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 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 cellsBuild(imIn, imInOut, grid=mamba.DEFAULT_GRID):
"""
Geodesic reconstruction of the cells of the partition image 'imIn' which
are marked by the image 'imInOut'. The marked cells take the value of
their corresponding marker. Note that the background cells (labelled by 0)
are also modified if they are marked.
The result is stored in 'imInOut'.
The images can be 8-bit or 32-bit images.
'grid' can be set to HEXAGONAL or SQUARE.
"""
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn)
imWrk3 = mamba.imageMb(imIn, 1)
vol = 0
prec_vol = -1
dirs = mamba.getDirections(grid)[1:]
while (prec_vol != vol):
prec_vol = vol
for d in dirs:
ed = 1 << d
mamba.copy(imIn, imWrk1)
mamba.copy(imIn, imWrk2)
mamba.supNeighbor(imWrk1, imWrk1, ed, grid=grid)
mamba.infNeighbor(imWrk2, imWrk2, ed, grid=grid)
mamba.generateSupMask(imWrk2, imWrk1, imWrk3, False)
mamba.convertByMask(imWrk3, imWrk1, 0,
mamba.computeMaxRange(imIn)[1])
mamba.linearDilate(imInOut, imWrk2, d, 1, grid=grid)
mamba.logic(imWrk2, imWrk1, imWrk2, "inf")
v = mamba.buildNeighbor(imWrk1, imWrk2, d, grid=grid)
mamba.logic(imWrk2, imInOut, imInOut, "sup")
vol = mamba.computeVolume(imInOut)
def lowerGeodesicErode(imIn, imMask, imOut, n=1, se=mamba.DEFAULT_SE):
"""
Performs a lower geodesic erosion of image 'imIn' under 'imMask'.
The result is put inside 'imOut', 'n' controls the size of the erosion.
'se' specifies the type of structuring element used to perform the
computation (DEFAULT_SE by default).
The binary lower geodesic erosion is realised using the fact that the
dilation is the dual operation of the erosion.
Warning! 'imMask' and 'imOut' must be different.
"""
if imIn.getDepth() == 1:
mamba.diff(imMask, imIn, imOut)
lowerGeodesicDilate(imOut, imMask, imOut, n, se=se)
mamba.diff(imMask, imOut, imOut)
else:
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn, 1)
mamba.logic(imIn, imMask, imOut, "inf")
for i in range(n):
mamba.generateSupMask(imOut, imMask, imWrk2, False)
mamba.convertByMask(imWrk2, imWrk1, 0, mamba.computeMaxRange(imWrk1)[1])
mamba.logic(imOut, imWrk1, imOut, "sup")
mamba.erode(imOut, imOut, se=se)
mamba.logic(imOut, imMask, imOut, "inf")
def cellsBuild(imIn, imInOut, grid=mamba.DEFAULT_GRID):
"""
Geodesic reconstruction of the cells of the partition image 'imIn' which
are marked by the image 'imInOut'. The marked cells take the value of
their corresponding marker. Note that the background cells (labelled by 0)
are also modified if they are marked.
The result is stored in 'imInOut'.
The images can be 8-bit or 32-bit images.
'grid' can be set to HEXAGONAL or SQUARE.
"""
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn)
imWrk3 = mamba.imageMb(imIn, 1)
vol = 0
prec_vol = -1
dirs = mamba.getDirections(grid)[1:]
while (prec_vol!=vol):
prec_vol = vol
for d in dirs:
ed = 1<<d
mamba.copy(imIn, imWrk1)
mamba.copy(imIn, imWrk2)
mamba.supNeighbor(imWrk1, imWrk1, ed, grid=grid)
mamba.infNeighbor(imWrk2, imWrk2, ed, grid=grid)
mamba.generateSupMask(imWrk2, imWrk1, imWrk3, False)
mamba.convertByMask(imWrk3, imWrk1, 0, mamba.computeMaxRange(imIn)[1])
mamba.linearDilate(imInOut, imWrk2, d, 1, grid=grid)
mamba.logic(imWrk2, imWrk1, imWrk2, "inf")
v = mamba.buildNeighbor(imWrk1, imWrk2, d, grid=grid)
mamba.logic(imWrk2, imInOut, imInOut, "sup")
vol = mamba.computeVolume(imInOut)
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 _initialQuasiDist_(imIn, imOut1, imOut2, grid=mamba.DEFAULT_GRID):
"""
Computes the initial quasi-distance. For internal use only. The resulting
quasi-distance is not lipchitzian (see MM documentation for details).
