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Commit f1fcb2d7 authored by Josef Brandt's avatar Josef Brandt
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Autofocus Option selectable by checkbox in WITecCom

parent d6102fea
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Showing with 139 additions and 107 deletions
.gitignore
View file @ f1fcb2d7
......@@ -18,3 +18,9 @@ external/build/
ramancom/renishawcom.py
ramancom/renishawtesting.py
*.exp
*.lib
*.obj
ramancom/WITecCOM.py
View file @ f1fcb2d7
......@@ -53,7 +53,9 @@ class WITecCOM(RamanBase):
hostname = gethostname()
self.IBUCSAccess = win32com.client.DispatchEx(self.CLSID, machine=hostname,
clsctx=pythoncom.CLSCTX_REMOTE_SERVER)
self.advancedInterface = False
self.doAutoFocus = False
def connect(self):
if not self.IBUCSAccess.RequestWriteAccess(True):
self.connected = False
......@@ -167,9 +169,8 @@ class WITecCOM(RamanBase):
try:
self.getAdvancedCOMFeatures(IBUCSCore)
self.advancedInterface = True
print('using advanced WITec features!')
self.doAutoFocus = False
except pythoncom.com_error:
print('not using advanced WITec features!')
self.advancedInterface = False
self.connected = True
......@@ -201,9 +202,9 @@ class WITecCOM(RamanBase):
from .advancedWITec import AdvancedWITecSpectra
except ModuleNotFoundError:
from advancedWITec import AdvancedWITecSpectra
self.advSpec = AdvancedWITecSpectra()
self.ramanParameters.append(RamanSettingParam('Autofocus', 'checkBox', default=True))
self.advSpec = AdvancedWITecSpectra()
if 'Autofocus' not in [param.name for param in self.ramanParameters]:
self.ramanParameters.append(RamanSettingParam('Autofocus', 'checkBox', default=False))
def getBrightness(self):
assert self.connected
......@@ -342,7 +343,8 @@ class WITecCOM(RamanBase):
if not self.advancedInterface:
self.triggerTimeSeriesMeasurement(num)
else:
self.doSpectralAutoFocus()
if self.doAutoFocus:
self.doSpectralAutoFocus()
self.acquireSilentSpectrum(num)
def finishMeasurement(self):
......@@ -378,6 +380,9 @@ class WITecCOM(RamanBase):
def initateSilentSpectrumAcquisition(self, ramanSettings):
self.advSpec.setDatasetPath(ramanSettings['path'])
self.doAutoFocus = ramanSettings['Autofocus']
self.advSpec.createTmpSpecFolder()
state = self.BeamPathState.GetValue()
if state == 'Video':
......
ramanscanui.py
View file @ f1fcb2d7
......@@ -99,33 +99,13 @@ class RamanScanUI(QtWidgets.QWidget):
hbox = QtWidgets.QHBoxLayout()
self.params = []
for param in self.ramanctrl.ramanParameters:
if param.dtype == 'int':
self.params.append(QtWidgets.QSpinBox())
self.params[-1].setMinimum(param.minVal)
self.params[-1].setMaximum(param.maxVal)
self.params[-1].setValue(param.value)
if param.dtype == 'double':
self.params.append(QtWidgets.QDoubleSpinBox())
self.params[-1].setMinimum(param.minVal)
self.params[-1].setMaximum(param.maxVal)
self.params[-1].setValue(param.value)
if param.dtype == 'combobox':
self.params.append(QtWidgets.QComboBox())
self.params[-1].addItems([str(i) for i in param.valList])
self.group2 = QtWidgets.QGroupBox("Raman settings", self)
grid2 = QtWidgets.QFormLayout()
for index, param in enumerate(self.params):
param.setMinimumWidth(70)
