def setInputNode(self, node):
if node.GetClassName() == "vtkMRMLStreamingVolumeNode":
try:
self.videoImage = slicer.util.getNode(node.GetName() +
'_Image')
self.videoImage.SetAndObserveImageData(node.GetImageData())
node.AddObserver(
slicer.vtkMRMLStreamingVolumeNode.FrameModifiedEvent,
self.referenceImageModified)
self.streamingNode = node
except slicer.util.MRMLNodeNotFoundException:
#Create a node to store the image data of the video so that IGTLink is
# sending an IMAGE message, not a VIDEO message
imageSpacing = [0.2, 0.2, 0.2]
# Create volume node
self.videoImage = slicer.vtkMRMLVectorVolumeNode()
self.videoImage.SetName(node.GetName() + '_Image')
self.videoImage.SetSpacing(imageSpacing)
self.videoImage.SetAndObserveImageData(node.GetImageData())
#self.videoImage.SetImageDataConnection(node.GetImageDataConnection())
node.AddObserver(
slicer.vtkMRMLStreamingVolumeNode.FrameModifiedEvent,
self.referenceImageModified)
# Add volume to scene
slicer.mrmlScene.AddNode(self.videoImage)
self.inputNode = self.videoImage
self.streamingNode = node
else:
self.inputNode = node
def onWIPButtonClicked(self):
import urllib2
reply = urllib2.urlopen('http://www.osa.sunysb.edu/erich.png')
byte_array = reply.read()
image = qt.QImage(qt.QImage.Format_RGB888)
image.loadFromData(byte_array)
imageData = self.QImage2vtkImage(image)
volumeNode = slicer.vtkMRMLVectorVolumeNode()
volumeNode.SetName("WEB")
volumeNode.SetAndObserveImageData(imageData)
displayNode = slicer.vtkMRMLVectorVolumeDisplayNode()
slicer.mrmlScene.AddNode(volumeNode)
slicer.mrmlScene.AddNode(displayNode)
volumeNode.SetAndObserveDisplayNodeID(displayNode.GetID())
displayNode.SetAndObserveColorNodeID('vtkMRMLColorTableNodeGrey')
self.mutate()
def openTargetImage(self):
import string
p = self.v.page()
m = p.mainFrame()
imageBound=m.evaluateJavaScript('viewer.viewport.viewportToImageRectangle(viewer.viewport.getBounds().x, viewer.viewport.getBounds().y, viewer.viewport.getBounds().width, viewer.viewport.getBounds().height)')
x=imageBound[u'x']
y=imageBound[u'y']
width=imageBound[u'width']
height=imageBound[u'height']
self.j['x'] = x
self.j['y'] = y
self.j['width'] = width
self.j['height'] = height
imagedata = m.evaluateJavaScript('imagedata')
tmpfilename= imagedata[u'metaData'][1]
imageFileName=string.rstrip(tmpfilename,'.dzi')
self.tilename = imagedata[u'imageId']
print self.tilename
self.parameterNode.SetParameter("SlicerPathology,tilename", self.tilename)
current_weburl ='http://quip1.uhmc.sunysb.edu/fcgi-bin/iipsrv.fcgi?IIIF=' + imageFileName +'/' + str(x) + ','+ str(y) + ',' + str(width) + ',' + str(height) + '/full/0/default.jpg'
print current_weburl
self.v.setUrl(qt.QUrl(current_weburl))
self.v.show()
reply = urllib2.urlopen(current_weburl)
byte_array = reply.read()
image = qt.QImage(qt.QImage.Format_RGB888)
image.loadFromData(byte_array)
imageData = self.QImage2vtkImage(image)
volumeNode = slicer.vtkMRMLVectorVolumeNode()
volumeNode.SetName("WEB")
volumeNode.SetAndObserveImageData(imageData)
displayNode = slicer.vtkMRMLVectorVolumeDisplayNode()
slicer.mrmlScene.AddNode(volumeNode)
slicer.mrmlScene.AddNode(displayNode)
