def hierarchicalLevel(imIn, imOut, grid=mamba.DEFAULT_GRID):
"""
Computes the next hierarchical level of image 'imIn' in the
waterfalls transformation and puts the result in 'imOut'.
This operation makes sure that the next hierarchical level is embedded
in the previous one.
'imIn' must be a valued watershed image.
"""
imWrk0 = mamba.imageMb(imIn)
imWrk1 = mamba.imageMb(imIn, 1)
imWrk2 = mamba.imageMb(imIn, 1)
imWrk3 = mamba.imageMb(imIn, 1)
imWrk4 = mamba.imageMb(imIn, 32)
mamba.threshold(imIn, imWrk1, 0, 0)
mamba.negate(imWrk1, imWrk2)
hierarchy(imIn, imWrk2, imWrk0, grid=grid)
mamba.minima(imWrk0, imWrk2, grid=grid)
mamba.label(imWrk2, imWrk4, grid=grid)
mamba.watershedSegment(imWrk0, imWrk4, grid=grid)
mamba.copyBytePlane(imWrk4, 3, imWrk0)
mamba.threshold(imWrk0, imWrk2, 0, 0)
mamba.diff(imWrk1, imWrk2, imWrk3)
mamba.build(imWrk1, imWrk3)
se = mamba.structuringElement(mamba.getDirections(grid), grid)
mamba.dilate(imWrk3, imWrk1, 1, se)
mamba.diff(imWrk2, imWrk1, imWrk1)
mamba.logic(imWrk1, imWrk3, imWrk1, "sup")
mamba.convertByMask(imWrk1, imWrk0, 255, 0)
mamba.logic(imIn, imWrk0, imOut, "inf")
def partitionLabel(imIn, imOut):
"""
This procedure labels each cell of image 'imIn' and puts the result in
'imOut'. The number of cells is returned. 'imIn' can be a 1-bit, 8-bit or a
32-bit image. 'imOut' is a 32-bit image. When 'imIn' is a binary image, all the
connected components of the background are also labelled. When 'imIn' is a grey
image, the 0-valued cells are also labelled (which is not the case with the label
operator.
Warning! The label values of adjacent cells are not necessarily consecutive.
"""
imWrk1 = mamba.imageMb(imIn, 1)
imWrk2 = mamba.imageMb(imIn, 32)
if imIn.getDepth() == 1:
mamba.negate(imIn, imWrk1)
else:
mamba.threshold(imIn, imWrk1, 0, 0)
nb1 = mamba.label(imWrk1, imWrk2)
mamba.convertByMask(imWrk1, imOut, mamba.computeMaxRange(imOut)[1], 0)
mamba.logic(imOut, imWrk2, imOut, "sup")
nb2 = mamba.label(imIn, imWrk2)
mamba.addConst(imWrk2, nb1, imWrk2)
mamba.logic(imOut, imWrk2, imOut, "inf")
return nb1 + nb2
def partitionLabel(imIn, imOut):
"""
This procedure labels each cell of image 'imIn' and puts the result in
'imOut'. The number of cells is returned. 'imIn' can be a 1-bit, 8-bit or a
32-bit image. 'imOut' is a 32-bit image. When 'imIn' is a binary image, all the
connected components of the background are also labelled. When 'imIn' is a grey
image, the 0-valued cells are also labelled (which is not the case with the label
operator.
Warning! The label values of adjacent cells are not necessarily consecutive.
"""
imWrk1 = mamba.imageMb(imIn, 1)
imWrk2 = mamba.imageMb(imIn, 32)
if imIn.getDepth() == 1:
mamba.negate(imIn, imWrk1)
else:
mamba.threshold(imIn, imWrk1, 0, 0)
nb1 = mamba.label(imWrk1, imWrk2)
mamba.convertByMask(imWrk1, imOut, mamba.computeMaxRange(imOut)[1], 0)
mamba.logic(imOut, imWrk2, imOut, "sup")
nb2 = mamba.label(imIn, imWrk2)
mamba.addConst(imWrk2, nb1, imWrk2)
mamba.logic(imOut, imWrk2, imOut, "inf")
return nb1 + nb2
def hierarchicalLevel(imIn, imOut, grid=mamba.DEFAULT_GRID):
"""
Computes the next hierarchical level of image 'imIn' in the
waterfalls transformation and puts the result in 'imOut'.
This operation makes sure that the next hierarchical level is embedded
in the previous one.
'imIn' must be a valued watershed image.
