::make_pair(labels.back(), info));
+                labelsInfo.insert(std::make_pair(label, info));
+            // 'path' can be file, dir or wildcard path
+            String root(path.c_str());
+            vector<String> files;
+            glob(root, files, true);
+            for(vector<String>::const_iterator f = files.begin(); f != files.end(); ++f) {
+                cout << "\t" << *f << endl;
+                Mat img = imread(*f, CV_LOAD_IMAGE_GRAYSCALE);
+                static int w=-1, h=-1;
+                static bool showSmallSizeWarning = true;
+                if(w>0 && h>0 && (w!=img.cols || h!=img.rows)) cout << "\t* Warning: images should be of the same size!" << endl;
+                if(showSmallSizeWarning && (img.cols<50 || img.rows<50)) {
+                    cout << "* Warning: for better results images should be not smaller than 50x50!" << endl;
+                    showSmallSizeWarning = false;
+                }
+                images.push_back(img);
+                labels.push_back(label);
+            }
         }
     }
 }
@@ -63,8 +66,17 @@ static void read_csv(const string& filename, vector<Mat>& images, vector<int>& l
 int main(int argc, const char *argv[]) {
     // Check for valid command line arguments, print usage
     // if no arguments were given.
-    if (argc != 2) {
-        cout << "usage: " << argv[0] << " <csv.ext>" << endl;
+    if (argc != 2 && argc != 3) {
+        cout << "Usage: " << argv[0] << " <csv> [arg2]\n"
+             << "\t<csv> - path to config file in CSV format\n"
+             << "\targ2 - if the 2nd argument is provided (with any value) "
+             << "the advanced stuff is run and shown to console.\n"
+             << "The CSV config file consists of the following lines:\n"
+             << "<path>;<label>[;<comment>]\n"
+             << "\t<path> - file, dir or wildcard path\n"
+             << "\t<label> - non-negative integer person label\n"
+             << "\t<comment> - optional comment string (e.g. person name)"
+             << endl;
         exit(1);
     }
     // Get the path to your CSV.
@@ -88,10 +100,6 @@ int main(int argc, const char *argv[]) {
         string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
         CV_Error(CV_StsError, error_message);
     }
-    // Get the height from the first image. We'll need this
-    // later in code to reshape the images to their original
-    // size:
-    int height = images[0].rows;
     // The following lines simply get the last images from
     // your dataset and remove it from the vector. This is
     // done, so that the training data (which we learn the
@@ -118,6 +126,9 @@ int main(int argc, const char *argv[]) {
     Ptr<FaceRecognizer> model = createEigenFaceRecognizer();
     model->setLabelsInfo(labelsInfo);
     model->train(images, labels);
+    string saveModelPath = "face-rec-model.txt";
+    cout << "Saving the trained model to " << saveModelPath << endl;
+    model->save(saveModelPath);
 
     // The following line predicts the label of a given
     // test image:
@@ -133,39 +144,43 @@ int main(int argc, const char *argv[]) {
     cout << result_message << endl;
     if( (predictedLabel == testLabel) && !model->getLabelInfo(predictedLabel).empty() )
         cout << format("%d-th label's info: %s", predictedLabel, model->getLabelInfo(predictedLabel).c_str()) << endl;
-    // Sometimes you'll need to get/set internal model data,
-    // which isn't exposed by the public cv::FaceRecognizer.
-    // Since each cv::FaceRecognizer is derived from a
-    // cv::Algorithm, you can query the data.
-    //
-    // First we'll use it to set the threshold of the FaceRecognizer
-    // to 0.0 without retraining the model. This can be useful if
-    // you are evaluating the model:
-    //
-    model->set("threshold", 0.0);
-    // Now the threshold of this model is set to 0.0. A prediction
-    // now returns -1, as it's impossible to have a distance below
-    // it
-    predictedLabel = model->predict(testSample);
-    cout << "Predicted class = " << predictedLabel << endl;
-    // Here is how to get the eigenvalues of this Eigenfaces model:
-    Mat eigenvalues = model->getMat("eigenvalues");
-    // And we can do the same to display the Eigenvectors (read Eigenfaces):
-    Mat W = model->getMat("eigenvectors");
-    // From this we will display the (at most) first 10 Eigenfaces:
-    for (int i = 0; i < min(10, W.cols); i++) {
-        string msg = format("Eigenvalue #%d = %.5f", i, eigenvalues.at<double>(i));
-        cout << msg << endl;
-        // get eigenvector #i
-        Mat ev = W.col(i).clone();
-        // Reshape to original size & normalize to [0...255] for imshow.
-        Mat grayscale = toGrayscale(ev.reshape(1, height));
-        // Show the image & apply a Jet colormap for better sensing.
-        Mat cgrayscale;
-        applyColorMap(grayscale, cgrayscale, COLORMAP_JET);
-        imshow(format("%d", i), cgrayscale);
-    }
-    waitKey(0);
 
+    // advanced stuff
+    if(argc>2) {
+        // Sometimes you'll need to get/set internal model data,
+        // which isn't exposed by the public cv::FaceRecognizer.
+        // Since each cv::FaceRecognizer is derived from a
+        // cv::Algorithm, you can query the data.
+        //
+        // First we'll use it to set the threshold of the FaceRecognizer
+        // to 0.0 without retraining the model. This can be useful if
+        // you are evaluating the model:
+        //
+        model->set("threshold", 0.0);
+        // Now the threshold of this model is set to 0.0. A prediction
+        // now returns -1, as it's impossible to have a distance below
+        // it
+        predictedLabel = model->predict(testSample);
+        cout << "Predicted class = " << predictedLabel << endl;
+        // Here is how to get the eigenvalues of this Eigenfaces model:
+        Mat eigenvalues = model->getMat("eigenvalues");
+        // And we can do the same to display the Eigenvectors (read Eigenfaces):
+        Mat W = model->getMat("eigenvectors");
+        // From this we will display the (at most) first 10 Eigenfaces:
+        for (int i = 0; i < min(10, W.cols); i++) {
+            string msg = format("Eigenvalue #%d = %.5f", i, eigenvalues.at<double>(i));
+            cout << msg << endl;
+            // get eigenvector #i
+            Mat ev = W.col(i).clone();
+            // Reshape to original size & normalize to [0...255] for imshow.
+            Mat grayscale;
+            normalize(ev.reshape(1), grayscale, 0, 255, NORM_MINMAX, CV_8UC1);
+            // Show the image & apply a Jet colormap for better sensing.
+            Mat cgrayscale;
+            applyColorMap(grayscale, cgrayscale, COLORMAP_JET);
+            imshow(format("%d", i), cgrayscale);
+        }
+        waitKey(0);
+    }
     return 0;
 }