volumeNode.SetAndObserveDisplayNodeID(displayNode.GetID())
displayNode.SetAndObserveColorNodeID('vtkMRMLColorTableNodeGrey')
self.mutate()
def setup(self):
ScriptedLoadableModuleWidget.setup(self)
self.logic = DataCollectionLogic()
self.moduleDir = os.path.dirname(slicer.modules.datacollection.path)
datasetDirectory = os.path.join(self.moduleDir, os.pardir, "Datasets")
try:
os.listdir(datasetDirectory)
except FileNotFoundError:
os.mkdir(datasetDirectory)
#
# Parameters Area
#
parametersCollapsibleButton = ctk.ctkCollapsibleButton()
parametersCollapsibleButton.text = "Parameters"
self.layout.addWidget(parametersCollapsibleButton)
# Layout within the dummy collapsible button
parametersFormLayout = qt.QFormLayout(parametersCollapsibleButton)
#self.imageSaveDirectory = qt.QLineEdit("Select directory to save images")
#parametersFormLayout.addRow(self.imageSaveDirectory)
self.selectRecordingNodeComboBox = qt.QComboBox()
self.selectRecordingNodeComboBox.addItems(["Select Image Node"])
self.recordingNodes = slicer.util.getNodesByClass("vtkMRMLVolumeNode")
recordingNodeNames = []
for recordingNode in self.recordingNodes:
recordingNodeNames.append(recordingNode.GetName())
self.selectRecordingNodeComboBox.addItems(recordingNodeNames)
parametersFormLayout.addRow(self.selectRecordingNodeComboBox)
self.datasetSelector = qt.QComboBox()
self.datasetSelector.addItems(["Select Dataset"])
datasetDirectoryContents = os.listdir(
os.path.join(self.moduleDir, os.pardir, "Datasets"))
datasetNames = [
dir for dir in datasetDirectoryContents if dir.find(".") == -1
]
self.datasetSelector.addItems(["Create New Dataset"])
self.datasetSelector.addItems(datasetNames)
parametersFormLayout.addRow(self.datasetSelector)
self.videoIDComboBox = qt.QComboBox()
self.videoIDComboBox.addItems(
["Select video ID", "Create new video ID"])
parametersFormLayout.addRow(self.videoIDComboBox)
self.fileTypeComboBox = qt.QComboBox()
self.fileTypeComboBox.addItems([".jpg", ".png", ".bmp", ".tiff"])
parametersFormLayout.addRow(self.fileTypeComboBox)
self.fileType = self.fileTypeComboBox.currentText
self.collectFromSequenceCheckBox = qt.QCheckBox(
"Collect from Sequence")
self.collectingFromSequence = False
parametersFormLayout.addRow(self.collectFromSequenceCheckBox)
self.problemTypeComboBox = qt.QComboBox()
self.problemTypeComboBox.addItems([
"Select problem type", "Classification", "Detection",
"Segmentation"
])
parametersFormLayout.addRow(self.problemTypeComboBox)
self.classificationFrame = qt.QFrame()
self.classificationLayout()
parametersFormLayout.addRow(self.classificationFrame)
self.classificationFrame.visible = False
self.detectionFrame = qt.QFrame()
self.detectionLayout()
parametersFormLayout.addRow(self.detectionFrame)
self.detectionFrame.visible = False
self.segmentationFrame = qt.QFrame()
self.segmentationLayout()
parametersFormLayout.addRow(self.segmentationFrame)
self.segmentationFrame.visible = False
#
# Start/Stop Image Collection Button
#
self.startStopCollectingImagesButton = qt.QPushButton(
"Start Image Collection")
self.startStopCollectingImagesButton.toolTip = "Collect images."
