def trim_edge_cube(cube):
""" trim_edge_cube: Function that reads in a cube and removes the edges
in the cube.
It runs the erode function to make sure that pixels within 3 pixels away
from the edges are blanked.
This is useful to remove very noisy pixels due to lower coverage by KFPA.
----------------------------------------
Warning: This function modifies the cube.
"""
#
mask = np.isfinite(cube)
if len(cube.shape) == 2:
mask_2d = mask[:,:]
else:
mask_2d = mask[0,:,:]
# remove image edges
mask_2d[:,0] = mask_2d[:,-1] = False
mask_2d[0,:] = mask_2d[-1,:] = False
# now erode image (using disk) and convert back to 3D mask
# then replace all voxels with NaN
mask &= erosion(mask_2d,disk(5))
cube[~mask] = np.nan
python类erosion()的实例源码
def erosion(x, radius=3):
""" Return greyscale morphological erosion of an image,
see `skimage.morphology.erosion <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.erosion>`_.
Parameters
-----------
x : 2D array image.
radius : int for the radius of mask.
"""
from skimage.morphology import disk, dilation, erosion
mask = disk(radius)
x = erosion(x, selem=mask)
return x
## Object Detection
def extract_voxels(images,pred_1,truths=None):
eroded = morphology.erosion(pred_1,np.ones([3,3,3]))
dilation = morphology.dilation(eroded,np.ones([3,3,3]))
labels = measure.label(dilation) # Different labels are displayed in different colors
label_vals = np.unique(labels)
regions = measure.regionprops(labels)
kept = 0
removed = 0
data = []
for idx in range(len(regions)):
b = regions[idx].bbox
if regions[idx].area < 50:
removed += 1
continue
kept += 1
print "before->",b
b = get_bounding_box(b,images.shape)
print "after->",b
image_voxel = images[b[0]:b[3],b[1]:b[4],b[2]:b[5]]
label = 0
if not truths is None:
print "finding region in truths"
truth_voxel = truths[b[0]:b[3],b[1]:b[4],b[2]:b[5]]
nonzeros = np.count_nonzero(truth_voxel)
if nonzeros > 0:
label = 1
assert(image_voxel.size==(VOXEL_DEPTH*VOXEL_DEPTH*VOXEL_DEPTH))
print "Appending voxel with label ",label
data.append((image_voxel,label,b))
print "kept",kept,"removed",removed
sys.stdout.flush()
return data
def binary_erosion(x, radius=3):
""" Return binary morphological erosion of an image,
see `skimage.morphology.binary_erosion <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.binary_erosion>`_.
Parameters
-----------
x : 2D array image.
radius : int for the radius of mask.
"""
from skimage.morphology import disk, dilation, binary_erosion
mask = disk(radius)
x = binary_erosion(x, selem=mask)
return x
def TF_erosion(img):
return erosion(img)
