def test_sereflect():
yield _brute_test, pymorph.secross()
se = pymorph.secross()
se[1, 1] = 1 # This makes it non-symmetric
yield _brute_test, se
se[2, 1] = 1 # This makes it non-symmetric
yield _brute_test, se
se = np.zeros((9, 9), bool)
se[0, 4] = 1
yield _brute_test, se
se = np.zeros((9, 9), bool)
se[1, 1] = se[1, 2] = se[0, 4] = 1
yield _brute_test, se
def fast_conditional_dilate(f, g, Bc=None, n=1):
if Bc is None: Bc = pymorph.secross()
f = pymorph.intersec(f, g)
for i in xrange(n):
prev = f
f = pymorph.intersec(mahotas.dilate(f, Bc), g)
if pymorph.isequal(f, prev): break
return f
def fast_conditional_dilate(f, g, Bc=None, n=1):
if Bc is None: Bc = pymorph.secross()
f = pymorph.intersec(f,g)
for i in xrange(n):
prev = f
f = pymorph.intersec(mahotas.dilate(f, Bc), g)
if pymorph.isequal(f, prev): break
return f
def colorize_segmentation(self, f_, g, k=0.15, t=3): # 25jan2013
'''
Parameters
----------
f: original image;
g: labeled segmentation;
k: transparency level;
t: thickness
'''
# f = uint8(ianormalize(f_, (0, 255)) + 0.5)
f = (ianormalize(f_, (0, 255)) + 0.5).astype(np.uint8)
# g = g-1 #remove background if watershed was used
z = mm.lblshow(g)
# z = uint8(ianormalize(z,(0,255))+0.5)
# z = (ianormalize(z,(0,255))+0.5).astype(np.uint8)
zc = mm.gradm(z, mm.secross(0), mm.secross(t))
# Regions:
m = z[0] == 0
z[0] = m * f + k * (1 - m) * z[0] + (1 - k) * (1 - m) * f
m = z[1] == 0
z[1] = m * f + k * (1 - m) * z[1] + (1 - k) * (1 - m) * f
m = z[2] == 0
z[2] = m * f + k * (1 - m) * z[2] + (1 - k) * (1 - m) * f
# Contours:
m = zc[0] == 0
z[0] = m * z[0] + (1 - m) * zc[0]
m = zc[1] == 0
z[1] = m * z[1] + (1 - m) * zc[1]
m = zc[2] == 0
z[2] = m * z[2] + (1 - m) * zc[2]
return z
def infrec(f, g, Bc=None):
if Bc is None: Bc = pymorph.secross()
n = f.size
return fast_conditional_dilate(f, g, Bc, n);
def areaclose(image):
return pymorph.areaclose(image, 4, pymorph.secross())
def test_neg():
pymorph.neg(pymorph.secross()) == ~pymorph.secross()
def addCell(self, eventTuple):
if self.maskOn:
if self.data.ndim == 2:
self.aveData = self.data.copy()
else:
self.aveData = self.data.mean(axis=2)
x, y = eventTuple
localValue = self.currentMask[x, y]
print str(self.mode) + " " + "x: " + str(x) + ", y: " + str(y) + ", mask val: " + str(localValue)
# ensure mask is uint16
self.currentMask = self.currentMask.astype("uint16")
sys.stdout.flush()
########## NORMAL MODE
if self.mode is None:
if localValue > 0 and localValue != self.currentMaskNumber:
print "we are altering mask at at %d, %d" % (x, y)
# copy the old mask
newMask = self.currentMask.copy()
# make a labeled image of the current mask
labeledCurrentMask = mahotas.label(newMask)[0]
roiNumber = labeledCurrentMask[x, y]
# set that ROI to zero
newMask[labeledCurrentMask == roiNumber] = self.currentMaskNumber
newMask = newMask.astype("uint16")
self.listOfMasks.append(newMask)
self.currentMask = self.listOfMasks[-1]
elif localValue > 0 and self.data.ndim == 3:
# update info panel
labeledCurrentMask = mahotas.label(self.currentMask.copy())[0]
roiNumber = labeledCurrentMask[x, y]
self.updateInfoPanel(ROI_number=roiNumber)
