def resample_img(data, new_shape):
"""resamples d"""
d1_g = OCLImage.from_array(data)
d2_g = OCLImage.empty(new_shape,np.float32,num_channels = 2 if np.iscomplexobj(data) else 1)
d2_g.copy_image_resampled(d1_g)
return d2_g.get()
def resample_img(data, new_shape):
"""resamples d"""
d1_g = OCLImage.from_array(data)
d2_g = OCLImage.empty(new_shape,
np.float32,
num_channels=2 if np.iscomplexobj(data) else 1)
d2_g.copy_image_resampled(d1_g)
return d2_g.get()
def nlm3(data,sigma, size_filter = 2, size_search = 3):
"""for noise level of sigma_0, choose sigma = 1.5*sigma_0
"""
prog = OCLProgram(abspath("kernels/nlm3.cl"),
build_options="-D FS=%i -D BS=%i"%(size_filter,size_search))
data = data.astype(np.float32, copy = False)
img = OCLImage.from_array(data)
distImg = OCLImage.empty_like(data)
distImg = OCLImage.empty_like(data)
tmpImg = OCLImage.empty_like(data)
tmpImg2 = OCLImage.empty_like(data)
accBuf = OCLArray.zeros(data.shape,np.float32)
weightBuf = OCLArray.zeros(data.shape,np.float32)
for dx in range(size_search+1):
for dy in range(-size_search,size_search+1):
for dz in range(-size_search,size_search+1):
prog.run_kernel("dist",img.shape,None,
img,tmpImg,np.int32(dx),np.int32(dy),np.int32(dz))
prog.run_kernel("convolve",img.shape,None,
tmpImg,tmpImg2,np.int32(1))
prog.run_kernel("convolve",img.shape,None,
tmpImg2,tmpImg,np.int32(2))
prog.run_kernel("convolve",img.shape,None,
tmpImg,distImg,np.int32(4))
prog.run_kernel("computePlus",img.shape,None,
img,distImg,accBuf.data,weightBuf.data,
np.int32(img.shape[0]),
np.int32(img.shape[1]),
np.int32(img.shape[2]),
np.int32(dx),np.int32(dy),np.int32(dz),
np.float32(sigma))
if any([dx,dy,dz]):
prog.run_kernel("computeMinus",img.shape,None,
img,distImg,accBuf.data,weightBuf.data,
np.int32(img.shape[0]),
np.int32(img.shape[1]),
np.int32(img.shape[2]),
np.int32(dx),np.int32(dy),np.int32(dz),
np.float32(sigma))
acc = accBuf.get()
weights = weightBuf.get()
return acc/weights
def nlm3(data,sigma, size_filter = 2, size_search = 3):
"""for noise level of sigma_0, choose sigma = 1.5*sigma_0
"""
prog = OCLProgram(abspath("kernels/nlm3.cl"),
build_options="-D FS=%i -D BS=%i"%(size_filter,size_search))
img = OCLImage.from_array(data)
distImg = OCLImage.empty_like(data)
distImg = OCLImage.empty_like(data)
tmpImg = OCLImage.empty_like(data)
tmpImg2 = OCLImage.empty_like(data)
accBuf = OCLArray.zeros(data.shape,np.float32)
weightBuf = OCLArray.zeros(data.shape,np.float32)
for dx in range(size_search+1):
for dy in range(-size_search,size_search+1):
for dz in range(-size_search,size_search+1):
prog.run_kernel("dist",img.shape,None,
img,tmpImg,np.int32(dx),np.int32(dy),np.int32(dz))
prog.run_kernel("convolve",img.shape,None,
tmpImg,tmpImg2,np.int32(1))
prog.run_kernel("convolve",img.shape,None,
tmpImg2,tmpImg,np.int32(2))
prog.run_kernel("convolve",img.shape,None,
tmpImg,distImg,np.int32(4))
prog.run_kernel("computePlus",img.shape,None,
img,distImg,accBuf.data,weightBuf.data,
np.int32(img.shape[0]),
np.int32(img.shape[1]),
np.int32(img.shape[2]),
np.int32(dx),np.int32(dy),np.int32(dz),
np.float32(sigma))
if any([dx,dy,dz]):
prog.run_kernel("computeMinus",img.shape,None,
img,distImg,accBuf.data,weightBuf.data,
np.int32(img.shape[0]),
np.int32(img.shape[1]),
np.int32(img.shape[2]),
np.int32(dx),np.int32(dy),np.int32(dz),
np.float32(sigma))
acc = accBuf.get()
weights = weightBuf.get()
return acc/weights
def transfer(data):
"""transfers data"""
d1_g = OCLArray.from_array(data)
d2_g = OCLArray.empty_like(data)
if data.dtype.type == np.float32:
im = OCLImage.empty(data.shape[::1],dtype = np.float32)
elif data.dtype.type == np.complex64:
im = OCLImage.empty(data.shape[::1],dtype = np.float32, num_channels=2)
im.copy_buffer(d1_g)
d2_g.copy_image(im)
return d2_g.get()
def resample_buf(data, new_shape):
"""resamples d"""
d1_g = OCLArray.from_array(data)
d2_g = OCLArray.empty(new_shape, data.dtype)
if data.dtype.type == np.float32:
im = OCLImage.empty(data.shape[::1], dtype=np.float32)
elif data.dtype.type == np.complex64:
im = OCLImage.empty(data.shape[::1], dtype=np.float32, num_channels=2)
im.copy_buffer(d1_g)
d2_g.copy_image_resampled(im)
return d2_g.get()
def transfer(data):
"""transfers data"""
d1_g = OCLArray.from_array(data)
d2_g = OCLArray.empty_like(data)
if data.dtype.type == np.float32:
im = OCLImage.empty(data.shape[::1], dtype=np.float32)
elif data.dtype.type == np.complex64:
im = OCLImage.empty(data.shape[::1], dtype=np.float32, num_channels=2)
im.copy_buffer(d1_g)
d2_g.copy_image(im)
return d2_g.get()
def resample_buf(data, new_shape):
"""resamples d"""
d1_g = OCLArray.from_array(data)
d2_g = OCLArray.empty(new_shape,data.dtype)
if data.dtype.type == np.float32:
im = OCLImage.empty(data.shape[::1],dtype = np.float32)
elif data.dtype.type == np.complex64:
im = OCLImage.empty(data.shape[::1],dtype = np.float32, num_channels=2)
im.copy_buffer(d1_g)
d2_g.copy_image_resampled(im)
return d2_g.get()
def scale(data, scale=(1., 1., 1.), interp="linear"):
"""returns a interpolated, scaled version of data
scale = (scale_z,scale_y,scale_x)
or
scale = scale_all
interp = "linear" | "nearest"
"""
bop = {"linear": "", "nearest": "-D USENEAREST"}
if not interp in bop.keys():
raise KeyError("interp = '%s' not defined ,valid: %s" %
(interp, bop.keys()))
if not isinstance(scale, (tuple, list, np.ndarray)):
scale = (scale, ) * 3
if len(scale) != 3:
raise ValueError("scale = %s misformed" % scale)
d_im = OCLImage.from_array(data)
nshape = np.array(data.shape) * np.array(scale)
nshape = tuple(nshape.astype(np.int))
res_g = OCLArray.empty(nshape, np.float32)
prog = OCLProgram(abspath("kernels/scale.cl"), build_options=[bop[interp]])
prog.run_kernel("scale", res_g.shape[::-1], None, d_im, res_g.data)
return res_g.get()
def bilateral3(data, size_filter, sigma_p, sigma_x=10.):
"""bilateral filter """
dtype = data.dtype.type
dtypes_kernels = {
np.float32: "bilat3_float",
}
if not dtype in dtypes_kernels:
logger.info("data type %s not supported yet (%s), casting to float:" %
(dtype, list(dtypes_kernels.keys())))
data = data.astype(np.float32)
dtype = data.dtype.type
img = OCLImage.from_array(data)
res = OCLArray.empty_like(data)
prog = OCLProgram(abspath("kernels/bilateral3.cl"))
logger.debug("in bilateral3, image shape: {}".format(img.shape))
prog.run_kernel(dtypes_kernels[dtype], img.shape, None, img, res.data,
np.int32(img.shape[0]), np.int32(img.shape[1]),
np.int32(size_filter), np.float32(sigma_x),
np.float32(sigma_p))
return res.get()
def _transfer_dn(self, dn):
if self._is_subsampled:
self._im_dn = OCLImage.from_array(
self._copy_arr_with_correct_type(dn))
else:
self._buf_dn = OCLArray.from_array(
self._copy_arr_with_correct_type(dn))
def tv2(data, weight, Niter=50):
"""
chambolles tv regularized denoising
weight should be around 2+1.5*noise_sigma
"""
prog = OCLProgram(abspath("kernels/tv2.cl"))
data_im = OCLImage.from_array(data.astype(np, float32, copy=False))
pImgs = [
dev.createImage(data.shape[::-1],
mem_flags=cl.mem_flags.READ_WRITE,
dtype=np.float32,
channel_order=cl.channel_order.RGBA) for i in range(2)
]
outImg = dev.createImage(data.shape[::-1],
dtype=np.float32,
mem_flags=cl.mem_flags.READ_WRITE)
dev.writeImage(inImg, data.astype(np.float32))
dev.writeImage(pImgs[0], np.zeros((4, ) + data.shape, dtype=np.float32))
dev.writeImage(pImgs[1], np.zeros((4, ) + data.shape, dtype=np.float32))
for i in range(Niter):
proc.runKernel("div_step", inImg.shape, None, inImg, pImgs[i % 2],
outImg)
proc.runKernel("grad_step", inImg.shape, None, outImg, pImgs[i % 2],
pImgs[1 - i % 2], np.float32(weight))
return dev.readImage(outImg, dtype=np.float32)
def tv2(data, weight, Niter=50):
"""
chambolles tv regularized denoising
weight should be around 2+1.5*noise_sigma
"""
prog = OCLProgram(abspath("kernels/tv2.cl"))
data_im = OCLImage.from_array(data.astype(np, float32, copy=False))
pImgs = [
dev.createImage(
data.shape[::-1], mem_flags=cl.mem_flags.READ_WRITE, dtype=np.float32, channel_order=cl.channel_order.RGBA
)
for i in range(2)
]
outImg = dev.createImage(data.shape[::-1], dtype=np.float32, mem_flags=cl.mem_flags.READ_WRITE)
dev.writeImage(inImg, data.astype(np.float32))
dev.writeImage(pImgs[0], np.zeros((4,) + data.shape, dtype=np.float32))
dev.writeImage(pImgs[1], np.zeros((4,) + data.shape, dtype=np.float32))
for i in range(Niter):
proc.runKernel("div_step", inImg.shape, None, inImg, pImgs[i % 2], outImg)
proc.runKernel("grad_step", inImg.shape, None, outImg, pImgs[i % 2], pImgs[1 - i % 2], np.float32(weight))
return dev.readImage(outImg, dtype=np.float32)
def bilateral3(data, size_filter, sigma_p, sigma_x = 10.):