"""
imWrk0 = mamba.imageMb(imIn)
imWrk1 = mamba.imageMb(imIn, 1)
imWrk2 = mamba.imageMb(imIn, 1)
imWrk3 = mamba.imageMb(imIn, 1)
imWrk4 = mamba.imageMb(imIn, 32)
mamba.threshold(imIn,imWrk1, 0, 0)
mamba.negate(imWrk1, imWrk2)
hierarchy(imIn, imWrk2, imWrk0, grid=grid)
mamba.minima(imWrk0, imWrk2, grid=grid)
mamba.label(imWrk2, imWrk4, grid=grid)
mamba.watershedSegment(imWrk0, imWrk4, grid=grid)
mamba.copyBytePlane(imWrk4, 3, imWrk0)
mamba.threshold(imWrk0, imWrk2, 0, 0)
mamba.diff(imWrk1, imWrk2, imWrk3)
mamba.build(imWrk1, imWrk3)
se = mamba.structuringElement(mamba.getDirections(grid), grid)
mamba.dilate(imWrk3, imWrk1, 1, se)
mamba.diff(imWrk2, imWrk1, imWrk1)
mamba.logic(imWrk1, imWrk3, imWrk1, "sup")
mamba.convertByMask(imWrk1, imWrk0, 255, 0)
mamba.logic(imIn, imWrk0, imOut, "inf")
def generalSegment(imIn, imOut, gain=2.0, offset=1, grid=mamba.DEFAULT_GRID):
"""
General segmentation algorithm. This algorithm is controlled by two
parameters: the 'gain' (identical to the gain used in standard and P
segmentation) and a new one, the 'offset'. The 'offset' indicates which
level of hierarchy is compared to the current hierarchical image.
The 'offset' is relative to the current hierarchical level. If 'offset' is
equal to 1, this operator corresponds to the standard segmentation, if
'offset' is equal to 255 (this value stands for the infinity), the operator
is equivalent to P algorithm.
Image 'imOut' contains all these hierarchies which are embedded.
'imIn' and 'imOut' must be greyscale images. 'imIn' and 'imOut' must be
different.
This transformation returns the number of hierarchical levels.
"""
imWrk0 = mamba.imageMb(imIn)
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn)
imWrk3 = mamba.imageMb(imIn)
imWrk4 = mamba.imageMb(imIn, 1)
imWrk5 = mamba.imageMb(imIn, 1)
imWrk6 = mamba.imageMb(imIn, 32)
mamba.copy(imIn, imWrk1)
mamba.mulRealConst(imIn, gain, imWrk6)
mamba.floorSubConst(imWrk6, 1, imWrk6)
mamba.threshold(imWrk6, imWrk4, 255, mamba.computeMaxRange(imWrk6)[1])
mamba.copyBytePlane(imWrk6, 0, imWrk0)
mamba.convert(imWrk4, imWrk2)
mamba.logic(imWrk0, imWrk2, imWrk0, "sup")
mamba.logic(imWrk0, imWrk1, imWrk0, "sup")
imOut.reset()
nbLevels = 0
mamba.threshold(imWrk1, imWrk4, 1, 255)
flag = not (mamba.checkEmptiness(imWrk4))
while flag:
nbLevels += 1
hierarchy(imWrk1, imWrk4, imWrk2, grid=grid)
mamba.add(imOut, imWrk4, imOut)
v = max(nbLevels - offset, 0) + 1
mamba.threshold(imOut, imWrk4, v, 255)
mamba.valuedWatershed(imWrk2, imWrk3, grid=grid)
mamba.threshold(imWrk3, imWrk5, 1, 255)
flag = not (mamba.checkEmptiness(imWrk5))
hierarchy(imWrk3, imWrk5, imWrk2, grid=grid)
mamba.generateSupMask(imWrk0, imWrk2, imWrk5, strict=False)
mamba.logic(imWrk4, imWrk5, imWrk4, "inf")
mamba.convertByMask(imWrk4, imWrk3, 0, 255)
mamba.logic(imWrk1, imWrk3, imWrk3, "inf")
mamba.negate(imWrk4, imWrk4)
mamba.label(imWrk4, imWrk6, grid=grid)
mamba.watershedSegment(imWrk3, imWrk6, grid=grid)
mamba.copyBytePlane(imWrk6, 3, imWrk3)
mamba.logic(imWrk1, imWrk2, imWrk1, "sup")
mamba.logic(imWrk1, imWrk3, imWrk1, "inf")
mamba.threshold(imWrk1, imWrk4, 1, 255)
return nbLevels
def generalSegment(imIn, imOut, gain=2.0, offset=1, grid=mamba.DEFAULT_GRID):
"""
General segmentation algorithm. This algorithm is controlled by two
parameters: the 'gain' (identical to the gain used in standard and P
segmentation) and a new one, the 'offset'. The 'offset' indicates which
level of hierarchy is compared to the current hierarchical image.
