Number and character recognition using ANN OpenCV 3.1 - c++

Question

I have implemented Neural network using OpenCV ANN Library. I am newbie in this field and I learn everything about it online (Mostly StackOverflow).
I am using this ANN for detection of number plate. I did segmentation part using OpenCV image processing library and it is working good. It performs character segmentation and gives it to the NN part of the project. NN is going to recognize the number plate.
I have sample images of 20x30, therefore I have 600 neurons in input layer. As there are 36 possibilities (0-9,A-Z) I have 36 output neurons. I kept 100 neurons in hidden layer. The predict function of OpenCV is giving me the same output for every segmented image. That output is also showing some large negative(< -1). I have used cv::ml::ANN_MLP::SIGMOID_SYM as an activation function.
Please don't mind as there is lot of code wrongly commented (I am doing trial and error).
I need to find out what is the output of predict function. Thank you for your help.
#include <opencv2/opencv.hpp>
int inputLayerSize = 1;
int outputLayerSize = 1;
int numSamples = 2;
Mat layers = Mat(3, 1, CV_32S);
layers.row(0) =Scalar(600) ;
layers.row(1) = Scalar(20);
layers.row(2) = Scalar(36);
vector<int> layerSizes = { 600,100,36 };
Ptr<ml::ANN_MLP> nnPtr = ml::ANN_MLP::create();
vector <int> n;
//nnPtr->setLayerSizes(3);
nnPtr->setLayerSizes(layers);
nnPtr->setTrainMethod(ml::ANN_MLP::BACKPROP);
nnPtr->setTermCriteria(TermCriteria(cv::TermCriteria::COUNT | cv::TermCriteria::EPS, 1000, 0.00001f));
nnPtr->setActivationFunction(cv::ml::ANN_MLP::SIGMOID_SYM, 1, 1);
nnPtr->setBackpropWeightScale(0.5f);
nnPtr->setBackpropMomentumScale(0.5f);
/*CvANN_MLP_TrainParams params = CvANN_MLP_TrainParams(
// terminate the training after either 1000
// iterations or a very small change in the
// network wieghts below the specified value
cvTermCriteria(CV_TERMCRIT_ITER + CV_TERMCRIT_EPS, 1000, 0.000001),
// use backpropogation for training
CvANN_MLP_TrainParams::BACKPROP,
// co-efficents for backpropogation training
// (refer to manual)
0.1,
0.1);*/
/* Mat samples(Size(inputLayerSize, numSamples), CV_32F);
samples.at<float>(Point(0, 0)) = 0.1f;
samples.at<float>(Point(0, 1)) = 0.2f;
Mat responses(Size(outputLayerSize, numSamples), CV_32F);
responses.at<float>(Point(0, 0)) = 0.2f;
responses.at<float>(Point(0, 1)) = 0.4f;
*/
//reading chaos image
// we will read the classification numbers into this variable as though it is a vector
// close the traning images file
/*vector<int> layerInfo;
layerInfo=nnPtr->get;
for (int i = 0; i < layerInfo.size(); i++) {
cout << "size of 0" <<layerInfo[i] << endl;
}*/
cv::imshow("chaos", matTrainingImagesAsFlattenedFloats);
// cout <<abc << endl;
matTrainingImagesAsFlattenedFloats.convertTo(matTrainingImagesAsFlattenedFloats, CV_32F);
//matClassificationInts.reshape(1, 496);
matClassificationInts.convertTo(matClassificationInts, CV_32F);
matSamples.convertTo(matSamples, CV_32F);
std::cout << matClassificationInts.rows << " " << matClassificationInts.cols << " ";
std::cout << matTrainingImagesAsFlattenedFloats.rows << " " << matTrainingImagesAsFlattenedFloats.cols << " ";
std::cout << matSamples.rows << " " << matSamples.cols;
imshow("Samples", matSamples);
imshow("chaos", matTrainingImagesAsFlattenedFloats);
Ptr<ml::TrainData> trainData = ml::TrainData::create(matTrainingImagesAsFlattenedFloats, ml::SampleTypes::ROW_SAMPLE, matSamples);
nnPtr->train(trainData);
bool m = nnPtr->isTrained();
if (m)
std::cout << "training complete\n\n";
// cv::Mat matCurrentChar = Mat(cv::Size(matTrainingImagesAsFlattenedFloats.cols, matTrainingImagesAsFlattenedFloats.rows), CV_32F);
// cout << "samples:\n" << samples << endl;
//cout << "\nresponses:\n" << responses << endl;
/* if (!nnPtr->train(trainData))
return 1;*/
/* cout << "\nweights[0]:\n" << nnPtr->getWeights(0) << endl;
cout << "\nweights[1]:\n" << nnPtr->getWeights(1) << endl;
