View Source Evision.DNN.Model (Evision v0.1.8)
Link to this section Summary
Functions
Raising version of dnn_Model/1
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Raising version of dnn_Model/2
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Variant 1:
Create model from deep learning network.
Create model from deep learning network represented in one of the supported formats. An order of @p model and @p config arguments does not matter.
Raising version of predict/2
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Raising version of predict/3
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Given the @p input frame, create input blob, run net and return the output @p blobs.
Given the @p input frame, create input blob, run net and return the output @p blobs.
Raising version of setInputCrop/2
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Set flag crop for frame.
Raising version of setInputMean/2
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Set mean value for frame.
Raising version of setInputParams/1
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Set preprocessing parameters for frame.
Raising version of setInputScale/2
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Set scalefactor value for frame.
Raising version of setInputSize/2
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Raising version of setInputSize/3
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Set input size for frame.
Positional Arguments
width:
int
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Raising version of setInputSwapRB/2
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Set flag swapRB for frame.
Raising version of setPreferableBackend/2
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Positional Arguments
- backendId:
dnn_Backend
Python prototype (for reference):
Raising version of setPreferableTarget/2
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Positional Arguments
- targetId:
dnn_Target
Python prototype (for reference):
Link to this section Functions
Raising version of dnn_Model/1
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Raising version of dnn_Model/2
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Variant 1:
Create model from deep learning network.
Positional Arguments
network:
Net
.Net object.
Python prototype (for reference):
Model(network) -> <dnn_Model object>
Variant 2:
Create model from deep learning 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):
Model(model[, config]) -> <dnn_Model object>
Create model from deep learning 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):
Model(model[, config]) -> <dnn_Model object>
Raising version of predict/2
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Raising version of predict/3
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Given the @p input frame, create input blob, run net and return the output @p blobs.
Positional Arguments
- frame:
Evision.Mat
Return
outs:
[Evision.Mat]
.Allocated output blobs, which will store results of the computation.
Python prototype (for reference):
predict(frame[, outs]) -> outs
Given the @p input frame, create input blob, run net and return the output @p blobs.
Positional Arguments
- frame:
Evision.Mat
Return
outs:
[Evision.Mat]
.Allocated output blobs, which will store results of the computation.
Python prototype (for reference):
predict(frame[, outs]) -> outs
Raising version of setInputCrop/2
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Set flag crop for frame.
Positional Arguments
crop:
bool
.Flag which indicates whether image will be cropped after resize or not.
Python prototype (for reference):
setInputCrop(crop) -> retval
Raising version of setInputMean/2
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Set mean value for frame.
Positional Arguments
mean:
Scalar
.Scalar with mean values which are subtracted from channels.
Python prototype (for reference):
setInputMean(mean) -> retval
Raising version of setInputParams/1
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Set preprocessing parameters for frame.
Keyword Arguments
scale:
double
.Multiplier for frame values.
size:
Size
.New input size.
mean:
Scalar
.Scalar with mean values which are subtracted from channels.
swapRB:
bool
.Flag which indicates that swap first and last channels.
crop:
bool
.Flag which indicates whether image will be cropped after resize or not. blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) )
Python prototype (for reference):
setInputParams([, scale[, size[, mean[, swapRB[, crop]]]]]) -> None
Raising version of setInputScale/2
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Set scalefactor value for frame.
Positional Arguments
scale:
double
.Multiplier for frame values.
Python prototype (for reference):
setInputScale(scale) -> retval
Raising version of setInputSize/2
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Raising version of setInputSize/3
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Set input size for frame.
Positional Arguments
size:
Size
.New input size.
Note: If shape of the new blob less than 0, then frame size not change.
Python prototype (for reference):
setInputSize(size) -> retval
Positional Arguments
width:
int
.New input width.
height:
int
.New input height.
Has overloading in C++
Python prototype (for reference):
setInputSize(width, height) -> retval
Raising version of setInputSwapRB/2
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Set flag swapRB for frame.
Positional Arguments
swapRB:
bool
.Flag which indicates that swap first and last channels.
Python prototype (for reference):
setInputSwapRB(swapRB) -> retval
Raising version of setPreferableBackend/2
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Positional Arguments
- backendId:
dnn_Backend
Python prototype (for reference):
setPreferableBackend(backendId) -> retval
Raising version of setPreferableTarget/2
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Positional Arguments
- targetId:
dnn_Target
Python prototype (for reference):
setPreferableTarget(targetId) -> retval