Mercurial > hg > svcore
view transform/FeatureExtractionPluginTransform.cpp @ 0:da6937383da8
initial import
author | Chris Cannam |
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date | Tue, 10 Jan 2006 16:33:16 +0000 |
parents | |
children | d86891498eef |
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/* -*- c-basic-offset: 4 -*- vi:set ts=8 sts=4 sw=4: */ /* A waveform viewer and audio annotation editor. Chris Cannam, Queen Mary University of London, 2005 This is experimental software. Not for distribution. */ #include "FeatureExtractionPluginTransform.h" #include "plugin/FeatureExtractionPluginFactory.h" #include "plugin/FeatureExtractionPlugin.h" #include "base/Model.h" #include "model/SparseOneDimensionalModel.h" #include "model/SparseTimeValueModel.h" #include "model/DenseThreeDimensionalModel.h" #include "model/DenseTimeValueModel.h" #include <iostream> FeatureExtractionPluginTransform::FeatureExtractionPluginTransform(Model *inputModel, QString pluginId, QString outputName) : Transform(inputModel), m_plugin(0), m_descriptor(0), m_outputFeatureNo(0) { std::cerr << "FeatureExtractionPluginTransform::FeatureExtractionPluginTransform: plugin " << pluginId.toStdString() << ", outputName " << outputName.toStdString() << std::endl; FeatureExtractionPluginFactory *factory = FeatureExtractionPluginFactory::instanceFor(pluginId); if (!factory) { std::cerr << "FeatureExtractionPluginTransform: No factory available for plugin id \"" << pluginId.toStdString() << "\"" << std::endl; return; } m_plugin = factory->instantiatePlugin(pluginId, m_input->getSampleRate()); if (!m_plugin) { std::cerr << "FeatureExtractionPluginTransform: Failed to instantiate plugin \"" << pluginId.toStdString() << "\"" << std::endl; return; } FeatureExtractionPlugin::OutputList outputs = m_plugin->getOutputDescriptors(); if (outputs.empty()) { std::cerr << "FeatureExtractionPluginTransform: Plugin \"" << pluginId.toStdString() << "\" has no outputs" << std::endl; return; } for (size_t i = 0; i < outputs.size(); ++i) { if (outputName == "" || outputs[i].name == outputName.toStdString()) { m_outputFeatureNo = i; m_descriptor = new FeatureExtractionPlugin::OutputDescriptor (outputs[i]); break; } } if (!m_descriptor) { std::cerr << "FeatureExtractionPluginTransform: Plugin \"" << pluginId.toStdString() << "\" has no output named \"" << outputName.toStdString() << "\"" << std::endl; return; } std::cerr << "FeatureExtractionPluginTransform: output sample type " << m_descriptor->sampleType << std::endl; int valueCount = 1; float minValue = 0.0, maxValue = 0.0; if (m_descriptor->hasFixedValueCount) { valueCount = m_descriptor->valueCount; } if (valueCount > 0 && m_descriptor->hasKnownExtents) { minValue = m_descriptor->minValue; maxValue = m_descriptor->maxValue; } size_t modelRate = m_input->getSampleRate(); size_t modelResolution = 1; switch (m_descriptor->sampleType) { case FeatureExtractionPlugin::OutputDescriptor::VariableSampleRate: if (m_descriptor->sampleRate != 0.0) { modelResolution = size_t(modelRate / m_descriptor->sampleRate + 0.001); } break; case FeatureExtractionPlugin::OutputDescriptor::OneSamplePerStep: modelResolution = m_plugin->getPreferredStepSize(); break; case FeatureExtractionPlugin::OutputDescriptor::FixedSampleRate: modelRate = m_descriptor->sampleRate; break; } if (valueCount == 0) { m_output = new SparseOneDimensionalModel(modelRate, modelResolution); } else if (valueCount == 1 || // We don't have a sparse 3D model m_descriptor->sampleType == FeatureExtractionPlugin::OutputDescriptor::VariableSampleRate) { m_output = new SparseTimeValueModel(modelRate, modelResolution, minValue, maxValue, false); } else { m_output = new DenseThreeDimensionalModel(modelRate, modelResolution, valueCount); } } FeatureExtractionPluginTransform::~FeatureExtractionPluginTransform() { delete m_plugin; delete m_descriptor; } DenseTimeValueModel * FeatureExtractionPluginTransform::getInput() { DenseTimeValueModel *dtvm = dynamic_cast<DenseTimeValueModel *>(getInputModel()); if (!dtvm) { std::cerr << "FeatureExtractionPluginTransform::getInput: WARNING: Input model is not conformable to DenseTimeValueModel" << std::endl; } return dtvm; } void FeatureExtractionPluginTransform::run() { DenseTimeValueModel *input = getInput(); if (!input) return; if (!m_output) return; size_t channelCount = input->getChannelCount(); if (m_plugin->getMaxChannelCount() < channelCount) { channelCount = 1; } if (m_plugin->getMinChannelCount() > channelCount) { std::cerr << "FeatureExtractionPluginTransform::run: " << "Can't provide enough channels to plugin (plugin min " << m_plugin->getMinChannelCount() << ", max " << m_plugin->getMaxChannelCount() << ", input model has " << input->getChannelCount() << ")" << std::endl; return; } size_t sampleRate = m_input->getSampleRate(); size_t stepSize = m_plugin->getPreferredStepSize(); size_t blockSize = m_plugin->getPreferredBlockSize(); m_plugin->initialise(channelCount, stepSize, blockSize); float **buffers = new float*[channelCount]; for (size_t ch = 0; ch < channelCount; ++ch) { buffers[ch] = new float[blockSize]; } size_t startFrame = m_input->getStartFrame(); size_t endFrame = m_input->getEndFrame(); size_t blockFrame = startFrame; size_t prevCompletion = 0; while (blockFrame < endFrame) { // std::cerr << "FeatureExtractionPluginTransform::run: blockFrame " // << blockFrame << std::endl; size_t completion = (((blockFrame - startFrame) / stepSize) * 99) / ( (endFrame - startFrame) / stepSize); // channelCount is either m_input->channelCount or 1 size_t got = 0; if (channelCount == 1) { got = input->getValues (-1, blockFrame, blockFrame + blockSize, buffers[0]); while (got < blockSize) { buffers[0][got++] = 0.0; } } else { for (size_t ch = 0; ch < channelCount; ++ch) { got = input->getValues (ch, blockFrame, blockFrame + blockSize, buffers[ch]); while (got < blockSize) { buffers[ch][got++] = 0.0; } } } FeatureExtractionPlugin::FeatureSet features = m_plugin->process (buffers, RealTime::frame2RealTime(blockFrame, sampleRate)); for (size_t fi = 0; fi < features[m_outputFeatureNo].size(); ++fi) { FeatureExtractionPlugin::Feature feature = features[m_outputFeatureNo][fi]; addFeature(blockFrame, feature); } if (blockFrame == startFrame || completion > prevCompletion) { setCompletion(completion); prevCompletion = completion; } blockFrame += stepSize; } FeatureExtractionPlugin::FeatureSet features = m_plugin->getRemainingFeatures(); for (size_t fi = 0; fi < features[m_outputFeatureNo].size(); ++fi) { FeatureExtractionPlugin::Feature feature = features[m_outputFeatureNo][fi]; addFeature(blockFrame, feature); } setCompletion(100); } void FeatureExtractionPluginTransform::addFeature(size_t blockFrame, const FeatureExtractionPlugin::Feature &feature) { size_t inputRate = m_input->getSampleRate(); // std::cerr << "FeatureExtractionPluginTransform::addFeature(" // << blockFrame << ")" << std::endl; int valueCount = 1; if (m_descriptor->hasFixedValueCount) { valueCount = m_descriptor->valueCount; } size_t frame = blockFrame; if (m_descriptor->sampleType == FeatureExtractionPlugin::OutputDescriptor::VariableSampleRate) { if (!feature.hasTimestamp) { std::cerr << "WARNING: FeatureExtractionPluginTransform::addFeature: " << "Feature has variable sample rate but no timestamp!" << std::endl; return; } else { frame = RealTime::realTime2Frame(feature.timestamp, inputRate); } } else if (m_descriptor->sampleType == FeatureExtractionPlugin::OutputDescriptor::FixedSampleRate) { if (feature.hasTimestamp) { //!!! warning: sampleRate may be non-integral frame = RealTime::realTime2Frame(feature.timestamp, m_descriptor->sampleRate); } else { frame = m_output->getEndFrame() + 1; } } if (valueCount == 0) { SparseOneDimensionalModel *model = getOutput<SparseOneDimensionalModel>(); if (!model) return; model->addPoint(SparseOneDimensionalModel::Point(frame, feature.label.c_str())); } else if (valueCount == 1 || m_descriptor->sampleType == FeatureExtractionPlugin::OutputDescriptor::VariableSampleRate) { float value = 0.0; if (feature.values.size() > 0) value = feature.values[0]; SparseTimeValueModel *model = getOutput<SparseTimeValueModel>(); if (!model) return; model->addPoint(SparseTimeValueModel::Point(frame, value, feature.label.c_str())); } else { DenseThreeDimensionalModel::BinValueSet values = feature.values; DenseThreeDimensionalModel *model = getOutput<DenseThreeDimensionalModel>(); if (!model) return; model->setBinValues(frame, values); } } void FeatureExtractionPluginTransform::setCompletion(int completion) { int valueCount = 1; if (m_descriptor->hasFixedValueCount) { valueCount = m_descriptor->valueCount; } if (valueCount == 0) { SparseOneDimensionalModel *model = getOutput<SparseOneDimensionalModel>(); if (!model) return; model->setCompletion(completion); } else if (valueCount == 1 || m_descriptor->sampleType == FeatureExtractionPlugin::OutputDescriptor::VariableSampleRate) { SparseTimeValueModel *model = getOutput<SparseTimeValueModel>(); if (!model) return; model->setCompletion(completion); } else { //!!! Can't actually do this with the 3D model (yet?) } }