Added second threshold in detectionview.py (4a04d651) · Commits · GEPARD / GEPARD

detectionview.py

+40 −7

Original line number	Original line	Diff line number	Diff line
	@@ -275,7 +275,7 @@ class ParticleDetectionView(QtWidgets.QWidget):
	self.dataset = self.verifySeedpoints(dataset)		self.dataset = self.verifySeedpoints(dataset)
	self.img = img		self.img = img
	self.imgclip = 0,0,0,0		self.imgclip = 0,0,0,0
	self.seg = Segmentation(self.dataset)		self.seg = Segmentation(self.dataset, self)
	self.thread = None		self.thread = None
	self.view = parent		self.view = parent

	@@ -303,10 +303,12 @@ class ParticleDetectionView(QtWidgets.QWidget):
	group = QtWidgets.QGroupBox("Detection settings", self)		group = QtWidgets.QGroupBox("Detection settings", self)
	grid = QtWidgets.QGridLayout()		grid = QtWidgets.QGridLayout()
	self.parameters = []		self.parameters = []
			checkBoxesToLink = {}

	# create editable parameters:		# create editable parameters:
	for i, p in enumerate(self.seg.parlist):		for i, p in enumerate(self.seg.parlist):
	label, colstretch = None, 1		label, colstretch = None, 1
	if p.name == "points":		if p.name == "contrastCurve":
	paramui = HistWidget(lambda : self.seg.calculateHist(self.seg.convert2Gray(self.subimg)),		paramui = HistWidget(lambda : self.seg.calculateHist(self.seg.convert2Gray(self.subimg)),
	self.seg.calculateHistFunction, self)		self.seg.calculateHistFunction, self)
	valuefunc = makeValueLambda(paramui.value)		valuefunc = makeValueLambda(paramui.value)
	@@ -316,6 +318,8 @@ class ParticleDetectionView(QtWidgets.QWidget):
	paramui.setChecked(p.value)		paramui.setChecked(p.value)
	valuefunc = makeValueLambda(paramui.isChecked)		valuefunc = makeValueLambda(paramui.isChecked)
	colstretch = 2		colstretch = 2
			if p.linkedParameter is not None:
			checkBoxesToLink[paramui] = p.linkedParameter
	elif p.dtype == int or p.dtype == float:		elif p.dtype == int or p.dtype == float:
	label = QtWidgets.QLabel(p.helptext, self)		label = QtWidgets.QLabel(p.helptext, self)
	if p.dtype == int:		if p.dtype == int:
	@@ -334,19 +338,45 @@ class ParticleDetectionView(QtWidgets.QWidget):
	elif p.dtype is None:		elif p.dtype is None:
	label = QtWidgets.QLabel(p.helptext, self)		label = QtWidgets.QLabel(p.helptext, self)
	paramui = None		paramui = None

	if paramui is not None:		if paramui is not None:
	self.parameters.append([paramui, p.name, valuefunc])		self.parameters.append([paramui, p.name, valuefunc, None])
	if colstretch == 1:		if colstretch == 1:
	grid.addWidget(paramui, i, 1, QtCore.Qt.AlignLeft)		grid.addWidget(paramui, i, 1, QtCore.Qt.AlignLeft)
	else:		else:
	grid.addWidget(paramui, i, 0, 1, 2, QtCore.Qt.AlignLeft)		grid.addWidget(paramui, i, 0, 1, 2, QtCore.Qt.AlignLeft)


	if label is not None:		if label is not None:
	grid.addWidget(label, i, 0, QtCore.Qt.AlignLeft)		grid.addWidget(label, i, 0, QtCore.Qt.AlignLeft)

	if p.show is True:		if p.show is True:
	pshow = QtWidgets.QPushButton(">", self)		pshow = QtWidgets.QPushButton(">", self)
	pshow.released.connect(makeShowLambda(p.name))		pshow.released.connect(makeShowLambda(p.name))
	grid.addWidget(pshow, i, 2, QtCore.Qt.AlignRight)		grid.addWidget(pshow, i, 2, QtCore.Qt.AlignRight)

