def blackClip(imIn, imOut, step=0, grid=mamba.DEFAULT_GRID):
"""
Performs a black skeleton clipping (clipping of a black skeleton image).
If 'step' is not defined (or equal to 0), the clipping is performed until
idempotence. If 'step' is defined, 'step' black points (if possible) will be
removed from each branch of the black skeleton.
'edge' is always set to FILLED.
"""
imWrk = mamba.imageMb(imIn)
mamba.negate(imIn, imOut)
if step == 0:
v1 = mamba.computeVolume(imOut)
v2 = 0
while v1 != v2:
v2 = v1
endPoints(imOut, imWrk, grid=grid, edge=mamba.FILLED)
mamba.diff(imOut, imWrk, imOut)
v1 = mamba.computeVolume(imOut)
else:
for i in range(step):
endPoints(imOut, imWrk, grid=grid, edge=mamba.FILLED)
mamba.diff(imOut, imWrk, imOut)
mamba.negate(imOut, imOut)
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 rotatingThin(imIn, imOut, dse, edge=mamba.FILLED):
"""
Performs a complete rotation of thinnings , the initial 'dse' double
structuring element being turned one step clockwise after each thinning.
At each rotation step, the previous result is used as input for the next
thinning (chained thinnings). Depending on the grid where 'dse' is defined,
6 or 8 rotations are performed.
'imIn' and 'imOut' are binary images.
'edge' is set to FILLED by default (default value is EMPTY in simple thin).
"""
imWrk = mamba.imageMb(imIn)
if edge == mamba.FILLED:
mamba.negate(imIn, imOut)
for d in mamba.getDirections(dse.getGrid(), True):
hitOrMiss(imOut, imWrk, dse.flip(), edge=mamba.EMPTY)
mamba.logic(imWrk, imOut, imOut, "sup")
dse = dse.rotate()
mamba.negate(imOut, imOut)
else:
mamba.copy(imIn, imOut)
for d in mamba.getDirections(dse.getGrid(), True):
hitOrMiss(imOut, imWrk, dse, edge=mamba.EMPTY)
mamba.diff(imOut, imWrk, imOut)
dse = dse.rotate()
def fullThin(imIn, imOut, dse, edge=mamba.EMPTY):
"""
Performs a complete thinning of 'imIn' with the successive rotations of 'dse'
(until idempotence) and puts the result in 'imOut'.
'imIn' and 'imOut' are binary images.
'edge' is set to EMPTY by default.
"""
if edge == mamba.EMPTY:
imWrk = mamba.imageMb(imIn)
mamba.copy(imIn, imOut)
v1 = mamba.computeVolume(imOut)
v2 = 0
while v1 != v2:
v2 = v1
for i in range(mamba.gridNeighbors(dse.getGrid())):
hitOrMiss(imOut, imWrk, dse)
mamba.diff(imOut, imWrk, imOut)
dse = dse.rotate()
v1 = mamba.computeVolume(imOut)
else:
mamba.negate(imIn, imOut)
v1 = mamba.computeVolume(imOut)
v2 = 0
while v1 != v2:
v2 = v1
rotatingThick(imOut, imOut, dse.flip())
v1 = mamba.computeVolume(imOut)
mamba.negate(imOut, imOut)
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 fullThin(imIn, imOut, dse, edge=mamba.EMPTY):
"""
Performs a complete thinning of 'imIn' with the successive rotations of 'dse'
(until idempotence) and puts the result in 'imOut'.
'imIn' and 'imOut' are binary images.
'edge' is set to EMPTY by default.
"""
if edge == mamba.EMPTY:
imWrk = mamba.imageMb(imIn)
mamba.copy(imIn, imOut)
v1 = mamba.computeVolume(imOut)
v2 = 0
while v1 != v2:
v2 = v1
for i in range(mamba.gridNeighbors(dse.getGrid())):
hitOrMiss(imOut, imWrk, dse)
mamba.diff(imOut, imWrk, imOut)
dse = dse.rotate()
v1 = mamba.computeVolume(imOut)
else:
mamba.negate(imIn, imOut)
v1 = mamba.computeVolume(imOut)
v2 = 0
while v1 != v2:
v2 = v1
rotatingThick(imOut, imOut, dse.flip())
v1 = mamba.computeVolume(imOut)
mamba.negate(imOut, imOut)
def blackClip(imIn, imOut, step=0, grid=mamba.DEFAULT_GRID):
"""
Performs a black skeleton clipping (clipping of a black skeleton image).
