Tomna's profile - activity

For now I am trying to predict only 1 or -1

the error is

OpenCV Error: Assertion failed (samples.cols == var_count && samples.type() == CV_32F) in predict

my code

public static Mat getMat(String path) {
    Mat img = new Mat();
    Mat convert_to_gray = Imgcodecs.imread(path, Imgcodecs.CV_LOAD_IMAGE_GRAYSCALE);
    convert_to_gray.convertTo(img, CvType.CV_8U);
    return img;
}

public static void test() {
    System.out.println("Testing..");
    Mat input = Imgcodecs.imread(new File(FILE_TEST).getAbsolutePath(),0);

    Mat output = new Mat();
    input.convertTo(output,CvType.CV_32SC1);
    output = output.reshape(1, 1);
    System.out.println(output);
    System.out.println(clasificador.predict(output)); <-the error points here 

}

public static void trains() {
    System.out.println("Training...");
    v_descriptors.copyTo(trainingData);
    trainingData.convertTo(trainingData, CvType.CV_32F);
    trainingLabels.copyTo(classes);
    classes.convertTo(classes, CvType.CV_32S);

    clasificador.setType(SVM.C_SVC);
    clasificador.setKernel(SVM.LINEAR);
    clasificador.setGamma(0.5);
    clasificador.setNu(0.5);
    clasificador.setC(1);
    clasificador.setTermCriteria(S);

    clasificador.train(trainingData, Ml.ROW_SAMPLE, classes);
    System.out.println("Done Training");

}

public static void trainPositives() {
    MatOfFloat descriptorsValues = new MatOfFloat();
    int count = 0;
    for (File file : new File(PATH_POSITIVE).listFiles()) {
        count++;
        Mat img = getMat(file.getAbsolutePath());
        HOGDescriptor d = new HOGDescriptor(new Size(32, 16), new Size(8, 8), new Size(4, 4), new Size(4, 4), 9);
        d.compute(img, descriptorsValues);
        Mat labelsMat = new Mat(1, 1, CvType.CV_32SC1, new Scalar(1));
        v_descriptors.push_back(descriptorsValues.reshape(1, 1));
        trainingLabels.push_back(labelsMat);
    }
    System.out.println(v_descriptors);
    System.out.println(trainingLabels);
}

public static void trainNegatives() {
    int count = 0;
    MatOfFloat descriptorsValues2 = new MatOfFloat();
    for (File file : new File(PATH_NEGATIVE).listFiles()) {
        count++;
        Mat img = getMat(file.getAbsolutePath());
        HOGDescriptor d = new HOGDescriptor(new Size(32, 16), new Size(8, 8), new Size(4, 4), new Size(4, 4), 9);
        d.compute(img, descriptorsValues2);
        Mat labelsMat = new Mat(1, 1, CvType.CV_32SC1, new Scalar(-1));

        v_descriptors.push_back(descriptorsValues2.reshape(1, 1));
        trainingLabels.push_back(labelsMat);
    }
    System.out.println(v_descriptors);
    System.out.println(trainingLabels);
}

i cant seem to detect the plate using the findcontour method here is my code i am using openCV java api

     Mat image = Imgcodecs.imread("/home/tomna/NetBeansProjects/main/src/main/Dadcar.png");
        Mat image_gray = new Mat();
        Mat image_sobel = new Mat();
        Mat image_threshold = new Mat();
        Mat strel = getStructuringElement(MORPH_RECT, new Size(20,36));


        Imgproc.cvtColor(image, image_gray, Imgproc.COLOR_BGR2GRAY);  // make image gray

        Imgproc.blur(image_gray, image_gray, new Size(5, 5));
        Imgproc.Sobel(image_gray, image_sobel, CvType.CV_8U, 1, 0, 3, 1, 0);
        Imgproc.threshold(image_sobel, image_threshold, 0, 255, Imgproc.THRESH_OTSU + Imgproc.THRESH_BINARY);
        Imgproc.morphologyEx(image_threshold, image_threshold, Imgproc.MORPH_CLOSE, strel);





        double[] centers = {image_gray.width() / 2, image_gray.height() / 2};
        Point image_center = new Point(centers);



        Mat hierarchy = new Mat();
        Imgproc.findContours(image_threshold, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_NONE);


        Rect rect_min = new Rect();


        for (MatOfPoint contour : contours) {

            Rect rec = Imgproc.boundingRect(contour);
            if (rec.height > image.height() & rec.width > image.width())
            {

                continue;
            }

        for (int i = 0; i < contours.size(); i++) {
                 Imgproc.drawContours(image, contours, i, new Scalar(0, 0, 255), 1, 8, hierarchy, 0, new Point());

            Imgproc.rectangle(image, rec.tl(), rec.br(), new Scalar(0, 0, 255), 2);

            }

        }

@berak any ideas?

so i have been successful with making ANN_MLP work and output results since i had a problem with the sigmoid not initialized however i am still not sure if there is a problem with having not enough training images or i have something wrong with my code making the results not true every time with different results is there a possible way to check if the problem is with the data set i have ?

