comparison plugin/transform/FeatureExtractionModelTransformer.cpp @ 331:f620ce48c950

* Further naming change: Transformer -> ModelTransformer.
  The Transform class now describes a thing that can be done, and the
  ModelTransformer does it to a Model.

+/* -*- c-basic-offset: 4 indent-tabs-mode: nil -*-  vi:set ts=8 sts=4 sw=4: */
+
+/*
+    Sonic Visualiser
+    An audio file viewer and annotation editor.
+    Centre for Digital Music, Queen Mary, University of London.
+    This file copyright 2006 Chris Cannam and QMUL.
+    
+    This program is free software; you can redistribute it and/or
+    modify it under the terms of the GNU General Public License as
+    published by the Free Software Foundation; either version 2 of the
+    License, or (at your option) any later version.  See the file
+    COPYING included with this distribution for more information.
+*/
+
+#include "FeatureExtractionModelTransformer.h"
+
+#include "plugin/FeatureExtractionPluginFactory.h"
+#include "plugin/PluginXml.h"
+#include "vamp-sdk/Plugin.h"
+
+#include "data/model/Model.h"
+#include "base/Window.h"
+#include "data/model/SparseOneDimensionalModel.h"
+#include "data/model/SparseTimeValueModel.h"
+#include "data/model/EditableDenseThreeDimensionalModel.h"
+#include "data/model/DenseTimeValueModel.h"
+#include "data/model/NoteModel.h"
+#include "data/model/FFTModel.h"
+#include "data/model/WaveFileModel.h"
+
+#include <QMessageBox>
+
+#include <iostream>
+
+FeatureExtractionModelTransformer::FeatureExtractionModelTransformer(Model *inputModel,
+								   QString pluginId,
+                                                                   const ExecutionContext &context,
+                                                                   QString configurationXml,
+								   QString outputName) :
+    PluginTransformer(inputModel, context),
+    m_plugin(0),
+    m_descriptor(0),
+    m_outputFeatureNo(0)
+{
+//    std::cerr << "FeatureExtractionModelTransformer::FeatureExtractionModelTransformer: plugin " << pluginId.toStdString() << ", outputName " << outputName.toStdString() << std::endl;
+
+    FeatureExtractionPluginFactory *factory =
+	FeatureExtractionPluginFactory::instanceFor(pluginId);
+
+    if (!factory) {
+	std::cerr << "FeatureExtractionModelTransformer: No factory available for plugin id \""
+		  << pluginId.toStdString() << "\"" << std::endl;
+	return;
+    }
+
+    m_plugin = factory->instantiatePlugin(pluginId, m_input->getSampleRate());
+
+    if (!m_plugin) {
+	std::cerr << "FeatureExtractionModelTransformer: Failed to instantiate plugin \""
+		  << pluginId.toStdString() << "\"" << std::endl;
+	return;
+    }
+
+    if (configurationXml != "") {
+        PluginXml(m_plugin).setParametersFromXml(configurationXml);
+    }
+
+    DenseTimeValueModel *input = getInput();
+    if (!input) return;
+
+    size_t channelCount = input->getChannelCount();
+    if (m_plugin->getMaxChannelCount() < channelCount) {
+	channelCount = 1;
+    }
+    if (m_plugin->getMinChannelCount() > channelCount) {
+	std::cerr << "FeatureExtractionModelTransformer:: "
+		  << "Can't provide enough channels to plugin (plugin min "
+		  << m_plugin->getMinChannelCount() << ", max "
+		  << m_plugin->getMaxChannelCount() << ", input model has "
+		  << input->getChannelCount() << ")" << std::endl;
+	return;
+    }
+
+    std::cerr << "Initialising feature extraction plugin with channels = "
