def remove_outlier_cuda(arr, dif, size=3, axis=0):
"""
Remove high intensity bright spots from a 3D array along axis 0
dimension using GPU.
Parameters
----------
arr : ndarray
Input array.
dif : float
Expected difference value between outlier value and
the median value of the array.
size : int
Size of the median filter.
axis : int, optional
Axis along which outlier removal is performed.
Returns
-------
ndarray
Corrected array.
Example
-------
>>> import tomocuda
>>> tomocuda.remove_outlier_cuda(arr, dif, 5)
For more information regarding install and using tomocuda, check
https://github.com/kyuepublic/tomocuda for more information
"""
arr = dtype.as_float32(arr)
dif = np.float32(dif)
try:
import tomocuda
winAllow = range(2, 16)
if(axis != 0):
arr = np.swapaxes(arr, 0, axis)
if size in winAllow:
prjsize = arr.shape[0]
loffset = int(size/2)
roffset = int((size-1)/2)
imsizex = arr.shape[2]
imsizey = arr.shape[1]
filter = tomocuda.mFilter(imsizex, imsizey, prjsize, size)
out = np.zeros(shape=(prjsize, imsizey, imsizex), dtype=np.float32)
for step in range(prjsize):
im_noisecu = arr[step].astype(np.float32)
im_noisecu = np.lib.pad(im_noisecu, ((loffset, roffset),
(loffset, roffset)), 'symmetric')
im_noisecu = im_noisecu.flatten()
filter.setCuImage(im_noisecu)
filter.run2DRemoveOutliner(size, dif)
results = filter.retreive()
results = results.reshape(imsizey, imsizex)
out[step] = results
if(axis != 0):
out = np.swapaxes(out, 0, axis)
else:
warnings.warn("Window size not support, using cpu outlier removal")
out = remove_outlier(arr, dif, size)
except ImportError:
warnings.warn("The tomocuda is not support, using cpu outlier removal")
out = remove_outlier(arr, dif, size)
return out
def remove_outlier_cuda(arr, dif, size=3, axis=0):
"""
Remove high intensity bright spots from a 3D array along axis 0
dimension using GPU.
Parameters
----------
arr : ndarray
Input array.
dif : float
Expected difference value between outlier value and
the median value of the array.
size : int
Size of the median filter.
axis : int, optional
Axis along which outlier removal is performed.
Returns
-------
ndarray
Corrected array.
Example
-------
>>> import tomocuda
>>> tomocuda.remove_outlier_cuda(arr, dif, 5)
For more information regarding install and using tomocuda, check
https://github.com/kyuepublic/tomocuda for more information
"""
arr = dtype.as_float32(arr)
dif = np.float32(dif)
try:
import tomocuda
winAllow = range(2, 16)
if(axis != 0):
arr = np.swapaxes(arr, 0, axis)
if size in winAllow:
prjsize = arr.shape[0]
loffset = int(size/2)
roffset = int((size-1)/2)
imsizex = arr.shape[2]
imsizey = arr.shape[1]
filter = tomocuda.mFilter(imsizex, imsizey, prjsize, size)
out = np.zeros(shape=(prjsize, imsizey, imsizex), dtype=np.float32)
for step in range(prjsize):
im_noisecu = arr[step].astype(np.float32)
im_noisecu = np.lib.pad(im_noisecu, ((loffset, roffset),
(loffset, roffset)), 'symmetric')
im_noisecu = im_noisecu.flatten()
filter.setCuImage(im_noisecu)
filter.run2DRemoveOutliner(size, dif)
results = filter.retreive()
results = results.reshape(imsizey, imsizex)
out[step] = results
if(axis != 0):
out = np.swapaxes(out, 0, axis)
else:
warnings.warn("Window size not support, using cpu outlier removal")
out = remove_outlier(arr, dif, size)
except ImportError:
warnings.warn("The tomocuda is not support, using cpu outlier removal")
out = remove_outlier(arr, dif, size)
return out
def median_filter_cuda(arr, size=3, axis=0):
"""
Apply median filter to 3D array along 0 axis with GPU support.
The winAllow is for A6000, Tian X support 3 to 8
Parameters
----------
arr : ndarray
Input array.
size : int, optional
The size of the filter.
axis : int, optional
Axis along which median filtering is performed.
Returns
-------
ndarray
Median filtered 3D array.
Example
-------
import tomocuda
tomocuda.remove_outlier_cuda(arr, dif, 5)
For more information regarding install and using tomocuda, check
https://github.com/kyuepublic/tomocuda for more information
"""
try:
import tomocuda
winAllow = range(2, 16)
if(axis != 0):
arr = np.swapaxes(arr, 0, axis)
if size in winAllow:
loffset = int(size/2)
roffset = int((size-1)/2)
prjsize = arr.shape[0]
imsizex = arr.shape[2]
imsizey = arr.shape[1]
filter = tomocuda.mFilter(imsizex, imsizey, prjsize, size)
out = np.zeros(
shape=(prjsize, imsizey, imsizex), dtype=np.float32)
for step in range(prjsize):
# im_noisecu = arr[:][step][:].astype(np.float32)
im_noisecu = arr[step].astype(np.float32)
im_noisecu = np.lib.pad(im_noisecu, ((loffset, roffset),
(loffset, roffset)), 'symmetric')
im_noisecu = im_noisecu.flatten()
filter.setCuImage(im_noisecu)
filter.run2DFilter(size)
results = filter.retreive()
results = results.reshape(imsizey, imsizex)
out[step] = results
if(axis != 0):
out = np.swapaxes(out, 0, axis)
else:
warnings.warn("Window size not support, using cpu median filter")
out = median_filter(arr, size, axis)
except ImportError:
warnings.warn("The tomocuda is not support, using cpu median filter")
out = median_filter(arr, size, axis)
return out
def median_filter_cuda(arr, size=3, axis=0):
"""
Apply median filter to 3D array along 0 axis with GPU support.
The winAllow is for A6000, Tian X support 3 to 8
Parameters
----------
arr : ndarray
Input array.
size : int, optional
The size of the filter.
axis : int, optional
Axis along which median filtering is performed.
Returns
-------
ndarray
Median filtered 3D array.
Example
-------
import tomocuda
tomocuda.remove_outlier_cuda(arr, dif, 5)
For more information regarding install and using tomocuda, check
https://github.com/kyuepublic/tomocuda for more information
"""
try:
import tomocuda
winAllow = range(2, 16)
if(axis != 0):
arr = np.swapaxes(arr, 0, axis)
if size in winAllow:
loffset = int(size/2)
roffset = int((size-1)/2)
prjsize = arr.shape[0]
imsizex = arr.shape[2]
imsizey = arr.shape[1]
filter = tomocuda.mFilter(imsizex, imsizey, prjsize, size)
out = np.zeros(
shape=(prjsize, imsizey, imsizex), dtype=np.float32)
for step in range(prjsize):
# im_noisecu = arr[:][step][:].astype(np.float32)
im_noisecu = arr[step].astype(np.float32)
im_noisecu = np.lib.pad(im_noisecu, ((loffset, roffset),
(loffset, roffset)), 'symmetric')
im_noisecu = im_noisecu.flatten()
filter.setCuImage(im_noisecu)
filter.run2DFilter(size)
results = filter.retreive()
results = results.reshape(imsizey, imsizex)
out[step] = results
if(axis != 0):
out = np.swapaxes(out, 0, axis)
else:
warnings.warn("Window size not support, using cpu median filter")
out = median_filter(arr, size, axis)
except ImportError:
warnings.warn("The tomocuda is not support, using cpu median filter")
out = median_filter(arr, size, axis)
return out