If 'step' is not defined (or equal to 0), the clipping is performed until
idempotence. If 'step' is defined, 'step' black points (if possible) will be
removed from each branch of the black skeleton.
'edge' is always set to FILLED.
"""
imWrk = mamba.imageMb(imIn)
mamba.negate(imIn, imOut)
if step == 0:
v1 = mamba.computeVolume(imOut)
v2 = 0
while v1 != v2:
v2 = v1
endPoints(imOut, imWrk, grid=grid, edge=mamba.FILLED)
mamba.diff(imOut, imWrk, imOut)
v1 = mamba.computeVolume(imOut)
else:
for i in range(step):
endPoints(imOut, imWrk, grid=grid, edge=mamba.FILLED)
mamba.diff(imOut, imWrk, imOut)
mamba.negate(imOut, imOut)
def mosaicGradient(imIn, imOut, grid=mamba.DEFAULT_GRID):
"""
Builds the mosaic-gradient image of 'imIn' and puts the result in 'imOut'.
The mosaic-gradient image is built by computing the differences of two
mosaic images generated from 'imIn', the first one having its watershed
lines valued by the suprema of the adjacent catchment basins values, the
second one been valued by the infima.
"""
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn)
imWrk3 = mamba.imageMb(imIn)
imWrk4 = mamba.imageMb(imIn)
imWrk5 = mamba.imageMb(imIn)
imWrk6 = mamba.imageMb(imIn, 1)
mosaic(imIn, imWrk2, imWrk3, grid=grid)
mamba.sub(imWrk2, imWrk3, imWrk1)
mamba.logic(imWrk2, imWrk3, imWrk2, "sup")
mamba.negate(imWrk2, imWrk2)
mamba.threshold(imWrk3, imWrk6, 1, 255)
mamba.multiplePoints(imWrk6, imWrk6, grid=grid)
mamba.convertByMask(imWrk6, imWrk3, 0, 255)
se = mamba.structuringElement(mamba.getDirections(grid), grid)
mamba.dilate(imWrk1, imWrk4, se=se)
mamba.dilate(imWrk2, imWrk5, se=se)
while mamba.computeVolume(imWrk3) != 0:
mamba.dilate(imWrk1, imWrk1, 2, se=se)
mamba.dilate(imWrk2, imWrk2, 2, se=se)
mamba.logic(imWrk1, imWrk3, imWrk1, "inf")
mamba.logic(imWrk2, imWrk3, imWrk2, "inf")
mamba.logic(imWrk1, imWrk4, imWrk4, "sup")
mamba.logic(imWrk2, imWrk5, imWrk5, "sup")
mamba.erode(imWrk3, imWrk3, 2, se=se)
mamba.negate(imWrk5, imWrk5)
mamba.sub(imWrk4, imWrk5, imOut)
def rotatingThin(imIn, imOut, dse, edge=mamba.FILLED):
"""
Performs a complete rotation of thinnings , the initial 'dse' double
structuring element being turned one step clockwise after each thinning.
At each rotation step, the previous result is used as input for the next
thinning (chained thinnings). Depending on the grid where 'dse' is defined,
6 or 8 rotations are performed.
'imIn' and 'imOut' are binary images.
'edge' is set to FILLED by default (default value is EMPTY in simple thin).
"""
imWrk = mamba.imageMb(imIn)
if edge == mamba.FILLED:
mamba.negate(imIn, imOut)
for d in mamba.getDirections(dse.getGrid(), True):
hitOrMiss(imOut, imWrk, dse.flip(), edge=mamba.EMPTY)
mamba.logic(imWrk, imOut, imOut, "sup")
dse = dse.rotate()
mamba.negate(imOut, imOut)
else:
mamba.copy(imIn, imOut)
for d in mamba.getDirections(dse.getGrid(), True):
hitOrMiss(imOut, imWrk, dse, edge=mamba.EMPTY)
mamba.diff(imOut, imWrk, imOut)
dse = dse.rotate()
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 mosaicGradient(imIn, imOut, grid=mamba.DEFAULT_GRID):
"""
Builds the mosaic-gradient image of 'imIn' and puts the result in 'imOut'.
