本文整理汇总了C++中GestureRecognitionPipeline::getPostProcessingModule方法的典型用法代码示例。如果您正苦于以下问题：C++ GestureRecognitionPipeline::getPostProcessingModule方法的具体用法？C++ GestureRecognitionPipeline::getPostProcessingModule怎么用？C++ GestureRecognitionPipeline::getPostProcessingModule使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类GestureRecognitionPipeline的用法示例。
在下文中一共展示了GestureRecognitionPipeline::getPostProcessingModule方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: setup
void setup() {
stream.setLabelsForAllDimensions({"x", "y", "z"});
useInputStream(stream);
DTW dtw(false, true, null_rej);
dtw.enableTrimTrainingData(true, 0.1, 75);
pipeline.setClassifier(dtw);
pipeline.addPostProcessingModule(ClassLabelTimeoutFilter(timeout));
usePipeline(pipeline);
registerTuneable(
null_rej, 0.1, 5.0, "Variability",
"How different from the training data a new gesture can be and "
"still be considered the same gesture. The higher the number, the "
"more different it can be.",
[](double new_null_rej) {
pipeline.getClassifier()->setNullRejectionCoeff(new_null_rej);
pipeline.getClassifier()->recomputeNullRejectionThresholds();
});
registerTuneable(
timeout, 1, 3000, "Timeout",
"How long (in milliseconds) to wait after recognizing a "
"gesture before recognizing another one.",
[](double new_timeout) {
ClassLabelTimeoutFilter* filter =
dynamic_cast<ClassLabelTimeoutFilter*>(
pipeline.getPostProcessingModule(0));
assert(filter != nullptr);
filter->setTimeoutDuration(new_timeout);
});
}示例2: setup
void setup() {
stream.setLabelsForAllDimensions({"audio"});
pipeline.addFeatureExtractionModule(
FFT(kFftWindowSize, kFftHopSize,
DIM, FFT::HAMMING_WINDOW, true, false));
MFCC::Options options;
options.sample_rate = kSampleRate;
options.fft_size = kFftWindowSize / 2;
options.start_freq = 300;
options.end_freq = 3700;
options.num_tri_filter = 26;
options.num_cepstral_coeff = 12;
options.lifter_param = 22;
options.use_vad = true;
options.noise_level = noise_level;
pipeline.addFeatureExtractionModule(MFCC(options));
pipeline.setClassifier(SVM());
// GMM(16, true, false, 1, 100, 0.001));
// In post processing, we wait #n predicitons. If m out of n predictions are
// from the same class, we declare the class as the right one.
//
// n = (duration * sample_rate) / frame_size
// where duration = post_duration
// sample_rate = kSampleRate
// frame_size = kFftHopSize
// m = n * post_ratio
int num_predictions = post_duration / 1000 * kSampleRate / kFftHopSize;
pipeline.addPostProcessingModule(
ClassLabelFilter(num_predictions * post_ratio, num_predictions));
auto ratio_updater = [](double new_ratio) {
ClassLabelFilter* filter =
dynamic_cast<ClassLabelFilter*>(pipeline.getPostProcessingModule(0));
// Recalculate num_predictions as post_duration might have been changed
int num_predictions = post_duration / 1000 * kSampleRate / kFftHopSize;
filter->setMinimumCount(new_ratio * num_predictions);
};
auto duration_updater = [](int new_duration) {
ClassLabelFilter* filter =
dynamic_cast<ClassLabelFilter*>(pipeline.getPostProcessingModule(0));
// Recalculate num_predictions as post_duration might have been changed
int num_predictions = post_duration / 1000 * kSampleRate / kFftHopSize;
filter->setBufferSize(num_predictions);
};
auto noise_updater = [](int new_noise_level) {
MFCC *mfcc = dynamic_cast<MFCC*>(pipeline.getFeatureExtractionModule(1));
mfcc->setNoiseLevel(new_noise_level);
};
registerTuneable(noise_level, 0, 20,
"Noise Level",
"The threshold for the system to distinguish between "
"ambient noise and speech/sound",
noise_updater);
registerTuneable(post_duration, 0, 2000,
"Duration",
"Time (in ms) that is considered as a whole "
"for smoothing the prediction",
duration_updater);
registerTuneable(post_ratio, 0.0f, 1.0f,
"Ratio",
"The portion of time in duration that "
"should be from the same class",
ratio_updater);
useInputStream(stream);
useOutputStream(oStream);
usePipeline(pipeline);
useLeaveOneOutScoring(false);
setGUIBufferSize(kSampleRate);
}