so my input is this image

and the dataset used for training

so the result should be 6.0

but what i get is 5.0 [0.52028143, 0.44897014, 0.54281932, 0.50860959, 0.47123247, 0.54682046, 0.48321274, 0.54106969, 0.45284992]

and

when input this image the result should be 0.0

but it gives me also

5.0 [0.52415699, 0.44889876, 0.54271841, 0.51714647, 0.48295537, 0.54325002, 0.48789713, 0.53129482, 0.45263588]

so without further due here is the code

       String PATH_numbers = "/home/tomna/NetBeansProjects/Parking-tracking-system/src/mainclass/chdataset/Numb";
        Mat img = Imgcodecs.imread("/home/tomna/NetBeansProjects/Parking-tracking-system/src/mainclass/characters/8.jpg");

        Mat trainingData = new Mat();
        Mat trainingLabels = new Mat();
        ANN_MLP knn = ANN_MLP.create();
        TermCriteria s = new TermCriteria(TermCriteria.MAX_ITER + TermCriteria.EPS, 100, 0.00001);

        knn.setTrainMethod(ANN_MLP.BACKPROP, 0.5, 0.5);
        knn.setBackpropWeightScale(0.05f);
        knn.setBackpropMomentumScale(0.05f);
        knn.setTermCriteria(s);

        Mat layers = new Mat(3, 1, CvType.CV_32SC1);
        layers.row(0).setTo(new Scalar(4440));
        layers.row(1).setTo(new Scalar(2000));
        layers.row(2).setTo(new Scalar(9));

        knn.setLayerSizes(layers);
        knn.setActivationFunction(ANN_MLP.SIGMOID_SYM, 0.1, 0.1);

        trainingLabels = new Mat(45, 9, CvType.CV_32F, Scalar.all(0));

        for (File file : new File(PATH_numbers).listFiles()) {

            Mat image = getMat(file.getAbsolutePath());
            image.convertTo(image, CvType.CV_32F); // Convert to float
            Imgproc.resize(image, image, new Size(60, 74));
            Imgproc.GaussianBlur(image, image, new Size(5, 5), 0);
            Core.addWeighted(image, 0.5, image, 0, 0, image);
            Mat imgresized = image.reshape(1, 1); // make continous
            trainingData.push_back(imgresized);
        }
        int index = 0;
        int numImgPerClass = 5;
        int rowstart = 0;
        for (int i = 0; i < 9; i++) {
            trainingLabels.submat(rowstart, numImgPerClass, i, i + 1).setTo(new Scalar(1));
            rowstart += 5;
            numImgPerClass += 5;

        }
        knn.train(trainingData, Ml.ROW_SAMPLE, trainingLabels);
        Mat test = new Mat();
        Imgproc.cvtColor(img, test, Imgproc.COLOR_RGB2GRAY);
        Imgproc.resize(test, test, new Size(60, 74));
        test.convertTo(test, CvType.CV_32FC1);
        Mat results = new Mat();
        test = test.reshape(1, 1);
        float label = knn.predict(test.reshape(1, 1), results, 0);
        System.out.println(label + " " + results.dump());

@StevenPuttemans and actually where exactly did you provide me with any code ?

@StevenPuttemans no i am not asking you to write me code i am asking for some explanation on why the output is this way

@StevenPuttemans could you check the updated code and see if you can help me understand why i get a 0 as an output no matter what the user input image is ? i mean the trainig part and going down and thank you :)

this means

  Mat layers = new Mat(3, 1, CvType.CV_32SC1);
    layers.row(0).setTo(new Scalar(4440));
    layers.row(1).setTo(new Scalar(2000));
    layers.row(2).setTo(new Scalar(9));  <---- here 

knn.setLayerSizes(layers);
trainingLabels = new Mat(45, 9, CvType.CV_32F, Scalar.all(0)); <----here 

int index = 0;
int numImgPerClass = 5;
int rowstart = 0;
for (int i = 0; i < 9; i++) {
    trainingLabels.submat(rowstart, numImgPerClass, i, i + 1).setTo(new Scalar(1));
    rowstart += 5;
    numImgPerClass += 5;
}

@StevenPuttemans i do understand how it works but the thing is i have 9 charachter and only 5 classess so my only solution is to increase the number of classes to 9 which i am not sure if correct since the layers output is 5

[1,0,0,0,0,0,0,0,0;  //image 1 
[1,0,0,0,0,0,0,0,0;  //image 1 
[1,0,0,0,0,0,0,0,0;  //image 1 
[0,1,0,0,0,0,0,0,0;  //image 2 
[0,1,0,0,0,0,0,0,0;  //image 2 
[0,1,0,0,0,0,0,0,0;  //image 2

@berak mm ok this means the code above is correct.. could you check the UPDATE cuz it outputs an error if i added a reshaper and convert to the output to the training labels but if i dont the output is 0

what i did in a loop outside and this output just fine

int index = 0;
int numImgPerClass = 5;
int rowstart = 0;
for (int i = 0; i < 9; i++) {
    trainingLabels.submat(rowstart, numImgPerClass, i, i + 1).setTo(new Scalar(1));
    rowstart += 5;
    numImgPerClass += 5;
}