+              << channelCount << ", step = " << m_context.stepSize
+              << ", block = " << m_context.blockSize << std::endl;
+
+    if (!m_plugin->initialise(channelCount,
+                              m_context.stepSize,
+                              m_context.blockSize)) {
+        std::cerr << "FeatureExtractionModelTransformer: Plugin "
+                  << m_plugin->getIdentifier() << " failed to initialise!" << std::endl;
+        return;
+    }
+
+    Vamp::Plugin::OutputList outputs = m_plugin->getOutputDescriptors();
+
+    if (outputs.empty()) {
+	std::cerr << "FeatureExtractionModelTransformer: Plugin \""
+		  << pluginId.toStdString() << "\" has no outputs" << std::endl;
+	return;
+    }
+    
+    for (size_t i = 0; i < outputs.size(); ++i) {
+	if (outputName == "" || outputs[i].identifier == outputName.toStdString()) {
+	    m_outputFeatureNo = i;
+	    m_descriptor = new Vamp::Plugin::OutputDescriptor
+		(outputs[i]);
+	    break;
+	}
+    }
+
+    if (!m_descriptor) {
+	std::cerr << "FeatureExtractionModelTransformer: Plugin \""
+		  << pluginId.toStdString() << "\" has no output named \""
+		  << outputName.toStdString() << "\"" << std::endl;
+	return;
+    }
+
+//    std::cerr << "FeatureExtractionModelTransformer: output sample type "
+//	      << m_descriptor->sampleType << std::endl;
+
+    int binCount = 1;
+    float minValue = 0.0, maxValue = 0.0;
+    bool haveExtents = false;
+    
+    if (m_descriptor->hasFixedBinCount) {
+	binCount = m_descriptor->binCount;
+    }
+
+//    std::cerr << "FeatureExtractionModelTransformer: output bin count "
+//	      << binCount << std::endl;
+
+    if (binCount > 0 && m_descriptor->hasKnownExtents) {
+	minValue = m_descriptor->minValue;
+	maxValue = m_descriptor->maxValue;
+        haveExtents = true;
+    }
+
+    size_t modelRate = m_input->getSampleRate();
+    size_t modelResolution = 1;
+    
+    switch (m_descriptor->sampleType) {
+
+    case Vamp::Plugin::OutputDescriptor::VariableSampleRate:
+	if (m_descriptor->sampleRate != 0.0) {
+	    modelResolution = size_t(modelRate / m_descriptor->sampleRate + 0.001);
+	}
+	break;
+
+    case Vamp::Plugin::OutputDescriptor::OneSamplePerStep:
+	modelResolution = m_context.stepSize;
+	break;
+
+    case Vamp::Plugin::OutputDescriptor::FixedSampleRate:
+	modelRate = size_t(m_descriptor->sampleRate + 0.001);
+	break;
+    }
+
+    if (binCount == 0) {
+
+	m_output = new SparseOneDimensionalModel(modelRate, modelResolution,
+						 false);
+
+    } else if (binCount == 1) {
+
+        SparseTimeValueModel *model;
+        if (haveExtents) {
+            model = new SparseTimeValueModel
+                (modelRate, modelResolution, minValue, maxValue, false);
+        } else {
+            model = new SparseTimeValueModel
+                (modelRate, modelResolution, false);
+        }
+        model->setScaleUnits(outputs[m_outputFeatureNo].unit.c_str());
+
+        m_output = model;
+
+    } else if (m_descriptor->sampleType ==
+	       Vamp::Plugin::OutputDescriptor::VariableSampleRate) {
+
+        // We don't have a sparse 3D model, so interpret this as a
+        // note model.  There's nothing to define which values to use
+        // as which parameters of the note -- for the moment let's
+        // treat the first as pitch, second as duration in frames,
+        // third (if present) as velocity. (Our note model doesn't
+        // yet store velocity.)
+        //!!! todo: ask the user!