"""bilateral filter """
dtype = data.dtype.type
dtypes_kernels = {np.float32:"bilat3_float",}
if not dtype in dtypes_kernels.keys():
logger.info("data type %s not supported yet (%s), casting to float:"%(dtype,dtypes_kernels.keys()))
data = data.astype(np.float32)
dtype = data.dtype.type
img = OCLImage.from_array(data)
res = OCLArray.empty_like(data)
prog = OCLProgram(abspath("kernels/bilateral3.cl"))
print img.shape
prog.run_kernel(dtypes_kernels[dtype],
img.shape,None,
img,res.data,
np.int32(img.shape[0]),np.int32(img.shape[1]),
np.int32(size_filter),np.float32(sigma_x),np.float32(sigma_p))
return res.get()
def _setup_gpu(self):
dev = get_device()
self._queue = dev.queue
self._ctx = dev.context
prog = OCLProgram(absPath("kernels/bpm_3d_kernels.cl"))
# the buffers/ images
Nx, Ny = self.simul_xy
Nx0, Ny0 = self.shape[:2]
self._plan = fft_plan((Ny, Nx), **self.fftplan_kwargs)
self._buf_plane = OCLArray.empty((Ny, Nx), np.complex64)
self._buf_H = OCLArray.empty((Ny, Nx), np.complex64)
self._img_xy = OCLImage.empty((Ny, Nx),
dtype=np.float32,
num_channels=2)
# buffer for the weighted dn average
self.intens_g = OCLArray.empty((1, Ny, Nx), dtype=Bpm3d._real_type)
self.intens_dn_g = OCLArray.empty((1, Ny, Nx), dtype=Bpm3d._real_type)
self.intens_sum_g = OCLArray.zeros((), dtype=Bpm3d._real_type)
self.intens_dn_sum_g = OCLArray.zeros((), dtype=Bpm3d._real_type)
# the kernels
self._kernel_compute_propagator = prog.compute_propagator
self._kernel_compute_propagator.set_scalar_arg_dtypes((None, ) +
(np.float32, ) *
5)
self._kernel_compute_propagator_buf = prog.compute_propagator_buf
self._kernel_compute_propagator_buf.set_scalar_arg_dtypes(
(None, ) + (np.float32, ) * 5 + (None, ) * 2)
self._kernel_mult_complex = prog.mult
self._kernel_im_to_buf_field = prog.img_to_buf_field
self._kernel_im_to_buf_intensity = prog.img_to_buf_intensity
self._kernel_im_to_im_intensity = prog.img_to_img_intensity
self._kernel_buf_to_buf_field = prog.buf_to_buf_field
self._kernel_buf_to_buf_intensity = prog.buf_to_buf_intensity
self._kernel_mult_dn_img_float = prog.mult_dn_image
self._kernel_mult_dn_buf_float = prog.mult_dn
self._kernel_mult_dn_img_complex = prog.mult_dn_image_complex
self._kernel_mult_dn_buf_complex = prog.mult_dn_complex
self._kernel_mult_dn_img_float_local = prog.mult_dn_image_local
self._kernel_mult_dn_buf_float_local = prog.mult_dn_local
self._kernel_mult_dn_img_complex_local = prog.mult_dn_image_complex_local
self._kernel_mult_dn_buf_complex_local = prog.mult_dn_complex_local
self._kernel_reduction = OCLMultiReductionKernel(
np.float32,
neutral="0",
reduce_expr="a+b",
map_exprs=["a[i]", "b[i]"],
arguments="__global float *a, __global float *b")
self._fill_propagator(self.n0)
def _ocl_star_dist(a, n_rays=32):
from gputools import OCLProgram, OCLArray, OCLImage
(np.isscalar(n_rays) and 0 < int(n_rays)) or _raise(ValueError())
n_rays = int(n_rays)
src = OCLImage.from_array(a.astype(np.uint16, copy=False))
dst = OCLArray.empty(a.shape + (n_rays, ), dtype=np.float32)
program = OCLProgram(path_absolute("kernels/stardist2d.cl"),
build_options=['-D', 'N_RAYS=%d' % n_rays])
program.run_kernel('star_dist', src.shape, None, dst.data, src)
return dst.get()
def _fill_buf_plane(self, u0):
"""fills buf plane from the array u0 with the correct sizes..."""
u0 = u0.astype(np.complex64, copy=False)
if u0.shape == self._buf_plane.shape:
self._buf_plane.write_array(u0)
else:
# interpolate
self._buf_plane.copy_image_resampled(OCLImage.from_array(u0))
def _fill_buf_plane(self, u0):
"""fills buf plane from the array u0 with the correct sizes..."""
u0 = u0.astype(np.complex64, copy=False)
if u0.shape==self._buf_plane.shape:
self._buf_plane.write_array(u0)
else:
# interpolate
self._buf_plane.copy_image_resampled(OCLImage.from_array(u0))
def scale_bicubic(data, scale=(1., 1., 1.)):
"""
returns a interpolated, scaled version of data
the output shape is scaled too.
Parameters
----------
data: ndarray
3d input array
scale: float, tuple
scaling factor along each axis (x,y,z)
interpolation: str
either "nearest" or "linear"
Returns
-------
scaled output
"""
if not (isinstance(data, np.ndarray) and data.ndim == 3):
raise ValueError("input data has to be a 3d array!")
options_types = {
np.uint8: ["-D", "TYPENAME=uchar", "-D", "READ_IMAGE=read_imageui"],
np.uint16: ["-D", "TYPENAME=short", "-D", "READ_IMAGE=read_imageui"],
np.float32: ["-D", "TYPENAME=float", "-D", "READ_IMAGE=read_imagef"],
}
dtype = data.dtype.type
if not dtype in options_types:
raise ValueError("type %s not supported! Available: %s" %
(dtype, str(list(options_types.keys()))))
if not isinstance(scale, (tuple, list, np.ndarray)):
scale = (scale, ) * 3
if len(scale) != 3:
raise ValueError("scale = %s misformed" % scale)
d_im = OCLImage.from_array(data)
nshape = _scale_shape(data.shape, scale)
res_g = OCLArray.empty(nshape, dtype)
prog = OCLProgram(abspath("kernels/scale.cl"),
build_options=options_types[dtype])
prog.run_kernel("scale_bicubic", res_g.shape[::-1], None, d_im, res_g.data)
return res_g.get()
def stardist_from_labels(a, n_rays=32):
""" assumes a to be a label image with integer values that encode object ids. id 0 denotes background. """
out_shape = a.shape + (n_rays, )
src = OCLImage.from_array(a.astype(np.uint16, copy=False))
dst = OCLArray.empty(out_shape, dtype=np.float32)
# program = OCLProgram("/home/uschmidt/research/dsb2018/notebooks/kernel.cl", build_options=["-D", "N_RAYS=%d" % n_rays])
# program = OCLProgram("kernel.cl", build_options=["-D", "N_RAYS=%d" % n_rays])
program = OCLProgram(src_str=kernel,
build_options=["-D", "N_RAYS=%d" % n_rays])
program.run_kernel('star_dist', src.shape, None, dst.data, src)
return dst.get()
def _setup_gpu(self):
dev = get_device()
self._queue = dev.queue
self._ctx = dev.context
prog = OCLProgram(absPath("kernels/bpm_3d_kernels.cl"))
# the buffers/ images
Nx, Ny = self.simul_xy
Nx0, Ny0 = self.shape[:2]
self._plan = fft_plan((Ny, Nx), **self.fftplan_kwargs)
self._buf_plane = OCLArray.empty((Ny, Nx), np.complex64)
self._buf_H = OCLArray.empty((Ny, Nx), np.complex64)
self._img_xy = OCLImage.empty((Ny, Nx), dtype=np.float32, num_channels=2)
# buffer for the weighted dn average
self.intens_g = OCLArray.empty((1, Ny, Nx), dtype=Bpm3d._real_type)
self.intens_dn_g = OCLArray.empty((1, Ny, Nx), dtype=Bpm3d._real_type)
self.intens_sum_g = OCLArray.zeros((), dtype=Bpm3d._real_type)
self.intens_dn_sum_g = OCLArray.zeros((), dtype=Bpm3d._real_type)
# the kernels
self._kernel_compute_propagator = prog.compute_propagator
self._kernel_compute_propagator.set_scalar_arg_dtypes((None,)+(np.float32,)*5)
self._kernel_compute_propagator_buf = prog.compute_propagator_buf
self._kernel_compute_propagator_buf.set_scalar_arg_dtypes((None,)+(np.float32,)*5+(None,)*2)
self._kernel_mult_complex = prog.mult
self._kernel_im_to_buf_field = prog.img_to_buf_field
self._kernel_im_to_buf_intensity = prog.img_to_buf_intensity
self._kernel_im_to_im_intensity = prog.img_to_img_intensity
self._kernel_buf_to_buf_field = prog.buf_to_buf_field
self._kernel_buf_to_buf_intensity = prog.buf_to_buf_intensity
self._kernel_mult_dn_img_float = prog.mult_dn_image
self._kernel_mult_dn_buf_float = prog.mult_dn
self._kernel_mult_dn_img_complex = prog.mult_dn_image_complex
self._kernel_mult_dn_buf_complex = prog.mult_dn_complex
self._kernel_mult_dn_img_float_local = prog.mult_dn_image_local
self._kernel_mult_dn_buf_float_local = prog.mult_dn_local
self._kernel_mult_dn_img_complex_local = prog.mult_dn_image_complex_local
self._kernel_mult_dn_buf_complex_local = prog.mult_dn_complex_local
self._kernel_reduction = OCLMultiReductionKernel(np.float32,
neutral="0", reduce_expr="a+b",
map_exprs=["a[i]", "b[i]"],
arguments="__global float *a, __global float *b")
self._fill_propagator(self.n0)
def _ocl_star_dist(lbl, n_rays=32, grid=(1, 1)):
from gputools import OCLProgram, OCLArray, OCLImage
(np.isscalar(n_rays) and 0 < int(n_rays)) or _raise(ValueError())
n_rays = int(n_rays)
# slicing with grid is done with tuple(slice(0, None, g) for g in grid)
res_shape = tuple((s - 1) // g + 1 for s, g in zip(lbl.shape, grid))
src = OCLImage.from_array(lbl.astype(np.uint16, copy=False))
dst = OCLArray.empty(res_shape + (n_rays, ), dtype=np.float32)
program = OCLProgram(path_absolute("kernels/stardist2d.cl"),
build_options=['-D', 'N_RAYS=%d' % n_rays])
program.run_kernel('star_dist', res_shape[::-1], None, dst.data, src,
np.int32(grid[0]), np.int32(grid[1]))
return dst.get()
def _ocl_star_dist3D(lbl, rays, grid=(1, 1, 1)):
from gputools import OCLProgram, OCLArray, OCLImage
grid = _normalize_grid(grid, 3)
