void checkImagesEqual(vtkImageDataPtr input1, vtkImageDataPtr input2)
{
REQUIRE(input1.Get()!=(vtkImageData*)NULL);
REQUIRE(input2.Get()!=(vtkImageData*)NULL);
REQUIRE(input1->GetDataDimension() == input2->GetDataDimension());
REQUIRE(input1->GetScalarType() == input2->GetScalarType());
REQUIRE(input1->GetNumberOfScalarComponents() == input2->GetNumberOfScalarComponents());
REQUIRE(Eigen::Array3i(input1->GetDimensions()).isApprox(Eigen::Array3i(input2->GetDimensions())));
CHECK(Eigen::Array3d(input1->GetSpacing()).isApprox(Eigen::Array3d(input2->GetSpacing()), 1.0E-2));
CHECK(Eigen::Array3d(input1->GetOrigin()).isApprox(Eigen::Array3d(input2->GetOrigin())));
// check spacing, dim, type, origin
vtkImageMathematicsPtr diff = vtkImageMathematicsPtr::New();
diff->SetOperationToSubtract();
diff->SetInput1Data(input1);
diff->SetInput2Data(input2);
diff->Update();
vtkImageAccumulatePtr histogram = vtkImageAccumulatePtr::New();
histogram->SetInputData(0, diff->GetOutput());
histogram->Update();
Eigen::Array3d histogramRange = Eigen::Array3d(histogram->GetMax()) - Eigen::Array3d(histogram->GetMin());
for (int i=0; i<input1->GetNumberOfScalarComponents(); ++i)
{
CHECK(histogramRange[i] < 0.01);
CHECK(histogramRange[i] > -0.01);
}
}
bool VNNclAlgorithm::reconstruct(ProcessedUSInputDataPtr input, vtkImageDataPtr outputData, float radius, int nClosePlanes)
{
mMeasurementNames.clear();
int numBlocks = 10; // FIXME? needs to be the same as the number of input bscans to the voxel_method kernel
// Split input US into blocks
// Splits and copies data from the processed input in the way the kernel will processes it, which is per frameBlock
frameBlock_t* inputBlocks = new frameBlock_t[numBlocks];
size_t nPlanes_numberOfInputImages = input->getDimensions()[2];
this->initializeFrameBlocks(inputBlocks, numBlocks, input);
// Allocate CL memory for each frame block
VECTOR_CLASS<cl::Buffer> clBlocks;
report("Allocating OpenCL input block buffers");
for (int i = 0; i < numBlocks; i++)
{
//TODO why does the context suddenly contain a "dummy" device?
cl::Buffer buffer = mOulContex->createBuffer(mOulContex->getContext(), CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR, inputBlocks[i].length, inputBlocks[i].data, "block buffer "+QString::number(i).toStdString());
clBlocks.push_back(buffer);
}
// Allocate output memory
int *outputDims = outputData->GetDimensions();
size_t outputVolumeSize = outputDims[0] * outputDims[1] * outputDims[2] * sizeof(unsigned char);
report(QString("Allocating CL output buffer, size %1").arg(outputVolumeSize));
cl_ulong globalMemUse = 10 * inputBlocks[0].length + outputVolumeSize + sizeof(float) * 16 * nPlanes_numberOfInputImages + sizeof(cl_uchar) * input->getDimensions()[0] * input->getDimensions()[1];
if(isUsingTooMuchMemory(outputVolumeSize, inputBlocks[0].length, globalMemUse))
return false;
cl::Buffer outputBuffer = mOulContex->createBuffer(mOulContex->getContext(), CL_MEM_WRITE_ONLY, outputVolumeSize, NULL, "output volume buffer");
// Fill the plane matrices
float *planeMatrices = new float[16 * nPlanes_numberOfInputImages]; //4x4 (matrix) = 16
this->fillPlaneMatrices(planeMatrices, input);
cl::Buffer clPlaneMatrices = mOulContex->createBuffer(mOulContex->getContext(), CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR, nPlanes_numberOfInputImages * sizeof(float) * 16, planeMatrices, "plane matrices buffer");
// US Probe mask
cl::Buffer clMask = mOulContex->createBuffer(mOulContex->getContext(), CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR,
sizeof(cl_uchar) * input->getMask()->GetDimensions()[0] * input->getMask()->GetDimensions()[1],
input->getMask()->GetScalarPointer(), "mask buffer");
double *out_spacing = outputData->GetSpacing();
float spacings[2];
float f_out_spacings[3];
f_out_spacings[0] = out_spacing[0];
f_out_spacings[1] = out_spacing[1];
f_out_spacings[2] = out_spacing[2];
spacings[0] = input->getSpacing()[0];
spacings[1] = input->getSpacing()[1];
//TODO why 4? because float4 is used??
size_t planes_eqs_size = sizeof(cl_float)*4*nPlanes_numberOfInputImages;
// Find the optimal local work size
size_t local_work_size;
unsigned int deviceNumber = 0;
cl::Device device = mOulContex->getDevice(deviceNumber);
mKernel.getWorkGroupInfo(device, CL_KERNEL_PREFERRED_WORK_GROUP_SIZE_MULTIPLE, &local_work_size);
size_t close_planes_size = this->calculateSpaceNeededForClosePlanes(mKernel, device, local_work_size, nPlanes_numberOfInputImages, nClosePlanes);
this->setKernelArguments(
mKernel,
outputDims[0],
outputDims[1],
outputDims[2],
f_out_spacings[0],
f_out_spacings[1],
f_out_spacings[2],
input->getDimensions()[0],
input->getDimensions()[1],
spacings[0],
spacings[1],
clBlocks,
outputBuffer,
clPlaneMatrices,
clMask,
planes_eqs_size,
close_planes_size,
radius);
report(QString("Using %1 as local workgroup size").arg(local_work_size));
// We will divide the work into cubes of CUBE_DIM^3 voxels. The global work size is the total number of voxels divided by that.
int cube_dim = 4;
int cube_dim_pow3 = cube_dim * cube_dim * cube_dim;
// Global work items:
size_t global_work_size = (((outputDims[0] + cube_dim) * (outputDims[1] + cube_dim) * (outputDims[2] + cube_dim)) / cube_dim_pow3); // = number of cubes = number of kernels to run
// Round global_work_size up to nearest multiple of local_work_size
if (global_work_size % local_work_size)
global_work_size = ((global_work_size / local_work_size) + 1) * local_work_size; // ceil(...)
unsigned int queueNumber = 0;
cl::CommandQueue queue = mOulContex->getQueue(queueNumber);
this->measureAndExecuteKernel(queue, mKernel, global_work_size, local_work_size, mKernelMeasurementName);
this->measureAndReadBuffer(queue, outputBuffer, outputVolumeSize, outputData->GetScalarPointer(), "vnncl_read_buffer");
setDeepModified(outputData);
// Cleaning up
report(QString("Done, freeing GPU memory"));
this->freeFrameBlocks(inputBlocks, numBlocks);
delete[] inputBlocks;
inputBlocks = NULL;
return true;
}