+	
+        NoteModel *model;
+        if (haveExtents) {
+            model = new NoteModel
+                (modelRate, modelResolution, minValue, maxValue, false);
+        } else {
+            model = new NoteModel
+                (modelRate, modelResolution, false);
+        }            
+        model->setScaleUnits(outputs[m_outputFeatureNo].unit.c_str());
+
+        m_output = model;
+
+    } else {
+
+        EditableDenseThreeDimensionalModel *model =
+            new EditableDenseThreeDimensionalModel
+            (modelRate, modelResolution, binCount, false);
+
+	if (!m_descriptor->binNames.empty()) {
+	    std::vector<QString> names;
+	    for (size_t i = 0; i < m_descriptor->binNames.size(); ++i) {
+		names.push_back(m_descriptor->binNames[i].c_str());
+	    }
+	    model->setBinNames(names);
+	}
+        
+        m_output = model;
+    }
+}
+
+FeatureExtractionModelTransformer::~FeatureExtractionModelTransformer()
+{
+    std::cerr << "FeatureExtractionModelTransformer::~FeatureExtractionModelTransformer()" << std::endl;
+    delete m_plugin;
+    delete m_descriptor;
+}
+
+DenseTimeValueModel *
+FeatureExtractionModelTransformer::getInput()
+{
+    DenseTimeValueModel *dtvm =
+	dynamic_cast<DenseTimeValueModel *>(getInputModel());
+    if (!dtvm) {
+	std::cerr << "FeatureExtractionModelTransformer::getInput: WARNING: Input model is not conformable to DenseTimeValueModel" << std::endl;
+    }
+    return dtvm;
+}
+
+void
+FeatureExtractionModelTransformer::run()
+{
+    DenseTimeValueModel *input = getInput();
+    if (!input) return;
+
+    if (!m_output) return;
+
+    while (!input->isReady()) {
+/*
+        if (dynamic_cast<WaveFileModel *>(input)) {
+            std::cerr << "FeatureExtractionModelTransformer::run: Model is not ready, but it's not a WaveFileModel (it's a " << typeid(input).name() << "), so that's OK" << std::endl;
+            sleep(2);
+            break; // no need to wait
+        }
+*/
+        std::cerr << "FeatureExtractionModelTransformer::run: Waiting for input model to be ready..." << std::endl;
+        sleep(1);
+    }
+
+    size_t sampleRate = m_input->getSampleRate();
+
+    size_t channelCount = input->getChannelCount();
+    if (m_plugin->getMaxChannelCount() < channelCount) {
+	channelCount = 1;
+    }
+
+    float **buffers = new float*[channelCount];
+    for (size_t ch = 0; ch < channelCount; ++ch) {
+	buffers[ch] = new float[m_context.blockSize + 2];
+    }
+
+    bool frequencyDomain = (m_plugin->getInputDomain() ==
+                            Vamp::Plugin::FrequencyDomain);
+    std::vector<FFTModel *> fftModels;
+
+    if (frequencyDomain) {
+        for (size_t ch = 0; ch < channelCount; ++ch) {
+            FFTModel *model = new FFTModel
+                                  (getInput(),
+                                   channelCount == 1 ? m_context.channel : ch,
+                                   m_context.windowType,
+                                   m_context.blockSize,
+                                   m_context.stepSize,
+                                   m_context.blockSize,
+                                   false);
+            if (!model->isOK()) {
+                QMessageBox::critical
+                    (0, tr("FFT cache failed"),
+                     tr("Failed to create the FFT model for this transform.\n"
+                        "There may be insufficient memory or disc space to continue."));
+                delete model;
+                setCompletion(100);
+                return;
+            }
+            model->resume();
+            fftModels.push_back(model);
+        }
+    }
+
+    long startFrame = m_input->getStartFrame();
+    long   endFrame = m_input->getEndFrame();
+
+    long contextStart = m_context.startFrame;
+    long contextDuration = m_context.duration;
+
+    if (contextStart == 0 || contextStart < startFrame) {
+        contextStart = startFrame;
+    }
+
+    if (contextDuration == 0) {
+        contextDuration = endFrame - contextStart;
+    }
+    if (contextStart + contextDuration > endFrame) {
