Packages

class ImageFeaturizer extends Transformer with HasInputCol with HasOutputCol with Wrappable with ComplexParamsWritable with SynapseMLLogging

The ImageFeaturizer relies on a ONNX model to do the featurization. One can set this model using the setOnnxModel parameter with a model you create yourself, or setModel to get a predefined named model from the ONNXHub.

The ImageFeaturizer takes an input column of images (the type returned by the ImageReader), and automatically resizes them to fit the ONNXModel's inputs. It then feeds them through a pre-trained ONNX model.

Linear Supertypes
SynapseMLLogging, ComplexParamsWritable, MLWritable, Wrappable, DotnetWrappable, RWrappable, PythonWrappable, BaseWrappable, HasOutputCol, HasInputCol, Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. ImageFeaturizer
  2. SynapseMLLogging
  3. ComplexParamsWritable
  4. MLWritable
  5. Wrappable
  6. DotnetWrappable
  7. RWrappable
  8. PythonWrappable
  9. BaseWrappable
  10. HasOutputCol
  11. HasInputCol
  12. Transformer
  13. PipelineStage
  14. Logging
  15. Params
  16. Serializable
  17. Serializable
  18. Identifiable
  19. AnyRef
  20. Any
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  1. Public
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Instance Constructors

  1. new ImageFeaturizer()
  2. new ImageFeaturizer(uid: String)

    uid

    the uid of the image transformer

Value Members

  1. val autoConvertToColor: BooleanParam
  2. val channelNormalizationMeans: DoubleArrayParam
  3. val channelNormalizationStds: DoubleArrayParam
  4. final def clear(param: Param[_]): ImageFeaturizer.this.type
    Definition Classes
    Params
  5. val colorScaleFactor: DoubleParam
  6. val convertFeaturesToVector: (Seq[Seq[Seq[Float]]]) ⇒ DenseVector
  7. val convertOutputToVector: (Seq[Float]) ⇒ DenseVector
  8. def copy(extra: ParamMap): Transformer
    Definition Classes
    ImageFeaturizer → Transformer → PipelineStage → Params
  9. def dotnetAdditionalMethods: String
    Definition Classes
    DotnetWrappable
  10. val dropNa: BooleanParam
  11. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  12. def explainParams(): String
    Definition Classes
    Params
  13. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  14. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  15. val featureTensorName: Param[String]

    Name of the output node which represents features

  16. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  17. def getAutoConvertToColor: Boolean
  18. def getChannelNormalizationMeans: Array[Double]

  19. def getChannelNormalizationStds: Array[Double]

  20. def getColorScaleFactor: Double

  21. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  22. def getDropNa: Boolean

  23. def getFeatureTensorName: String

  24. def getHeadless: Boolean

  25. def getIgnoreDecodingErrors: Boolean

  26. def getImageHeight: Int

  27. def getImageTensorName: String

  28. def getImageWidth: Int

  29. def getInputCol: String

    Definition Classes
    HasInputCol
  30. def getMiniBatchSize: Int

  31. def getModel: Array[Byte]

  32. def getOnnxModel: ONNXModel

  33. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  34. def getOutputCol: String

    Definition Classes
    HasOutputCol
  35. def getOutputTensorName: String

  36. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  37. def getParamInfo(p: Param[_]): ParamInfo[_]
    Definition Classes
    BaseWrappable
  38. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  39. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  40. val headless: BooleanParam
  41. val ignoreDecodingErrors: BooleanParam
  42. val imageHeight: IntParam
  43. val imageTensorName: Param[String]

    Name of the input node for images

  44. val imageWidth: IntParam
  45. val inputCol: Param[String]

    The name of the input column

    The name of the input column

    Definition Classes
    HasInputCol
  46. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  47. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  48. def logClass(): Unit
    Definition Classes
    SynapseMLLogging
  49. def logFit[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  50. def logTrain[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  51. def logTransform[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  52. def logVerb[T](verb: String, f: ⇒ T, columns: Int = -1): T
    Definition Classes
    SynapseMLLogging
  53. def makeDotnetFile(conf: CodegenConfig): Unit
    Definition Classes
    DotnetWrappable
  54. def makePyFile(conf: CodegenConfig): Unit
    Definition Classes
    PythonWrappable
  55. def makeRFile(conf: CodegenConfig): Unit
    Definition Classes
    RWrappable
  56. val onnxModel: TransformerParam
  57. val outputCol: Param[String]

    The name of the output column

    The name of the output column

    Definition Classes
    HasOutputCol
  58. val outputTensorName: Param[String]

    Name of the output node which represents probabilities

  59. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  60. def pyAdditionalMethods: String
    Definition Classes
    PythonWrappable
  61. def pyInitFunc(): String
    Definition Classes
    PythonWrappable
  62. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  63. final def set[T](param: Param[T], value: T): ImageFeaturizer.this.type
    Definition Classes
    Params
  64. def setAutoConvertToColor(value: Boolean): ImageFeaturizer.this.type
  65. def setChannelNormalizationMeans(value: Array[Double]): ImageFeaturizer.this.type

  66. def setChannelNormalizationStds(value: Array[Double]): ImageFeaturizer.this.type

  67. def setColorScaleFactor(value: Double): ImageFeaturizer.this.type

  68. def setDropNa(value: Boolean): ImageFeaturizer.this.type

  69. def setFeatureTensorName(value: String): ImageFeaturizer.this.type

  70. def setHeadless(value: Boolean): ImageFeaturizer.this.type

  71. def setIgnoreDecodingErrors(value: Boolean): ImageFeaturizer.this.type

  72. def setImageHeight(value: Int): ImageFeaturizer.this.type

  73. def setImageTensorName(value: String): ImageFeaturizer.this.type

  74. def setImageWidth(value: Int): ImageFeaturizer.this.type

  75. def setInputCol(value: String): ImageFeaturizer.this.type

    Definition Classes
    HasInputCol
  76. def setMiniBatchSize(value: Int): ImageFeaturizer.this.type

  77. def setModel(bytes: Array[Byte]): ImageFeaturizer.this.type

  78. def setModel(name: String): ImageFeaturizer.this.type
  79. def setModelInfo(info: ONNXModelInfo): ImageFeaturizer.this.type
  80. def setModelLocation(path: String): ImageFeaturizer.this.type
  81. def setOnnxModel(value: ONNXModel): ImageFeaturizer.this.type

  82. def setOutputCol(value: String): ImageFeaturizer.this.type

    Definition Classes
    HasOutputCol
  83. def setOutputTensorName(value: String): ImageFeaturizer.this.type

  84. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  85. def transform(dataset: Dataset[_]): DataFrame
    Definition Classes
    ImageFeaturizer → Transformer
  86. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  87. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  88. def transformSchema(schema: StructType): StructType

    Add the features column to the schema

    Add the features column to the schema

    schema

    Schema to transform

    returns

    schema with features column

    Definition Classes
    ImageFeaturizer → PipelineStage
  89. val uid: String
    Definition Classes
    ImageFeaturizer → SynapseMLLogging → Identifiable
  90. def write: MLWriter
    Definition Classes
    ComplexParamsWritable → MLWritable