def run(self, data: np.ndarray):
if data.shape != self.shape:
raise ValueError("data and h have to be same shape")
# set up some gpu buffers
data64 = data.astype(np.complex64)
y_g = OCLArray.from_array(data64)
u_g = OCLArray.from_array(data64)
# hflipped_g = OCLArray.from_array(h.astype(np.complex64))
for i in range(self.n_iter):
# logger.info("Iteration: {}".format(i))
fft_convolve(u_g,
self.psf_g,
plan=self.plan,
res_g=self.tmp_g,
kernel_is_fft=True)
_complex_divide_inplace(y_g, self.tmp_g)
fft_convolve(self.tmp_g,
self.psfflip_f_g,
plan=self.plan,
inplace=True,
kernel_is_fft=True)
_complex_multiply_inplace(u_g, self.tmp_g)
# can abs be calculated on the gpu ?
return np.abs(u_g.get())
def _deconv_rl_np_fft(data, h, Niter = 10,
h_is_fftshifted = False):
""" deconvolves data with given psf (kernel) h
data and h have to be same shape
via lucy richardson deconvolution
"""
if data.shape != h.shape:
raise ValueError("data and h have to be same shape")
if not h_is_fftshifted:
h = np.fft.fftshift(h)
hflip = h[::-1,::-1]
#set up some gpu buffers
y_g = OCLArray.from_array(data.astype(np.complex64))
u_g = OCLArray.from_array(data.astype(np.complex64))
tmp_g = OCLArray.empty(data.shape,np.complex64)
hf_g = OCLArray.from_array(h.astype(np.complex64))
hflip_f_g = OCLArray.from_array(hflip.astype(np.complex64))
# hflipped_g = OCLArray.from_array(h.astype(np.complex64))
plan = fft_plan(data.shape)
#transform psf
fft(hf_g,inplace = True)
fft(hflip_f_g,inplace = True)
for i in range(Niter):
print i
fft_convolve(u_g, hf_g,
res_g = tmp_g,
kernel_is_fft = True)
_complex_divide_inplace(y_g,tmp_g)
fft_convolve(tmp_g,hflip_f_g,
inplace = True,
kernel_is_fft = True)
_complex_multiply_inplace(u_g,tmp_g)
return np.abs(u_g.get())
def _deconv_rl_np_fft(data, h, Niter=10, h_is_fftshifted=False):
""" deconvolves data with given psf (kernel) h
data and h have to be same shape
via lucy richardson deconvolution
"""
if data.shape != h.shape:
raise ValueError("data and h have to be same shape")
if not h_is_fftshifted:
h = np.fft.fftshift(h)
hflip = h[::-1, ::-1]
#set up some gpu buffers
y_g = OCLArray.from_array(data.astype(np.complex64))
u_g = OCLArray.from_array(data.astype(np.complex64))
tmp_g = OCLArray.empty(data.shape, np.complex64)
hf_g = OCLArray.from_array(h.astype(np.complex64))
hflip_f_g = OCLArray.from_array(hflip.astype(np.complex64))
# hflipped_g = OCLArray.from_array(h.astype(np.complex64))
plan = fft_plan(data.shape)
#transform psf
fft(hf_g, inplace=True)
fft(hflip_f_g, inplace=True)
for i in range(Niter):
logger.info("Iteration: {}".format(i))
fft_convolve(u_g, hf_g, res_g=tmp_g, kernel_is_fft=True)
_complex_divide_inplace(y_g, tmp_g)
fft_convolve(tmp_g, hflip_f_g, inplace=True, kernel_is_fft=True)
_complex_multiply_inplace(u_g, tmp_g)
return np.abs(u_g.get())
def _deconv_rl_gpu_fft(data_g, h_g, Niter = 10):
"""
using fft_convolve
"""
if data_g.shape != h_g.shape:
