Added second threshold in detectionview.py

detectionview.py

@@ -275,7 +275,7 @@ class ParticleDetectionView(QtWidgets.QWidget):
         self.dataset = self.verifySeedpoints(dataset)
         self.img = img
         self.imgclip = 0,0,0,0
-        self.seg = Segmentation(self.dataset)
+        self.seg = Segmentation(self.dataset, self)
         self.thread = None
         self.view = parent
         
@@ -303,10 +303,12 @@ class ParticleDetectionView(QtWidgets.QWidget):
         group = QtWidgets.QGroupBox("Detection settings", self)
         grid = QtWidgets.QGridLayout()
         self.parameters = []
+        checkBoxesToLink = {}
+        
         # create editable parameters:
         for i, p in enumerate(self.seg.parlist):
             label, colstretch = None, 1
-            if p.name == "points":
+            if p.name == "contrastCurve":
                 paramui = HistWidget(lambda : self.seg.calculateHist(self.seg.convert2Gray(self.subimg)),
                                      self.seg.calculateHistFunction, self)
                 valuefunc = makeValueLambda(paramui.value)
@@ -316,6 +318,8 @@ class ParticleDetectionView(QtWidgets.QWidget):
                 paramui.setChecked(p.value)
                 valuefunc = makeValueLambda(paramui.isChecked)
                 colstretch = 2
+                if p.linkedParameter is not None:
+                    checkBoxesToLink[paramui] = p.linkedParameter
             elif p.dtype == int or p.dtype == float:
                 label = QtWidgets.QLabel(p.helptext, self)
                 if p.dtype == int:
@@ -334,18 +338,44 @@ class ParticleDetectionView(QtWidgets.QWidget):
             elif p.dtype is None:
                 label = QtWidgets.QLabel(p.helptext, self)
                 paramui = None
+            
             if paramui is not None:
-                self.parameters.append([paramui, p.name, valuefunc])
+                self.parameters.append([paramui, p.name, valuefunc, None])
                 if colstretch == 1:
                     grid.addWidget(paramui, i, 1, QtCore.Qt.AlignLeft)
                 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:
                 grid.addWidget(label, i, 0, QtCore.Qt.AlignLeft)
+                
             if p.show is True:
                 pshow = QtWidgets.QPushButton(">", self)
                 pshow.released.connect(makeShowLambda(p.name))
                 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)
         grid.addWidget(label, i+1, 0, QtCore.Qt.AlignLeft)
@@ -384,7 +414,7 @@ class ParticleDetectionView(QtWidgets.QWidget):
     def saveDetectParams(self, ds=None):
         if ds is not None:
             for param in self.parameters:
-                if param[1] == 'points':
+                if param[1] == 'contrastCurve':
                     print(param[0].value())
                 try:                # is it a spinbox or the histWidget? Read out the value
                     ds.detectParams[param[1]] = param[0].value()
@@ -535,7 +565,7 @@ class ParticleDetectionView(QtWidgets.QWidget):
         self.saveDetectParams(self.dataset)
         img = self.subimg.copy()
         kwargs = {}
-        for ui, name, valuefunc in self.parameters:
+        for ui, name, valuefunc, showbtn in self.parameters:
             kwargs[name] = valuefunc()
         self.seg.setParameters(**kwargs)
         seedradius = self.seedradiusedit.value()
@@ -579,6 +609,9 @@ class ParticleDetectionView(QtWidgets.QWidget):
         self.pdetectsub.setEnabled(True)
         self.pclear.setEnabled(True)
     
+    def raiseWarning(self, warning):
+        QtWidgets.QMessageBox.critical(self, "Warning", warning)
+    
     @QtCore.pyqtSlot()
     def detectParticles(self):
         self.saveDetectParams(self.dataset)
@@ -615,7 +648,7 @@ class ParticleDetectionView(QtWidgets.QWidget):
         if self.dataset is not None:
             seedpoints = self.dataset.seedpoints
             deletepoints = self.dataset.seeddeletepoints
-        for ui, name, valuefunc in self.parameters:
+        for ui, name, valuefunc, showbtn in self.parameters:
             kwargs[name] = valuefunc()
         seedradius = self.seedradiusedit.value()
         self.seg.setParameters(**kwargs)

segmentation.py

@@ -30,7 +30,7 @@ from random import random
 
 class Parameter(object):
     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.dtype = dtype
         self.value = value
@@ -39,36 +39,50 @@ class Parameter(object):
         self.stepsize = stepsize
         self.helptext = helptext
         self.show = show
+        self.linkedParameter = linkedParameter
 