+        contextDuration = endFrame - contextStart;
+    }
+
+    long blockFrame = contextStart;
+
+    long prevCompletion = 0;
+
+    setCompletion(0);
+
+    while (!m_abandoned) {
+
+        if (frequencyDomain) {
+            if (blockFrame - int(m_context.blockSize)/2 >
+                contextStart + contextDuration) break;
+        } else {
+            if (blockFrame >= 
+                contextStart + contextDuration) break;
+        }
+
+//	std::cerr << "FeatureExtractionModelTransformer::run: blockFrame "
+//		  << blockFrame << ", endFrame " << endFrame << ", blockSize "
+//                  << m_context.blockSize << std::endl;
+
+	long completion =
+	    (((blockFrame - contextStart) / m_context.stepSize) * 99) /
+	    (contextDuration / m_context.stepSize);
+
+	// channelCount is either m_input->channelCount or 1
+
+        for (size_t ch = 0; ch < channelCount; ++ch) {
+            if (frequencyDomain) {
+                int column = (blockFrame - startFrame) / m_context.stepSize;
+                for (size_t i = 0; i <= m_context.blockSize/2; ++i) {
+                    fftModels[ch]->getValuesAt
+                        (column, i, buffers[ch][i*2], buffers[ch][i*2+1]);
+                }
+            } else {
+                getFrames(ch, channelCount, 
+                          blockFrame, m_context.blockSize, buffers[ch]);
+            }                
+        }
+
+	Vamp::Plugin::FeatureSet features = m_plugin->process
+	    (buffers, Vamp::RealTime::frame2RealTime(blockFrame, sampleRate));
+
+	for (size_t fi = 0; fi < features[m_outputFeatureNo].size(); ++fi) {
+	    Vamp::Plugin::Feature feature =
+		features[m_outputFeatureNo][fi];
+	    addFeature(blockFrame, feature);
+	}
+
+	if (blockFrame == contextStart || completion > prevCompletion) {
+	    setCompletion(completion);
+	    prevCompletion = completion;
+	}
+
+	blockFrame += m_context.stepSize;
+    }
+
+    if (m_abandoned) return;
+
+    Vamp::Plugin::FeatureSet features = m_plugin->getRemainingFeatures();
+
+    for (size_t fi = 0; fi < features[m_outputFeatureNo].size(); ++fi) {
+	Vamp::Plugin::Feature feature =
+	    features[m_outputFeatureNo][fi];
+	addFeature(blockFrame, feature);
+    }
+
+    if (frequencyDomain) {
+        for (size_t ch = 0; ch < channelCount; ++ch) {
+            delete fftModels[ch];
+        }
+    }
+
+    setCompletion(100);
+}
+
+void
+FeatureExtractionModelTransformer::getFrames(int channel, int channelCount,
+                                            long startFrame, long size,
+                                            float *buffer)
+{
+    long offset = 0;
+
+    if (startFrame < 0) {
+        for (int i = 0; i < size && startFrame + i < 0; ++i) {
+            buffer[i] = 0.0f;
+        }
+        offset = -startFrame;
+        size -= offset;
+        if (size <= 0) return;
+        startFrame = 0;
+    }
+
+    long got = getInput()->getData
+        ((channelCount == 1 ? m_context.channel : channel),
+         startFrame, size, buffer + offset);
+
+    while (got < size) {
+        buffer[offset + got] = 0.0;
+        ++got;
+    }
+
+    if (m_context.channel == -1 && channelCount == 1 &&
+        getInput()->getChannelCount() > 1) {
+        // use mean instead of sum, as plugin input
+        int cc = getInput()->getChannelCount();
+        for (long i = 0; i < size; ++i) {
+            buffer[i] /= cc;
+        }
+    }
+}
+
+void
+FeatureExtractionModelTransformer::addFeature(size_t blockFrame,
+					     const Vamp::Plugin::Feature &feature)
+{
+    size_t inputRate = m_input->getSampleRate();
+
+//    std::cerr << "FeatureExtractionModelTransformer::addFeature("
+//	      << blockFrame << ")" << std::endl;
+
+    int binCount = 1;
+    if (m_descriptor->hasFixedBinCount) {
+	binCount = m_descriptor->binCount;
+    }
+
+    size_t frame = blockFrame;
+
+    if (m_descriptor->sampleType ==
+	Vamp::Plugin::OutputDescriptor::VariableSampleRate) {
+
+	if (!feature.hasTimestamp) {
+	    std::cerr
+		<< "WARNING: FeatureExtractionModelTransformer::addFeature: "
+		<< "Feature has variable sample rate but no timestamp!"
