Debug assertion failed - C++ (OpenCV) - pops up after the program terminates - c++

Question

I am using OpenCV version 2.4.9 and Visual Studio 2015. I am sure all the dependencies between them are working, since other sample programs worked perfectly, using OpenCV libraries.
You can find here my code:
#include <opencv2/opencv.hpp>
#include <stdio.h>
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/core/core.hpp"
#include "opencv2/contrib/contrib.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <iostream>
#include <fstream>
#include <sstream>
using namespace cv;
using namespace std;
String face_cascade_name = "C:\\opencv\\sources\\data\\haarcascades\\haarcascade_frontalface_alt.xml";
String eye_cascade_name = "C:\\opencv\\sources\\data\\haarcascades\\haarcascade_eye.xml";
Mat faceDetect(Mat img);
CascadeClassifier face_cascade;
CascadeClassifier eyes_cascade;
using namespace cv;
using namespace std;
enum EmotionState_t {
SERIOUS = 0, // 0
SMILE, // 1
SURPRISED, // 2
};
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[])
{
EmotionState_t emotion;
if (!face_cascade.load(face_cascade_name)) {
printf("--(!)Error loading\n"); return -1; };
if (!eyes_cascade.load(eye_cascade_name)) {
printf("--(!)Error loading\n"); return -1; };
// 0 is the ID of the built-in laptop camera, change if you want to useother camera
VideoCapture cap(0);
//check if the file was opened properly
if (!cap.isOpened())
{
std::cout << "Capture could not be opened succesfully" << endl;
return -1;
}
else
{
std::cout << "camera is ok.. Stay 2 ft away from your camera\n" << endl;
}
int w = 432;
int h = 240;
cap.set(CV_CAP_PROP_FRAME_WIDTH, w);
cap.set(CV_CAP_PROP_FRAME_HEIGHT, h);
Mat frame;
cap >> frame;
std::cout << "processing the image...." << endl;
Mat testSample = faceDetect(frame);
// Get the path to your CSV.
string fn_csv = "C:\\Users\\Omar\\Downloads\\test_canny\\my_csv.txt";
// These vectors hold the images and corresponding labels.
vector<Mat>* images;
images = new vector<Mat>;
vector<int>* labels;
labels = new vector<int>;
// Read in the data. This can fail if no valid
// input filename is given.
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);
}
// Quit if there are not enough images for this demo.
if ((*images).size() <= 1)
{
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 create an Fisherfaces model for
// face recognition and train it with the images and
// labels read from the given CSV file.
// If you just want to keep 10 Fisherfaces, then call
// the factory method like this:
//
// cv::createFisherFaceRecognizer(10);
//
// However it is not useful to discard Fisherfaces! Please
// always try to use _all_ available Fisherfaces for
// classification.
//
// If you want to create a FaceRecognizer with a
// confidence threshold (e.g. 123.0) and use _all_
// Fisherfaces, then call it with:
//
// cv::createFisherFaceRecognizer(0, 123.0);
//
Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
model->train(*images, *labels);
// The following line predicts the label of a given
// test image:
int predictedLabel = model->predict(testSample);
// To get the confidence of a prediction call the model with:
//
// int predictedLabel = -1;
// double confidence = 0.0;
// model->predict(testSample, predictedLabel, confidence);
//
string result_message = format("Predicted class = %d", predictedLabel);
std::cout << result_message << endl;
// giving the result
switch (predictedLabel)
{
case SMILE:
std::cout << "You are happy!" << endl;
break;
case SURPRISED:
std::cout << "You are surprised!" << endl;
break;
case SERIOUS:
std::cout << "You are serious!" << endl;
