PrepareInput::PrepareInput(Buffer* dataMean, bool useCenterOnly, bool needsFlip, bool doRandomSample, int imageSize, int rescaledSize, bool isMeanChanneled) :
_useCenterOnly(useCenterOnly),
_needsFlip(needsFlip),
_doRandomSample(doRandomSample),
_imageSize(imageSize),
_rescaledSize(rescaledSize) {
assert(dataMean != NULL);
Dimensions expectedDims(_rescaledSize, _rescaledSize, kOutputChannels);
dataMean->reshape(expectedDims);
Dimensions outputDims(_imageSize, _imageSize, kOutputChannels);
_dataMean = new Buffer(outputDims);
const int deltaX = (_rescaledSize - _imageSize);
const int deltaY = (_rescaledSize - _imageSize);
const int marginX = (deltaX / 2);
const int marginY = (deltaY / 2);
if (isMeanChanneled) {
Buffer* fromChanneled = convert_from_channeled_rgb_image(dataMean);
crop_and_flip_image(_dataMean, fromChanneled, marginX, marginY, false);
delete fromChanneled;
} else {
crop_and_flip_image(_dataMean, dataMean, marginX, marginY, false);
}
_dataMean->setName("_dataMean");
setClassName("PrepareInput");
}
void Foam::equationReader::checkFinalDimensions
(
const label& equationIndex,
dimensionSet& expectedDimensions,
const word& outputName
) const
{
const equation& eqn(operator[](equationIndex));
dimensionSet outputDims(evaluateDimensions(equationIndex));
if ((outputDims != expectedDimensions) && (dimensionSet::debug))
{
WarningIn("equationReader::checkFinalDimenions")
<< "Dimension error thrown for equation " << eqn.name()
<< ". Output dimensions: " << outputDims << "do not match "
<< "dimensions of destination field " << outputName << ", "
<< expectedDimensions << ". You can initialize " << outputName
<< "'s dimensions with:" << token::NL
<< token::TAB << outputName << ".dimensions().reset" << token::NL
<< token::TAB << "(" << token::NL << token::TAB << token::TAB
<< "equationReaderObject.evaluateDimensions(" << equationIndex
<< "))" << endl;
}
expectedDimensions = outputDims;
}
Buffer* PrepareInput::run(Buffer* input) {
if (_output != NULL) {
delete _output;
}
Dimensions rescaledDims(_rescaledSize, _rescaledSize, kOutputChannels);
Buffer* rescaled = new Buffer(rescaledDims);
rescaled->setName("rescaled");
input->setName("input");
rescale_image_to_fit(input, rescaled, _needsFlip);
const int deltaX = (_rescaledSize - _imageSize);
const int deltaY = (_rescaledSize - _imageSize);
const int marginX = (deltaX / 2);
const int marginY = (deltaY / 2);
if (_useCenterOnly) {
Dimensions outputDims(1, _imageSize, _imageSize, kOutputChannels);
_output = new Buffer(outputDims);
_output->setName("prepareInput_output");
int sourceX;
int sourceY;
if (_doRandomSample) {
sourceX = (int)(rand() * (deltaX / (float)(RAND_MAX)));
sourceY = (int)(rand() * (deltaY / (float)(RAND_MAX)));
} else {
sourceX = marginX;
sourceY = marginY;
}
Buffer* blitDestination = buffer_view_at_top_index(_output, 0);
crop_and_flip_image(blitDestination, rescaled, sourceX, sourceY, false);
matrix_add_inplace(blitDestination, _dataMean, -1.0f);
} else {
Dimensions outputDims(10, _imageSize, _imageSize, kOutputChannels);
_output = new Buffer(outputDims);
_output->setName("prepareInput_output");
for (int flipPass = 0; flipPass < 2; flipPass += 1) {
const bool doFlip = (flipPass == 1);
Buffer* blitDestination = buffer_view_at_top_index(_output, (flipPass * 5));
crop_and_flip_image(blitDestination, rescaled, marginX, marginY, doFlip);
for (int yIndex = 0; yIndex < 2; yIndex += 1) {
for (int xIndex = 0; xIndex < 2; xIndex += 1) {
const int viewIndex = ((flipPass * 5) + (yIndex * 2) + xIndex + 1);
Buffer* blitDestination = buffer_view_at_top_index(_output, viewIndex);
const int sourceX = (xIndex * deltaX);
const int sourceY = (yIndex * deltaY);
crop_and_flip_image(blitDestination, rescaled, sourceX, sourceY, doFlip);
}
}
}
}
delete rescaled;
return _output;
}