elif localValue == 0:
xmin = int(x - self.diskSize)
xmax = int(x + self.diskSize)
ymin = int(y - self.diskSize)
ymax = int(y + self.diskSize)
sub_region_image = self.aveData[xmin:xmax, ymin:ymax].copy()
# threshold = mahotas.otsu(self.data[xmin:xmax, ymin:ymax].astype('uint16'))
# do a gaussian_laplacian filter to find the edges and the center
g_l = nd.gaussian_laplace(
sub_region_image, 1
) # second argument is a free parameter, std of gaussian
g_l = mahotas.dilate(mahotas.erode(g_l >= 0))
g_l = mahotas.label(g_l)[0]
center = g_l == g_l[g_l.shape[0] / 2, g_l.shape[0] / 2]
# edges = mahotas.dilate(mahotas.dilate(mahotas.dilate(center))) - center
newCell = np.zeros_like(self.currentMask)
newCell[xmin:xmax, ymin:ymax] = center
newCell = mahotas.dilate(newCell)
if self.useNMF:
modes, thresh_modes, fit_data, this_cell, is_cell, nmf_limits = self.doLocalNMF(x, y, newCell)
for mode, mode_thresh, t, i in zip(modes, thresh_modes, this_cell, is_cell):
# need to place it in the right place
# have x and y
mode_width, mode_height = mode_thresh.shape
mode_thresh_fullsize = np.zeros_like(newCell)
mode_thresh_fullsize[
nmf_limits[0] : nmf_limits[1], nmf_limits[2] : nmf_limits[3]
] = mode_thresh
# need to add all modes belonging to this cell first,
# then remove the ones nearby.
if i:
if t:
valid_area = np.logical_and(
mahotas.dilate(
mahotas.dilate(mahotas.dilate(mahotas.dilate(newCell.astype(bool))))
),
mode_thresh_fullsize,
)
newCell = np.logical_or(newCell.astype(bool), valid_area)
else:
newCell = np.logical_and(
newCell.astype(bool), np.logical_not(mahotas.dilate(mode_thresh_fullsize))
)
newCell = mahotas.close_holes(newCell.astype(bool))
self.excludePixels(newCell, 2)
newCell = newCell.astype(self.currentMask.dtype)
# remove all pixels in and near current mask and filter for ROI size
newCell[mahotas.dilate(self.currentMask > 0)] = 0
newCell = self.excludePixels(newCell, 10)
newMask = (newCell * self.currentMaskNumber) + self.currentMask
newMask = newMask.astype("uint16")
self.listOfMasks.append(newMask.copy())
self.currentMask = newMask.copy()
elif self.mode is "OGB":
# build structuring elements
se = pymorph.sebox()
se2 = pymorph.sedisk(self.cellRadius, metric="city-block")
seJunk = pymorph.sedisk(max(np.floor(self.cellRadius / 4.0), 1), metric="city-block")
seExpand = pymorph.sedisk(self.diskSize, metric="city-block")
# add a disk around selected point, non-overlapping with adjacent cells
dilatedOrignal = mahotas.dilate(self.currentMask.astype(bool), Bc=se)
safeUnselected = np.logical_not(dilatedOrignal)
# tempMask is
tempMask = np.zeros_like(self.currentMask, dtype=bool)
tempMask[x, y] = True
tempMask = mahotas.dilate(tempMask, Bc=se2)
tempMask = np.logical_and(tempMask, safeUnselected)
# calculate the area we should add to this disk based on % of a threshold
cellMean = self.aveData[tempMask == 1.0].mean()
allMeanBw = self.aveData >= (cellMean * float(self.contrastThreshold))
tempLabel = mahotas.label(np.logical_and(allMeanBw, safeUnselected).astype(np.uint16))[0]
connMeanBw = tempLabel == tempLabel[x, y]
connMeanBw = np.logical_and(np.logical_or(connMeanBw, tempMask), safeUnselected).astype(np.bool)