The mosaic-gradient image is built by computing the differences of two
mosaic images generated from 'imIn', the first one having its watershed
lines valued by the suprema of the adjacent catchment basins values, the
second one been valued by the infima.
"""
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn)
imWrk3 = mamba.imageMb(imIn)
imWrk4 = mamba.imageMb(imIn)
imWrk5 = mamba.imageMb(imIn)
imWrk6 = mamba.imageMb(imIn, 1)
mosaic(imIn, imWrk2, imWrk3, grid=grid)
mamba.sub(imWrk2, imWrk3, imWrk1)
mamba.logic(imWrk2, imWrk3, imWrk2, "sup")
mamba.negate(imWrk2, imWrk2)
mamba.threshold(imWrk3, imWrk6, 1, 255)
mamba.multiplePoints(imWrk6, imWrk6, grid=grid)
mamba.convertByMask(imWrk6, imWrk3, 0, 255)
se = mamba.structuringElement(mamba.getDirections(grid), grid)
mamba.dilate(imWrk1, imWrk4, se=se)
mamba.dilate(imWrk2, imWrk5, se=se)
while mamba.computeVolume(imWrk3) != 0:
mamba.dilate(imWrk1, imWrk1, 2, se=se)
mamba.dilate(imWrk2, imWrk2, 2, se=se)
mamba.logic(imWrk1, imWrk3, imWrk1, "inf")
mamba.logic(imWrk2, imWrk3, imWrk2, "inf")
mamba.logic(imWrk1, imWrk4, imWrk4, "sup")
mamba.logic(imWrk2, imWrk5, imWrk5, "sup")
mamba.erode(imWrk3, imWrk3, 2, se=se)
mamba.negate(imWrk5, imWrk5)
mamba.sub(imWrk4, imWrk5, imOut)
def hitOrMiss3D(imIn, imOut, dse, edge=mamba.EMPTY):
"""
Performs a binary Hit-or-miss operation on 3D image 'imIn' using the
doubleStructuringElement3D 'dse'. Result is put in 'imOut'.
WARNING! 'imIn' and 'imOut' must be different images.
"""
(width, height, length) = imIn.getSize()
depth = imIn.getDepth()
if depth != 1:
mamba.raiseExceptionOnError(core.MB_ERR_BAD_DEPTH)
if length != len(imOut):
mamba.raiseExceptionOnError(core.MB_ERR_BAD_SIZE)
zext = dse.grid.getZExtension()
imWrk = m3D.image3DMb(width, height, length + zext * 2, depth)
# Border handling
imWrk.reset()
m3D.copy3D(imIn, imWrk, firstPlaneOut=1)
if edge == mamba.FILLED:
m3D.negate3D(imWrk, imWrk)
for i in range(zext):
imWrk[i].reset()
imWrk[length + zext * 2 - 1 - i].reset()
dse = dse.flip()
# Central point
if dse.se1.hasZero():
m3D.copy3D(imWrk, imOut, firstPlaneIn=1)
else:
if dse.se0.hasZero():
for i in range(length):
mamba.negate(imWrk[i + 1], imOut[i])
else:
imOut.fill(1)
# Other directions
dirs = m3D.getDirections3D(dse.getGrid(), True)
dirs0 = dse.se0.getDirections()
dirs1 = dse.se1.getDirections()
grid2D = dse.getGrid().get2DGrid()
for d in dirs:
if d in dirs1:
for i in range(length):
(planeOffset, dc) = dse.getGrid().convertFromDir(d, i)
mamba.infNeighbor(imWrk[i + 1 + planeOffset],
imOut[i],
1 << dc,
grid=grid2D,
edge=edge)
elif d in dirs0:
for i in range(length):
(planeOffset, dc) = dse.getGrid().convertFromDir(d, i)
mamba.diffNeighbor(imWrk[i + 1 + planeOffset],
imOut[i],
1 << dc,
grid=grid2D,
edge=edge)
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 _generateMask_(imIn1, imIn2, imOut):
#This procedure is used internally by the residues operators. It computes
#a mask indicating the points in the image where 'imIn1' is greater or equal
#to 'imIn2' with 'imIn1' strictly positive.
