DaisyExtractor

Instance Constructors

new DaisyExtractor(daisyT: Int = 8, daisyQ: Int = 3, daisyR: Int = 7, daisyH: Int = 8, pixelBorder: Int = 16, stride: Int = 4, patchSize: Int = 24)

daisyT
Number of histograms at a single layer
daisyQ
Number of convolved orientations layers with different sigmas
daisyR
Distance from the center pixel to the outer most grid point
daisyH
Number of bins in the histogram
pixelBorder
Starting offset for keypoints
stride
Stride for keypoints on the grid
patchSize
Size of neighborhood to consider for constructing patches

Value Members

final def !=(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def !=(arg0: Any): Boolean

Definition Classes
Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def ==(arg0: Any): Boolean

Definition Classes
Any
final def andThen[C, L](est: LabelEstimator[DenseMatrix[Float], C, L], data: PipelineDataset[Image], labels: PipelineDataset[L]): Pipeline[Image, C]

Chains a label estimator onto the end of this pipeline, producing a new pipeline.
Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.
est
The estimator to chain onto the end of this pipeline
data
The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)
labels
The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

Definition Classes
Chainable
final def andThen[C, L](est: LabelEstimator[DenseMatrix[Float], C, L], data: RDD[Image], labels: PipelineDataset[L]): Pipeline[Image, C]

Chains a label estimator onto the end of this pipeline, producing a new pipeline.
Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.
est
The estimator to chain onto the end of this pipeline
data
The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)
labels
The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

Definition Classes
Chainable
final def andThen[C, L](est: LabelEstimator[DenseMatrix[Float], C, L], data: PipelineDataset[Image], labels: RDD[L]): Pipeline[Image, C]

Chains a label estimator onto the end of this pipeline, producing a new pipeline.
Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.
est
The estimator to chain onto the end of this pipeline
data
The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)
labels
The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

Definition Classes
Chainable
final def andThen[C, L](est: LabelEstimator[DenseMatrix[Float], C, L], data: RDD[Image], labels: RDD[L]): Pipeline[Image, C]

Chains a label estimator onto the end of this pipeline, producing a new pipeline.
Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.
est
The estimator to chain onto the end of this pipeline
data
The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)
labels
The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

Definition Classes
Chainable
final def andThen[C](est: Estimator[DenseMatrix[Float], C], data: PipelineDataset[Image]): Pipeline[Image, C]

Chains an estimator onto the end of this pipeline, producing a new pipeline.
Chains an estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.
est
The estimator to chain onto the end of this pipeline
data
The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

Definition Classes
Chainable
final def andThen[C](est: Estimator[DenseMatrix[Float], C], data: RDD[Image]): Pipeline[Image, C]

Chains an estimator onto the end of this pipeline, producing a new pipeline.
Chains an estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.
est
The estimator to chain onto the end of this pipeline
data
The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

Definition Classes
Chainable
final def andThen[C](next: Chainable[DenseMatrix[Float], C]): Pipeline[Image, C]

Chains a pipeline onto the end of this one, producing a new pipeline.
Chains a pipeline onto the end of this one, producing a new pipeline. If either this pipeline or the following has already been executed, it will not need to be fit again.
next
the pipeline to chain

Definition Classes
Chainable
def apply(in: Image): DenseMatrix[Float]

Computes daisy features of an image.
Computes daisy features of an image.
in
Image
returns
Daisy features.

