def multiplePoints(imIn, imOut, grid=mamba.DEFAULT_GRID):
"""
Extracts multiple points in 'imIn', supposed to be a "skeleton" image (connected
components without thickness), and puts the result in 'imOut'.
Note that, on a square grid, the resulting skeleton is supposed to be defined
on a 4-connectivity grid. if it is not the case, some multiple points are likely
to be missed.
"""
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn)
endPoints(imIn, imWrk2)
if grid == mamba.HEXAGONAL:
dse_list = [hexagonalS1, hexagonalS2]
step = 1
nb = 6
else:
dse_list = [squareS1, squareS2]
step = 2
nb = 4
for dse in dse_list:
for i in range(nb):
hitOrMiss(imIn, imWrk1, dse)
mamba.logic(imWrk1, imWrk2, imWrk2, "sup")
dse = dse.rotate(step)
mamba.diff(imIn, imWrk2, imOut)
def endPoints(imIn, imOut, grid=mamba.DEFAULT_GRID, edge=mamba.FILLED):
"""
Extracts end points in 'imIn', supposed to be a "skeleton" image (connected
components without thickness), and puts them in 'imOut'.
'edge' is FILLED by default and it can be modified to take into account
extremities touching the edge.
"""
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn)
if grid == mamba.HEXAGONAL:
dse1 = hexagonalE
dse2 = hexagonalL
nb = 6
step = 1
else:
dse1 = squareE
dse2 = squareL
nb = 4
step = 2
rotatingThin(imIn, imWrk1, dse2, edge=edge)
# added to avoid blocking of the process in clipping
mamba.diff(imIn, imWrk1, imOut)
for i in range(nb):
hitOrMiss(imWrk1, imWrk2, dse1, edge=edge)
mamba.logic(imOut, imWrk2, imOut, "sup")
dse1 = dse1.rotate(step)
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 cellsHMT(imIn, imOut, dse, edge=mamba.EMPTY):
"""
A Hit-Or-Miss transform is performed on each cell of the partition 'imIn'.
'dse' is a double structuring element (see thinthick.py module).
The result is put in 'imOut'. 'edge' is set to EMPTY by default.
"""
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn)
grid = dse.getGrid()
cse0 = dse.getStructuringElement(0)
cse1 = dse.getStructuringElement(1)
mamba.copy(imIn, imOut)
mamba.copy(imIn, imWrk1)
equalNeighbors(imWrk1,
imWrk2,
cse1.getEncodedDirections(withoutZero=True),
grid=grid,
edge=edge)
mamba.logic(imOut, imWrk2, imOut, "inf")
nonEqualNeighbors(imWrk1,
imWrk2,
cse0.getEncodedDirections(withoutZero=True),
grid=grid,
edge=edge)
mamba.logic(imOut, imWrk2, imOut, "inf")
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 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 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 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 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 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 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 mulRealConst(imIn, v, imOut, nearest=False, precision=2):
"""
Multiplies image 'imIn' by a real positive constant value 'v' and puts the
result in image 'imOut'. 'imIn' and 'imOut' can be 8-bit or 32-bit images.
If 'imOut' is greyscale (8-bit), the result is saturated (results
of the multiplication greater than 255 are limited to this value).
'precision' indicates the number of decimal digits taken into account for
the constant 'v' (default is 2).
If 'nearest' is true, the result is rounded to the nearest integer value.
If not (default), the result is simply truncated.
