def input_wrapper(f):
image = misc.imread(f)
sx,sy = image.shape
diff = np.abs(sx-sy)
sx,sy = image.shape
image = np.pad(image,((sx//8,sx//8),(sy//8,sy//8)),'constant')
if sx > sy:
image = np.pad(image,((0,0),(diff//2,diff//2)),'constant')
else:
image = np.pad(image,((diff//2,diff//2),(0,0)),'constant')
image = dilation(image,disk(max(sx,sy)/32))
image = misc.imresize(image,(32,32))
if np.max(image) > 1:
image = image/255.
return image
python类dilation()的实例源码
def input_wrapper(f):
image = misc.imread(f)
# image[image>50]=255
# image[image<=50]=0
sx,sy = image.shape
diff = np.abs(sx-sy)
sx,sy = image.shape
image = np.pad(image,((sx//8,sx//8),(sy//8,sy//8)),'constant')
if sx > sy:
image = np.pad(image,((0,0),(diff//2,diff//2)),'constant')
else:
image = np.pad(image,((diff//2,diff//2),(0,0)),'constant')
image = dilation(image,disk(max(sx,sy)/32))
image = misc.imresize(image,(32,32))
if np.max(image) > 1:
image = image/255.
return image
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 input_wrapper_arr(self,image):
sx,sy = image.shape
diff = np.abs(sx-sy)
sx,sy = image.shape
image = np.pad(image,((sx//8,sx//8),(sy//8,sy//8)),'constant')
if sx > sy:
image = np.pad(image,((0,0),(diff//2,diff//2)),'constant')
else:
image = np.pad(image,((diff//2,diff//2),(0,0)),'constant')
image = dilation(image,disk(max(sx,sy)/32))
image = misc.imresize(image,(32,32))
if np.max(image) > 1:
image = image/255.
return image
def whiteout_ramp(image, linecoords):
# Dilation enlarge the bright segments and cut them out off the original image
imagesection = image[linecoords.object]
count = 0
for i in morph.dilation(linecoords.object_matrix, morph.square(10)):
whitevalue = measurements.find_objects(i == linecoords.object_value + 1)
if whitevalue:
whitevalue = whitevalue[0][0]
imagesection[count,whitevalue.start:whitevalue.stop] = 255
count +=1
return 0
def binary_dilation(x, radius=3):
""" Return fast binary morphological dilation of an image.
see `skimage.morphology.binary_dilation <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.binary_dilation>`_.
Parameters
-----------
x : 2D array image.
radius : int for the radius of mask.
"""
from skimage.morphology import disk, binary_dilation
mask = disk(radius)
x = binary_dilation(x, selem=mask)
return x
def dilation(x, radius=3):
""" Return greyscale morphological dilation of an image,
see `skimage.morphology.dilation <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.dilation>`_.
Parameters
-----------
x : 2D array image.
radius : int for the radius of mask.
"""
from skimage.morphology import disk, dilation
mask = disk(radius)
x = dilation(x, selem=mask)
return x
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_dilation(img):
return dilation(img)
def get_stomata(max_proj_image, min_obj_size=200, max_obj_size=1000):
"""Performs image segmentation from a max_proj_image.
Disposes of objects in range min_obj_size to
max_obj_size
:param max_proj_image: the maximum projection image
:type max_proj_image: numpy.ndarray, uint16
:param min_obj_size: minimum size of object to keep
:type min_obj_size: int
:param max_obj_size: maximum size of object to keep
:type max_obj_size: int
:returns: list of [ [coordinates of kept objects - list of slice objects],
binary object image - numpy.ndarray,
labelled object image - numpy.ndarray
]
"""
# pore_margin = 10
# max_obj_size = 1000
# min_obj_size = 200
# for prop, value in segment_options:
# if prop == 'pore_margin':
# pore_margin = value
# if prop == 'max_obj_size':
# max_obj_size = value
# if prop == 'min_obj_size':
# min_obj_size = value
#
# print(pore_margin)
# print(max_obj_size)
# print(min_obj_size)
#rescale_min = 50
#rescale_max= 100
#rescaled = exposure.rescale_intensity(max_proj_image, in_range=(rescale_min,rescale_max))
rescaled = max_proj_image
seed = np.copy(rescaled)
seed[1:-1, 1:-1] = rescaled.max()
#mask = rescaled
#if gamma != None:
# rescaled = exposure.adjust_gamma(max_proj_image, gamma)
#filled = reconstruction(seed, mask, method='erosion')
closed = dilation(rescaled)
seed = np.copy(closed)
seed[1:-1, 1:-1] = closed.max()
mask = closed
filled = reconstruction(seed, mask, method='erosion')
label_objects, nb_labels = ndimage.label(filled)
sizes = np.bincount(label_objects.ravel())
mask_sizes = sizes
mask_sizes = (sizes > min_obj_size) & (sizes < max_obj_size)
#mask_sizes = (sizes > 200) & (sizes < 1000)
mask_sizes[0] = 0
big_objs = mask_sizes[label_objects]
stomata, _ = ndimage.label(big_objs)
obj_slices = ndimage.find_objects(stomata)
return [obj_slices, big_objs, stomata]
