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https://github.com/OpenSpace/OpenSpace.git
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527 lines
18 KiB
C++
527 lines
18 KiB
C++
// /*****************************************************************************************
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// * *
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// * OpenSpace *
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// * *
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// * Copyright (c) 2014-2016 *
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// * *
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// * Permission is hereby granted, free of charge, to any person obtaining a copy of this *
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// * software and associated documentation files (the "Software"), to deal in the Software *
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// * without restriction, including without limitation the rights to use, copy, modify, *
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// * merge, publish, distribute, sublicense, and/or sell copies of the Software, and to *
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// * permit persons to whom the Software is furnished to do so, subject to the following *
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// * conditions: *
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// * *
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// * The above copyright notice and this permission notice shall be included in all copies *
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// * or substantial portions of the Software. *
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// * *
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// * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, *
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// * INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A *
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// * PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT *
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// * HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF *
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// * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE *
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// * OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. *
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// ****************************************************************************************/
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#include <modules/iswa/rendering/dataplane.h>
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#include <fstream>
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#include <ghoul/io/texture/texturereader.h>
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#include <ghoul/opengl/programobject.h>
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#include <ghoul/opengl/textureunit.h>
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#include <openspace/scene/scene.h>
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#include <openspace/scene/scenegraphnode.h>
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#include <openspace/engine/openspaceengine.h>
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#include <openspace/rendering/renderengine.h>
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#include <openspace/util/spicemanager.h>
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#include <ghoul/filesystem/filesystem.h>
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namespace {
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const std::string _loggerCat = "DataPlane";
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}
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namespace openspace {
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DataPlane::DataPlane(const ghoul::Dictionary& dictionary)
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:CygnetPlane(dictionary)
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,_useLog("useLog","Use Logarithm", false)
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,_useHistogram("_useHistogram", "Use Histogram", true)
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,_normValues("normValues", "Normalize Values", glm::vec2(1.0,1.0), glm::vec2(0), glm::vec2(5.0))
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,_backgroundValues("backgroundValues", "Background Values", glm::vec2(0.0), glm::vec2(0), glm::vec2(1.0))
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,_transferFunctionsFile("transferfunctions", "Transfer Functions", "${SCENE}/iswa/tfs/hot.tf")
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,_dataOptions("dataOptions", "Data Options")
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// ,_colorbar(nullptr)
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{
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std::string name;
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dictionary.getValue("Name", name);
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setName(name);
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registerProperties();
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addProperty(_useLog);
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addProperty(_useHistogram);
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addProperty(_normValues);
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addProperty(_backgroundValues);
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addProperty(_transferFunctionsFile);
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addProperty(_dataOptions);
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if(_data->groupId < 0){
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OsEng.gui()._iswa.registerProperty(&_useLog);
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OsEng.gui()._iswa.registerProperty(&_useHistogram);
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OsEng.gui()._iswa.registerProperty(&_normValues);
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OsEng.gui()._iswa.registerProperty(&_backgroundValues);
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OsEng.gui()._iswa.registerProperty(&_transferFunctionsFile);
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OsEng.gui()._iswa.registerProperty(&_dataOptions);
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}
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_normValues.onChange([this](){
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// FOR TESTING (should be done on all onChange)
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// _avgBenchmarkTime = 0.0;
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// _numOfBenchmarks = 0;
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loadTexture();});
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_useLog.onChange([this](){loadTexture();});
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_useHistogram.onChange([this](){loadTexture();});
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_dataOptions.onChange([this](){ loadTexture();} );
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_transferFunctionsFile.onChange([this](){
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setTransferFunctions(_transferFunctionsFile.value());
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});
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_type = ISWAManager::CygnetType::Data;
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setTransferFunctions(_transferFunctionsFile.value());
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}
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DataPlane::~DataPlane(){}
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bool DataPlane::loadTexture() {
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// if The future is done then get the new dataFile
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if(_futureObject.valid() && DownloadManager::futureReady(_futureObject)){
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_dataFile = _futureObject.get();
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if(_dataFile.corrupted)
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return false;
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}
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// if the buffer in the datafile is empty, do not proceed
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if(_dataFile.buffer.empty())
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return false;
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std::vector<float*> data = readData(_dataFile.buffer);
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if(data.empty())
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return false;
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bool texturesReady = false;
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std::vector<int> selectedOptions = _dataOptions.value();
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for(int option: selectedOptions){
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float* values = data[option];
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if(!values) continue;
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if(!_textures[option]){
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std::unique_ptr<ghoul::opengl::Texture> texture = std::make_unique<ghoul::opengl::Texture>(
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values,
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_dimensions,
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ghoul::opengl::Texture::Format::Red,
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GL_RED,
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GL_FLOAT,
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ghoul::opengl::Texture::FilterMode::Linear,
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ghoul::opengl::Texture::WrappingMode::ClampToEdge
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);
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if(texture){
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texture->uploadTexture();
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texture->setFilter(ghoul::opengl::Texture::FilterMode::Linear);
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_textures[option] = std::move(texture);
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}
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}else{
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_textures[option]->setPixelData(values);
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_textures[option]->uploadTexture();
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}
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texturesReady = true;
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}
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return texturesReady;
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}
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bool DataPlane::updateTexture(){
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if(_futureObject.valid())
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return false;
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std::future<DownloadManager::MemoryFile> future = ISWAManager::ref().fetchDataCygnet(_data->id);
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if(future.valid()){
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_futureObject = std::move(future);
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return true;
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}
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return false;
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}
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bool DataPlane::readyToRender(){
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return (!_textures.empty());
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}
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bool DataPlane::setUniformAndTextures(){
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// _shader->setUniform("textures", 1, units[1]);
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// _shader->setUniform("textures", 2, units[2]);
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// }
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std::vector<int> selectedOptions = _dataOptions.value();
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int activeTextures = selectedOptions.size();
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int activeTransferfunctions = _transferFunctions.size();
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ghoul::opengl::TextureUnit txUnits[10];
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int j = 0;
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for(int option : selectedOptions){
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if(_textures[option]){
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txUnits[j].activate();
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_textures[option]->bind();
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_shader->setUniform(
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"textures[" + std::to_string(j) + "]",
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txUnits[j]
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);
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j++;
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}
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}
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ghoul::opengl::TextureUnit tfUnits[10];
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j = 0;
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if((activeTransferfunctions == 1) && (_textures.size() != _transferFunctions.size())){
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tfUnits[0].activate();
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_transferFunctions[0]->bind();
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_shader->setUniform(
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"transferFunctions[0]",
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tfUnits[0]
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);
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}else{
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for(int option : selectedOptions){
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if(_transferFunctions[option]){
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tfUnits[j].activate();
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_transferFunctions[option]->bind();
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_shader->setUniform(
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"transferFunctions[" + std::to_string(j) + "]",
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tfUnits[j]
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);
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j++;
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}
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}
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}
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_shader->setUniform("numTextures", activeTextures);
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_shader->setUniform("numTransferFunctions", activeTransferfunctions);
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_shader->setUniform("backgroundValues", _backgroundValues.value());
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}
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bool DataPlane::createShader(){
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if (_shader == nullptr) {
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// DatePlane Program
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RenderEngine& renderEngine = OsEng.renderEngine();
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_shader = renderEngine.buildRenderProgram("DataPlaneProgram",
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"${MODULE_ISWA}/shaders/dataplane_vs.glsl",
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"${MODULE_ISWA}/shaders/dataplane_fs.glsl"
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);
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if (!_shader) return false;
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}
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}
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void DataPlane::readHeader(std::string& _memorybuffer){
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if(!_memorybuffer.empty()){
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std::stringstream memorystream(_memorybuffer);
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std::string line;
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int numOptions = 0;
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while(getline(memorystream,line)){
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if(line.find("#") == 0){
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if(line.find("# Output data:") == 0){
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line = line.substr(26);
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std::stringstream ss(line);
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std::string token;
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getline(ss, token, 'x');
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int x = std::stoi(token);
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getline(ss, token, '=');
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int y = std::stoi(token);
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_dimensions = glm::size3_t(x, y, 1);
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getline(memorystream, line);
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line = line.substr(1);
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ss = std::stringstream(line);
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std::string option;
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while(ss >> option){
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if(option != "x" && option != "y" && option != "z"){
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_dataOptions.addOption({numOptions, name()+"_"+option});
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numOptions++;
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_textures.push_back(nullptr);
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}
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}
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_dataOptions.setValue(std::vector<int>(1,0));
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if(_data->groupId > 0)
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ISWAManager::ref().registerOptionsToGroup(_data->groupId, _dataOptions.options());
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}
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}else{
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break;
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}
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}
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}
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}
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std::vector<float*> DataPlane::readData(std::string& _memorybuffer){
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if(!_memorybuffer.empty()){
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if(!_dataOptions.options().size()) // load options for value selection
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readHeader(_memorybuffer);
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std::stringstream memorystream(_memorybuffer);
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std::string line;
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std::vector<int> selectedOptions = _dataOptions.value();
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int numSelected = selectedOptions.size();
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std::vector<float> min(numSelected, std::numeric_limits<float>::max());
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std::vector<float> max(numSelected, std::numeric_limits<float>::min());
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std::vector<float> sum(numSelected, 0.0f);
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std::vector<std::vector<float>> optionValues(numSelected, std::vector<float>());
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std::vector<float*> data(_dataOptions.options().size(), nullptr);
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for(int option : selectedOptions){
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data[option] = new float[_dimensions.x*_dimensions.y]{0.0f};
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}
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int numValues = 0;
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while(getline(memorystream, line)){
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if(line.find("#") == 0){ //part of the header
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continue;
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}
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std::stringstream ss(line);
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std::vector<float> value;
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float v;
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while(ss >> v){
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value.push_back(v);
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}
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if(value.size()){
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for(int i=0; i<numSelected; i++){
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float v = value[selectedOptions[i]+3]; //+3 because "options" x, y and z.
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if(_useLog.value()){
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int sign = (v>0)? 1:-1;
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if(v != 0){
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v = sign*log(fabs(v));
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}
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}
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optionValues[i].push_back(v);
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min[i] = std::min(min[i], v);
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max[i] = std::max(max[i], v);
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sum[i] += v;
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}
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numValues++;
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}
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}
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if(numValues != _dimensions.x*_dimensions.y){
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LWARNING("Number of values read and expected are not the same");
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return std::vector<float*>();
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}
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// FOR TESTING
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// ===========
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// std::chrono::time_point<std::chrono::system_clock> start, end;
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// start = std::chrono::system_clock::now();
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// ===========
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for(int i=0; i<numSelected; i++){
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processData(data[ selectedOptions[i] ], optionValues[i], min[i], max[i], sum[i]);
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}
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// FOR TESTING
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// ===========
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// end = std::chrono::system_clock::now();
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// _numOfBenchmarks++;
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// std::chrono::duration<double> elapsed_seconds = end-start;
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// _avgBenchmarkTime = ( (_avgBenchmarkTime * (_numOfBenchmarks-1)) + elapsed_seconds.count() ) / _numOfBenchmarks;
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// std::cout << " readData():" << std::endl;
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// std::cout << "avg elapsed time: " << _avgBenchmarkTime << "s\n";
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// std::cout << "num Benchmarks: " << _numOfBenchmarks << "\n";
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// ===========
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return data;
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}
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else {
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// LWARNING("Nothing in memory buffer, are you connected to the information super highway?");
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return std::vector<float*>();
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}
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}
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void DataPlane::processData(float* outputData, std::vector<float>& inputData, float min, float max,float sum){
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// HISTOGRAM
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// number of levels/bins/values
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const int levels = 512;
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// Normal Histogram where "levels" is the number of steps/bins
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std::vector<int> histogram = std::vector<int>(levels, 0);
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// Maps the old levels to new ones.
