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facerec_demo model->predict exception thrown

Hello, I am trying to use the facerec_demo.cpp file to start off with face recognition using a video feed. This demo does extactly that.

So I can get it to compile properly inputting all the command arguments and it runs except when it reaches the predict function of the code.

int prediction = model->predict(face_resized);

I know its throwing the exception here because I inset a breakpoint at this location the exception happens when this line executes.

Does any one have any ideas as to why this may be happening?

Thank you, Sonny.

facerec_demo model->predict exception thrown

Hello, I am trying to use the facerec_demo.cpp file to start off with face recognition using a video feed. This demo does extactly that.

So I can get it to compile properly inputting all the command arguments and it runs except when it reaches the predict function of the code.

int prediction = model->predict(face_resized);

I know its throwing the exception here because I inset a breakpoint at this location the exception happens when this line executes.

Does any one have any ideas as to why this may be happening?

UPDATE: CODE

include "opencv2/core/core.hpp"

include "opencv2/contrib/contrib.hpp"

include "opencv2/highgui/highgui.hpp"

include "opencv2/imgproc/imgproc.hpp"

include "opencv2/objdetect/objdetect.hpp"

include <iostream>

include <fstream>

include <sstream>

using namespace cv; using namespace std;

static void read_csv(const string& filename, vector<mat>& images, vector<int>& labels, 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); } string line, path, classlabel; while (getline(file, line)) { stringstream liness(line); getline(liness, path, separator); getline(liness, classlabel); if(!path.empty() && !classlabel.empty()) { images.push_back(imread(path, 0)); labels.push_back(atoi(classlabel.c_str())); } } }

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] << " </path> </path> </path>" << endl; // cout << "\t </path> -- Path to the Haar Cascade for face detection." << endl; cout << "\t </path> -- Path to the CSV file with the face database." << endl; // cout << "\t <device id=""> -- The webcam device id to grab frames from." << endl; system("pause"); exit(1); } // Get the path to your CSV: //string fn_haar = string(argv[1]);

string fn_haar = "C:\\opencv\\data\\haarcascades\\haarscascade_frontalface_default.xml";

string fn_csv = string(argv[1]);
//int deviceId = atoi(argv[3]);

// These vectors hold the images and corresponding labels:
vector<Mat> images;
vector<int> labels;
// Read in the data (fails if no valid input filename is given, but you'll get an error message):
try {
    read_csv(fn_csv, images, labels);
} catch (cv::Exception& e) {
    cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
    // nothing more we can do
    exit(1);
}
// Get the height from the first image. We'll need this
// later in code to reshape the images to their original
// size AND we need to reshape incoming faces to this size:
int im_width = images[0].cols;
int im_height = images[0].rows;
// Create a FaceRecognizer and train it on the given images:
Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
model->train(images, labels);
// That's it for learning the Face Recognition model. You now
// need to create the classifier for the task of Face Detection.
// We are going to use the haar cascade you have specified in the
// command line arguments:
//
CascadeClassifier haar_cascade;
haar_cascade.load(fn_haar);
// Get a handle to the Video device:
VideoCapture cap("C:\\ImageFolder\\Video2.mpg");
// Check if we can use this device at all:
if(!cap.isOpened()) {
   // cerr << "Capture Device ID " << deviceId << "cannot be opened." << endl;
    return -1;
}
// Holds the current frame from the Video device:
Mat frame;

for(;;) { cap >> frame; // Clone the current frame: Mat original = frame.clone(); // Convert the current frame to grayscale: Mat gray; cvtColor(original, gray, CV_BGR2GRAY);

imshow("gray face", gray); // Find the faces in the frame: vector< Rect_<int> > faces; haar_cascade.detectMultiScale(gray, faces); // At this point you have the position of the faces in // faces. Now we'll get the faces, make a prediction and // annotate it in the video. Cool or what? for(int i = 0; i < faces.size(); i++) { // Process face by face: Rect face_i = faces[i]; // Crop the face from the image. So simple with OpenCV C++: Mat face = gray(face_i); // Resizing the face is necessary for Eigenfaces and Fisherfaces. You can easily // verify this, by reading through the face recognition tutorial coming with OpenCV. // Resizing IS NOT NEEDED for Local Binary Patterns Histograms, so preparing the // input data really depends on the algorithm used. // // I strongly encourage you to play around with the algorithms. See which work best // in your scenario, LBPH should always be a contender for robust face recognition. // // Since I am showing the Fisherfaces algorithm here, I also show how to resize the // face you have just found: Mat face_resized; cv::resize(face, face_resized, Size(im_width, im_height), 1.0, 1.0, INTER_CUBIC); // Now perform the prediction, see how easy that is: int prediction = model->predict(face_resized); // And finally write all we've found out to the original image! // First of all draw a green rectangle around the detected face: rectangle(original, face_i, CV_RGB(0, 255,0), 1); // Create the text we will annotate the box with: string box_text = format("Prediction = %d", prediction); // Calculate the position for annotated text (make sure we don't // put illegal values in there): int pos_x = std::max(face_i.tl().x - 10, 0); int pos_y = std::max(face_i.tl().y - 10, 0); // And now put it into the image: putText(original, box_text, Point(pos_x, pos_y), FONT_HERSHEY_PLAIN, 1.0, CV_RGB(0,255,0), 2.0); } // Show the result: imshow("face_recognizer", original); // And display it: char key = (char) waitKey(20); // Exit this loop on escape: if(key == 27) break; } return 0; }

Thank you, Sonny.

click to hide/show revision 3
code formating

updated 2013-02-22 10:08:47 -0500

berak gravatar image

facerec_demo model->predict exception thrown

Hello, I am trying to use the facerec_demo.cpp file to start off with face recognition using a video feed. This demo does extactly that.