			if paramui is not None:
			self.parameters[-1][3] = pshow




			#link checkboxes to other parameters:
			def makeEnableLambda(checkbox, parameter):
			return lambda: parameter.setEnabled(checkbox.isChecked())

			for box in checkBoxesToLink:
			linkedName = checkBoxesToLink[box]
			for p in self.parameters:
			if p[1] == linkedName:
			p[0].setEnabled(box.isChecked())
			box.stateChanged.connect(makeEnableLambda(box, p[0])) #the actual control element
			if p[3] is not None: #the "show" box, if present
			box.stateChanged.connect(makeEnableLambda(box, p[3]))
			p[3].setEnabled(box.isChecked())



	label = QtWidgets.QLabel("Seed radius", self)		label = QtWidgets.QLabel("Seed radius", self)
	grid.addWidget(label, i+1, 0, QtCore.Qt.AlignLeft)		grid.addWidget(label, i+1, 0, QtCore.Qt.AlignLeft)
	grid.addWidget(self.seedradiusedit, i+1, 1, QtCore.Qt.AlignLeft)		grid.addWidget(self.seedradiusedit, i+1, 1, QtCore.Qt.AlignLeft)
	@@ -384,7 +414,7 @@ class ParticleDetectionView(QtWidgets.QWidget):
	def saveDetectParams(self, ds=None):		def saveDetectParams(self, ds=None):
	if ds is not None:		if ds is not None:
	for param in self.parameters:		for param in self.parameters:
	if param[1] == 'points':		if param[1] == 'contrastCurve':
	print(param[0].value())		print(param[0].value())
	try: # is it a spinbox or the histWidget? Read out the value		try: # is it a spinbox or the histWidget? Read out the value
	ds.detectParams[param[1]] = param[0].value()		ds.detectParams[param[1]] = param[0].value()
	@@ -535,7 +565,7 @@ class ParticleDetectionView(QtWidgets.QWidget):
	self.saveDetectParams(self.dataset)		self.saveDetectParams(self.dataset)
	img = self.subimg.copy()		img = self.subimg.copy()
	kwargs = {}		kwargs = {}
	for ui, name, valuefunc in self.parameters:		for ui, name, valuefunc, showbtn in self.parameters:
	kwargs[name] = valuefunc()		kwargs[name] = valuefunc()
	self.seg.setParameters(**kwargs)		self.seg.setParameters(**kwargs)
	seedradius = self.seedradiusedit.value()		seedradius = self.seedradiusedit.value()
	@@ -579,6 +609,9 @@ class ParticleDetectionView(QtWidgets.QWidget):
	self.pdetectsub.setEnabled(True)		self.pdetectsub.setEnabled(True)
	self.pclear.setEnabled(True)		self.pclear.setEnabled(True)

			def raiseWarning(self, warning):
			QtWidgets.QMessageBox.critical(self, "Warning", warning)

	@QtCore.pyqtSlot()		@QtCore.pyqtSlot()
	def detectParticles(self):		def detectParticles(self):
	self.saveDetectParams(self.dataset)		self.saveDetectParams(self.dataset)
	@@ -615,7 +648,7 @@ class ParticleDetectionView(QtWidgets.QWidget):
	if self.dataset is not None:		if self.dataset is not None:
	seedpoints = self.dataset.seedpoints		seedpoints = self.dataset.seedpoints
	deletepoints = self.dataset.seeddeletepoints		deletepoints = self.dataset.seeddeletepoints
	for ui, name, valuefunc in self.parameters:		for ui, name, valuefunc, showbtn in self.parameters:
	kwargs[name] = valuefunc()		kwargs[name] = valuefunc()
	seedradius = self.seedradiusedit.value()		seedradius = self.seedradiusedit.value()
	self.seg.setParameters(**kwargs)		self.seg.setParameters(**kwargs)

segmentation.py

+76 −38

Original line number	Original line	Diff line number	Diff line
	@@ -30,7 +30,7 @@ from random import random