+		<< std::endl;
+	    return;
+	} else {
+	    frame = Vamp::RealTime::realTime2Frame(feature.timestamp, inputRate);
+	}
+
+    } else if (m_descriptor->sampleType ==
+	       Vamp::Plugin::OutputDescriptor::FixedSampleRate) {
+
+	if (feature.hasTimestamp) {
+	    //!!! warning: sampleRate may be non-integral
+	    frame = Vamp::RealTime::realTime2Frame(feature.timestamp,
+                                                   lrintf(m_descriptor->sampleRate));
+	} else {
+	    frame = m_output->getEndFrame();
+	}
+    }
+	
+    if (binCount == 0) {
+
+	SparseOneDimensionalModel *model = getOutput<SparseOneDimensionalModel>();
+	if (!model) return;
+	model->addPoint(SparseOneDimensionalModel::Point(frame, feature.label.c_str()));
+	
+    } else if (binCount == 1) {
+
+	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()));
+//        std::cerr << "SparseTimeValueModel::addPoint(" << frame << ", " << value << "), " << feature.label.c_str() << std::endl;
+
+    } else if (m_descriptor->sampleType == 
+	       Vamp::Plugin::OutputDescriptor::VariableSampleRate) {
+
+        float pitch = 0.0;
+        if (feature.values.size() > 0) pitch = feature.values[0];
+
+        float duration = 1;
+        if (feature.values.size() > 1) duration = feature.values[1];
+        
+        float velocity = 100;
+        if (feature.values.size() > 2) velocity = feature.values[2];
+
+        NoteModel *model = getOutput<NoteModel>();
+        if (!model) return;
+
+        model->addPoint(NoteModel::Point(frame, pitch,
+                                         lrintf(duration),
+                                         feature.label.c_str()));
+	
+    } else {
+	
+	DenseThreeDimensionalModel::Column values = feature.values;
+	
+	EditableDenseThreeDimensionalModel *model =
+            getOutput<EditableDenseThreeDimensionalModel>();
+	if (!model) return;
+
+	model->setColumn(frame / model->getResolution(), values);
+    }
+}
+
+void
+FeatureExtractionModelTransformer::setCompletion(int completion)
+{
+    int binCount = 1;
+    if (m_descriptor->hasFixedBinCount) {
+	binCount = m_descriptor->binCount;
+    }
+
+//    std::cerr << "FeatureExtractionModelTransformer::setCompletion("
+//              << completion << ")" << std::endl;
+
+    if (binCount == 0) {
+
+	SparseOneDimensionalModel *model = getOutput<SparseOneDimensionalModel>();
+	if (!model) return;
+	model->setCompletion(completion);
+
+    } else if (binCount == 1) {
+
+	SparseTimeValueModel *model = getOutput<SparseTimeValueModel>();
+	if (!model) return;
+	model->setCompletion(completion);
+
+    } else if (m_descriptor->sampleType ==
+	       Vamp::Plugin::OutputDescriptor::VariableSampleRate) {
+
+	NoteModel *model = getOutput<NoteModel>();
+	if (!model) return;
+	model->setCompletion(completion);
+
+    } else {
+
+	EditableDenseThreeDimensionalModel *model =
+            getOutput<EditableDenseThreeDimensionalModel>();
+	if (!model) return;
+	model->setCompletion(completion);
+    }
+}
+