break;
}
return 0;
}
Mat faceDetect(Mat img)
{
std::vector<Rect>* faces;
faces = new vector<Rect>;
std::vector<Rect>* eyes;
eyes = new vector<Rect>;
bool two_eyes = false;
bool any_eye_detected = false;
//detecting faces
face_cascade.detectMultiScale(img, *faces, 1.1, 2, 0 | CV_HAAR_SCALE_IMAGE,
Size(30, 30));
if ((*faces).size() == 0)
{
std::cout << "Try again.. I did not dectected any faces..." << endl;
exit(-1); // abort everything
}
Point p1 = Point(0, 0);
for (size_t i = 0; i < (*faces).size(); i++)
{
// we cannot draw in the image !!! otherwise will mess with the prediction
// rectangle( img, faces[i], Scalar( 255, 100, 0 ), 4, 8, 0 );
Mat frame_gray;
cvtColor(img, frame_gray, CV_BGR2GRAY);
// croping only the face in region defined by faces[i]
std::vector<Rect>* eyes;
eyes = new vector<Rect>;
Mat faceROI = frame_gray((*faces)[i]);
//In each face, detect eyes
eyes_cascade.detectMultiScale(faceROI, *eyes, 1.1, 3, 0
| CV_HAAR_SCALE_IMAGE, Size(30, 30));
for (size_t j = 0; j < (*eyes).size(); j++)
{
Point center((*faces)[i].x + (*eyes)[j].x + (*eyes)[j].width*0.5,
(*faces)[i].y + (*eyes)[j].y + (*eyes)[j].height*0.5);
// we cannot draw in the image !!! otherwise will mess with the prediction
// int radius = cvRound( (eyes[j].width + eyes[j].height)*0.25 );
// circle( img, center, radius, Scalar( 255, 0, 0 ), 4, 8, 0 );
if (j == 0)
{
p1 = center;
any_eye_detected = true;
}
else
{
two_eyes = true;
}
}
}
std::cout << "SOME DEBUG" << endl;
std::cout << "-------------------------" << endl;
std::cout << "faces detected:" << (*faces).size() << endl;
std::cout << "x: " << (*faces)[0].x << endl;
std::cout << "y: " << (*faces)[0].y << endl;
std::cout << "w: " << (*faces)[0].width << endl;
std::cout << "h: " << (*faces)[0].height << endl << endl;
Mat imageInRectangle;
imageInRectangle = img((*faces)[0]);
Size recFaceSize = imageInRectangle.size();
std::cout << recFaceSize << endl;
// for debug
imwrite("C:\\Users\\Omar\\Downloads\\test_canny\\imageInRectangle.jpg", imageInRectangle);
int rec_w = 0;
int rec_h = (*faces)[0].height * 0.64;
// checking the (x,y) for cropped rectangle
// based in human anatomy
int px = 0;
int py = 2 * 0.125 * (*faces)[0].height;
Mat cropImage;
std::cout << "faces[0].x:" << (*faces)[0].x << endl;
p1.x = p1.x - (*faces)[0].x;
std::cout << "p1.x:" << p1.x << endl;
if (any_eye_detected)
{
if (two_eyes)
{
std::cout << "two eyes detected" << endl;
// we have detected two eyes
// we have p1 and p2
// left eye
px = p1.x / 1.35;
}
else
{
// only one eye was found.. need to check if the
// left or right eye
// we have only p1
if (p1.x > recFaceSize.width / 2)
{
// right eye
std::cout << "only right eye detected" << endl;
px = p1.x / 1.75;
}
else
{
// left eye
std::cout << "only left eye detected" << endl;
px = p1.x / 1.35;
}
}
}
else
{
// no eyes detected but we have a face
px = 25;
py = 25;
rec_w = recFaceSize.width - 50;
rec_h = recFaceSize.height - 30;
}
rec_w = ((*faces)[0].width - px) * 0.75;
std::cout << "px :" << px << endl;
std::cout << "py :" << py << endl;
std::cout << "rec_w:" << rec_w << endl;
std::cout << "rec_h:" << rec_h << endl;
cropImage = imageInRectangle(Rect(px, py, rec_w, rec_h));
Size dstImgSize(70, 70); // same image size of db
Mat finalSizeImg;
resize(cropImage, finalSizeImg, dstImgSize);
// for debug
imwrite("C:\\Users\\Omar\\Downloads\\test_canny\\onlyface.jpg", finalSizeImg);
cvtColor(finalSizeImg, finalSizeImg, CV_BGR2GRAY);
return finalSizeImg;
}
I've debugged it and the error only pops up when I reach return 0 in the main method.
And here's an image of the error (too large to embed)
Any help will be appreciated.