void mlp ( cv :: Mat & trainingData , cv :: Mat & trainingClasses , cv :: Mat & testData , cv :: Mat &testClasses ) {
cv :: Mat layers = cv :: Mat (4 , 1 , CV_32SC1 ) ;
layers . row (0) = cv :: Scalar (2) ;
layers . row (1) = cv :: Scalar (10) ;
layers . row (2) = cv :: Scalar (15) ;
layers . row (3) = cv :: Scalar (1) ;
CvANN_MLP mlp ;
CvANN_MLP_TrainParams params;
CvTermCriteria criteria ;
criteria.max_iter = 100;
criteria.epsilon = 0.00001f ;
criteria.type = CV_TERMCRIT_ITER | CV_TERMCRIT_EPS ;
params.train_method = CvANN_MLP_TrainParams :: BACKPROP ;
params.bp_dw_scale = 0.05f;
params.bp_moment_scale = 0.05f;
params.term_crit = criteria;
mlp.create ( layers ) ;
// train
mlp.train ( trainingData , trainingClasses , cv :: Mat () , cv :: Mat () , params ) ;
cv::Mat response (1 , 1 , CV_32FC1 ) ;
cv::Mat predicted ( testClasses . rows , 1 , CV_32F ) ;
for ( int i = 0; i < testData . rows ; i ++) {
cv :: Mat response (1 , 1 , CV_32FC1 ) ;
cv :: Mat sample = testData . row ( i ) ;
mlp . predict ( sample , response ) ;
predicted . at < float >( i ,0) = response . at < float >(0 ,0) ;
}
cout << " Accuracy_ { MLP } = " << evaluate ( predicted , testClasses ) << endl ;
plot_binary ( testData , predicted , " Predictions Backpropagation " ) ;
}
void Model::Predict_mlp( const SampleSet& samples, SampleSet& outError )
{
int true_resp = 0;
CvANN_MLP *model = (CvANN_MLP*)m_pModel;
cv::Mat result;
float temp[40];
model->predict(samples.Samples(), result);
for (int i = 0; i < samples.N(); i++)
{
float maxcol = -1;
int index = -1;
for (int j = 0; j < result.cols; j++)
{
if (result.at<float>(i,j) > maxcol)
{
maxcol = result.at<float>(i,j);
index = j;
}
}
float label = samples.Classes()[index];
if (label != samples.GetLabelAt(i))
{
outError.Add(samples.GetSampleAt(i), samples.GetLabelAt(i));
}
else
{
true_resp++;
}
}
printf("%d %d",samples.N(), true_resp);
}
void train(Mat TrainData, Mat classes, int nlayers){
Mat layers(1,3,CV_32SC1);
layers.at<int>(0)= TrainData.cols;
layers.at<int>(1)= nlayers;
layers.at<int>(2)= numberCharacters;
ann.create(layers, CvANN_MLP::SIGMOID_SYM, 1, 1);
//Prepare trainClases
//Create a mat with n trained data by m classes
Mat trainClasses;
trainClasses.create( TrainData.rows, numberCharacters, CV_32FC1 );
for( int i = 0; i < trainClasses.rows; i++ )
{
for( int k = 0; k < trainClasses.cols; k++ )
{
//If class of data i is same than a k class
if( k == classes.at<int>(i) )
trainClasses.at<float>(i,k) = 1;
else
trainClasses.at<float>(i,k) = 0;
}
}
Mat weights( 1, TrainData.rows, CV_32FC1, Scalar::all(1) );
//Learn classifier
ann.train( TrainData, trainClasses, weights );
}
void CImageProcess::ANN_Region()
{
char path[512] = {0};
float obj[MAX_TRAIN_COLS] = {0};
Sample("./sources/0 (1).bmp", obj, MAX_TRAIN_COLS);
CvANN_MLP bpANN;
CvANN_MLP_TrainParams param;
param.term_crit = cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,5000,0.01);
param.train_method = CvANN_MLP_TrainParams::BACKPROP;
param.bp_dw_scale = 0.1;
param.bp_moment_scale = 0.1;
Mat layerSize = (Mat_<int>(1,3)<<MAX_TRAIN_COLS ,MAX_OBJ_COLS,MAX_OBJ_COLS);
bpANN.create(layerSize, CvANN_MLP::SIGMOID_SYM);
//m_bpANN.load("./sources/mlp.xml");
Mat input(1, MAX_TRAIN_COLS, CV_32FC1, obj);
float _obj[MAX_OBJ_COLS] ={0};
Mat out(1, MAX_OBJ_COLS, CV_32FC1, _obj);
//Mat out;
bpANN.predict(input,out);
int i=0;
i+=i;
}
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 classify_emotion(Mat& face, const char* ann_file, int tagonimg)
{
int ret = 0;
Mat output(1, OUTPUT_SIZE, CV_64FC1);
Mat data(1, nn_input_size, CV_64FC1);
CvANN_MLP nnetwork;
nnetwork.load(ann_file, "facial_ann");
vector<Point_<double> > points;
vector<double> distances;
if(!get_facial_points(face, points)) {
return -1;
}
get_euler_distance_sets(points, distances);
int j = 0;
while(!distances.empty()) {
data.at<double>(0,j) = distances.back();
distances.pop_back();
j++;
}
nnetwork.predict(data, output);
/* Find the biggest value in the output vector, that is what we want. */
double b = 0;
int k = 1;
for (j = 0; j < OUTPUT_SIZE; j++) {
cout<<output.at<double>(0, j)<<" ";
if (b < output.at<double>(0, j)) {
b = output.at<double>(0, j);
k = j + 1;
}
}
/* Print the result on the image. */
if (tagonimg) {
putText(face, get_emotion(k), Point(30, 30), FONT_HERSHEY_SIMPLEX,
0.7, Scalar(0, 255, 0), 2);
draw_distance(face, points);
}
return k;
}
void annTrain(Mat TrainData, Mat classes, int nNeruns) {
ann.clear();
Mat layers(1, 3, CV_32SC1);
layers.at<int>(0) = TrainData.cols;
layers.at<int>(1) = nNeruns;
layers.at<int>(2) = numAll;
ann.create(layers, CvANN_MLP::SIGMOID_SYM, 1, 1);
// Prepare trainClases
// Create a mat with n trained data by m classes
Mat trainClasses;
trainClasses.create(TrainData.rows, numAll, CV_32FC1);
for (int i = 0; i < trainClasses.rows; i++) {
for (int k = 0; k < trainClasses.cols; k++) {
// If class of data i is same than a k class
if (k == classes.at<int>(i))
trainClasses.at<float>(i, k) = 1;
else
trainClasses.at<float>(i, k) = 0;
}
}
Mat weights(1, TrainData.rows, CV_32FC1, Scalar::all(1));
// Learn classifier
// ann.train( TrainData, trainClasses, weights );
// Setup the BPNetwork
// Set up BPNetwork's parameters
CvANN_MLP_TrainParams params;
params.train_method = CvANN_MLP_TrainParams::BACKPROP;
params.bp_dw_scale = 0.1;
params.bp_moment_scale = 0.1;
// params.train_method=CvANN_MLP_TrainParams::RPROP;
// params.rp_dw0 = 0.1;
// params.rp_dw_plus = 1.2;
// params.rp_dw_minus = 0.5;
// params.rp_dw_min = FLT_EPSILON;
// params.rp_dw_max = 50.;
ann.train(TrainData, trainClasses, Mat(), Mat(), params);
}
int classify(Mat f){
Mat output(1, numberCharacters, CV_32FC1);
ann.predict(f, output);
Point maxLoc;
double maxVal;
minMaxLoc(output, 0, &maxVal, 0, &maxLoc);
//We need know where in output is the max val, the x (cols) is the class.
cout<<maxLoc.x<<endl;
return maxLoc.x;
}
void Model::Train_mlp( const SampleSet& samples )
{
CvANN_MLP* model = (CvANN_MLP*)m_pModel;
CvANN_MLP_TrainParams* para = (CvANN_MLP_TrainParams*)m_trainPara;
int dim = samples.Dim();
vector<float> classes = samples.Classes();
cv::Mat layerSize = (cv::Mat_<int>(1, 3) << dim, 100, classes.size());
model->create(layerSize);
cv::Mat newLaybels = cv::Mat::zeros(samples.N(), classes.size(), CV_32F);
for (int n=0; n<samples.N(); n++)
{
int label = samples.GetLabelAt(n);
for (int c=0; c<classes.size(); c++)
{
if (label == classes[c])
newLaybels.at<float>(n, c) = 1.0f;
}
}
model->train(samples.Samples(), newLaybels, cv::Mat::ones(samples.N(), 1, CV_32F), cv::Mat(), *para);
}
// Predict the output with the trained ANN given the two inputs.
void Predict(float data1, float data2)
{
float _sample[2];
CvMat sample = cvMat(1, 2, CV_32FC1, _sample);
float _predout[1];
CvMat predout = cvMat(1, 1, CV_32FC1, _predout);
sample.data.fl[0] = data1;
sample.data.fl[1] = data2;
machineBrain.predict(&sample, &predout);
printf("%f %f -> %f \n", data1, data2, predout.data.fl[0]);
}
// Predict the output with the trained ANN given the two inputs.
void predict()
{
int test_sample_count = 78;
//The test data matrix.