"""
if imIn.getDepth()==1 or imOut.getDepth()==1:
mamba.raiseExceptionOnError(core.MB_ERR_BAD_DEPTH)
imWrk1 = mamba.imageMb(imIn, 32)
imWrk2 = mamba.imageMb(imIn, 1)
precVal = (10 ** precision)
v1 = int(v * precVal)
if imIn.getDepth()==8:
imWrk1.reset()
mamba.copyBytePlane(imIn, 0, imWrk1)
else:
mamba.copy(imIn, imWrk1)
mulConst(imWrk1, v1, imWrk1)
if nearest:
adjVal = int(5 * (10 ** (precision - 1)))
addConst(imWrk1, adjVal , imWrk1)
divConst(imWrk1, precVal, imWrk1)
if imOut.getDepth()==8:
mamba.threshold(imWrk1, imWrk2, 255, mamba.computeMaxRange(imWrk1)[1])
mamba.copyBytePlane(imWrk1, 0, imOut)
imWrk2.convert(8)
mamba.logic(imOut, imWrk2, imOut, "sup")
else:
mamba.copy(imWrk1, imOut)
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 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 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 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 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 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 multiplePoints(imIn, imOut, grid=mamba.DEFAULT_GRID):
"""
Extracts multiple points in 'imIn', supposed to be a "skeleton" image (connected
components without thickness), and puts the result in 'imOut'.
Note that, on a square grid, the resulting skeleton is supposed to be defined
on a 4-connectivity grid. if it is not the case, some multiple points are likely
to be missed.
"""
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn)
endPoints(imIn, imWrk2)
if grid == mamba.HEXAGONAL:
dse_list = [hexagonalS1, hexagonalS2]
step = 1
nb = 6
else:
dse_list = [squareS1, squareS2]
step = 2
nb = 4
for dse in dse_list:
for i in range(nb):
hitOrMiss(imIn, imWrk1, dse)
mamba.logic(imWrk1, imWrk2, imWrk2, "sup")
dse = dse.rotate(step)
mamba.diff(imIn, imWrk2, imOut)
def endPoints(imIn, imOut, grid=mamba.DEFAULT_GRID, edge=mamba.FILLED):
"""
Extracts end points in 'imIn', supposed to be a "skeleton" image (connected
components without thickness), and puts them in 'imOut'.
'edge' is FILLED by default and it can be modified to take into account
extremities touching the edge.
"""
imWrk1 = mamba.imageMb(imIn)
imWrk2 = mamba.imageMb(imIn)
if grid == mamba.HEXAGONAL:
dse1 = hexagonalE
dse2 = hexagonalL
nb = 6
step = 1
else:
dse1 = squareE
dse2 = squareL
nb = 4
step = 2
rotatingThin(imIn, imWrk1, dse2, edge=edge)
# added to avoid blocking of the process in clipping
mamba.diff(imIn, imWrk1, imOut)
for i in range(nb):
hitOrMiss(imWrk1, imWrk2, dse1, edge=edge)
mamba.logic(imOut, imWrk2, imOut, "sup")
dse1 = dse1.rotate(step)
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 geodesicThick(imIn, imMask, imOut, dse):
"""
Geodesic thickening of image 'imIn' inside 'imMask' by the double
structuring element 'dse'. The result is stored in 'imOut'.
'imIn', 'imMask' and 'imOut' are binary images.
"""
thick(imIn, imOut, dse)
mamba.logic(imOut, imMask, imOut, "inf")
def geodesicThick(imIn, imMask, imOut, dse):
"""
Geodesic thickening of image 'imIn' inside 'imMask' by the double
structuring element 'dse'. The result is stored in 'imOut'.
'imIn', 'imMask' and 'imOut' are binary images.
"""
thick(imIn, imOut, dse)
mamba.logic(imOut, imMask, imOut, "inf")
def thick(imIn, imOut, dse):
"""
Elementary thickening operator with 'dse' double structuring element. The
'imIn' and 'imOut' are binary images.
The edge is always EMPTY (as for hitOrMiss).
"""
imWrk = mamba.imageMb(imIn)
hitOrMiss(imIn, imWrk, dse, edge=mamba.EMPTY)
mamba.logic(imIn, imWrk, imOut, "or")
def thick(imIn, imOut, dse):
"""
Elementary thickening operator with 'dse' double structuring element. The
'imIn' and 'imOut' are binary images.
The edge is always EMPTY (as for hitOrMiss).
