Convolver

Value Members

1. final def !=(arg0: AnyRef): Boolean

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2. final def !=(arg0: Any): Boolean

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3. final def ##(): Int

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4. final def ==(arg0: AnyRef): Boolean

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5. final def ==(arg0: Any): Boolean

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6. def apply(filters: Array[Image], imgInfo: ImageMetadata, whitener: Option[ZCAWhitener] = None, normalizePatches: Boolean = true, varConstant: Double = 10.0, flipFilters: Boolean = false): Convolver

   User-friendly constructor interface for the Conovler.

   User-friendly constructor interface for the Conovler.

   filters

   An array of images with which we convolve each input image. These images should *not* be pre-whitened.

   imgInfo

   Metadata of a typical image we will be convolving. All images must have the same size/shape.

   whitener

   Whitener to be applied to both the input images and the filters before convolving.

   normalizePatches

   Should the patches be normalized before convolution?

   varConstant

   Constant to be used in scaling.

   flipFilters

   Should the filters be flipped before convolution is applied (used for comparability to MATLAB's convnd function.)

7. final def asInstanceOf[T0]: T0

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8. def clone(): AnyRef

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9. def convolve(img: Image, patchMat: DenseMatrix[Double], resWidth: Int, resHeight: Int, imgChannels: Int, convSize: Int, normalizePatches: Boolean, whitener: Option[ZCAWhitener], convolutions: DenseMatrix[Double], varConstant: Double = 10.0): Image

10. def convolvePartitions(imgs: Iterator[Image], resWidth: Int, resHeight: Int, imgChannels: Int, convSize: Int, normalizePatches: Boolean, whitener: Option[ZCAWhitener], convolutions: DenseMatrix[Double], varConstant: Double): Iterator[Image]

11. final def eq(arg0: AnyRef): Boolean

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12. def equals(arg0: Any): Boolean

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13. def finalize(): Unit

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14. final def getClass(): Class[_]

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15. def hashCode(): Int

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16. final def isInstanceOf[T0]: Boolean

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17. def makePatches(img: Image, patchMat: DenseMatrix[Double], resWidth: Int, resHeight: Int, imgChannels: Int, convSize: Int, normalizePatches: Boolean, whitener: Option[ZCAWhitener], varConstant: Double): DenseMatrix[Double]

    This function takes an image and generates a matrix of all of its patches.

    This function takes an image and generates a matrix of all of its patches. Patches are expected to have indexes of the form: c + x*numChannels + y*numChannels*xDim

    img

    returns

18. final def ne(arg0: AnyRef): Boolean

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19. final def notify(): Unit

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20. final def notifyAll(): Unit

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21. def packFilters(filters: Array[Image]): DenseMatrix[Double]

    Given an array of filters, packs the filters into a DenseMatrix[Double] which has the following form: for a row i, column c+y*numChannels+x*numChannels*yDim corresponds to the pixel value at (x,y,c) in image i of the filters array.

    Given an array of filters, packs the filters into a DenseMatrix[Double] which has the following form: for a row i, column c+y*numChannels+x*numChannels*yDim corresponds to the pixel value at (x,y,c) in image i of the filters array.

    filters

    Array of filters.

    returns

    DenseMatrix of filters, as described above.

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

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23. def toString(): String

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24. final def wait(): Unit

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

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26. final def wait(arg0: Long): Unit

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