[RNE Rewrite] style(ts): standardize task naming, preprocessor options, and TSDoc conventions

packages/react-native-executorch/src/extensions/cv/ops/boxes.ts

@@ -8,9 +8,24 @@ import { scalePoint } from './points';
  * @category Types
  */
 export type BoxMap = {
-  xyxy: { xmin: number; ymin: number; xmax: number; ymax: number };
-  xywh: { xmin: number; ymin: number; w: number; h: number };
-  cxcywh: { cx: number; cy: number; w: number; h: number };
+  xyxy: {
+    readonly xmin: number;
+    readonly ymin: number;
+    readonly xmax: number;
+    readonly ymax: number;
+  };
+  xywh: {
+    readonly xmin: number;
+    readonly ymin: number;
+    readonly w: number;
+    readonly h: number;
+  };
+  cxcywh: {
+    readonly cx: number;
+    readonly cy: number;
+    readonly w: number;
+    readonly h: number;
+  };
 };
 
 /**
@@ -60,8 +75,8 @@ export function decodeBox<F extends BoxFormat>(
  * @param opts Options defining dimensions and resize modes.
  * @param opts.from The source bounds (e.g. model input dimensions).
  * @param opts.to The destination bounds (e.g. original image dimensions).
- * @param opts.resizeMode The mode used to resize the image ('letterbox' or
- * 'stretch').
+ * @param opts.resizeMode The mode used to resize the image {@link ResizeMode}
+ * (excluding `'crop'`).
  * @returns The scaled BoundingBox object.
  */
 export function scaleBox<F extends BoxFormat>(
@@ -130,9 +145,16 @@ export function scaleBox<F extends BoxFormat>(
  * @category Types
  */
 export type NmsOptions = {
+  /** How bounding box coordinates are interpreted {@link BoxFormat}. */
   readonly boxFormat: BoxFormat;
+  /** Intersection over Union (IoU) threshold for suppressing overlapping boxes. */
   readonly iouThreshold: number;
+  /** Minimum confidence score threshold for filtering candidate boxes. */
   readonly confidenceThreshold: number;
+  /**
+   * NMS algorithm variant (`standard` for hard suppression, `weighted` for soft
+   * coordinate averaging).
+   */
   readonly nmsType: 'standard' | 'weighted';
 };
 
@@ -142,7 +164,13 @@ export type NmsOptions = {
  * @category Utils
  * @param boxes Bounding boxes coordinate tensor.
  * @param scores Bounding boxes confidence scores tensor.
- * @param opts Options configure NMS thresholds and execution mode.
+ * @param opts Options configuring NMS thresholds and execution mode.
+ * @param opts.boxFormat The bounding box format {@link BoxFormat}.
+ * @param opts.iouThreshold Intersection over Union (IoU) threshold for
+ * suppression.
+ * @param opts.confidenceThreshold Minimum confidence score for candidate
+ * selection.
+ * @param opts.nmsType The NMS algorithm variant {@link NmsOptions.nmsType}.
  * @returns The resulting indices of the non-suppressed boxes:
  * - For `standard` NMS: A 1D array of indices (`number[]`) representing the
  *   selected boxes.

packages/react-native-executorch/src/extensions/cv/ops/image.ts

@@ -74,8 +74,11 @@ export type InterpolationMethod = 'nearest' | 'area' | 'cubic' | 'lanczos' | 'li
  * @category Types
  */
 export type ResizeOptions = {
+  /** How the image is resized {@link ResizeMode}. */
   readonly mode?: ResizeMode;
+  /** Background fill value used when letterboxing. */
   readonly padValue?: number;
+  /** Pixel interpolation method {@link InterpolationMethod}. */
   readonly interpolation?: InterpolationMethod;
 };
 
@@ -84,21 +87,30 @@ export type ResizeOptions = {
  * @category Types
  */
 export type NormalizeOptions = {
+  /**
+   * Multiplicative coefficient applied as `pixel * alpha`. Single value for
+   * uniform scaling, array for per-channel.
+   */
   readonly alpha?: number | readonly number[];
+  /** Additive offset applied as `pixel * alpha + beta`. Single value or per-channel array. */
   readonly beta?: number | readonly number[];
 };
 
