class ImageAssembler extends Transformer with DefaultParamsWritable with HasOutputAnnotatorType with HasOutputAnnotationCol
Prepares images read by Spark into a format that is processable by Spark NLP. This component is needed to process images.
Example
import com.johnsnowlabs.nlp.ImageAssembler import org.apache.spark.ml.Pipeline val imageDF: DataFrame = spark.read .format("image") .option("dropInvalid", value = true) .load("src/test/resources/image/") val imageAssembler = new ImageAssembler() .setInputCol("image") .setOutputCol("image_assembler") val pipeline = new Pipeline().setStages(Array(imageAssembler)) val pipelineDF = pipeline.fit(imageDF).transform(imageDF) pipelineDF.printSchema() root |-- image_assembler: array (nullable = true) | |-- element: struct (containsNull = true) | | |-- annotatorType: string (nullable = true) | | |-- origin: string (nullable = true) | | |-- height: integer (nullable = false) | | |-- width: integer (nullable = false) | | |-- nChannels: integer (nullable = false) | | |-- mode: integer (nullable = false) | | |-- result: binary (nullable = true) | | |-- metadata: map (nullable = true) | | | |-- key: string | | | |-- value: string (valueContainsNull = true)
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- ImageAssembler
- HasOutputAnnotationCol
- HasOutputAnnotatorType
- DefaultParamsWritable
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Instance Constructors
Type Members
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type
AnnotatorType = String
- Definition Classes
- HasOutputAnnotatorType
Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
$[T](param: Param[T]): T
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final
def
==(arg0: Any): Boolean
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final
def
asInstanceOf[T0]: T0
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final
def
clear(param: Param[_]): ImageAssembler.this.type
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def
clone(): AnyRef
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- protected[lang]
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- @throws( ... ) @native()
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def
copy(extra: ParamMap): Transformer
- Definition Classes
- ImageAssembler → Transformer → PipelineStage → Params
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def
copyValues[T <: Params](to: T, extra: ParamMap): T
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final
def
defaultCopy[T <: Params](extra: ParamMap): T
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
explainParam(param: Param[_]): String
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def
explainParams(): String
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final
def
extractParamMap(): ParamMap
- Definition Classes
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final
def
extractParamMap(extra: ParamMap): ParamMap
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def
finalize(): Unit
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final
def
get[T](param: Param[T]): Option[T]
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final
def
getClass(): Class[_]
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final
def
getDefault[T](param: Param[T]): Option[T]
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def
getInputCol: String
Input text column for processing
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final
def
getOrDefault[T](param: Param[T]): T
- Definition Classes
- Params
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final
def
getOutputCol: String
Gets annotation column name going to generate
Gets annotation column name going to generate
- Definition Classes
- HasOutputAnnotationCol
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def
getParam(paramName: String): Param[Any]
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- Params
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final
def
hasDefault[T](param: Param[T]): Boolean
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def
hasParam(paramName: String): Boolean
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def
hashCode(): Int
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def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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def
initializeLogIfNecessary(isInterpreter: Boolean): Unit
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val
inputCol: Param[String]
Input text column for processing
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final
def
isDefined(param: Param[_]): Boolean
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final
def
isInstanceOf[T0]: Boolean
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final
def
isSet(param: Param[_]): Boolean
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def
isTraceEnabled(): Boolean
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def
log: Logger
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def
logDebug(msg: ⇒ String, throwable: Throwable): Unit
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def
logDebug(msg: ⇒ String): Unit
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def
logError(msg: ⇒ String, throwable: Throwable): Unit
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def
logError(msg: ⇒ String): Unit
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def
logInfo(msg: ⇒ String, throwable: Throwable): Unit
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def
logInfo(msg: ⇒ String): Unit
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def
logName: String
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def
logTrace(msg: ⇒ String, throwable: Throwable): Unit
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def
logTrace(msg: ⇒ String): Unit
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def
logWarning(msg: ⇒ String, throwable: Throwable): Unit
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def
logWarning(msg: ⇒ String): Unit
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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val
outputAnnotatorType: AnnotatorType
Output Annotator Type: DOCUMENT
Output Annotator Type: DOCUMENT
- Definition Classes
- ImageAssembler → HasOutputAnnotatorType
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final
val
outputCol: Param[String]
- Attributes
- protected
- Definition Classes
- HasOutputAnnotationCol
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lazy val
params: Array[Param[_]]
- Definition Classes
- Params
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def
save(path: String): Unit
- Definition Classes
- MLWritable
- Annotations
- @Since( "1.6.0" ) @throws( ... )
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final
def
set(paramPair: ParamPair[_]): ImageAssembler.this.type
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- protected
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final
def
set(param: String, value: Any): ImageAssembler.this.type
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final
def
set[T](param: Param[T], value: T): ImageAssembler.this.type
- Definition Classes
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final
def
setDefault(paramPairs: ParamPair[_]*): ImageAssembler.this.type
- Attributes
- protected
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final
def
setDefault[T](param: Param[T], value: T): ImageAssembler.this.type
- Attributes
- protected[org.apache.spark.ml]
- Definition Classes
- Params
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def
setInputCol(value: String): ImageAssembler.this.type
Input text column for processing
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final
def
setOutputCol(value: String): ImageAssembler.this.type
Overrides annotation column name when transforming
Overrides annotation column name when transforming
- Definition Classes
- HasOutputAnnotationCol
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final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
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def
toString(): String
- Definition Classes
- Identifiable → AnyRef → Any
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def
transform(dataset: Dataset[_]): DataFrame
- Definition Classes
- ImageAssembler → Transformer
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def
transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" )
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def
transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" ) @varargs()
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final
def
transformSchema(schema: StructType): StructType
requirement for pipeline transformation validation.
requirement for pipeline transformation validation. It is called on fit()
- Definition Classes
- ImageAssembler → PipelineStage
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def
transformSchema(schema: StructType, logging: Boolean): StructType
- Attributes
- protected
- Definition Classes
- PipelineStage
- Annotations
- @DeveloperApi()
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val
uid: String
- Definition Classes
- ImageAssembler → Identifiable
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final
def
wait(): Unit
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- @throws( ... )
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
- Definition Classes
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- @throws( ... ) @native()
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def
write: MLWriter
- Definition Classes
- DefaultParamsWritable → MLWritable
Inherited from HasOutputAnnotationCol
Inherited from HasOutputAnnotatorType
Inherited from DefaultParamsWritable
Inherited from MLWritable
Inherited from Transformer
Inherited from PipelineStage
Inherited from Logging
Inherited from Params
Inherited from Serializable
Inherited from Serializable
Inherited from Identifiable
Inherited from AnyRef
Inherited from Any
Parameters
A list of (hyper-)parameter keys this annotator can take. Users can set and get the parameter values through setters and getters, respectively.
Annotator types
Required input and expected output annotator types