Package org.jetbrains.kotlinx.dl.impl.preprocessing

Types

ConvertToFloatArray

android

class ConvertToFloatArray(layout: TensorLayout) : Operation<Bitmap, Pair<FloatArray, TensorShape>>

Converts Bitmap to float array representation. Only Bitmap.Config.ARGB_8888 is supported.

FloatArrayOperation

common

The aim of this class is to provide common functionality for all Operations that can be applied to Pair and simplify the implementation of a new Operations.

Normalizing

common

class Normalizing : FloatArrayOperation

This preprocessor defines normalizing operation. Given mean and std for n channels, this operation normalizes each channel of the input array, i.e.

Rescaling

common

class Rescaling(scalingCoefficient: Float) : FloatArrayOperation

This preprocessor defines the Rescaling operation. It scales each pixel pixel_i = pixel_i / scalingCoefficient.

TensorLayout

android

enum TensorLayout : Enum<TensorLayout>

Supported layouts for flat representations of tensors.

Functions

call

common

fun <I, M, O> <ERROR CLASS><I, M>.call(operation: <ERROR CLASS><M, O>): <ERROR CLASS><I, O>

Applies provided operation to the preprocessing pipeline.

crop

android

fun Operation<I, Bitmap>.crop(block: Crop.() -> Unit): Operation<I, Bitmap>

Applies Crop operation to crop the Bitmap at a specified region.

mean

common

fun FloatArray.mean(channels: Int = 1): FloatArray

Computes mean value for each channel of the array. Array size should be divisible by the passed channels number.

fun mean(vararg arrays: FloatArray, channels: Int = 1): FloatArray

Computes mean value for each channel of the provided arrays.

normalize

common

fun <ERROR CLASS><I, <ERROR CLASS><FloatArray, <ERROR CLASS>>>.normalize(block: Normalizing.() -> Unit): <ERROR CLASS><I, <ERROR CLASS><FloatArray, <ERROR CLASS>>>

Applies Normalizing preprocessor to the tensor to normalize it with given mean and std values.

onResult

common

fun <I, O> <ERROR CLASS><I, O>.onResult(block: (O) -> Unit): <ERROR CLASS><I, O>

Convenience functions for executing custom logic after applying Operation. Could be useful for debugging purposes.

rescale

common

fun <ERROR CLASS><I, <ERROR CLASS><FloatArray, <ERROR CLASS>>>.rescale(block: Rescaling.() -> Unit): <ERROR CLASS><I, <ERROR CLASS><FloatArray, <ERROR CLASS>>>

Applies Rescaling preprocessor to the tensor to scale each value by a given coefficient.

resize

android