C++ FileStorage::root方法代码示例

static void readDetections( FileStorage fs, const string& nodeName, vector<LatentSvmDetector::ObjectDetection>& detections )
{
    detections.clear();

    FileNode fn = fs.root()[nodeName];
    FileNodeIterator fni = fn.begin();
    while( fni != fn.end() )
    {
        LatentSvmDetector::ObjectDetection d;
        fni >> d.rect.x >> d.rect.y >> d.rect.width >> d.rect.height
            >> d.score >> d.classID;
        detections.push_back( d );
    }
}

int main()
{
//vars for 120 fps
time_duration td, td1;
ptime nextFrameTimestamp, currentFrameTimestamp, initialLoopTimestamp, finalLoopTimestamp;
int delayFound = 0;

//Setting up communication with arduino
HANDLE hSerial;
hSerial = CreateFile("COM4",GENERIC_READ|GENERIC_WRITE,0,0,OPEN_EXISTING,FILE_ATTRIBUTE_NORMAL,0);
if(hSerial==INVALID_HANDLE_VALUE)
{
    if(GetLastError()==ERROR_FILE_NOT_FOUND)
    {
        cout<<"File not found"<<endl;//serial port does not exist. Inform user.
    }
    cout<<"error :/"<<endl;//some other error occurred. Inform user.
}

DCB dcbSerialParams = {0};
dcbSerialParams.DCBlength=sizeof(dcbSerialParams);
if (!GetCommState(hSerial, &dcbSerialParams))
    {
        printf("error getting state\n");
    }
dcbSerialParams.BaudRate=CBR_9600;
dcbSerialParams.ByteSize=8;
dcbSerialParams.StopBits=ONESTOPBIT;
dcbSerialParams.Parity=NOPARITY;
if(!SetCommState(hSerial, &dcbSerialParams))
    {
        printf("error setting serial port state\n");
    }
int n=1;
DWORD bytesWritten;

ifstream infile("..//..//..//data.csv");		//Takes input from basic(GUI) for reading patient data and stores in the data.csv file
infile>>patdat;			//reading patients first name
infile>>patdat1;		//reading patients last name
infile>>patdat2;        //reading patients age
infile>>patdat3;        //reading patients gender

//Snippet for reading camera settings from xml file
FileStorage fs;
fs.open("file1.xml", FileStorage::READ);

FileNode q = fs.root();
for (FileNodeIterator current = q.begin(); current != q.end(); current++)
    {
        FileNode item = *current;

        //cout<<"Success"<<endl;
        item["Exposure"] >> exposure;
        item["FPS"] >> framerate;
        item["Framewidth"] >> framewidth;
        item["Frameheight"] >> frameheight;
        //cout << exposure << endl;
    }


VideoCapture capture(0);	//Opens the camera of the device connected
VideoCapture capture1(1);

capture.set(CV_CAP_PROP_FRAME_WIDTH, framewidth);
capture.set(CV_CAP_PROP_FRAME_HEIGHT,frameheight);
capture.set(CV_CAP_PROP_EXPOSURE, exposure);

capture1.set(CV_CAP_PROP_FRAME_WIDTH, framewidth);
capture1.set(CV_CAP_PROP_FRAME_HEIGHT,frameheight);
capture1.set(CV_CAP_PROP_EXPOSURE, exposure);

capture>>image;			//Extract a frame and store in image matrix.
capture1>>image1;



strcpy(fname, "..//..//Videos//");	//declaring path for storing video
strcat(fname, patdat);		//appending patients first name on the video
strcat(fname, "_");		//appending patients name on the video
strcat(fname, patdat1);		//appending patients last name on the video
strcat(fname, "_");
strcat(fname, patdat2);
strcat(fname, "_");
strcat(fname, patdat3);
strcat(fname, "_");
strcat(fname, "left");
strcat(fname,".avi");

strcpy(fname1, "..//..//Videos//");	//declaring path for storing video
strcat(fname1, patdat);		//appending patients first name on the video
strcat(fname1, "_");		//appending patients name on the video
strcat(fname1, patdat1);		//appending patients last name on the video
strcat(fname1, "_");
strcat(fname1, patdat2);
strcat(fname1, "_");
strcat(fname1, patdat3);
strcat(fname1, "_");
strcat(fname1, "right");
strcat(fname1,".avi");
int fps1=20;
//.........这里部分代码省略.........