float td[78][61];
float _sample[60];
CvMat sample = cvMat(1, 60, CV_32FC1, _sample);
float _predout[1];
CvMat predout = cvMat(1, 1, CV_32FC1, _predout);
//Read the test file
FILE *fin;
fin = fopen("data/sonar_test.csv", "r");
for (int i=0; i<test_sample_count; i++)
fscanf(fin,"%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f",
&td[i][0],&td[i][1],&td[i][2],&td[i][3],&td[i][4],&td[i][5],&td[i][6],&td[i][7],&td[i][8],&td[i][9],&td[i][10],&td[i][11],&td[i][12],&td[i][13],&td[i][14],&td[i][15],&td[i][16],&td[i][17],&td[i][18],&td[i][19],&td[i][20],&td[i][21],&td[i][22],&td[i][23],&td[i][24],&td[i][25],&td[i][26],&td[i][27],&td[i][28],&td[i][29],&td[i][30],&td[i][31],&td[i][32],&td[i][33],&td[i][34],&td[i][35],&td[i][36],&td[i][37],&td[i][38],&td[i][39],&td[i][40],&td[i][41],&td[i][42],&td[i][43],&td[i][44],&td[i][45],&td[i][46],&td[i][47],&td[i][48],&td[i][49],&td[i][50],&td[i][51],&td[i][52],&td[i][53],&td[i][54],&td[i][55],&td[i][56],&td[i][57],&td[i][58],&td[i][59],&td[i][60]);
fclose(fin);
int fnCount = 0;
int fpCount = 0;
for (int i=0; i < test_sample_count; i++)
{
for (int j=0; j < 60; j++) {
sample.data.fl[j] = td[i][j];
}
float actual = td[i][60];
ann.predict(&sample, &predout);
float predicted = predout.data.fl[0];
if (actual == 1.0f && predicted < 0.0f)
{
fnCount++;
std::cout << "BOOM! ";
}
else if (actual == -1.0f && predicted > 0.0f)
{
fpCount++;
}
printf("predicted: %f, actual: %f\n", predicted, actual);
}
std::cout << "False Negative %: " << ((float)fnCount / test_sample_count)*100 << "%\n";
std::cout << "False Positive %: " << ((float)fpCount / test_sample_count)*100 << "%\n";
std::cout << "Total Misses: " << ((float)(fpCount+fnCount) / test_sample_count)*100 << "%\n";
}
// Predict the output with the trained ANN given the two inputs.
void Predict(float data0, float data1, float data2, float data3, float data4, float data5)
{
float _sample[6];
CvMat sample = cvMat(1, 6, CV_32FC1, _sample);
float _predout[1];
CvMat predout = cvMat(1, 1, CV_32FC1, _predout);
sample.data.fl[0] = data0;
sample.data.fl[1] = data1;
sample.data.fl[2] = data2;
sample.data.fl[3] = data3;
sample.data.fl[4] = data4;
sample.data.fl[5] = data5;
machineBrain.predict(&sample, &predout);
printf("%f \n",predout.data.fl[0]);
}
void Training::testModel(string testPath,CvANN_MLP &neural_network){
int success(0),fail(0);
loadDataSet(testPath, testSet, testClassification, NB_TEST_SAMPLES);
cout<<"Test set loaded"<<endl;
cv::Mat classificationResult(1, CLASSES, CV_32F);
Mat testSample;
for(int i=0; i < NB_TEST_SAMPLES;i++){
testSample = testSet.row(i);
//predict
neural_network.predict(testSample, classificationResult);
int maxIndex = 0;
float value = 0.0f;
float maxValue = classificationResult.at<float>(0,0);
for(int j=1; j<CLASSES;j++){
value=classificationResult.at<float>(0,j);
if(value>maxValue){
maxValue = value;
maxIndex = j;
}
}
if(testClassification.at<float>(i,maxIndex)!=1.0f)
fail++;
else
success++;
}
cout<<"Successfully classified : "<<success<<endl;
cout<<"Wrongly classified ! "<<fail<<endl;
cout<<"Succes % : "<<success * 100 / NB_TEST_SAMPLES<<endl;
}
void saveModel(int _predictsize, int _neurons)
{
FileStorage fs;
fs.open("train/ann_data.xml", FileStorage::READ);
Mat TrainingData;
Mat Classes;
string training;
if(1)
{
stringstream ss(stringstream::in | stringstream::out);
ss << "TrainingDataF" << _predictsize;
training = ss.str();
}
fs[training] >> TrainingData;
fs["classes"] >> Classes;
//train the Ann
cout << "Begin to saveModelChar predictSize:" << _predictsize
<< " neurons:" << _neurons << endl;
double start = cv::getTickCount();
annTrain(TrainingData, Classes, _neurons);
double end = cv::getTickCount();
cout << "GetTickCount:" << (end-start)/1000 << endl;
cout << "End the saveModelChar" << endl;
string model_name = "train/ann.xml";
//if(1)
//{
// stringstream ss(stringstream::in | stringstream::out);
// ss << "ann_prd" << _predictsize << "_neu"<< _neurons << ".xml";
// model_name = ss.str();
//}
FileStorage fsTo(model_name, cv::FileStorage::WRITE);
ann.write(*fsTo, "ann");
}
//---------------------------------------------------------
bool COpenCV_NNet::On_Execute(void)
{
//-------------------------------------------------
bool b_updateWeights, b_noInputScale, b_noOutputScale, b_NoData;
int i_matType, i_layers, i_maxIter, i_neurons, i_areasClassId, i_trainFeatTotalCount, *i_outputFeatureIdxs, i_outputFeatureCount, i_Grid, x, y, i_evalOut, i_winner;
double d_alpha, d_beta, d_eps;
DATA_TYPE e_dataType;
TRAINING_METHOD e_trainMet;
ACTIVATION_FUNCTION e_actFunc;
CSG_Table *t_Weights, *t_Indices, *t_TrainInput, *t_EvalInput, *t_EvalOutput;
CSG_Parameter_Grid_List *gl_TrainInputs;
CSG_Grid *g_EvalOutput, *g_EvalOutputCert;
CSG_Shapes *s_TrainInputAreas;
CSG_Parameters *p_TrainFeatures;
TSG_Point p;
CvMat *mat_Weights, *mat_Indices, **mat_data, *mat_neuralLayers, mat_layerSizesSub, *mat_EvalInput, *mat_EvalOutput; // todo: mat_indices to respect input indices, mat_weights for initialization
CvANN_MLP_TrainParams tp_trainParams;
CvANN_MLP model;
b_updateWeights = Parameters("UPDATE_WEIGHTS" )->asBool();
b_noInputScale = Parameters("NO_INPUT_SCALE" )->asBool();
b_noOutputScale = Parameters("NO_OUTPUT_SCALE" )->asBool();
i_layers = Parameters("NNET_LAYER" )->asInt();
i_neurons = Parameters("NNET_NEURONS" )->asInt();
i_maxIter = Parameters("MAX_ITER" )->asInt();
i_areasClassId = Parameters("TRAIN_INPUT_AREAS_CLASS_FIELD" )->asInt();
e_dataType = (DATA_TYPE)Parameters("DATA_TYPE" )->asInt();
e_trainMet = (TRAINING_METHOD)Parameters("TRAINING_METHOD" )->asInt();
e_actFunc = (ACTIVATION_FUNCTION)Parameters("ACTIVATION_FUNCTION" )->asInt();
d_alpha = Parameters("ALPHA" )->asDouble();
d_beta = Parameters("BETA" )->asDouble();
d_eps = Parameters("EPSILON" )->asDouble();
t_Weights = Parameters("WEIGHTS" )->asTable();
t_Indices = Parameters("INDICES" )->asTable();
t_TrainInput = Parameters("TRAIN_INPUT_TABLE" )->asTable();
t_EvalInput = Parameters("EVAL_INPUT_TABLE" )->asTable();
t_EvalOutput = Parameters("EVAL_OUTPUT_TABLE" )->asTable();
p_TrainFeatures = Parameters("TRAIN_FEATURES_TABLE" )->asParameters();
gl_TrainInputs = Parameters("TRAIN_INPUT_GRIDS" )->asGridList();
g_EvalOutput = Parameters("EVAL_OUTPUT_GRID_CLASSES" )->asGrid();
g_EvalOutputCert = Parameters("EVAL_OUTPUT_GRID_CERTAINTY" )->asGrid();
s_TrainInputAreas = Parameters("TRAIN_INPUT_AREAS" )->asShapes();
// Fixed matrix type (TODO: Analyze what to do for other types of data (i.e. images))
i_matType = CV_32FC1;
//-------------------------------------------------
if (e_dataType == TABLE)
{
// We are working with TABLE data
if( t_TrainInput->Get_Count() == 0 || p_TrainFeatures->Get_Count() == 0 )
{
Error_Set(_TL("Select an input table and at least one output feature!"));
return( false );
}
// Count the total number of available features
i_trainFeatTotalCount = t_TrainInput->Get_Field_Count();
// Count the number of selected output features
i_outputFeatureIdxs = (int *)SG_Calloc(i_trainFeatTotalCount, sizeof(int));
i_outputFeatureCount = 0;
for(int i=0; i<p_TrainFeatures->Get_Count(); i++)
{
if( p_TrainFeatures->Get_Parameter(i)->asBool() )
{
i_outputFeatureIdxs[i_outputFeatureCount++] = CSG_String(p_TrainFeatures->Get_Parameter(i)->Get_Identifier()).asInt();
}
}
// Update the number of training features
i_trainFeatTotalCount = i_trainFeatTotalCount-i_outputFeatureCount;
if( i_outputFeatureCount <= 0 )
{
Error_Set(_TL("Select at least one output feature!"));
return( false );
}
// Now convert the input and output training data into a OpenCV matrix objects
mat_data = GetTrainAndOutputMatrix(t_TrainInput, i_matType, i_outputFeatureIdxs, i_outputFeatureCount);
}
else
{
// TODO: Add some grid validation logic
i_trainFeatTotalCount = gl_TrainInputs->Get_Count();
i_outputFeatureCount = s_TrainInputAreas->Get_Count();
// Convert the data from the grid into the matrix from
mat_data = GetTrainAndOutputMatrix(gl_TrainInputs, i_matType, s_TrainInputAreas, i_areasClassId, g_EvalOutput, g_EvalOutputCert);
}
//-------------------------------------------------
// Add two additional layer to the network topology (0-th layer for input and the last as the output)
i_layers = i_layers + 2;
mat_neuralLayers = cvCreateMat(i_layers, 1, CV_32SC1);
cvGetRows(mat_neuralLayers, &mat_layerSizesSub, 0, i_layers);
//Setting the number of neurons on each layer
for (int i = 0; i < i_layers; i++)
{
if (i == 0)
{
