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 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 _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 _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 _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(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",
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_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
