com.intel.analytics.bigdl.transform.vision.image.util

BboxUtil

object BboxUtil

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  7. def bboxTransformInv(boxes: Tensor[Float], deltas: Tensor[Float]): 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)

    returns

  8. 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

    returns

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

  10. 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)

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  12. 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]]]

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

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

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  18. 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

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

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

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

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

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

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

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

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

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