// The first layer needs the same size (number of nerons) as the number of columns in the training data
cvSet1D(&mat_layerSizesSub, i, cvScalar(i_trainFeatTotalCount));
}
else if (i == i_layers-1)
{
// The last layer needs the same size (number of neurons) as the number of output columns
cvSet1D(&mat_layerSizesSub, i, cvScalar(i_outputFeatureCount));
}
else
{
// On every other layer set the layer size selected by the user
cvSet1D(&mat_layerSizesSub, i, cvScalar(i_neurons));
}
}
//-------------------------------------------------
// Create the training params object
tp_trainParams = CvANN_MLP_TrainParams();
tp_trainParams.term_crit = cvTermCriteria(CV_TERMCRIT_ITER + CV_TERMCRIT_EPS, i_maxIter, d_eps);
// Check which training method was selected and set corresponding params
if(e_trainMet == RPROP)
{
// Set all RPROP specific params
tp_trainParams.train_method = CvANN_MLP_TrainParams::RPROP;
tp_trainParams.rp_dw0 = Parameters("RP_DW0" )->asDouble();
tp_trainParams.rp_dw_plus = Parameters("RP_DW_PLUS" )->asDouble();
tp_trainParams.rp_dw_minus = Parameters("RP_DW_MINUS" )->asDouble();
tp_trainParams.rp_dw_min = Parameters("RP_DW_MIN" )->asDouble();
tp_trainParams.rp_dw_max = Parameters("RP_DW_MAX" )->asDouble();
}
else
{
// Set all BPROP specific params
tp_trainParams.train_method = CvANN_MLP_TrainParams::BACKPROP;
tp_trainParams.bp_dw_scale = Parameters("BP_DW_SCALE" )->asDouble();
tp_trainParams.bp_moment_scale = Parameters("BP_MOMENT_SCALE" )->asInt();
}
//-------------------------------------------------
// Create the model (depending on the activation function)
if(e_actFunc == SIGMOID)
{
model.create(mat_neuralLayers);
}
else
{
model.create(mat_neuralLayers, CvANN_MLP::GAUSSIAN, d_alpha, d_beta);
}
//-------------------------------------------------
// Now train the network
// TODO: Integrate init weights and indicies for record selection
// mat_Weights = GetMatrix(t_Weights, i_matType);
// mat_Indices = GetMatrix(t_Indices, i_matType);
//model.train(mat_TrainInput, mat_TrainOutput, NULL, NULL, tp_trainParams);
model.train(mat_data[0], mat_data[1], NULL, NULL, tp_trainParams);
//-------------------------------------------------
// Predict data
if (e_dataType == TABLE)
{
// Get the eavaluation/test matrix from the eval table
mat_EvalInput = GetEvalMatrix(t_EvalInput, i_matType);
}
else
{
// Train and eval data overlap in grid mode
mat_EvalInput = GetEvalMatrix(gl_TrainInputs, i_matType);
}
// Prepare output matrix
mat_EvalOutput = cvCreateMat(mat_EvalInput->rows, i_outputFeatureCount, i_matType);
// Start prediction
model.predict(mat_EvalInput, mat_EvalOutput);
Message_Add(_TL("Successfully trained the network and predicted the values. Here comes the output."));
//-------------------------------------------------
// Save and print results
if (e_dataType == TABLE)
{
// DEBUG -> Save results to output table and print results
for (int i = 0; i < i_outputFeatureCount; i++)
{
t_EvalOutput->Add_Field(CSG_String(t_TrainInput->Get_Field_Name(i_outputFeatureIdxs[i])), SG_DATATYPE_Float);
}
for (int i = 0; i < mat_EvalOutput->rows; i++)
{
CSG_Table_Record* tr_record = t_EvalOutput->Add_Record();
for (int j = 0; j < i_outputFeatureCount; j++)
{
float f_targetValue = mat_EvalOutput->data.fl[i*i_outputFeatureCount+j];
tr_record->Set_Value(j, f_targetValue);
}
}
}
else
{
// Fill the output table output
for (int i = 0; i < i_outputFeatureCount; i++)
{
// TODO: Get the class name
t_EvalOutput->Add_Field(CSG_String::Format(SG_T("CLASS_%d"), i), SG_DATATYPE_Float);
}
for (int i = 0; i < mat_EvalOutput->rows; i++)
{
CSG_Table_Record* tr_record = t_EvalOutput->Add_Record();
for (int j = 0; j < i_outputFeatureCount; j++)
{
float f_targetValue = mat_EvalOutput->data.fl[i*i_outputFeatureCount+j];
tr_record->Set_Value(j, f_targetValue);
}
}
i_evalOut = 0;
// Fill the output grid
for(y=0, p.y=Get_YMin(); y<Get_NY() && Set_Progress(y); y++, p.y+=Get_Cellsize())
{
for(x=0, p.x=Get_XMin(); x<Get_NX(); x++, p.x+=Get_Cellsize())
{
for(i_Grid=0, b_NoData=false; i_Grid<gl_TrainInputs->Get_Count() && !b_NoData; i_Grid++)
{
// If there is one grid that has no data in this point p, then set the no data flag
if( gl_TrainInputs->asGrid(i_Grid)->is_NoData(x, y) )
{
b_NoData = true;
}
}
if (!b_NoData)
{
// We have data in all grids, so this is a point that was predicted
// Get the winner class for this point and set it to the output grid
float f_targetValue = 0;
for (int j = 0; j < i_outputFeatureCount; j++)
{
if (mat_EvalOutput->data.fl[i_evalOut*i_outputFeatureCount+j] > f_targetValue)
{
// The current value is higher than the last one, so lets memorize the current class
f_targetValue = mat_EvalOutput->data.fl[i_evalOut*i_outputFeatureCount+j];
i_winner = j;
}
}
// Now finally set the values to the grids
g_EvalOutput->Set_Value(x, y, i_winner);
g_EvalOutputCert->Set_Value(x, y, f_targetValue);
i_evalOut++;
}
}
}
}
return( true );
}
// Read the training data and train the network.
void trainMachine()
{ int i; //The number of training samples.
int train_sample_count;
//The training data matrix.
//Note that we are limiting the number of training data samples to 1000 here.
//The data sample consists of two inputs and an output. That's why 3.
float td[10000][3];
//Read the training file
/*
A sample file contents(say we are training the network for generating
the mean given two numbers) would be:
5
12 16 14
10 5 7.5
8 10 9
5 4 4.5
12 6 9
*/
FILE *fin;
fin = fopen("train.txt", "r");
//Get the number of samples.
fscanf(fin, "%d", &train_sample_count);
printf("Found training file with %d samples...\n", train_sample_count);
//Create the matrices
//Input data samples. Matrix of order (train_sample_count x 2)
CvMat* trainData = cvCreateMat(train_sample_count, 2, CV_32FC1);
//Output data samples. Matrix of order (train_sample_count x 1)
CvMat* trainClasses = cvCreateMat(train_sample_count, 1, CV_32FC1);
//The weight of each training data sample. We'll later set all to equal weights.
CvMat* sampleWts = cvCreateMat(train_sample_count, 1, CV_32FC1);
//The matrix representation of our ANN. We'll have four layers.
CvMat* neuralLayers = cvCreateMat(4, 1, CV_32SC1);
CvMat trainData1, trainClasses1, neuralLayers1, sampleWts1;
cvGetRows(trainData, &trainData1, 0, train_sample_count);
cvGetRows(trainClasses, &trainClasses1, 0, train_sample_count);
cvGetRows(trainClasses, &trainClasses1, 0, train_sample_count);
cvGetRows(sampleWts, &sampleWts1, 0, train_sample_count);
cvGetRows(neuralLayers, &neuralLayers1, 0, 4);
//Setting the number of neurons on each layer of the ANN
/*
We have in Layer 1: 2 neurons (2 inputs)
Layer 2: 3 neurons (hidden layer)
Layer 3: 3 neurons (hidden layer)
Layer 4: 1 neurons (1 output)
*/
cvSet1D(&neuralLayers1, 0, cvScalar(2));
cvSet1D(&neuralLayers1, 1, cvScalar(3));
cvSet1D(&neuralLayers1, 2, cvScalar(3));
cvSet1D(&neuralLayers1, 3, cvScalar(1));
//Read and populate the samples.
for (i=0;i<train_sample_count;i++)
fscanf(fin,"%f %f %f",&td[i][0],&td[i][1],&td[i][2]);
fclose(fin);
//Assemble the ML training data.
for (i=0; i<train_sample_count; i++)
{
//Input 1
cvSetReal2D(&trainData1, i, 0, td[i][0]);
//Input 2
cvSetReal2D(&trainData1, i, 1, td[i][1]);
//Output
cvSet1D(&trainClasses1, i, cvScalar(td[i][2]));
//Weight (setting everything to 1)
cvSet1D(&sampleWts1, i, cvScalar(1));
}
//Create our ANN.
machineBrain.create(neuralLayers);//sigmoid 0 0(激活函数的两个参数)
//Train it with our data.
machineBrain.train(
trainData,//输入
trainClasses,//输出
sampleWts,//输入项的权值
0,
CvANN_MLP_TrainParams(
cvTermCriteria(
CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,///类型 CV_TERMCRIT_ITER 和CV_TERMCRIT_EPS二值之一，或者二者的组合
10000000,//最大迭代次数
0.00000001//结果的精确性 两次迭代间权值变化量
),
CvANN_MLP_TrainParams::BACKPROP,//BP算法
0.01,//几个可显式调整的参数 学习速率 阿尔法
0.05 //惯性参数
)
);
}
// Read the training data and train the network.
void trainMachine()
{
int i;
//The number of training samples.
int train_sample_count = 130;
//The training data matrix.
float td[130][61];
//Read the training file
FILE *fin;
fin = fopen("data/sonar_train.csv", "r");
//Create the matrices
//Input data samples. Matrix of order (train_sample_count x 60)
CvMat* trainData = cvCreateMat(train_sample_count, 60, CV_32FC1);
//Output data samples. Matrix of order (train_sample_count x 1)
CvMat* trainClasses = cvCreateMat(train_sample_count, 1, CV_32FC1);
//The weight of each training data sample. We'll later set all to equal weights.
