int main() {
std::vector<ContourWithData> allContoursWithData;
std::vector<ContourWithData> validContoursWithData;
cv::Mat matClassificationFloats;
cv::FileStorage fsClassifications("classifications.xml", cv::FileStorage::READ);
if (fsClassifications.isOpened() == false) {
std::cout << "error, unable to open training classifications file, exiting program\n\n";
return(0);
}
fsClassifications["classifications"] >> matClassificationFloats;
fsClassifications.release();
cv::Mat matTrainingImages;
cv::FileStorage fsTrainingImages("images.xml", cv::FileStorage::READ);
if (fsTrainingImages.isOpened() == false) {
std::cout << "error, unable to open training images file, exiting program\n\n";
return(0);
}
fsTrainingImages["images"] >> matTrainingImages;
fsTrainingImages.release();
cv::Ptr<cv::ml::KNearest> kNearest = cv::ml::KNearest::create();
cv::Ptr<cv::ml::TrainData> trainingData = cv::ml::TrainData::create(matTrainingImages, cv::ml::SampleTypes::ROW_SAMPLE, matClassificationFloats);
kNearest->setIsClassifier(true);
kNearest->setAlgorithmType(cv::ml::KNearest::Types::BRUTE_FORCE);
kNearest->setDefaultK(101);
cv::Mat matResults(0, 0, CV_32F);
kNearest->train(trainingData);
cv::Mat matTestingNumbers = cv::imread("test_numbers.png");
if (matTestingNumbers.empty()) {
std::cout << "error: image not read from file\n\n";
return(0);
}
cv::Mat matGrayscale;
cv::Mat matBlurred;
cv::Mat matThresh;
cv::Mat matThreshCopy;
cv::cvtColor(matTestingNumbers, matGrayscale, CV_BGR2GRAY);
threshold(matGrayscale, matThresh, 10, 255, CV_THRESH_BINARY_INV);
matThreshCopy = matThresh.clone();
std::vector<std::vector<cv::Point> > ptContours;
std::vector<cv::Vec4i> v4iHierarchy;
cv::findContours(matThreshCopy,
ptContours,
v4iHierarchy,
CV_RETR_EXTERNAL,
CV_CHAIN_APPROX_TC89_KCOS);
for (int i = 0; i < ptContours.size(); i++) {
ContourWithData contourWithData;
contourWithData.ptContour = ptContours[i];
contourWithData.boundingRect = cv::boundingRect(contourWithData.ptContour);
contourWithData.fltArea = cv::contourArea(contourWithData.ptContour);
allContoursWithData.push_back(contourWithData);
}
for (int i = 0; i < allContoursWithData.size(); i++) {
if (allContoursWithData[i].checkIfContourIsValid()) {
validContoursWithData.push_back(allContoursWithData[i]);
}
}
std::sort(validContoursWithData.begin(), validContoursWithData.end(), ContourWithData::sortByBoundingRectXPosition);
std::string strFinalString;
for (int i = 0; i < validContoursWithData.size(); i++) {
cv::rectangle(matTestingNumbers,
validContoursWithData[i].boundingRect,
cv::Scalar(0, 255, 0),
2);
cv::Mat matROI = matThresh(validContoursWithData[i].boundingRect);
cv::Mat matROIResized;
cv::resize(matROI, matROIResized, cv::Size(RESIZED_IMAGE_WIDTH, RESIZED_IMAGE_HEIGHT));
cv::Mat matROIFloat;
matROIResized.convertTo(matROIFloat, CV_32FC1);
float fltCurrentChar = kNearest->findNearest(matROIFloat.reshape(1,1), kNearest->getDefaultK(), matResults);
strFinalString = strFinalString + char(int(fltCurrentChar));
}
cv::Mat string_box(100,500,CV_8UC3, cv::Scalar::all(0));
int baseLine = 0;
cv::Size string_size = cv::getTextSize(strFinalString, CV_FONT_HERSHEY_DUPLEX, 1, 2, &baseLine);
baseLine += 2;
cv::Point textOrg((string_box.cols - string_size.width) / 2, (string_box.rows + string_size.height) / 2);
cv::putText(string_box, strFinalString, textOrg, CV_FONT_HERSHEY_DUPLEX, 1, cv::Scalar::all(255), 2, 8);
cv::imshow("result", string_box);
cv::imshow("matTestingNumbers", matTestingNumbers);
cv::waitKey(0);
return(0);
}
int main() {
std::vector<ContourWithData> allContoursWithData; // declare empty vectors,
std::vector<ContourWithData> validContoursWithData; // we will fill these shortly
// read in training classifications ///////////////////////////////////////////////////
cv::Mat matClassificationInts; // we will read the classification numbers into this variable as though it is a vector
cv::FileStorage fsClassifications("classifications.xml", cv::FileStorage::READ); // 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; // read classifications section into Mat classifications variable
fsClassifications.release(); // close the classifications file