# if not all(g==1 for g in grid):
# raise NotImplementedError("grid not yet implemented for OpenCL version of star_dist3D()...")
res_shape = tuple(s // g for s, g in zip(lbl.shape, grid))
lbl_g = OCLImage.from_array(lbl.astype(np.uint16, copy=False))
dist_g = OCLArray.empty(res_shape + (len(rays), ), dtype=np.float32)
rays_g = OCLArray.from_array(rays.vertices.astype(np.float32, copy=False))
program = OCLProgram(path_absolute("kernels/stardist3d.cl"),
build_options=['-D', 'N_RAYS=%d' % len(rays)])
program.run_kernel('stardist3d', res_shape[::-1], None, lbl_g, rays_g.data,
dist_g.data, np.int32(grid[0]), np.int32(grid[1]),
np.int32(grid[2]))
return dist_g.get()
def bilateral2(data, fSize, sigma_p, sigma_x=10.):
"""bilateral filter """
dtype = data.dtype.type
dtypes_kernels = {np.float32: "bilat2_float", np.uint16: "bilat2_short"}
if not dtype in dtypes_kernels.keys():
logger.info("data type %s not supported yet (%s), casting to float:" %
(dtype, dtypes_kernels.keys()))
data = data.astype(np.float32)
dtype = data.dtype.type
img = OCLImage.from_array(data)
res = OCLArray.empty_like(data)
prog = OCLProgram(abspath("kernels/bilateral2.cl"))
prog.run_kernel(dtypes_kernels[dtype], img.shape, None, img, res.data,
np.int32(img.shape[0]), np.int32(img.shape[1]),
np.int32(fSize), np.float32(sigma_x), np.float32(sigma_p))
return res.get()
def affine(data, mat = np.identity(4), interp = "linear"):
"""affine transform data with matrix mat
"""
bop = {"linear":"","nearest":"-D USENEAREST"}
if not interp in bop.keys():
raise KeyError("interp = '%s' not defined ,valid: %s"%(interp,bop.keys()))
d_im = OCLImage.from_array(data)
res_g = OCLArray.empty(data.shape,np.float32)
mat_g = OCLArray.from_array(np.linalg.inv(mat).astype(np.float32,copy=False))
prog = OCLProgram(abspath("kernels/transformations.cl")
, build_options=[bop[interp]])
prog.run_kernel("affine",
data.shape[::-1],None,
d_im,res_g.data,mat_g.data)
return res_g.get()
def affine(data, mat = np.identity(4), mode ="linear"):
"""affine transform data with matrix mat
"""
bop = {"linear":"","nearest":"-D USENEAREST"}
if not mode in bop.keys():
raise KeyError("mode = '%s' not defined ,valid: %s"%(mode, bop.keys()))
d_im = OCLImage.from_array(data)
res_g = OCLArray.empty(data.shape,np.float32)
mat_g = OCLArray.from_array(np.linalg.inv(mat).astype(np.float32,copy=False))
prog = OCLProgram(abspath("kernels/transformations.cl")
, build_options=[bop[mode]])
prog.run_kernel("affine",
data.shape[::-1],None,
d_im,res_g.data,mat_g.data)
return res_g.get()
def _convolve3_old(data,h, dev = None):
"""convolves 3d data with kernel h on the GPU Device dev
boundary conditions are clamping to edge.
h is converted to float32
if dev == None the default one is used
"""
if dev is None:
dev = get_device()
if dev is None:
raise ValueError("no OpenCLDevice found...")
dtype = data.dtype.type
dtypes_options = {np.float32:"",
np.uint16:"-D SHORTTYPE"}
if not dtype in dtypes_options.keys():
raise TypeError("data type %s not supported yet, please convert to:"%dtype,dtypes_options.keys())
prog = OCLProgram(abspath("kernels/convolve3.cl"),
build_options = dtypes_options[dtype])
hbuf = OCLArray.from_array(h.astype(np.float32))
img = OCLImage.from_array(data)
res = OCLArray.empty(data.shape,dtype=np.float32)
Ns = [np.int32(n) for n in data.shape+h.shape]
prog.run_kernel("convolve3d",img.shape,None,
img,hbuf.data,res.data,
*Ns)
return res.get()
def scale(data, scale = (1.,1.,1.), interp = "linear"):
"""returns a interpolated, scaled version of data
scale = (scale_z,scale_y,scale_x)
or
scale = scale_all
interp = "linear" | "nearest"
"""
bop = {"linear":[],"nearest":["-D","USENEAREST"]}
if not interp in bop.keys():
raise KeyError("interp = '%s' not defined ,valid: %s"%(interp,bop.keys()))
if not isinstance(scale,(tuple, list, np.ndarray)):
scale = (scale,)*3
if len(scale) != 3:
raise ValueError("scale = %s misformed"%scale)
d_im = OCLImage.from_array(data)
nshape = np.array(data.shape)*np.array(scale)
nshape = tuple(nshape.astype(np.int))
res_g = OCLArray.empty(nshape,np.float32)
prog = OCLProgram(abspath("kernels/scale.cl"), build_options=bop[interp])
prog.run_kernel("scale",
res_g.shape[::-1],None,
d_im,res_g.data)
return res_g.get()
def _transfer_dn(self, dn):
if self._is_subsampled:
self._im_dn = OCLImage.from_array(self._copy_arr_with_correct_type(dn))
else:
self._buf_dn = OCLArray.from_array(self._copy_arr_with_correct_type(dn))
def image_from_array(data):
im = OCLImage.from_array(data)
assert np.allclose(data,im.get())
def convolve_spatial2(im, hs, mode="constant", plan=None, return_plan=False):
"""
spatial varying convolution of an 2d image with a 2d grid of psfs
shape(im_ = (Ny,Nx)
shape(hs) = (Gy,Gx, Hy,Hx)
the input image im is subdivided into (Gy,Gz) blocks
hs[j,i] is the psf at the center of each block (i,j)
as of now each image dimension has to be divisble by the grid dim, i.e.