self.startStopCollectingImagesButton.enabled = False
parametersFormLayout.addRow(self.startStopCollectingImagesButton)
self.infoLabel = qt.QLabel("")
parametersFormLayout.addRow(self.infoLabel)
# connections
self.fileTypeComboBox.connect('currentIndexChanged(int)',
self.onFileTypeSelected)
self.inputSegmentationSelector.connect(
'currentIndexChanged(int)', self.onSegmentationInputSelected)
self.selectRecordingNodeComboBox.connect("currentIndexChanged(int)",
self.onRecordingNodeSelected)
self.datasetSelector.connect('currentIndexChanged(int)',
self.onDatasetSelected)
self.startStopCollectingImagesButton.connect(
'clicked(bool)', self.onStartStopCollectingImagesButton)
self.videoIDComboBox.connect('currentIndexChanged(int)',
self.onVideoIDSelected)
self.collectFromSequenceCheckBox.connect(
'stateChanged(int)', self.onCollectFromSequenceChecked)
self.problemTypeComboBox.connect('currentIndexChanged(int)',
self.onProblemTypeSelected)
# Add vertical spacer
self.layout.addStretch(1)
# Refresh Start/Stop Collecting Images Button state
self.onSelect()
try:
self.webcamReference = slicer.util.getNode('Webcam_Reference')
except slicer.util.MRMLNodeNotFoundException:
#if not self.webcamReference:
imageSpacing = [0.2, 0.2, 0.2]
imageData = vtk.vtkImageData()
imageData.SetDimensions(640, 480, 1)
imageData.AllocateScalars(vtk.VTK_UNSIGNED_CHAR, 1)
thresholder = vtk.vtkImageThreshold()
thresholder.SetInputData(imageData)
thresholder.SetInValue(0)
thresholder.SetOutValue(0)
# Create volume node
self.webcamReference = slicer.vtkMRMLVectorVolumeNode()
self.webcamReference.SetName('Webcam_Reference')
self.webcamReference.SetSpacing(imageSpacing)
self.webcamReference.SetImageDataConnection(
thresholder.GetOutputPort())
# Add volume to scene
slicer.mrmlScene.AddNode(self.webcamReference)
displayNode = slicer.vtkMRMLVectorVolumeDisplayNode()
slicer.mrmlScene.AddNode(displayNode)
self.webcamReference.SetAndObserveDisplayNodeID(
displayNode.GetID())
self.webcamConnectorNode = self.createWebcamPlusConnector()
self.webcamConnectorNode.Start()
self.setupWebcamResliceDriver()
def run(self, charVolume, testThicknessVolume, testLabel, outputLabel):
"""
Run characterization step
"""
# Checks if parameters and conditions are set to run algorithm
# Tests characterization input nodes:
if not charVolume:
slicer.util.errorDisplay('No input volumes were selected.')
return False
if not self.isValidInputNode(charVolume):
slicer.util.errorDisplay(
'Characterization selected volumes is invalid.')
return False
if not outputLabel:
slicer.util.errorDisplay('No output volume was selected.')
return False
# Default parameters
doHaralick = True
doHistogram = False
logging.info('Processing started')
# Runs characterization step ******************************
vectorFeatureNodes = slicer.vtkMRMLVectorVolumeNode()
slicer.mrmlScene.AddNode(vectorFeatureNodes)
listFeatures = []
listZFeatures = []
if testThicknessVolume is None:
doWithThickness = False
else:
doWithThickness = True
self.runCharacterizationCLI(charVolume, vectorFeatureNodes, doHaralick,
doHistogram)
# Creates list from vector image
self.createListFromVectorImage(vectorFeatureNodes, listFeatures)
# Runs Z-Score step ***************************************
self.generateZScore(charVolume, listFeatures, testThicknessVolume,
listZFeatures)
# Creates list with all features in correct order for classifier
listFull = []
self.composeListWithAllFeatures(listFeatures, listZFeatures,
doWithThickness, listFull)
# Check for dependencies
# from pip._internal import main as pipmain
import subprocess
pip_modules = [['scipy', 'scipy'], ['scikit-learn', 'sklearn'],
['joblib', 'joblib']]
for module in pip_modules:
try:
module_obj = __import__(module[1])
logging.info("{0} was found. Successfully imported".format(
module[0]))
except ImportError:
logging.info(
"{0} was not found. Attempting to install {0}.".format(
module[0]))
# pipmain(['install', '--user', module[0]])
subprocess.call([
sys.executable, "-m", "pip", "install", "--user", module[0]