	class Parameter(object):		class Parameter(object):
	def __init__(self, name, dtype, value=None, minval=None, maxval=None,		def __init__(self, name, dtype, value=None, minval=None, maxval=None,
	decimals=0, stepsize=1, helptext=None, show=False):		decimals=0, stepsize=1, helptext=None, show=False, linkedParameter=None):
	self.name = name		self.name = name
	self.dtype = dtype		self.dtype = dtype
	self.value = value		self.value = value
	@@ -39,36 +39,50 @@ class Parameter(object):
	self.stepsize = stepsize		self.stepsize = stepsize
	self.helptext = helptext		self.helptext = helptext
	self.show = show		self.show = show
			self.linkedParameter = linkedParameter

	class Segmentation(object):		class Segmentation(object):
	def __init__(self, dataset=None):		def __init__(self, dataset=None, parent=None):
	self.cancelcomputation = False		self.cancelcomputation = False
	if dataset is not None:		self.parent = parent
	self.detectParams = dataset.detectParams		self.defaultParams = {'contrastCurve': np.array([[50,0],[100,200],[200,255]]),
	else:		'activateContrastCurve': True,
	self.detectParams = {'points': np.array([[50,0],[100,200],[200,255]]),
	'contrastcurve': True,
	'blurRadius': 9,		'blurRadius': 9,
	'threshold': 0.2,		'activateLowThresh': True,
			'lowThresh': 0.2,
			'activateUpThresh': False,
			'upThresh': 0.5,
	'maxholebrightness': 0.5,		'maxholebrightness': 0.5,
	'erodeconvexdefects': 0,		# 'erodeconvexdefects': 0,
	'minparticlearea': 20,		'minparticlearea': 20,
	'minparticledistance': 20,		'minparticledistance': 20,
			'closeBackground': True,
	'measurefrac': 1,		'measurefrac': 1,
	'compactness': 0.,		'compactness': 0.,
	'seedRad': 3}		'seedRad': 3}
	self.initialParameters()		if dataset is not None:
			self.detectParams = dataset.detectParams
			for key in self.defaultParams:
			if key not in self.detectParams:
			self.detectParams[key] = self.defaultParams[key]
			else:
			self.detectParams = self.defaultParams
			self.initializeParameters()