grid2.addRow(QtWidgets.QLabel(self.ramanctrl.ramanParameters[index].name), param)
self.paramsGroup = QtWidgets.QGroupBox("Raman settings")
self.paramsLayout = QtWidgets.QFormLayout()
self.prun = QtWidgets.QPushButton("Raman scan", self)
self.prun.released.connect(self.run)
grid2.addRow(self.prun)
self.group2.setLayout(grid2)
self.paramsGroup.setLayout(self.paramsLayout)
self.updateRamanParameters()
self.pexit = QtWidgets.QPushButton("Cancel", self)
self.pexit.released.connect(self.stopScan)
......@@ -139,7 +119,7 @@ class RamanScanUI(QtWidgets.QWidget):
hbox.addStretch()
hbox.addWidget(self.pexit)
vbox.addWidget(self.group2)
vbox.addWidget(self.paramsGroup)
vbox.addLayout(hbox)
vbox.addWidget(self.progresstime)
vbox.addWidget(self.progressbar)
......@@ -148,6 +128,42 @@ class RamanScanUI(QtWidgets.QWidget):
self.setWindowTitle("Raman Scan")
self.setVisible(False)
def updateRamanParameters(self):
"""
Update the raman parameters in the layout
:return:
"""
for index in reversed(range(self.paramsLayout.count())):
widget = self.paramsLayout.itemAt(index).widget()
self.paramsLayout.removeWidget(widget)
widget.setParent(None)
self.params = []
for param in self.ramanctrl.ramanParameters:
if param.dtype == 'int':
self.params.append(QtWidgets.QSpinBox())
self.params[-1].setMinimum(param.minVal)
self.params[-1].setMaximum(param.maxVal)
self.params[-1].setValue(param.value)
if param.dtype == 'double':
self.params.append(QtWidgets.QDoubleSpinBox())
self.params[-1].setMinimum(param.minVal)
self.params[-1].setMaximum(param.maxVal)
self.params[-1].setValue(param.value)
if param.dtype == 'combobox':
self.params.append(QtWidgets.QComboBox())
self.params[-1].addItems([str(i) for i in param.valList])
if param.dtype== 'checkBox':
self.params.append(QtWidgets.QCheckBox())
self.params[-1].setChecked(param.value)
for index, param in enumerate(self.params):
param.setMinimumWidth(70)
self.paramsLayout.addRow(QtWidgets.QLabel(self.ramanctrl.ramanParameters[index].name), param)
self.paramsLayout.addRow(self.prun)
self.paramsGroup.setLayout(self.paramsLayout)
def makeGetFnameLambda(self, msg, path, fileType, btn):
return lambda : self.getFName(msg, path, fileType, btn)
......@@ -247,7 +263,7 @@ class RamanScanUI(QtWidgets.QWidget):
else:
self.view.highLightRamanIndex(0)
self.view.blockUI()
self.group2.setEnabled(False)
self.paramsGroup.setEnabled(False)
self.progresstime.setEnabled(True)
self.progressbar.setEnabled(True)
self.progressbar.setRange(0, len(scanpoints))
......
sampleview.py
View file @ f1fcb2d7
......@@ -220,6 +220,7 @@ class SampleView(QtWidgets.QGraphicsView):
elif mode == "RamanScan":
self.removeSeedPointMarkers()
self.ramanwidget.resetDataset(self.dataset)
self.ramanwidget.updateRamanParameters()
self.ramanwidget.setVisible(True)
#show legend:
......@@ -350,9 +351,9 @@ class SampleView(QtWidgets.QGraphicsView):
p0 = self.mapToScene(event.pos())
self.moveStageToPosition(p0)
# elif self.mode=="ParticleDetection":
# p0 = self.mapToScene(event.pos())
# self.detectionwidget.setImageCenter([p0.x(), p0.y()])
elif self.mode=="ParticleDetection":
p0 = self.mapToScene(event.pos())
self.detectionwidget.setImageCenter([p0.x(), p0.y()])
else:
p0 = self.mapToScene(event.pos())
......