# erode and then dilate to remove sharp bits and edges
erodedMean = mahotas.erode(connMeanBw, Bc=seJunk)
dilateMean = mahotas.dilate(erodedMean, Bc=seJunk)
dilateMean = mahotas.dilate(dilateMean, Bc=seExpand)
modes, thresh_modes, fit_data, this_cell, is_cell, limits = self.doLocaNMF(x, y)
newCell = np.logical_and(dilateMean, safeUnselected)
newMask = (newCell * self.currentMaskNumber) + self.currentMask
newMask = newMask.astype("uint16")
self.listOfMasks.append(newMask.copy())
self.currentMask = newMask.copy()
########## SQUARE MODE
elif self.mode is "square":
self.modeData.append((x, y))
if len(self.modeData) == 2:
square_mask = np.zeros_like(self.currentMask)
xstart = self.modeData[0][0]
ystart = self.modeData[0][1]
xend = self.modeData[1][0]
yend = self.modeData[1][1]
square_mask[xstart:xend, ystart:yend] = 1
# check if square_mask interfers with current mask, if so, abort
if np.any(np.logical_and(square_mask, self.currentMask)):
return None
# add square_mask to mask
newMask = (square_mask * self.currentMaskNumber) + self.currentMask
newMask = newMask.astype("uint16")
self.listOfMasks.append(newMask)
self.currentMask = self.listOfMasks[-1]
# clear current mode data
self.clearModeData()
########## CIRCLE MODE
elif self.mode is "circle":
# make a strel and move it in place to make circle_mask
if self.diskSize < 1:
return None
if self.diskSize is 1:
se = np.ones((1, 1))
elif self.diskSize is 2:
se = pymorph.secross(r=1)
else:
se = pymorph.sedisk(r=(self.diskSize - 1))
se_extent = int(se.shape[0] / 2)
circle_mask = np.zeros_like(self.currentMask)
circle_mask[x - se_extent : x + se_extent + 1, y - se_extent : y + se_extent + 1] = se * 1.0
circle_mask = circle_mask.astype(bool)
# check if circle_mask interfers with current mask, if so, abort
if np.any(np.logical_and(circle_mask, mahotas.dilate(self.currentMask.astype(bool)))):
return None
# add circle_mask to mask
newMask = (circle_mask * self.currentMaskNumber) + self.currentMask
newMask = newMask.astype("uint16")
self.listOfMasks.append(newMask)
self.currentMask = self.listOfMasks[-1]
########## POLY MODE
elif self.mode is "poly":
self.modeData.append((x, y))
sys.stdout.flush()
self.makeNewMaskAndBackgroundImage()
def tentDetection_MM(strInputFile, maxTentArea, strOutputFile, strShape='box', iThresh_coeff=0):
# five step to do this
# 1. opening-determine the square structure element (6-60 m2/resolution)
# 2. opening by reconstruction
# 3. top-hat by reconstruction
# 4. lower threshold
# 5. double threshold
import pymorph as pymm
objImg = osgeo.gdal.Open(strInputFile, GA_ReadOnly)
nRasterCount = objImg.RasterCount
poDataset = objImg.ReadAsArray().astype(np.float)
geotransform = objImg.GetGeoTransform()
pixelWidth = np.fabs(geotransform[1])
pixelHeight = np.fabs(geotransform[5])
resolution = pixelWidth * pixelHeight
# NoDataValue = objImg.GetRasterBand(1).GetNoDataValue()
# gray scale image
if (nRasterCount == 1):
objnImg = pymm.to_int32(poDataset)
# RGB image
elif(nRasterCount == 3):
objnImg = pymm.to_gray(poDataset)
else:
print 'it only supports gray-scale or RGB image'
sys.exit(1)
