View Source Evision.DNN.KeypointsModel (Evision v0.1.7)
Link to this section Summary
Functions
Raising version of dnn_KeypointsModel/1
.
Raising version of dnn_KeypointsModel/2
.
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
.
Raising version of estimate/3
.
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_KeypointsModel/1
.
Raising version of dnn_KeypointsModel/2
.
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>
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
.
Raising version of estimate/3
.
Given the @p input frame, create input blob, run net
Positional Arguments
- frame:
Evision.Mat
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
Given the @p input frame, create input blob, run net
Positional Arguments
- frame:
Evision.Mat
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