def __init__(self, name, param):
core.CWorkflowTask.__init__(self, name)
# Add input/output of the process here
self.addOutput(datasetio.IkDatasetIO("yolo"))
self.addOutput(dataprocess.CNumericIO())
# Create parameters class
if param is None:
self.setParam(DatasetYoloParam())
else:
self.setParam(copy.deepcopy(param))
def __init__(self, name, param):
core.CWorkflowTask.__init__(self, name)
# Add input/output of the process here
# Example : self.addInput(PyDataProcess.CImageProcessIO())
# self.addOutput(PyDataProcess.CImageProcessIO())
self.addOutput(datasetio.IkDatasetIO("other"))
self.addOutput(dataprocess.CNumericIO())
# Create parameters class
if param is None:
self.setParam(DatasetCwfidParam())
else:
self.setParam(copy.deepcopy(param))
def __init__(self, name, param):
dataprocess.C2dImageTask.__init__(self, name)
self.model = None
self.names = None
self.colors = None
self.update = False
# Detect if we have a GPU available
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Add graphics output
self.addOutput(dataprocess.CGraphicsOutput())
# Add numeric output
self.addOutput(dataprocess.CNumericIO())
# Create parameters class
if param is None:
self.setParam(InferYoloV5Param())
else:
self.setParam(copy.deepcopy(param))
def __init__(self, name, param):
dataprocess.C2dImageTask.__init__(self, name)
self.model = None
self.class_names = []
# Detect if we have a GPU available
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Remove graphics input
self.removeInput(1)
# Add graphics output
self.addOutput(dataprocess.CGraphicsOutput())
# Add numeric output
self.addOutput(dataprocess.CNumericIO())
# Create parameters class
if param is None:
self.setParam(MnasnetParam())
else:
self.setParam(copy.deepcopy(param))
def __init__(self, name, param):
dataprocess.C2dImageTask.__init__(self, name)
# Add graphics output
self.addOutput(dataprocess.CGraphicsOutput())
# Add numeric output
self.addOutput(dataprocess.CNumericIO())
# Network members
self.net = None
self.class_names = []
# Create parameters class
if param is None:
self.setParam(EmotionFerPlusParam())
else:
self.setParam(copy.deepcopy(param))
# Load class names
model_folder = os.path.dirname(os.path.realpath(__file__)) + "/models"
with open(model_folder + "/class_names") as f:
for row in f:
self.class_names.append(row[:-1])
def __init__(self, name, param):
dataprocess.C2dImageTask.__init__(self, name)
self.model = None
self.class_names = []
# Detect if we have a GPU available
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Remove graphics input
self.removeInput(1)
# Segmentation mask output
self.setOutputDataType(core.IODataType.IMAGE_LABEL, 0)
# Result image
self.addOutput(dataprocess.CImageIO(core.IODataType.IMAGE))
# Add graphics output
self.addOutput(dataprocess.CGraphicsOutput())
# Add numeric output
self.addOutput(dataprocess.CNumericIO())
# Create parameters class
if param is None:
self.setParam(MaskRcnnParam())
else:
self.setParam(copy.deepcopy(param))
def __init__(self, name, param):
dataprocess.C2dImageTask.__init__(self, name)
# Add graphics output
self.addOutput(dataprocess.CGraphicsOutput())
# Add numeric output
self.addOutput(dataprocess.CNumericIO())
# Create parameters class
if param is None:
self.setParam(CovidNetParam())
else:
self.setParam(copy.deepcopy(param))
param = self.getParam()
self.covid_model = CovidNet(model_path=param.model_path)
# Load class names
self.class_names = []
class_names_path = os.path.dirname(os.path.realpath(__file__)) + "/models/class_names"
with open(class_names_path) as f:
for row in f:
self.class_names.append(row[:-1])