segmentation.py
View file @ f1fcb2d7
......@@ -110,7 +110,7 @@ class Segmentation(object):
Parameter("maxholebrightness", float, self.detectParams['maxholebrightness'], 0, 1, 2, 0.02, helptext="Close holes brighter than..", show = True),
Parameter("minparticlearea", int, self.detectParams['minparticlearea'], 1, 1000, 0, 50, helptext="Min. particle pixel area", show=False),
Parameter("enableMaxArea", np.bool, self.detectParams['enableMaxArea'], helptext="enable filtering for maximal pixel area", show=False, linkedParameter='maxparticlearea'),
Parameter("maxparticlearea", int, self.detectParams['maxparticlearea'], 10, 1E9, 0, 50, helptext="Max. particle pixel area", show=True),
Parameter("maxparticlearea", int, self.detectParams['maxparticlearea'], 10, 1E9, 0, 50, helptext="Max. particle pixel area", show=False),
Parameter("minparticledistance", int, self.detectParams['minparticledistance'], 5, 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),
......@@ -253,83 +253,87 @@ class Segmentation(object):
for label in range(1, n):
area = stats[label, cv2.CC_STAT_AREA]
if self.minparticlearea < area < maxArea:
print(f'processing {label} of {n} compontents')
up = stats[label, cv2.CC_STAT_TOP]
left = stats[label, cv2.CC_STAT_LEFT]
width = stats[label, cv2.CC_STAT_WIDTH]
height = stats[label, cv2.CC_STAT_HEIGHT]
subthresh = np.uint8(255 * (labels[up:(up+height), left:(left+width)] == label))
subdist = cv2.distanceTransform(subthresh, cv2.DIST_L2, 3)
sure_fg = self.getSureForeground(subthresh, subdist, self.minparticledistance)
sure_bg = cv2.dilate(subthresh, np.ones((5, 5)), iterations = 1)
if self.closeBackground:
sure_bg = self.closeHoles(sure_bg)
# modify sure_fg and sure_bg with seedpoints and deletepoints
for p in np.int32(seedpoints):
cv2.circle(sure_fg, tuple([p[0]-left, p[1]-up]), int(p[2]), 1, -1)
cv2.circle(sure_bg, tuple([p[0]-left, p[1]-up]), int(p[2]), 1, -1)
for p in np.int32(deletepoints):
cv2.circle(sure_fg, tuple([p[0]-left, p[1]-up]), int(p[2]), 0, -1)
cv2.circle(sure_bg, tuple([p[0]-left, p[1]-up]), int(p[2]), 0, -1)
if self.cancelcomputation:
return None, None
if return_step=="sure_fg":
preview_surefg = self.addToPreviewImage(sure_fg, up, left, preview_surefg)
preview_surebg = self.addToPreviewImage(sure_bg, up, left, preview_surebg)
continue
unknown = cv2.subtract(sure_bg, sure_fg)
ret, markers = cv2.connectedComponents(sure_fg)
markers = markers+1
markers[unknown==255] = 0
markers = ndi.label(sure_fg)[0]
markers = watershed(-subdist, markers, mask=sure_bg, compactness = self.compactness, watershed_line = True) #labels = 0 for background, 1... for particles
if self.cancelcomputation:
return None, None
if return_step=="watershed":
previewImage = self.addToPreviewImage(markers, up, left, previewImage)
continue
if cv2.__version__ > '3.5':
contours, hierarchy = cv2.findContours(markers, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
# if width > 25000 or height > 25000:
if False:
print(f'skipping{label} of {n} compontents, too large: {width} x {height} pixel!!!')
