C++ CvANN_MLP::read方法代码示例

void predict(int nbSamples, int size)
{
	CvANN_MLP network;
	CvFileStorage* storage = cvOpenFileStorage( "data/neural_model.xml", 0, CV_STORAGE_READ);
	CvFileNode *n = cvGetFileNodeByName(storage, 0, "neural_model");
	network.read(storage, n);

	Mat toPredict(nbSamples, size * size, CV_32F);

	int label;
	float pixel;
	FILE *file = fopen("data/predict.txt", "r");

	for(int i=0; i < nbSamples; i++){

			for(int j=0; j < size * size; j++){

					// WHILE ITS PIXEL VALUE
					if(j < size * size){


						fscanf(file, "%f,", &pixel);
						toPredict.at<float>(i,j) = pixel;
					}
			}
	}
	fclose(file);

	Mat classOut(nbSamples, 62,CV_32F);

	network.predict(toPredict,classOut);

	float value;
	int maxIndex = 0;
	float maxValue;

	for(int k = 0; k < nbSamples; k++)
	{
		maxIndex = 0;
		maxValue = classOut.at<float>(0,0);
		for(int index=1;index<62;index++){

			value = classOut.at<float>(0,index);
			if(value>maxValue){

				maxValue = value;
				maxIndex = index;
			}
		}
	}

	cout<<"Index predicted : " << maxIndex + 1 << endl;

	cvReleaseFileStorage(&storage);
}

int CheckCircle( Mat src)//Matとcircleの組を渡すこと
{
     //XMLを読み込んでニューラルネットワークの構築
    CvANN_MLP nnetwork;
    CvFileStorage* storage = cvOpenFileStorage( "param.xml", 0, CV_STORAGE_READ );
    CvFileNode *n = cvGetFileNodeByName(storage,0,"DigitOCR");
    nnetwork.read(storage,n);
    cvReleaseFileStorage(&storage);

        //特徴ベクトルの生成
        int index;
        float train[64];
        for(int i=0; i<64; i++) train[i] = 0;
        Mat norm(src.size(), src.type());
        Mat sample(src.size(), src.type());
        normalize(src, norm, 0, 255, NORM_MINMAX, CV_8UC3);
        
        for(int y=0; y<sample.rows; y++){
            for(int x=0; x<sample.cols; x++){
                index = y*sample.step+x*sample.elemSize();
                int color = (norm.data[index+0]/64)+
                    (norm.data[index+1]/64)*4+
                    (norm.data[index+2]/64)*16;
                train[color]+=1;
            }
        }
        int pixel = sample.cols * sample.rows;
        for(int i=0; i<64; i++){
            train[i] /= pixel;
        }

        //分類の実行
        Mat data(1, ATTRIBUTES, CV_32F);
        for(int col=0; col<ATTRIBUTES; col++){
            data.at<float>(0,col) = train[col];
        }
        int maxIndex = 0;
        Mat classOut(1,CLASSES,CV_32F);
        nnetwork.predict(data, classOut);
        float value;
        float maxValue=classOut.at<float>(0,0);
        for(int index=1;index<CLASSES;index++){
            value = classOut.at<float>(0,index);
            if(value > maxValue){
                maxValue = value;
                maxIndex=index;
            }
		}
		return maxIndex;
}

int main( int argc, char** argv ) {

	//To avoid preprocessing the images all the time
	std::cout << "Have you preprocessed files already? Y/N ";
	char temp = toupper( getchar() ); std::cin.get();
	if (temp == 'N'){
		std::cout << "Preprocessing images";
		if (preprocess(ImageSize, alphabetSize, dataSet, letters) == 0){
			std::cout << "\nSomething went wrong when preprocessing the files" << endl;
			std::cin.get(); return -1;
		}
	}
	
	std::cout << "\nDo you need to make and train a new Neural Net? Y/N ";
	temp = toupper( getchar() ); 
	std::cin.get();

	if (temp == 'Y'){

		Mat training_set = Mat::zeros(trainingSamples, attributes,CV_32F);					//zeroed matrix to hold the training samples.
		Mat training_results = Mat::zeros(trainingSamples, alphabetSize, CV_32F);			//zeroed matrix to hold the training results.

		Mat test_set = Mat::zeros(testSamples,attributes,CV_32F);							//zeroed matrix to hold the test samples.
		Mat test_results = Mat::zeros(testSamples,alphabetSize,CV_32F);						//zeroed matrix to hold the test results.
	
		std::cout << "\nReading training data";
		if (readPreprocessed(training_set, training_results, ImageSize, alphabetSize, letters, (dataSet-dataSet+1), trainingSet) == 0) {
			std::cout << "\nSomething went wrong when opening preprocessed files" << endl;
			std::cin.get(); return -1;
		}
	
		std::cout << "\nReading test data";
		if (readPreprocessed(test_set, test_results, ImageSize, alphabetSize, letters, (dataSet-trainingSet+1), dataSet) == 0) {
			std::cout << "\nSomething went wrong when opening preprocessed files" << endl;
			std::cin.get(); return -1;
		}
	
		std::cout << "\nSetting up Neural Net";
	