cout << "\nweights[2]:\n" << nnPtr->getWeights(2) << endl;
cout << "\nweights[3]:\n" << nnPtr->getWeights(3) << endl;*/
//predicting
std::vector <cv::String> filename;
cv::String folder = "./plate/";
cv::glob(folder, filename);
if (filename.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
}
String strFinalString;
for (int i = 0; i < filename.size(); i++) {
cv::Mat matTestingNumbers = cv::imread(filename[i]);
cv::Mat matGrayscale; //
cv::Mat matBlurred; // declare more image variables
cv::Mat matThresh; //
cv::Mat matThreshCopy;
cv::Mat matCanny;
//
cv::cvtColor(matTestingNumbers, matGrayscale, CV_BGR2GRAY); // convert to grayscale
matThresh = cv::Mat(cv::Size(matGrayscale.cols, matGrayscale.rows), CV_8UC1);
for (int i = 0; i < matGrayscale.cols; i++) {
for (int j = 0; j < matGrayscale.rows; j++) {
if (matGrayscale.at<uchar>(j, i) <= 130) {
matThresh.at<uchar>(j, i) = 255;
}
else {
matThresh.at<uchar>(j, i) = 0;
}
}
}
// blur
cv::GaussianBlur(matThresh, // 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
// cv::imshow("thresh" + std::to_string(i), matThresh);
matThreshCopy = matThresh.clone();
std::vector<std::vector<cv::Point> > ptContours; // declare a vector for the contours
std::vector<cv::Vec4i> v4iHierarchy;// make a copy of the thresh image, this in necessary b/c findContours modifies the image
cv::Canny(matBlurred, matCanny, 20, 40, 3);
/*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
/*std::vector<std::vector<cv::Point> > contours_poly(ptContours.size());
std::vector<cv::Rect> boundRect(ptContours.size());
for (int i = 0; i < ptContours.size(); i++)
{
approxPolyDP(cv::Mat(ptContours[i]), contours_poly[i], 3, true);
boundRect[i] = cv::boundingRect(cv::Mat(contours_poly[i]));
}*/
/*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;
cv::resize(matThresh, matThresh, cv::Size(RESIZED_IMAGE_WIDTH, RESIZED_IMAGE_HEIGHT));
matThresh.convertTo(matROIFloat, CV_32FC1, 1.0 / 255.0); // convert Mat to float, necessary for call to find_nearest
cv::Mat matROIFlattenedFloat = matROIFloat.reshape(1, 1);
cv::Point maxLoc = { 0,0 };
cv::Point minLoc;
cv::Mat output = cv::Mat(cv::Size(36, 1), CV_32F);
vector<float>output2;
// cv::Mat output2 = cv::Mat(cv::Size(36, 1), CV_32F);
nnPtr->predict(matROIFlattenedFloat, output2);
// float max = output.at<float>(0, 0);
int fo = 0;
float m = output2[0];
imshow("predicted input", matROIFlattenedFloat);
// float b = output.at<float>(0, 0);
// cout <<"\n output0,0:"<<b<<endl;
// minMaxLoc(output, 0, 0, &minLoc, &maxLoc, Mat());
// cout << "\noutput:\n" << maxLoc.x << endl;
for (int j = 1; j < 36; j++) {
float value =output2[j];
if (value > m) {
m = value;
fo = j;
}
}
float * p = 0;
p = &m;
cout << "j value in output " << fo << " Max value " << p << endl;
//imshow("output image" + to_string(i), output);
// cout << "\noutput:\n" << minLoc.x << endl;
//float fltCurrentChar = (float)maxLoc.x;
output.release();
m = 0;
fo = 0;
}
// strFinalString = strFinalString + char(int(fltCurrentChar)); // append current char to full string
// cv::imshow("Predict output", output);
/*cv::Point maxLoc = {0,0};
Mat output=Mat (cv::Size(matSamples.cols,matSamples.rows),CV_32F);
nnPtr->predict(matTrainingImagesAsFlattenedFloats, output);
minMaxLoc(output, 0, 0, 0, &maxLoc, 0);
cout << "\noutput:\n" << maxLoc.x << endl;*/
// getchar();
/*for (int i = 0; i < 10;i++) {
for (int j = 0; j < 36; j++) {
if (matCurrentChar.at<float>(i, j) >= 0.6) {
cout << " "<<j<<" ";
}
}
}*/
waitKey(0);
return(0);
}
void gen() {
std::string dir, filepath;
int num, imgArea, minArea;
int pos = 0;
bool f = true;
struct stat filestat;
cv::Mat imgTrainingNumbers;
cv::Mat imgGrayscale;
cv::Mat imgBlurred;
cv::Mat imgThresh;
cv::Mat imgThreshCopy;
cv::Mat matROIResized=cv::Mat (cv::Size(RESIZED_IMAGE_WIDTH,RESIZED_IMAGE_HEIGHT),CV_8UC1);
cv::Mat matROI;
std::vector <cv::String> filename;
std::vector<std::vector<cv::Point> > ptContours;
