[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;
+  };
 };
 
 /**
@@ -130,9 +145,16 @@ export function scaleBox<F extends BoxFormat>(
  * @category Types
  */
 export type NmsOptions = {
+  /** Bounding box format (`xyxy`, `cxcywh`, etc.). */
   readonly boxFormat: BoxFormat;
+  /** 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 Bounding box format (`xyxy`, `cxcywh`, etc.).
+ * @param opts.iouThreshold Intersection over Union (IoU) threshold for
+ * suppression.
+ * @param opts.confidenceThreshold Minimum confidence score for candidate
+ * selection.
+ * @param opts.nmsType Suppression algorithm mode (`standard` or `weighted`).
  * @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 = {
+  /** Resize algorithm mode (`stretch`, `letterbox`, `crop`). */
   readonly mode?: ResizeMode;
+  /** Background fill value used when letterboxing. */
   readonly padValue?: number;
+  /** Pixel interpolation method. */
   readonly interpolation?: InterpolationMethod;
 };
 
@@ -84,7 +87,9 @@ export type ResizeOptions = {
  * @category Types
  */
 export type NormalizeOptions = {
+  /** Multiplicative scaling coefficient(s). */
   readonly alpha?: number | readonly number[];
+  /** Additive offset coefficient(s). */
   readonly beta?: number | readonly number[];
 };
 
@@ -99,6 +104,11 @@ export type NormalizeOptions = {
  * 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 Resize algorithm mode (`stretch`, `letterbox`, `crop`).
+ * Defaults to `'stretch'`.
+ * @param opts.interpolation Pixel interpolation method (`linear`, `lanczos`,
+ * etc.). 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 +183,9 @@ 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/tasks/classification.ts

@@ -14,23 +14,30 @@ 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 preprocessor and vocabulary options. */
+  readonly opts: 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 +81,7 @@ export async function createClassifier<L>(
    */
   classifyWorklet: (input: ImageBuffer, options?: { topk?: number }) => Classification<L>[];
 }> {
-  const { modelPath, classifierOpts } = config;
+  const { modelPath, opts } = config;
   const model = await wrapAsync(loadModel, runtime)(modelPath);
 
   const meta = validateModelSchema(
@@ -87,9 +94,9 @@ export async function createClassifier<L>(
   const outShape = meta.outputTensorMeta[0]!.shape;
 
   const numLabels = outShape[outShape.length - 1]!;
-  if (classifierOpts.labels.length !== numLabels) {
+  if (opts.labels.length !== numLabels) {
     throw new Error(
-      `Classifier labels length (${classifierOpts.labels.length}) must match model output dimension (${numLabels}).`
+      `Classifier labels length (${opts.labels.length}) must match model output dimension (${numLabels}).`
     );
   }
 
@@ -100,7 +107,7 @@ export async function createClassifier<L>(
   ] as const;
 
   const [tLogits, tProbas] = tensors;
-  const preprocessor = createImagePreprocessor(classifierOpts, inpShape);
+  const preprocessor = createImagePreprocessor(opts, inpShape);
 
   const dispose = () => {
     preprocessor.dispose();
@@ -125,7 +132,7 @@ 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: opts.labels[index]! }))
       .sort((a, b) => b.confidence - a.confidence)
       .slice(0, options?.topk);
   };

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

@@ -13,7 +13,9 @@ import { createImagePreprocessor, type ImagePreprocessorOptions } from './prepro
  * @category Types
  */
 export type ImageEmbedderModel = {
+  /** Local path or remote URL of the `.pte` model file. */
   readonly modelPath: string;
+  /** Image preprocessor options. */
   readonly opts: ImagePreprocessorOptions;
 };
 

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. */
   readonly resizeMode: 'stretch';
+  /** Array of class labels matching the model's output vocabulary. */
   readonly labels: readonly L[];
+  /** Bounding box format exported by the model (`xyxy`, `cxcywh`, etc.). */
   readonly boxFormat: F;
+  /** Default 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;
 };
 
@@ -46,7 +52,9 @@ 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;
+  /** Instance segmenter options. */
   readonly opts: InstanceSegmenterOptions<F, L>;
 };
 
@@ -58,9 +66,13 @@ 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 +105,12 @@ 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 `opts.defaultConfidenceThreshold`.
+   * @param options.iouThreshold IoU threshold in NMS. If omitted, uses
+   * `opts.defaultIouThreshold`.
+   * @param options.maskThreshold Mask binarization threshold. If omitted,
+   * uses `opts.defaultMaskThreshold`.
    * @returns A promise resolving to a list of detected instances.
    */
   segmentInstances: (

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

@@ -22,10 +22,15 @@ export type KeypointDetectorOptions<F extends BoxFormat, L extends PropertyKey>
   ImagePreprocessorOptions,
   'resizeMode'
 > & {
+  /** Resize mode for preprocessing input images (`stretch` or `letterbox`). */
   readonly resizeMode: Exclude<ResizeMode, 'crop'>;
+  /** Bounding box format exported by the model (`xyxy`, `cxcywh`, etc.). */
   readonly boxFormat: F;
+  /** Array of landmark names matching the model output keypoint locations. */
   readonly landmarks: readonly L[];
+  /** Default IoU threshold for Non-Maximum Suppression (NMS). */
   readonly defaultIouThreshold: number;
+  /** Default minimum confidence score threshold for keypoint detections. */
   readonly defaultConfidenceThreshold: number;
 };
 
@@ -34,7 +39,9 @@ export type KeypointDetectorOptions<F extends BoxFormat, L extends PropertyKey>
  * @category Types
  */
 export type KeypointDetectorModel<F extends BoxFormat, L extends PropertyKey> = {
+  /** Local path or remote URL of the `.pte` model file. */
   readonly modelPath: string;
+  /** Keypoint detector options. */
   readonly opts: KeypointDetectorOptions<F, L>;
 };
 
