Support new output format for ImageSegmenter
PiperOrigin-RevId: 524371021
This commit is contained in:
parent
f5197a3adc
commit
92f45c98d8
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@ -59,13 +59,12 @@ export function drawCategoryMask(
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const isFloatArray = image instanceof Float32Array;
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for (let i = 0; i < image.length; i++) {
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const colorIndex = isFloatArray ? Math.round(image[i] * 255) : image[i];
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const color = COLOR_MAP[colorIndex];
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let color = COLOR_MAP[colorIndex % COLOR_MAP.length];
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// When we're given a confidence mask by accident, we just log and return.
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// TODO: We should fix this.
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if (!color) {
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// TODO: We should fix this.
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console.warn('No color for ', colorIndex);
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return;
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color = COLOR_MAP[colorIndex % COLOR_MAP.length];
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}
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rgbaArray[4 * i] = color[0];
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@ -29,7 +29,10 @@ mediapipe_ts_library(
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mediapipe_ts_declaration(
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name = "image_segmenter_types",
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srcs = ["image_segmenter_options.d.ts"],
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srcs = [
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"image_segmenter_options.d.ts",
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"image_segmenter_result.d.ts",
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],
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deps = [
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"//mediapipe/tasks/web/core",
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"//mediapipe/tasks/web/core:classifier_options",
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@ -22,33 +22,48 @@ import {ImageSegmenterGraphOptions as ImageSegmenterGraphOptionsProto} from '../
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import {SegmenterOptions as SegmenterOptionsProto} from '../../../../tasks/cc/vision/image_segmenter/proto/segmenter_options_pb';
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import {WasmFileset} from '../../../../tasks/web/core/wasm_fileset';
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import {ImageProcessingOptions} from '../../../../tasks/web/vision/core/image_processing_options';
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import {SegmentationMask, SegmentationMaskCallback} from '../../../../tasks/web/vision/core/types';
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import {SegmentationMask} from '../../../../tasks/web/vision/core/types';
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import {VisionGraphRunner, VisionTaskRunner} from '../../../../tasks/web/vision/core/vision_task_runner';
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import {LabelMapItem} from '../../../../util/label_map_pb';
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import {ImageSource, WasmModule} from '../../../../web/graph_runner/graph_runner';
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// Placeholder for internal dependency on trusted resource url
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import {ImageSegmenterOptions} from './image_segmenter_options';
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import {ImageSegmenterResult} from './image_segmenter_result';
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export * from './image_segmenter_options';
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export {SegmentationMask, SegmentationMaskCallback};
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export * from './image_segmenter_result';
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export {SegmentationMask};
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export {ImageSource}; // Used in the public API
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const IMAGE_STREAM = 'image_in';
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const NORM_RECT_STREAM = 'norm_rect';
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const GROUPED_SEGMENTATIONS_STREAM = 'segmented_masks';
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const CONFIDENCE_MASKS_STREAM = 'confidence_masks';
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const CATEGORY_MASK_STREAM = 'category_mask';
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const IMAGE_SEGMENTER_GRAPH =
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'mediapipe.tasks.vision.image_segmenter.ImageSegmenterGraph';
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const TENSORS_TO_SEGMENTATION_CALCULATOR_NAME =
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'mediapipe.tasks.TensorsToSegmentationCalculator';
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const DEFAULT_OUTPUT_CATEGORY_MASK = false;
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const DEFAULT_OUTPUT_CONFIDENCE_MASKS = true;
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// The OSS JS API does not support the builder pattern.
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// tslint:disable:jspb-use-builder-pattern
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/**
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* A callback that receives the computed masks from the image segmenter. The
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* returned data is only valid for the duration of the callback. If
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* asynchronous processing is needed, all data needs to be copied before the
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* callback returns.
