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. def argmax2(arr: Tensor[Float], d: Int): Array[Int]

   

   return the max value in rows(d=0) or in cols(d=1) arr = [4 9 5 7 8 5]

   return the max value in rows(d=0) or in cols(d=1) arr = [4 9 5 7 8 5]

   argmax2(arr, 1) will return 3, 1 argmax2(arr, 2) will return 2, 2, 1

   returns

   todo: this maybe removed

5. final def asInstanceOf[T0]: T0

   

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6. def bboxOverlap(boxes: Tensor[Float], queryBoxes: Tensor[Float]): Tensor[Float]

   

   Get the overlap between boxes and query_boxes

   Get the overlap between boxes and query_boxes

   boxes

   (N, 4) ndarray of float

   queryBoxes

   (K, >=4) ndarray of float

   returns

   overlaps: (N, K) ndarray of overlap between boxes and query_boxes

7. def bboxTransform(sampledRois: Tensor[Float], gtRois: Tensor[Float]): Tensor[Float]

   

8. 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)

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

   

   BBox vote result

   BBox vote result

   scoresNms

   N

   bboxNms

   N * 4

   scoresAll

   M

   bboxAll

   M * 4

10. def clipBox(box: BoundingBox, clipedBox: BoundingBox): Unit

    

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

    

12. 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)

13. def clone(): AnyRef

    

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14. def decodeBatchOutput(output: Tensor[Float], nClass: Int): Array[Array[RoiLabel]]

    

    decode batch

15. 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]]]

    

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

    

17. def encodeBBox(priorBox: Tensor[Float], priorVariance: Tensor[Float], gtBox: Tensor[Float], enodeBbox: Tensor[Float]): Unit

    

    Encode BBox

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

    

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19. def equals(arg0: Any): Boolean

    

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20. def finalize(): Unit

    

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21. 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

22. def getBboxRegressionLabels(bboxTargetData: Tensor[Float], numClasses: Int): (Tensor[Float], Tensor[Float])

    

    Bounding-box regression targets (bboxTargetData) are stored in a compact form N x (class, tx, ty, tw, th) * This function expands those targets into the 4-of-4*K representation used by the network (i.e.

    Bounding-box regression targets (bboxTargetData) are stored in a compact form N x (class, tx, ty, tw, th) * This function expands those targets into the 4-of-4*K representation used by the network (i.e. only one class has non-zero targets). * Returns: bbox_target (ndarray): N x 4K blob of regression targets bbox_inside_weights (ndarray): N x 4K blob of loss weights

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

    

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

    

    Get Confidence scores

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

    

    Get groud truth indices from result

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

    

    Get ground truth from result

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

    

    Get location prediction result

28. def getMaxOverlaps(gtBboxes: Tensor[Float], gtAreas: Tensor[Float], gtInds: Array[Int], bbox: Tensor[Float], normalized: Boolean = false, overlaps: Tensor[Float] = null, gtOffset: Int = 0): (Float, Int)

    

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

    

30. def hashCode(): Int

    

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31. def horzcat(tensors: Tensor[Float]*): Tensor[Float]

    

    Concat multiple 2D tensors horizontally

32. final def isInstanceOf[T0]: Boolean

    

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33. def jaccardOverlap(bbox: BoundingBox, bbox2: BoundingBox): Float

    

34. def locateBBox(srcBox: BoundingBox, box: BoundingBox, locBox: BoundingBox): Unit

    

35. val logger: Logger

    

36. def meetEmitCenterConstraint(srcBox: BoundingBox, bbox: BoundingBox): Boolean

    

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

    

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38. final def notify(): Unit

    

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39. final def notifyAll(): Unit

    

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40. def projectBbox(srcBox: BoundingBox, bbox: BoundingBox, projBox: BoundingBox): Boolean

    

    Project bbox onto the coordinate system defined by src_bbox.

41. def scaleBox(box: BoundingBox, height: Int, width: Int, scaledBox: BoundingBox): Unit

    

42. def selectMatrix(matrix: Tensor[Float], indices: Array[Int], dim: Int, indiceLen: Int = 1, out: Tensor[Float] = null): Tensor[Float]

    

    Select tensor from matrix

    Select tensor from matrix

    matrix

    source matrix

    indices

    indices array

    dim

    dimension

    indiceLen

    indice length

    out

    based tensor

    returns

    selected tensor

43. def selectTensor(matrix: Tensor[Float], indices: Array[Int], dim: Int, indiceLen: Int = 1, out: Tensor[Float] = null): Tensor[Float]

    

    Select tensor from matrix

    Select tensor from matrix

    matrix

    source matrix

    indices

    indices array

    dim

    dimension

    indiceLen

    indice length

    out

    based tensor

    returns

    selected tensor

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

    

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45. def toString(): String

    

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46. def updateRange(dest: Tensor[Float], startR: Int, endR: Int, startC: Int, endC: Int, src: Tensor[Float]): Unit

    

    update with 2d tensor, the range must be equal to the src tensor size

47. def vertcat1D[T](tensors: Tensor[T]*)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    

    Concat multiple 1D tensors vertically

48. def vertcat2D[T](tensors: Tensor[T]*)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    

    Concat multiple 2D tensors vertically

    Concat multiple 2D tensors vertically

    tensors

    tensor list

49. final def wait(): Unit

    

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

    

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

    

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