View Source Evision.DNN.SegmentationModel (Evision v0.1.9)

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Functions

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.

Given the @p input frame, create input blob, run net

Given the @p input frame, create input blob, run net

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dnn_SegmentationModel!(network)

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Raising version of dnn_SegmentationModel/1.

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dnn_SegmentationModel!(model, opts)

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Raising version of dnn_SegmentationModel/2.

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dnn_SegmentationModel(network)

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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>
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dnn_SegmentationModel(model, opts)

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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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segment!(self, frame, opts)

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Raising version of segment/3.

Given the @p input frame, create input blob, run net

Positional Arguments
Return
  • mask: Evision.Mat.

    Allocated class prediction for each pixel

Python prototype (for reference):

segment(frame[, mask]) -> mask
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segment(self, frame, opts)

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Given the @p input frame, create input blob, run net

Positional Arguments
Return
  • mask: Evision.Mat.

    Allocated class prediction for each pixel

Python prototype (for reference):

segment(frame[, mask]) -> mask