CvMat* sampleWts = cvCreateMat(train_sample_count, 1, CV_32FC1);
//The matrix representation of our ANN. We'll have four layers.
CvMat* neuralLayers = cvCreateMat(4, 1, CV_32SC1);
//Setting the number of neurons on each layer of the ANN
/*
We have in Layer 1: 60 neurons (60 inputs)
Layer 2: 150 neurons (hidden layer)
Layer 3: 225 neurons (hidden layer)
Layer 4: 1 neurons (1 output)
*/
cvSet1D(neuralLayers, 0, cvScalar(60));
cvSet1D(neuralLayers, 1, cvScalar(150));
cvSet1D(neuralLayers, 2, cvScalar(225));
cvSet1D(neuralLayers, 3, cvScalar(1));
//Read and populate the samples.
for (i=0;i<train_sample_count;i++)
fscanf(fin,"%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f",
&td[i][0],&td[i][1],&td[i][2],&td[i][3],&td[i][4],&td[i][5],&td[i][6],&td[i][7],&td[i][8],&td[i][9],&td[i][10],&td[i][11],&td[i][12],&td[i][13],&td[i][14],&td[i][15],&td[i][16],&td[i][17],&td[i][18],&td[i][19],&td[i][20],&td[i][21],&td[i][22],&td[i][23],&td[i][24],&td[i][25],&td[i][26],&td[i][27],&td[i][28],&td[i][29],&td[i][30],&td[i][31],&td[i][32],&td[i][33],&td[i][34],&td[i][35],&td[i][36],&td[i][37],&td[i][38],&td[i][39],&td[i][40],&td[i][41],&td[i][42],&td[i][43],&td[i][44],&td[i][45],&td[i][46],&td[i][47],&td[i][48],&td[i][49],&td[i][50],&td[i][51],&td[i][52],&td[i][53],&td[i][54],&td[i][55],&td[i][56],&td[i][57],&td[i][58],&td[i][59],&td[i][60]);
//we are done reading the file, so close it
fclose(fin);
//Assemble the ML training data.
for (i=0; i<train_sample_count; i++)
{
//inputs
for (int j = 0; j < 60; j++)
cvSetReal2D(trainData, i, j, td[i][j]);
//Output
cvSet1D(trainClasses, i, cvScalar(td[i][60]));
//Weight (setting everything to 1)
cvSet1D(sampleWts, i, cvScalar(1));
}
//Create our ANN.
ann.create(neuralLayers);
cout << "training...\n";
//Train it with our data.
ann.train(
trainData,
trainClasses,
sampleWts,
0,
CvANN_MLP_TrainParams(
cvTermCriteria(
CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,
100000,
0.000001),
CvANN_MLP_TrainParams::BACKPROP,
0.01,
0.05));
}
int main()
{
const int sampleTypeCount = 7; //共有几种字体
const int sampleCount = 50; //每种字体的样本数
const int sampleAllCount = sampleCount*sampleTypeCount;
const int featureCount = 256; //特征维数
CvANN_MLP bp;// = CvANN_MLP(layerSizes,CvANN_MLP::SIGMOID_SYM,1,1);
string str_dir[sampleTypeCount];
str_dir[0] = "A水滴渍";
str_dir[1] = "B水纹";
str_dir[2] = "C指纹";
str_dir[3] = "D釉面凹凸";
str_dir[4] = "X凹点";
str_dir[5] = "Y杂质";
str_dir[6] = "Z划痕";
float trainingData[sampleAllCount][featureCount] = { 0 };
float outputData[sampleAllCount][sampleTypeCount] = { 0 };
int itemIndex = 0;
for (int index = 0; index < 7; index++)
{
for (int i = 1; i <= 50; i++)
{
outputData[itemIndex][index] = 1;
cout << str_dir[index] << "_" << i << endl;
stringstream ss;
char num[4];
sprintf(num, "%03d", i);
ss << "特征样本库\\" << str_dir[index] << "\\" << num << ".jpg";
string path;
ss >> path;
//读取灰度图像以便计算灰度直方图
cv::Mat f = cv::imread(path, 0);
cv::Mat grayHist;
// 设定bin数目，也就是灰度级别，这里选择的是0-255灰度
int histSize = 256;
//cv::equalizeHist(f, f);
cv::normalize(f, f, histSize, 0, cv::NORM_MINMAX);
//cv::bitwise_xor(f, cv::Scalar(255), f);//反相
FeatureMaker::GetGrayHist(f, grayHist, histSize);
for (int j = 0; j < 256; j++)
{
trainingData[itemIndex][j] = grayHist.ptr<float>(j)[0];
}
itemIndex++;
}
}
//创建一个网络
cv::Mat layerSizes = (cv::Mat_<int>(1, 3) << featureCount, 25, sampleTypeCount);//创建一个featureCount输入 IDC_EDIT_YinCangCount隐藏 sampleTypeCount输出的三层网络
CvANN_MLP_TrainParams param;
param.term_crit = cvTermCriteria(CV_TERMCRIT_ITER + CV_TERMCRIT_EPS, 50000, 0.002);
param.train_method = CvANN_MLP_TrainParams::BACKPROP;
param.bp_dw_scale = 0.01;//权值更新率
param.bp_moment_scale = 0.03;//权值更新冲量
cv::Mat inputs(sampleAllCount, featureCount, CV_32FC1, trainingData);//样品总数，特征维数，储存的数据类型
cv::Mat outputs(sampleAllCount, sampleTypeCount, CV_32FC1, outputData);
bp.create(layerSizes, CvANN_MLP::SIGMOID_SYM);
bp.train(inputs, outputs, cv::Mat(), cv::Mat(), param);
bp.save("ANN_mlp.xml");
itemIndex = 0;
int zhengque = 0;
for (int index = 0; index < 7; index++)
{
for (int i = 1; i <= 50; i++)
{
cv::Mat sampleMat(1, featureCount, CV_32FC1, trainingData[itemIndex]);//样品总数，特征维数，储存的数据类型
cv::Mat nearest(1, sampleTypeCount, CV_32FC1, cv::Scalar(0));
bp.predict(sampleMat, nearest);
float possibility = -1;
int outindex = 0;
for (int i = 0; i < nearest.size().width; i++){
float x = nearest.at<float>(0, i);
if (x>possibility){
possibility = x;
outindex = i;
}
}
if (outindex == index)
zhengque++;
cout << str_dir[index] << "_" << i << "：" << outindex << "->" << possibility << "->" << str_dir[outindex] << endl;
itemIndex++;
}
}
cout << "正确率" << ((double)zhengque / (double)sampleAllCount);
return 0;
}
// Train
void *train(Mat &trainData, Mat &response)
{
if(conf.classifier == "SVM")
{
std::cout<<"--->SVM Training ..."<<std::endl;
CvSVMParams params;
params.kernel_type = CvSVM::LINEAR;
params.svm_type = CvSVM::C_SVC;
params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,1000,1e-6);
CvSVM *classifier = new CvSVM;
classifier->train(trainData,response,Mat(),Mat(),params);
int c = classifier->get_support_vector_count();
printf(" %d support vectors founded.\n",c);
return (void *)classifier;
}
else if(conf.classifier == "BP")
{
std::cout<<"--->BP Training ..."<<std::endl;
// Data transforming.
int numClass = (int)conf.classes.size();
cv::Mat labelMat = Mat::zeros(response.rows, numClass, CV_32F);
for(int i = 0;i<response.rows;i++)
{
int k = response.ptr<int>()[i];
labelMat.ptr<float>(i)[k-1] = 1.0;
}
// Set up BP network parameters
CvANN_MLP_TrainParams params;
params.term_crit = cvTermCriteria( CV_TERMCRIT_ITER + CV_TERMCRIT_EPS, 1000, 0.01 ),
params.train_method = CvANN_MLP_TrainParams::BACKPROP;
params.bp_dw_scale = 0.1;
params.bp_moment_scale = 0.1;
CvANN_MLP *classifier = new CvANN_MLP;
// Set up the topology structure of BP network.