Nx % Gx == 0
Ny % Gy == 0
mode can be:
"constant" - assumed values to be zero
"wrap" - periodic boundary condition
"""
if im.ndim != 2 or hs.ndim != 4:
raise ValueError("wrong dimensions of input!")
if not np.all([n % g == 0 for n, g in zip(im.shape, hs.shape[:2])]):
raise NotImplementedError(
"shape of image has to be divisible by Gx Gy = %s shape mismatch"
% (str(hs.shape[:2])))
mode_str = {"constant": "CLK_ADDRESS_CLAMP", "wrap": "CLK_ADDRESS_REPEAT"}
Ny, Nx = im.shape
Gy, Gx = hs.shape[:2]
# the size of each block within the grid
Nblock_y, Nblock_x = Ny / Gy, Nx / Gx
# the size of the overlapping patches with safety padding
Npatch_x, Npatch_y = _next_power_of_2(3 * Nblock_x), _next_power_of_2(
3 * Nblock_y)
#Npatch_x, Npatch_y = _next_power_of_2(2*Nblock_x), _next_power_of_2(2*Nblock_y)
print(Nblock_x, Npatch_x)
hs = np.fft.fftshift(pad_to_shape(hs, (Gy, Gx, Npatch_y, Npatch_x)),
axes=(2, 3))
prog = OCLProgram(abspath("kernels/conv_spatial.cl"),
build_options=["-D",
"ADDRESSMODE=%s" % mode_str[mode]])
if plan is None:
plan = fft_plan((Npatch_y, Npatch_x))
patches_g = OCLArray.empty((Gy, Gx, Npatch_y, Npatch_x), np.complex64)
h_g = OCLArray.from_array(hs.astype(np.complex64))
im_g = OCLImage.from_array(im.astype(np.float32, copy=False))
x0s = Nblock_x * np.arange(Gx)
y0s = Nblock_y * np.arange(Gy)
print(x0s)
for i, _x0 in enumerate(x0s):
for j, _y0 in enumerate(y0s):
prog.run_kernel(
"fill_patch2", (Npatch_x, Npatch_y), None, im_g,
np.int32(_x0 + Nblock_x / 2 - Npatch_x / 2),
np.int32(_y0 + Nblock_y / 2 - Npatch_y / 2), patches_g.data,
np.int32(i * Npatch_x * Npatch_y +
j * Gx * Npatch_x * Npatch_y))
# convolution
fft(patches_g, inplace=True, batch=Gx * Gy, plan=plan)
fft(h_g, inplace=True, batch=Gx * Gy, plan=plan)
prog.run_kernel("mult_inplace", (Npatch_x * Npatch_y * Gx * Gy, ), None,
patches_g.data, h_g.data)
fft(patches_g, inplace=True, inverse=True, batch=Gx * Gy, plan=plan)
#return patches_g.get()
#accumulate
res_g = OCLArray.empty(im.shape, np.float32)
for i in range(Gx + 1):
for j in range(Gy + 1):
prog.run_kernel("interpolate2", (Nblock_x, Nblock_y),
None, patches_g.data, res_g.data, np.int32(i),
np.int32(j), np.int32(Gx), np.int32(Gy),
np.int32(Npatch_x), np.int32(Npatch_y))
res = res_g.get()
if return_plan:
return res, plan
else:
return res
def convolve_spatial3(im,
hs,
mode="constant",
plan=None,
return_plan=False,
pad_factor=2):
"""
spatial varying convolution of an 3d image with a 3d grid of psfs
shape(im_ = (Nz,Ny,Nx)
shape(hs) = (Gz,Gy,Gx, Hz,Hy,Hx)
the input image im is subdivided into (Gx,Gy,Gz) blocks
hs[k,j,i] is the psf at the center of each block (i,j,k)
as of now each image dimension has to be divisble by the grid dim, i.e.
Nx % Gx == 0
Ny % Gy == 0
Nz % Gz == 0
mode can be:
"constant" - assumed values to be zero
"wrap" - periodic boundary condition
"""
if im.ndim != 3 or hs.ndim != 6:
raise ValueError("wrong dimensions of input!")
if not np.all([n % g == 0 for n, g in zip(im.shape, hs.shape[:3])]):
raise NotImplementedError(
"shape of image has to be divisible by Gx Gy = %s !" %
(str(hs.shape[:3])))
mode_str = {"constant": "CLK_ADDRESS_CLAMP", "wrap": "CLK_ADDRESS_REPEAT"}
Ns = tuple(im.shape)
Gs = tuple(hs.shape[:3])
# the size of each block within the grid
Nblocks = [n / g for n, g in zip(Ns, Gs)]
# the size of the overlapping patches with safety padding
Npatchs = tuple([_next_power_of_2(pad_factor * nb) for nb in Nblocks])
print(hs.shape)
hs = np.fft.fftshift(pad_to_shape(hs, Gs + Npatchs), axes=(3, 4, 5))
prog = OCLProgram(abspath("kernels/conv_spatial.cl"),
build_options=["-D",
"ADDRESSMODE=%s" % mode_str[mode]])
if plan is None:
plan = fft_plan(Npatchs)
patches_g = OCLArray.empty(Gs + Npatchs, np.complex64)
h_g = OCLArray.from_array(hs.astype(np.complex64))
im_g = OCLImage.from_array(im.astype(np.float32, copy=False))
Xs = [nb * np.arange(g) for nb, g in zip(Nblocks, Gs)]
print(Nblocks)
# this loops over all i,j,k
for (k, _z0), (j, _y0), (i, _x0) in product(*[enumerate(X) for X in Xs]):
prog.run_kernel(
"fill_patch3", Npatchs[::-1], None, im_g,
np.int32(_x0 + Nblocks[2] / 2 - Npatchs[2] / 2),
np.int32(_y0 + Nblocks[1] / 2 - Npatchs[1] / 2),
np.int32(_z0 + Nblocks[0] / 2 - Npatchs[0] / 2), patches_g.data,
np.int32(i * np.prod(Npatchs) + j * Gs[2] * np.prod(Npatchs) +
k * Gs[2] * Gs[1] * np.prod(Npatchs)))
print(patches_g.shape, h_g.shape)
# convolution
fft(patches_g, inplace=True, batch=np.prod(Gs), plan=plan)
fft(h_g, inplace=True, batch=np.prod(Gs), plan=plan)
prog.run_kernel("mult_inplace", (np.prod(Npatchs) * np.prod(Gs), ), None,
patches_g.data, h_g.data)
fft(patches_g, inplace=True, inverse=True, batch=np.prod(Gs), plan=plan)
#return patches_g.get()
#accumulate
res_g = OCLArray.zeros(im.shape, np.float32)
for k, j, i in product(*[list(range(g + 1)) for g in Gs]):
prog.run_kernel("interpolate3", Nblocks[::-1], None, patches_g.data,
res_g.data, np.int32(i), np.int32(j), np.int32(k),
np.int32(Gs[2]), np.int32(Gs[1]), np.int32(Gs[0]),
np.int32(Npatchs[2]), np.int32(Npatchs[1]),
np.int32(Npatchs[0]))
res = res_g.get()
if return_plan:
return res, plan
else:
return res
def affine(data, mat=np.identity(4), mode="constant", interpolation="linear"):
"""
affine transform data with matrix mat, which is the inverse coordinate transform matrix
(similar to ndimage.affine_transform)
Parameters
----------
data, ndarray
3d array to be transformed
mat, ndarray
3x3 or 4x4 inverse coordinate transform matrix
mode: string
boundary mode, one of the following:
'constant'
pads with zeros
'edge'
pads with edge values
'wrap'
pads with the repeated version of the input
interpolation, string
interpolation mode, one of the following
'linear'
'nearest'
Returns
-------
res: ndarray
transformed array (same shape as input)
"""
warnings.warn(
"gputools.transform.affine: API change as of gputools>= 0.2.8: the inverse of the matrix is now used as in scipy.ndimage.affine_transform"
)
if not (isinstance(data, np.ndarray) and data.ndim == 3):
raise ValueError("input data has to be a 3d array!")
interpolation_defines = {
"linear": ["-D", "SAMPLER_FILTER=CLK_FILTER_LINEAR"],
"nearest": ["-D", "SAMPLER_FILTER=CLK_FILTER_NEAREST"]
}
mode_defines = {
"constant": ["-D", "SAMPLER_ADDRESS=CLK_ADDRESS_CLAMP"],
"wrap": ["-D", "SAMPLER_ADDRESS=CLK_ADDRESS_REPEAT"],
"edge": ["-D", "SAMPLER_ADDRESS=CLK_ADDRESS_CLAMP_TO_EDGE"]
}
if not interpolation in interpolation_defines:
raise KeyError("interpolation = '%s' not defined ,valid: %s" %
(interpolation, list(interpolation_defines.keys())))
if not mode in mode_defines:
raise KeyError("mode = '%s' not defined ,valid: %s" %
(mode, list(mode_defines.keys())))
# reorder matrix, such that x,y,z -> z,y,x (as the kernel is assuming that)
d_im = OCLImage.from_array(data.astype(np.float32, copy=False))
res_g = OCLArray.empty(data.shape, np.float32)
mat_inv_g = OCLArray.from_array(mat.astype(np.float32, copy=False))
prog = OCLProgram(abspath("kernels/affine.cl"),
build_options=interpolation_defines[interpolation] +
mode_defines[mode])
prog.run_kernel("affine3", data.shape[::-1], None, d_im, res_g.data,
mat_inv_g.data)
return res_g.get()
def geometric_transform(data,
mapping="c0,c1",
output_shape=None,
mode='constant',
interpolation="linear"):
"""
Apply an arbitrary geometric transform.
The given mapping function is used to find, for each point in the
output, the corresponding coordinates in the input. The value of the
input at those coordinates is determined by spline interpolation of
the requested order.
Parameters
----------
%(input)s
mapping : {callable, scipy.LowLevelCallable}
A callable object that accepts a tuple of length equal to the output
array rank, and returns the corresponding input coordinates as a tuple
of length equal to the input array rank.
