def maximum_filter_2d(
arr2D, footprint
): ## Make sure arr2D is our datatype float32 and footprint of int32
arr2DMaxed = numpy.empty_like(arr2D)
head, tail = os.path.split(
os.path.abspath(__file__)
) # Used so that we can always get the kernel which should be in the same directory as this file
maxFunction = open(head + "/2DSlidingMaxFootprintKernel.c", "rt")
maxFunction = SourceModule(maxFunction.read())
slidingMaxKernel = maxFunction.get_function("slidingMaxiumum2D")
blockSize = [
16, 16
] # To-do: Add a variable to this, can affect performance based on GPU
gridSize = getGridSize(
blockSize, arr2D.shape
) # Get the size of our grid based on the size of a grid (blocksize)
slidingMaxKernel(
cuda.In(arr2D), # Input
cuda.Out(arr2DMaxed), # Output
numpy.int32(footprint.shape[1]), # Kernel Size
numpy.int32(arr2D.shape[1]), # Row Stride
numpy.int32(1), # Column Stride
numpy.int32(int(arr2D.shape[1])), # Array Column Count
numpy.int32(int(arr2D.shape[0])), # Array Row Count
cuda.In(footprint),
block=(blockSize[0], blockSize[1], 1),
grid=(gridSize[0], gridSize[1], 1))
return arr2DMaxed