# determine the structure element
iNum = int(np.sqrt(maxTentArea) / resolution) + 1
if (strShape == 'box'):
objStructureElement = pymm.sebox(iNum)
elif (strShape == 'cross'):
objStructureElement = pymm.secross(iNum)
else:
objStructureElement = pymm.sedisk(iNum)
# opening
objOpen = pymm.open(objnImg, objStructureElement)
# opening by reconstruction
objOpenRec = pymm.openrec(objOpen, objStructureElement, objStructureElement)
objtophat = pymm.openrecth(objnImg, objStructureElement, objStructureElement)
# objtophat = pymm.subm(objnImg, objOpenRec)
# objTent = pymm.threshad(objtophat, 0.25 * objnImg, 0.40 * objnImg)
# y = mean + k*std
(minValue, maxValue, meanValue, stdValue) = objImg.GetRasterBand(1).GetStatistics(0, 1)
if (nRasterCount == 3):
(minValue2, maxValue2, meanValue2, stdValue2) = objImg.GetRasterBand(2).GetStatistics(0, 1)
(minValue3, maxValue3, meanValue3, stdValue3) = objImg.GetRasterBand(3).GetStatistics(0, 1)
meanValue = 0.2989 * meanValue + 0.5870 * meanValue2 + 0.1140 * meanValue3
maxValue = 0.2989 * maxValue + 0.5870 * maxValue2 + 0.1140 * maxValue3
# meanValue = 438
# maxValue = 2047
threshad = meanValue + iThresh_coeff * stdValue
objTent = pymm.threshad(objtophat, threshad, maxValue)
data_list = []
data_list.append(objTent)
WriteOutputImage(strOutputFile, 1, data_list, 0, 0, 0, strInputFile)
'''
def tentDetection_wt_mm(strInputFile, maxTentArea, strOutputFile, strShape='box', iThresh_coeff=0):
import pywt
import pymorph as pymm
objImg = osgeo.gdal.Open(strInputFile, GA_ReadOnly)
nRasterCount = objImg.RasterCount
poDataset = objImg.ReadAsArray().astype(np.float)
geotransform = objImg.GetGeoTransform()
pixelWidth = np.fabs(geotransform[1])
pixelHeight = np.fabs(geotransform[5])
resolution = pixelWidth * pixelHeight
# NoDataValue = objImg.GetRasterBand(1).GetNoDataValue()
# gray scale image
if (nRasterCount == 1):
objnImg = pymm.to_int32(poDataset)
# RGB image
elif(nRasterCount == 3):
objnImg = pymm.to_gray(poDataset)
else:
print 'it only supports gray-scale or RGB image'
sys.exit(1)
# determine the structure element
iNum = int(np.sqrt(maxTentArea) / resolution) + 1
if (strShape == 'box'):
objStructureElement = pymm.sebox(iNum)
elif (strShape == 'cross'):
objStructureElement = pymm.secross(iNum)
else:
objStructureElement = pymm.sedisk(iNum)
# decomposition until 1 level
wp = pywt.WaveletPacket2D(data=objnImg, wavelet='db4', mode='sym', maxlevel=1)
# iMaxLevel = wp.maxlevel()
# top-hat
wp['h'].data = pymm.openrecth(pymm.to_int32(wp['h'].data), objStructureElement, objStructureElement)
wp['v'].data = pymm.openrecth(pymm.to_int32(wp['v'].data), objStructureElement, objStructureElement)
wp['d'].data = pymm.openrecth(pymm.to_int32(wp['d'].data), objStructureElement, objStructureElement)
wp['a'].data = 0.5 * wp['a'].data
# reconstruction
wp.reconstruct(update=True)
# top-hat for reconstructed image
objtophat = pymm.openrecth(pymm.to_int32(wp.data), objStructureElement, objStructureElement)
# y = mean + k*std
(minValue, maxValue, meanValue, stdValue) = objImg.GetRasterBand(1).GetStatistics(0, 1)
if (nRasterCount == 3):