The 'offset' is relative to the current hierarchical level. If 'offset' is
equal to 1, this operator corresponds to the standard segmentation, if
'offset' is equal to 255 (this value stands for the infinity), the operator
is equivalent to P algorithm.
Image 'imOut' contains all these hierarchies which are embedded.
'imIn' and 'imOut' must be greyscale images. 'imIn' and 'imOut' must be
different.
This transformation returns the number of hierarchical levels.
"""
imWrk0 = mamba.imageMb(imIn)
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn)
imWrk3 = mamba.imageMb(imIn)
imWrk4 = mamba.imageMb(imIn, 1)
imWrk5 = mamba.imageMb(imIn, 1)
imWrk6 = mamba.imageMb(imIn, 32)
mamba.copy(imIn, imWrk1)
mamba.mulRealConst(imIn, gain, imWrk6)
mamba.floorSubConst(imWrk6, 1, imWrk6)
mamba.threshold(imWrk6, imWrk4, 255, mamba.computeMaxRange(imWrk6)[1])
mamba.copyBytePlane(imWrk6, 0, imWrk0)
mamba.convert(imWrk4, imWrk2)
mamba.logic(imWrk0, imWrk2, imWrk0, "sup")
mamba.logic(imWrk0, imWrk1, imWrk0, "sup")
imOut.reset()
nbLevels = 0
mamba.threshold(imWrk1, imWrk4, 1, 255)
flag = not(mamba.checkEmptiness(imWrk4))
while flag:
nbLevels += 1
hierarchy(imWrk1, imWrk4, imWrk2, grid=grid)
mamba.add(imOut, imWrk4, imOut)
v = max(nbLevels - offset, 0) + 1
mamba.threshold(imOut, imWrk4, v, 255)
mamba.valuedWatershed(imWrk2, imWrk3, grid=grid)
mamba.threshold(imWrk3, imWrk5, 1, 255)
flag = not(mamba.checkEmptiness(imWrk5))
hierarchy(imWrk3, imWrk5, imWrk2, grid=grid)
mamba.generateSupMask(imWrk0, imWrk2, imWrk5, strict=False)
mamba.logic(imWrk4, imWrk5, imWrk4, "inf")
mamba.convertByMask(imWrk4, imWrk3, 0, 255)
mamba.logic(imWrk1, imWrk3, imWrk3, "inf")
mamba.negate(imWrk4, imWrk4)
mamba.label(imWrk4, imWrk6, grid=grid)
mamba.watershedSegment(imWrk3, imWrk6, grid=grid)
mamba.copyBytePlane(imWrk6, 3, imWrk3)
mamba.logic(imWrk1, imWrk2, imWrk1, "sup")
mamba.logic(imWrk1, imWrk3, imWrk1, "inf")
mamba.threshold(imWrk1, imWrk4, 1, 255)
return nbLevels
def standardSegment(imIn, imOut, gain=2.0, grid=mamba.DEFAULT_GRID):
"""
General standard segmentation. This algorithm keeps the contours of the
watershed transform which are above or equal to the hierarchical image
associated to the next level of hierarchy when the altitude of the contour
is multiplied by a 'gain' factor (default is 2.0). This transform also ends
by idempotence. All the hierarchical levels of image 'imIn'(which is a
valued watershed) are computed. 'imOut' contains all these hierarchies which
are embedded, so that hierarchy i is simply obtained by a threshold
[i+1, 255] of image 'imOut'.
'imIn' and 'imOut' must be greyscale images. 'imIn' and 'imOut' must be
different.
This transformation returns the number of hierarchical levels.