int inputNodeNum = trainData.cols;
float ratio = 2;
int hiddenNodeNum = static_cast<float>(inputNodeNum) * ratio;
int outputNodeNum = numClass;
int maxHiddenLayersNum = 1;
// It could be better! esp. Output node amount
printf(" %d input nodes, %d output nodes.\n",inputNodeNum, outputNodeNum);
Mat layerSizes;
layerSizes.push_back(inputNodeNum);
printf(" Hidden layers nodes' amount:");
layerSizes.push_back(hiddenNodeNum);
for(int i = 0 ;i<maxHiddenLayersNum;i++)
{
if(hiddenNodeNum > outputNodeNum * ratio + 1 )
{
hiddenNodeNum = static_cast<float>(hiddenNodeNum) / ratio;
layerSizes.push_back(hiddenNodeNum);
printf(" %d ",hiddenNodeNum);
}
else
break;
}
printf("\n");
layerSizes.push_back(outputNodeNum);
classifier->create(layerSizes, CvANN_MLP::SIGMOID_SYM);
classifier->train(trainData, labelMat, Mat(),Mat(), params);
return (void *)classifier;
}
else{
std::cout<<"--->Error: wrong classifier."<<std::endl;
return NULL;
}
}
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;
break;
}
}
int width = rx - lx;
int height = by - ty;
// Cropping image
Mat crop(originalImage, Rect(lx, ty, width, height));
// Cloning image
Mat splittedImage;
splittedImage = crop.clone();
// imwrite("/home/andersson/Escritorio/Temporales/splitted.png",
// splittedImage);
// Processing image
Mat output;
cv::GaussianBlur(splittedImage, output, cv::Size(5, 5), 0);
cv::threshold(output, output, 50, ATTRIBUTES - 1, 0);
cv::Mat scaledDownImage(ROWCOLUMN, ROWCOLUMN, CV_8U, cv::Scalar(0));
scaleDownImage(output, scaledDownImage);
int pixelValueArray[ATTRIBUTES];
cv::Mat testSet(1, ATTRIBUTES, CV_32F);
// Mat to Pixel Value Array
convertToPixelValueArray(scaledDownImage, pixelValueArray);
// Pixel Value Array to Mat CV_32F
cv::Mat classificationResult(1, CLASSES, CV_32F);
for (int i = 0; i <= ATTRIBUTES; i++) {
testSet.at<float> (0, i) = pixelValueArray[i];
}
// Predicting the number
nnetwork.predict(testSet, classificationResult);
// Selecting the correct response
int maxIndex = 0;
float value = 0.0f;
float maxValue = classificationResult.at<float> (0, 0);
for (int index = 1; index < CLASSES; index++) {
value = classificationResult.at<float> (0, index);
if (value > maxValue) {
maxValue = value;
maxIndex = index;
}
}
printf("Class result: %d\n", maxIndex);
numbers = numbers + convertIntToString(maxIndex);
Scalar colorRect = Scalar(0.0, 0.0, 255.0);
rectangle(clon, Point(lx, ty), Point(rx, by), colorRect, 1, 8, 0);
namedWindow("Clon", CV_WINDOW_NORMAL);
imshow("Clon", clon);
waitKey(0);
namedWindow("Test", CV_WINDOW_NORMAL);
imshow("Test", splittedImage);
waitKey(0);
}
imwrite("/home/andersson/Escritorio/Temporales/clon.png", clon);
return numbers;
}
int CTrain::excuteIndefine()
{
char path[256] = {0};
int i_c = 4;
sprintf_s(path,"./sample/%d (%d).bmp",i_c,5);
IplImage *pSourec = cvLoadImage(path);
if(nullptr == pSourec){
cout<<"features:"<<path<<" is not vailed"<<endl;
return -1;
}
cout<<"start idenfie:" << path<<endl;
#ifdef Debug
cvNamedWindow("source");
cvShowImage("source", pSourec);
#endif
//out
IplImage *pOut = nullptr;
//opencv 灰度化
IplImage *gray = cvCreateImage(cvGetSize(pSourec), IPL_DEPTH_8U, 1);
cvCvtColor(pSourec, gray, CV_BGR2GRAY);
//opencv 二值化
cvThreshold(gray, gray, 175, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);
//去边框
cvRectangle(gray, cvPoint(0, 0), cvPoint(gray->width-1, gray->height-1), CV_RGB(255, 255, 255));
#ifdef Debug
cvNamedWindow("cvRectangle");
cvShowImage("cvRectangle", gray);
#endif
//去噪
//IplConvKernel *se = cvCreateStructuringElementEx(2, 2, 1, 1, CV_SHAPE_CROSS);
//cvDilate(gray, gray, se);
#ifdef Debug
//cvNamedWindow("IplConvKernel");
//cvShowImage("IplConvKernel", gray);
#endif
//计算连通域contoure
cvXorS(gray, cvScalarAll(255), gray, 0);
#ifdef Debug
cvNamedWindow("cvXorS");
cvShowImage("cvXorS", gray);
#endif
CvMemStorage *storage = cvCreateMemStorage();
CvSeq *contour = nullptr;
cvFindContours(gray, storage, &contour, sizeof(CvContour),CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);
//分析连通域
CvSeq *p = contour;
while(p){
CvRect rect = cvBoundingRect(p, 0);
if(rect.height < 10){//图像文字需要15像素高度
p = p->h_next;
continue;
}
//绘制该连通到character
cvZero(gray);
IplImage *character = cvCreateImage(cvSize(rect.width, rect.height), IPL_DEPTH_8U, 1);
cvZero(character);
cvDrawContours(character, p, CV_RGB(255, 255, 255), CV_RGB(0, 0, 0), -1, -1, 8, cvPoint(-rect.x, -rect.y));
#ifdef Debug
cvNamedWindow("character");
cvShowImage("character", character);
#endif
// 归一化
pOut = cvCreateImage(cvSize(16, 16), IPL_DEPTH_8U, 1);
cvResize(character, pOut, CV_INTER_AREA);
#ifdef Debug
cvNamedWindow("cvResize");
cvShowImage("cvResize", pOut);
#endif
// 修正
cvThreshold(pOut, pOut, 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);
#ifdef Debug
cvNamedWindow("show");
cvShowImage("show", pOut);
#endif
//cvReleaseImage(&character);
p = p->h_next;
}
// 计算输入向量
float input[256];
for(int i=0; i<256; i++)
input[i] = (pOut->imageData[i]==-1);
// 识别
CvANN_MLP mlp;
mlp.load( "mpl.xml" );
CvMat* output = cvCreateMat( 1, 36, CV_32F );
CvMat inputMat = cvMat( 1, 256, CV_32F, input);
mlp.predict( &inputMat, output );
CvPoint max_loc = {0,0};
cvMinMaxLoc( output, NULL, NULL, NULL, &max_loc, NULL );
int best = max_loc.x;// 识别结果
char c = (char)( best<10 ? '0'+best : 'A'+best-10 );
cout<<"indefine="<<c<<"<=====>pratics="<<i_c<<endl;
cvReleaseMat( &output );
cvReleaseImage(&gray);
cvReleaseImage(&pOut);
cvReleaseImage(&pSourec);
cvReleaseMemStorage(&storage);
//cvReleaseStructuringElement(&se);
#ifdef Debug
cvWaitKey(0);
cvDestroyAllWindows();
#endif
return 0;
}
int cv_ann()
{
//Setup the BPNetwork
CvANN_MLP bp;
// Set up BPNetwork's parameters
CvANN_MLP_TrainParams params;
params.train_method=CvANN_MLP_TrainParams::BACKPROP; //(Back Propagation,BP)反向传播算法
params.bp_dw_scale=0.1;
params.bp_moment_scale=0.1;
// Set up training data
float labels[10][2] = {{0.9,0.1},{0.1,0.9},{0.9,0.1},{0.1,0.9},{0.9,0.1},{0.9,0.1},{0.1,0.9},{0.1,0.9},{0.9,0.1},{0.9,0.1}};
//这里对于样本标记为0.1和0.9而非0和1，主要是考虑到sigmoid函数的输出为一般为0和1之间的数，只有在输入趋近于-∞和+∞才逐渐趋近于0和1，而不可能达到。
Mat labelsMat(10, 2, CV_32FC1, labels);
float trainingData[10][2] = { {11,12},{111,112}, {21,22}, {211,212},{51,32}, {71,42}, {441,412},{311,312}, {41,62}, {81,52} };
Mat trainingDataMat(10, 2, CV_32FC1, trainingData);
Mat layerSizes=(Mat_<int>(1,5) << 2, 2, 2, 2, 2); //5层：输入层，3层隐藏层和输出层，每层均为两个perceptron
bp.create(layerSizes,CvANN_MLP::SIGMOID_SYM);//CvANN_MLP::SIGMOID_SYM ,选用sigmoid作为激励函数
bp.train(trainingDataMat, labelsMat, Mat(),Mat(), params); //训练
// Data for visual representation
int width = 512, height = 512;
Mat image = Mat::zeros(height, width, CV_8UC3);
Vec3b green(0,255,0), blue (255,0,0);
// Show the decision regions
for (int i = 0; i < image.rows; ++i)
{
for (int j = 0; j < image.cols; ++j)
{
Mat sampleMat = (Mat_<float>(1,2) << i,j);
Mat responseMat;
bp.predict(sampleMat,responseMat);
float* p=responseMat.ptr<float>(0);
//
if (p[0] > p[1])
{
image.at<Vec3b>(j, i) = green;
}
else
{
image.at<Vec3b>(j, i) = blue;
}
}
}
// Show the training data
int thickness = -1;
int lineType = 8;
circle( image, Point(111, 112), 5, Scalar( 0, 0, 0), thickness, lineType);
circle( image, Point(211, 212), 5, Scalar( 0, 0, 0), thickness, lineType);
circle( image, Point(441, 412), 5, Scalar( 0, 0, 0), thickness, lineType);
circle( image, Point(311, 312), 5, Scalar( 0, 0, 0), thickness, lineType);
circle( image, Point(11, 12), 5, Scalar(255, 255, 255), thickness, lineType);
circle( image, Point(21, 22), 5, Scalar(255, 255, 255), thickness, lineType);
circle( image, Point(51, 32), 5, Scalar(255, 255, 255), thickness, lineType);
circle( image, Point(71, 42), 5, Scalar(255, 255, 255), thickness, lineType);
circle( image, Point(41, 62), 5, Scalar(255, 255, 255), thickness, lineType);
circle( image, Point(81, 52), 5, Scalar(255, 255, 255), thickness, lineType);
imwrite("result.png", image); // save the image
imshow("BP Simple Example", image); // show it to the user
waitKey(0);
return 0;
}
int CTrain::excuteTrain()
{
// 读入结果responses 特征data
FILE* f = fopen( "batch", "rb" );
fseek(f, 0l, SEEK_END);
long size = ftell(f);
fseek(f, 0l, SEEK_SET);
int count = size/4/(36+256);
CvMat* batch = cvCreateMat( count, 36+256, CV_32F );
fread(batch->data.fl, size-1, 1, f);
CvMat outputs, inputs;
cvGetCols(batch, &outputs, 0, 36);
cvGetCols(batch, &inputs, 36, 36+256);
fclose(f);
// 新建MPL
CvANN_MLP mlp;
int layer_sz[] = { 256, 20, 36 };
CvMat layer_sizes = cvMat( 1, 3, CV_32S, layer_sz );
mlp.create( &layer_sizes );
// 训练
//system( "time" );
mlp.train( &inputs, &outputs, NULL, NULL,
CvANN_MLP_TrainParams(cvTermCriteria(CV_TERMCRIT_ITER,300,0.01), CvANN_MLP_TrainParams::RPROP, 0.01)
);
//system( "time" );
// 存储MPL
mlp.save( "mpl.xml" );
// 测试
int right = 0;
CvMat* output = cvCreateMat( 1, 36, CV_32F );
for(int i=0; i<count; i++)
{
CvMat input;
cvGetRow( &inputs, &input, i );
mlp.predict( &input, output );
CvPoint max_loc = {0,0};
cvMinMaxLoc( output, NULL, NULL, NULL, &max_loc, NULL );
int best = max_loc.x;// 识别结果
int ans = -1;// 实际结果
for(int j=0; j<36; j++)
{
if( outputs.data.fl[i*(outputs.step/4)+j] == 1.0f )
{
ans = j;
break;
}
}
cout<<(char)( best<10 ? '0'+best : 'A'+best-10 );
cout<<(char)( ans<10 ? '0'+ans : 'A'+ans-10 );
if( best==ans )
{
cout<<"+";
right++;
}
//cin.get();
cout<<endl;
}
cvReleaseMat( &output );
cout<<endl<<right<<"/"<<count<<endl;
cvReleaseMat( &batch );
system( "pause" );
return 0;
}
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);
if( value > maxValue )
{
maxValue = value;
maxIndex=index;
}
}
//Now compare the predicted class to the actural class. if the prediction is correct then\
//test_set_classifications[tsample][ maxIndex] should be 1.