"""
maskIm = mamba.imageMb(imIn, 1)
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn)
imWrk3 = mamba.imageMb(imIn, 32)
se = mamba.structuringElement(mamba.getDirections(grid), grid)
i = 0
mamba.copy(imIn, imWrk1)
v2 = mamba.computeVolume(imWrk1)
v1 = v2 + 1
imOut1.reset()
imOut2.reset()
while v1 > v2:
i += 1
v1 = v2
mamba.erode(imWrk1, imWrk2, se=se)
mamba.sub(imWrk1, imWrk2, imWrk1)
_generateMask_(imWrk1, imOut1, maskIm)
mamba.convertByMask(maskIm, imWrk3, 0, i)
mamba.logic(imOut1, imWrk1, imOut1, "sup")
mamba.logic(imOut2, imWrk3, imOut2, "sup")
mamba.copy(imWrk2, imWrk1)
v2 = mamba.computeVolume(imWrk1)
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 binaryUltimateErosion(imIn, imOut1, imOut2, grid=mamba.DEFAULT_GRID, edge=mamba.FILLED):
"""
Ultimate erosion of binary image 'imIn'. 'imOut1' contains the ultimate
eroded set and 'imOut2' contains the associated function (that is the
height of each connected component of the ultimate erosion).
An ultimate erosion is composed of the union of the last connected
components of the successive erosions of the initial set. The associated
function provides the size of the corresponding erosion.
Depth of 'imOut1' is 1, depth of 'imOut2' is 32.
The operation is fast because it is computed using the distance function
of 'imIn' (the ultimate erosion is identical to the maxima of this
distance function).
The edge is set to 'FILLED' by default.
"""
imWrk1 = mamba.imageMb(imIn, 32)
imWrk2 = mamba.imageMb(imIn, 32)
mamba.computeDistance(imIn, imWrk1, grid=grid, edge=edge)
mamba.maxima(imWrk1, imOut1, grid=grid)
mamba.convertByMask(imOut1, imWrk2, 0, mamba.computeMaxRange(imWrk2)[1])
mamba.logic(imWrk1, imWrk2, imOut2, "inf")
def vectorGradient(imIn, imModul, imAzim, size=1):
"""
Computes modulus (result in 'imModul') and azimut (in 'imAzim') of image
'imIn'. The 'size' of each directional gradient is set to 1 by default.
This operator is defined on the hexagonal grid (12 directions are used).
Note that this operator belongs to the residual operators class.
"""
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn)
imWrk3 = mamba.imageMb(imIn)
imWrk4 = mamba.imageMb(imIn, 1)
mamba.copy(imIn, imWrk1)
imModul.reset()
imAzim.reset()
for i in range(12):
d = i + 1
# Directional gradient obtained with linear erosions and dilations.
directionalDilate(imWrk1, imWrk2, d, size)
directionalErode(imWrk1, imWrk3, d, size)
mamba.sub(imWrk2, imWrk3, imWrk2)
# For each direction, maximal pixels are indexed in a mask image.
mamba.generateSupMask(imWrk2, imModul, imWrk4, True)
mamba.convertByMask(imWrk4, imWrk3, 0, d)
# Modulus and azimut are calculated.
mamba.logic(imWrk2, imModul, imModul, "sup")
mamba.logic(imWrk3, imAzim, imAzim, "sup")
def quasiDistance(imIn, imOut1, imOut2, grid=mamba.DEFAULT_GRID):
"""
Quasi-distance of image 'imIn'. 'imOut1' contains the residues image
and 'imOut2' contains the quasi-distance (associated function).
The quasi-distance of a greytone image is made of a patch of distance
functions of some almost flat regions in the image. When the image is a
simple indicator function of a set, the quasi-distance and the distance
function are identical.
Depth of 'imOut1' is the same as 'imIn', depth of 'imOut2' is 32.