])
self.doClassification(listFull, testLabel, outputLabel,
doWithThickness)
logging.info('Processing completed')
return True
def setup(self):
ScriptedLoadableModuleWidget.setup(self)
self.logic = Collect_Training_ImagesLogic()
self.moduleDir = os.path.dirname(slicer.modules.collect_training_images.path)
#
# Parameters Area
#
parametersCollapsibleButton = ctk.ctkCollapsibleButton()
parametersCollapsibleButton.text = "Parameters"
self.layout.addWidget(parametersCollapsibleButton)
# Layout within the dummy collapsible button
parametersFormLayout = qt.QFormLayout(parametersCollapsibleButton)
#self.imageSaveDirectory = qt.QLineEdit("Select directory to save images")
#parametersFormLayout.addRow(self.imageSaveDirectory)
self.modelSelector = qt.QComboBox()
self.modelSelector.addItems(["Select model"])
modelDirectoryContents = os.listdir(os.path.join(self.moduleDir,os.pardir,"Models"))
modelNames = [dir for dir in modelDirectoryContents if dir.find(".") == -1 and dir != "Dockerfile"]
self.modelSelector.addItems(["Create new model"])
self.modelSelector.addItems(modelNames)
parametersFormLayout.addRow(self.modelSelector)
#self.imageSaveDirectoryLabel = qt.QLabel("Training photo directory:")
#parametersFormLayout.addRow(self.imageSaveDirectoryLabel)
self.imageSaveDirectoryLineEdit = ctk.ctkPathLineEdit()
#node = self.logic.getParameterNode()
imageSaveDirectory = os.path.dirname(slicer.modules.collect_training_images.path)
self.imageSaveDirectoryLineEdit.currentPath = imageSaveDirectory
self.imageSaveDirectoryLineEdit.filters = ctk.ctkPathLineEdit.Dirs
self.imageSaveDirectoryLineEdit.options = ctk.ctkPathLineEdit.DontUseSheet
self.imageSaveDirectoryLineEdit.options = ctk.ctkPathLineEdit.ShowDirsOnly
self.imageSaveDirectoryLineEdit.showHistoryButton = False
self.imageSaveDirectoryLineEdit.setMinimumWidth(100)
self.imageSaveDirectoryLineEdit.setMaximumWidth(500)
#parametersFormLayout.addRow(self.imageSaveDirectoryLineEdit)
self.imageClassComboBox = qt.QComboBox()
self.imageClassComboBox.addItems(["Select image class","Create new image class"])
parametersFormLayout.addRow(self.imageClassComboBox)
#
# Start/Stop Image Collection Button
#
self.startStopCollectingImagesButton = qt.QPushButton("Start Image Collection")
self.startStopCollectingImagesButton.toolTip = "Collect training images."
self.startStopCollectingImagesButton.enabled = False
parametersFormLayout.addRow(self.startStopCollectingImagesButton)
self.infoLabel = qt.QLabel("")
parametersFormLayout.addRow(self.infoLabel)
# connections
self.modelSelector.connect('currentIndexChanged(int)',self.onModelSelected)
self.startStopCollectingImagesButton.connect('clicked(bool)', self.onStartStopCollectingImagesButton)
self.imageClassComboBox.connect('currentIndexChanged(int)',self.onImageClassSelected)
# Add vertical spacer
self.layout.addStretch(1)
# Refresh Start/Stop Collecting Images Button state
self.onSelect()
try:
self.webcamReference = slicer.util.getNode('Webcam_Reference')
except slicer.util.MRMLNodeNotFoundException:
#if not self.webcamReference:
imageSpacing = [0.2, 0.2, 0.2]
imageData = vtk.vtkImageData()
imageData.SetDimensions(640, 480, 1)
imageData.AllocateScalars(vtk.VTK_UNSIGNED_CHAR, 1)
thresholder = vtk.vtkImageThreshold()
thresholder.SetInputData(imageData)
thresholder.SetInValue(0)
thresholder.SetOutValue(0)
# Create volume node
self.webcamReference = slicer.vtkMRMLVectorVolumeNode()
self.webcamReference.SetName('Webcam_Reference')
self.webcamReference.SetSpacing(imageSpacing)
self.webcamReference.SetImageDataConnection(thresholder.GetOutputPort())
# Add volume to scene
slicer.mrmlScene.AddNode(self.webcamReference)
displayNode = slicer.vtkMRMLVectorVolumeDisplayNode()
slicer.mrmlScene.AddNode(displayNode)
self.webcamReference.SetAndObserveDisplayNodeID(displayNode.GetID())
self.webcamConnectorNode = self.createWebcamPlusConnector()
self.webcamConnectorNode.Start()
self.setupWebcamResliceDriver()
def loadVolume(self, outputNode=None):
"""
Load the files in paths to outputNode.