//if the classification is wrong, note that.
if (test_results.at<float>(tsample, maxIndex)!=1.0f) {
// if they differ more than floating point error => wrong class
wrong_class++;
//find the actual label 'class_index'
for(int class_index = 0; class_index < alphabetSize; class_index++)
{
if(test_results.at<float>(tsample, class_index)==1.0f)
{
classification_matrix[class_index][maxIndex]++;// A class_index sample was wrongly classified as maxindex.
break;
}
}
} else {
// otherwise correct
correct_class++;
classification_matrix[maxIndex][maxIndex]++;
}
}
//Getting the % of correct and wrong characters
cout << "Number of correct letters: " << correct_class << " / ";
cout << correct_class*100.f/testSamples << "% \n";
cout << "Number of wrong lettesr: " << wrong_class << " / ";
cout << wrong_class*100.f/testSamples << "%\n";
cin.get();
//Writing a 2d matrix that shows what the ANN guessed
for (int i = 0; i < alphabetSize; i++) {
std::cout << "\t" << char(i+'A');
}
std::cout<<"\n\n";
for(int row = 0; row < alphabetSize; row++) {
std::cout << row << "\t";
for(int col = 0; col < alphabetSize; col++) {
std::cout << classification_matrix[row][col]<<"\t";
}
std::cout<<"\n\n";
}
//No need to train
} else {
//read the model from the XML file and create the neural network.
cout << "\nEnter the path to the stored xml file of the Neural Net: ";
string path; getline( cin, path );
CvANN_MLP NeuralNet;
CvFileStorage* storage = cvOpenFileStorage( path.c_str(), 0, CV_STORAGE_READ );
CvFileNode *n = cvGetFileNodeByName(storage,0,"DigitOCR");
NeuralNet.read(storage,n);
cvReleaseFileStorage(&storage);
//reading a single preprocessedfile for predicting.
cout << "\nEnter path to file for testing: ";
getline( cin, path );
Mat data = Mat::zeros(1, attributes,CV_32F); //Zeroed matrix for single test
Mat goal = Mat::zeros(1, alphabetSize, CV_32F); //Zeroed matrix for result
if (readPreprocessed( data, goal, ImageSize, path) == 0){
std::cout << "\nSomething went wrong while reading file." << endl;
cin.get(); return 1;
}
//prediction
Mat prediction = Mat::zeros(1, alphabetSize, CV_32F); //Zeroed matrix for prediction
NeuralNet.predict(data, prediction);
//converting prediction to human readable.
float maxres = 0;
float maxtar = 0;
int numres = 0;
int numtar = 0;
for(int z = 0; z < alphabetSize; z++){
if (maxres <= prediction.at<float>(0, z)){
maxres = prediction.at<float>(0, z);
numres = z;
};
if (maxtar <= goal.at<float>(0, z)){
maxtar = goal.at<float>(0, z);
numtar = z;
}
}
std::cout << "\nPrediction: " << char(numres+'A') << " target is: " << char(numtar+'A');
}
std::cin.get();
return 0;
}
void MainWindow::on_pushButton_test_clicked()
{
QString str = QFileDialog::getExistingDirectory();
QByteArray ba = str.toLocal8Bit();
char *c_str = ba.data();
string slash = "/";
Mat training;
Mat response;
read_num_class_data("train.txt", 4, training, response);
cout<<training.rows<<endl;
cout<<response.rows<<endl;
ofstream output_file;
output_file.open("Ratio.txt");
Mat layers = Mat(3,1,CV_32SC1);
int sz = training.cols ;
layers.row(0) = Scalar(sz);
layers.row(1) = Scalar(16);
layers.row(2) = Scalar(1);
CvANN_MLP mlp;
CvANN_MLP_TrainParams params;
CvTermCriteria criteria;
criteria.max_iter = 1000;
criteria.epsilon = 0.00001f;
criteria.type = CV_TERMCRIT_ITER | CV_TERMCRIT_EPS;
params.train_method = CvANN_MLP_TrainParams::BACKPROP;
params.bp_dw_scale = 0.1f;
params.bp_moment_scale = 0.1f;
params.term_crit = criteria;
mlp.create(layers,CvANN_MLP::SIGMOID_SYM);
int i = mlp.train(training, response, Mat(),Mat(),params); // Train dataset
FileStorage fs("mlp.xml", FileStorage::WRITE); // or xml
mlp.write(*fs, "mlp"); // don't think too much about the deref, it casts to a FileNode
ui->label_training->setText("Training finish");
//mlp.load("mlp.xml","mlp"); //Load ANN weights for each layer
vector<string> img_name;
string output_directory = "output_img/";
img_name = listFile(c_str);
Mat testing(1, 3, CV_32FC1);
Mat predict (1 , 1, CV_32F );
int file_num = 0;
for(int i = 0; i < img_name.size(); i++) //size of the img_name
{
ui->progressBar->setValue(i*100/img_name.size());
string file_name = c_str + slash + img_name[i];
Mat img_test = imread(file_name);
Mat img_test_clone = img_test.clone();
Mat img_thresh, img_thresh_copy, img_HSV, img_gray;
vector<Mat> img_split;
cvtColor(img_test_clone, img_HSV, CV_RGB2HSV);
cvtColor(img_test_clone, img_gray, CV_RGB2GRAY);
split(img_HSV, img_split);
threshold(img_split[0], img_thresh, 75, 255, CV_THRESH_BINARY);
img_thresh_copy = img_thresh.clone();
Mat hole = img_thresh_copy.clone();
floodFill(hole, Point(0,0), Scalar(255));
bitwise_not(hole, hole);
img_thresh_copy = (img_thresh_copy | hole);
Mat element = getStructuringElement(MORPH_RECT, Size(15, 15));
Mat open_result;
morphologyEx(img_thresh, open_result, MORPH_CLOSE, element );
int infected_num = 0;
int total_pixels = 0;
if(img_test.data)
{
file_num++;
for (int m = 0; m < img_test.rows; m++)
{
for (int n = 0; n < img_test.cols; n++)
{
if (img_thresh_copy.at<uchar>(m, n) == 255)
{
total_pixels++;
testing.at<float>(0, 0) = (float)img_test.at<Vec3b>(m, n)[0];
testing.at<float>(0, 1) = (float)img_test.at<Vec3b>(m, n)[1];
testing.at<float>(0, 2) = (float)img_test.at<Vec3b>(m, n)[2];
mlp.predict(testing,predict);
float a = predict.at<float>(0,0);
if (a < 0.4) //0.4
{
img_test.at<Vec3b>(m, n)[0] = 0;
img_test.at<Vec3b>(m, n)[1] = 0;
img_test.at<Vec3b>(m, n)[2] = 255;
infected_num++;
}
}
}
}
float ratio = (float)infected_num / total_pixels * 100;
output_file<<img_name[i]<<" "<<(ratio)<<endl;
string output_file_name = output_directory + img_name[i];
cout<<output_file_name<<endl;
imwrite(output_file_name, img_test);
QImage img_qt = QImage((const unsigned char*)(img_test_clone.data), img_test_clone.cols, img_test_clone.rows, QImage::Format_RGB888);
QImage img_qt_result = QImage((const unsigned char*)(img_test.data), img_test.cols, img_test.rows, QImage::Format_RGB888);
//ui->label_original->setPixmap(QPixmap::fromImage(img_qt.rgbSwapped()));
//ui->label_resulting->setPixmap(QPixmap::fromImage((img_qt_result.rgbSwapped())));
// imshow("Ori", img_thresh_copy);
imshow("split", img_test);
waitKey(0);
QThread::msleep(100);
}
else
{
continue;
}
}
ui->progressBar->setValue(100);
output_file<<endl<<endl<<"Number of processed images: "<<file_num<<endl;
cout<<"Test finished!";
}
static
int build_mlp_classifier( char* data_filename,
char* filename_to_save, char* filename_to_load )
{
const int class_count = 26;
CvMat* data = 0;
CvMat train_data;
CvMat* responses = 0;
CvMat* mlp_response = 0;
int ok = read_num_class_data( data_filename, 16, &data, &responses );
int nsamples_all = 0, ntrain_samples = 0;
int i, j;
double train_hr = 0, test_hr = 0;
CvANN_MLP mlp;
if( !ok )
{
printf( "Could not read the database %s\n", data_filename );
return -1;
}
printf( "The database %s is loaded.\n", data_filename );
nsamples_all = data->rows;
ntrain_samples = (int)(nsamples_all*0.8);
// Create or load MLP classifier
if( filename_to_load )
{
// load classifier from the specified file
mlp.load( filename_to_load );
ntrain_samples = 0;
if( !mlp.get_layer_count() )
{
printf( "Could not read the classifier %s\n", filename_to_load );
return -1;
}
printf( "The classifier %s is loaded.\n", data_filename );
}
else
{
// !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
//
// MLP does not support categorical variables by explicitly.