"""
imWrk0 = mamba.imageMb(imIn)
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn)
imWrk3 = mamba.imageMb(imIn)
imWrk4 = mamba.imageMb(imIn, 1)
imWrk5 = mamba.imageMb(imIn, 1)
imWrk6 = mamba.imageMb(imIn, 32)
mamba.copy(imIn, imWrk1)
mamba.mulRealConst(imIn, gain, imWrk6)
mamba.floorSubConst(imWrk6, 1, imWrk6)
mamba.threshold(imWrk6, imWrk4, 255, mamba.computeMaxRange(imWrk6)[1])
mamba.copyBytePlane(imWrk6, 0, imWrk0)
mamba.convert(imWrk4, imWrk2)
mamba.logic(imWrk0, imWrk2, imWrk0, "sup")
mamba.logic(imWrk0, imWrk1, imWrk0, "sup")
imOut.reset()
nbLevels = 0
mamba.threshold(imWrk1, imWrk4, 1, 255)
flag = not (mamba.checkEmptiness(imWrk4))
while flag:
hierarchy(imWrk1, imWrk4, imWrk2, grid=grid)
mamba.add(imOut, imWrk4, imOut)
mamba.valuedWatershed(imWrk2, imWrk3, grid=grid)
mamba.threshold(imWrk3, imWrk5, 1, 255)
flag = not (mamba.checkEmptiness(imWrk5))
hierarchy(imWrk3, imWrk5, imWrk2, grid=grid)
mamba.generateSupMask(imWrk0, imWrk2, imWrk5, strict=False)
mamba.logic(imWrk4, imWrk5, imWrk4, "inf")
mamba.convertByMask(imWrk4, imWrk3, 0, 255)
mamba.logic(imWrk1, imWrk3, imWrk3, "inf")
mamba.negate(imWrk4, imWrk4)
mamba.label(imWrk4, imWrk6, grid=grid)
mamba.watershedSegment(imWrk3, imWrk6, grid=grid)
mamba.copyBytePlane(imWrk6, 3, imWrk3)
mamba.logic(imWrk1, imWrk2, imWrk1, "sup")
mamba.logic(imWrk1, imWrk3, imWrk1, "inf")
mamba.threshold(imWrk1, imWrk4, 1, 255)
nbLevels += 1
return nbLevels
def standardSegment(imIn, imOut, gain=2.0, grid=mamba.DEFAULT_GRID):
"""
General standard segmentation. This algorithm keeps the contours of the
watershed transform which are above or equal to the hierarchical image
associated to the next level of hierarchy when the altitude of the contour
is multiplied by a 'gain' factor (default is 2.0). This transform also ends
by idempotence. All the hierarchical levels of image 'imIn'(which is a
valued watershed) are computed. 'imOut' contains all these hierarchies which
are embedded, so that hierarchy i is simply obtained by a threshold
[i+1, 255] of image 'imOut'.
'imIn' and 'imOut' must be greyscale images. 'imIn' and 'imOut' must be
different.
This transformation returns the number of hierarchical levels.
"""
imWrk0 = mamba.imageMb(imIn)
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn)
imWrk3 = mamba.imageMb(imIn)
imWrk4 = mamba.imageMb(imIn, 1)
imWrk5 = mamba.imageMb(imIn, 1)
imWrk6 = mamba.imageMb(imIn, 32)
mamba.copy(imIn, imWrk1)
mamba.mulRealConst(imIn, gain, imWrk6)
mamba.floorSubConst(imWrk6, 1, imWrk6)
mamba.threshold(imWrk6, imWrk4, 255, mamba.computeMaxRange(imWrk6)[1])
mamba.copyBytePlane(imWrk6, 0, imWrk0)
mamba.convert(imWrk4, imWrk2)
mamba.logic(imWrk0, imWrk2, imWrk0, "sup")
mamba.logic(imWrk0, imWrk1, imWrk0, "sup")
imOut.reset()
nbLevels = 0
mamba.threshold(imWrk1, imWrk4, 1, 255)
flag = not(mamba.checkEmptiness(imWrk4))
while flag:
hierarchy(imWrk1, imWrk4, imWrk2, grid=grid)
mamba.add(imOut, imWrk4, imOut)
mamba.valuedWatershed(imWrk2, imWrk3, grid=grid)
mamba.threshold(imWrk3, imWrk5, 1, 255)
flag = not(mamba.checkEmptiness(imWrk5))
hierarchy(imWrk3, imWrk5, imWrk2, grid=grid)
mamba.generateSupMask(imWrk0, imWrk2, imWrk5, strict=False)
mamba.logic(imWrk4, imWrk5, imWrk4, "inf")
mamba.convertByMask(imWrk4, imWrk3, 0, 255)
mamba.logic(imWrk1, imWrk3, imWrk3, "inf")
mamba.negate(imWrk4, imWrk4)
mamba.label(imWrk4, imWrk6, grid=grid)
mamba.watershedSegment(imWrk3, imWrk6, grid=grid)
mamba.copyBytePlane(imWrk6, 3, imWrk3)
mamba.logic(imWrk1, imWrk2, imWrk1, "sup")
mamba.logic(imWrk1, imWrk3, imWrk1, "inf")
mamba.threshold(imWrk1, imWrk4, 1, 255)
nbLevels += 1
return nbLevels
def extendedSegment(imIn, imTest, imOut, offset=255, grid=mamba.DEFAULT_GRID):
"""
Extended (experimental) segmentation algorithm. This algorithm is controlled
by image 'imTest'. The current hierarchical image is compared to image
'imTest'. This image must be a greyscale image. The 'offset' indicates which
level of hierarchy is compared to the current hierarchical image.