So I can get it to compile properly inputting all the command arguments and it runs except when it reaches the predict function of the code.

int prediction = model->predict(face_resized);

I know its throwing the exception here because I inset a breakpoint at this location the exception happens when this line executes.

Does any one have any ideas as to why this may be happening?

UPDATE: CODE

include "opencv2/core/core.hpp"

include "opencv2/contrib/contrib.hpp"

include "opencv2/highgui/highgui.hpp"

include "opencv2/imgproc/imgproc.hpp"

include "opencv2/objdetect/objdetect.hpp"

include <iostream>

include <fstream>

include <sstream>

#include "opencv2/core/core.hpp"
#include "opencv2/contrib/contrib.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/objdetect/objdetect.hpp"

#include <iostream>
#include <fstream>
#include <sstream>

using namespace cv;
using namespace std;

std; static void read_csv(const string& filename, vector<mat>& vector<Mat>& images, vector<int>& labels, 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); } string line, path, classlabel; while (getline(file, line)) { stringstream liness(line); getline(liness, path, separator); getline(liness, classlabel); if(!path.empty() && !classlabel.empty()) { images.push_back(imread(path, 0)); labels.push_back(atoi(classlabel.c_str())); } } }

}

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] << " </path> </path> </path>" << endl; // cout << "\t </path> -- Path to the Haar Cascade for face detection." << endl; cout << "\t </path> -- Path to the CSV file with the face database." << endl; // cout << "\t <device id=""> -- The webcam device id to grab frames from." << endl; system("pause"); exit(1); } // Get the path to your CSV: //string fn_haar = string(argv[1]);

string fn_haar = "C:\\opencv\\data\\haarcascades\\haarscascade_frontalface_default.xml"; "C:\opencv\data\haarcascades\haarscascade_frontalface_default.xml";

string fn_csv = string(argv[1]); //int deviceId = atoi(argv[3]); atoi(argv[3]);

// These vectors hold the images and corresponding labels: vector<Mat> vector<mat> images; vector<int> labels; // Read in the data (fails if no valid input filename is given, but you'll get an error message): try { read_csv(fn_csv, images, labels); } catch (cv::Exception& e) { cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl; // nothing more we can do exit(1); } // Get the height from the first image. We'll need this // later in code to reshape the images to their original // size AND we need to reshape incoming faces to this size: int im_width = images[0].cols; int im_height = images[0].rows; // Create a FaceRecognizer and train it on the given images: Ptr<FaceRecognizer> Ptr<facerecognizer> model = createFisherFaceRecognizer(); model->train(images, labels); // That's it for learning the Face Recognition model. You now // need to create the classifier for the task of Face Detection. // We are going to use the haar cascade you have specified in the // command line arguments: // CascadeClassifier haar_cascade; haar_cascade.load(fn_haar); // Get a handle to the Video device: VideoCapture cap("C:\\ImageFolder\\Video2.mpg"); cap("C:\ImageFolder\Video2.mpg"); // Check if we can use this device at all: if(!cap.isOpened()) { // cerr << "Capture Device ID " << deviceId << "cannot be opened." << endl; return -1; } // Holds the current frame from the Video device: Mat frame;

for(;;) { cap >> frame; // Clone the current frame: Mat original = frame.clone(); // Convert the current frame to grayscale: Mat gray; cvtColor(original, gray, CV_BGR2GRAY);

imshow("gray face", gray); // Find the faces in the frame: vector< Rect_<int> > faces; haar_cascade.detectMultiScale(gray, faces); // At this point you have the position of the faces in // faces. Now we'll get the faces, make a prediction and // annotate it in the video. Cool or what? for(int i = 0; i < faces.size(); i++) { // Process face by face: Rect face_i = faces[i]; // Crop the face from the image. So simple with OpenCV C++: Mat face = gray(face_i); // Resizing the face is necessary for Eigenfaces and Fisherfaces. You can easily // verify this, by reading through the face recognition tutorial coming with OpenCV. // Resizing IS NOT NEEDED for Local Binary Patterns Histograms, so preparing the // input data really depends on the algorithm used. // // I strongly encourage you to play around with the algorithms. See which work best // in your scenario, LBPH should always be a contender for robust face recognition. // // Since I am showing the Fisherfaces algorithm here, I also show how to resize the // face you have just found: Mat face_resized; cv::resize(face, face_resized, Size(im_width, im_height), 1.0, 1.0, INTER_CUBIC); // Now perform the prediction, see how easy that is: int prediction = model->predict(face_resized); // And finally write all we've found out to the original image! // First of all draw a green rectangle around the detected face: rectangle(original, face_i, CV_RGB(0, 255,0), 1); // Create the text we will annotate the box with: string box_text = format("Prediction = %d", prediction); // Calculate the position for annotated text (make sure we don't // put illegal values in there): int pos_x = std::max(face_i.tl().x - 10, 0); int pos_y = std::max(face_i.tl().y - 10, 0); // And now put it into the image: putText(original, box_text, Point(pos_x, pos_y), FONT_HERSHEY_PLAIN, 1.0, CV_RGB(0,255,0), 2.0); } // Show the result: imshow("face_recognizer", original); // And display it: char key = (char) waitKey(20); // Exit this loop on escape: if(key == 27) break; } return 0; }

Thank you, Sonny.