def decimate(image, shape, sigma=None, average=False, queue=None, block=False):
"""Decimate *image* so that its dimensions match the final *shape*, which has to be a divisor of
the original shape. Remove low frequencies by a Gaussian filter with *sigma* pixels. If *sigma*
is None, use the FWHM of one low resolution pixel. Use command *queue*, if *block* is True, wait
for the copy to finish.
"""
if queue is None:
queue = cfg.OPENCL.queue
image = g_util.get_array(image, queue=queue)
shape = make_tuple(shape)
pow_shape = tuple([next_power_of_two(n) for n in image.shape])
orig_shape = image.shape
if image.shape != pow_shape:
image = pad(image, region=(0, 0) + pow_shape, queue=queue)
if sigma is None:
sigma = tuple([fwnm_to_sigma(float(image.shape[i]) / shape[i], n=2) for i in range(2)])
LOG.debug(
"decimate, shape: %s, final_shape: %s, sigma: %s, average: %s",
image.shape,
shape,
sigma,
average,
)
fltr = get_gauss_2d(image.shape, sigma, fourier=True, queue=queue, block=block)
image = image.astype(cfg.PRECISION.np_cplx)
fft_2(image, queue=queue, block=block)
image *= fltr
ifft_2(image, queue=queue, block=block)
image = crop(image.real, (0, 0) + orig_shape, queue=queue, block=block)
return bin_image(image, shape, average=average, queue=queue, block=block)
def pad(image, region=None, out=None, value=0, queue=None, block=False):
"""Pad a 2D *image*. *region* is the region to pad as (y_0, x_0, height, width). If not
specified, the next power of two dimensions are used and the image is centered in the padded
one. The final image dimensions are height x width and the filling starts at (y_0, x_0), *out*
is the pyopencl Array instance, if not specified it will be created. *out* is also returned.
*value* is the padded value. If *block* is True, wait for the copy to finish.
"""
if region is None:
shape = tuple([next_power_of_two(n) for n in image.shape])
y_0 = (shape[0] - image.shape[0]) / 2
x_0 = (shape[1] - image.shape[1]) / 2
region = (y_0, x_0) + shape
if queue is None:
queue = cfg.OPENCL.queue
if out is None:
out = cl_array.zeros(queue, (region[2], region[3]), dtype=image.dtype) + value
image = g_util.get_array(image, queue=queue)
n_bytes = image.dtype.itemsize
y_0, x_0, height, width = region
src_origin = (0, 0, 0)
dst_origin = (n_bytes * x_0, y_0, 0)
region = (n_bytes * image.shape[1], image.shape[0], 1)
LOG.debug('pad, shape: %s, src_origin: %s, dst_origin: %s, region: %s', image.shape,
src_origin, dst_origin, region)
_copy_rect(image, out, src_origin, dst_origin, region, queue, block=block)
return out
def pad(image, region=None, out=None, value=0, queue=None, block=False):
"""Pad a 2D *image*. *region* is the region to pad as (y_0, x_0, height, width). If not
specified, the next power of two dimensions are used and the image is centered in the padded
one. The final image dimensions are height x width and the filling starts at (y_0, x_0), *out*
is the pyopencl Array instance, if not specified it will be created. *out* is also returned.
*value* is the padded value. If *block* is True, wait for the copy to finish.
"""
if region is None:
shape = tuple([next_power_of_two(n) for n in image.shape])
y_0 = (shape[0] - image.shape[0]) // 2
x_0 = (shape[1] - image.shape[1]) // 2
region = (y_0, x_0) + shape
if queue is None:
queue = cfg.OPENCL.queue
if out is None:
out = cl_array.zeros(queue, (region[2], region[3]), dtype=image.dtype) + value
image = g_util.get_array(image, queue=queue)
n_bytes = image.dtype.itemsize
y_0, x_0, height, width = region
src_origin = (0, 0, 0)
dst_origin = (n_bytes * x_0, y_0, 0)
region = (n_bytes * image.shape[1], image.shape[0], 1)
LOG.debug(
"pad, shape: %s, src_origin: %s, dst_origin: %s, region: %s",
image.shape,
src_origin,
dst_origin,
region,
)
_copy_rect(image, out, src_origin, dst_origin, region, queue, block=block)
return out
def decimate(image, shape, sigma=None, average=False, queue=None, block=False):
"""Decimate *image* so that its dimensions match the final *shape*, which has to be a divisor of
the original shape. Remove low frequencies by a Gaussian filter with *sigma* pixels. If *sigma*
is None, use the FWHM of one low resolution pixel. Use command *queue*, if *block* is True, wait
for the copy to finish.
"""
if queue is None:
queue = cfg.OPENCL.queue
image = g_util.get_array(image, queue=queue)
shape = make_tuple(shape)
pow_shape = tuple([next_power_of_two(n) for n in image.shape])
orig_shape = image.shape
if image.shape != pow_shape:
image = pad(image, region=(0, 0) + pow_shape, queue=queue)
if sigma is None:
sigma = tuple([fwnm_to_sigma(float(image.shape[i]) / shape[i], n=2) for i in range(2)])
LOG.debug('decimate, shape: %s, final_shape: %s, sigma: %s, average: %s', image.shape, shape,
sigma, average)
fltr = get_gauss_2d(image.shape, sigma, fourier=True, queue=queue, block=block)
image = image.astype(cfg.PRECISION.np_cplx)
fft_2(image, queue=queue, block=block)
image *= fltr
ifft_2(image, queue=queue, block=block)
image = crop(image.real, (0, 0) + orig_shape, queue=queue, block=block)
return bin_image(image, shape, average=average, queue=queue, block=block)