View Source Evision.DNN.KeypointsModel (Evision v0.1.7)

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Functions

Variant 1:

Create model from deep learning network.

Create keypoints 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 estimate/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_KeypointsModel!(network)

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Raising version of dnn_KeypointsModel/1.

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dnn_KeypointsModel!(model, opts)

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Raising version of dnn_KeypointsModel/2.

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dnn_KeypointsModel(network)

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Variant 1:

Create model from deep learning network.

Positional Arguments
  • network: Net.

    Net object.

Python prototype (for reference):

KeypointsModel(network) -> <dnn_KeypointsModel object>

Variant 2:

Create keypoints 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):

KeypointsModel(model[, config]) -> <dnn_KeypointsModel object>
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dnn_KeypointsModel(model, opts)

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Create keypoints 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):

KeypointsModel(model[, config]) -> <dnn_KeypointsModel object>

Raising version of estimate/2.

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estimate!(self, frame, opts)

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Raising version of estimate/3.

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

Positional Arguments
Keyword Arguments
  • thresh: float.

    minimum confidence threshold to select a keypoint

@returns a vector holding the x and y coordinates of each detected keypoint

Python prototype (for reference):

estimate(frame[, thresh]) -> retval
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estimate(self, frame, opts)

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

Positional Arguments
Keyword Arguments
  • thresh: float.

    minimum confidence threshold to select a keypoint

@returns a vector holding the x and y coordinates of each detected keypoint

Python prototype (for reference):

estimate(frame[, thresh]) -> retval