View Source Evision.DNN.SegmentationModel (Evision v0.1.8)
Link to this section Summary
Functions
Raising version of dnn_SegmentationModel/1
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Raising version of dnn_SegmentationModel/2
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Variant 1:
Create model from deep learning network.
Create segmentation model from network represented in one of the supported formats. An order of @p model and @p config arguments does not matter.
Raising version of segment/2
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Raising version of segment/3
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Given the @p input frame, create input blob, run net
Given the @p input frame, create input blob, run net
Link to this section Functions
Raising version of dnn_SegmentationModel/1
.
Raising version of dnn_SegmentationModel/2
.
Variant 1:
Create model from deep learning network.
Positional Arguments
network:
Net
.Net object.
Python prototype (for reference):
SegmentationModel(network) -> <dnn_SegmentationModel object>
Variant 2:
Create segmentation model from network represented in one of the supported formats. An order of @p model and @p config arguments does not matter.
Positional Arguments
model:
String
.Binary file contains trained weights.
Keyword Arguments
config:
String
.Text file contains network configuration.
Python prototype (for reference):
SegmentationModel(model[, config]) -> <dnn_SegmentationModel object>
Create segmentation model from network represented in one of the supported formats. An order of @p model and @p config arguments does not matter.
Positional Arguments
model:
String
.Binary file contains trained weights.
Keyword Arguments
config:
String
.Text file contains network configuration.
Python prototype (for reference):
SegmentationModel(model[, config]) -> <dnn_SegmentationModel object>
Raising version of segment/2
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Raising version of segment/3
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Given the @p input frame, create input blob, run net
Positional Arguments
- frame:
Evision.Mat
Return
mask:
Evision.Mat
.Allocated class prediction for each pixel
Python prototype (for reference):
segment(frame[, mask]) -> mask
Given the @p input frame, create input blob, run net
Positional Arguments
- frame:
Evision.Mat
Return
mask:
Evision.Mat
.Allocated class prediction for each pixel
Python prototype (for reference):
segment(frame[, mask]) -> mask