I am trying to classify an Image using support vector machines (SVM) of OpenCV and I get this error (OpenCV Error: Bad argument (train data must be floating-point matrix) in cvCheck
TrainData, file ......\modules\ml\src\inner_functions.cpp, line 857) during SVM training.
So far I converted the data to rows of floating data type but still get an error. I would also like to predict and save the final classes of the image. Kindly advice me, my code is as below.
Is there a multiclass SVM classification code example somewhere I can follow?
Thanks.
int main()
{
//Read image;
register int iii, jjj;
Mat image = imread("E:\\DATA\\Dummy\\Input\\ImageFeatures.jpg");
const int rows = image.rows, cols = image.cols, bands = image.channels();
Mat reshapedImage;
reshapedImage = image.reshape(bands, image.rows * image.cols);
reshapedImage.convertTo(reshapedImage,CV_32FC1);
// Set up labels
Mat imagelabels = imread("E:\\DATA\\Dummy\\Input\\TrainingSet.bmp", CV_8UC1);
Mat labels;
labels = imagelabels.reshape(1, rows * cols);
labels.convertTo(labels, CV_32FC1);
// Set up SVM's parameters
CvSVMParams params;
params.svm_type = CvSVM::C_SVC;
params.kernel_type = CvSVM::RBF; //RBF
params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);
params.C = 10;
params.gamma = 0.9;
// Train the SVM
CvSVM SVM;
SVM.train(reshapedImage, labels, Mat(), Mat(), params);
//Vec3b green(0,255,0), blue (255,0,0), red(0,0,255), gray(230,230,230);
float response = SVM.predict(reshapedImage);
Mat solution;
for (iii = 0; iii < image.rows; iii++)
for (jjj = 0; jjj < image.cols; jjj++)
{
float response = SVM.predict(reshapedImage);
solution.at<float>(iii,jjj) = response;
}
// Show the training data
imwrite("result.bmp", solution); // save the image
solution.convertTo(solution,CV_8UC1);
imshow("SVM Simple Example", solution); // show it to the user
waitKey(0);
}
In my case I have a 1000 by 1000 pixel image and a corresponding fully labelled 1000 by 1000 pixel image with five classes (with labels from 0 to 4). How best do I deal with this scenario?
Related
I am play with OpenCV and SVM to make a classifier to predict facial expression. I have no problem to classify test dadaset, but when I try to predict a new image, I get this:
OpenCV Error: Assertion failed (samples.cols == var_count && samples.type() == CV_32F) in cv::ml::SVMImpl::predict
Error is pretty clear and I have a different number of columns, but of the same type.
I do not know how to achieve that, because I have a matrix of dimensions 1xnumber_of_features, but numbers_of_features is not the same of the trained and tested samples. How can I extract the same number of features from another image? Am I missing something?
To train classifier I did:
Detect face and save ROI;
Sift to extract features;
kmeans to cluster them;
bag of words to get the same numbers of features for each image;
pca to reduce;
train on train dadaset;
predict on test dadaset;
On the new image I did the same thing.
I tried to resize the new image to the same size, but nothing, same error ( and different number of columns, aka features). Vectors are of the same type (CF_32F).
[EDIT 1] Let's try to be more specific.
After succesfuly trained my classifier, I save SVM model in this way
svmClassifier->save(baseDatabasePath);
Then I load it when I need to do real time prediction in this way
cv::Ptr<cv::ml::SVM> svmClassifier;
svmClassifier = cv::ml::StatModel::load<ml::SVM>(path);
Then loop,
while (true)
{
getOneImage();
cv::Mat feature = extractFeaturesFromSingleImage();
float labelPredicted = svmClassifier->predict(feature);
cout << "Label predicted is: " << labelPredicted << endl;
}
But predict returns the error. feature dimension is 1x66, for example. As you can see below, I need like 140 features
<?xml version="1.0"?>
<opencv_storage>
<opencv_ml_svm>
<format>3</format>
<svmType>C_SVC</svmType>
<kernel>
<type>RBF</type>
<gamma>5.0625000000000009e-01</gamma></kernel>
<C>1.2500000000000000e+01</C>
<term_criteria><epsilon>1.1920928955078125e-07</epsilon>
<iterations>1000</iterations></term_criteria>
<var_count>140</var_count>
<class_count>7</class_count>
<class_labels type_id="opencv-matrix">
<rows>7</rows>
<cols>1</cols>
<dt>i</dt>
<data>
0 1 2 3 4 5 6</data></class_labels>
<sv_total>172</sv_total>
I do not know how achieve 140 features, when SIFT, FAST or SURF just give me around 60 features. What am I missing?
