com.intel.analytics.bigdl.transform.vision.image.augmentation
FixedCrop
Companion object FixedCrop
class FixedCrop extends Crop
Crop a fixed area of image
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Instance Constructors
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new
FixedCrop(x1: Float, y1: Float, x2: Float, y2: Float, normalized: Boolean, isClip: Boolean)
- x1
start in width
- y1
start in height
- x2
end in width
- y2
end in height
- normalized
whether args are normalized, i.e. in range [0, 1]
- isClip
whether to clip the roi to image boundaries
Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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def
->[C](other: Transformer[ImageFeature, C]): Transformer[ImageFeature, C]
- Definition Classes
- FeatureTransformer → Transformer
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def
->(other: FeatureTransformer): FeatureTransformer
- Definition Classes
- FeatureTransformer
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final
def
==(arg0: Any): Boolean
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def
apply(imageFrame: ImageFrame): ImageFrame
- Definition Classes
- FeatureTransformer
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def
apply(prev: Iterator[ImageFeature]): Iterator[ImageFeature]
- Definition Classes
- FeatureTransformer → Transformer
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def
apply(dataset: RDD[ImageFeature])(implicit evidence: ClassTag[ImageFeature]): RDD[ImageFeature]
Apply this transformer to rdd
Apply this transformer to rdd
- Definition Classes
- Transformer
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final
def
asInstanceOf[T0]: T0
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def
clone(): AnyRef
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- @native() @throws( ... )
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def
cloneTransformer(): Transformer[ImageFeature, ImageFeature]
- Definition Classes
- Transformer
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def
enableIgnoreException(): FixedCrop.this.type
catch the exception of the transformer to avoid crashing.
catch the exception of the transformer to avoid crashing.
- Definition Classes
- FeatureTransformer
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
finalize(): Unit
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def
generateRoi(feature: ImageFeature): BoundingBox
how to generate crop roi
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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final
def
isInstanceOf[T0]: Boolean
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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def
setOutKey(key: String): FixedCrop.this.type
set the output key to store current transformed result if the key is not set, or same as default, then the transformed result will be overwritted by the following transformer
set the output key to store current transformed result if the key is not set, or same as default, then the transformed result will be overwritted by the following transformer
- key
output key
- Definition Classes
- FeatureTransformer
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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def
transform(feature: ImageFeature): ImageFeature
transform feature
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def
transformMat(feature: ImageFeature): Unit
transform mat
transform mat
- Definition Classes
- Crop → FeatureTransformer
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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