"""
imWrk1 = mamba.imageMb(imIn, 32)
imWrk2 = mamba.imageMb(imIn, 32)
imWrk3 = mamba.imageMb(imIn, 32)
maskIm = mamba.imageMb(imIn, 1)
se = mamba.structuringElement(mamba.getDirections(grid), grid)
_initialQuasiDist_(imIn, imOut1, imOut2, grid=grid)
mamba.copy(imOut2, imWrk1)
v1 = mamba.computeVolume(imOut2)
v2 = v1 + 1
while v2 > v1:
v2 = v1
mamba.erode(imWrk1, imWrk2, se=se)
mamba.sub(imWrk1, imWrk2, imWrk2)
mamba.threshold(imWrk2, maskIm, 2, mamba.computeMaxRange(imWrk2)[1])
mamba.convertByMask(maskIm, imWrk3, 0, mamba.computeMaxRange(imWrk3)[1])
mamba.logic(imWrk2, imWrk3, imWrk2, "inf")
mamba.subConst(imWrk2, 1, imWrk3)
mamba.logic(imWrk2, imWrk3, imWrk2, "inf") # Patch non saturated subtraction
mamba.sub(imWrk1, imWrk2, imWrk1)
v1 = mamba.computeVolume(imWrk1)
mamba.copy(imWrk1, imOut2)
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 vectorGradient(imIn, imModul, imAzim, size=1):
"""
Computes modulus (result in 'imModul') and azimut (in 'imAzim') of image
'imIn'. The 'size' of each directional gradient is set to 1 by default.
This operator is defined on the hexagonal grid (12 directions are used).
Note that this operator belongs to the residual operators class.
"""
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn)
imWrk3 = mamba.imageMb(imIn)
imWrk4 = mamba.imageMb(imIn, 1)
mamba.copy(imIn, imWrk1)
imModul.reset()
imAzim.reset()
for i in range(12):
d = i + 1
# Directional gradient obtained with linear erosions and dilations.
directionalDilate(imWrk1, imWrk2, d, size)
directionalErode(imWrk1, imWrk3, d, size)
mamba.sub(imWrk2, imWrk3, imWrk2)
# For each direction, maximal pixels are indexed in a mask image.
mamba.generateSupMask(imWrk2, imModul, imWrk4, True)
mamba.convertByMask(imWrk4, imWrk3, 0, d)
# Modulus and azimut are calculated.
mamba.logic(imWrk2, imModul, imModul, "sup")
mamba.logic(imWrk3, imAzim, imAzim, "sup")
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 nonEqualNeighbors(imIn, imOut, nb, grid=mamba.DEFAULT_GRID, edge=mamba.FILLED):
"""
This operator compares the value of each pixel of image 'imIn'
with the value of its neighbors encoded in 'nb'.
If all the neighbor values are different, the pixel is unchanged.
Otherwise, it takes value 0.
This operator works on hexagonal or square 'grid' and
'edge' is set to FILLED by default.
This operator works for 8-bit and 32-bit images.
"""
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn)
imWrk3 = mamba.imageMb(imIn, 1)
imWrk4 = mamba.imageMb(imIn, 1)
for d in mamba.getDirections(grid):
ed = 1<<d
if (nb & ed):
mamba.copy(imIn, imWrk1)
mamba.copy(imIn, imWrk2)
mamba.supNeighbor(imWrk1, imWrk1, ed, grid=grid, edge=edge)
mamba.infNeighbor(imWrk2, imWrk2, ed, grid=grid, edge=edge)
mamba.generateSupMask(imWrk2, imWrk1, imWrk3, False)
mamba.logic(imWrk4, imWrk3, imWrk4, "or")
mamba.convertByMask(imWrk4, imWrk1, mamba.computeMaxRange(imIn)[1], 0)
mamba.logic(imIn, imWrk1, imOut, "inf")
def upperGeodesicDilate(imIn, imMask, imOut, n=1, se=mamba.DEFAULT_SE):
"""
Performs an upper geodesic dilation of image 'imIn' above 'imMask'.
The result is put inside 'imOut', 'n' controls the size of the dilation.
'se' specifies the type of structuring element used to perform the
computation (DEFAULT_SE by default).
Warning! 'imMask' and 'imOut' must be different.