 class Segmentation(object):
-    def __init__(self, dataset=None):
+    def __init__(self, dataset=None, parent=None):        
         self.cancelcomputation = False
-        if dataset is not None:
-            self.detectParams = dataset.detectParams
-        else:
-            self.detectParams = {'points': np.array([[50,0],[100,200],[200,255]]),
-                                 'contrastcurve': True,
+        self.parent = parent
+        self.defaultParams = {'contrastCurve': np.array([[50,0],[100,200],[200,255]]),
+                                 'activateContrastCurve': True,
                                 'blurRadius': 9,
-                                'threshold': 0.2,
+                                'activateLowThresh': True,
+                                'lowThresh': 0.2,
+                                'activateUpThresh': False,
+                                'upThresh': 0.5,
                                 'maxholebrightness': 0.5,
-                                'erodeconvexdefects': 0,
+#                                'erodeconvexdefects': 0,
                                 'minparticlearea': 20,
                                 'minparticledistance': 20,
+                                'closeBackground': True,
                                 'measurefrac': 1,
                                 'compactness': 0.,
                                 '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):
-        parlist = [Parameter("points", np.ndarray, self.detectParams['points'], helptext="Curve contrast"),
-                   Parameter("contrastcurve", np.bool, self.detectParams['contrastcurve'], helptext="Contrast curve", show=True),
+    def initializeParameters(self):
+        parlist = [Parameter("contrastCurve", np.ndarray, self.detectParams['contrastCurve'], helptext="Curve contrast"),
+                   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("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("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("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("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("compactness", float, self.detectParams['compactness'], 0, 1, 2, 0.05, helptext="watershed compactness", show=False),
                    Parameter("watershed", None, helptext="Show watershed markers", show=True),
@@ -284,15 +298,16 @@ class Segmentation(object):
         # convert to gray image and do histrogram normalization        
         gray = self.convert2Gray(img)
         print("gray")
-        if self.contrastcurve:
-            xi, arr = self.calculateHistFunction(self.points)
+        
+        if self.activateContrastCurve:
+            xi, arr = self.calculateHistFunction(self.contrastCurve)
             gray = arr[gray]
         print("contrast curve")
         if self.cancelcomputation:
             return None, None, None
             
         # return even if inactive!
-        if return_step=="contrastcurve": return gray, 0
+        if return_step=="activateContrastCurve": return gray, 0
         
         # image blur for noise-reduction
         blur = cv2.medianBlur(gray, self.blurRadius)
@@ -303,11 +318,36 @@ class Segmentation(object):
             return None, None, None
         
         # thresholding
-        thresh = cv2.threshold(blur, int(255*self.threshold), 255, cv2.THRESH_BINARY)[1]
-        if return_step=="threshold": return thresh, 0
-        print("threshold")
-        if self.cancelcomputation:
+        if self.activateLowThresh and not self.activateUpThresh:
+            thresh = cv2.threshold(blur, int(255*self.lowThresh), 255, cv2.THRESH_BINARY)[1]
+            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.lowThresh*255), np.round(self.upThresh*255)
+            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:
+                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
         self.closeBrightHoles(thresh, blur, self.maxholebrightness)
@@ -317,20 +357,20 @@ class Segmentation(object):
         if self.cancelcomputation:
             return None, None, None
                 
-        if self.erodeconvexdefects>0:
-            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?
-        else:
-            erthresh = thresh
-        print("erodeconvexdefects")
-        if self.cancelcomputation:
-            return None, None, None
+#        if self.erodeconvexdefects>0:     #TODO: Consider removing
+#            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:
+#            erthresh = thresh
+#        print("erodeconvexdefects")
+#        if self.cancelcomputation:
+#            return None, None, None
         # return even if inactive!
         
-        if return_step=="erodeconvexdefects": 
-            if self.erodeconvexdefects > 0: return erthresh, 0
-            else: return thresh, 0
+#        if return_step=="erodeconvexdefects": 
+#            if self.erodeconvexdefects > 0: 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")
         if self.cancelcomputation:
             return None, None, None
@@ -339,13 +379,11 @@ class Segmentation(object):
         '''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'''
         
-        sure_fg = self.getSureForeground(erthresh, self.minparticledistance, self.minparticlearea)
-        
-        sure_bg = cv2.dilate(erthresh, np.ones((5, 5)), iterations = 1)
-        sure_bg = self.closeHoles(sure_bg)
+        sure_fg = self.getSureForeground(thresh, self.minparticledistance, self.minparticlearea)
         
         sure_bg = cv2.dilate(thresh, np.ones((5, 5)), iterations = 1)
-        sure_bg = self.closeHoles(sure_bg)
+        if self.closeBackground:
+            sure_bg = self.closeHoles(sure_bg)
         
         # modify sure_fg and sure_bg with seedpoints and deletepoints
         if len(deletepoints)>0: