def correlate2(data, h, dev=None):
"""computes normalized cross correlation of 2d <data> with template <h> on the GPU Device <dev>
boundary conditions are clamping to edge.
h is converted to float32
if dev == None a new one is created
"""
if not dev:
dev = OCLDevice(useDevice=imgtools.__OPENCLDEVICE__)
# normalize data and template
#data
dtype = data.dtype.type
dtypes_kernels = {
np.float32: "mean_var_2d_float",
np.uint16: "mean_var_2d_short"
}
if not dtype in dtypes_kernels.keys():
raise TypeError(
"data type %s not supported yet, please convert to:" % dtype,
dtypes_kernels.keys())
proc = OCLProcessor(dev, absPath("kernels/correlate_kernels.cl"))
Ny, Nx = h.shape
inImg = dev.createImage_like(data)
meanBuf = dev.createBuffer(data.size,
dtype=dtype,
mem_flags=cl.mem_flags.READ_WRITE)
varBuf = dev.createBuffer(data.size,
dtype=dtype,
mem_flags=cl.mem_flags.READ_WRITE)
dev.writeImage(inImg, data)
proc.runKernel(dtypes_kernels[dtype], inImg.shape, None, inImg,
np.int32(data.shape[1]), np.int32(Nx), np.int32(Ny),
meanBuf, varBuf)
dataMean, dataVar = dev.readBuffer(meanBuf, dtype).reshape(
data.shape), dev.readBuffer(varBuf, dtype).reshape(data.shape)
#template
hMean = 1. * np.mean(h.flatten())
hVar = 1. * np.var(h.flatten())
res = convolve2(dev, data - dataMean, h - hMean)
print hMean, hVar
# # res = convolve2(dev,data-dataMean,h)
# return res
# return dataVar
return res / np.maximum(1.e-6, np.sqrt(dataVar * hVar))
def _correlate2(data, h, dev=None):
"""computes normalized cross correlation of 2d <data> with template <h> on the GPU Device <dev>
boundary conditions are clamping to edge.
h is converted to float32
if dev == None a new one is created
"""
if not dev:
dev = OCLDevice(useDevice=imgtools.__OPENCLDEVICE__)
dtype = data.dtype.type
dtypes_kernels = {
np.float32: "correlate2d_float",
np.uint16: "correlate2d_short"
}
if not dtype in dtypes_kernels.keys():
raise TypeError(
"data type %s not supported yet, please convert to:" % dtype,
dtypes_kernels.keys())
proc = OCLProcessor(dev, absPath("kernels/correlate_kernels.cl"))
Ny, Nx = h.shape
hbuf = dev.createBuffer(Nx * Ny,
dtype=np.float32,
mem_flags=cl.mem_flags.READ_ONLY)
inImg = dev.createImage_like(data)
outImg = dev.createImage_like(data, mem_flags="READ_WRITE")
dev.writeImage(inImg, data)
dev.writeBuffer(hbuf, h.astype(np.float32).flatten())
proc.runKernel(dtypes_kernels[dtype], inImg.shape, None, inImg, hbuf,
np.int32(Nx), np.int32(Ny), outImg)
return dev.readImage(outImg)
res = convolve2(dev, data - dataMean, h - hMean)
print hMean, hVar
# # res = convolve2(dev,data-dataMean,h)
# return res
# return dataVar
return res / np.maximum(1.e-6, np.sqrt(dataVar * hVar))
# return dataMean, dataVar
if __name__ == '__main__':
N, Nh = 64, 11
data = np.zeros((N, ) * 2, np.float32)
t = np.linspace(-1, 1, Nh)
hx = np.exp(-3 * t**2)
hy = np.exp(-3 * t**2)
h = np.outer(hy, hx)
# h = np.ones((Nh,)*2)
data[10:10 + Nh, 1:1 + Nh] = 1. * h
dev = OCLDevice()
out = correlate2(data, h, dev)