// So, instead of the output class label, we will use
// a binary vector of <class_count> components for training and,
// therefore, MLP will give us a vector of "probabilities" at the
// prediction stage
//
// !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
CvMat* new_responses = cvCreateMat( ntrain_samples, class_count, CV_32F );
// 1. unroll the responses
printf( "Unrolling the responses...\n");
for( i = 0; i < ntrain_samples; i++ )
{
int cls_label = cvRound(responses->data.fl[i]) - 'A';
float* bit_vec = (float*)(new_responses->data.ptr + i*new_responses->step);
for( j = 0; j < class_count; j++ )
bit_vec[j] = 0.f;
bit_vec[cls_label] = 1.f;
}
cvGetRows( data, &train_data, 0, ntrain_samples );
// 2. train classifier
int layer_sz[] = { data->cols, 100, 100, class_count };
CvMat layer_sizes =
cvMat( 1, (int)(sizeof(layer_sz)/sizeof(layer_sz[0])), CV_32S, layer_sz );
mlp.create( &layer_sizes );
printf( "Training the classifier (may take a few minutes)...\n");
mlp.train( &train_data, new_responses, 0, 0,
CvANN_MLP_TrainParams(cvTermCriteria(CV_TERMCRIT_ITER,300,0.01),
#if 1
CvANN_MLP_TrainParams::BACKPROP,0.001));
#else
CvANN_MLP_TrainParams::RPROP,0.05));
#endif
cvReleaseMat( &new_responses );
printf("\n");
}
mlp_response = cvCreateMat( 1, class_count, CV_32F );
// compute prediction error on train and test data
for( i = 0; i < nsamples_all; i++ )
{
int best_class;
CvMat sample;
cvGetRow( data, &sample, i );
CvPoint max_loc = {0,0};
mlp.predict( &sample, mlp_response );
cvMinMaxLoc( mlp_response, 0, 0, 0, &max_loc, 0 );
best_class = max_loc.x + 'A';
int r = fabs((double)best_class - responses->data.fl[i]) < FLT_EPSILON ? 1 : 0;
if( i < ntrain_samples )
train_hr += r;
else
test_hr += r;
}
test_hr /= (double)(nsamples_all-ntrain_samples);
train_hr /= (double)ntrain_samples;
printf( "Recognition rate: train = %.1f%%, test = %.1f%%\n",
train_hr*100., test_hr*100. );
// Save classifier to file if needed
if( filename_to_save )
mlp.save( filename_to_save );
cvReleaseMat( &mlp_response );
cvReleaseMat( &data );
cvReleaseMat( &responses );
return 0;
}
double test(cv::Mat &vocabulary, void *src)
{
// Test
std::vector<BOWImg> images;
conf.max_num = conf.max_num * 2;
std::cout<<"--->Loading testing images ... "<<std::endl;
int numImages = imgRead(images);
std::cout<<" "<<numImages<<" images loaded."<<std::endl;
if(numImages < 0)
return -1;
printf("--->Extracting %s features ...\n", conf.extractor.c_str());
features(images, conf.extractor, conf.detector);
std::cout<<"--->Extracting BOW features ..."<<std::endl;
bowFeatures(images, vocabulary, conf.extractor);
cv::Mat rawData;
for(std::vector<BOWImg>::iterator iter = images.begin();iter != images.end(); iter++)
rawData.push_back(iter->BOWDescriptor);
//PCA
#ifdef _USE_PCA_
float factor = 1;
int maxComponentsNum = static_cast<float>(conf.numClusters) * factor;
cv::PCA pca(rawData, Mat(),CV_PCA_DATA_AS_ROW, maxComponentsNum);
cv::Mat pcaData;
for(int i = 0;i<rawData.rows;i++)
{
cv::Mat vec = rawData.row(i);
cv::Mat coeffs = pca.project(vec);
pcaData.push_back(coeffs);
}
cv::Mat testData = pcaData;
#else
cv::Mat testData = rawData;
#endif
std::cout<<"--->Executing predictions ..."<<std::endl;
cv::Mat output;
double ac = 0;
double ac_rate = 0;
if(conf.classifier == "BP")
{
CvANN_MLP *classifier = (CvANN_MLP *)src;
classifier->predict(testData,output);
cout<<"--->Predict answer: "<<std::endl;
for(int i = 0;i < output.rows;i++)
{
float *p = output.ptr<float>(i);
int k = 0;
int tmp = 0;
for(int j = 0;j < output.cols;j++)
{
if(p[j] > tmp )
{
tmp = p[j];
k = j;
}
}
std::cout<<" "<<images[i].imgName<<" ---- "<<conf.classes[k]<<endl;
if(images[i].label == k+1)
ac++;
}
ac_rate = ac / static_cast<double>(output.rows);
}
else if(conf.classifier == "SVM")
{
CvSVM *classifier = (CvSVM *)src;
classifier->predict(testData,output);
cout<<"--->Predict answer: "<<std::endl;
for(int i = 0;i < output.rows;i++)
{
int k = (int)output.ptr<float>()[i]-1;
std::cout<<" "<<images[i].imgName<<" ---- "<<conf.classes[k]<<endl;
if(images[i].label == k+1)
ac++;
}
ac_rate = ac / static_cast<double>(output.rows);
}
else {
std::cout<<"--->Error: wrong classifier."<<std::endl;
}
return ac_rate;
}
int main (int argc, char** argv){
CvANN_MLP* neuron = NULL ;
IplImage *img = cvLoadImage(argv[1], CV_LOAD_IMAGE_COLOR);
CvMat *input3Ch = cvCreateMat(img->height, img->width, CV_32FC3);
CvMat *output1Ch = cvCreateMat(img->height, img->width, CV_32FC1);
CvMat *input_morph = cvCreateMat(img->height, img->width, CV_32FC1);
//cvConvertScale(img, output1Ch);
cvConvertScale(img, input3Ch);
if (neuron == NULL )
neuron = new CvANN_MLP();
else
neuron->clear();
ByteArrayToANN("cia.tmp", neuron);
CvMat input_nn = cvMat(input3Ch->height*input3Ch->width, 3, CV_32FC1, input3Ch->data.fl);
CvMat output_nn = cvMat(output1Ch->height*output1Ch->width, 1, CV_32FC1, output1Ch->data.fl);
neuron->predict(&input_nn, &output_nn);
//do threshold
cvThreshold(output1Ch, input_morph, -0.5, 255.0, CV_THRESH_BINARY);
// morph open
IplConvKernel *se1 = cvCreateStructuringElementEx(3, 3, 1, 1, CV_SHAPE_ELLIPSE);
cvMorphologyEx(input_morph, input_morph, NULL, se1, CV_MOP_OPEN); // remove noise
CvMat *out_single = cvCreateMat(input_morph->height, input_morph->width, CV_32FC1);
cvSetZero(out_single);
IplImage *tmp8UC1 = cvCreateImage(cvGetSize(input_morph), IPL_DEPTH_8U, 1);
// remove small cells and fill holes.
CvMemStorage *storage = cvCreateMemStorage();
CvSeq *first_con = NULL;
CvSeq *cur = NULL;
cvConvert(input_morph, tmp8UC1);
cvFindContours(tmp8UC1, storage, &first_con, sizeof(CvContour), CV_RETR_EXTERNAL);
cur = first_con;
while (cur != NULL) {
double area = fabs(cvContourArea(cur));
int npts = cur->total;
CvPoint *p = new CvPoint[npts];
cvCvtSeqToArray(cur, p);
//~ cout<<area<<" ";
if (area < 1500.0) // remove small area
cvFillPoly(input_morph, &p, &npts, 1, cvScalar(0.0)); // remove from input
else if (area < 7500.0) {
cvFillPoly(out_single, &p, &npts, 1, cvScalar(255.0)); // move to single
cvFillPoly(input_morph, &p, &npts, 1, cvScalar(0.0)); // remove from input
}else
cvFillPoly(input_morph, &p, &npts, 1, cvScalar(255.0)); // fill hole
delete[] p;
cur = cur->h_next;
}
//~ cout<<endl;
//~ Mat tmpmat = cvarrToMat(out_single, true);
//~ tmpmat.convertTo(tmpmat, CV_8UC1);
//~ if(!imwrite("c_out_single.jpg", tmpmat))
//~ {
//~ cout<<"error writing image"<<endl;
//~ }
//~
//~ tmpmat = cvarrToMat(input_morph, true);
//~ tmpmat.convertTo(tmpmat, CV_8UC1);
//~ if(!imwrite("c_input_morph.jpg", tmpmat))
//~ {
//~ cout<<"error writing image"<<endl;
//~ }
//~
//~ cvSaveImage("c_tmp8UC1.jpg", tmp8UC1);
CvMat *output_morph = cvCreateMat(input_morph->height, input_morph->width, CV_32FC1);
cvOr(input_morph, out_single, output_morph);
cvReleaseStructuringElement(&se1);
//~ tmpmat = cvarrToMat(output_morph, true);
//~ tmpmat.convertTo(tmpmat, CV_8UC1);
//~ if(!imwrite("c_output_morph_or.jpg", tmpmat))
//~ {
//~ cout<<"error writing image"<<endl;
//~ }
//## Scanning Cells ##//
int ncell = 0, prev_ncontour = 0, same_count = 0, ncontour = 1;
while(ncontour != 0){
cvConvert(input_morph, tmp8UC1);
cvClearMemStorage(storage);
int ncontour = cvFindContours(tmp8UC1, storage, &first_con, sizeof(CvContour), CV_RETR_EXTERNAL);
if (ncontour == 0)
break; // finish extract cell
if (ncontour == prev_ncontour) {
cvErode(input_morph, input_morph);
same_count++;
} else
same_count = 0;
prev_ncontour = ncontour;
cur = first_con;
while (cur != NULL) {
double area = fabs(cvContourArea(cur));
if ((area < 3000.0) || (same_count > 10)) {
int npts = cur->total;
CvPoint *p = new CvPoint[npts];
cvCvtSeqToArray(cur, p);
cvFillPoly(out_single, &p, &npts, 1, cvScalar(255.0)); // move to single
cvFillPoly(input_morph, &p, &npts, 1, cvScalar(0.0)); // remove from input
delete[] p;
ncell++;
}
cur = cur->h_next;
}
}
Mat tmpmat = cvarrToMat(out_single, true);
//tmpmat.convertTo(tmpmat, CV_8UC1);