#Depth of 'imOut' is 1.
imWrk = mamba.imageMb(imOut)
mamba.generateSupMask(imIn1, imIn2, imOut, False)
if imIn1.getDepth()==1:
mamba.negate(imIn1, imWrk)
else:
mamba.threshold(imIn1, imWrk, 0, 0)
mamba.diff(imOut, imWrk, imOut)
def hitOrMiss3D(imIn, imOut, dse, edge=mamba.EMPTY):
"""
Performs a binary Hit-or-miss operation on 3D image 'imIn' using the
doubleStructuringElement3D 'dse'. Result is put in 'imOut'.
WARNING! 'imIn' and 'imOut' must be different images.
"""
(width,height,length) = imIn.getSize()
depth = imIn.getDepth()
if depth!=1:
mamba.raiseExceptionOnError(core.MB_ERR_BAD_DEPTH)
if length!=len(imOut):
mamba.raiseExceptionOnError(core.MB_ERR_BAD_SIZE)
zext = dse.grid.getZExtension()
imWrk = m3D.image3DMb(width, height, length+zext*2, depth)
# Border handling
imWrk.reset()
m3D.copy3D(imIn, imWrk, firstPlaneOut=1)
if edge==mamba.FILLED:
m3D.negate3D(imWrk, imWrk)
for i in range(zext):
imWrk[i].reset()
imWrk[length+zext*2-1-i].reset()
dse = dse.flip()
# Central point
if dse.se1.hasZero():
m3D.copy3D(imWrk, imOut, firstPlaneIn=1)
else:
if dse.se0.hasZero():
for i in range(length):
mamba.negate(imWrk[i+1], imOut[i])
else:
imOut.fill(1)
# Other directions
dirs = m3D.getDirections3D(dse.getGrid(), True)
dirs0 = dse.se0.getDirections()
dirs1 = dse.se1.getDirections()
grid2D = dse.getGrid().get2DGrid()
for d in dirs:
if d in dirs1:
for i in range(length):
(planeOffset, dc) = dse.getGrid().convertFromDir(d,i)
mamba.infNeighbor(imWrk[i+1+planeOffset], imOut[i], 1<<dc, grid=grid2D, edge=edge)
elif d in dirs0:
for i in range(length):
(planeOffset, dc) = dse.getGrid().convertFromDir(d,i)
mamba.diffNeighbor(imWrk[i+1+planeOffset], imOut[i], 1<<dc, grid=grid2D, edge=edge)
def negate3D(imIn, imOut):
"""
Negates the 3D image 'imIn' and puts the result in 'imOut'.
The operation is a binary complement for binary images and a negation for
greyscale and 32-bit images.
"""
outl = len(imOut)
inl = len(imIn)
if inl!=outl:
mamba.raiseExceptionOnError(core.MB_ERR_BAD_SIZE)
for i in range(outl):
mamba.negate(imIn[i], imOut[i])
def negate3D(imIn, imOut):
"""
Negates the 3D image 'imIn' and puts the result in 'imOut'.
The operation is a binary complement for binary images and a negation for
greyscale and 32-bit images.
"""
outl = len(imOut)
inl = len(imIn)
if inl != outl:
mamba.raiseExceptionOnError(core.MB_ERR_BAD_SIZE)
for i in range(outl):
mamba.negate(imIn[i], imOut[i])
def partitionErode(imIn, imOut, n=1, grid=mamba.DEFAULT_GRID):
"""
Graph erosion of the corresponding partition image 'imIn'. The size is given
by 'n'. The corresponding partition image of the resulting eroded graph is
put in 'imOut'.
'grid' can be set to HEXAGONAL or SQUARE.
"""
imWrk = mamba.imageMb(imIn)
mamba.negate(imIn, imWrk)
mamba.copy(imWrk, imOut)
se = mamba.structuringElement(mamba.getDirections(grid), grid)
for i in range(n):
mamba.dilate(imOut, imOut, se=se)
cellsBuild(imWrk, imOut, grid=grid)
mamba.negate(imOut, imOut)
def partitionErode(imIn, imOut, n=1, grid=mamba.DEFAULT_GRID):
"""
Graph erosion of the corresponding partition image 'imIn'. The size is given
by 'n'. The corresponding partition image of the resulting eroded graph is
put in 'imOut'.
'grid' can be set to HEXAGONAL or SQUARE.