@@ -51,8 +58,11 @@ export type Landmarks<L extends PropertyKey> = Record<L, Point & { readonly conf
  * @category Types
  */
 export type KeypointDetection<F extends BoxFormat, L extends PropertyKey> = {
+  /** Scaled bounding box coordinates matching the input image resolution. */
   readonly box: BoundingBox<F>;
+  /** Overall confidence score of the detection (between 0.0 and 1.0). */
   readonly confidence: number;
+  /** Map of landmark names to their scaled pixel coordinates and individual confidence scores. */
   readonly landmarks: Landmarks<L>;
 };
 
@@ -160,9 +170,9 @@ export async function createKeypointDetector<F extends BoxFormat, L extends Prop
    * @param input The input image buffer.
    * @param options Configuration options for keypoint detection.
    * @param options.confidenceThreshold Minimum confidence score for a
-   * detection. If omitted, uses the model default.
+   * detection. If omitted, uses `opts.defaultConfidenceThreshold`.
    * @param options.iouThreshold Intersection over Union (IoU) threshold for
-   * NMS. If omitted, uses the model default.
+   * NMS. If omitted, uses `opts.defaultIouThreshold`.
    * @returns A promise resolving to the list of keypoint detections.
    */
   detectKeypoints: (

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

@@ -21,10 +21,15 @@ export type ObjectDetectorOptions<F extends BoxFormat, L> = Omit<
   ImagePreprocessorOptions,
   'resizeMode'
 > & {
+  /** Resize mode for preprocessing input images (`stretch` or `letterbox`). */
   readonly resizeMode: Exclude<ResizeMode, 'crop'>;
+  /** Array of class labels matching the model's output vocabulary. */
   readonly labels: readonly L[];
+  /** Bounding box format exported by the model (`xyxy`, `cxcywh`, etc.). */
   readonly boxFormat: F;
+  /** Default IoU threshold for Non-Maximum Suppression (NMS). */
   readonly defaultIouThreshold: number;
+  /** Default minimum confidence score threshold for detections. */
   readonly defaultConfidenceThreshold: number;
 };
 
@@ -33,7 +38,9 @@ export type ObjectDetectorOptions<F extends BoxFormat, L> = Omit<
  * @category Types
  */
 export type ObjectDetectorModel<F extends BoxFormat, L> = {
+  /** Local path or remote URL of the `.pte` model file. */
   readonly modelPath: string;
+  /** Object detector preprocessor and threshold options. */
   readonly opts: ObjectDetectorOptions<F, L>;
 };
 
@@ -42,8 +49,11 @@ export type ObjectDetectorModel<F extends BoxFormat, L> = {
  * @category Types
  */
 export type ObjectDetection<F extends BoxFormat, L> = {
+  /** Scaled bounding box coordinates matching the input image resolution. */
   readonly box: BoundingBox<F>;
+  /** Predicted object class label. */
   readonly label: L;
+  /** Confidence score of the detection (between 0.0 and 1.0). */
   readonly confidence: number;
 };
 
@@ -72,12 +82,12 @@ export async function createObjectDetector<F extends BoxFormat, L>(
    */
   dispose: () => void;
   /**
-   * Performs asynchronous object detection on the given input image.
    * @param input The input image buffer.
    * @param options Configuration options for object detection.
-   * @param options.confidenceThreshold Minimum confidence score for returned
-   * detections.
-   * @param options.iouThreshold Non-maximum suppression IoU threshold.
+   * @param options.confidenceThreshold Minimum confidence score threshold. If
+   * omitted, uses `opts.defaultConfidenceThreshold`.
+   * @param options.iouThreshold Non-maximum suppression IoU threshold. If
+   * omitted, uses `opts.defaultIouThreshold`.
    * @returns A promise resolving to the list of object detections.
    */
   detectObjects: (

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

@@ -5,6 +5,7 @@ import type { ImageBuffer } from '../image';
 import {
   type ResizeMode,
   type InterpolationMethod,
+  type NormalizeOptions,
   FORMAT_CONVERSION,
   FORMAT_CHANNELS,
   resize,
@@ -18,10 +19,13 @@ import {
  * @category Types
  */
 export type ImagePreprocessorOptions = {
+  /** Resize algorithm mode (`stretch`, `letterbox`, `crop`). */
   readonly resizeMode: ResizeMode;
+  /** Pixel interpolation method (`linear`, `lanczos`, etc.). */
   readonly interpolation: InterpolationMethod;
-  readonly alpha: number | readonly number[];
-  readonly beta: number | readonly number[];
+  /** Normalization scaling coefficients. */
+  readonly normalizeOpts: NormalizeOptions;
+  /** Optional background fill value used when letterboxing. */
   readonly padValue?: number;
 };
 
@@ -84,7 +88,7 @@ export function createImagePreprocessor(
   ] as const;
 
   const [tColor, tChanFirst, tNorm, tOutput] = tensors;
-  const { resizeMode, interpolation, alpha, beta, padValue } = opts;
+  const { resizeMode, interpolation, normalizeOpts, padValue } = opts;
 
   const dispose = () => tensors.forEach((t) => t.dispose());
   const process = (input: ImageBuffer): Tensor => {
@@ -105,7 +109,7 @@ export function createImagePreprocessor(
         })
         .throughIf(colorCode !== null, cvtColor, tColor, colorCode!)
         .through(toChannelsFirst, tChanFirst)
-        .through(normalize, tNorm, { alpha, beta })
+        .through(normalize, tNorm, normalizeOpts)
         .copyTo(tOutput);
     } finally {
       tInput.dispose();