The 'offset' is relative to the current hierarchical level (by default,
'offset' is equal to 255, so that the initial segmentation is used).
Image 'imOut' contains all these hierarchies which are embedded.
'imIn', 'imTest' and 'imOut' must be greyscale images.
'imIn', 'imTest' and 'imOut' must be different.
This transformation returns the number of hierarchical levels.
"""
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn)
imWrk3 = mamba.imageMb(imIn)
imWrk4 = mamba.imageMb(imIn, 1)
imWrk5 = mamba.imageMb(imIn, 1)
imWrk6 = mamba.imageMb(imIn, 32)
mamba.copy(imIn, imWrk1)
imOut.reset()
nbLevels = 0
mamba.threshold(imWrk1, imWrk4, 1, 255)
flag = not(mamba.checkEmptiness(imWrk4))
while flag:
nbLevels += 1
hierarchy(imWrk1, imWrk4, imWrk2, grid=grid)
mamba.add(imOut, imWrk4, imOut)
v = max(nbLevels - offset, 0) + 1
mamba.threshold(imOut, imWrk4, v, 255)
mamba.valuedWatershed(imWrk2, imWrk3, grid=grid)
mamba.threshold(imWrk3, imWrk5, 1, 255)
flag = not(mamba.checkEmptiness(imWrk5))
hierarchy(imWrk3, imWrk5, imWrk2, grid=grid)
mamba.generateSupMask(imTest, imWrk2, imWrk5, strict=False)
mamba.logic(imWrk4, imWrk5, imWrk4, "inf")
mamba.convertByMask(imWrk4, imWrk3, 0, 255)
mamba.logic(imWrk1, imWrk3, imWrk3, "inf")
mamba.negate(imWrk4, imWrk4)
mamba.label(imWrk4, imWrk6, grid=grid)
mamba.watershedSegment(imWrk3, imWrk6, grid=grid)
mamba.copyBytePlane(imWrk6, 3, imWrk3)
mamba.logic(imWrk1, imWrk2, imWrk1, "sup")
mamba.logic(imWrk1, imWrk3, imWrk1, "inf")
mamba.threshold(imWrk1, imWrk4, 1, 255)
return nbLevels
def extendedSegment(imIn, imTest, imOut, offset=255, grid=mamba.DEFAULT_GRID):
"""
Extended (experimental) segmentation algorithm. This algorithm is controlled
by image 'imTest'. The current hierarchical image is compared to image
'imTest'. This image must be a greyscale image. The 'offset' indicates which
level of hierarchy is compared to the current hierarchical image.
The 'offset' is relative to the current hierarchical level (by default,
'offset' is equal to 255, so that the initial segmentation is used).
Image 'imOut' contains all these hierarchies which are embedded.
'imIn', 'imTest' and 'imOut' must be greyscale images.
'imIn', 'imTest' and 'imOut' must be different.
This transformation returns the number of hierarchical levels.