"""
imWrk = mamba.imageMb(imIn)
hitOrMiss(imIn, imWrk, dse, edge=mamba.EMPTY)
mamba.logic(imIn, imWrk, imOut, "or")
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 segmentByP(imIn, imOut, gain=2.0, grid=mamba.DEFAULT_GRID):
"""
General segmentation by P algorithm. This algorithm keeps or reintroduces
the contours of the initial 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, 32)
mamba.copy(imIn, imWrk1)
mamba.mulRealConst(imIn, gain, imWrk5)
mamba.floorSubConst(imWrk5, 1, imWrk5)
mamba.threshold(imWrk5, imWrk4, 255, mamba.computeMaxRange(imWrk5)[1])
mamba.copyBytePlane(imWrk5, 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, imWrk4, 1, 255)
flag = not(mamba.checkEmptiness(imWrk4))
hierarchy(imWrk3, imWrk4, imWrk2, grid=grid)
mamba.generateSupMask(imWrk0, imWrk2, imWrk4, strict=False)
mamba.convertByMask(imWrk4, imWrk3, 0, 255)
mamba.logic(imWrk1, imWrk3, imWrk3, "inf")
mamba.negate(imWrk4, imWrk4)
mamba.label(imWrk4, imWrk5, grid=grid)
mamba.watershedSegment(imWrk3, imWrk5, grid=grid)
mamba.copyBytePlane(imWrk5, 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 segmentByP(imIn, imOut, gain=2.0, grid=mamba.DEFAULT_GRID):
"""
General segmentation by P algorithm. This algorithm keeps or reintroduces
the contours of the initial 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, 32)
mamba.copy(imIn, imWrk1)
mamba.mulRealConst(imIn, gain, imWrk5)
mamba.floorSubConst(imWrk5, 1, imWrk5)
mamba.threshold(imWrk5, imWrk4, 255, mamba.computeMaxRange(imWrk5)[1])
mamba.copyBytePlane(imWrk5, 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, imWrk4, 1, 255)
flag = not (mamba.checkEmptiness(imWrk4))
hierarchy(imWrk3, imWrk4, imWrk2, grid=grid)
mamba.generateSupMask(imWrk0, imWrk2, imWrk4, strict=False)
mamba.convertByMask(imWrk4, imWrk3, 0, 255)
mamba.logic(imWrk1, imWrk3, imWrk3, "inf")
mamba.negate(imWrk4, imWrk4)
mamba.label(imWrk4, imWrk5, grid=grid)
mamba.watershedSegment(imWrk3, imWrk5, grid=grid)
mamba.copyBytePlane(imWrk5, 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 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 markerControlledWatershed(imIn, imMarkers, imOut, grid=mamba.DEFAULT_GRID):
"""
Marker-controlled watershed transform of greytone image 'imIn'. The binary
image 'imMarkers' contains the markers which control the flooding process.
'imOut' contains the valued watershed.
"""
im_mark = mamba.imageMb(imIn, 32)
imWrk = mamba.imageMb(imIn)
label(imMarkers, im_mark, grid=grid)
watershedSegment(imIn, im_mark, grid=grid)
mamba.copyBytePlane(im_mark, 3, imWrk)
mamba.logic(imWrk, imIn, imOut, 'inf')
def markerControlledWatershed(imIn, imMarkers, imOut, grid=mamba.DEFAULT_GRID):
"""
Marker-controlled watershed transform of greytone image 'imIn'. The binary
image 'imMarkers' contains the markers which control the flooding process.
'imOut' contains the valued watershed.
"""
im_mark = mamba.imageMb(imIn, 32)
imWrk = mamba.imageMb(imIn)
label(imMarkers, im_mark, grid=grid)
watershedSegment(imIn, im_mark, grid=grid)
mamba.copyBytePlane(im_mark, 3, imWrk)
mamba.logic(imWrk, imIn, imOut, "inf")
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 upperGeodesicErode(imIn, imMask, imOut, n=1, se=mamba.DEFAULT_SE):
"""
Performs a upper geodesic erosion of image 'imIn' above '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).