std::vector<cv::Vec4i> v4iHierarchy;
int count = 0, contoursCount = 0;
matSamples = cv::Mat(cv::Size(36, 496), CV_32FC1);
matTrainingImagesAsFlattenedFloats = cv::Mat(cv::Size(600, 496), CV_32FC1);
for (int j = 0; j <= 35; j++) {
int tmp = j;
cv::String folder = "./Training Data/" + std::to_string(tmp);
cv::glob(folder, filename);
for (int k = 0; k < filename.size(); k++) {
count++;
// If the file is a directory (or is in some way invalid) we'll skip it
// if (stat(filepath.c_str(), &filestat)) continue;
//if (S_ISDIR(filestat.st_mode)) continue;
imgTrainingNumbers = cv::imread(filename[k]);
imgArea = imgTrainingNumbers.cols*imgTrainingNumbers.rows;
// read in training numbers image
minArea = imgArea * 50 / 100;
if (imgTrainingNumbers.empty()) {
std::cout << "error: image not read from file\n\n";
//return(0);
}
cv::cvtColor(imgTrainingNumbers, imgGrayscale, CV_BGR2GRAY);
//cv::equalizeHist(imgGrayscale, imgGrayscale);
imgThresh = cv::Mat(cv::Size(imgGrayscale.cols, imgGrayscale.rows), CV_8UC1);
/*cv::adaptiveThreshold(imgGrayscale,
imgThresh,
255,
cv::ADAPTIVE_THRESH_GAUSSIAN_C,
cv::THRESH_BINARY_INV,
3,
0);
*/
for (int i = 0; i < imgGrayscale.cols; i++) {
for (int j = 0; j < imgGrayscale.rows; j++) {
if (imgGrayscale.at<uchar>(j, i) <= 130) {
imgThresh.at<uchar>(j, i) = 255;
}
else {
imgThresh.at<uchar>(j, i) = 0;
}
}
}
// cv::imshow("imgThresh"+std::to_string(count), imgThresh);
imgThreshCopy = imgThresh.clone();
cv::GaussianBlur(imgThreshCopy,
imgBlurred,
cv::Size(5, 5),
0);
cv::Mat imgCanny;
// cv::Canny(imgBlurred,imgCanny,20,40,3);
cv::findContours(imgBlurred,
ptContours,
v4iHierarchy,
cv::RETR_EXTERNAL,
cv::CHAIN_APPROX_SIMPLE);
for (int i = 0; i < ptContours.size(); i++) {
if (cv::contourArea(ptContours[i]) > MIN_CONTOUR_AREA) {
contoursCount++;
cv::Rect boundingRect = cv::boundingRect(ptContours[i]);
cv::rectangle(imgTrainingNumbers, boundingRect, cv::Scalar(0, 0, 255), 2); // draw red rectangle around each contour as we ask user for input
matROI = imgThreshCopy(boundingRect); // get ROI image of bounding rect
std::string path = "./" + std::to_string(contoursCount) + ".JPG";
cv::imwrite(path, matROI);
// cv::imshow("matROI" + std::to_string(count), matROI);
cv::resize(matROI, matROIResized, cv::Size(RESIZED_IMAGE_WIDTH, RESIZED_IMAGE_HEIGHT)); // resize image, this will be more consistent for recognition and storage
std::cout << filename[k] << " " << contoursCount << "\n";
//cv::imshow("matROI", matROI);
//cv::imshow("matROIResized"+std::to_string(count), matROIResized);
// cv::imshow("imgTrainingNumbers" + std::to_string(contoursCount), imgTrainingNumbers);
int intChar;
if (j<10)
intChar = j + 48;
else {
intChar = j + 55;
}
/*if (intChar == 27) { // if esc key was pressed
return(0); // exit program
}*/
// if (std::find(intValidChars.begin(), intValidChars.end(), intChar) != intValidChars.end()) { // else if the char is in the list of chars we are looking for . . .
// append classification char to integer list of chars
cv::Mat matImageFloat;
matROIResized.convertTo(matImageFloat,CV_32FC1);// 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);
//matTrainingImagesAsFlattenedFloats.push_back(matImageFlattenedFloat);// flatten
try {
//matTrainingImagesAsFlattenedFloats.push_back(matImageFlattenedFloat);
std::cout << matTrainingImagesAsFlattenedFloats.rows << " " << matTrainingImagesAsFlattenedFloats.cols;
//unsigned char* re;
int ii = 0; // Current column in training_mat
for (int i = 0; i<matImageFloat.rows; i++) {
for (int j = 0; j < matImageFloat.cols; j++) {
matTrainingImagesAsFlattenedFloats.at<float>(contoursCount-1, ii++) = matImageFloat.at<float>(i,j);
}
}
}
catch (std::exception &exc) {
f = false;
exc.what();
}
if (f) {
matClassificationInts.push_back((float)intChar);
matSamples.at<float>(contoursCount-1, j) = 1.0;
}
f = true;
// 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
}//end i
}//end j
}
Output of predict function