TODO: currently downsample is done with nearest neighbor filtering
(i.e. skip slices and take every other row/column).
It would be better to do a high order spline or other
method, but there is no convenient streaming option for these.
One option will be do to a box filter by averaging adjacent
slices/row/columns which should be easy in numpy
and give good results for a pixel aligned 50% scale operation.
"""
ijkToRAS, extent, numberOfScalarComponents = self.outputVolumeGeometry(
)
outputSpacing = [numpy.linalg.norm(ijkToRAS[0:3, i]) for i in range(3)]
originalVolumeSpacing = [
numpy.linalg.norm(self.originalVolumeIJKToRAS[0:3, i])
for i in range(3)
]
stepSize = [
int(outputSpacing[i] / originalVolumeSpacing[i]) for i in range(3)
]
paths = self._filePaths[::stepSize[2]]
if self.reverseSliceOrder:
paths.reverse()
volumeArray = None
sliceIndex = 0
firstArrayFullShape = None
for inputSliceIndex, path in enumerate(paths):
if inputSliceIndex < extent[4] or inputSliceIndex > extent[5]:
# out of selected bounds
continue
reader = sitk.ImageFileReader()
reader.SetFileName(path)
image = reader.Execute()
sliceArray = sitk.GetArrayFromImage(image)
if len(sliceArray.shape) == 3 and self.outputGrayscale:
# We convert to grayscale by simply taking the first component, which is appropriate for cases when grayscale image is stored as R=G=B,
# but to convert real RGB images it could better to compute the mean or luminance.
sliceArray = sitk.GetArrayFromImage(image)[:, :, 0]
currentArrayFullShape = sliceArray.shape
if firstArrayFullShape is None:
firstArrayFullShape = currentArrayFullShape
if volumeArray is None:
shape = [
extent[5] - extent[4] + 1, extent[3] - extent[2] + 1,
extent[1] - extent[0] + 1
]
if len(sliceArray.shape) == 3:
shape.append(sliceArray.shape[2])
volumeArray = numpy.zeros(shape, dtype=sliceArray.dtype)
if len(sliceArray.shape) == 3:
# vector volume
sliceArray = sliceArray[extent[2] *
stepSize[1]:(extent[3] + 1) *
stepSize[1]:stepSize[1], extent[0] *
stepSize[0]:(extent[1] + 1) *
stepSize[0]:stepSize[0], :]
else:
# grayscale volume
sliceArray = sliceArray[extent[2] *
stepSize[1]:(extent[3] + 1) *
stepSize[1]:stepSize[1], extent[0] *
stepSize[0]:(extent[1] + 1) *
stepSize[0]:stepSize[0]]
if (sliceIndex > 0) and (volumeArray[sliceIndex].shape !=
sliceArray.shape):
logging.debug(
"After downsampling, {} size is {} x {}\n\n{} size is {} x {} ({} scalar components)"
.format(paths[0], volumeArray[0].shape[0],
volumeArray[0].shape[1], path, sliceArray.shape[0],
sliceArray.shape[1]),
sliceArray.shape[2] if len(sliceArray.shape) == 3 else 1)
message = "There are multiple datasets in the folder. Please select a single file as a sample or specify a pattern.\nDetails:\n{0} size is {1} x {2} ({6} scalar components)\n\n{3} size is {4} x {5} ({7} scalar components)".format(
paths[0], firstArrayFullShape[0], firstArrayFullShape[1],
path, currentArrayFullShape[0], currentArrayFullShape[1],
firstArrayFullShape.shape[2]
if len(firstArrayFullShape.shape) == 3 else 1,
currentArrayFullShape.shape[2]
if len(currentArrayFullShape.shape) == 3 else 1)
raise ValueError(message)
volumeArray[sliceIndex] = sliceArray
sliceIndex += 1
if not outputNode:
if len(volumeArray.shape) == 3:
outputNode = slicer.vtkMRMLScalarVolumeNode()
else:
outputNode = slicer.vtkMRMLVectorVolumeNode()
if volumeArray.shape[3] == 3:
outputNode.SetVoxelVectorType(
outputNode.VoxelVectorTypeColorRGB)
elif volumeArray.shape[3] == 4:
outputNode.SetVoxelVectorType(
outputNode.VoxelVectorTypeColorRGBA)
slicer.mrmlScene.AddNode(outputNode)
path = paths[0]
fileName = os.path.basename(path)
name = os.path.splitext(fileName)[0]
outputNode.SetName(name)
else:
# Output volume already exists, check if it is the correct type
if len(volumeArray.shape) == 4:
if outputNode.IsA("vtkMRMLScalarVolumeNode"):
raise ValueError(
"Select a vector volume as output volume or force grayscale output."
