def __init__(self, name, param):
dataprocess.C2dImageTask.__init__(self, name)
# Create parameters class
if param is None:
self.setParam(PointRendParam())
else:
self.setParam(copy.deepcopy(param))
self.threshold = 0.5
self.MODEL_NAME_CONFIG = "pointrend_rcnn_R_50_FPN_3x_coco"
self.MODEL_NAME = "model_final_3c3198"
self.path_to_config = "/PointRend_git/configs/InstanceSegmentation/" + self.MODEL_NAME_CONFIG + ".yaml"
self.path_to_model = "/models/" + self.MODEL_NAME + ".pkl"
self.folder = os.path.dirname(os.path.realpath(__file__))
self.cfg = get_cfg()
add_pointrend_config(self.cfg)
self.cfg.merge_from_file(self.folder + self.path_to_config)
self.cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = self.threshold
self.cfg.MODEL.WEIGHTS = self.folder + self.path_to_model
self.loaded = False
self.deviceFrom = ""
# add output + set data type
self.setOutputDataType(core.IODataType.IMAGE_LABEL, 0)
self.addOutput(dataprocess.CImageIO(core.IODataType.IMAGE))
self.addOutput(dataprocess.CGraphicsOutput())
def __init__(self, name, param):
dataprocess.C2dImageTask.__init__(self, name)
# Create parameters class
if param is None:
self.setParam(MorphoSnakesParam())
else:
self.setParam(copy.deepcopy(param))
# add input -> initial level set
self.addInput(dataprocess.CImageIO())
# add output -> results image
self.addOutput(dataprocess.CImageIO())
# set color mask
self.setOutputColorMap(1, 0, [[255, 0, 0]])
def __init__(self, name, param):
dataprocess.C2dImageTask.__init__(self, name)
# add output + set data type
self.setOutputDataType(core.IODataType.IMAGE_LABEL, 0)
self.addOutput(dataprocess.CImageIO(core.IODataType.IMAGE))
self.addOutput(dataprocess.CImageIO(core.IODataType.IMAGE))
self.model = None
self.cfg = None
self.colors = None
self.dataset = ""
self.update = False
self.classes = None
# Create parameters class
if param is None:
self.setParam(Deeplabv3plusParam())
else:
self.setParam(copy.deepcopy(param))
def __init__(self, name, param):
dataprocess.C2dImageTask.__init__(self, name)
self.net = None
# Add input_img/output of the process here
self.addOutput(dataprocess.CImageIO())
# Create parameters class
if param is None:
self.setParam(NeuralStyleTransferParam())
else:
self.setParam(copy.deepcopy(param))
def __init__(self, name, param):
dataprocess.CVideoTask.__init__(self, name)
# Add input/output of the process here
# Set this variable to True if you want to work with the raw Optical Flow (vector field)
self.rawOutput = False
if self.rawOutput:
self.addOutput(dataprocess.CImageIO())
self.frame_1 = None
# Create parameters class
if param is None:
self.setParam(RaftOpticalFlowParam())
else:
self.setParam(copy.deepcopy(param))
def __init__(self, name, param):
dataprocess.C2dImageTask.__init__(self, name)
if param is None:
self.setParam(MaskRcnnParam())
else:
self.setParam(copy.deepcopy(param))
# get and set config model
self.threshold = 0.5
self.cfg = None
self.model_link = "COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml"
self.predictor = None
# add output + set data type
self.setOutputDataType(core.IODataType.IMAGE_LABEL, 0)
self.addOutput(dataprocess.CImageIO(core.IODataType.IMAGE))
self.addOutput(dataprocess.CGraphicsOutput())
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 input/output of the process here
self.setOutputDataType(core.IODataType.IMAGE_LABEL, 0)
self.addOutput(dataprocess.CImageIO(core.IODataType.IMAGE))
# Add graphics output
self.addOutput(dataprocess.CGraphicsOutput())
self.net = None
self.class_names = []
# Create parameters class
if param is None:
self.setParam(InferYolactParam())
else:
self.setParam(copy.deepcopy(param))
# Load class names
model_folder = os.path.dirname(os.path.realpath(__file__)) + "/models"
with open(model_folder + "/Coco_names.txt") as f:
for row in f:
self.class_names.append(row[:-1])
def __init__(self, name, param):
dataprocess.C2dImageTask.__init__(self, name)
# Create parameters class
if param is None:
self.setParam(PanopticFpnParam())
else:
self.setParam(copy.deepcopy(param))
# get and set config model
self.LINK_MODEL = "COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml"
self.cfg = get_cfg()
self.threshold = 0.5
self.cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = self.threshold
self.cfg.merge_from_file(model_zoo.get_config_file(
self.LINK_MODEL)) # load config from file(.yaml)
self.cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(
self.LINK_MODEL) # download the model (.pkl)
self.loaded = False
self.deviceFrom = ""
# add outputs
self.addOutput(dataprocess.CImageIO())
self.addOutput(dataprocess.CGraphicsOutput())