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, 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. RWrappable
  7. PythonWrappable
  8. BaseWrappable
  9. HasOutputCol
  10. HasInputCol
  11. Transformer
  12. PipelineStage
  13. Logging
  14. Params
  15. Serializable
  16. Serializable
  17. Identifiable
  18. AnyRef
  19. Any
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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. val dropNa: BooleanParam
  10. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  11. def explainParams(): String
    Definition Classes
    Params
  12. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  13. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  14. val featureTensorName: Param[String]

    Name of the output node which represents features

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

  18. def getChannelNormalizationStds: Array[Double]

  19. def getColorScaleFactor: Double

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

  22. def getFeatureTensorName: String

  23. def getHeadless: Boolean

  24. def getIgnoreDecodingErrors: Boolean

  25. def getImageHeight: Int

  26. def getImageTensorName: String

  27. def getImageWidth: Int

  28. def getInputCol: String

    Definition Classes
    HasInputCol
  29. def getMiniBatchSize: Int

  30. def getModel: Array[Byte]

  31. def getOnnxModel: ONNXModel

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

    Definition Classes
    HasOutputCol
  34. def getOutputTensorName: String

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

    Name of the input node for images

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

    The name of the input column

    The name of the input column

    Definition Classes
    HasInputCol
  45. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  46. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  47. def logClass(featureName: String): Unit
    Definition Classes
    SynapseMLLogging
  48. def logFit[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  49. def logTransform[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  50. def logVerb[T](verb: String, f: ⇒ T, columns: Option[Int] = None): T
    Definition Classes
    SynapseMLLogging
  51. def makePyFile(conf: CodegenConfig): Unit
    Definition Classes
    PythonWrappable
  52. def makeRFile(conf: CodegenConfig): Unit
    Definition Classes
    RWrappable
  53. val onnxModel: TransformerParam
  54. val outputCol: Param[String]

    The name of the output column

    The name of the output column

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

    Name of the output node which represents probabilities

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

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

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

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

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

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

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

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

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

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

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

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

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

  75. def setModel(name: String): ImageFeaturizer.this.type
  76. def setModelInfo(info: ONNXModelInfo): ImageFeaturizer.this.type
  77. def setModelLocation(path: String): ImageFeaturizer.this.type
  78. def setOnnxModel(value: ONNXModel): ImageFeaturizer.this.type

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

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

  81. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  82. def transform(dataset: Dataset[_]): DataFrame
    Definition Classes
    ImageFeaturizer → Transformer
  83. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  84. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  85. 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
  86. val uid: String
    Definition Classes
    ImageFeaturizer → SynapseMLLogging → Identifiable
  87. def write: MLWriter
    Definition Classes
    ComplexParamsWritable → MLWritable