// read in training images ////////////////////////////////////////////////////////////
cv::Mat matTrainingImagesAsFlattenedFloats; // we will read multiple images into this single image variable as though it is a vector
cv::FileStorage fsTrainingImages("images.xml", cv::FileStorage::READ); // 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; // read images section into Mat training images variable
fsTrainingImages.release(); // close the traning images file
// train //////////////////////////////////////////////////////////////////////////////
cv::Ptr<cv::ml::KNearest> kNearest(cv::ml::KNearest::create()); // instantiate the KNN object
// finally we get to the call to train, note that both parameters have to be of type Mat (a single Mat)
// even though in reality they are multiple images / numbers
kNearest->train(matTrainingImagesAsFlattenedFloats, cv::ml::ROW_SAMPLE, matClassificationInts);
// test ///////////////////////////////////////////////////////////////////////////////
cv::Mat matTestingNumbers = cv::imread("test1.png"); // read in the test numbers image
if (matTestingNumbers.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::Mat matGrayscale; //
cv::Mat matBlurred; // declare more image variables
cv::Mat matThresh; //
cv::Mat matThreshCopy; //
cv::cvtColor(matTestingNumbers, matGrayscale, CV_BGR2GRAY); // convert to grayscale
// blur
cv::GaussianBlur(matGrayscale, // input image
matBlurred, // 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(matBlurred, // input image
matThresh, // 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
matThreshCopy = matThresh.clone(); // make a copy of the thresh image, this in necessary b/c findContours modifies the image
std::vector<std::vector<cv::Point> > ptContours; // declare a vector for the contours
std::vector<cv::Vec4i> v4iHierarchy; // declare a vector for the hierarchy (we won't use this in this program but this may be helpful for reference)
cv::findContours(matThreshCopy, // 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
ContourWithData contourWithData; // instantiate a contour with data object
contourWithData.ptContour = ptContours[i]; // assign contour to contour with data
contourWithData.boundingRect = cv::boundingRect(contourWithData.ptContour); // get the bounding rect
contourWithData.fltArea = cv::contourArea(contourWithData.ptContour); // calculate the contour area
allContoursWithData.push_back(contourWithData); // add contour with data object to list of all contours with data
}
for (int i = 0; i < allContoursWithData.size(); i++) { // for all contours
if (allContoursWithData[i].checkIfContourIsValid()) { // check if valid
validContoursWithData.push_back(allContoursWithData[i]); // if so, append to valid contour list
}
}
// sort contours from left to right
std::sort(validContoursWithData.begin(), validContoursWithData.end(), ContourWithData::sortByBoundingRectXPosition);
std::string strFinalString; // declare final string, this will have the final number sequence by the end of the program
for (int i = 0; i < validContoursWithData.size(); i++) { // for each contour
// draw a green rect around the current char
cv::rectangle(matTestingNumbers, // draw rectangle on original image
validContoursWithData[i].boundingRect, // rect to draw
cv::Scalar(0, 255, 0), // green
2); // thickness
cv::Mat matROI = matThresh(validContoursWithData[i].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::Mat matROIFloat;
matROIResized.convertTo(matROIFloat, CV_32FC1); // convert Mat to float, necessary for call to find_nearest
cv::Mat matROIFlattenedFloat = matROIFloat.reshape(1, 1);
cv::Mat matCurrentChar(0, 0, CV_32F);
kNearest->findNearest(matROIFlattenedFloat, 1, matCurrentChar); // finally we can call find_nearest !!!
float fltCurrentChar = (float)matCurrentChar.at<float>(0, 0);
strFinalString = strFinalString + char(int(fltCurrentChar)); // append current char to full string
}
std::cout << "\n\n" << "numbers read = " << strFinalString << "\n\n"; // show the full string
cv::imshow("matTestingNumbers", matTestingNumbers); // show input image with green boxes drawn around found digits
cv::waitKey(0); // wait for user key press
return(0);
}