"""
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn)
imWrk3 = mamba.imageMb(imIn)
imWrk4 = mamba.imageMb(imIn, 1)
imWrk5 = mamba.imageMb(imIn, 1)
imWrk6 = mamba.imageMb(imIn, 32)
mamba.copy(imIn, imWrk1)
imOut.reset()
nbLevels = 0
mamba.threshold(imWrk1, imWrk4, 1, 255)
flag = not (mamba.checkEmptiness(imWrk4))
while flag:
nbLevels += 1
hierarchy(imWrk1, imWrk4, imWrk2, grid=grid)
mamba.add(imOut, imWrk4, imOut)
v = max(nbLevels - offset, 0) + 1
mamba.threshold(imOut, imWrk4, v, 255)
mamba.valuedWatershed(imWrk2, imWrk3, grid=grid)
mamba.threshold(imWrk3, imWrk5, 1, 255)
flag = not (mamba.checkEmptiness(imWrk5))
hierarchy(imWrk3, imWrk5, imWrk2, grid=grid)
mamba.generateSupMask(imTest, imWrk2, imWrk5, strict=False)
mamba.logic(imWrk4, imWrk5, imWrk4, "inf")
mamba.convertByMask(imWrk4, imWrk3, 0, 255)
mamba.logic(imWrk1, imWrk3, imWrk3, "inf")
mamba.negate(imWrk4, imWrk4)
mamba.label(imWrk4, imWrk6, grid=grid)
mamba.watershedSegment(imWrk3, imWrk6, grid=grid)
mamba.copyBytePlane(imWrk6, 3, imWrk3)
mamba.logic(imWrk1, imWrk2, imWrk1, "sup")
mamba.logic(imWrk1, imWrk3, imWrk1, "inf")
mamba.threshold(imWrk1, imWrk4, 1, 255)
return nbLevels
def computeComponentsNumber(imIn, grid=mamba.DEFAULT_GRID):
"""
Computes the number of connected components in image 'imIn'. The result is
an integer value.
"""
imWrk = mamba.imageMb(imIn, 32)
return mamba.label(imIn, imWrk, grid=grid)
def computeComponentsNumber(imIn, grid=mamba.DEFAULT_GRID):
"""
Computes the number of connected components in image 'imIn'. The result is
an integer value.
"""
imWrk = mamba.imageMb(imIn, 32)
return mamba.label(imIn, imWrk, grid=grid)
def measureLabelling(imIn, imMeasure, imOut):
"""
Labelling each particle of the binary image or each cell of the partition 'imIn'
with the number of pixels in the binary image 'imMeasure' contained in each particle
or each cell of the partition. The result is put is the 32-bit image 'imOut'.
"""
imWrk1 = mamba.imageMb(imIn, 32)
imWrk2 = mamba.imageMb(imIn, 1)
imWrk3 = mamba.imageMb(imIn, 8)
imWrk4 = mamba.imageMb(imIn, 8)
imWrk5 = mamba.imageMb(imIn, 8)
imWrk6 = mamba.imageMb(imIn, 32)
# Output image is emptied.
imOut.reset()
# Labelling the initial image.
if imIn.getDepth() == 1:
nbParticles = mamba.label(imIn, imWrk1)
else:
nbParticles = partitionLabel(imIn, imWrk1)
# Defining output LUTs.
outLuts = [[0 for i in range(256)] for i in range(4)]
# Converting the imMeasure image to 8-bit.
mamba.convert(imMeasure, imWrk4)
while nbParticles > 0:
# Particles with labels between 1 and 255 are extracted.
mamba.threshold(imWrk1, imWrk2, 0, 255)
mamba.convert(imWrk2, imWrk3)
mamba.copyBytePlane(imWrk1, 0, imWrk5)
mamba.logic(imWrk3, imWrk5, imWrk3, "inf")
# The points contained in each particle are labelled.
mamba.logic(imWrk3, imWrk4, imWrk5, "inf")
# The histogram is computed.
histo = mamba.getHistogram(imWrk5)
# The same operation is performed for the 255 particles.
for i in range(1, 256):
# The number of points in each particle is obtained from the histogram.
value = histo[i]
j = 3
# This value is splitted in powers of 256 and stored in the four
# output LUTs.
while j >= 0:
n = 2**(8 * j)
outLuts[j][i] = value // n
value = value % n
j -= 1
# Each LUT is used to label each byte plane of a temporary image with the
# corresponding value.
for i in range(4):
mamba.lookup(imWrk3, imWrk5, outLuts[i])
mamba.copyBytePlane(imWrk5, i, imWrk6)
# The intermediary result is accumulated in the final image.
mamba.logic(imOut, imWrk6, imOut, "sup")
# 255 is subtracted from the initial labelled image in order to process
# the next 255 particles.
mamba.floorSubConst(imWrk1, 255, imWrk1)
nbParticles -= 255
def measureLabelling(imIn, imMeasure, imOut):
"""
Labelling each particle of the binary image or each cell of the partition 'imIn'
with the number of pixels in the binary image 'imMeasure' contained in each particle
or each cell of the partition. The result is put is the 32-bit image 'imOut'.