"""
mamba.logic(imIn, imMask, imOut, "sup")
if imIn.getDepth() == 1:
for i in range(n):
mamba.diff(imOut, imMask, imOut)
mamba.dilate(imOut, imOut, se=se)
mamba.logic(imMask, imOut, imOut, "sup")
else:
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn, 1)
for i in range(n):
mamba.generateSupMask(imOut, imMask, imWrk2, True)
mamba.convertByMask(imWrk2, imWrk1, 0, mamba.computeMaxRange(imWrk1)[1])
mamba.logic(imOut, imWrk1, imOut, "inf")
mamba.dilate(imOut, imOut, se=se)
mamba.logic(imOut, imMask, imOut, "sup")
def nonEqualNeighbors(imIn,
imOut,
nb,
grid=mamba.DEFAULT_GRID,
edge=mamba.FILLED):
"""
This operator compares the value of each pixel of image 'imIn'
with the value of its neighbors encoded in 'nb'.
If all the neighbor values are different, the pixel is unchanged.
Otherwise, it takes value 0.
This operator works on hexagonal or square 'grid' and
'edge' is set to FILLED by default.
This operator works for 8-bit and 32-bit images.
"""
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn)
imWrk3 = mamba.imageMb(imIn, 1)
imWrk4 = mamba.imageMb(imIn, 1)
for d in mamba.getDirections(grid):
ed = 1 << d
if (nb & ed):
mamba.copy(imIn, imWrk1)
mamba.copy(imIn, imWrk2)
mamba.supNeighbor(imWrk1, imWrk1, ed, grid=grid, edge=edge)
mamba.infNeighbor(imWrk2, imWrk2, ed, grid=grid, edge=edge)
mamba.generateSupMask(imWrk2, imWrk1, imWrk3, False)
mamba.logic(imWrk4, imWrk3, imWrk4, "or")
mamba.convertByMask(imWrk4, imWrk1, mamba.computeMaxRange(imIn)[1], 0)
mamba.logic(imIn, imWrk1, 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 fastSKIZ(imIn, imOut, grid=mamba.DEFAULT_GRID):
"""
Fast skeleton by zones of influence of binary image 'imIn'. Result is put in
binary image 'imOut'. The transformation is faster as it uses the watershed
transform by hierarchical queues.
"""
imWrk = mamba.imageMb(imIn, 8)
mamba.convertByMask(imIn, imWrk, 1, 0)
markerControlledWatershed(imWrk, imIn, imWrk, grid=grid)
mamba.threshold(imWrk, imOut, 0, 0)
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 fastSKIZ(imIn, imOut, grid=mamba.DEFAULT_GRID):
"""
Fast skeleton by zones of influence of binary image 'imIn'. Result is put in
binary image 'imOut'. The transformation is faster as it uses the watershed
transform by hierarchical queues.
"""
imWrk = mamba.imageMb(imIn, 8)
mamba.convertByMask(imIn, imWrk, 1, 0)
markerControlledWatershed(imWrk, imIn, imWrk, grid=grid)
mamba.threshold(imWrk, imOut, 0, 0)
def multiSuperpose(imInout, *imIns):
"""
Superposes multiple binary images ('imIns') to the greyscale image
'imInout'. The binary images are put above the greyscale. The
result is meant to be seen with an appropriate color palette.
"""
imWrk = mamba.imageMb(imInout)
mamba.subConst(imInout, len(imIns), imInout)
for i,im in enumerate(imIns):
mamba.convertByMask(im, imWrk, 0, 256-len(imIns)+i)
mamba.logic(imInout, imWrk, imInout, "sup")
def cellsExtract(imIn, imMarkers, imOut, grid=mamba.DEFAULT_GRID):
"""
Geodesic reconstruction and extraction of the cells of the partition image
'imIn' which are marked by the binary marker image 'imMarkers'. The marked
cells keep their initial value. The result is stored in 'imOut'.
The images can be 8-bit or 32-bit images.
'grid' can be set to HEXAGONAL or SQUARE.
"""
imWrk1 = mamba.imageMb(imIn)
mamba.convertByMask(imMarkers, imWrk1, 0, mamba.computeMaxRange(imIn)[1])
mamba.logic(imIn, imWrk1, imOut, "inf")
cellsBuild(imIn, imOut, grid=grid)
def cellsExtract(imIn, imMarkers, imOut, grid=mamba.DEFAULT_GRID):
"""
Geodesic reconstruction and extraction of the cells of the partition image
'imIn' which are marked by the binary marker image 'imMarkers'. The marked
cells keep their initial value. The result is stored in 'imOut'.
The images can be 8-bit or 32-bit images.
'grid' can be set to HEXAGONAL or SQUARE.
