View Source Evision.DNN.TextDetectionModelDB (Evision v0.1.12)
Link to this section Summary
Types
Type that represents an Evision.DNN.TextDetectionModelDB
struct.
Functions
Return
- retval:
float
Python prototype (for reference):
Return
- retval:
int
Python prototype (for reference):
Return
- retval:
float
Python prototype (for reference):
Return
- retval:
double
Python prototype (for reference):
Positional Arguments
- binaryThreshold:
float
Return
- retval:
Evision.DNN.TextDetectionModelDB
Python prototype (for reference):
Positional Arguments
- maxCandidates:
int
Return
- retval:
Evision.DNN.TextDetectionModelDB
Python prototype (for reference):
Positional Arguments
- polygonThreshold:
float
Return
- retval:
Evision.DNN.TextDetectionModelDB
Python prototype (for reference):
Positional Arguments
- unclipRatio:
double
Return
- retval:
Evision.DNN.TextDetectionModelDB
Python prototype (for reference):
Variant 1:
Create text detection model from network represented in one of the supported formats. An order of @p model and @p config arguments does not matter.
Create text detection model from network represented in one of the supported formats. An order of @p model and @p config arguments does not matter.
Link to this section Types
@type t() :: %Evision.DNN.TextDetectionModelDB{ref: reference()}
Type that represents an Evision.DNN.TextDetectionModelDB
struct.
ref.
reference()
The underlying erlang resource variable.
Link to this section Functions
Return
- retval:
float
Python prototype (for reference):
getBinaryThreshold() -> retval
Return
- retval:
int
Python prototype (for reference):
getMaxCandidates() -> retval
Return
- retval:
float
Python prototype (for reference):
getPolygonThreshold() -> retval
Return
- retval:
double
Python prototype (for reference):
getUnclipRatio() -> retval
Positional Arguments
- binaryThreshold:
float
Return
- retval:
Evision.DNN.TextDetectionModelDB
Python prototype (for reference):
setBinaryThreshold(binaryThreshold) -> retval
Positional Arguments
- maxCandidates:
int
Return
- retval:
Evision.DNN.TextDetectionModelDB
Python prototype (for reference):
setMaxCandidates(maxCandidates) -> retval
Positional Arguments
- polygonThreshold:
float
Return
- retval:
Evision.DNN.TextDetectionModelDB
Python prototype (for reference):
setPolygonThreshold(polygonThreshold) -> retval
Positional Arguments
- unclipRatio:
double
Return
- retval:
Evision.DNN.TextDetectionModelDB
Python prototype (for reference):
setUnclipRatio(unclipRatio) -> retval
@spec textDetectionModelDB(binary()) :: t() | {:error, String.t()}
@spec textDetectionModelDB(Evision.DNN.Net.t()) :: t() | {:error, String.t()}
Variant 1:
Create text 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.
Return
Python prototype (for reference):
TextDetectionModel_DB(model[, config]) -> <dnn_TextDetectionModel_DB object>
Variant 2:
Create text detection algorithm from deep learning network.
Positional Arguments
network:
Evision.DNN.Net
.Net object.
Return
Python prototype (for reference):
TextDetectionModel_DB(network) -> <dnn_TextDetectionModel_DB object>
Create text 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.
Return
Python prototype (for reference):
TextDetectionModel_DB(model[, config]) -> <dnn_TextDetectionModel_DB object>