"""
imWrk = mamba.imageMb(imIn)
mamba.negate(imIn, imWrk)
mamba.copy(imWrk, imOut)
se = mamba.structuringElement(mamba.getDirections(grid), grid)
for i in range(n):
mamba.dilate(imOut, imOut, se=se)
cellsBuild(imWrk, imOut, grid=grid)
mamba.negate(imOut, imOut)
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 simpleLevelling(imIn, imMask, imOut, grid=mamba.DEFAULT_GRID):
"""
Performs a simple levelling of image 'imIn' controlled by image 'imMask'
and puts the result in 'imOut'. This operation is composed of two
geodesic reconstructions. This filter tends to level regions in the
image of homogeneous grey values.
"""
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn)
mask_im = mamba.imageMb(imIn, 1)
mamba.logic(imIn, imMask, imWrk1, "inf")
mamba.build(imIn, imWrk1, grid=grid)
mamba.logic(imIn, imMask, imWrk2, "sup")
mamba.dualBuild(imIn, imWrk2, grid=grid)
mamba.generateSupMask(imIn, imMask, mask_im, False)
mamba.convertByMask(mask_im, imOut, 0, mamba.computeMaxRange(imIn)[1])
mamba.logic(imOut, imWrk1, imWrk1, "inf")
mamba.negate(imOut, imOut)
mamba.logic(imOut, imWrk2, imOut, "inf")
mamba.logic(imWrk1, imOut, imOut, "sup")
def tlaferm((name, thresh, outpath)):
# =============== Load numpy file and create contours then save png =========
# threshold=25
# print 'name= ',name
zr = np.load(name)
im = Image.fromarray(zr.T)
#
xpixels, ypixels = im.size[1], im.size[0]
dpi, scalefactor = 72, 1
xinch = xpixels * scalefactor / dpi
yinch = ypixels * scalefactor / dpi
#
fig = plt.figure(figsize=(xinch, yinch))
ax = plt.axes([0, 0, 1, 1], frame_on=False, xticks=[], yticks=[])
imc = plt.contourf(zr, [0, thresh], colors='black', origin='image')
##
imfile = 'image_' + os.path.basename(name) + '.png'
impath = os.path.dirname(name)
plt.savefig(os.path.join(outpath, imfile), dpi=dpi)
plt.close('all')
#
# =================== load png and use mamba then save ======================================
im = mamba.imageMb(os.path.join(outpath, imfile))
im1, im2, im3, im4 = mamba.imageMb(im), mamba.imageMb(im), mamba.imageMb(
im), mamba.imageMb(im)
se = mamba.structuringElement([0, 1, 2, 3, 4, 5, 6], grid=mamba.HEXAGONAL)
mamba.closeHoles(im, im)
mamba.negate(im, im)
mamba.closeHoles(im, im)
mamba.negate(im, im)
#
#mamba.opening(im, im1, n=2, se=se)
ma_file = 'mamba_' + imfile + '.png'
im.save(os.path.join(outpath, ma_file))
def tlaferm((name,thresh,outpath)):
# =============== Load numpy file and create contours then save png =========
# threshold=25
# print 'name= ',name
zr=np.load(name)
im=Image.fromarray(zr.T)
#
xpixels,ypixels = im.size[1],im.size[0]
dpi,scalefactor = 72,1
xinch = xpixels * scalefactor / dpi
yinch = ypixels * scalefactor / dpi
#
fig = plt.figure(figsize=(xinch,yinch))
ax = plt.axes([0, 0, 1, 1], frame_on=False, xticks=[], yticks=[])
imc=plt.contourf(zr, [0,thresh], colors='black', origin='image')
##
imfile='image_'+os.path.basename(name)+'.png'
impath=os.path.dirname(name)
plt.savefig(os.path.join(outpath,imfile), dpi=dpi)
plt.close('all')
#
# =================== load png and use mamba then save ======================================
im = mamba.imageMb(os.path.join(outpath,imfile))
im1,im2,im3,im4=mamba.imageMb(im),mamba.imageMb(im),mamba.imageMb(im),mamba.imageMb(im)
se = mamba.structuringElement([0,1,2,3,4,5,6],grid=mamba.HEXAGONAL)
mamba.closeHoles(im, im)
mamba.negate(im, im)
mamba.closeHoles(im, im)
mamba.negate(im, im)
#
#mamba.opening(im, im1, n=2, se=se)
ma_file='mamba_'+imfile+'.png'
im.save(os.path.join(outpath,ma_file))
def closeHoles(imIn, imOut, grid=mamba.DEFAULT_GRID):
"""
Close holes in image 'imIn' and puts the result in 'imOut'. This operator
works on binary and greytone images. In this case, however, it should be
used cautiously.
