| 1 | #include "training.h" |
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| 2 | |
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| 3 | /** |
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| 4 | * Dataset destructor, which calls clear. |
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| 5 | */ |
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| 6 | Training::~Training() |
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| 7 | { |
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| 8 | clear(); |
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| 9 | } |
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| 10 | |
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| 11 | /** |
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| 12 | * Load a training stored in a file. |
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| 13 | * |
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| 14 | * @param training File stream of the contained training |
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| 15 | */ |
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| 16 | bool Training::loadTraining(ifstream& training) |
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| 17 | { |
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| 18 | string sample, cmd; |
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| 19 | |
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| 20 | // Get a sample |
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| 21 | getline(training, sample); |
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| 22 | |
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| 23 | // Parse every sample to get the log type |
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| 24 | istringstream sStr(sample); |
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| 25 | sStr >> cmd; |
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| 26 | |
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| 27 | while(!training.eof() && cmd != "END" && cmd != "") { |
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| 28 | if(cmd == "ACC") |
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| 29 | addSample(new AccSample(sStr.str())); |
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| 30 | else if(cmd == "GYRO") |
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| 31 | addSample(new GyroSample(sStr.str())); |
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| 32 | else { |
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| 33 | cout << "[Error] Bad log type." << endl; |
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| 34 | return false; |
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| 35 | } |
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| 36 | |
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| 37 | getline(training, sample); |
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| 38 | sStr.str(sample); |
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| 39 | sStr >> cmd; |
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| 40 | } |
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| 41 | |
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| 42 | return true; |
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| 43 | } |
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| 44 | |
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| 45 | /** |
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| 46 | * Save the training into a file for training and recognition. |
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| 47 | * |
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| 48 | * @param out Stream of the destination file |
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| 49 | */ |
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| 50 | void Training::save(ofstream& out) const |
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| 51 | { |
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| 52 | // Training Header |
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| 53 | out << "START " << timestamp << endl; |
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| 54 | |
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| 55 | // Samples |
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| 56 | for(unsigned int i = 0 ; i < samples.size() ; i++) |
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| 57 | samples[i]->save(out); |
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| 58 | |
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| 59 | out << "END" << endl; |
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| 60 | } |
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| 61 | |
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| 62 | /** |
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| 63 | * Add a new training to the dataset. |
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| 64 | * |
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| 65 | * @param sample Add a sample to the training set. |
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| 66 | */ |
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| 67 | void Training::addSample(Sample* sample) |
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| 68 | { |
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| 69 | // We retrieve the overall gesture timestamp |
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| 70 | struct timeval t; |
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| 71 | gettimeofday(&t,0); |
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| 72 | unsigned long sampleTs = (t.tv_sec % 86400) * 1000 + t.tv_usec / 1000; |
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| 73 | |
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| 74 | // We compute the relative timestamp in msec |
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| 75 | unsigned long deltaT = sampleTs - timestamp; |
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| 76 | |
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| 77 | if(sample) { |
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| 78 | sample->setTimestampFromGestureStart(deltaT); |
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| 79 | samples.push_back(sample); |
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| 80 | } |
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| 81 | } |
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| 82 | |
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| 83 | /** |
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| 84 | * Delete all samples and clear the buffer. This method will take |
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| 85 | * care of freeing the memory of each sample in the training set, |
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| 86 | * hence you don't need to free them in your code. |
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| 87 | */ |
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| 88 | void Training::clear() |
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| 89 | { |
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| 90 | for(unsigned int i = 0 ; i < samples.size() ; i++) { |
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| 91 | if(samples[i]) { |
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| 92 | delete samples[i]; |
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| 93 | samples[i] = 0; |
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| 94 | } |
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| 95 | } |
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| 96 | samples.clear(); |
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| 97 | } |
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