Answer 1

Unfortunately, I don't have the necessary time to really review the code, but I can say off the top that to train a model that performs well for prediction with 36 classes, you will need several things:
A large number of good quality images. Ideally, you'd want thousands of images for each class. Of course, you can see somewhat decent results with less than that, but if you only have a few images per class, it's never going to be able to generalize adequately.
You need a model that is large and sophisticated enough to provide the necessary expressiveness to solve the problem. For a problem like this, a plain old multi-layer perceptron with one hidden layer with 100 units may not be enough. This is actually a problem that would benefit from using a Convolutional Neural Net (CNN) with a couple layers just to extract useful features first. But assuming you don't want to go down that path, you may at least want to tweak the size of your hidden layer.
To even get to a point where the training process converges, you will probably need to experiment and crucially, you need an effective way to test the accuracy of the ANN after each experiment. Ideally, you want to observe the loss as the training is proceeding, but I'm not sure whether that's possible using OpenCV's ML functionality. At a minimum, you should fully expect to have to play around with the various so-called "hyper-parameters" and run many experiments before you have a reasonable model.
Anyway, the most important thing is to make sure you have a solid mechanism for validating the accuracy of the model after training. If you aren't already doing so, set aside some images as a separate test set, and after each experiment, use the trained ANN to predict each test image to see the accuracy.
One final general note: what you're trying to do is complex. You will save yourself a huge number of headaches if you take the time early and often to refactor your code. No matter how many experiments you run, if there's some defect causing (for example) your training data to be fundamentally different in some way than your test data, you will never see good results.
Good luck!
EDIT: I should also point out that seeing the same result for every input image is a classic sign that training failed. Unfortunately, there are many reasons why that might happen and it will be very difficult for anyone to isolate that for you without some cleaner code and access to your image data.

Answer 2

I have solved the issue of not getting the output of predict. The issue was created because of the input Mat image to train (ie. matTrainingImagesAsFlattenedFloats) was having values 255.0 for a white pixel. This happened because I haven't use convertTo() properly. You need to use convertTo(OutputImage name, CV_32FC1, 1.0 / 255.0); like this which will convert all the pixel values with 255.0 to 1.0 and after that I am getting the correct output.
Thank you for all the help.

Answer 3

This is too broad to be in one question. Sorry for the bad news. I tried this over and over and couldn't find a solution. I recommend that you implement a simple AND, OR or XOR first just to make sure that the learning part is working and that you are getting better results the more passes you do. Also I suggest to try the Tangent Hyperbolic as a Transfer Function instead of Sigmoid. And Good luck!
Here is some of my own posts that might help you:
Exact results as yours: HERE
Some codes: HERE
I don't want to say that, but several professors I met said Backpropagation just doesn't work and they had (and me have) to implement my own method of teaching the network.