 /**
  * Resizes an image tensor from a source dimension to a destination dimension.
  *
- * Supports various resize modes (`stretch`, `letterbox`, `crop`) and
- * interpolation algorithms (`linear`, `lanczos`, etc.).
+ * Supports various {@link ResizeMode} and {@link InterpolationMethod} options.
  * @category Typescript API
  * @param src The source image tensor in HWC layout. Shape [H,W,C].
  * @param dst The pre-allocated destination tensor to write the resized image
  * to. `dst` must be in HWC layout and its number of channels must match `src`.
  * Shape [H',W',C].
  * @param opts Configuration options for resizing.
+ * @param opts.mode The resize algorithm mode {@link ResizeMode}. Defaults to
+ * `'stretch'`.
+ * @param opts.interpolation The pixel interpolation method
+ * {@link InterpolationMethod}. Defaults to `'lanczos'`.
+ * @param opts.padValue Fill value for letterboxing. Defaults to `0`.
  * @returns The destination tensor containing the resized image.
  */
 export function resize(src: Tensor, dst: Tensor, opts?: ResizeOptions): Tensor {
@@ -173,6 +185,9 @@ export function toChannelsLast(src: Tensor, dst: Tensor): Tensor {
  * @param dst The pre-allocated destination tensor to write the normalized
  * values to. `dst` must have the same shape as `src`. Shape [C,H,W].
  * @param opts Normalization scaling coefficients.
+ * @param opts.alpha Multiplicative scaling coefficient(s). Defaults to
+ * `1 / 255.0`.
+ * @param opts.beta Additive offset coefficient(s). Defaults to `0.0`.
  * @returns The destination tensor containing the normalized image.
  */
 export function normalize(src: Tensor, dst: Tensor, opts?: NormalizeOptions): Tensor {

packages/react-native-executorch/src/extensions/cv/ops/points.ts

@@ -17,8 +17,8 @@ export type Point = {
  * @param opts Options detailing the scaling factors and resize mode.
  * @param opts.from The source bounds (e.g. model input dimensions).
  * @param opts.to The destination bounds (e.g. original image dimensions).
- * @param opts.resizeMode The mode used to resize the image ('letterbox' or
- * 'stretch').
+ * @param opts.resizeMode The mode used to resize the image {@link ResizeMode}
+ * (excluding `'crop'`).
  * @returns The scaled coordinate point.
  */
 export function scalePoint(

packages/react-native-executorch/src/extensions/cv/tasks/classification.ts

@@ -14,23 +14,34 @@ import { createImagePreprocessor, type ImagePreprocessorOptions } from './prepro
  * vocabulary.
  * @category Types
  */
-export type ClassifierOptions<L> = ImagePreprocessorOptions & { readonly labels: readonly L[] };
+export type ClassifierOptions<L> = ImagePreprocessorOptions & {
+  /** Array of class labels matching the model's output vocabulary. */
+  readonly labels: readonly L[];
+};
 
 /**
  * Model configuration required to instantiate a classifier task runner.
  * @category Types
  */
 export type ClassifierModel<L> = {
+  /** Local path or remote URL of the `.pte` model file. */
   readonly modelPath: string;
-  readonly classifierOpts: ClassifierOptions<L>;
+  /**
+   * Image preprocessing and label vocabulary
+   * {@link ClassifierOptions}. The `labels` array length must
+   * match the model's output dimension.
+   */
+  readonly modelOpts: ClassifierOptions<L>;
 };
 
 /**
  * Result structure representing a single classification prediction.
  * @category Types
  */
 export type Classification<L> = {
+  /** Predicted class label. */
   readonly label: L;
+  /** Confidence score of the prediction (between 0.0 and 1.0). */
   readonly confidence: number;
 };
 
@@ -74,7 +85,7 @@ export async function createClassifier<L>(
    */
   classifyWorklet: (input: ImageBuffer, options?: { topk?: number }) => Classification<L>[];
 }> {
-  const { modelPath, classifierOpts } = config;
+  const { modelPath, modelOpts } = config;
   const model = await wrapAsync(loadModel, runtime)(modelPath);
 
   const meta = validateModelSchema(
@@ -87,9 +98,9 @@ export async function createClassifier<L>(
   const outShape = meta.outputTensorMeta[0]!.shape;
 
   const numLabels = outShape[outShape.length - 1]!;
-  if (classifierOpts.labels.length !== numLabels) {
+  if (modelOpts.labels.length !== numLabels) {
     throw new Error(
-      `Classifier labels length (${classifierOpts.labels.length}) must match model output dimension (${numLabels}).`
+      `Classifier labels length (${modelOpts.labels.length}) must match model output dimension (${numLabels}).`
     );
   }
 
