BboxUtil

Value Members

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

   

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2. final def ##(): Int

   

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

   

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4. final def asInstanceOf[T0]: T0

   

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5. def bboxTransformInv(boxes: Tensor[Float], deltas: Tensor[Float], normalized: Boolean = false): Tensor[Float]

   

   Note that the output are stored in input deltas

   Note that the output are stored in input deltas

   boxes

   (N, 4)

   deltas

   (N, 4a)

6. def bboxVote(scoresNms: Tensor[Float], bboxNms: Tensor[Float], scoresAll: Tensor[Float], bboxAll: Tensor[Float], areasBuf: Tensor[Float] = null): RoiLabel

   
   scoresNms

   N

   bboxNms

   N * 4

   scoresAll

   M

   bboxAll

   M * 4

7. def clipBoxes(bboxes: Tensor[Float]): Tensor[Float]

   

8. def clipBoxes(boxes: Tensor[Float], height: Float, width: Float, minH: Float = 0, minW: Float = 0, scores: Tensor[Float] = null): Int

   

   Clip boxes to image boundaries.

   Clip boxes to image boundaries. set the score of all boxes with any side smaller than minSize to 0

   boxes

   N * 4a

   height

   height of image

   width

   width of image

   minH

   min height limit

   minW

   min width limit

   scores

   scores for boxes

   returns

   the number of boxes kept (score > 0)

9. def clone(): AnyRef

   

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10. def decodeBboxesAll(allLocPreds: Array[Array[Tensor[Float]]], priorBoxes: Tensor[Float], priorVariances: Tensor[Float], nClasses: Int, bgLabel: Int, clipBoxes: Boolean, varianceEncodedInTarget: Boolean, shareLocation: Boolean, output: Array[Array[Tensor[Float]]] = null): Array[Array[Tensor[Float]]]

    

11. def decodeBoxes(priorBoxes: Tensor[Float], priorVariances: Tensor[Float], isClipBoxes: Boolean, bboxes: Tensor[Float], varianceEncodedInTarget: Boolean, output: Tensor[Float] = null): Tensor[Float]

    

12. def decodeRois(output: Tensor[Float]): Tensor[Float]

    

13. def decodeWithWeight(encodeBox: Tensor[Float], bbox: Tensor[Float], weight: Array[Float], decodeBox: Tensor[Float]): Unit

    

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

    

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15. def equals(arg0: Any): Boolean

    

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16. def finalize(): Unit

    

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17. def getAreas(boxes: Tensor[Float], areas: Tensor[Float], startInd: Int = 1, normalized: Boolean = false): Tensor[Float]

    

    get the areas of boxes

    get the areas of boxes

    boxes

    N * 4 tensor

    areas

    buffer to store the results

    returns

    areas array

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

    

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19. def getConfidenceScores(conf: Tensor[Float], numPredsPerClass: Int, numClasses: Int, confBuf: Array[Array[Tensor[Float]]] = null): Array[Array[Tensor[Float]]]

    

20. def getGroundTruthIndices(result: Tensor[Float]): Map[Int, (Int, Int)]

    

21. def getGroundTruths(result: Tensor[Float]): Map[Int, Tensor[Float]]

    

22. def getLocPredictions(loc: Tensor[Float], numPredsPerClass: Int, numClasses: Int, shareLocation: Boolean, locPredsBuf: Array[Array[Tensor[Float]]] = null): Array[Array[Tensor[Float]]]

    

23. def getPriorBboxes(prior: Tensor[Float], nPriors: Int): (Tensor[Float], Tensor[Float])

    

24. def hashCode(): Int

    

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25. final def isInstanceOf[T0]: Boolean

    

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26. val logger: Logger

    

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

    

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28. final def notify(): Unit

    

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29. final def notifyAll(): Unit

    

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30. def scaleBBox(bboxes: Tensor[Float], height: Float, width: Float): Unit

    

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

    

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32. def toString(): String

    

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33. final def wait(): Unit

    

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

    

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

    

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