"""
imWrk1 = mamba.imageMb(imIn)
mamba.convertByMask(imMarkers, imWrk1, 0, mamba.computeMaxRange(imIn)[1])
mamba.logic(imIn, imWrk1, imOut, "inf")
cellsBuild(imIn, imOut, grid=grid)
def minPartialBuild(imIn, imMask, imOut, grid=mamba.DEFAULT_GRID):
"""
Performs the partial reconstruction of 'imIn' with its minima which are
contained in the binary mask 'imMask'. The result is put in 'imOut'.
'imIn' and 'imOut' must be different and greyscale images.
"""
imWrk = mamba.imageMb(imIn, 1)
minima(imIn, imWrk, 1, grid=grid)
mamba.logic(imMask, imWrk, imWrk, "inf")
mamba.convertByMask(imWrk, imOut, mamba.computeMaxRange(imIn)[1], 0)
mamba.logic(imIn, imOut, imOut, "sup")
mamba.dualBuild(imIn, imOut)
def floorSubConst(imIn, v, imOut):
"""
Subtracts a constant value 'v' to image 'imIn' and puts the result in 'imOut'.
If imIn - v is negative, the result is truncated and limited to 0.
Note that this operator is mainly useful for 32-bit images, as the result
of the subtraction is always truncated for 8-bit images.
"""
imMask = mamba.imageMb(imIn, 1)
imWrk = mamba.imageMb(imIn)
mamba.subConst(imIn, v, imWrk)
mamba.generateSupMask(imIn, imWrk, imMask, False)
mamba.convertByMask(imMask, imOut, 0, mamba.computeMaxRange(imOut)[1])
mamba.logic(imOut, imWrk, imOut, "inf")
def geodesicSKIZ(imIn, imMask, imOut, grid=mamba.DEFAULT_GRID):
"""
Geodesic skeleton by zones of influence of binary image 'imIn' inside the
geodesic mask 'imMask'. The result is in binary image 'imOut'.
"""
imWrk1 = mamba.imageMb(imIn, 8)
imWrk2 = mamba.imageMb(imIn)
mamba.copy(imIn, imWrk2)
mamba.build(imMask, imWrk2, grid=grid)
mamba.convertByMask(imWrk2, imWrk1, 2, 1)
mamba.sub(imWrk1, imIn, imWrk1)
markerControlledWatershed(imWrk1, imIn, imWrk1, grid=grid)
mamba.threshold(imWrk1, imOut, 0, 0)
mamba.logic(imOut, imWrk2, imOut, "inf")
def geodesicSKIZ(imIn, imMask, imOut, grid=mamba.DEFAULT_GRID):
"""
Geodesic skeleton by zones of influence of binary image 'imIn' inside the
geodesic mask 'imMask'. The result is in binary image 'imOut'.
"""
imWrk1 = mamba.imageMb(imIn, 8)
imWrk2 = mamba.imageMb(imIn)
mamba.copy(imIn, imWrk2)
mamba.build(imMask, imWrk2, grid=grid)
mamba.convertByMask(imWrk2, imWrk1, 2, 1)
mamba.sub(imWrk1, imIn, imWrk1)
markerControlledWatershed(imWrk1, imIn, imWrk1, grid=grid)
mamba.threshold(imWrk1, imOut, 0, 0)
mamba.logic(imOut, imWrk2, imOut, "inf")
def ceilingAddConst(imIn, v, imOut):
"""
Adds a constant value 'v' to image 'imIn' and puts the result in 'imOut'. If
imIn + v is larger than the maximal possible value in imOut, the result is
truncated and limited to this maximal value.
Note that this operator is mainly useful for 32-bit images, as the result
of the addition is always truncated for 8-bit images.
"""
imMask = mamba.imageMb(imIn, 1)
imWrk = mamba.imageMb(imIn)
mamba.addConst(imIn, v, imWrk)
mamba.generateSupMask(imIn, imWrk, imMask, True)
mamba.convertByMask(imMask, imOut, 0, mamba.computeMaxRange(imOut)[1])
mamba.logic(imOut, imWrk, imOut, "sup")
def cellsErode(imIn, imOut, n=1, se=mamba.DEFAULT_SE, edge=mamba.FILLED):
"""
Simultaneous erosion of size 'n' (default 1) of all the cells of the
partition image 'imIn' with 'se' structuring element.
The resulting partition is put in 'imOut'.
'edge' is set to FILLED by default.
This operation works on 8-bit and 32-bit partitions.