if(!saveMat("outSingle", tmpmat))
{
cout << "outSingle: cant save mat to binary file" << endl;
}
tmpmat = cvarrToMat(output_morph, true);
//tmpmat.convertTo(tmpmat, CV_8UC1);
if(!saveMat("outputMorph", tmpmat))
{
cout << "outMorph : save mat to binary file" << endl;
}
//~ if(!imwrite("c_out_single_scanningCell.jpg", tmpmat))
//~ {
//~ cout<<"error writing image"<<endl;
//~ }
//~
//~
//~ tmpmat = cvarrToMat(input_morph, true);
//~ tmpmat.convertTo(tmpmat, CV_8UC1);
//~ if(!imwrite("c_input_morph_scanningCell.jpg", tmpmat))
//~ {
//~ cout<<"error writing c_out_single_scanningCell.jpg"<<endl;
//~ }
//~
//~ tmpmat = cvarrToMat(output_morph, true);
//~ tmpmat.convertTo(tmpmat, CV_8UC1);
//~ if(!imwrite("c_output_morph_scanningCell.jpg", tmpmat))
//~ {
//~ cout<<"error writing c_output_morph_scanningCell.jpg"<<endl;
//~ }
//~ cvSaveImage("c_tmp8UC1_scanningCell.jpg", tmp8UC1);
/* ### separate cells ### */
cvConvert(out_single, tmp8UC1);
cvClearMemStorage(storage);
cvFindContours(tmp8UC1, storage, &first_con, sizeof(CvContour), CV_RETR_EXTERNAL);
int count = 1;
cur = first_con;
while (cur != NULL) {
int npts = cur->total;
CvPoint *p = new CvPoint[npts];
cvCvtSeqToArray(cur, p);
cvFillPoly(out_single, &p, &npts, 1, cvScalar((count++%254)+1)); // fill label, must be 1-255
delete[] p;
cur = cur->h_next;
}
cvConvertScale(output_morph, tmp8UC1);
CvMat *inwater = cvCreateMat(out_single->height, out_single->width, CV_8UC3);
CvMat outwater = cvMat(out_single->height, out_single->width, CV_32SC1, out_single->data.fl);
cvMerge(tmp8UC1, tmp8UC1, tmp8UC1, NULL, inwater);
cvWatershed(inwater, &outwater);
cvErode(out_single, out_single, NULL, 2);
cvConvertScale(output_morph, tmp8UC1);
cvSub(output_morph, out_single, output_morph, tmp8UC1);
cvReleaseMat(&inwater);
//~
//~ tmpmat = cvarrToMat(out_single, true);
//~ tmpmat.convertTo(tmpmat, CV_8UC1);
//~ if(!imwrite("c_out_single_sep.jpg", tmpmat))
//~ {
//~ cout<<"error writing c_out_single_sep.jpg"<<endl;
//~ }
//~
//~
//~ tmpmat = cvarrToMat(input_morph, true);
//~ tmpmat.convertTo(tmpmat, CV_8UC1);
//~ if(!imwrite("c_input_morph_sep.jpg", tmpmat))
//~ {
//~ cout<<"error writing c_input_morph_sep.jpg"<<endl;
//~ }
//~
//~ cvSaveImage("c_tmp8UC1_sep.jpg", tmp8UC1);
//~
//~ tmpmat = cvarrToMat(output_morph, true);
//~ tmpmat.convertTo(tmpmat, CV_8UC1);
//~ if(!imwrite("c_output_morph_sep.jpg", tmpmat))
//~ {
//~ cout<<"error writing c_output_morph_sep.jpg"<<endl;
//~ }
/* ### prepare result ### */
cvConvertScale(output_morph, tmp8UC1);
cvClearMemStorage(storage);
cvFindContours(tmp8UC1, storage, &first_con, sizeof(CvContour), CV_RETR_EXTERNAL);
IplImage *tmpImage = cvCreateImage(cvSize(tmp8UC1->width, tmp8UC1->height), IPL_DEPTH_8U, 3);
cvSet(tmpImage, CV_RGB(0,0,255)); // Background, blue
cvSetZero(tmp8UC1);
CvScalar pixel;
cur = first_con;
ncell = 0; // total cells
while (cur != NULL) {
if ((cur->total > 2) && (fabs(cvContourArea(cur)) > 1500.0)) { // remove small area
int npts = cur->total;
CvPoint *p = new CvPoint[npts];
cvCvtSeqToArray(cur, p);
cvFillPoly(tmp8UC1, &p, &npts, 1, cvScalar(255)); // set mask
pixel = cvAvg(output1Ch, tmp8UC1);
cvFillPoly(tmp8UC1, &p, &npts, 1, cvScalar(0)); // clear mask
if (pixel.val[0] > 0.5) { // Negative, green
if (tmpImage != NULL)
cvFillPoly(tmpImage, &p, &npts, 1, CV_RGB(0,255,0));
} else if (pixel.val[0] > -0.5) { // Positive, red
if (tmpImage != NULL)
cvFillPoly(tmpImage, &p, &npts, 1, CV_RGB(255,0,0));
}
delete[] p;
}
cur = cur->h_next;
}
cvSaveImage("result_fullCIA.jpg", tmpImage);
cvReleaseMat(&inwater);
//cvReleaseMat(outwater);
if (tmp8UC1 != NULL) cvReleaseImage(&tmp8UC1);
if (input_morph != NULL) cvReleaseMat(&input_morph);
if (out_single != NULL) cvReleaseMat(&out_single);
if (output_morph != NULL) cvReleaseMat(&output_morph);
//if (input3Ch != NULL) cvReleaseMat(&input3Ch);
if (output1Ch != NULL) cvReleaseMat(&output1Ch);
if (tmpImage != NULL) cvReleaseImageHeader(&tmpImage);
if (storage != NULL) cvReleaseMemStorage(&storage);
return 0;
}
int main(int argc, char ** argv)
{
// read command line arguments
if (argc != 5)
{
cerr << "usage: " << argv[0] << " <training samples> <training labels>"
<< " <testing samples> <testing labels>" << endl;
exit(-1);
}
const string trainingSamplesFilename = argv[1];
const string trainingLabelsFilename = argv[2];
const string testingSamplesFilename = argv[3];
const string testingLabelsFilename = argv[4];
try
{
Chrono chrono;
cout << fixed << showpoint << setprecision(2);
//////////////////////////////////////////////////
// training
//////////////////////////////////////////////////
int trainingN, trainingQ, trainingK;
cv::Mat trainingSamples, trainingLabels;
loadSamplesLabels(trainingSamplesFilename, trainingSamples,
trainingLabelsFilename, trainingLabels,
trainingN, trainingQ, trainingK);
// build class probabilities
cv::Mat trainingLabelsK = cv::Mat::zeros(trainingN, trainingK, CV_64FC1);
cv::Mat trainingHist = cv::Mat::zeros(1, trainingK, CV_32SC1);
for (int n=0; n<trainingN; n++)
{
int r = trainingLabels.at<int>(n, 0);
assert(r>=0 and r<trainingK);
trainingLabelsK.at<double>(n, r) = 1.0;
trainingHist.at<int>(0, r)++;
}
cout << "trainingHist: " << format(trainingHist, "csv") << endl;
// TODO tune network
// init layers (2 hidden layers with 10 neurons per layer)
cv::Mat layers = cv::Mat(3, 1, CV_32SC1);
layers.row(0) = cv::Scalar(trainingQ);
layers.row(1) = cv::Scalar(trainingQ*trainingK);
layers.row(2) = cv::Scalar(trainingK);
CvANN_MLP network;
network.create(layers);
// init training params
CvANN_MLP_TrainParams params;
CvTermCriteria criteria;
criteria.max_iter = 100;
criteria.epsilon = 0.00001f;
criteria.type = CV_TERMCRIT_ITER | CV_TERMCRIT_EPS;
params.term_crit = criteria;
params.train_method = CvANN_MLP_TrainParams::BACKPROP;
params.bp_dw_scale = 0.05f;
params.bp_moment_scale = 0.05f;
// compute training
network.train(trainingSamples, trainingLabelsK, cv::Mat(), cv::Mat(), params);
double trainingTime = chrono.elapsedAndReset();
cout << "trainingTime: " << trainingTime << endl << endl;
//////////////////////////////////////////////////
// testing
//////////////////////////////////////////////////
int testingN, testingQ, testingK;
cv::Mat testingSamples, testingLabels;
loadSamplesLabels(testingSamplesFilename, testingSamples,
testingLabelsFilename, testingLabels,
testingN, testingQ, testingK);
// compute testing
int K = max(trainingK, testingK);
cv::Mat testingHist = cv::Mat::zeros(1, K, CV_32SC1);
cv::Mat positives = cv::Mat::zeros(K, K, CV_32SC1);
unsigned nbTp = 0;
unsigned nbFp = 0;
for(int n = 0; n < testingN; n++)
{
// compute prediction
cv::Mat sample = testingSamples.row(n);
// TODO remove allocation ?
cv::Mat prediction;
network.predict(sample, prediction);
//cout << "sample: " << format(sample, "csv") << endl;
//cout << "prediction: " << format(prediction, "csv") << endl;
// get the class corresponding to the prediction
double vMin, vMax;
cv::Point pMin, pMax;
cv::minMaxLoc(prediction, &vMin, &vMax, &pMin, &pMax);
// test if expected == predicted
int predicted = pMax.x;
int expected = testingLabels.at<int>(n, 0);
//cout << "expected: " << expected << endl;
//cout << "predicted: " << predicted << endl;
testingHist.at<int>(0, expected)++;
positives.at<int>(predicted, expected)++;
if (expected == predicted)
nbTp++;
else
nbFp++;
}
cout << "testingHist: " << format(testingHist, "csv") << endl;
double accuracy = nbTp / double(nbTp+nbFp);
double testingTime = chrono.elapsed();
cout << "testingTime: " << testingTime << endl << endl;
//////////////////////////////////////////////////
// display results
//////////////////////////////////////////////////
// TODO display network params
cout << " " << format(positives,"csv") << endl;
cout << "accuracy: " << accuracy << endl;
}
catch (cv::Exception & e)
{
cerr << "exception caught: " << e.what() << endl;
}
return 0;
}