@berak this outputs an error

OpenCV Error: Assertion failed (0 <= _rowRange.start && _rowRange.start <= _rowRange.end && _rowRange.end <= m.rows) in Mat, file /home/tomna/Desktop/opencv-3.1.0/modules/core/src/matrix.cpp, line 469
Exception in thread "main" CvException [org.opencv.core.CvException: cv::Exception: /home/tomna/Desktop/opencv-3.1.0/modules/core/src/matrix.cpp:469: error: (-215) 0 <= _rowRange.start && _rowRange.start <= _rowRange.end && _rowRange.end <= m.rows in function Mat
]

@StevenPuttemans i do understand that i should make a row for each training sample but what i dont understand is based on what the 1 will be placed at which column ? so far what i see is its index like 0 then index 0 will contain the 1

@berak i just updated the the question check the UPDATE and i really wanna know how close am i from fininshing this and thank you

@ StevenPuttemans any idea what to do?

@berak so there is something i just noticed if according to your code

Mat labels = new Mat(numImages, numClasses, CV_32F, Scalar.all(0)); Mat labels = new Mat( 45, 5 , CV_32F, Scalar.all(0)); <-- this means that

the output of the labels will be something like that

[1, 0, 0, 0, 0;
 1, 0, 0, 0, 0;
 1, 0, 0, 0, 0;
 1, 0, 0, 0, 0;
 1, 0, 0, 0, 0;
 0, 1, 0, 0, 0;
 0, 1, 0, 0, 0;
 0, 1, 0, 0, 0;
 0, 1, 0, 0, 0;
 0, 1, 0, 0, 0;
 0, 0, 1, 0, 0;
 0, 0, 1, 0, 0;
 0, 0, 1, 0, 0;
 0, 0, 1, 0, 0;
 0, 0, 1, 0, 0;
 0, 0, 0, 1, 0;
 0, 0, 0, 1, 0;
 0, 0, 0, 1, 0;
 0, 0, 0, 1, 0;
 0, 0, 0, 1, 0;
 0, 0, 0, 0, 1;
 0, 0, 0, 0, 1;
 0, 0, 0, 0, 1;
 0, 0, 0, 0, 1;
 0, 0, 0, 0, 1;
 0, 0, 0, 0, 0;    <<--- it wont assign anymore 
 0, 0, 0, 0, 0;
 0, 0, 0, 0, 0;
 0, 0, 0, 0, 0;
 0, 0, 0, 0, 0;
 0, 0, 0, 0, 0;
 0, 0, 0, 0, 0;
 0, 0, 0, 0, 0;
 0, 0, 0, 0, 0;
 0, 0, 0, 0, 0;
 0, 0, 0, 0, 0;
 0, 0, 0, 0, 0;
 0, 0, 0, 0, 0;
 0, 0, 0, 0, 0;
 0, 0, 0, 0, 0;
 0, 0, 0, 0, 0;
 0, 0, 0, 0, 0;
 0, 0, 0, 0, 0;
 0, 0, 0, 0, 0;
 0, 0, 0, 0, 0]

@berak mm ok good and now there is a problem with part two i cant seem to update the parameters of submat

    for (File file : new File(PATH_numbers).listFiles()) {
                 int numImgPerClass = 5;
    int rowstart = 0;

        trainingLabels.submat(rowstart, numImgPerClass, index, index + 1).setTo(new Scalar(1));
        index++;
        rowstart += 5;
        numImgPerClass += 5;
}
System.out.println(trainingLabels.dump());

[1, 0, 0, 0, 0;
 1, 0, 0, 0, 0;
 1, 0, 0, 0, 0;
 1, 0, 0, 0, 0;
 1, 0, 0, 0, 0;
 0, 0, 0, 0, 0;
 0, 0, 0, 0, 0;
 0, 0, 0, 0, 0;
 0, 0, 0, 0, 0;
 0, 0, 0, 0, 0;]  it out puts the 45 all "0"

@berak ok soo i understood the second part the one-hot encoded but the first part setting the network layers is what i dont understand

according to your explanation this is how the network will look like ?

 Mat layers = new Mat(3,1,CvType.CV_32SC1);
    layers.row(0).setTo(new Scalar(4440));
    layers.row(1).setTo(new Scalar(2000));
    layers.row(2).setTo(new Scalar(5));

@StevenPuttemans ya i know but i need to implement this using opencv so ya this is why i am using neural network ANN_MLP now

@StevenPuttemans do you have any links on how to implement it using java and opencv lib ?

@berak man sorry for tagging you in every question i post but man you seem like the only one who understand image processing here once again sorry and hope you help here :)

@berak so the training label will be something like this [1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9] and after changing this to this for both the training images and test image

 Imgproc.resize(image, image, new Size(74, 60));
Imgproc.resize(image, image, new Size(60, 74));

now what processing could be done ?

@berak do you mean i use the code before the edit but the i/5+1 where will this be exactly