Definition Classes
DaisyExtractor → Transformer
def apply(in: RDD[Image]): RDD[DenseMatrix[Float]]

The application of this Transformer to an RDD of input items.
The application of this Transformer to an RDD of input items. This method may optionally be overridden by ML developers.
in
The bulk RDD input to pass into this transformer
returns
The bulk RDD output for the given input

Definition Classes
Transformer
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
val convThreshold: Double
val daisyFeatureSize: Int
val daisyH: Int

Number of bins in the histogram
val daisyQ: Int

Number of convolved orientations layers with different sigmas
val daisyR: Int

Distance from the center pixel to the outer most grid point
val daisySigmaSq: IndexedSeq[Double]
val daisySigmaSqDiff: IndexedSeq[Double]
val daisyT: Int

Number of histograms at a single layer
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def execute(deps: Seq[Expression]): Expression

Definition Classes
TransformerOperator → Operator
val featureThreshold: Double
val filter1: Array[Double]
val filter2: Array[Double]
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
val g: Array[Array[Double]]
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def label: String

Definition Classes
Operator
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def normalize(x: DenseVector[Double]): DenseVector[Double]
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val patchSize: Int

Size of neighborhood to consider for constructing patches
val pixelBorder: Int

Starting offset for keypoints
val stride: Int

Stride for keypoints on the grid
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
val tDaisy: IndexedSeq[Int]
def toPipeline: Pipeline[Image, DenseMatrix[Float]]

A method that converts this object into a Pipeline.
A method that converts this object into a Pipeline. Must be implemented by anything that extends Chainable.

Definition Classes
Transformer → Chainable
def toString(): String

Definition Classes
AnyRef → Any
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

class DaisyExtractor extends Transformer[Image, DenseMatrix[Float]]

Instance Constructors

new DaisyExtractor(daisyT: Int = 8, daisyQ: Int = 3, daisyR: Int = 7, daisyH: Int = 8, pixelBorder: Int = 16, stride: Int = 4, patchSize: Int = 24)

Value Members

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

final def andThen[C, L](est: LabelEstimator[DenseMatrix[Float], C, L], data: PipelineDataset[Image], labels: PipelineDataset[L]): Pipeline[Image, C]

final def andThen[C, L](est: LabelEstimator[DenseMatrix[Float], C, L], data: RDD[Image], labels: PipelineDataset[L]): Pipeline[Image, C]

final def andThen[C, L](est: LabelEstimator[DenseMatrix[Float], C, L], data: PipelineDataset[Image], labels: RDD[L]): Pipeline[Image, C]

final def andThen[C, L](est: LabelEstimator[DenseMatrix[Float], C, L], data: RDD[Image], labels: RDD[L]): Pipeline[Image, C]

final def andThen[C](est: Estimator[DenseMatrix[Float], C], data: PipelineDataset[Image]): Pipeline[Image, C]

final def andThen[C](est: Estimator[DenseMatrix[Float], C], data: RDD[Image]): Pipeline[Image, C]

final def andThen[C](next: Chainable[DenseMatrix[Float], C]): Pipeline[Image, C]

def apply(in: Image): DenseMatrix[Float]

def apply(in: RDD[Image]): RDD[DenseMatrix[Float]]

final def asInstanceOf[T0]: T0

def clone(): AnyRef

val convThreshold: Double

val daisyFeatureSize: Int

val daisyH: Int

val daisyQ: Int

val daisyR: Int

val daisySigmaSq: IndexedSeq[Double]

val daisySigmaSqDiff: IndexedSeq[Double]

val daisyT: Int

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def execute(deps: Seq[Expression]): Expression

val featureThreshold: Double

val filter1: Array[Double]

val filter2: Array[Double]

def finalize(): Unit

val g: Array[Array[Double]]

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def label: String

final def ne(arg0: AnyRef): Boolean

def normalize(x: DenseVector[Double]): DenseVector[Double]

final def notify(): Unit

final def notifyAll(): Unit

val patchSize: Int

val pixelBorder: Int

val stride: Int

final def synchronized[T0](arg0: ⇒ T0): T0

val tDaisy: IndexedSeq[Int]

def toPipeline: Pipeline[Image, DenseMatrix[Float]]

def toString(): String

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

Inherited from Transformer[Image, DenseMatrix[Float]]

Inherited from Chainable[Image, DenseMatrix[Float]]

Inherited from TransformerOperator

Inherited from Serializable

Inherited from Serializable

Inherited from Operator

Inherited from AnyRef

Inherited from Any

Ungrouped