"""
imWrk = mamba.imageMb(imIn)
mamba.negate(imIn, imIn)
mamba.drawEdge(imWrk)
mamba.logic(imIn, imWrk, imWrk, "inf")
build(imIn, imWrk, grid=grid)
mamba.negate(imIn, imIn)
mamba.negate(imWrk, imOut)
def feretDiameterLabelling(imIn, imOut, direc):
"""
The Feret diameter of each connected component of the binary image or the
partition image 'imIn' is computed and its value labels the corresponding
component. The labelled image is stored in the 32-bit image 'imOut'.
If 'direc' is "vertical", the vertical Feret diameter is computed. If it is
set to "horizontal", the corresponding diameter is used.
"""
imWrk1 = mamba.imageMb(imIn, 1)
imWrk2 = mamba.imageMb(imIn, 32)
imWrk3 = mamba.imageMb(imIn, 32)
imWrk4 = mamba.imageMb(imIn, 32)
imWrk1.fill(1)
if direc == "horizontal":
dir = 7
elif direc == "vertical":
dir = 1
else:
mamba.raiseExceptionOnError(core.MB_ERR_BAD_DIRECTION)
# The above statement generates an error ('direc' is not horizontal or
# vertical.
# An horizontal or vertical distance function is generated.
mamba.linearErode(imWrk1, imWrk1, dir, grid=mamba.SQUARE, edge=mamba.EMPTY)
mamba.computeDistance(imWrk1, imOut, grid=mamba.SQUARE, edge=mamba.FILLED)
mamba.addConst(imOut, 1, imOut)
if imIn.getDepth() == 1:
# Each particle is valued with the distance.
mamba.convertByMask(imIn, imWrk2, 0, mamba.computeMaxRange(imWrk3)[1])
mamba.logic(imOut, imWrk2, imWrk3, "inf")
# The valued image is preserved.
mamba.copy(imWrk3, imWrk4)
# Each component is labelled by the maximal coordinate.
mamba.build(imWrk2, imWrk3)
# Using the dual reconstruction, we label the particles with the
# minimal ccordinate.
mamba.negate(imWrk2, imWrk2)
mamba.logic(imWrk2, imWrk4, imWrk4, "sup")
mamba.dualBuild(imWrk2, imWrk4)
# We subtract 1 because the selected coordinate must be outside the particle.
mamba.subConst(imWrk4, 1, imWrk4)
mamba.negate(imWrk2, imWrk2)
mamba.logic(imWrk2, imWrk4, imWrk4, "inf")
# Then, the subtraction gives the Feret diameter.
mamba.sub(imWrk3, imWrk4, imOut)
else:
mamba.copy(imOut, imWrk3)
if imIn.getDepth() == 32:
mamba.copy(imIn, imWrk2)
else:
mamba.convert(imIn, imWrk2)
# Using the cells builds (direct and dual to label the cells with the maximum
# and minimum distance.
mamba.cellsBuild(imWrk2, imWrk3)
mamba.cellsBuild(imWrk2, imWrk3)
mamba.negate(imOut, imOut)
mamba.cellsBuild(imWrk2, imOut)
mamba.negate(imOut, imOut)
# Subtracting 1...
mamba.subConst(imOut, 1, imOut)
# ... and getting the final result.
mamba.sub(imWrk3, imOut, imOut)
# a problem of detection and counting of the teeth of a notched wheel when it is
# associated to a preliminary selection of the zone where these teeth should be.
## SCRIPT ######################################################################
# Importing mamba
import mamba
import mambaDisplay
im = mamba.imageMb("wheel.png", 1)
im1 = mamba.imageMb(im, 1)
im2 = mamba.imageMb(im, 1)
# Opening of image
mamba.opening(im, im1, 3)
# Selection of the outside region
mamba.negate(im1, im2)
mamba.removeEdgeParticles(im2, im1)
mamba.diff(im2, im1, im2)
# Extracting the wheel teeth
mamba.logic(im, im2, im2, "inf")
# Cleaning the image
mamba.opening(im2, im2)
# Counting and marking each tooth
mamba.thinD(im2, im1)
nb_teeth = mamba.computeVolume(im1)
print("Number of teeth: %d" % (nb_teeth))
mamba.dilate(im1, im1, 3, mamba.SQUARE3X3)
im1.convert(8)
im8 = mamba.imageMb(im, 8)
mamba.convert(im, im8)
mamba.subConst(im8, 1, im8)
def feretDiameterLabelling(imIn, imOut, direc):
"""
The Feret diameter of each connected component of the binary image or the
partition image 'imIn' is computed and its value labels the corresponding
component. The labelled image is stored in the 32-bit image 'imOut'.