@@ -100,7 +111,7 @@ export async function createClassifier<L>(
   ] as const;
 
   const [tLogits, tProbas] = tensors;
-  const preprocessor = createImagePreprocessor(classifierOpts, inpShape);
+  const preprocessor = createImagePreprocessor(modelOpts, inpShape);
 
   const dispose = () => {
     preprocessor.dispose();
@@ -125,7 +136,7 @@ export async function createClassifier<L>(
       .getData(new Float32Array(tProbas.numel));
 
     return Array.from(probas)
-      .map((confidence, index) => ({ confidence, label: classifierOpts.labels[index]! }))
+      .map((confidence, index) => ({ confidence, label: modelOpts.labels[index]! }))
       .sort((a, b) => b.confidence - a.confidence)
       .slice(0, options?.topk);
   };

packages/react-native-executorch/src/extensions/cv/tasks/imageEmbedding.ts

@@ -13,8 +13,13 @@ import { createImagePreprocessor, type ImagePreprocessorOptions } from './prepro
  * @category Types
  */
 export type ImageEmbedderModel = {
+  /** Local path or remote URL of the `.pte` model file. */
   readonly modelPath: string;
-  readonly opts: ImagePreprocessorOptions;
+  /**
+   * Image preprocessing (resize, color conversion, normalization)
+   * for embedding models {@link ImagePreprocessorOptions}.
+   */
+  readonly modelOpts: ImagePreprocessorOptions;
 };
 
 /**
@@ -53,7 +58,7 @@ export async function createImageEmbedder(
    */
   embedWorklet: (input: ImageBuffer) => Float32Array;
 }> {
-  const { modelPath, opts } = config;
+  const { modelPath, modelOpts } = config;
   const model = await wrapAsync(loadModel, runtime)(modelPath);
 
   const meta = validateModelSchema(
@@ -67,7 +72,7 @@ export async function createImageEmbedder(
 
   const tensors = [tensor('float32', outShape)] as const;
   const [tEmbedding] = tensors;
-  const preprocessor = createImagePreprocessor(opts, inpShape);
+  const preprocessor = createImagePreprocessor(modelOpts, inpShape);
 
   const dispose = () => {
     preprocessor.dispose();

packages/react-native-executorch/src/extensions/cv/tasks/instanceSegmentation.ts

@@ -31,11 +31,17 @@ export type InstanceSegmenterOptions<F extends BoxFormat, L> = Omit<
   ImagePreprocessorOptions,
   'resizeMode'
 > & {
+  /** Resize mode for input images. Must be `'stretch'`. */
   readonly resizeMode: 'stretch';
+  /** Array of class labels matching the model's output vocabulary. */
   readonly labels: readonly L[];
+  /** How bounding box coordinates are interpreted {@link BoxFormat}. */
   readonly boxFormat: F;
+  /** Default Intersection over Union (IoU) threshold for Non-Maximum Suppression (NMS). */
   readonly defaultIouThreshold: number;
+  /** Default probability threshold for mask values. */
   readonly defaultMaskThreshold: number;
+  /** Default minimum confidence score threshold for detected instances. */
   readonly defaultConfidenceThreshold: number;
 };
 
@@ -46,8 +52,14 @@ export type InstanceSegmenterOptions<F extends BoxFormat, L> = Omit<
  * @typeParam L The label type.
  */
 export type InstanceSegmenterModel<F extends BoxFormat, L> = {
+  /** Local path or remote URL of the `.pte` model file. */
   readonly modelPath: string;
-  readonly opts: InstanceSegmenterOptions<F, L>;
+  /**
+   * Image preprocessing, label vocabulary, bounding box format,
+   * and default NMS/mask/confidence thresholds
+   * {@link InstanceSegmenterOptions}.
+   */
+  readonly modelOpts: InstanceSegmenterOptions<F, L>;
 };
 
 /**
@@ -58,9 +70,13 @@ export type InstanceSegmenterModel<F extends BoxFormat, L> = {
  * @typeParam L The label type.
  */
 export type InstanceSegmentationResult<F extends BoxFormat, L> = {
+  /** Scaled bounding box coordinates matching the input image resolution. */
   readonly box: BoundingBox<F>;
+  /** Binary segmentation mask buffer cropped to the instance bounding box. */
   readonly mask: ImageBuffer;
+  /** Predicted instance class label. */
   readonly label: L;
+  /** Confidence score of the instance detection (between 0.0 and 1.0). */
   readonly confidence: number;
 };
 
