def regional_filter(z, h):
"""Perform a h-dome regional filtering of the an image for background
subtraction.
Parameters
----------
h : float
h-dome cutoff value.
Returns
-------
h-dome subtracted image as np.array
"""
seed = np.copy(z)
seed = z - h
mask = z
dilated = morphology.reconstruction(seed, mask, method='dilation')
return z - dilated
python类reconstruction()的实例源码
def regional_flattener(z, h):
"""Localised erosion of the image 'z' for features below a value 'h'"""
seed = np.copy(z) + h
mask = z
eroded = morphology.reconstruction(seed, mask, method='erosion')
return eroded - h
def execMorphologicalDilation(filteredImg, h=0.4):
mask = filteredImg
seed = filteredImg - h
dilatedImg = reconstruction(seed, mask, method='dilation')
return dilatedImg
def bg_sub_signPreserveNorm(imageFile, path, extent, extension):
vec = signPreserveNorm(imageFile, path, extent, extension)
image = np.reshape(vec, (20,20), order="F")
image = gaussian_filter(image, 1)
seed = np.copy(image)
seed[1:-1, 1:-1] = image.min()
mask = image
dilated = reconstruction(seed, mask, method='dilation')
return np.ravel(image - dilated, order="F")
def bg_sub_signPreserveNorm(imageFile, path, extent, extension):
vec = signPreserveNorm(imageFile, path, extent, extension)
image = np.reshape(vec, (20,20), order="F")
image = gaussian_filter(image, 1)
seed = np.copy(image)
seed[1:-1, 1:-1] = image.min()
mask = image
dilated = reconstruction(seed, mask, method='dilation')
return np.ravel(image - dilated, order="F")
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]