	def initialParameters(self):		def initializeParameters(self):
	parlist = [Parameter("points", np.ndarray, self.detectParams['points'], helptext="Curve contrast"),		parlist = [Parameter("contrastCurve", np.ndarray, self.detectParams['contrastCurve'], helptext="Curve contrast"),
	Parameter("contrastcurve", np.bool, self.detectParams['contrastcurve'], helptext="Contrast curve", show=True),		Parameter("activateContrastCurve", np.bool, self.detectParams['activateContrastCurve'], helptext="activate Contrast curve", show=True, linkedParameter='contrastCurve'),
	Parameter("blurRadius", int, self.detectParams['blurRadius'], 3, 99, 1, 2, helptext="Blur radius", show=True),		Parameter("blurRadius", int, self.detectParams['blurRadius'], 3, 99, 1, 2, helptext="Blur radius", show=True),
	Parameter("threshold", float, self.detectParams['threshold'], .01, .9, 2, .02, helptext="Basic threshold", show=True),		Parameter("activateLowThresh", np.bool, self.detectParams['activateThresh2'], helptext="activate lower threshold", show=False, linkedParameter='lowThresh'),
			Parameter("lowThresh", float, self.detectParams['lowThresh'], .01, .9, 2, .02, helptext="Lower threshold", show=True),
			Parameter("activateUpThresh", np.bool, self.detectParams['activateThresh2'], helptext="activate upper threshold", show=False, linkedParameter='upThresh'),
			Parameter("upThresh", float, self.detectParams['upThresh'], .01, 1.0, 2, .02, helptext="Upper threshold", show=True),
	Parameter("maxholebrightness", float, self.detectParams['maxholebrightness'], 0, 1, 2, 0.02, helptext="Close holes brighter than..", show = True),		Parameter("maxholebrightness", float, self.detectParams['maxholebrightness'], 0, 1, 2, 0.02, helptext="Close holes brighter than..", show = True),
	Parameter("erodeconvexdefects", int, self.detectParams['erodeconvexdefects'], 0, 20, helptext="Erode convex defects", show=True),		# Parameter("erodeconvexdefects", int, self.detectParams['erodeconvexdefects'], 0, 20, helptext="Erode convex defects", show=True), #TODO: Consider removing it entirely. It is usually not used...
	Parameter("minparticlearea", int, self.detectParams['minparticlearea'], 10, 1000, 0, 50, helptext="Min. particle pixel area", show=False),		Parameter("minparticlearea", int, self.detectParams['minparticlearea'], 10, 1000, 0, 50, helptext="Min. particle pixel area", show=False),
	Parameter("minparticledistance", int, self.detectParams['minparticledistance'], 10, 1000, 0, 5, helptext="Min. distance between particles", show=False),		Parameter("minparticledistance", int, self.detectParams['minparticledistance'], 10, 1000, 0, 5, helptext="Min. distance between particles", show=False),
	Parameter("measurefrac", float, self.detectParams['measurefrac'], 0, 1, 2, stepsize = 0.05, helptext="measure fraction of particles", show=False),		Parameter("measurefrac", float, self.detectParams['measurefrac'], 0, 1, 2, stepsize = 0.05, helptext="measure fraction of particles", show=False),
			Parameter("closeBackground", np.bool, self.detectParams['closeBackground'], helptext="close holes in sure background", show=False),
	Parameter("sure_fg", None, helptext="Show sure foreground", show=True),		Parameter("sure_fg", None, helptext="Show sure foreground", show=True),
	Parameter("compactness", float, self.detectParams['compactness'], 0, 1, 2, 0.05, helptext="watershed compactness", show=False),		Parameter("compactness", float, self.detectParams['compactness'], 0, 1, 2, 0.05, helptext="watershed compactness", show=False),
	Parameter("watershed", None, helptext="Show watershed markers", show=True),		Parameter("watershed", None, helptext="Show watershed markers", show=True),
	@@ -284,15 +298,16 @@ class Segmentation(object):
	# convert to gray image and do histrogram normalization		# convert to gray image and do histrogram normalization
	gray = self.convert2Gray(img)		gray = self.convert2Gray(img)
	print("gray")		print("gray")
	if self.contrastcurve:
	xi, arr = self.calculateHistFunction(self.points)		if self.activateContrastCurve:
			xi, arr = self.calculateHistFunction(self.contrastCurve)
	gray = arr[gray]		gray = arr[gray]
	print("contrast curve")		print("contrast curve")
	if self.cancelcomputation:		if self.cancelcomputation:
	return None, None, None		return None, None, None

	# return even if inactive!		# return even if inactive!
	if return_step=="contrastcurve": return gray, 0		if return_step=="activateContrastCurve": return gray, 0

	# image blur for noise-reduction		# image blur for noise-reduction
	blur = cv2.medianBlur(gray, self.blurRadius)		blur = cv2.medianBlur(gray, self.blurRadius)
	@@ -303,12 +318,37 @@ class Segmentation(object):
	return None, None, None		return None, None, None

	# thresholding		# thresholding
	thresh = cv2.threshold(blur, int(255*self.threshold), 255, cv2.THRESH_BINARY)[1]		if self.activateLowThresh and not self.activateUpThresh:
	if return_step=="threshold": return thresh, 0		thresh = cv2.threshold(blur, int(255*self.lowThresh), 255, cv2.THRESH_BINARY)[1]
	print("threshold")		if return_step=="lowThresh": return thresh, 0
			print("lower threshold")
			if self.cancelcomputation:
			return None, None, None

			elif self.activateLowThresh and self.activateUpThresh:
			lowerLimit, upperLimit = np.round(self.lowThresh255), np.round(self.upThresh255)
			thresh = np.zeros_like(blur)
			thresh[np.where(np.logical_and(blur >= lowerLimit, blur <= upperLimit))] = 255

