Class

org.apache.mxnet.infer

ObjectDetector

Related Doc: package infer

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class ObjectDetector extends AnyRef

The ObjectDetector class helps to run ObjectDetection tasks where the goal is to find bounding boxes and corresponding labels for objects in a image.

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Instance Constructors

  1. new ObjectDetector(modelPathPrefix: String, inputDescriptors: IndexedSeq[DataDesc], contexts: Array[Context] = Context.cpu(), epoch: Option[Int] = Some(0))

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    modelPathPrefix

    Path prefix from where to load the model artifacts. These include the symbol, parameters, and synset.txt. Example: file://model-dir/ssd_resnet50_512 (containing ssd_resnet50_512-symbol.json, ssd_resnet50_512-0000.params, and synset.txt)

    inputDescriptors

    Descriptors defining the input node names, shape, layout and type parameters

    contexts

    Device contexts on which you want to run inference. Defaults to CPU.

    epoch

    Model epoch to load; defaults to 0

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 clone(): AnyRef

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

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  7. def equals(arg0: Any): Boolean

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  8. def finalize(): Unit

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

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  10. val handler: MXNetHandler

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  11. def hashCode(): Int

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  12. val height: Int

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  13. def imageBatchObjectDetect(inputBatch: Traversable[BufferedImage], topK: Option[Int] = None): IndexedSeq[IndexedSeq[(String, Array[Float])]]

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    To classify batch of input images according to the provided model

    To classify batch of input images according to the provided model

    inputBatch

    Input array of buffered images

    topK

    Number of result elements to return, sorted by probability

    returns

    List of list of tuples of (class, probability)

  14. def imageObjectDetect(inputImage: BufferedImage, topK: Option[Int] = None): IndexedSeq[IndexedSeq[(String, Array[Float])]]

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    Detects objects and returns bounding boxes with corresponding class/label

    Detects objects and returns bounding boxes with corresponding class/label

    inputImage

    Path prefix of the input image

    topK

    Number of result elements to return, sorted by probability

    returns

    List of list of tuples of (class, [probability, xmin, ymin, xmax, ymax])

  15. val imgClassifier: ImageClassifier

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  16. val inputShape: Shape

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  17. final def isInstanceOf[T0]: Boolean

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  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 objectDetectWithNDArray(input: IndexedSeq[NDArray], topK: Option[Int]): IndexedSeq[IndexedSeq[(String, Array[Float])]]

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    Takes input images as NDArrays.

    Takes input images as NDArrays. Useful when you want to perform multiple operations on the input array, or when you want to pass a batch of input images.

    input

    Indexed Sequence of NDArrays

    topK

    (Optional) How many top_k (sorting will be based on the last axis) elements to return. If not passed, returns all unsorted output.

    returns

    List of list of tuples of (class, [probability, xmin, ymin, xmax, ymax])

  22. val predictor: PredictBase

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

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  24. val synset: IndexedSeq[String]

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  25. def toString(): String

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  26. final def wait(): Unit

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

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

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  29. val width: Int

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