		Mat layers(numberOfLayers, 1, CV_32S);

		layers.at<int>(0,0) = attributes;			//input layer
		layers.at<int>(1,0) = sizeOfHiddenLayer;	//hidden layer
		layers.at<int>(2,0) = alphabetSize;			//output layer
	
		//create the neural network.
		CvANN_MLP NeuralNet(layers, CvANN_MLP::SIGMOID_SYM,alpha,beta);

		// terminate the training after either 10 000
		// iterations or a very small change in the
		// network wieghts below the specified value

		// use backpropogation for training

		// co-efficents for backpropogation training
		// recommended values taken from http://docs.opencv.org/modules/ml/doc/neural_networks.html#cvann-mlp-trainparams
		CvANN_MLP_TrainParams params( cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 10000, 0.005), CvANN_MLP_TrainParams::BACKPROP, 0.1, 0.5 );

		std::cout << "\nTraining Neural Net" << endl;
	
		int iterations = NeuralNet.train(training_set, training_results, Mat(), Mat(), params);
		std::cout << "\nCompleted after " << iterations << " iterations trough the training data set." << endl;

		// Save the model generated into an xml file.
		std::cout << "\nWriting to file param.xml ..." << endl;
		CvFileStorage* storage = cvOpenFileStorage( "param.xml", 0, CV_STORAGE_WRITE );
		NeuralNet.write(storage,"DigitOCR");
		cvReleaseFileStorage(&storage);
		std::cout << "\t\t ...Done." << endl; std::cin.get();

		cv::Mat classificationResult(1, alphabetSize, CV_32F);
		// Test the generated model with the test samples.
		cv::Mat test_sample;
		//count of correct prediction
		int correct_class = 0;
		//count of wrong prediction
		int wrong_class = 0;
 
		//classification matrix gives the count of classes to which the samples were classified.
		int classification_matrix[alphabetSize][alphabetSize] = {{}};
  
		// for each sample in the test set.
		for (int tsample = 0; tsample < testSamples; tsample++) {
 
			// extract the sample
 
			test_sample = test_set.row(tsample);
 
			//try to predict its class
 
			NeuralNet.predict(test_sample, classificationResult);
			/*The classification result matrix holds weightage  of each class.
			we take the class with the highest weightage as the resultant class */
 
			// find the class with maximum weightage.
			int maxIndex = 0;
			float value = 0.0f;
			float maxValue = classificationResult.at<float>(0,0);
			for(int index = 1; index < alphabetSize; index++) {   
				value = classificationResult.at<float>(0,index);
//.........这里部分代码省略.........

string predictDigits(Mat &originalImage) {
	string numbers = "";
	Mat clon = originalImage.clone();

	// Read the model from the XML file and create the neural network.
	CvANN_MLP nnetwork;
	CvFileStorage* storage = cvOpenFileStorage(
			"/home/andersson/Escritorio/Temporales/neural_network.xml", 0,
			CV_STORAGE_READ);
	CvFileNode *n = cvGetFileNodeByName(storage, 0, "DigitOCR");
	nnetwork.read(storage, n);
	cvReleaseFileStorage(&storage);

	int rows = originalImage.rows;
	int cols = originalImage.cols;

	int lx = 0;
	int ty = 0;
	int by = 0;
	int rx = 0;
	int flag = 0;
	int currentColumn = 1;
	bool temp = false;

	while (!temp) {
		/* Left X */
		for (int i = currentColumn; i < cols; i++) {
			for (int j = 1; j < rows; j++) {
				if (i != (cols - 1)) {
					if (originalImage.at<uchar> (j, i) == 0) {
						lx = i;
						flag = 1;
						break;
					}
				} else {
					temp = true;
					break;
				}
			}

			if (!temp) {
				if (flag == 1) {
					flag = 0;
					break;
				}
			} else {
				break;
			}
		}

		if (temp) {
			continue;
		}

		/* Right X */
		int tempNum;
		for (int i = lx; i < cols; i++) {
			tempNum = 0;
			for (int j = 1; j < rows; j++) {
				if (originalImage.at<uchar> (j, i) == 0) {
					tempNum += 1;
				}
			}

			if (tempNum == 0) {
				rx = (i - 1);
				break;
			}
		}

		currentColumn = rx + 1;

		/* Top Y */
		for (int i = 1; i < rows; i++) {
			for (int j = lx; j <= rx; j++) {
				if (originalImage.at<uchar> (i, j) == 0) {
					ty = i;
					flag = 1;
					break;
				}
			}

			if (flag == 1) {
				flag = 0;
				break;
			}
		}

		/* Bottom Y */
		for (int i = (rows - 1); i >= 1; i--) {
			for (int j = lx; j <= rx; j++) {
				if (originalImage.at<uchar> (i, j) == 0) {
					by = i;
					flag = 1;
					break;
				}
			}

			if (flag == 1) {
				flag = 0;
//.........这里部分代码省略.........