How to get my C++ program to detect the Ñ - c++

Question

I am working on a OCR project and I am in the training phase, so the goal is for my program to detect the characters as objects in an image and allow them to be classified.
The problem is that I cannot get it with the Ñ because it is formed by two objects (the N and the stick).
Do you know if there is any way to get it classified as a single object?
Here is the program:
#include<opencv2/core/core.hpp>
#include<opencv2/highgui/highgui.hpp>
#include<opencv2/imgproc/imgproc.hpp>
#include<opencv2/ml/ml.hpp>
#include<iostream>
#include<vector>
// global variables
const int MIN_CONTOUR_AREA = 30;
const int RESIZED_IMAGE_WIDTH = 20;
const int RESIZED_IMAGE_HEIGHT = 30;
//////////////////////////////////////
int main() {
cv::Mat imgTrainingNumbers; // input image
cv::Mat imgGrayscale; //
cv::Mat imgBlurred; // declare various images
cv::Mat imgThresh; //
cv::Mat imgThreshCopy; //
std::vector<std::vector<cv::Point> > ptContours; // declare contours vector
std::vector<cv::Vec4i> v4iHierarchy; // declare contours hierarchy
cv::Mat matClassificationInts; // these are our training classifications, note we will have to perform some conversions before writing to file later
// these are our training images, due to the data types that the KNN object KNearest requires, we have to declare a single Mat,
// then append to it as though it's a vector, also we will have to perform some conversions before writing to file later
cv::Mat matTrainingImagesAsFlattenedFloats;
// possible chars we are interested in are digits 0 through 9 and capital letters A through Z, put these in vector intValidChars
std::vector<int> intValidChars = { '0', '1', '2', '3', '4', '5', '6', '7', '8', '9',
'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J',
'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T',
'U', 'V', 'W', 'X', 'Y', 'Z'};
imgTrainingNumbers = cv::imread("caracteres_prueba.png"); // read in training numbers image
if (imgTrainingNumbers.empty()) { // if unable to open image
std::cout << "error: image not read from file\n\n"; // show error message on command line
return(0); // and exit program
}
cv::cvtColor(imgTrainingNumbers, imgGrayscale, CV_BGR2GRAY); // convert to grayscale
cv::GaussianBlur(imgGrayscale, // input image
imgBlurred, // output image
cv::Size(5, 5), // smoothing window width and height in pixels
0); // sigma value, determines how much the image will be blurred, zero makes function choose the sigma value
// filter image from grayscale to black and white
cv::adaptiveThreshold(imgBlurred, // input image
imgThresh, // output image
255, // make pixels that pass the threshold full white
cv::ADAPTIVE_THRESH_GAUSSIAN_C, // use gaussian rather than mean, seems to give better results
cv::THRESH_BINARY_INV, // invert so foreground will be white, background will be black
11, // size of a pixel neighborhood used to calculate threshold value
2); // constant subtracted from the mean or weighted mean
cv::imshow("imgThresh", imgThresh); // show threshold image for reference
imgThreshCopy = imgThresh.clone(); // make a copy of the thresh image, this in necessary b/c findContours modifies the image
cv::findContours(imgThreshCopy, // input image, make sure to use a copy since the function will modify this image in the course of finding contours
ptContours, // output contours
v4iHierarchy, // output hierarchy
cv::RETR_EXTERNAL, // retrieve the outermost contours only
cv::CHAIN_APPROX_SIMPLE); // compress horizontal, vertical, and diagonal segments and leave only their end points
for (int i = 0; i < ptContours.size(); i++) { // for each contour
if (cv::contourArea(ptContours[i]) > MIN_CONTOUR_AREA) { // if contour is big enough to consider
cv::Rect boundingRect = cv::boundingRect(ptContours[i]); // get the bounding rect
cv::rectangle(imgTrainingNumbers, boundingRect, cv::Scalar(0, 0, 255), 2); // draw red rectangle around each contour as we ask user for input
cv::Mat matROI = imgThresh(boundingRect); // get ROI image of bounding rect
cv::Mat matROIResized;
cv::resize(matROI, matROIResized, cv::Size(RESIZED_IMAGE_WIDTH, RESIZED_IMAGE_HEIGHT)); // resize image, this will be more consistent for recognition and storage
cv::imshow("matROI", matROI); // show ROI image for reference
cv::imshow("matROIResized", matROIResized); // show resized ROI image for reference
cv::imshow("imgTrainingNumbers", imgTrainingNumbers); // show training numbers image, this will now have red rectangles drawn on it
int intChar = cv::waitKey(0); // get key press
if (intChar == 27) { // if esc key was pressed
return(0); // exit program
}
else if (std::find(intValidChars.begin(), intValidChars.end(), intChar) != intValidChars.end()) { // else if the char is in the list of chars we are looking for . . .
matClassificationInts.push_back(intChar); // append classification char to integer list of chars
cv::Mat matImageFloat; // now add the training image (some conversion is necessary first) . . .
matROIResized.convertTo(matImageFloat, CV_32FC1); // convert Mat to float
cv::Mat matImageFlattenedFloat = matImageFloat.reshape(1, 1); // flatten
matTrainingImagesAsFlattenedFloats.push_back(matImageFlattenedFloat); // add to Mat as though it was a vector, this is necessary due to the
// data types that KNearest.train accepts
} // end if
} // end if
} // end for
std::cout << "training complete\n\n";
// save classifications to file ///////////////////////////////////////////////////////
cv::FileStorage fsClassifications("classifications.xml", cv::FileStorage::WRITE); // open the classifications file
if (fsClassifications.isOpened() == false) { // if the file was not opened successfully
std::cout << "error, unable to open training classifications file, exiting program\n\n"; // show error message
return(0); // and exit program
}
fsClassifications << "classifications" << matClassificationInts; // write classifications into classifications section of classifications file
fsClassifications.release(); // close the classifications file
// save training images to file ///////////////////////////////////////////////////////
cv::FileStorage fsTrainingImages("images.xml", cv::FileStorage::WRITE); // open the training images file
if (fsTrainingImages.isOpened() == false) { // if the file was not opened successfully
std::cout << "error, unable to open training images file, exiting program\n\n"; // show error message
return(0); // and exit program
}
fsTrainingImages << "images" << matTrainingImagesAsFlattenedFloats; // write training images into images section of images file
fsTrainingImages.release(); // close the training images file
return(0);
}

Answer 1

first you have an error in
int intChar = cv::waitKey(0);
it must be
char intChar = cv::waitKey(0);
try it and if ther are error tell me