EDIT 2: I am going to try to be more formal: how can I put my real time sample on the same dimension of train and test dataset?
EDIT 3:
Extract features with sift and push on a vector of mat.
std::vector<cv::Mat> featuresVector;
for (int i = 0; i < numberImages; ++i)
{
cv::Mat face = cv::imread(facePath, CV_LOAD_IMAGE_GRAYSCALE);
cv::Mat featuresExtracted = runExtractFeature(face, featuresExtractionAlgorithm);
featuresVector.push_back(featuresExtracted);
}
Get total features extracted from all images.
int numberFeatures = 0;
for (int i = 0; i < featuresVector.size(); ++i)
{
numberFeatures += featuresVector[i].rows;
}
Prepare a mat to cluster features (I tried to follow this example)
cv::Mat featuresData = cv::Mat::zeros(numberFeatures, featuresVector[0].cols, CV_32FC1);
int currentIndex = 0;
for (int i = 0; i < featuresVector.size(); ++i)
{
featuresVector[i].copyTo(featuresData.rowRange(currentIndex, currentIndex + featuresVector[i].rows));
currentIndex += featuresVector[i].rows;
}
Perform clustering (I do not know how this parameter suite my case, my I think can be ok for now)
cv::Mat labels;
cv::Mat centers;
int binSize = 1000;
kmeans(featuresData, binSize, labels, cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 100, 1.0), 3, KMEANS_PP_CENTERS, centers);
Prepare a mat to perform bow.
cv::Mat featuresDataHist = cv::Mat::zeros(numberImages, binSize, CV_32FC1);
for (int i = 0; i < numberImages; ++i)
{
cv::Mat feature = cv::Mat::zeros(1, binSize, CV_32FC1);
int numberImageFeatures = featuresVector[i].rows;
for (int j = 0; j < numberImageFeatures; ++j)
{
int bin = labels.at<int>(currentIndex + j);
feature.at<float>(0, bin) += 1;
}
cv::normalize(feature, feature);
feature.copyTo(featuresDataHist.row(i));
currentIndex += featuresVector[i].rows;
}
PCA to try to reduce dimension.
cv::PCA pca(featuresDataHist, cv::Mat(), CV_PCA_DATA_AS_ROW, 50/*0.90*/);
cv::Mat feature;
for (int i = 0; i < numberImages; ++i)
{
feature = pca.project(featuresDataHist.row(i));
}
Hi i've been trying to train a svm with features, but i don't understand what to do with the descriptors that are computed of the keypoints using ORB. I know that svm needs a data matrix and a label matrix, but i don't know how can i pass the descriptors Mat to a valid format.
I've read about the BoF (Bag of Words/Features) but i don't know how to use it.
Thanks for any help.
The code below allows me to get the descriptors of an image. What's the next step?
std::vector<KeyPoint> kp;
Mat desc;
// Default parameters of ORB
int nfeatures = 128;
float scaleFactor = 1.2f;
int nlevels = 8;
int edgeThreshold = 15; // Changed default (31);
int firstLevel = 0;
int WTA_K = 2;
int scoreType = ORB::HARRIS_SCORE;
int patchSize = 31;
int fastThreshold = 20;
Ptr<ORB> myORB = ORB::create(nfeatures, scaleFactor, nlevels, edgeThreshold, firstLevel, WTA_K, scoreType,
patchSize, fastThreshold);
myORB->detectAndCompute(src, Mat(), kp, desc);
features.push_back(desc);
I would highly recommend you to use python with OpenCV that will save you a lot of time. In python, this will be just 10 lines of code.
You can refer to this link for ORB. And once you get features then you can use scikit-learn svm for training an SVM classifier.
I was trying to use the SVM included in the OpenCV library. I made two labels: either 1 or -1. However, sometimes the predict() function returns 0. (It works as expected sometimes, when testing cases are close to my training data, but fails when the testing case is far away or exactly same as my training data) My guess is that the data can't be separated linearly. I couldn't find any information online about what the function returns 0 implies.
Also, if I should try to use the method for non-linearly separable data. What should the settings be for CvSVMParams?
Thank you.