"""
imWrk1 = mamba.imageMb(imIn, 32)
imWrk2 = mamba.imageMb(imIn, 1)
imWrk3 = mamba.imageMb(imIn, 8)
imWrk4 = mamba.imageMb(imIn, 8)
imWrk5 = mamba.imageMb(imIn, 8)
imWrk6 = mamba.imageMb(imIn, 32)
# Output image is emptied.
imOut.reset()
# Labelling the initial image.
if imIn.getDepth() == 1:
nbParticles = mamba.label(imIn, imWrk1)
else:
nbParticles = partitionLabel(imIn, imWrk1)
# Defining output LUTs.
outLuts = [[0 for i in range(256)] for i in range(4)]
# Converting the imMeasure image to 8-bit.
mamba.convert(imMeasure, imWrk4)
while nbParticles > 0:
# Particles with labels between 1 and 255 are extracted.
mamba.threshold(imWrk1, imWrk2, 0, 255)
mamba.convert(imWrk2, imWrk3)
mamba.copyBytePlane(imWrk1, 0, imWrk5)
mamba.logic(imWrk3, imWrk5, imWrk3, "inf")
# The points contained in each particle are labelled.
mamba.logic(imWrk3, imWrk4, imWrk5, "inf")
# The histogram is computed.
histo = mamba.getHistogram(imWrk5)
# The same operation is performed for the 255 particles.
for i in range(1, 256):
# The number of points in each particle is obtained from the histogram.
value = histo[i]
j = 3
# This value is splitted in powers of 256 and stored in the four
# output LUTs.
while j >= 0:
n = 2 ** (8 * j)
outLuts[j][i] = value // n
value = value % n
j -= 1
# Each LUT is used to label each byte plane of a temporary image with the
# corresponding value.
for i in range(4):
mamba.lookup(imWrk3, imWrk5, outLuts[i])
mamba.copyBytePlane(imWrk5, i, imWrk6)
# The intermediary result is accumulated in the final image.
mamba.logic(imOut, imWrk6, imOut, "sup")
# 255 is subtracted from the initial labelled image in order to process
# the next 255 particles.
mamba.floorSubConst(imWrk1, 255, imWrk1)
nbParticles -= 255
def enhancedWaterfalls(imIn, imOut, grid=mamba.DEFAULT_GRID):
"""
Enhanced waterfall algorithm. Compared to the classical waterfalls
algorithm, this one adds the contours of the watershed transform which are
above the hierarchical image associated to the next level of hierarchy. This
waterfalls transform also ends to an empty set. All the hierarchical levels
of image 'imIn' (which is a valued watershed) are computed. 'imOut' contains
all these hierarchies which are embedded, so that hierarchy i is simply
obtained by a threshold [i+1, 255] of image 'imOut'.
'imIn' and 'imOut' must be greyscale images. 'imIn' and 'imOu't must be
different.
This transformation returns the number of hierarchical levels.
"""
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn)
imWrk3 = mamba.imageMb(imIn)
imWrk4 = mamba.imageMb(imIn, 1)
imWrk5 = mamba.imageMb(imIn, 32)
mamba.copy(imIn, imWrk1)
imOut.reset()
nbLevels = 0
mamba.threshold(imWrk1, imWrk4, 1, 255)
flag = not (mamba.checkEmptiness(imWrk4))
while flag:
mamba.add(imOut, imWrk4, imOut)
hierarchy(imWrk1, imWrk4, imWrk2, grid=grid)
mamba.valuedWatershed(imWrk2, imWrk3, grid=grid)
mamba.threshold(imWrk3, imWrk4, 1, 255)
flag = not (mamba.checkEmptiness(imWrk4))
hierarchy(imWrk3, imWrk4, imWrk2, grid=grid)
mamba.generateSupMask(imWrk2, imWrk1, imWrk4, strict=True)
mamba.convertByMask(imWrk4, imWrk3, 255, 0)
mamba.logic(imWrk1, imWrk3, imWrk3, "inf")
mamba.label(imWrk4, imWrk5, grid=grid)
mamba.watershedSegment(imWrk3, imWrk5, grid=grid)
mamba.copyBytePlane(imWrk5, 3, imWrk1)
mamba.logic(imWrk1, imWrk3, imWrk1, "inf")
mamba.threshold(imWrk1, imWrk4, 1, 255)
nbLevels += 1
return nbLevels
def enhancedWaterfalls(imIn, imOut, grid=mamba.DEFAULT_GRID):
"""
Enhanced waterfall algorithm. Compared to the classical waterfalls
algorithm, this one adds the contours of the watershed transform which are
above the hierarchical image associated to the next level of hierarchy. This
waterfalls transform also ends to an empty set. All the hierarchical levels
of image 'imIn' (which is a valued watershed) are computed. 'imOut' contains
all these hierarchies which are embedded, so that hierarchy i is simply
obtained by a threshold [i+1, 255] of image 'imOut'.
