Lucas Amparo Barbosa's profile - activity

Pre-stitching data Hi, folks. I need to access the data from the stitching algorithm "seconds" before the blending... S

computed by numpy.linalg.matrix_rank function

[[ 1.95163442e-08 1.34608880e-07 -3.20936720e-04] [ 4.76676164e-08 5.75973164e-07 -1.02011608e-03] [-5.95314437e-05

[[-2.29390496e-08 8.55007340e-07 -1.07108724e-03] [ 2.87605686e-08 -2.30601171e-06 2.86179300e-03] [ 2.94004722e

[[-2.29390496e-08 8.55007340e-07 -1.07108724e-03] [ 2.87605686e-08 -2.30601171e-06 2.86179300e-03] [ 2.94004722e-05

[[-2.29390496e-08 8.55007340e-07 -1.07108724e-03] [ 2.87605686e-08 -2.30601171e-06 2.86179300e-03] [ 2.94004722e-05

Wrong rank in Fundamental Matrix Hi guys, I'm using the OpenCV for Python3 and, based on the Mastering OpenCV Book, try

i think this code willl works for me. Thanks berak.

Hi everyone,

Someone already does a Rank N for the algorithms from OpenCV faces?

Have some way to take only the feature set from the eigen/fisher/lbph processing to build my onw matching process?

Thanks guys.

I've solved that. Thanks everyone.

Rank N for OpenCV Face Recognition Hi everyone, Someone already does a Rank N for the algorithms from OpenCV faces? Ha

Angle between two lines: Give two lines, s and r, when ms and mr are the angular coeff for the lines. So, the angle a is

Tensorflow Import Assert Guys, I'm trying to import a tensorflow model (protobuffer file) but the opencv returns Ass

I added the flag -lopencv_face, and the error turns to: OpenCV Error: Bad argument (Wrong shapes for given matrices. Wa

No, man... I've declared the namespace before. But thanks...

Problems with FaceRecognition - URGENT Hello everyone, I'm try to follow the tutorial from docs (here) , but when I try

Problems with FaceRecognition - URGENT Hello everyone, I'm try to follow the tutorial from docs (here) , but when I try

I've build a knn model for regression, but isn't work.

When I call for a prediction, with a set of numbers, the answer always be the same value, like this >>

In = 0.742781 || Out = 0.917355 In = 0.557086 || Out = 0.917355 In = 0.19518 || Out = 0.917355 In = 0.9759 || Out = 0.917355 In = 0.09759 || Out = 0.917355

the model is setted to regression (setIsClassifier(false)) and the Algorithm Type is Brute Force (The KdTree isn't work too, showing a error).

Please, guys. I need so much this help.

@berak , the code

//Loading the model, early trained.
Ptr<KNearest> knn = Algorithm::load<KNearest>(str.str());

//Loading the value for regression
sampleMat.at<float>(0,0) = cloud_normals->points[i/3].normal_x;
knns[i]->findNearest(sampleMat, 5, response);
//Saving the regression
tmp.x = response.at<float>(0,0);

Visual Results. After the train, i've tested the model.

OpenCV v2 (2.4), Ubuntu 14.04

Hello everyone,

I have a dataset assembled by pairs formed by two float numbers, like (-87.884599 , 45.453324).

I need to build a regression model to predict the second value for a single input.

Can someone help me with this?

Thanks

@berak still not working... Change the multiple call

for(int j = 0; j < 12; j++){
        for(int i = 0; i < matTrainFeatures.rows; i++){
            lr->train(matTrainFeatures.row(i),ROW_SAMPLE,matTrainLabels.row(i));   
        }  
    }

To

lr->train(matTrainFeatures,ROW_SAMPLE,matTrainLabels);

Now, the output is

matSample: [53.028278; 19.925919; 40.105942; 81.438507]

matResults: [52.965919; 48.013771; 51.669022; 51.085236]

When the correct answer would be [84.443771; 3.7859535; 72.47551; 55.815529;]

Guys... I'm using the ANN_MLP to predict values like a simple regression.

But, don't know why, isn't working.

My code >> http://pastebin.com/nrczCHxW The output from this code >> http://pastebin.com/BWCY9ZFp

It's should work like this: Train with random numbers and random labels, like ordened pairs (x,y). Test with the first fours elements on the training set. The output should be the first fours elements from the label set...

Someone can help me??

#include <opencv2/highgui.hpp>
#include <opencv2/ml.hpp>

using namespace cv;
using namespace cv::ml;
using namespace std;

int main( int argc, char** argv ){
    Mat matTrainFeatures(100,1,CV_32F);
    randu(matTrainFeatures,0,100);

    Mat matTrainLabels(100,1,CV_32F);
    randu(matTrainLabels,0,100);

    Mat matSample(4,1,CV_32F);
    //randu(matSample,0,100);
    matSample.at<float>(0,0) = matTrainFeatures.at<float>(0,0);
    matSample.at<float>(1,0) = matTrainFeatures.at<float>(1,0);
    matSample.at<float>(2,0) = matTrainFeatures.at<float>(2,0);
    matSample.at<float>(3,0) = matTrainFeatures.at<float>(3,0);

    Mat matSampleLabels(1,1,CV_32F);

    Mat matResults(4,1,CV_32F);
    //Mat matResults(5,1,CV_32F);

    Ptr<TrainData> trainingData;
    trainingData=TrainData::create(matTrainFeatures,ROW_SAMPLE,matTrainLabels);

    Ptr<ANN_MLP> lr = ANN_MLP::create();
    Mat layers(3,1,CV_32FC1);
    layers.row(0) = 1;
    layers.row(1) = 100;
    layers.row(2) = 1;
    lr->setBackpropMomentumScale(0.05f);
    lr->setLayerSizes(layers);
    lr->setBackpropWeightScale(0.05f);
    lr->setActivationFunction(ANN_MLP::SIGMOID_SYM, 1, 1);
    lr->setTrainMethod(ANN_MLP::BACKPROP, 0.001);
    lr->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 300, FLT_EPSILON));

    //lr->train(trainingData);
    for(int j = 0; j < 12; j++){
        for(int i = 0; i < matTrainFeatures.rows; i++){
            lr->train(matTrainFeatures.row(i),ROW_SAMPLE,matTrainLabels.row(i));   
        }  
    }  
    lr->predict(matSample,matResults);

    //Just checking the settings
    cout<<"Training data: "<<endl
        <<"getNSample\t"<<trainingData->getNSamples()<<endl
        <<"getSamples\n"<<trainingData->getSamples()<<endl
        <<"getResponses\n"<<trainingData->getTrainResponses()<<endl
        <<endl;

    //confirming sample order
    cout<<"matSample: "<<endl
        <<matSample<<endl
        <<endl;

    //displaying the results
    cout<<"matResults: "<<endl
        <<matResults<<endl
        <<endl;

    return 0;

    }