"""
if not (isinstance(data, np.ndarray) and data.ndim in (2, 3)):
raise ValueError("input data has to be a 2d or 3d array!")
interpolation_defines = {
"linear": ["-D", "SAMPLER_FILTER=CLK_FILTER_LINEAR"],
"nearest": ["-D", "SAMPLER_FILTER=CLK_FILTER_NEAREST"]
}
mode_defines = {
"constant": ["-D", "SAMPLER_ADDRESS=CLK_ADDRESS_CLAMP"],
"wrap": ["-D", "SAMPLER_ADDRESS=CLK_ADDRESS_REPEAT"],
"edge": ["-D", "SAMPLER_ADDRESS=CLK_ADDRESS_CLAMP_TO_EDGE"]
}
if not interpolation in interpolation_defines:
raise KeyError("interpolation = '%s' not defined ,valid: %s" %
(interpolation, list(interpolation_defines.keys())))
if not mode in mode_defines:
raise KeyError("mode = '%s' not defined ,valid: %s" %
(mode, list(mode_defines.keys())))
if not data.dtype.type in cl_buffer_datatype_dict:
raise KeyError(
"dtype %s not supported yet (%s)" %
(data.dtype.type, tuple(cl_buffer_datatype_dict.keys())))
dtype_defines = [
"-D",
"DTYPE={type}".format(type=cl_buffer_datatype_dict[data.dtype.type])
]
image_functions = {
np.float32: "read_imagef",
np.uint8: "read_imageui",
np.uint16: "read_imageui",
np.int32: "read_imagei"
}
image_read_defines = [
"-D", "READ_IMAGE=%s" % image_functions[data.dtype.type]
]
with open(abspath("kernels/geometric_transform.cl"), "r") as f:
tpl = Template(f.read())
output_shape = tuple(output_shape)
mappings = {"FUNC2": "c1,c0", "FUNC3": "c2,c1,c0"}
mappings["FUNC%d" % data.ndim] = ",".join(reversed(mapping.split(",")))
rendered = tpl.render(**mappings)
d_im = OCLImage.from_array(data)
res_g = OCLArray.empty(output_shape, data.dtype)
prog = OCLProgram(src_str=rendered,
build_options=interpolation_defines[interpolation] +
mode_defines[mode] + dtype_defines + image_read_defines)
kernel = "geometric_transform{ndim}".format(ndim=data.ndim)
prog.run_kernel(kernel, output_shape[::-1], None, d_im, res_g.data)
return res_g.get()
def _bpm_3d_image(size,
units,
lam = .5,
u0 = None, dn = None,
subsample = 1,
n0 = 1.,
return_scattering = False,
return_g = False,
return_full_last = False,
use_fresnel_approx = False,
):
"""
simulates the propagation of monochromativ wave of wavelength lam with initial conditions u0 along z in a media filled with dn
size - the dimension of the image to be calulcated in pixels (Nx,Ny,Nz)
units - the unit lengths of each dimensions in microns
lam - the wavelength
u0 - the initial field distribution, if u0 = None an incident plane wave is assumed
dn - the refractive index of the medium (can be complex)
"""
clock = StopWatch()
clock.tic("setup")
Nx, Ny, Nz = size
dx, dy, dz = units
# subsampling
Nx2, Ny2, Nz2 = (subsample*N for N in size)
dx2, dy2, dz2 = (1.*d/subsample for d in units)
#setting up the propagator
k0 = 2.*np.pi/lam
kxs = 2.*np.pi*np.fft.fftfreq(Nx2,dx2)
kys = 2.*np.pi*np.fft.fftfreq(Ny2,dy2)
KY, KX = np.meshgrid(kys,kxs, indexing= "ij")
#H0 = np.sqrt(0.j+n0**2*k0**2-KX**2-KY**2)
H0 = np.sqrt(n0**2*k0**2-KX**2-KY**2)
if use_fresnel_approx:
H0 = 0.j+n0**2*k0-.5*(KX**2+KY**2)
outsideInds = np.isnan(H0)
H = np.exp(-1.j*dz2*H0)
H[outsideInds] = 0.
H0[outsideInds] = 0.
if u0 is None:
u0 = np.ones((Ny2,Nx2),np.complex64)
else:
if subsample >1:
u0 = zoom(np.real(u0),subsample) + 1.j*zoom(np.imag(u0),subsample)
# setting up the gpu buffers and kernels
program = OCLProgram(absPath("kernels/bpm_3d_kernels.cl"))
plan = fft_plan((Ny2,Nx2))
plane_g = OCLArray.from_array(u0.astype(np.complex64))
h_g = OCLArray.from_array(H.astype(np.complex64))
if dn is not None:
if isinstance(dn,OCLImage):
dn_g = dn
else:
if dn.dtype.type in (np.complex64,np.complex128):
dn_complex = np.zeros(dn.shape+(2,),np.float32)
dn_complex[...,0] = np.real(dn)
dn_complex[...,1] = np.imag(dn)
dn_g = OCLImage.from_array(dn_complex)
else:
dn_g = OCLImage.from_array(dn.astype(np.float32))
isComplexDn = dn.dtype.type in (np.complex64,np.complex128)
else:
#dummy dn
dn_g = OCLArray.empty((1,)*3,np.float16)
if return_scattering:
cos_theta = np.real(H0)/n0/k0
# = cos(theta)
scatter_weights = cos_theta
scatter_weights_g = OCLArray.from_array(scatter_weights.astype(np.float32))
# = cos(theta)^2
gfactor_weights = cos_theta**2
gfactor_weights_g = OCLArray.from_array(gfactor_weights.astype(np.float32))
#return None,None,scatter_weights, gfactor_weights
scatter_cross_sec_g = OCLArray.zeros(Nz,"float32")
gfactor_g = OCLArray.zeros(Nz,"float32")
plain_wave_dct = Nx2*Ny2*np.exp(-1.j*k0*n0*np.arange(Nz)*dz).astype(np.complex64)
reduce_kernel = OCLReductionKernel(
np.float32, neutral="0",
reduce_expr="a+b",
map_expr="weights[i]*cfloat_abs(field[i]-(i==0)*plain)*cfloat_abs(field[i]-(i==0)*plain)",
arguments="__global cfloat_t *field, __global float * weights,cfloat_t plain")
# reduce_kernel = OCLReductionKernel(
# np.float32, neutral="0",
# reduce_expr="a+b",
# map_expr = "weights[i]*(i!=0)*cfloat_abs(field[i])*cfloat_abs(field[i])",
# arguments = "__global cfloat_t *field, __global float * weights,cfloat_t plain")
u_g = OCLArray.empty((Nz,Ny,Nx),dtype=np.complex64)
program.run_kernel("copy_subsampled_buffer",(Nx,Ny),None,
u_g.data,plane_g.data,
np.int32(subsample),
np.int32(0))
clock.toc("setup")
clock.tic("run")
for i in range(Nz-1):
for substep in range(subsample):
fft(plane_g,inplace = True, plan = plan)
program.run_kernel("mult",(Nx2*Ny2,),None,
plane_g.data,h_g.data)
if return_scattering and substep == (subsample-1):
scatter_cross_sec_g[i+1] = reduce_kernel(plane_g,
scatter_weights_g,
plain_wave_dct[i+1])
gfactor_g[i+1] = reduce_kernel(plane_g,
gfactor_weights_g,
plain_wave_dct[i+1])
fft(plane_g,inplace = True, inverse = True, plan = plan)
if dn is not None:
if isComplexDn:
program.run_kernel("mult_dn_complex_image",(Nx2,Ny2),None,
plane_g.data,dn_g,
np.float32(k0*dz2),
np.float32(n0),
np.int32(subsample*(i+1.)+substep),
np.int32(subsample))
else:
program.run_kernel("mult_dn_image",(Nx2,Ny2),None,
plane_g.data,dn_g,
np.float32(k0*dz2),
np.float32(n0),
np.int32(subsample*(i+1.)+substep),
np.int32(subsample))
program.run_kernel("copy_subsampled_buffer",(Nx,Ny),None,
u_g.data,plane_g.data,
np.int32(subsample),
np.int32((i+1)*Nx*Ny))
clock.toc("run")
print clock
result = (u_g.get(), dn_g.get(),)
if return_scattering:
# normalizing prefactor dkx = dx2/Nx2
# prefac = 1./Nx2/Ny2*dx2*dy2/4./np.pi/n0
prefac = 1./Nx2/Ny2*dx2*dy2
p = prefac*scatter_cross_sec_g.get()
result += (p,)
if return_g:
prefac = 1./Nx2/Ny2*dx2*dy2
g = prefac*gfactor_g.get()/p
result += (g,)
if return_full_last:
result += (plane_g.get(),)
return result
def nlm3(data, sigma, size_filter=2, size_search=3):
"""
Fast version of Non local mean denoising of 3 dimensional data
see [1]_
Parameters
----------
data: 3d ndarray
the input volume
sigma: float
denoising strength
size_filter: int
the half size of the image patches (i.e. width is 2*size_filter+1 along every dimension)
size_search: int
the half size of the search window (i.e. width is 2*size_search+1 along every dimension)
Returns
-------
ndarray
the denoised volume
Examples
--------
>>> d = np.random.uniform(0,1,(100,)*3)
>>> d[40:60,40:60,40:60] += 5
>>> res = nlm3(d,1.,3,4)
References
----------
.. [1] Buades, Antoni, Bartomeu Coll, and J-M. Morel. "A non-local algorithm for image denoising." CVPR 2005.
"""
prog = OCLProgram(abspath("kernels/nlm3.cl"),
build_options="-D FS=%i -D BS=%i" %
(size_filter, size_search))
data = data.astype(np.float32, copy=False)
img = OCLImage.from_array(data)
distImg = OCLImage.empty_like(data)
distImg = OCLImage.empty_like(data)
tmpImg = OCLImage.empty_like(data)
tmpImg2 = OCLImage.empty_like(data)
accBuf = OCLArray.zeros(data.shape, np.float32)
weightBuf = OCLArray.zeros(data.shape, np.float32)
for dx in range(size_search + 1):
for dy in range(-size_search, size_search + 1):
for dz in range(-size_search, size_search + 1):
prog.run_kernel("dist", img.shape, None, img, tmpImg,
np.int32(dx), np.int32(dy), np.int32(dz))
prog.run_kernel("convolve", img.shape, None, tmpImg, tmpImg2,
np.int32(1))
prog.run_kernel("convolve", img.shape, None, tmpImg2, tmpImg,
np.int32(2))
prog.run_kernel("convolve", img.shape, None, tmpImg, distImg,
np.int32(4))
prog.run_kernel("computePlus", img.shape, None, img, distImg,
accBuf.data, weightBuf.data,
np.int32(img.shape[0]), np.int32(img.shape[1]),
np.int32(img.shape[2]), np.int32(dx),
np.int32(dy), np.int32(dz), np.float32(sigma))
if any([dx, dy, dz]):
prog.run_kernel("computeMinus", img.shape, None, img,
distImg, accBuf.data, weightBuf.data,
np.int32(img.shape[0]),
np.int32(img.shape[1]),
np.int32(img.shape[2]), np.int32(dx),
np.int32(dy), np.int32(dz),
np.float32(sigma))
acc = accBuf.get()
weights = weightBuf.get()
return acc / weights
def convolve_spatial2(im, hs,
mode = "constant",
plan = None,
return_plan = False):
"""
spatial varying convolution of an 2d image with a 2d grid of psfs
shape(im_ = (Ny,Nx)
shape(hs) = (Gy,Gx, Hy,Hx)
the input image im is subdivided into (Gy,Gz) blocks
hs[j,i] is the psf at the center of each block (i,j)
as of now each image dimension has to be divisble by the grid dim, i.e.
