t24-white's profile - activity

Hi,

As many of you are aware i am using an MLP to use real time image recogniton to drive a car.

my testing data and training data are used from the same set of images- meaning all test data is currently also in the training set, which is why I cannot figure out when running my testNetwork funciton, it produces a score of 0/114 correct answers, as naturally the fact that it has been trained using this data and more, it should work. I know training data should be seperate but I wanted to get it working like this first..

all 3 functions are as follows, I used @berak 's code as a good baseline to adapt it to my needs, I highly recommend taking a look at it if you are learning to use Opencv MLP abilities as it is very useful.

void readScanStore(){
cv::Mat trainingData;//mat collectionn of images to train with 
cv::Mat TestData;
char const *path = "/home/pi/selfdrivingcarV1/train_data/";//needs folders of 0 1 2 3  holdig 50 pics each for representing outputs
int const numFilesDirs[]={128,217,217}; //number of photos for each direction ()LEFT =0 RIGHT =1 straight =2
char const strDirs[]={'0','1','2'}; //optional outputs " 0=go,1 right 2 left
int const numDirs = 3;//number of directions

cv::Mat TestLabels (0,0,(CV_32S));
cv::Mat trainingLabels (0,0,(CV_32S));
//same as svm??
for (int i=0;i!=numDirs; i++){//outer for loop to go through all 4 output options
    int numFiles = numFilesDirs[i];//assign inner loop based on size of samples from current outerloop val
        for (int j=0;j!=numFiles;j++){//loop through all files within current output value
            std::cout << "direction" << strDirs[i] << "file: " << j <<".jpg" << "\n";// print current output val and files associated with that direction
            std::stringstream ss;
            ss << path << strDirs[i] << "/" <<"i (" << j+1 << ").jpg";//print current working image
            cv::Mat img = cv::imread(ss.str(),0);


        if (!img.data)
        cout << "error no file found " << ss.str() << endl;


            Size size(10,10);
            Mat ImgCon;
            resize(img,ImgCon,size);
            ImgCon =ImgCon.reshape(1,1);
            //assume img is continous
            //reshape image to 1xtotal res 
            if (j%5==0){//push every 5 images to test set
                TestData.push_back(ImgCon);
                TestLabels.push_back(i);
            }
            trainingData.push_back(ImgCon); //push back image
            trainingLabels.push_back(i);//assign instruction corresponding to image in label set
        }
    }  
    TestData.convertTo(TestData, CV_32F);
trainingData.convertTo(trainingData, CV_32F);//,1/255.0);//convert all images to cv32f (numeric values)

cv::FileStorage fs("TRAIN_VALUES.xml",FileStorage::WRITE);//store numeric values in xml file as training data values for each image.
fs << "TrainingData" <<trainingData;//assign each image 
fs << "classes" << trainingLabels;//assign associated classes


cv::FileStorage ffs("TEST_VALUES.xml",FileStorage::WRITE);//store numeric values in xml file as training data values for each image.
ffs << "TestData" <<TestData;//assign each image 
ffs << "classes" << TestLabels;//assign associated classes


printf("complete");
    }

void trainNetwork() {

    int nclasses = 3;
    cv::FileStorage fsa;
    fsa.open("TRAIN_VALUES.xml", cv::FileStorage::READ);
    cv::Mat train_data;
    cv::Mat train_labels;
    fsa ...

I have been using the tutorial on this site titled "how to start implementing a neural network in C"( cant post link yet due to rep) and others: to create a neural network to process images of a track I have made for a car to be able to drive round it autonomously.

The process of reading in the training images (converted as required) and their respective classes works perfectly and stores to the data file Values.xml with the below code:

void readScanStore(){
cv::Mat trainingData;//mat collectionn of images to train with 
char const *path = "/home/pi/selfdrivingcarV1/train_data/";//needs folders of 0 1 2 3  holdig 50 pics each for representing outputs
int const numFilesDirs[]={110,95,63}; //number of photos for each direction (fwd l r)
char const strDirs[]={'0','1','2'}; //optional outputs " 0=go,1 right 2 left
int const numDirs = 3;//number of directions


cv::Mat trainingLabels (0,0,(CV_32S));
//same as svm??
for (int i=0;i!=numDirs; i++){//outer for loop to go through all 4 output options
    int numFiles = numFilesDirs[i];//assign inner loop based on size of samples from current outerloop val
        for (int j=0;j!=numFiles;j++){//loop through all files within current output value
            std::cout << "direction" << strDirs[i] << "file: " << j <<".jpg" << "\n";// print current output val and files associated with that direction
            std::stringstream ss;
            ss << path << strDirs[i] << "/" << j << ".jpg";//print current working image
            cv::Mat img = cv::imread(ss.str(),0);


        if (!img.data)
        cout << "error no file found " << ss << endl;


            Size size(10,10);
            Mat ImgCon;
            resize(img,ImgCon,size);
            ImgCon.reshape(1,1);
        //assume img is continous
            //reshape image to 1xtotal res 

            trainingData.push_back(ImgCon); //push back image
            trainingLabels.push_back(i);//assign instruction corresponding to image in label set
        }
    }  

trainingData.convertTo(trainingData, CV_32F);//,1/255.0);//convert all images to cv32f (numeric values)

cv::FileStorage fs("VALUES.xml",FileStorage::WRITE);//store numeric values in xml file as training data values for each image.
fs << "TrainingData" <<trainingData;//assign each image 
fs << "classes" << trainingLabels;//assign associated classes

printf("complete");
    }

which is then proceeded by the training procedure which loads the values of the images and uses them as training data to train the NN:

void trainNetwork(){
    int nclasses = 3;

    cv::FileStorage fs;
fs.open("VALUES.xml", cv::FileStorage::READ);
cv::Mat trainData;
cv::Mat classes;
fs["TrainingData"] >> trainData;
fs["classes"] >> classes;
    Mat confusion(nclasses,nclasses,CV_32S, Scalar(0)); // will hold our test results

 train_test(nclasses, trainData, classes, confusion);
}

which calls the following:

    void train_test(int nclasses, const Mat &train_data, const Mat &train_labels, Mat &confusion) {

        int nfeatures = train_data.cols;
        Ptr<ml::ANN_MLP> ann = ml::ANN_MLP::create();
        Mat_<int> layers(4,1);
        layers(0) = nfeatures;     // input
        layers(1) = nclasses * 8;  // hidden
        layers(2) = nclasses * 4;  // hidden
        layers(3) = nclasses;      // output, 1 pin per class.
        ann->setLayerSizes(layers);
        ann->setActivationFunction(ml::ANN_MLP::SIGMOID_SYM,0,0);
        ann->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 300, 0.0001));
        ann->setTrainMethod(ml::ANN_MLP::BACKPROP, 0.0001);
    printf("sending data to ...