else:
temp, contours, hierarchy = cv2.findContours(markers, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
if self.cancelcomputation:
return None, None
print(f'processing {label} of {n} compontents, {width} x {height} pixel')
subthresh = np.uint8(255 * (labels[up:(up+height), left:(left+width)] == label))
subdist = cv2.distanceTransform(subthresh, cv2.DIST_L2, 3)
tmpcontours = [contours[i] for i in range(len(contours)) if hierarchy[0,i,3]<0]
for cnt in tmpcontours:
if cv2.contourArea(cnt) >= self.minparticlearea:
label = markers[cnt[0,0,1],cnt[0,0,0]]
if label==0:
continue
x0, x1 = cnt[:,0,0].min(), cnt[:,0,0].max()
y0, y1 = cnt[:,0,1].min(), cnt[:,0,1].max()
subimg = (markers[y0:y1+1,x0:x1+1]).copy()
subimg[subimg!=label] = 0
y, x = self.getMeasurementPoints(subimg)
for i in range(len(cnt)):
cnt[i][0][0] += left
cnt[i][0][1] += up
finalcontours.append(cnt)
measurementPoints[particleIndex] = []
sure_fg = self.getSureForeground(subthresh, subdist, self.minparticledistance)
sure_bg = cv2.dilate(subthresh, np.ones((5, 5)), iterations = 1)
if self.closeBackground:
sure_bg = self.closeHoles(sure_bg)
# modify sure_fg and sure_bg with seedpoints and deletepoints
for p in np.int32(seedpoints):
cv2.circle(sure_fg, tuple([p[0]-left, p[1]-up]), int(p[2]), 1, -1)
cv2.circle(sure_bg, tuple([p[0]-left, p[1]-up]), int(p[2]), 1, -1)
for p in np.int32(deletepoints):
cv2.circle(sure_fg, tuple([p[0]-left, p[1]-up]), int(p[2]), 0, -1)
cv2.circle(sure_bg, tuple([p[0]-left, p[1]-up]), int(p[2]), 0, -1)
if self.cancelcomputation:
return None, None
if return_step=="sure_fg":
preview_surefg = self.addToPreviewImage(sure_fg, up, left, preview_surefg)
preview_surebg = self.addToPreviewImage(sure_bg, up, left, preview_surebg)
continue
unknown = cv2.subtract(sure_bg, sure_fg)
ret, markers = cv2.connectedComponents(sure_fg)
markers = markers+1
markers[unknown==255] = 0
markers = ndi.label(sure_fg)[0]
markers = watershed(-subdist, markers, mask=sure_bg, compactness = self.compactness, watershed_line = True) #labels = 0 for background, 1... for particles
if self.cancelcomputation:
return None, None
if return_step=="watershed":
previewImage = self.addToPreviewImage(markers, up, left, previewImage)
continue
if cv2.__version__ > '3.5':
contours, hierarchy = cv2.findContours(markers, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
else:
temp, contours, hierarchy = cv2.findContours(markers, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
if self.cancelcomputation:
return None, None
for index in range(0, len(x)):
newMeasPoint = MeasurementPoint(particleIndex, x[index] + x0 + left, y[index] + y0 + up)
measurementPoints[particleIndex].append(newMeasPoint)
particleIndex += 1
tmpcontours = [contours[i] for i in range(len(contours)) if hierarchy[0,i,3]<0]
for cnt in tmpcontours:
if cv2.contourArea(cnt) >= self.minparticlearea:
label = markers[cnt[0,0,1],cnt[0,0,0]]
if label==0:
continue
x0, x1 = cnt[:,0,0].min(), cnt[:,0,0].max()
y0, y1 = cnt[:,0,1].min(), cnt[:,0,1].max()
subimg = (markers[y0:y1+1,x0:x1+1]).copy()
subimg[subimg!=label] = 0
y, x = self.getMeasurementPoints(subimg)
for i in range(len(cnt)):
cnt[i][0][0] += left
cnt[i][0][1] += up
finalcontours.append(cnt)
measurementPoints[particleIndex] = []
for index in range(0, len(x)):
newMeasPoint = MeasurementPoint(particleIndex, x[index] + x0 + left, y[index] + y0 + up)
measurementPoints[particleIndex].append(newMeasPoint)
particleIndex += 1
if return_step == 'sure_fg':
img = np.zeros_like(preview_surefg)
......
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