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std::vector<float> newLevels = std::vector<float>(levels, 0.0f);
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const int numValues = inputData.size();
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//FOR TESTING ONLY
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//================
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// float entropyBefore;
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// float entropyAfter;
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// std::vector<int> histogramAfter = std::vector<int>(levels, 0);
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// auto calulateEntropy = [levels, numValues](std::vector<int> histogram){
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// float entropy;
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// for(auto frequency : histogram){
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// if(frequency != 0)
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// entropy -= ((float)frequency/numValues) * log2((float)frequency/numValues);
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// }
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// return entropy;
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// };
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//================
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// maps the data values to the histogram bin/index/level
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auto mapToHistogram = [levels](float val, float varMin, float varMax) {
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float probability = (val-varMin)/(varMax-varMin);
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float mappedValue = probability * levels;
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return glm::clamp(mappedValue, 0.0f, static_cast<float>(levels - 1));
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};
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//Calculate the mean
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float mean = (1.0 / numValues) * sum;
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//Calculate the Standard Deviation
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float standardDeviation = sqrt (((pow(sum, 2.0)) - ((1.0/numValues) * (pow(sum,2.0)))) / (numValues - 1.0));
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//calulate log mean
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// logmean /= numValues;
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//HISTOGRAM FUNCTIONALITY
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//======================
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if(_useHistogram.value()){
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for(int i = 0; i < numValues; i++){
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float v = inputData[i];
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float pixelVal = mapToHistogram(v, min, max);
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histogram[(int)pixelVal]++;
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inputData[i] = pixelVal;
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}
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// Map mean and standard deviation to histogram levels
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mean = mapToHistogram(mean , min, max);
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// logmean = mapToHistogram(logmean , min, max);
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standardDeviation = mapToHistogram(standardDeviation, min, max);
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min = 0.0f;
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max = levels - 1.0f;
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//FOR TESTING
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//entropyBefore = calulateEntropy(histogram);
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//Calculate the cumulative distributtion function (CDF)
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float previousCdf = 0.0f;
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for(int i = 0; i < levels; i++){
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float probability = histogram[i] / (float)numValues;
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float cdf = previousCdf + probability;
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cdf = glm::clamp(cdf, 0.0f, 1.0f); //just in case
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newLevels[i] = cdf * (levels-1);
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previousCdf = cdf;
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}
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}
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//======================
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for(int i=0; i< numValues; i++){
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float v = inputData[i];
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// if use histogram get the equalized values
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if(_useHistogram.value()){
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v = newLevels[(int)v];
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// FOR TESTING
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//histogramAfter[(int)v]++;
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// Map mean and standard deviation to new histogram levels
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mean = newLevels[(int) mean];
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// logmean = newLevels[(int) logmean];
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standardDeviation = newLevels[(int) standardDeviation];
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}
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v = normalizeWithStandardScore(v, mean, standardDeviation);
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outputData[i] += v;
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}
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// FOR TESTING
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// ===========
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// entropyAfter = calulateEntropy(histogramAfter);
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// std::cout << "Entropy Before: "<< entropyBefore << std::endl;
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// std::cout << "Entropy After: "<< entropyAfter << std::endl;
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// ===========
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}
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float DataPlane::normalizeWithStandardScore(float value, float mean, float sd){
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float zScoreMin = _normValues.value().x;
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float zScoreMax = _normValues.value().y;
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float standardScore = ( value - mean ) / sd;
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// Clamp intresting values
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standardScore = glm::clamp(standardScore, -zScoreMin, zScoreMax);
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//return and normalize
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return ( standardScore + zScoreMin )/(zScoreMin + zScoreMax );
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}
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void DataPlane::setTransferFunctions(std::string tfPath){
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std::string line;
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std::ifstream tfFile(absPath(tfPath));
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std::vector<std::shared_ptr<TransferFunction>> tfs;
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if(tfFile.is_open()){
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while(getline(tfFile, line)){
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std::shared_ptr<TransferFunction> tf = std::make_shared<TransferFunction>(absPath(line));
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if(tf)
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tfs.push_back(tf);
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}
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}
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if(!tfs.empty()){
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_transferFunctions.clear();
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_transferFunctions = tfs;
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}
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}
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}// namespace openspace
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