static float testNetwork(NeuralNetwork& neuralNetwork, const Image& image,
float noiseMagnitude, size_t iterations, size_t batchSize,
std::default_random_engine& engine)
{
float accuracy = 0.0f;
iterations = std::max(iterations, 1UL);
lucius::util::log("TestVisualization") << "Testing the accuracy of the trained network.\n";
for(size_t i = 0; i != iterations; ++i)
{
lucius::util::log("TestVisualization") << " Iteration " << i << " out of "
<< iterations << "\n";
ImageVector batch = generateBatch(image, noiseMagnitude,
batchSize, engine);
Matrix input = batch.convertToStandardizedMatrix(
neuralNetwork.getInputCount(),
neuralNetwork.getInputBlockingFactor(), image.colorComponents());
Matrix reference = generateReference(batch);
lucius::util::log("TestVisualization") << " Input: " << input.toString();
lucius::util::log("TestVisualization") << " Reference: " << reference.toString();
accuracy += neuralNetwork.computeAccuracy(input, reference);
}
return accuracy * 100.0f / iterations;
}
static ImageVector generateBatch(const Image& image, float noiseMagnitude,
size_t batchSize, std::default_random_engine& engine)
{
ImageVector images;
std::bernoulli_distribution distribution(0.5f);
for(size_t i = 0; i != batchSize; ++i)
{
bool generateRandom = distribution(engine);
if(generateRandom)
{
images.push_back(generateRandomImage(
image.y(), image.x(), image.colorComponents(), engine));
}
else
{
images.push_back(addRandomNoiseToImage(image,
noiseMagnitude, engine));
}
}
return images;
}
static Matrix generateReference(const ImageVector& images)
{
Matrix reference(images.size(), 1);
for(size_t i = 0; i < images.size(); ++i)
{
reference(i, 0) = images[i].label() == "reference" ? 1.0f : 0.0f;
//std::cout << "reference: " << reference(i, 0) << "\n";
}
return reference;
}
void CRemoteDoc::OnMENUITEMPCAFusion()
{
using namespace rss;
POSITION pos=GetDocTemplate()->GetFirstDocPosition();
ImageVector<image_type> images;
while(pos) {
CRemoteDoc *pDoc= (CRemoteDoc *) (GetDocTemplate()->GetNextDoc(pos));
if(pDoc)
images.push_back(pDoc->GetImage());
else
break;
}
if(images.size()<=1) return;
int width = images[0].width();
int height = images[0].height();
PCAFusion<image_type> fusion(width, height);
//images.resize_image(images[0].width(), images[0].height());
BilinearInterpolation<image_type> interpolate(Size(width, height));
for(size_t i=0; i<images.size(); i++) {
image_type result;
interpolate.operator ()(images[i], result);
images[i]=result;
}
//CRemoteDoc *pResultDoc=(CRemoteDoc *) GetDocTemplate()->CreateNewDocument();
//CFrameWnd * pWnd=GetDocTemplate()->CreateNewFrame(pResultDoc, 0);
image_type result;
clock_t start = clock();
fusion(images, result);
clock_t end = clock();
CString title;
title.Format("%f", static_cast<double>(end - start) / CLOCKS_PER_SEC);
theApp.GetMainWnd()->GetTopLevelParent()->SetWindowText(title.GetBuffer());
SetImage(result);
//pResultDoc->SetModifiedFlag( TRUE );
//pWnd->InitialUpdateFrame(pResultDoc, true);
}
void Machinery::initialize(ImageVector & images)
{
m_algorithm->initialize(images);
for(int i = 0; i < m_ios.size(); i++)
{
m_ios[i]->save(*images[images.size()-1]);
}
}
static void trainNetwork(NeuralNetwork& neuralNetwork, const Image& image,
float noiseMagnitude, size_t iterations, size_t batchSize,
std::default_random_engine& engine)
{
lucius::util::log("TestVisualization") << "Training the network.\n";
for(size_t i = 0; i != iterations; ++i)
{
lucius::util::log("TestVisualization") << " Iteration " << i << " out of "
<< iterations << "\n";
ImageVector batch = generateBatch(image, noiseMagnitude,
batchSize, engine);
Matrix input = batch.convertToStandardizedMatrix(
neuralNetwork.getInputCount(),
neuralNetwork.getInputBlockingFactor(), image.colorComponents());
Matrix reference = generateReference(batch);
lucius::util::log("TestVisualization") << " Input: " << input.toString();
lucius::util::log("TestVisualization") << " Reference: " << reference.toString();
neuralNetwork.train(input, reference);
}
}
void Storage::getBlendmaps(float chunkSize, const osg::Vec2f &chunkCenter,
bool pack, ImageVector &blendmaps, std::vector<Terrain::LayerInfo> &layerList)
{
// TODO - blending isn't completely right yet; the blending radius appears to be
// different at a cell transition (2 vertices, not 4), so we may need to create a larger blendmap
// and interpolate the rest of the cell by hand? :/
osg::Vec2f origin = chunkCenter - osg::Vec2f(chunkSize/2.f, chunkSize/2.f);
int cellX = static_cast<int>(origin.x());
int cellY = static_cast<int>(origin.y());
// Save the used texture indices so we know the total number of textures
// and number of required blend maps
std::set<UniqueTextureId> textureIndices;
// Due to the way the blending works, the base layer will always shine through in between
// blend transitions (eg halfway between two texels, both blend values will be 0.5, so 25% of base layer visible).
// To get a consistent look, we need to make sure to use the same base layer in all cells.
// So we're always adding _land_default.dds as the base layer here, even if it's not referenced in this cell.
textureIndices.insert(std::make_pair(0,0));
for (int y=0; y<ESM::Land::LAND_TEXTURE_SIZE+1; ++y)
for (int x=0; x<ESM::Land::LAND_TEXTURE_SIZE+1; ++x)
{
UniqueTextureId id = getVtexIndexAt(cellX, cellY, x, y);
textureIndices.insert(id);
}
// Makes sure the indices are sorted, or rather,
// retrieved as sorted. This is important to keep the splatting order
// consistent across cells.
std::map<UniqueTextureId, int> textureIndicesMap;
for (std::set<UniqueTextureId>::iterator it = textureIndices.begin(); it != textureIndices.end(); ++it)
{
int size = textureIndicesMap.size();
textureIndicesMap[*it] = size;
layerList.push_back(getLayerInfo(getTextureName(*it)));
}
int numTextures = textureIndices.size();
// numTextures-1 since the base layer doesn't need blending
int numBlendmaps = pack ? static_cast<int>(std::ceil((numTextures - 1) / 4.f)) : (numTextures - 1);
int channels = pack ? 4 : 1;
// Second iteration - create and fill in the blend maps
const int blendmapSize = ESM::Land::LAND_TEXTURE_SIZE+1;
for (int i=0; i<numBlendmaps; ++i)
{
GLenum format = pack ? GL_RGBA : GL_ALPHA;
osg::ref_ptr<osg::Image> image (new osg::Image);
image->allocateImage(blendmapSize, blendmapSize, 1, format, GL_UNSIGNED_BYTE);
unsigned char* pData = image->data();
for (int y=0; y<blendmapSize; ++y)
{
for (int x=0; x<blendmapSize; ++x)
{
UniqueTextureId id = getVtexIndexAt(cellX, cellY, x, y);
int layerIndex = textureIndicesMap.find(id)->second;
int blendIndex = (pack ? static_cast<int>(std::floor((layerIndex - 1) / 4.f)) : layerIndex - 1);
int channel = pack ? std::max(0, (layerIndex-1) % 4) : 0;
if (blendIndex == i)
pData[y*blendmapSize*channels + x*channels + channel] = 255;
else
pData[y*blendmapSize*channels + x*channels + channel] = 0;
}
}
blendmaps.push_back(image);
}
}
void ImageSeries::SetImageCollection(const ImageVector& images){
_imageCollection.assign(images.begin(), images.end());
}
int main(int argc, char *argv[])
{
int rc = 0;
bool first = true;
ImageVector v;
Client client(argc, argv);
if ((rc = parseArgs(argc, argv)))
return rc;
try {
// event handler
Event * event = new Event();
// Signal set to be handled by the event handler.
ACE_Sig_Set sig;
// register Signal handler for Ctr+C
sig.sig_add(SIGINT);
sig.sig_add(SIGTERM);
// Reactor, misused as signal handler
ACE_Reactor reactor;
if (reactor.register_handler(sig, event) == -1) {
throw Miro::ACE_Exception(errno, "failed to register signal handler");
}
// get reference to video service
Video_var video = client.resolveName<Video>(interfaceName.c_str());
Miro::VideoConnection connection(video.in());
ACE_Time_Value start;
while(!canceled) {
// wait for key
if (first || !streaming) {
first = false;
cout << "Press key to grab next image: " << flush;
getchar();
start = ACE_OS::gettimeofday();
}
// init streaming timer
if (!first && streaming) {
ACE_Time_Value elapsed = ACE_OS::gettimeofday();
elapsed -= start;
if (elapsed.msec() > stop)
break;
}
{
// get image
Miro::VideoAcquireImage
frame(connection,
(interval)?
Miro::VideoAcquireImage::Current :
Miro::VideoAcquireImage::Next);
// fill image structure
Image image;
image.fileName = path() + client.namingContextName + "_" + Miro::timeString() + ".ppm";
image.width = connection.handle->format.width;
image.height = connection.handle->format.height;
// fill image buffer
if (!bgr && !streaming)
image.buffer = (char*) frame.buffer;
else {
image.buffer = new char[3 * image.width * image.height];
// byte swapping
if (bgr) {
int offset = 0;
for (int i = 0; i < image.width; ++i) {
for (int j = 0; j < image.height; ++j, offset += 3) {
image.buffer[offset + 0] = frame.buffer[offset + 2]; // r
image.buffer[offset + 1] = frame.buffer[offset + 1]; // g
image.buffer[offset + 2] = frame.buffer[offset + 0]; // b
}
}
}
else
memcpy(image.buffer, frame.buffer, 3 * image.width * image.height);
}
// save image
if (!streaming) {
image.writePpm();
if (bgr)
delete[] image.buffer;
}
else
v.push_back(image);
}
// sleep
if (interval) {
ACE_Time_Value delta;
delta.msec(interval);
ACE_OS::sleep(delta);
}
}
cout << "exiting on signal" << endl;
for (ImageVector::const_iterator i = v.begin(); i != v.end(); ++i) {
i->writePpm();
delete i->buffer;
}
}
catch (const Miro::ETimeOut& e) {
cerr << "Miro Timeout Exception: " << e << endl;
rc = 1;
}
catch (const Miro::EDevIO & e) {
cerr << "Miro Device I/O exception: " << e << endl;
rc = 1;
}
catch (const Miro::EOutOfBounds & e) {
cerr << "Miro out of bounds exception: " << e << endl;
rc = 1;
}
catch (const CORBA::Exception & e) {
cerr << "Uncaught CORBA exception: " << e << endl;
rc = 1;
}
return rc;
}