raise ValueError("data and h have to be same shape")
#set up some gpu buffers
u_g = OCLArray.empty(data_g.shape,np.complex64)
u_g.copy_buffer(data_g)
tmp_g = OCLArray.empty(data_g.shape,np.complex64)
#fix this
hflip_g = OCLArray.from_array((h_g.get()[::-1,::-1]).copy())
plan = fft_plan(data_g.shape)
#transform psf
fft(h_g,inplace = True)
fft(hflip_g,inplace = True)
for i in range(Niter):
print i
fft_convolve(u_g, h_g,
res_g = tmp_g,
kernel_is_fft = True)
_complex_divide_inplace(data_g,tmp_g)
fft_convolve(tmp_g,hflip_g,
inplace = True,
kernel_is_fft = True)
_complex_multiply_inplace(u_g,tmp_g)
return u_g
def _deconv_rl_gpu_fft(data_g, h_g, Niter=10):
"""
using fft_convolve
"""
if data_g.shape != h_g.shape:
raise ValueError("data and h have to be same shape")
#set up some gpu buffers
u_g = OCLArray.empty(data_g.shape, np.complex64)
u_g.copy_buffer(data_g)
tmp_g = OCLArray.empty(data_g.shape, np.complex64)
#fix this
hflip_g = OCLArray.from_array((h_g.get()[::-1, ::-1]).copy())
plan = fft_plan(data_g.shape)
#transform psf
fft(h_g, inplace=True)
fft(hflip_g, inplace=True)
for i in range(Niter):
logger.info("Iteration: {}".format(i))
fft_convolve(u_g, h_g, res_g=tmp_g, kernel_is_fft=True)
_complex_divide_inplace(data_g, tmp_g)
fft_convolve(tmp_g, hflip_g, inplace=True, kernel_is_fft=True)
_complex_multiply_inplace(u_g, tmp_g)
return u_g
def test_convolve2():
pass
if __name__ == '__main__':
test_convolve2()
from scipy.misc import lena
# N = 256
# d = np.random.uniform(-1,1,(N,)*2)
# h = np.random.uniform(-1,1,(N,)*2)
d = lena()[100:164, 100:164]
x = np.linspace(-1, 1, 65)[:-1]
Y, X = np.meshgrid(x, x, indexing="ij")
h = np.exp(-1000 * (Y**2 + X**2))
h *= 1. / np.sum(h)
t = time()
res1 = convolve2d(d, h, mode="same")
print time() - t
t = time()
res2 = gputools.fft_convolve(d, h)
print time() - t
def test_fftconv_np():
d = np.ones((128,)*2, np.float32)
res = fft_convolve(d,d)
def test_convolve2():
pass
if __name__ == '__main__':
test_convolve2()
from scipy.misc import lena
# N = 256
# d = np.random.uniform(-1,1,(N,)*2)
# h = np.random.uniform(-1,1,(N,)*2)
d = lena()[100:164,100:164]
x = np.linspace(-1,1,65)[:-1]
Y,X = np.meshgrid(x,x,indexing="ij")
h = np.exp(-1000*(Y**2+X**2))
h *= 1./np.sum(h)
t = time()
res1 = convolve2d(d,h,mode="same")
print time()-t
t = time()
res2 = gputools.fft_convolve(d,h)
print time()-t
kernel_is_fft=True)
_complex_divide_inplace(data_g, tmp_g)
fft_convolve(tmp_g, hflip_g,
inplace=True,
kernel_is_fft=True)
_complex_multiply_inplace(u_g, tmp_g)
return u_g
if __name__=='__main__':
from scipy.misc import face
from gputools import pad_to_shape
d = np.pad(face()[200:642, 400:842, 0].astype(np.float32),((35,)*2,)*2, mode = "constant")
h = np.ones((31,)*2)
h *= 1./np.sum(h)
h = pad_to_shape(h,d.shape)
y = fft_convolve(d, h)
y += 0.1*np.max(d)*np.random.uniform(0, 1, d.shape)
print "start"
u = deconv_rl(y, h, 20, mode_conv="fft")