@@ -93,10 +109,12 @@ export async function createInstanceSegmenter<F extends BoxFormat, L>(
    * Performs asynchronous instance segmentation on the given input image.
    * @param input The input image buffer.
    * @param options Execution override options.
-   * @param options.confidenceThreshold Override for the minimum confidence
-   * threshold.
-   * @param options.iouThreshold Override for the IoU threshold in NMS.
-   * @param options.maskThreshold Override for the mask binarization threshold.
+   * @param options.confidenceThreshold Minimum confidence threshold. If
+   * omitted, uses `modelOpts.defaultConfidenceThreshold`.
+   * @param options.iouThreshold Intersection over Union (IoU) threshold in NMS. If omitted, uses
+   * `modelOpts.defaultIouThreshold`.
+   * @param options.maskThreshold Mask binarization threshold. If omitted,
+   * uses `modelOpts.defaultMaskThreshold`.
    * @returns A promise resolving to a list of detected instances.
    */
   segmentInstances: (
@@ -113,7 +131,7 @@ export async function createInstanceSegmenter<F extends BoxFormat, L>(
     options?: { confidenceThreshold?: number; iouThreshold?: number; maskThreshold?: number }
   ) => InstanceSegmentationResult<F, L>[];
 }> {
-  const { modelPath, opts } = config;
+  const { modelPath, modelOpts } = config;
   const model = await wrapAsync(loadModel, runtime)(modelPath);
   const meta = validateModelSchema(
     model,
@@ -149,7 +167,7 @@ export async function createInstanceSegmenter<F extends BoxFormat, L>(
 
   const [tBoxes, tScores, tClasses, tAllMasks, tMask] = tensors;
 
-  const preprocessor = createImagePreprocessor(opts, inpShape);
+  const preprocessor = createImagePreprocessor(modelOpts, inpShape);
 
   const dispose = () => {
     preprocessor.dispose();
@@ -165,16 +183,17 @@ export async function createInstanceSegmenter<F extends BoxFormat, L>(
     const tInput = preprocessor.process(input);
     model.execute('forward', [tInput], [tBoxes, tScores, tClasses, tAllMasks]);
 
-    const iouThreshold = options?.iouThreshold ?? opts.defaultIouThreshold;
-    const maskThreshold = options?.maskThreshold ?? opts.defaultMaskThreshold;
-    const confidenceThreshold = options?.confidenceThreshold ?? opts.defaultConfidenceThreshold;
+    const iouThreshold = options?.iouThreshold ?? modelOpts.defaultIouThreshold;
+    const maskThreshold = options?.maskThreshold ?? modelOpts.defaultMaskThreshold;
+    const confidenceThreshold =
+      options?.confidenceThreshold ?? modelOpts.defaultConfidenceThreshold;
 
     const eps = 1e-7;
     const clampedMaskThreshold = Math.max(eps, Math.min(1 - eps, maskThreshold));
     const logitMaskThreshold = Math.log(clampedMaskThreshold / (1 - clampedMaskThreshold));
 
     const indices = nms(tBoxes, tScores, {
-      boxFormat: opts.boxFormat,
+      boxFormat: modelOpts.boxFormat,
       iouThreshold,
       confidenceThreshold,
       nmsType: 'standard',
@@ -199,12 +218,12 @@ export async function createInstanceSegmenter<F extends BoxFormat, L>(
       for (const idx of indices) {
         const confidence = scores[idx]!;
         const classIdx = Math.round(classes[idx]!);
-        const label = opts.labels[classIdx];
+        const label = modelOpts.labels[classIdx];
 
         if (label === undefined) {
           throw new Error(
             `InstanceSegmenter: Predicted class index ${classIdx} is ` +
-              `out of bounds for labels array of size ${opts.labels.length}.`
+              `out of bounds for labels array of size ${modelOpts.labels.length}.`
           );
         }
 
@@ -213,7 +232,7 @@ export async function createInstanceSegmenter<F extends BoxFormat, L>(
         const c = boxes[idx * 4 + 2]!;
         const d = boxes[idx * 4 + 3]!;
 
-        const box = scaleBox(decodeBox([a, b, c, d], opts.boxFormat), {
+        const box = scaleBox(decodeBox([a, b, c, d], modelOpts.boxFormat), {
           from: { width: targetW, height: targetH },
           to: { width: input.width, height: input.height },
           resizeMode: 'stretch',