If 'direc' is "vertical", the vertical Feret diameter is computed. If it is
set to "horizontal", the corresponding diameter is used.
"""
imWrk1 = mamba.imageMb(imIn, 1)
imWrk2 = mamba.imageMb(imIn, 32)
imWrk3 = mamba.imageMb(imIn, 32)
imWrk4 = mamba.imageMb(imIn, 32)
imWrk1.fill(1)
if direc == "horizontal":
dir = 7
elif direc == "vertical":
dir = 1
else:
mamba.raiseExceptionOnError(core.MB_ERR_BAD_DIRECTION)
# The above statement generates an error ('direc' is not horizontal or
# vertical.
# An horizontal or vertical distance function is generated.
mamba.linearErode(imWrk1, imWrk1, dir, grid=mamba.SQUARE, edge=mamba.EMPTY)
mamba.computeDistance(imWrk1, imOut, grid=mamba.SQUARE, edge=mamba.FILLED)
mamba.addConst(imOut, 1, imOut)
if imIn.getDepth() == 1:
# Each particle is valued with the distance.
mamba.convertByMask(imIn, imWrk2, 0, mamba.computeMaxRange(imWrk3)[1])
mamba.logic(imOut, imWrk2, imWrk3, "inf")
# The valued image is preserved.
mamba.copy(imWrk3, imWrk4)
# Each component is labelled by the maximal coordinate.
mamba.build(imWrk2, imWrk3)
# Using the dual reconstruction, we label the particles with the
# minimal ccordinate.
mamba.negate(imWrk2, imWrk2)
mamba.logic(imWrk2, imWrk4, imWrk4, "sup")
mamba.dualBuild(imWrk2, imWrk4)
# We subtract 1 because the selected coordinate must be outside the particle.
mamba.subConst(imWrk4, 1, imWrk4)
mamba.negate(imWrk2, imWrk2)
mamba.logic(imWrk2, imWrk4, imWrk4, "inf")
# Then, the subtraction gives the Feret diameter.
mamba.sub(imWrk3, imWrk4, imOut)
else:
mamba.copy(imOut, imWrk3)
if imIn.getDepth() == 32:
mamba.copy(imIn, imWrk2)
else:
mamba.convert(imIn, imWrk2)
# Using the cells builds (direct and dual to label the cells with the maximum
# and minimum distance.
mamba.cellsBuild(imWrk2, imWrk3)
mamba.cellsBuild(imWrk2, imWrk3)
mamba.negate(imOut, imOut)
mamba.cellsBuild(imWrk2, imOut)
mamba.negate(imOut, imOut)
# Subtracting 1...
mamba.subConst(imOut, 1, imOut)
# ... and getting the final result.
mamba.sub(imWrk3, imOut, imOut)
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
# a problem of detection and counting of the teeth of a notched wheel when it is
# associated to a preliminary selection of the zone where these teeth should be.
## SCRIPT ######################################################################
# Importing mamba
import mamba
import mambaDisplay
im = mamba.imageMb("wheel.png", 1)
im1 = mamba.imageMb(im, 1)
im2 = mamba.imageMb(im, 1)
# Opening of image
mamba.opening(im, im1, 3)
# Selection of the outside region
mamba.negate(im1, im2)
mamba.removeEdgeParticles(im2, im1)
mamba.diff(im2, im1, im2)
# Extracting the wheel teeth
mamba.logic(im, im2, im2, "inf")
# Cleaning the image
mamba.opening(im2, im2)
# Counting and marking each tooth
mamba.thinD(im2, im1)
nb_teeth = mamba.computeVolume(im1)
print("Number of teeth: %d" % (nb_teeth))
mamba.dilate(im1, im1, 3, mamba.SQUARE3X3)
im1.convert(8)
im8 = mamba.imageMb(im, 8)
mamba.convert(im, im8)
mamba.subConst(im8, 1, im8)