View Source Evision.DNN.DetectionModel (Evision v0.1.9)
Link to this section Summary
Functions
Raising version of detect/2
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Raising version of detect/3
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Given the @p input frame, create input blob, run net and return result detections.
Given the @p input frame, create input blob, run net and return result detections.
Raising version of dnn_DetectionModel/1
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Raising version of dnn_DetectionModel/2
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Variant 1:
Create model from deep learning network.
Create detection 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 getNmsAcrossClasses/1
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Getter for nmsAcrossClasses. This variable defaults to false, such that when non max suppression is used during the detect() function, it will do so only per-class
Raising version of setNmsAcrossClasses/2
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nmsAcrossClasses defaults to false, such that when non max suppression is used during the detect() function, it will do so per-class. This function allows you to toggle this behaviour.
Link to this section Functions
Raising version of detect/2
.
Raising version of detect/3
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Given the @p input frame, create input blob, run net and return result detections.
Positional Arguments
- frame:
Evision.Mat
Keyword Arguments
confThreshold:
float
.A threshold used to filter boxes by confidences.
nmsThreshold:
float
.A threshold used in non maximum suppression.
Return
classIds:
[int]
.Class indexes in result detection.
confidences:
[float]
.A set of corresponding confidences.
boxes:
[Rect]
.A set of bounding boxes.
Python prototype (for reference):
detect(frame[, confThreshold[, nmsThreshold]]) -> classIds, confidences, boxes
Given the @p input frame, create input blob, run net and return result detections.
Positional Arguments
- frame:
Evision.Mat
Keyword Arguments
confThreshold:
float
.A threshold used to filter boxes by confidences.
nmsThreshold:
float
.A threshold used in non maximum suppression.
Return
classIds:
[int]
.Class indexes in result detection.
confidences:
[float]
.A set of corresponding confidences.
boxes:
[Rect]
.A set of bounding boxes.
Python prototype (for reference):
detect(frame[, confThreshold[, nmsThreshold]]) -> classIds, confidences, boxes
Raising version of dnn_DetectionModel/1
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Raising version of dnn_DetectionModel/2
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Variant 1:
Create model from deep learning network.
Positional Arguments
network:
Net
.Net object.
Python prototype (for reference):
DetectionModel(network) -> <dnn_DetectionModel object>
Variant 2:
Create detection 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):
DetectionModel(model[, config]) -> <dnn_DetectionModel object>
Create detection 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):
DetectionModel(model[, config]) -> <dnn_DetectionModel object>
Raising version of getNmsAcrossClasses/1
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Getter for nmsAcrossClasses. This variable defaults to false, such that when non max suppression is used during the detect() function, it will do so only per-class
Python prototype (for reference):
getNmsAcrossClasses() -> retval
Raising version of setNmsAcrossClasses/2
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nmsAcrossClasses defaults to false, such that when non max suppression is used during the detect() function, it will do so per-class. This function allows you to toggle this behaviour.
Positional Arguments
value:
bool
.The new value for nmsAcrossClasses
Python prototype (for reference):
setNmsAcrossClasses(value) -> retval