Optional information: I am working on color recognition. So my training cases are all the pixels from a sample photo. Since each pixel contains 3 data(RGB), my array is like arr[numOfPixels][3].
int main(void){
//read two source img for the training data
Mat color1 = imread("zi.png");
Mat color2 = imread("lv.png");
//mark 0-2499 to label 1 and 2500-5000 to label -1
float labels[5000] = {1.0};
for(int c=2500;c<5000;c++){
labels[c] = -1.0;
}
Mat LabelsMat(5000,1,CV_32FC1,labels);
//get 5000 pixels training data
float trainingdata[5000][3] = {{0,0,0}};
int count=0;
for(int i=0;i<50;i++){
for(int j=0;j<50;j++){
trainingdata[count][0] = (float)color1.at<Vec3b>(i,j)[0];
trainingdata[count][1] = (float)color1.at<Vec3b>(i,j)[1];
trainingdata[count][2] = (float)color1.at<Vec3b>(i,j)[2];
cout<<trainingdata[count][0]<<","<<trainingdata[count][1]<<","<<trainingdata[count][2]<<endl;
count++;
}}
cout<<"green"<<endl;
for(int i=0;i<50;i++){
for(int j=0;j<50;j++){
trainingdata[count][0] =(float)color2.at<Vec3b>(i,j)[0];
trainingdata[count][1] =(float)color2.at<Vec3b>(i,j)[1];
trainingdata[count][2] =(float)color2.at<Vec3b>(i,j)[2];
cout<<trainingdata[count][0]<<","<<trainingdata[count][1]<<","<<trainingdata[count][2]<<endl;
count++;
}}
Mat trainingDataMat(5000,3,CV_32FC1,trainingdata);
//sets SVM Parameters
CvSVMParams params;
params.svm_type = CvSVM::C_SVC;
params.kernel_type = CvSVM::LINEAR;
params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);
//train the SVM
CvSVM SVM;
SVM.train(trainingDataMat, LabelsMat, Mat(), Mat(), params);
//here's a quick test case
Mat sampleMat = (Mat_<float>(1,3) << 70,43,65);
cout<<(float)SVM.predict(sampleMat);
system("pause");
//set up the full testcase
Mat testcase = imread("zi.png");
if(!testcase.data)system("pause");
cout<<"testcase"<<endl;
float testdata[900][3] = {{0,0,0}};
count =0;
for(int i=0;i<30;i++){
for(int j=0;j<30;j++){
testdata[count][0]=(float)testcase.at<Vec3b>(i,j)[0];
testdata[count][1]=(float)testcase.at<Vec3b>(i,j)[1];
testdata[count][2]=(float)testcase.at<Vec3b>(i,j)[2];
cout<<testdata[count][0]<<","<<testdata[count][1]<<","<<testdata[count][2]<<endl;
count++;
}}
//test the testcase
for(int i=0;i<900;i++){
float temp[3] = {50,50,50};
Mat sampleMat(1,3,CV_32FC1,temp);
cout<< SVM.predict(sampleMat)<<endl;
}
system("pause");
return 0;
}
im learning about SVM, so im making a sample program that trains an SVM to detect if a symbol is in an image or if its not. All the images are black and white (the symbols would be black and the background white). I have 12 training images, 6 positives (with the symbol) and 6 negatives (without it). Im using hu moments to get the descriptors of every image and then i construct the training matrix with those descriptors. also i have a Labels matrix, which contains a label for each image: 1 if its positive and 0 if its negative. but im getting an error (something like a segmentation fault) at the line where i train the SVM. here is my code:
using namespace cv;
using namespace std;
int main(int argc, char* argv[])
{
//arrays where the labels and the features will be stored
float labels[12] ;
float trainingData[12][7] ;
Moments moment;
double hu[7];
//===============extracting the descriptos for each positive image=========
for ( int i = 0; i <= 5; i++){
//the images are called t0.png ... t5.png and are in the folder train
std::string path("train/t");
path += std::to_string(i);
path += ".png";
Mat input = imread(path, 0); //read the images
bitwise_not(input, input); //invert black and white
Mat BinaryInput;
threshold(input, BinaryInput, 100, 255, cv::THRESH_BINARY); //apply theshold
moment = moments(BinaryInput, true); //calculate the moments of the current image
HuMoments(moment, hu); //calculate the hu moments (this will be our descriptor)
//setting the row i of the training data as the hu moments
for (int j = 0; j <= 6; j++){
trainingData[i][j] = (float)hu[j];
}
labels[i] = 1; //label=1 because is a positive image
}
//===============extracting the descriptos for each negative image=========
for (int i = 0; i <= 5; i++){
//the images are called tn0.png ... tn5.png and are in the folder train
std::string path("train/tn");
path += std::to_string(i);
path += ".png";
Mat input = imread(path, 0); //read the images
bitwise_not(input, input); //invert black and white
Mat BinaryInput;
threshold(input, BinaryInput, 100, 255, cv::THRESH_BINARY); //apply theshold
moment = moments(BinaryInput, true); //calculate the moments of the current image
HuMoments(moment, hu); //calculate the hu moments (this will be our descriptor)
for (int j = 0; j <= 6; j++){
trainingData[i + 6][j] = (float)hu[j];
}
labels[i + 6] = 0; //label=0 because is a negative image
}
//===========================training the SVM================
//we convert the labels and trainingData matrixes to Mat objects
Mat labelsMat(12, 1, CV_32FC1, labels);
Mat trainingDataMat(12, 7, CV_32FC1, trainingData);
//create the SVM
Ptr<ml::SVM> svm = ml::SVM::create();
//set the parameters of the SVM
svm->setType(ml::SVM::C_SVC);
svm->setKernel(ml::SVM::LINEAR);
CvTermCriteria criteria = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);
svm->setTermCriteria(criteria);
//Train the SVM !!!!!HERE OCCURS THE ERROR!!!!!!