Nx % Gx == 0
Ny % Gy == 0
mode can be:
"constant" - assumed values to be zero
"wrap" - periodic boundary condition
"""
if im.ndim !=2 or hs.ndim !=4:
raise ValueError("wrong dimensions of input!")
if not np.all([n%g==0 for n,g in zip(im.shape,hs.shape[:2])]):
raise NotImplementedError("shape of image has to be divisible by Gx Gy = %s shape mismatch"%(str(hs.shape[:2])))
mode_str = {"constant":"CLK_ADDRESS_CLAMP",
"wrap":"CLK_ADDRESS_REPEAT"}
Ny, Nx = im.shape
Gy, Gx = hs.shape[:2]
# the size of each block within the grid
Nblock_y, Nblock_x = Ny/Gy, Nx/Gx
# the size of the overlapping patches with safety padding
Npatch_x, Npatch_y = _next_power_of_2(3*Nblock_x), _next_power_of_2(3*Nblock_y)
#Npatch_x, Npatch_y = _next_power_of_2(2*Nblock_x), _next_power_of_2(2*Nblock_y)
print Nblock_x, Npatch_x
hs = np.fft.fftshift(pad_to_shape(hs,(Gy,Gx,Npatch_y,Npatch_x)),axes=(2,3))
prog = OCLProgram(abspath("kernels/conv_spatial.cl"),
build_options=["-D","ADDRESSMODE=%s"%mode_str[mode]])
if plan is None:
plan = fft_plan((Npatch_y,Npatch_x))
patches_g = OCLArray.empty((Gy,Gx,Npatch_y,Npatch_x),np.complex64)
h_g = OCLArray.from_array(hs.astype(np.complex64))
im_g = OCLImage.from_array(im.astype(np.float32,copy=False))
x0s = Nblock_x*np.arange(Gx)
y0s = Nblock_y*np.arange(Gy)
print x0s
for i,_x0 in enumerate(x0s):
for j,_y0 in enumerate(y0s):
prog.run_kernel("fill_patch2",(Npatch_x,Npatch_y),None,
im_g,
np.int32(_x0+Nblock_x/2-Npatch_x/2),
np.int32(_y0+Nblock_y/2-Npatch_y/2),
patches_g.data,
np.int32(i*Npatch_x*Npatch_y+j*Gx*Npatch_x*Npatch_y))
# convolution
fft(patches_g,inplace=True, batch = Gx*Gy, plan = plan)
fft(h_g,inplace=True, batch = Gx*Gy, plan = plan)
prog.run_kernel("mult_inplace",(Npatch_x*Npatch_y*Gx*Gy,),None,
patches_g.data, h_g.data)
fft(patches_g,inplace=True, inverse = True, batch = Gx*Gy, plan = plan)
#return patches_g.get()
#accumulate
res_g = OCLArray.empty(im.shape,np.float32)
for i in xrange(Gx+1):
for j in xrange(Gy+1):
prog.run_kernel("interpolate2",(Nblock_x,Nblock_y),None,
patches_g.data,res_g.data,
np.int32(i),np.int32(j),
np.int32(Gx),np.int32(Gy),
np.int32(Npatch_x),np.int32(Npatch_y))
res = res_g.get()
if return_plan:
return res, plan
else:
return res
def image_create_write(data):
im = OCLImage.empty(data.shape,data.dtype)
im.write_array(data)
assert np.allclose(data,im.get())
def convolve_spatial3(im, hs,
mode = "constant",
plan = None,
return_plan = False,
pad_factor = 2):
"""
spatial varying convolution of an 3d image with a 3d grid of psfs
shape(im_ = (Nz,Ny,Nx)
shape(hs) = (Gz,Gy,Gx, Hz,Hy,Hx)
the input image im is subdivided into (Gx,Gy,Gz) blocks
hs[k,j,i] is the psf at the center of each block (i,j,k)
as of now each image dimension has to be divisble by the grid dim, i.e.
Nx % Gx == 0
Ny % Gy == 0
Nz % Gz == 0
mode can be:
"constant" - assumed values to be zero
"wrap" - periodic boundary condition
"""
if im.ndim !=3 or hs.ndim !=6:
raise ValueError("wrong dimensions of input!")
if not np.all([n%g==0 for n,g in zip(im.shape,hs.shape[:3])]):
raise NotImplementedError("shape of image has to be divisible by Gx Gy = %s !"%(str(hs.shape[:3])))
mode_str = {"constant":"CLK_ADDRESS_CLAMP",
"wrap":"CLK_ADDRESS_REPEAT"}
Ns = tuple(im.shape)
Gs = tuple(hs.shape[:3])
# the size of each block within the grid
Nblocks = [n/g for n,g in zip(Ns,Gs)]
# the size of the overlapping patches with safety padding
Npatchs = tuple([_next_power_of_2(pad_factor*nb) for nb in Nblocks])
print hs.shape
hs = np.fft.fftshift(pad_to_shape(hs,Gs+Npatchs),axes=(3,4,5))
prog = OCLProgram(abspath("kernels/conv_spatial.cl"),
build_options=["-D","ADDRESSMODE=%s"%mode_str[mode]])
if plan is None:
plan = fft_plan(Npatchs)
patches_g = OCLArray.empty(Gs+Npatchs,np.complex64)
h_g = OCLArray.from_array(hs.astype(np.complex64))
im_g = OCLImage.from_array(im.astype(np.float32,copy=False))
Xs = [nb*np.arange(g) for nb, g in zip(Nblocks,Gs)]
print Nblocks
# this loops over all i,j,k
for (k,_z0), (j,_y0),(i,_x0) in product(*[enumerate(X) for X in Xs]):
prog.run_kernel("fill_patch3",Npatchs[::-1],None,
im_g,
np.int32(_x0+Nblocks[2]/2-Npatchs[2]/2),
np.int32(_y0+Nblocks[1]/2-Npatchs[1]/2),
np.int32(_z0+Nblocks[0]/2-Npatchs[0]/2),
patches_g.data,
np.int32(i*np.prod(Npatchs)+
j*Gs[2]*np.prod(Npatchs)+
k*Gs[2]*Gs[1]*np.prod(Npatchs)))
print patches_g.shape, h_g.shape
# convolution
fft(patches_g,inplace=True, batch = np.prod(Gs), plan = plan)
fft(h_g,inplace=True, batch = np.prod(Gs), plan = plan)
prog.run_kernel("mult_inplace",(np.prod(Npatchs)*np.prod(Gs),),None,
patches_g.data, h_g.data)
fft(patches_g,
inplace=True,
inverse = True,
batch = np.prod(Gs),
plan = plan)
#return patches_g.get()
#accumulate
res_g = OCLArray.zeros(im.shape,np.float32)
for k, j, i in product(*[range(g+1) for g in Gs]):
prog.run_kernel("interpolate3",Nblocks[::-1],None,
patches_g.data,
res_g.data,
np.int32(i),np.int32(j),np.int32(k),
np.int32(Gs[2]),np.int32(Gs[1]),np.int32(Gs[0]),
np.int32(Npatchs[2]),np.int32(Npatchs[1]),np.int32(Npatchs[0]))
res = res_g.get()
if return_plan:
return res, plan
else:
return res
def _convolve_spatial3(im,
hs,
mode="constant",
grid_dim=None,
plan=None,
return_plan=False,
pad_factor=2):
if im.ndim != 3:
raise ValueError("wrong dimensions of input!")
if not (hs.ndim == 6 or (hs.ndim == 3 and grid_dim)):
raise ValueError("wrong dimensions of psf grid!")
if grid_dim:
if hs.shape != im.shape:
raise ValueError("if grid_dim is set, then im.shape = hs.shape !")