"""
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn, 1)
mamba.dilate(imIn, imWrk1, n=n, se=se)
mamba.erode(imIn, imOut, n=n, se=se, edge=edge)
mamba.generateSupMask(imOut, imWrk1, imWrk2, False)
mamba.convertByMask(imWrk2, imWrk1, 0, mamba.computeMaxRange(imIn)[1])
mamba.logic(imOut, imWrk1, imOut, "inf")
def cellsErode(imIn, imOut, n=1, se=mamba.DEFAULT_SE, edge=mamba.FILLED):
"""
Simultaneous erosion of size 'n' (default 1) of all the cells of the
partition image 'imIn' with 'se' structuring element.
The resulting partition is put in 'imOut'.
'edge' is set to FILLED by default.
This operation works on 8-bit and 32-bit partitions.
"""
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn, 1)
mamba.dilate(imIn, imWrk1, n=n, se=se)
mamba.erode(imIn, imOut, n=n, se=se, edge=edge)
mamba.generateSupMask(imOut, imWrk1, imWrk2, False)
mamba.convertByMask(imWrk2, imWrk1, 0, mamba.computeMaxRange(imIn)[1])
mamba.logic(imOut, imWrk1, imOut, "inf")
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 convertByMask3D(imIn, imOut, mFalse, mTrue):
"""
Converts a binary image 'imIn' into a greyscale image (8-bit) or a 32-bit
image and puts the result in 'imOut'.
White pixels of 'imIn' are set to value 'mTrue' in the output image and the
black pixels set to value 'mFalse'.
This function works with 3D images.
"""
outl = len(imOut)
inl = len(imIn)
if inl!=outl:
mamba.raiseExceptionOnError(core.MB_ERR_BAD_SIZE)
for i in range(outl):
mamba.convertByMask(imIn[i], imOut[i], mFalse, mTrue)
def convertByMask3D(imIn, imOut, mFalse, mTrue):
"""
Converts a binary image 'imIn' into a greyscale image (8-bit) or a 32-bit
image and puts the result in 'imOut'.
White pixels of 'imIn' are set to value 'mTrue' in the output image and the
black pixels set to value 'mFalse'.
This function works with 3D images.
"""
outl = len(imOut)
inl = len(imIn)
if inl != outl:
mamba.raiseExceptionOnError(core.MB_ERR_BAD_SIZE)
for i in range(outl):
mamba.convertByMask(imIn[i], imOut[i], mFalse, mTrue)
def hierarchy(imIn, imMask, imOut, grid=mamba.DEFAULT_GRID):
"""
Construction of a hierarchical image from image 'imIn' and with 'imMask'.
The binary image 'imMask' controls the dual reconstruction (propagation)
of 'imIn'.
This operator is mainly used to build hierarchical images from valued
watershed images.
The hierarchical image is put in 'imOut'.
"""
imWrk = mamba.imageMb(imIn)
if mamba.checkEmptiness(imIn):
mamba.copy(imIn, imOut)
else:
mamba.convertByMask(imMask, imWrk, 255, 0)
mamba.logic(imIn, imWrk, imWrk, "sup")
mamba.hierarDualBuild(imIn, imWrk)
mamba.copy(imWrk, imOut)
def floorSub(imIn1, imIn2, imOut):
"""
Subtracts image 'imIn2' from image 'imIn1' and puts the result in 'imOut'.
If imIn1 - imIn2 is negative, the result is truncated and limited to 0.
Although it is possible to use a 8-bit image for imIn2, it is recommended to
use the same depth for all the images.
Note that this operator is mainly useful for 32-bit images, as the result
of the subtraction is always truncated for 8-bit images.
"""
imMask = mamba.imageMb(imIn1, 1)
imWrk = mamba.imageMb(imIn1)
mamba.sub(imIn1, imIn2, imWrk)
mamba.generateSupMask(imIn1, imWrk, imMask, False)
mamba.convertByMask(imMask, imOut, 0, mamba.computeMaxRange(imOut)[1])
mamba.logic(imOut, imWrk, imOut, "inf")
def hierarchy(imIn, imMask, imOut, grid=mamba.DEFAULT_GRID):
"""
Construction of a hierarchical image from image 'imIn' and with 'imMask'.
The binary image 'imMask' controls the dual reconstruction (propagation)
of 'imIn'.
This operator is mainly used to build hierarchical images from valued
watershed images.