)
else:
if outputNode.IsA("vtkMRMLVectorVolumeNode"):
raise ValueError(
"Select a scalar volume as output volume.")
ijkToRAS = slicer.util.vtkMatrixFromArray(ijkToRAS)
outputNode.SetIJKToRASMatrix(ijkToRAS)
slicer.util.updateVolumeFromArray(outputNode, volumeArray)
slicer.util.setSliceViewerLayers(background=outputNode, fit=True)
return outputNode
def onSaveButtonClicked(self):
import zipfile
import os.path
import uuid
bundle = EditUtil.EditUtil().getParameterNode().GetParameter('QuickTCGAEffect,erich')
tran = json.loads(bundle)
layers = []
for key in tran:
nn = tran[key]
nn["file"] = key + '.tif'
layers.append(tran[key])
self.j['layers'] = layers
self.j['username'] = self.setupUserName.text
self.j['sourcetile'] = self.tilename
self.j['generator'] = slicer.app.applicationVersion
self.j['timestamp'] = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
self.j['execution_id'] = self.setupExecutionID.text + "-"+ uuid.uuid4().get_urn()
labelNodes = slicer.util.getNodes('vtkMRMLLabelMapVolumeNode*')
savedMessage = 'Segmentations for the following series were saved:\n\n'
zfname = os.path.join(self.dataDirButton.directory, self.tilename + "_" + datetime.datetime.now().strftime("%Y%m%d%H%M%S") + '.zip')
print "zipfile name"
print zfname
zf = zipfile.ZipFile(zfname, mode='w')
red_logic = slicer.app.layoutManager().sliceWidget("Red").sliceLogic()
red_cn = red_logic.GetSliceCompositeNode()
fg = red_cn.GetForegroundVolumeID()
ff = slicer.util.getNode(fg)
sNode = slicer.vtkMRMLVolumeArchetypeStorageNode()
sNode.SetFileName("original.tif")
sNode.SetWriteFileFormat('tif')
sNode.SetURI(None)
success = sNode.WriteData(ff)
zf.write("original.tif")
os.remove("original.tif")
for label in labelNodes.values():
labelName = label.GetName()
labelFileName = os.path.join(self.dataDirButton.directory, labelName + '.tif')
compFileName = os.path.join(self.dataDirButton.directory, labelName + '-comp.tif')
sNode.SetFileName(labelFileName)
success = sNode.WriteData(label)
if success:
print "adding "+labelFileName+" to zipfile"
zf.write(labelFileName,os.path.basename(labelFileName))
os.remove(labelFileName)
else:
print "failed writing "+labelFileName
comp = self.WriteLonI(label.GetImageData(),ff.GetImageData())
volumeNode = slicer.vtkMRMLVectorVolumeNode()
volumeNode.SetName("COMP")
volumeNode.SetAndObserveImageData(comp)
sNode.SetFileName(compFileName)
success = sNode.WriteData(volumeNode)
if success:
print "adding "+compFileName+" to zipfile"
zf.write(compFileName,os.path.basename(compFileName))
os.remove(compFileName)
else:
print "failed writing "+compFileName
jstr = json.dumps(self.j,sort_keys=True, indent=4, separators=(',', ': '))
mfname = os.path.join(self.dataDirButton.directory, 'manifest.json')
f = open(mfname,'w')
f.write(jstr)
f.close()
zf.write(mfname,os.path.basename(mfname))
zf.close()
os.remove(mfname)
def setup(self):
ScriptedLoadableModuleWidget.setup(self)
self.logic = CNN_Image_ClassifierLogic()
self.moduleDir = os.path.dirname(
slicer.modules.collect_training_images.path)