Gs = tuple(grid_dim)
else:
if not hs.ndim == 6:
raise ValueError("wrong dimensions of psf grid! (Gy,Gx,Ny,Nx)")
Gs = hs.shape[:3]
if not np.all([n % g == 0 for n, g in zip(im.shape, Gs)]):
raise NotImplementedError(
"shape of image has to be divisible by Gx Gy = %s shape mismatch"
% (str(hs.shape[:2])))
mode_str = {
"constant": "CLK_ADDRESS_CLAMP",
"wrap": "CLK_ADDRESS_REPEAT",
"edge": "CLK_ADDRESS_CLAMP_TO_EDGE",
"reflect": "CLK_ADDRESS_MIRRORED_REPEAT"
}
Ns = im.shape
# the size of each block within the grid
Nblocks = [n // g for n, g in zip(Ns, Gs)]
# the size of the overlapping patches with safety padding
Npatchs = tuple([next_power_of_2(pad_factor * nb) for nb in Nblocks])
prog = OCLProgram(abspath("kernels/conv_spatial3.cl"),
build_options=["-D",
"ADDRESSMODE=%s" % mode_str[mode]])
if plan is None:
plan = fft_plan(Gs + Npatchs, axes=(-3, -2, -1))
Xs = [nb * np.arange(g) for nb, g in zip(Nblocks, Gs)]
patches_g = OCLArray.empty(Gs + Npatchs, np.complex64)
# prepare psfs
if grid_dim:
h_g = OCLArray.zeros(Gs + Npatchs, np.complex64)
tmp_g = OCLArray.from_array(hs.astype(np.float32, copy=False))
for (k, _z0), (j, _y0), (i,
_x0) in product(*[enumerate(X) for X in Xs]):
prog.run_kernel(
"fill_psf_grid3", Nblocks[::-1], None, tmp_g.data,
np.int32(im.shape[2]), np.int32(im.shape[1]),
np.int32(i * Nblocks[2]), np.int32(j * Nblocks[1]),
np.int32(k * Nblocks[0]), h_g.data, np.int32(Npatchs[2]),
np.int32(Npatchs[1]), np.int32(Npatchs[0]),
np.int32(-Nblocks[2] // 2 + Npatchs[2] // 2),
np.int32(-Nblocks[1] // 2 + Npatchs[1] // 2),
np.int32(-Nblocks[0] // 2 + Npatchs[0] // 2),
np.int32(i * np.prod(Npatchs) + j * Gs[2] * np.prod(Npatchs) +
k * Gs[2] * Gs[1] * np.prod(Npatchs)))
else:
hs = np.fft.fftshift(pad_to_shape(hs, Gs + Npatchs), axes=(3, 4, 5))
h_g = OCLArray.from_array(hs.astype(np.complex64))
im_g = OCLImage.from_array(im.astype(np.float32, copy=False))
# this loops over all i,j,k
for (k, _z0), (j, _y0), (i, _x0) in product(*[enumerate(X) for X in Xs]):
prog.run_kernel(
"fill_patch3", Npatchs[::-1], None, im_g,
np.int32(_x0 + Nblocks[2] // 2 - Npatchs[2] // 2),
np.int32(_y0 + Nblocks[1] // 2 - Npatchs[1] // 2),
np.int32(_z0 + Nblocks[0] // 2 - Npatchs[0] // 2), patches_g.data,
np.int32(i * np.prod(Npatchs) + j * Gs[2] * np.prod(Npatchs) +
k * Gs[2] * Gs[1] * np.prod(Npatchs)))
# convolution
fft(patches_g, inplace=True, plan=plan)
fft(h_g, inplace=True, plan=plan)
prog.run_kernel("mult_inplace", (np.prod(Npatchs) * np.prod(Gs), ), None,
patches_g.data, h_g.data)
fft(patches_g, inplace=True, inverse=True, plan=plan)
# return patches_g.get()
# accumulate
res_g = OCLArray.zeros(im.shape, np.float32)
for k, j, i in product(*[list(range(g + 1)) for g in Gs]):
prog.run_kernel("interpolate3", Nblocks[::-1], None, patches_g.data,
res_g.data, np.int32(i), np.int32(j), np.int32(k),
np.int32(Gs[2]), np.int32(Gs[1]), np.int32(Gs[0]),
np.int32(Npatchs[2]), np.int32(Npatchs[1]),
np.int32(Npatchs[0]))
res = res_g.get()
if return_plan:
return res, plan
else:
return res
def _convolve_spatial3(im, hs,
mode = "constant",
grid_dim = None,
plan = None,
return_plan = False,
pad_factor = 2):
if im.ndim !=3:
raise ValueError("wrong dimensions of input!")
if not (hs.ndim==6 or (hs.ndim==3 and grid_dim)):
raise ValueError("wrong dimensions of psf grid!")
if grid_dim:
if hs.shape != im.shape:
raise ValueError("if grid_dim is set, then im.shape = hs.shape !")
Gs = tuple(grid_dim)
else:
if not hs.ndim==6:
raise ValueError("wrong dimensions of psf grid! (Gy,Gx,Ny,Nx)")
Gs = hs.shape[:3]
if not np.all([n%g==0 for n,g in zip(im.shape,Gs)]):
raise NotImplementedError("shape of image has to be divisible by Gx Gy = %s shape mismatch"%(str(hs.shape[:2])))
mode_str = {"constant":"CLK_ADDRESS_CLAMP",
"wrap":"CLK_ADDRESS_REPEAT"}
Ns = im.shape
# the size of each block within the grid
Nblocks = [n/g for n,g in zip(Ns,Gs)]
# the size of the overlapping patches with safety padding
Npatchs = tuple([_next_power_of_2(pad_factor*nb) for nb in Nblocks])
prog = OCLProgram(abspath("kernels/conv_spatial3.cl"),
build_options=["-D","ADDRESSMODE=%s"%mode_str[mode]])
if plan is None:
plan = fft_plan(Npatchs)
Xs = [nb*np.arange(g) for nb, g in zip(Nblocks,Gs)]
patches_g = OCLArray.empty(Gs+Npatchs,np.complex64)
#prepare psfs
if grid_dim:
h_g = OCLArray.zeros(Gs+Npatchs,np.complex64)
tmp_g = OCLArray.from_array(hs.astype(np.float32, copy = False))
for (k,_z0), (j,_y0),(i,_x0) in product(*[enumerate(X) for X in Xs]):
prog.run_kernel("fill_psf_grid3",
Nblocks[::-1],None,
tmp_g.data,
np.int32(im.shape[2]),
np.int32(im.shape[1]),
np.int32(i*Nblocks[2]),
np.int32(j*Nblocks[1]),
np.int32(k*Nblocks[0]),
h_g.data,
np.int32(Npatchs[2]),
np.int32(Npatchs[1]),
np.int32(Npatchs[0]),
np.int32(-Nblocks[2]/2+Npatchs[2]/2),
np.int32(-Nblocks[1]/2+Npatchs[1]/2),
np.int32(-Nblocks[0]/2+Npatchs[0]/2),
np.int32(i*np.prod(Npatchs)+
j*Gs[2]*np.prod(Npatchs)+
k*Gs[2]*Gs[1]*np.prod(Npatchs)))
else:
hs = np.fft.fftshift(pad_to_shape(hs,Gs+Npatchs),axes=(3,4,5))
h_g = OCLArray.from_array(hs.astype(np.complex64))
im_g = OCLImage.from_array(im.astype(np.float32,copy=False))
# this loops over all i,j,k
for (k,_z0), (j,_y0),(i,_x0) in product(*[enumerate(X) for X in Xs]):
prog.run_kernel("fill_patch3",Npatchs[::-1],None,
im_g,
np.int32(_x0+Nblocks[2]/2-Npatchs[2]/2),
np.int32(_y0+Nblocks[1]/2-Npatchs[1]/2),
np.int32(_z0+Nblocks[0]/2-Npatchs[0]/2),
patches_g.data,
np.int32(i*np.prod(Npatchs)+
j*Gs[2]*np.prod(Npatchs)+
k*Gs[2]*Gs[1]*np.prod(Npatchs)))
# convolution
fft(patches_g,inplace=True, batch = np.prod(Gs), plan = plan)
fft(h_g,inplace=True, batch = np.prod(Gs), plan = plan)
prog.run_kernel("mult_inplace",(np.prod(Npatchs)*np.prod(Gs),),None,
patches_g.data, h_g.data)
fft(patches_g,
inplace=True,
inverse = True,
batch = np.prod(Gs),
plan = plan)
#return patches_g.get()
#accumulate
res_g = OCLArray.zeros(im.shape,np.float32)
for k, j, i in product(*[range(g+1) for g in Gs]):
prog.run_kernel("interpolate3",Nblocks[::-1],None,
patches_g.data,
res_g.data,
np.int32(i),np.int32(j),np.int32(k),
np.int32(Gs[2]),np.int32(Gs[1]),np.int32(Gs[0]),
np.int32(Npatchs[2]),np.int32(Npatchs[1]),np.int32(Npatchs[0]))
res = res_g.get()
if return_plan:
return res, plan
else:
return res
def __init__(self, *args, **kwargs):
kwargs["enforce_subsampled"] = True
super(Bpm3d_img, self).__init__(*args, **kwargs)
self._is_subsampled = True
self.result_im = OCLImage.empty(self.shape[::-1], dtype=np.float32)
def set_shape(self, dataShape):
if self.isGPU:
self.dataImg = OCLImage.empty(dataShape[::-1], dtype=self.dtype)
else:
raise NotImplementedError("TODO")
def _convolve_spatial2(im, hs,
mode = "constant",
grid_dim = None,
pad_factor = 2,
plan = None,
return_plan = False):
"""
spatial varying convolution of an 2d image with a 2d grid of psfs
shape(im_ = (Ny,Nx)
shape(hs) = (Gy,Gx, Hy,Hx)
the input image im is subdivided into (Gy,Gx) blocks
hs[j,i] is the psf at the center of each block (i,j)
as of now each image dimension has to be divisible by the grid dim, i.e.
