sparkocr.transformers.image_adaptive_thresholding#

Classes#

class ImageAdaptiveThresholding(inputCol=None, outputCol=None, blockSize=None, method=None, offset=None, mode=None, cval=None)#

Bases: sparkocr.transformers.image_base_transformer.ImageBaseTransformer

Compute a threshold mask image based on local pixel neighborhood and apply it to image.

Also known as adaptive or dynamic thresholding. The threshold value is the weighted mean for the local neighborhood of a pixel subtracted by a constant.

blockSize#

cval#

inputCol :Param[str]#

keepInput#

method#

mode#

offset#

outputCol :Param[str]#

uid = ''#

clear(param: Param) → None#

Clears a param from the param map if it has been explicitly set.

copy(extra: pyspark.ml._typing.ParamMap | None = None) → P#

Creates a copy of this instance with the same uid and some extra params. The default implementation creates a shallow copy using copy.copy(), and then copies the embedded and extra parameters over and returns the copy. Subclasses should override this method if the default approach is not sufficient.

Parameters:

extra (dict, optional) – Extra parameters to copy to the new instance

Returns:

Copy of this instance

Return type:

Params

explainParam(param: str | Param) → str#

Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams() → str#

Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra: pyspark.ml._typing.ParamMap | None = None) → pyspark.ml._typing.ParamMap#

Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.

Parameters:

extra (dict, optional) – extra param values

Returns:

merged param map

Return type:

dict

getBlockSize()#

getCval()#

getInputCol() → str#

Gets the value of inputCol or its default value.

getMethod()#

getMode()#

getOffset()#

getOrDefault(param: str) → Any#

getOrDefault(param: Param[T]) → T

Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.

getOutputCol() → str#

Gets the value of outputCol or its default value.

getParam(paramName: str) → Param#

Gets a param by its name.

hasDefault(param: str | Param[Any]) → bool#

Checks whether a param has a default value.

hasParam(paramName: str) → bool#

Tests whether this instance contains a param with a given (string) name.

isDefined(param: str | Param[Any]) → bool#

Checks whether a param is explicitly set by user or has a default value.

isSet(param: str | Param[Any]) → bool#

Checks whether a param is explicitly set by user.

classmethod load(path: str) → RL#

Reads an ML instance from the input path, a shortcut of read().load(path).

classmethod read() → DefaultParamsReader[RL]#

Returns a DefaultParamsReader instance for this class.

save(path: str) → None#

Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param: Param, value: Any) → None#

Sets a parameter in the embedded param map.

setBlockSize(value)#

setCval(value)#

setInputCol(value)#

Sets the value of inputCol.

setKeepInput(value)#

Sets the value of keepInput.

setMethod(value)#

setMode(value)#

setOffset(value)#

setOutputCol(value)#

Sets the value of outputCol.

setParams(inputCol=None, outputCol=None, blockSize=None, method=None, offset=None, mode=None, cval=None)#

transform(dataset: pyspark.sql.dataframe.DataFrame, params: pyspark.ml._typing.ParamMap | None = None) → pyspark.sql.dataframe.DataFrame#

Transforms the input dataset with optional parameters.

New in version 1.3.0.

Parameters:

• dataset (pyspark.sql.DataFrame) – input dataset

• params (dict, optional) – an optional param map that overrides embedded params.

Returns:

transformed dataset

Return type:

pyspark.sql.DataFrame

transform_image(image)#

write() → MLWriter#

Returns a DefaultParamsWriter instance for this class.