(minValue2, maxValue2, meanValue2, stdValue2) = objImg.GetRasterBand(2).GetStatistics(0, 1)
(minValue3, maxValue3, meanValue3, stdValue3) = objImg.GetRasterBand(3).GetStatistics(0, 1)
meanValue = 0.2989 * meanValue + 0.5870 * meanValue2 + 0.1140 * meanValue3
maxValue = 0.2989 * maxValue + 0.5870 * maxValue2 + 0.1140 * maxValue3
# meanValue = 438
# maxValue = 2047
threshad = meanValue + iThresh_coeff * stdValue
objTent = pymm.threshad(objtophat, stdValue, maxValue)
data_list = []
data_list.append(objTent)
WriteOutputImage(strOutputFile, 1, data_list, 0, 0, 0, strInputFile)
def infrec(f, g, Bc=None):
if Bc is None: Bc = pymorph.secross()
n = f.size
return fast_conditional_dilate(f, g, Bc, n)
def test_serot():
se = pymorph.secross()
assert np.all(pymorph.serot(se, 90) == se)
assert not np.all(pymorph.serot(se, 45) == se)
def test_labelflat():
assert np.all(pymorph.labelflat(pymorph.secross()) == pymorph.secross())
def tentDetection_MM(strInputFile,
maxTentArea,
strOutputFile,
strShape='box',
iThresh_coeff=0):
# five step to do this
# 1. opening-determine the square structure element (6-60 m2/resolution)
# 2. opening by reconstruction
# 3. top-hat by reconstruction
# 4. lower threshold
# 5. double threshold
import pymorph as pymm
objImg = osgeo.gdal.Open(strInputFile, GA_ReadOnly)
nRasterCount = objImg.RasterCount
poDataset = objImg.ReadAsArray().astype(np.float)
geotransform = objImg.GetGeoTransform()
pixelWidth = np.fabs(geotransform[1])
pixelHeight = np.fabs(geotransform[5])
resolution = pixelWidth * pixelHeight
# NoDataValue = objImg.GetRasterBand(1).GetNoDataValue()
# gray scale image
if (nRasterCount == 1):
objnImg = pymm.to_int32(poDataset)
# RGB image
elif (nRasterCount == 3):
objnImg = pymm.to_gray(poDataset)
else:
print 'it only supports gray-scale or RGB image'
sys.exit(1)
# determine the structure element
iNum = int(np.sqrt(maxTentArea) / resolution) + 1
if (strShape == 'box'):
objStructureElement = pymm.sebox(iNum)
elif (strShape == 'cross'):
objStructureElement = pymm.secross(iNum)
else:
objStructureElement = pymm.sedisk(iNum)
# opening
objOpen = pymm.open(objnImg, objStructureElement)
# opening by reconstruction
objOpenRec = pymm.openrec(objOpen, objStructureElement,
objStructureElement)
objtophat = pymm.openrecth(objnImg, objStructureElement,
objStructureElement)
# objtophat = pymm.subm(objnImg, objOpenRec)
# objTent = pymm.threshad(objtophat, 0.25 * objnImg, 0.40 * objnImg)
# y = mean + k*std
(minValue, maxValue, meanValue,
stdValue) = objImg.GetRasterBand(1).GetStatistics(0, 1)
if (nRasterCount == 3):
(minValue2, maxValue2, meanValue2,
stdValue2) = objImg.GetRasterBand(2).GetStatistics(0, 1)
(minValue3, maxValue3, meanValue3,
stdValue3) = objImg.GetRasterBand(3).GetStatistics(0, 1)
meanValue = 0.2989 * meanValue + 0.5870 * meanValue2 + 0.1140 * meanValue3
maxValue = 0.2989 * maxValue + 0.5870 * maxValue2 + 0.1140 * maxValue3
# meanValue = 438
# maxValue = 2047
threshad = meanValue + iThresh_coeff * stdValue
objTent = pymm.threshad(objtophat, threshad, maxValue)
data_list = []
data_list.append(objTent)
WriteOutputImage(strOutputFile, 1, data_list, 0, 0, 0, strInputFile)
'''