Nx % Gx == 0
Ny % Gy == 0
mode can be:
"constant" - assumed values to be zero
"wrap" - periodic boundary condition
"""
if grid_dim:
Gs = tuple(grid_dim)
else:
Gs = hs.shape[:2]
mode_str = {"constant":"CLK_ADDRESS_CLAMP",
"wrap":"CLK_ADDRESS_REPEAT"}
Ny, Nx = im.shape
Gy, Gx = Gs
# the size of each block within the grid
Nblock_y, Nblock_x = Ny/Gy, Nx/Gx
# the size of the overlapping patches with safety padding
Npatch_x, Npatch_y = _next_power_of_2(pad_factor*Nblock_x), _next_power_of_2(pad_factor*Nblock_y)
prog = OCLProgram(abspath("kernels/conv_spatial2.cl"),
build_options=["-D","ADDRESSMODE=%s"%mode_str[mode]])
if plan is None:
plan = fft_plan((Npatch_y,Npatch_x))
x0s = Nblock_x*np.arange(Gx)
y0s = Nblock_y*np.arange(Gy)
patches_g = OCLArray.empty((Gy,Gx,Npatch_y,Npatch_x),np.complex64)
#prepare psfs
if grid_dim:
h_g = OCLArray.zeros((Gy,Gx,Npatch_y,Npatch_x),np.complex64)
tmp_g = OCLArray.from_array(hs.astype(np.float32, copy = False))
for i,_x0 in enumerate(x0s):
for j,_y0 in enumerate(y0s):
prog.run_kernel("fill_psf_grid2",
(Nblock_x,Nblock_y),None,
tmp_g.data,
np.int32(Nx),
np.int32(i*Nblock_x),
np.int32(j*Nblock_y),
h_g.data,
np.int32(Npatch_x),
np.int32(Npatch_y),
np.int32(-Nblock_x/2+Npatch_x/2),
np.int32(-Nblock_y/2+Npatch_y/2),
np.int32(i*Npatch_x*Npatch_y+j*Gx*Npatch_x*Npatch_y)
)
else:
hs = np.fft.fftshift(pad_to_shape(hs,(Gy,Gx,Npatch_y,Npatch_x)),axes=(2,3))
h_g = OCLArray.from_array(hs.astype(np.complex64))
#prepare image
im_g = OCLImage.from_array(im.astype(np.float32,copy=False))
for i,_x0 in enumerate(x0s):
for j,_y0 in enumerate(y0s):
prog.run_kernel("fill_patch2",(Npatch_x,Npatch_y),None,
im_g,
np.int32(_x0+Nblock_x/2-Npatch_x/2),
np.int32(_y0+Nblock_y/2-Npatch_y/2),
patches_g.data,
np.int32(i*Npatch_x*Npatch_y+j*Gx*Npatch_x*Npatch_y))
#return np.abs(patches_g.get())
# convolution
fft(patches_g,inplace=True, batch = Gx*Gy, plan = plan)
fft(h_g,inplace=True, batch = Gx*Gy, plan = plan)
prog.run_kernel("mult_inplace",(Npatch_x*Npatch_y*Gx*Gy,),None,
patches_g.data, h_g.data)
fft(patches_g,inplace=True, inverse = True, batch = Gx*Gy, plan = plan)
print Nblock_x, Npatch_x
#return np.abs(patches_g.get())
#accumulate
res_g = OCLArray.empty(im.shape,np.float32)
for j in xrange(Gy+1):
for i in xrange(Gx+1):
prog.run_kernel("interpolate2",(Nblock_x,Nblock_y),None,
patches_g.data,res_g.data,
np.int32(i),np.int32(j),
np.int32(Gx),np.int32(Gy),
np.int32(Npatch_x),np.int32(Npatch_y))
res = res_g.get()
if return_plan:
return res, plan
else:
return res
def __init__(self, *args, **kwargs):
kwargs["enforce_subsampled"] = True
super(Bpm3d_img, self).__init__(*args, **kwargs)
self._is_subsampled = True
self.result_im = OCLImage.empty(self.shape[::-1], dtype=np.float32)
def affine(data, mat=np.identity(4), mode="constant", interpolation="linear"):
"""
affine transform data with matrix mat
Parameters
----------
data, ndarray
3d array to be transformed
mat, ndarray
4x4 affine matrix
mode: string
boundary mode, one of the following:
'constant'
pads with zeros
'edge'
pads with edge values
'wrap'
pads with the repeated version of the input
interpolation, string
interpolation mode, one of the following
'linear'
'nearest'
Returns
-------
res: ndarray
transformed array (same shape as input)
"""
if not (isinstance(data, np.ndarray) and data.ndim == 3):
raise ValueError("input data has to be a 3d array!")
interpolation_defines = {
"linear": ["-D", "SAMPLER_FILTER=CLK_FILTER_LINEAR"],
"nearest": ["-D", "SAMPLER_FILTER=CLK_FILTER_NEAREST"]
}
mode_defines = {
"constant": ["-D", "SAMPLER_ADDRESS=CLK_ADDRESS_CLAMP"],
"wrap": ["-D", "SAMPLER_ADDRESS=CLK_ADDRESS_REPEAT"],
"edge": ["-D", "SAMPLER_ADDRESS=CLK_ADDRESS_CLAMP_TO_EDGE"]
}
if not interpolation in interpolation_defines:
raise KeyError("interpolation = '%s' not defined ,valid: %s" %
(interpolation, list(interpolation_defines.keys())))
if not mode in mode_defines:
raise KeyError("mode = '%s' not defined ,valid: %s" %
(mode, list(mode_defines.keys())))
d_im = OCLImage.from_array(data.astype(np.float32, copy=False))
res_g = OCLArray.empty(data.shape, np.float32)
mat_g = OCLArray.from_array(
np.linalg.inv(mat).astype(np.float32, copy=False))
prog = OCLProgram(abspath("kernels/transformations.cl"),
build_options=interpolation_defines[interpolation] +
mode_defines[mode])
prog.run_kernel("affine", data.shape[::-1], None, d_im, res_g.data,
mat_g.data)
return res_g.get()
def image_from_array(data):
im = OCLImage.from_array(data)
assert np.allclose(data, im.get())
def _convolve_spatial2(im,
hs,
mode="constant",
grid_dim=None,
pad_factor=2,
plan=None,
return_plan=False):
"""
spatial varying convolution of an 2d image with a 2d grid of psfs
shape(im_ = (Ny,Nx)
shape(hs) = (Gy,Gx, Hy,Hx)
the input image im is subdivided into (Gy,Gx) blocks
hs[j,i] is the psf at the center of each block (i,j)
as of now each image dimension has to be divisible by the grid dim, i.e.
Nx % Gx == 0
Ny % Gy == 0
mode can be:
"constant" - assumed values to be zero
"wrap" - periodic boundary condition
"""
if grid_dim:
Gs = tuple(grid_dim)
else:
Gs = hs.shape[:2]
mode_str = {"constant": "CLK_ADDRESS_CLAMP", "wrap": "CLK_ADDRESS_REPEAT"}
Ny, Nx = im.shape
Gy, Gx = Gs
# the size of each block within the grid
Nblock_y, Nblock_x = Ny // Gy, Nx // Gx
# the size of the overlapping patches with safety padding
Npatch_x, Npatch_y = _next_power_of_2(
pad_factor * Nblock_x), _next_power_of_2(pad_factor * Nblock_y)
prog = OCLProgram(abspath("kernels/conv_spatial2.cl"),
build_options=["-D",
"ADDRESSMODE=%s" % mode_str[mode]])
if plan is None:
plan = fft_plan((Gy, Gx, Npatch_y, Npatch_x), axes=(-2, -1))
x0s = Nblock_x * np.arange(Gx)
y0s = Nblock_y * np.arange(Gy)
patches_g = OCLArray.empty((Gy, Gx, Npatch_y, Npatch_x), np.complex64)
#prepare psfs
if grid_dim:
h_g = OCLArray.zeros((Gy, Gx, Npatch_y, Npatch_x), np.complex64)
tmp_g = OCLArray.from_array(hs.astype(np.float32, copy=False))
for i, _x0 in enumerate(x0s):
for j, _y0 in enumerate(y0s):
prog.run_kernel(
"fill_psf_grid2", (Nblock_x, Nblock_y), None, tmp_g.data,
np.int32(Nx),
np.int32(i * Nblock_x), np.int32(j * Nblock_y), h_g.data,
np.int32(Npatch_x), np.int32(Npatch_y),
np.int32(-Nblock_x // 2 + Npatch_x // 2),
np.int32(-Nblock_y // 2 + Npatch_y // 2),
np.int32(i * Npatch_x * Npatch_y +
j * Gx * Npatch_x * Npatch_y))
else:
hs = np.fft.fftshift(pad_to_shape(hs, (Gy, Gx, Npatch_y, Npatch_x)),
axes=(2, 3))
h_g = OCLArray.from_array(hs.astype(np.complex64))
#prepare image
im_g = OCLImage.from_array(im.astype(np.float32, copy=False))
for i, _x0 in enumerate(x0s):
for j, _y0 in enumerate(y0s):
prog.run_kernel(
"fill_patch2", (Npatch_x, Npatch_y), None, im_g,
np.int32(_x0 + Nblock_x // 2 - Npatch_x // 2),
np.int32(_y0 + Nblock_y // 2 - Npatch_y // 2), patches_g.data,
np.int32(i * Npatch_x * Npatch_y +
j * Gx * Npatch_x * Npatch_y))
#return np.abs(patches_g.get())
# convolution
fft(patches_g, inplace=True, plan=plan)
fft(h_g, inplace=True, plan=plan)
prog.run_kernel("mult_inplace", (Npatch_x * Npatch_y * Gx * Gy, ), None,
patches_g.data, h_g.data)
fft(patches_g, inplace=True, inverse=True, plan=plan)
logger.debug("Nblock_x: {}, Npatch_x: {}".format(Nblock_x, Npatch_x))
#return np.abs(patches_g.get())
#accumulate
res_g = OCLArray.empty(im.shape, np.float32)
for j in range(Gy + 1):
for i in range(Gx + 1):
prog.run_kernel("interpolate2", (Nblock_x, Nblock_y),
None, patches_g.data, res_g.data, np.int32(i),
np.int32(j), np.int32(Gx), np.int32(Gy),
np.int32(Npatch_x), np.int32(Npatch_y))
res = res_g.get()
if return_plan:
return res, plan
else:
return res
def image_create_write(data):
im = OCLImage.empty(data.shape, data.dtype)
im.write_array(data)
assert np.allclose(data, im.get())