Warning! 'imMask' and 'imOut' must be different.
"""
mamba.logic(imIn, imMask, imOut, "sup")
for i in range(n):
mamba.erode(imOut, imOut, se=se)
mamba.logic(imOut, imMask, imOut, "sup")
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 linearClose(imIn, imOut, dir, n, grid=mamba.DEFAULT_GRID, edge=mamba.FILLED):
"""
Performs a closing by a segment of size 'n' in direction 'dir'.
If 'edge' is set to 'EMPTY', the operation must be modified to remain extensive.
"""
imWrk = mamba.imageMb(imIn)
if edge==mamba.EMPTY:
mamba.copy(imIn, imWrk)
mamba.linearDilate(imIn, imOut, dir, n, grid=grid)
mamba.linearErode(imOut, imOut, mamba.transposeDirection(dir, grid=grid), n, edge=edge, grid=grid)
if edge==mamba.EMPTY:
mamba.logic(imOut, imWrk, imOut, "sup")
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 removeEdgeParticles(imIn, imOut, grid=mamba.DEFAULT_GRID):
"""
Removes particles (connected components) touching the edge in image 'imIn'.
The resulting image is put in image 'imOut'.
Although this operator may be used with greytone images, it should be
considered with caution.
"""
imWrk = mamba.imageMb(imIn)
se = mamba.structuringElement(mamba.getDirections(grid), grid)
mamba.dilate(imWrk, imWrk, se=se, edge=mamba.FILLED)
mamba.logic(imIn, imWrk, imWrk, "inf")
build(imIn, imWrk, grid=grid)
mamba.diff(imIn, imWrk, imOut)
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 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 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 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 dilateByCylinder3D(imInOut, height, section):
"""
Dilates 3D image 'imInOut' using a cylinder with an hexagonal section of size
2x'section' and a height of 2x'height'. The image is modified by this
function. The edge is always set to EMPTY.
"""
l = len(imInOut)
for im in imInOut:
mamba.dilate(im, im, section, se=mamba.HEXAGON)
provIm3D = m3D.image3DMb(imInOut)
for i in range(l):
mamba.copy(imInOut[i], provIm3D[i])
for j in range(max(0,i-height), min(l,i+height+1)):
mamba.logic(provIm3D[i], imInOut[j], provIm3D[i], "sup")
m3D.copy3D(provIm3D, imInOut)
def closing(imIn, imOut, n=1, se=mamba.DEFAULT_SE, edge=mamba.FILLED):
"""
Performs a closing operation on image 'imIn' and puts the result in 'imOut'.
'n' controls the size of the closing and 'se' the structuring element used.
The default edge is set to 'FILLED'. If 'edge' is set to 'EMPTY', the operation
is slightly modified to avoid errors (non extensivity).
"""
imWrk = mamba.imageMb(imIn)
if edge == mamba.EMPTY:
mamba.copy(imIn, imWrk)
mamba.dilate(imIn, imOut, n, se=se)
mamba.erode(imOut, imOut, n, se=se.transpose(), edge=edge)
if edge == mamba.EMPTY:
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 directionalClose(imIn, imOut, d, size, grid=mamba.DEFAULT_GRID, edge=mamba.FILLED):
"""
Directional closing of image 'imIn' defined by combining a dilation in
direction 'd' followed by an erosion in the transposed direction (which
depends on the grid in use). Result is put in 'imOut'.
If 'edge' is set to 'EMPTY', the operation must be modified to remain extensive.
"""
imWrk = mamba.imageMb(imIn)
if edge==mamba.EMPTY:
mamba.copy(imIn, imWrk)
directionalDilate(imIn, imOut, d, size, grid=grid, edge=edge)
j = (d + mamba.gridNeighbors(grid=grid) - 1) % (mamba.gridNeighbors(grid=grid) * 2) + 1
directionalErode(imOut, imOut, j, size, grid=grid)
if edge==mamba.EMPTY:
mamba.logic(imOut, imWrk, imOut, "sup")
