Functions Reference

top(_:_:)
Returns the top k predictions from Core ML classification results as an array of (String, Double) pairs.
Declaration
Swift

public func top(_ k: Int, _ prob: [String: Double]) -> [(String, Double)]
top(_:_:)
Returns the top k predictions from Vision classification results as an array of (String, Double) pairs.
Declaration
Swift

public func top(_ k: Int, _ observations: [VNClassificationObservation]) -> [(String, Double)]


                    
                    
                    clamp(_:min:max:)

Undocumented

Declaration

Swift

public func clamp<T: Comparable>(_ x: T, min: T, max: T) -> T

createPixelBuffer(width:height:)
Creates a RGB pixel buffer of the specified width and height.
Declaration
Swift

public func createPixelBuffer(width: Int, height: Int) -> CVPixelBuffer?


                    
                    
                    resizePixelBuffer(_:cropX:cropY:cropWidth:cropHeight:scaleWidth:scaleHeight:)

First crops the pixel buffer, then resizes it.

Declaration

Swift

public func resizePixelBuffer(_ srcPixelBuffer: CVPixelBuffer,
                              cropX: Int,
                              cropY: Int,
                              cropWidth: Int,
                              cropHeight: Int,
                              scaleWidth: Int,
                              scaleHeight: Int) -> CVPixelBuffer?


                    
                    
                    resizePixelBuffer(_:width:height:)

Resizes a CVPixelBuffer to a new width and height.

Declaration

Swift

public func resizePixelBuffer(_ pixelBuffer: CVPixelBuffer,
                              width: Int, height: Int) -> CVPixelBuffer?


                    
                    
                    resizePixelBuffer(_:width:height:output:context:)

Resizes a CVPixelBuffer to a new width and height.

Declaration

Swift

public func resizePixelBuffer(_ pixelBuffer: CVPixelBuffer,
                              width: Int, height: Int,
                              output: CVPixelBuffer, context: CIContext)

IOU(_:_:)
Computes intersection-over-union overlap between two bounding boxes.
Declaration
Swift

public func IOU(_ a: CGRect, _ b: CGRect) -> Float
nonMaxSuppression(predictions:iouThreshold:maxBoxes:)
Removes bounding boxes that overlap too much with other boxes that have a higher score.
Declaration
Swift

public func nonMaxSuppression(predictions: [NMSPrediction], iouThreshold: Float, maxBoxes: Int) -> [Int]


                    
                    
                    nonMaxSuppression(predictions:indices:iouThreshold:maxBoxes:)

Removes bounding boxes that overlap too much with other boxes that have a higher score.

Based on code from https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/non_max_suppression_op.cc

Note

This version of NMS ignores the class of the bounding boxes. Since it selects the bounding boxes in a greedy fashion, if a certain class has many boxes that are selected, then it is possible none of the boxes of the other classes get selected.

Declaration

Swift

public func nonMaxSuppression(predictions: [NMSPrediction],
                              indices: [Int],
                              iouThreshold: Float,
                              maxBoxes: Int) -> [Int]

Parameters

`predictions`	an array of bounding boxes and their scores
`indices`	which predictions to look at
`iouThreshold`	used to decide whether boxes overlap too much
`maxBoxes`	the maximum number of boxes that will be selected

Return Value

the array indices of the selected bounding boxes


                    
                    
                    nonMaxSuppressionMultiClass(numClasses:predictions:scoreThreshold:iouThreshold:maxPerClass:maxTotal:)

Multi-class version of non maximum suppression.

Where nonMaxSuppression() does not look at the class of the predictions at all, the multi-class version first selects the best bounding boxes for each class, and then keeps the best ones of those.

With this method you can usually expect to see at least one bounding box for each class (unless all the scores for a given class are really low).

Based on code from: https://github.com/tensorflow/models/blob/master/object_detection/core/post_processing.py

Declaration

Swift

public func nonMaxSuppressionMultiClass(numClasses: Int,
                                        predictions: [NMSPrediction],
                                        scoreThreshold: Float,
                                        iouThreshold: Float,
                                        maxPerClass: Int,
                                        maxTotal: Int) -> [Int]

Parameters

`numClasses`	the number of classes
`predictions`	an array of bounding boxes and their scores
`scoreThreshold`	used to only keep bounding boxes with a high enough score
`iouThreshold`	used to decide whether boxes overlap too much
`maxPerClass`	the maximum number of boxes that will be selected per class
`maxTotal`	maximum number of boxes that will be selected over all classes

Return Value

the array indices of the selected bounding boxes