svm->train(trainingDataMat, ml::ROW_SAMPLE, labelsMat);
//Testing the SVM...
Mat test = imread("train/t1.png", 0); //this should be a positive test
bitwise_not(test, test);
Mat testBin;
threshold(test, testBin, 100, 255, cv::THRESH_BINARY);
Moments momentP = moments(testBin, true); //calculate the moments of the test image
double huP[7];
HuMoments(momentP, huP);
Mat testMat(1, 7, CV_32FC1, huP); //setting the hu moments to the test matrix
double resp = svm->predict(testMat); //pretiction of the SVM
printf("%f", resp); //Response
getchar();
}
i know that the program is running fine until that line because i printed labelsMat and trainingDataMat and the values inside them are ok. Even in the console i can see that the program is running fine until that exact line executes. the console then shows this message:
OpenCV error: Bad argument (in the case of classification problem the responses must be categorical; either specify varType when creating TrainDatam or pass integer responses)
i dont really know what this means. any idea of what could be causing the problem? if you need any other details please tell me.
EDIT
for future readers:
the problem was in the way i defined the labels array as an array of float and the LabelsMat as a Mat of CV_32FC1. the array that contains the labels needs to have integers inside, so i changed:
float labels[12];
to
int labels[12];
and also changed
Mat labelsMat(12, 1, CV_32FC1, labels);
to
Mat labelsMat(12, 1, CV_32SC1, labels);
and that solved the error. Thank you
Trying changing:
Mat labelsMat(12, 1, CV_32FC1, labels);
to
Mat labelsMat(12, 1, CV_32SC1, labels);
From: http://answers.opencv.org/question/63715/svm-java-opencv-3/
If that doesn't work, hopefully one of these posts will help you:
Opencv 3.0 SVM train classification issues
OpenCV SVM Training Data
I'm beginner in opencv. I have not gotten main concepts of opencv in details.
So maybe my code it's too dumb;
Out of my curiosity I want to try machine learning functions like a KNN, ANN.
I have the set of images with size 28*28 pixels. I want to do train cassifier for digit recognition. So first I need to assemble train set and train classes;
Mat train_data = Mat(rows, cols, CV_32FC1);
Mat train_classes = Mat(rows, 1, CV_32SC1);
Mat img = imread(image);
Mat float_data(1, cols, CV_32FC1);
img.convertTo(float_data, CV_32FC1);
How to fill train_data with float_data ?
I thought It was smth like this:
for (int i = 0; i < train_data.rows; ++i)
{
... // image is a string which contains next image path
image = imread(image);
img.convertTo(float_data, CV_32FC1);
for( int x = 0; x < train_data.cols; x++ ){
train_data.at<float> (i, x) = float_data.at<float>( x);;
}
}
KNearest knn;
knn.train(train_data, train_classes);
but it's of course doesn't work . . .
Please, tell me how to do it right. Or at least suggest the books for dummies :)
Mat train_data; // initially empty
Mat train_labels; // empty, too.
// for each img in the train set :
Mat img = imread("image_path");
Mat float_data;
img.convertTo(float_data, CV_32FC1); // to float
train_data.push_back( float_data.reshape(1,1) ); // add 1 row (flattened image)
train_labels.push_back( label_for_image ); // add 1 item
KNearest knn;
knn.train(train_data, train_labels);
it's all the same for other ml algos !