'imIn' and 'imOut' must be greyscale images. 'imIn' and 'imOu't must be
different.
This transformation returns the number of hierarchical levels.
"""
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn)
imWrk3 = mamba.imageMb(imIn)
imWrk4 = mamba.imageMb(imIn, 1)
imWrk5 = mamba.imageMb(imIn, 32)
mamba.copy(imIn, imWrk1)
imOut.reset()
nbLevels = 0
mamba.threshold(imWrk1, imWrk4, 1, 255)
flag = not(mamba.checkEmptiness(imWrk4))
while flag:
mamba.add(imOut, imWrk4, imOut)
hierarchy(imWrk1, imWrk4, imWrk2, grid=grid)
mamba.valuedWatershed(imWrk2, imWrk3, grid=grid)
mamba.threshold(imWrk3, imWrk4, 1, 255)
flag = not(mamba.checkEmptiness(imWrk4))
hierarchy(imWrk3, imWrk4, imWrk2, grid=grid)
mamba.generateSupMask(imWrk2, imWrk1, imWrk4, strict=True)
mamba.convertByMask(imWrk4, imWrk3, 255, 0)
mamba.logic(imWrk1, imWrk3, imWrk3, "inf")
mamba.label(imWrk4, imWrk5, grid=grid)
mamba.watershedSegment(imWrk3, imWrk5, grid=grid)
mamba.copyBytePlane(imWrk5, 3, imWrk1)
mamba.logic(imWrk1, imWrk3, imWrk1, "inf")
mamba.threshold(imWrk1, imWrk4, 1, 255)
nbLevels += 1
return nbLevels
def _watershed_using_quasi_distance(self):
"""
疑似ユークリッド距離(Quasi Distance) に基づく
Watershed 領域分割
Returns
-------
numpy.ndarray
領域分割線の画像
"""
from mamba import (
imageMb,
gradient,
add,
negate,
quasiDistance,
copyBytePlane,
subConst,
build,
maxima,
label,
watershedSegment,
logic,
mix,
)
from utils.convert import mamba2np, np2mamba
# Channel Split
if self.src_img.ndim == 3:
b, g, r = [np2mamba(self.src_img[:, :, i]) for i in range(3)]
elif self.src_img.ndim == 2:
b, g, r = [np2mamba(self.src_img)] * 3
# We will perform a thick gradient on each color channel (contours in original
# picture are more or less fuzzy) and we add all these gradients
gradient = imageMb(r)
tmp_1 = imageMb(r)
gradient.reset()
gradient(r, tmp_1, 2)
add(tmp_1, gradient, gradient)
gradient(g, tmp_1, 2)
add(tmp_1, gradient, gradient)
gradient(b, tmp_1, 2)
add(tmp_1, gradient, gradient)
# Then we invert the gradient image and we compute its quasi-distance
quasi_dist = imageMb(gradient, 32)
negate(gradient, gradient)
quasiDistance(gradient, tmp_1, quasi_dist)
if self.is_logging:
self.logger.logging_img(tmp_1, "quasi_dist_gradient")
self.logger.logging_img(quasi_dist, "quasi_dist")
# The maxima of the quasi-distance are extracted and filtered (too close maxima,
# less than 6 pixels apart, are merged)
tmp_2 = imageMb(r)
marker = imageMb(gradient, 1)
copyBytePlane(quasi_dist, 0, tmp_1)
subConst(tmp_1, 3, tmp_2)
build(tmp_1, tmp_2)
maxima(tmp_2, marker)
# The marker-controlled watershed of the gradient is performed
watershed = imageMb(gradient)
label(marker, quasi_dist)
negate(gradient, gradient)
watershedSegment(gradient, quasi_dist)
copyBytePlane(quasi_dist, 3, watershed)
# The segmented binary and color image are stored
logic(r, watershed, r, "sup")
logic(g, watershed, g, "sup")
logic(b, watershed, b, "sup")
segmented_image = mix(r, g, b)
if self.is_logging:
self.logger.logging_img(segmented_image, "segmented_image")
watershed = mamba2np(watershed)
return watershed
def label(npImIn):
mbImIn = convertNumpy2Mamba(npImIn)
mbImOut = mamba.imageMb(mbImIn, 32)
mamba.label(mbImIn, mbImOut, grid=mamba.SQUARE)
height, width = npImIn.shape[-2:]
return convertMamba2Numpy(mbImOut)[:height, :width].astype(np.uint32)