The hierarchical image is put in 'imOut'.
"""
imWrk = mamba.imageMb(imIn)
if mamba.checkEmptiness(imIn):
mamba.copy(imIn, imOut)
else:
mamba.convertByMask(imMask, imWrk, 255, 0)
mamba.logic(imIn, imWrk, imWrk, "sup")
mamba.hierarDualBuild(imIn, imWrk)
mamba.copy(imWrk, imOut)
def ceilingAdd(imIn1, imIn2, imOut):
"""
Adds image 'imIn2' to image 'imIn1' and puts the result in 'imOut'. If
imIn1 + imIn2 is larger than the maximal possible value in imOut, the result
is truncated and limited to this maximal value.
Although it is possible to use a 8-bit image for imIn2, it is recommended to
use the same depth for all the images.
Note that this operator is mainly useful for 32-bit images, as the result
of the addition is always truncated for 8-bit images.
"""
imMask = mamba.imageMb(imIn1, 1)
imWrk = mamba.imageMb(imIn1)
mamba.add(imIn1, imIn2, imWrk)
mamba.generateSupMask(imIn1, imWrk, imMask, True)
mamba.convertByMask(imMask, imOut, 0, mamba.computeMaxRange(imOut)[1])
mamba.logic(imOut, imWrk, imOut, "sup")
def equalNeighbors(imIn, imOut, nb, grid=mamba.DEFAULT_GRID, edge=mamba.FILLED):
"""
This operator compares the value of each pixel of image 'imIn'
with the value of its neighbors encoded in 'nb'.
If all the neighbor values are equal, the pixel is unchanged.
Otherwise, it takes value 0.
This operator works on hexagonal or square 'grid' and
'edge' is set to FILLED by default.
This operator works for 8-bit and 32-bit images.
"""
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn, 1)
mamba.copy(imIn, imWrk1)
mamba.copy(imIn, imOut)
mamba.supNeighbor(imIn, imWrk1, nb, grid=grid, edge=edge)
mamba.infNeighbor(imOut, imOut, nb, grid=grid, edge=edge)
mamba.generateSupMask(imOut, imWrk1, imWrk2, False)
mamba.convertByMask(imWrk2, imWrk1, 0, mamba.computeMaxRange(imIn)[1])
mamba.logic(imOut, imWrk1, imOut, "inf")
def fullRegularisedGradient(imIn, imOut1, imOut2, grid=mamba.DEFAULT_GRID, maxSize=16):
"""
Full regularised morphological gradient of image 'imIn'. This operator
is a residual transform which uses the regularised gradient of size i as
a residue. The range of sizes i is limited to 16 by default, as beyond this
value, the residue is most of the time equal to 0.
Warning! 'imOut2' is a greyscale image (depth equal to 8).
"""
imWrk = mamba.imageMb(imIn)
maskIm = mamba.imageMb(imIn, 1)
imOut1.reset()
imOut2.reset()
for i in range(1, maxSize + 1):
mamba.regularisedGradient(imIn, imWrk, i, grid=grid)
_generateMask_(imWrk, imOut1, maskIm)
mamba.logic(imOut1, imWrk, imOut1, "sup")
mamba.convertByMask(maskIm, imWrk, 0, i)
mamba.logic(imOut2, imWrk, imOut2, "sup")
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 directionalCoding(imIn, imOut, grid=mamba.DEFAULT_GRID):
"""
Coding of the direction which, at each point of 'imIn', corresponds to the
direction of the maximal intercept through this point. The result of this
coding is put in the grey scale image 'imOut'.
6 directions are coded on the hexagonal grid, 8 on the square one.
"""
imWrk1 = mamba.imageMb(imIn, 32)
imWrk2 = mamba.imageMb(imIn, 32)
imWrk3 = mamba.imageMb(imIn)
imWrk4 = mamba.imageMb(imIn, 8)
imOut.reset()
imWrk1.reset()
for i in range(mamba.gridNeighbors(grid=grid)):
d = i + 1
linearUltimateOpen(imIn, imWrk2, d, grid=grid)
mamba.generateSupMask(imWrk2, imWrk1, imWrk3, True)
mamba.logic(imWrk2, imWrk1, imWrk1, "sup")
mamba.convertByMask(imWrk3, imWrk4, 0, d)
mamba.logic(imWrk4, imOut, imOut, "sup")
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")