# Instantiate and connect widgets ...
#
# Parameters Area
#
parametersCollapsibleButton = ctk.ctkCollapsibleButton()
parametersCollapsibleButton.text = "Parameters"
self.layout.addWidget(parametersCollapsibleButton)
# Layout within the dummy collapsible button
parametersFormLayout = qt.QFormLayout(parametersCollapsibleButton)
self.modelSelector = qt.QComboBox()
self.modelSelector.addItems(["Select model"])
modelDirectoryContents = os.listdir(
os.path.join(self.moduleDir, os.pardir, "Models/retrainContainer"))
modelNames = [
dir for dir in modelDirectoryContents
if dir.find(".") == -1 and dir != "Dockerfile"
]
self.modelSelector.addItems(["Create new model"])
self.modelSelector.addItems(modelNames)
parametersFormLayout.addRow(self.modelSelector)
#
# Apply Button
#
self.applyButton = qt.QPushButton("Start")
self.applyButton.toolTip = "Run the algorithm."
self.applyButton.enabled = False
parametersFormLayout.addRow(self.applyButton)
#
# Object table
#
self.objectTable = qt.QTableWidget()
self.objectTable.setColumnCount(3)
self.objectTable.setHorizontalHeaderLabels(
["Name", "Found", "Confidence"])
parametersFormLayout.addRow(self.objectTable)
#
# Adjust Confidence Thresholds
#
confidenceThresholdsCollapsibleButton = ctk.ctkCollapsibleButton()
confidenceThresholdsCollapsibleButton.text = "Confidence Thresholds"
self.layout.addWidget(confidenceThresholdsCollapsibleButton)
confidenceFormLayout = qt.QFormLayout(
confidenceThresholdsCollapsibleButton)
self.confidenceSlider = qt.QSlider(0x1) #horizontal slider
self.confidenceSlider.setRange(0, 100)
self.confidenceSlider.setTickInterval(5)
self.confidenceSlider.setTickPosition(2) #Ticks appear below slider
self.confidenceSlider.setSliderPosition(80)
self.confidenceSlider.setToolTip(
"Set the minimum degree of confidence that must be met for an object to be considered found"
)
confidenceFormLayout.addRow("Confidence: ", self.confidenceSlider)
self.confidenceLabel = qt.QLabel("80%")
confidenceFormLayout.addRow(self.confidenceLabel)
# connections
self.applyButton.connect('clicked(bool)', self.onApplyButton)
self.modelSelector.connect('currentIndexChanged(int)',
self.onModelSelected)
self.confidenceSlider.connect('sliderMoved(int)',
self.onConfidenceChanged)
# Add vertical spacer
self.layout.addStretch(1)
# Refresh Apply button state
self.onSelect()
self.webcamReference = slicer.util.getNode('Webcam_Reference')
if not self.webcamReference:
imageSpacing = [0.2, 0.2, 0.2]
imageData = vtk.vtkImageData()
imageData.SetDimensions(640, 480, 1)
imageData.AllocateScalars(vtk.VTK_UNSIGNED_CHAR, 1)
thresholder = vtk.vtkImageThreshold()
thresholder.SetInputData(imageData)
thresholder.SetInValue(0)
thresholder.SetOutValue(0)
# Create volume node
self.webcamReference = slicer.vtkMRMLVectorVolumeNode()
self.webcamReference.SetName('Webcam_Reference')
self.webcamReference.SetSpacing(imageSpacing)
self.webcamReference.SetImageDataConnection(
thresholder.GetOutputPort())
# Add volume to scene
slicer.mrmlScene.AddNode(self.webcamReference)
displayNode = slicer.vtkMRMLVectorVolumeDisplayNode()
slicer.mrmlScene.AddNode(displayNode)
self.webcamReference.SetAndObserveDisplayNodeID(
displayNode.GetID())
self.webcamConnectorNode = self.createWebcamPlusConnector()
self.webcamConnectorNode.Start()
self.setupWebcamResliceDriver()