TEST_P(ML_ANN_METHOD, Test)
{
    int methodType = get<0>(GetParam());
    string methodName = get<1>(GetParam());
    int N = get<2>(GetParam());

    String folder = string(cvtest::TS::ptr()->get_data_path());
    String original_path = folder + "waveform.data";
    String dataname = folder + "waveform" + '_' + methodName;

    Ptr<TrainData> tdata2 = TrainData::loadFromCSV(original_path, 0);
    Mat samples = tdata2->getSamples()(Range(0, N), Range::all());
    Mat responses(N, 3, CV_32FC1, Scalar(0));
    for (int i = 0; i < N; i++)
        responses.at<float>(i, static_cast<int>(tdata2->getResponses().at<float>(i, 0))) = 1;
    Ptr<TrainData> tdata = TrainData::create(samples, ml::ROW_SAMPLE, responses);

    ASSERT_FALSE(tdata.empty()) << "Could not find test data file : " << original_path;
    RNG& rng = theRNG();
    rng.state = 0;
    tdata->setTrainTestSplitRatio(0.8);

    Mat testSamples = tdata->getTestSamples();

#ifdef GENERATE_TESTDATA
    {
    Ptr<ml::ANN_MLP> xx = ml::ANN_MLP_ANNEAL::create();
    Mat_<int> layerSizesXX(1, 4);
    layerSizesXX(0, 0) = tdata->getNVars();
    layerSizesXX(0, 1) = 30;
    layerSizesXX(0, 2) = 30;
    layerSizesXX(0, 3) = tdata->getResponses().cols;
    xx->setLayerSizes(layerSizesXX);
    xx->setActivationFunction(ml::ANN_MLP::SIGMOID_SYM);
    xx->setTrainMethod(ml::ANN_MLP::RPROP);
    xx->setTermCriteria(TermCriteria(TermCriteria::COUNT, 1, 0.01));
    xx->train(tdata, ml::ANN_MLP::NO_OUTPUT_SCALE + ml::ANN_MLP::NO_INPUT_SCALE);
    FileStorage fs;
    fs.open(dataname + "_init_weight.yml.gz", FileStorage::WRITE + FileStorage::BASE64);
    xx->write(fs);
    fs.release();
    }
#endif
    {
        FileStorage fs;
        fs.open(dataname + "_init_weight.yml.gz", FileStorage::READ);
        Ptr<ml::ANN_MLP> x = ml::ANN_MLP_ANNEAL::create();
        x->read(fs.root());
        x->setTrainMethod(methodType);
        if (methodType == ml::ANN_MLP::ANNEAL)
        {
            x->setAnnealEnergyRNG(RNG(CV_BIG_INT(0xffffffff)));
            x->setAnnealInitialT(12);
            x->setAnnealFinalT(0.15);
            x->setAnnealCoolingRatio(0.96);
            x->setAnnealItePerStep(11);
        }
        x->setTermCriteria(TermCriteria(TermCriteria::COUNT, 100, 0.01));
        x->train(tdata, ml::ANN_MLP::NO_OUTPUT_SCALE + ml::ANN_MLP::NO_INPUT_SCALE + ml::ANN_MLP::UPDATE_WEIGHTS);
        ASSERT_TRUE(x->isTrained()) << "Could not train networks with  " << methodName;
        string filename = dataname + ".yml.gz";
        Mat r_gold;
#ifdef  GENERATE_TESTDATA
        x->save(filename);
        x->predict(testSamples, r_gold);
        {
            FileStorage fs_response(dataname + "_response.yml.gz", FileStorage::WRITE + FileStorage::BASE64);
            fs_response << "response" << r_gold;
        }
#else
        {
            FileStorage fs_response(dataname + "_response.yml.gz", FileStorage::READ);
            fs_response["response"] >> r_gold;
        }
#endif
        ASSERT_FALSE(r_gold.empty());
        Ptr<ml::ANN_MLP> y = Algorithm::load<ANN_MLP>(filename);
        ASSERT_TRUE(y != NULL) << "Could not load   " << filename;
        Mat rx, ry;
        for (int j = 0; j < 4; j++)
        {
            rx = x->getWeights(j);
            ry = y->getWeights(j);
            double n = cvtest::norm(rx, ry, NORM_INF);
            EXPECT_LT(n, FLT_EPSILON) << "Weights are not equal for layer: " << j;
        }
        x->predict(testSamples, rx);
        y->predict(testSamples, ry);
        double n = cvtest::norm(ry, rx, NORM_INF);
        EXPECT_LT(n, FLT_EPSILON) << "Predict are not equal to result of the saved model";
        n = cvtest::norm(r_gold, rx, NORM_INF);
        EXPECT_LT(n, FLT_EPSILON) << "Predict are not equal to 'gold' response";
    }
}