def test_labelflat():
assert np.all(pymorph.labelflat(pymorph.secross()) == pymorph.secross())
def test_sesum():
assert np.all(pymorph.sesum(pymorph.secross(), 1) == pymorph.secross())
assert len(pymorph.sesum(pymorph.secross(), 0).shape) == 2
def tentDetection_wt_mm(strInputFile,
maxTentArea,
strOutputFile,
strShape='box',
iThresh_coeff=0):
import pywt
import pymorph as pymm
objImg = osgeo.gdal.Open(strInputFile, GA_ReadOnly)
nRasterCount = objImg.RasterCount
poDataset = objImg.ReadAsArray().astype(np.float)
geotransform = objImg.GetGeoTransform()
pixelWidth = np.fabs(geotransform[1])
pixelHeight = np.fabs(geotransform[5])
resolution = pixelWidth * pixelHeight
# NoDataValue = objImg.GetRasterBand(1).GetNoDataValue()
# gray scale image
if (nRasterCount == 1):
objnImg = pymm.to_int32(poDataset)
# RGB image
elif (nRasterCount == 3):
objnImg = pymm.to_gray(poDataset)
else:
print 'it only supports gray-scale or RGB image'
sys.exit(1)
# determine the structure element
iNum = int(np.sqrt(maxTentArea) / resolution) + 1
if (strShape == 'box'):
objStructureElement = pymm.sebox(iNum)
elif (strShape == 'cross'):
objStructureElement = pymm.secross(iNum)
else:
objStructureElement = pymm.sedisk(iNum)
# decomposition until 1 level
wp = pywt.WaveletPacket2D(data=objnImg,
wavelet='db4',
mode='sym',
maxlevel=1)
# iMaxLevel = wp.maxlevel()
# top-hat
wp['h'].data = pymm.openrecth(pymm.to_int32(wp['h'].data),
objStructureElement, objStructureElement)
wp['v'].data = pymm.openrecth(pymm.to_int32(wp['v'].data),
objStructureElement, objStructureElement)
wp['d'].data = pymm.openrecth(pymm.to_int32(wp['d'].data),
objStructureElement, objStructureElement)
wp['a'].data = 0.5 * wp['a'].data
# reconstruction
wp.reconstruct(update=True)
# top-hat for reconstructed image
objtophat = pymm.openrecth(pymm.to_int32(wp.data), objStructureElement,
objStructureElement)
# y = mean + k*std
(minValue, maxValue, meanValue,
stdValue) = objImg.GetRasterBand(1).GetStatistics(0, 1)
if (nRasterCount == 3):
(minValue2, maxValue2, meanValue2,
stdValue2) = objImg.GetRasterBand(2).GetStatistics(0, 1)
(minValue3, maxValue3, meanValue3,
stdValue3) = objImg.GetRasterBand(3).GetStatistics(0, 1)
meanValue = 0.2989 * meanValue + 0.5870 * meanValue2 + 0.1140 * meanValue3
maxValue = 0.2989 * maxValue + 0.5870 * maxValue2 + 0.1140 * maxValue3
# meanValue = 438
# maxValue = 2047
threshad = meanValue + iThresh_coeff * stdValue
objTent = pymm.threshad(objtophat, stdValue, maxValue)
data_list = []
data_list.append(objTent)
WriteOutputImage(strOutputFile, 1, data_list, 0, 0, 0, strInputFile)
def areaclose(image): return pymorph.areaclose(image, 4, pymorph.secross())
def test_sesum():
assert np.all(pymorph.sesum(pymorph.secross(), 1) == pymorph.secross())
assert len(pymorph.sesum(pymorph.secross(), 0).shape) == 2
def test_neg():
pymorph.neg(pymorph.secross()) == ~pymorph.secross()
def test_secross():
assert np.all(pymorph.secross() == np.array([[0,1,0],[1,1,1],[0,1,0]]))
assert np.all(pymorph.secross(0) == np.array([[1]]))
assert np.all(pymorph.secross(2).shape == (5,5))
assert np.all(pymorph.secross(9).shape == (2*9+1,2*9+1))