			if return_step=="lowThresh" or return_step=="upThresh": return thresh, 0
			print("between threshold")
	if self.cancelcomputation:		if self.cancelcomputation:
	return None, None, None		return None, None, None

			elif not self.activateLowThresh and self.activateUpThresh:
			thresh = np.zeros_like(blur)
			thresh[np.where(blur <= np.round(self.upThresh*255))] = 255
			if return_step=="upThresh": return thresh, 0
			print("upper threshold")
			if self.cancelcomputation:
			return None, None, None
			else: #no checkbox checked
			if self.parent is not None:
			self.parent.raiseWarning('No thresholding method selected!\nAborted detection..')
			print('NO THRESHOLDING SELECTED!')
			return None, None, None


	#close holes darkter than self.max_brightness		#close holes darkter than self.max_brightness
	self.closeBrightHoles(thresh, blur, self.maxholebrightness)		self.closeBrightHoles(thresh, blur, self.maxholebrightness)
	print("closed holes")		print("closed holes")
	@@ -317,20 +357,20 @@ class Segmentation(object):
	if self.cancelcomputation:		if self.cancelcomputation:
	return None, None, None		return None, None, None

	if self.erodeconvexdefects>0:		# if self.erodeconvexdefects>0: #TODO: Consider removing
	erthresh = self.erodeConvexDefects(thresh, self.erodeconvexdefects) ##ist erthresh hier eigentlich notwendig? Können wir bei Bedarf nicht einfach "thresh" überschreiben, anstatt noch ein großes Bild in den Speicher zu laden?		# thresh = self.erodeConvexDefects(thresh, self.erodeconvexdefects) ##ist erthresh hier eigentlich notwendig? Können wir bei Bedarf nicht einfach "thresh" überschreiben, anstatt noch ein großes Bild in den Speicher zu laden?
	else:		# else:
	erthresh = thresh		# erthresh = thresh
	print("erodeconvexdefects")		# print("erodeconvexdefects")
	if self.cancelcomputation:		# if self.cancelcomputation:
	return None, None, None		# return None, None, None
	# return even if inactive!		# return even if inactive!

	if return_step=="erodeconvexdefects":		# if return_step=="erodeconvexdefects":
	if self.erodeconvexdefects > 0: return erthresh, 0		# if self.erodeconvexdefects > 0: return thresh, 0
	else: return thresh, 0		# else: return thresh, 0

	dist_transform = cv2.distanceTransform(erthresh, cv2.DIST_L2,5)		dist_transform = cv2.distanceTransform(thresh, cv2.DIST_L2,5)
	print("distanceTransform")		print("distanceTransform")
	if self.cancelcomputation:		if self.cancelcomputation:
	return None, None, None		return None, None, None
	@@ -339,12 +379,10 @@ class Segmentation(object):
	'''the peak_local_max function takes the min distance between peaks. Unfortunately, that means that individual		'''the peak_local_max function takes the min distance between peaks. Unfortunately, that means that individual
	particles smaller than that distance are consequently disregarded. Hence, we need a connectec_components approach'''		particles smaller than that distance are consequently disregarded. Hence, we need a connectec_components approach'''

	sure_fg = self.getSureForeground(erthresh, self.minparticledistance, self.minparticlearea)		sure_fg = self.getSureForeground(thresh, self.minparticledistance, self.minparticlearea)

	sure_bg = cv2.dilate(erthresh, np.ones((5, 5)), iterations = 1)
	sure_bg = self.closeHoles(sure_bg)

	sure_bg = cv2.dilate(thresh, np.ones((5, 5)), iterations = 1)		sure_bg = cv2.dilate(thresh, np.ones((5, 5)), iterations = 1)
			if self.closeBackground:
	sure_bg = self.closeHoles(sure_bg)		sure_bg = self.closeHoles(sure_bg)

	# modify sure_fg and sure_bg with seedpoints and deletepoints		# modify sure_fg and sure_bg with seedpoints and deletepoints