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*/
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export type ImageSegmenterCallack = (result: ImageSegmenterResult) => void;
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/** Performs image segmentation on images. */
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export class ImageSegmenter extends VisionTaskRunner {
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private userCallback: SegmentationMaskCallback = () => {};
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private result: ImageSegmenterResult = {width: 0, height: 0};
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private labels: string[] = [];
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private outputCategoryMask = DEFAULT_OUTPUT_CATEGORY_MASK;
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private outputConfidenceMasks = DEFAULT_OUTPUT_CONFIDENCE_MASKS;
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private readonly options: ImageSegmenterGraphOptionsProto;
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private readonly segmenterOptions: SegmenterOptionsProto;
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@ -109,7 +124,6 @@ export class ImageSegmenter extends VisionTaskRunner {
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this.options.setBaseOptions(new BaseOptionsProto());
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}
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protected override get baseOptions(): BaseOptionsProto {
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return this.options.getBaseOptions()!;
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}
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@ -137,12 +151,14 @@ export class ImageSegmenter extends VisionTaskRunner {
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this.options.clearDisplayNamesLocale();
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}
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if (options.outputType === 'CONFIDENCE_MASK') {
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this.segmenterOptions.setOutputType(
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SegmenterOptionsProto.OutputType.CONFIDENCE_MASK);
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} else {
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this.segmenterOptions.setOutputType(
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SegmenterOptionsProto.OutputType.CATEGORY_MASK);
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if ('outputCategoryMask' in options) {
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this.outputCategoryMask =
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options.outputCategoryMask ?? DEFAULT_OUTPUT_CATEGORY_MASK;
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}
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if ('outputConfidenceMasks' in options) {
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this.outputConfidenceMasks =
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options.outputConfidenceMasks ?? DEFAULT_OUTPUT_CONFIDENCE_MASKS;
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}
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return super.applyOptions(options);
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@ -192,7 +208,7 @@ export class ImageSegmenter extends VisionTaskRunner {
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* lifetime of the returned data is only guaranteed for the duration of the
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* callback.
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*/
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segment(image: ImageSource, callback: SegmentationMaskCallback): void;
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segment(image: ImageSource, callback: ImageSegmenterCallack): void;
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/**
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* Performs image segmentation on the provided single image and invokes the
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* callback with the response. The method returns synchronously once the
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@ -208,22 +224,77 @@ export class ImageSegmenter extends VisionTaskRunner {
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*/
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segment(
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image: ImageSource, imageProcessingOptions: ImageProcessingOptions,
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callback: SegmentationMaskCallback): void;
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callback: ImageSegmenterCallack): void;
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segment(
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image: ImageSource,
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imageProcessingOptionsOrCallback: ImageProcessingOptions|
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SegmentationMaskCallback,
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callback?: SegmentationMaskCallback): void {
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ImageSegmenterCallack,
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callback?: ImageSegmenterCallack): void {
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const imageProcessingOptions =
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typeof imageProcessingOptionsOrCallback !== 'function' ?
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imageProcessingOptionsOrCallback :
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{};
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this.userCallback = typeof imageProcessingOptionsOrCallback === 'function' ?
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const userCallback =
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typeof imageProcessingOptionsOrCallback === 'function' ?
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imageProcessingOptionsOrCallback :
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callback!;
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this.reset();
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this.processImageData(image, imageProcessingOptions);
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this.userCallback = () => {};
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userCallback(this.result);
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}
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/**
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* Performs image segmentation on the provided video frame and invokes the
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* callback with the response. The method returns synchronously once the
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* callback returns. Only use this method when the ImageSegmenter is
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* created with running mode `video`.
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*
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* @param videoFrame A video frame to process.
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* @param timestamp The timestamp of the current frame, in ms.
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* @param callback The callback that is invoked with the segmented masks. The
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* lifetime of the returned data is only guaranteed for the duration of the
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* callback.
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*/
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segmentForVideo(
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videoFrame: ImageSource, timestamp: number,
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callback: ImageSegmenterCallack): void;
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/**
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* Performs image segmentation on the provided video frame and invokes the
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* callback with the response. The method returns synchronously once the
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* callback returns. Only use this method when the ImageSegmenter is
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* created with running mode `video`.
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*
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* @param videoFrame A video frame to process.
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* @param imageProcessingOptions the `ImageProcessingOptions` specifying how
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* to process the input image before running inference.
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* @param timestamp The timestamp of the current frame, in ms.
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* @param callback The callback that is invoked with the segmented masks. The
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* lifetime of the returned data is only guaranteed for the duration of the
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* callback.
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*/
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segmentForVideo(
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videoFrame: ImageSource, imageProcessingOptions: ImageProcessingOptions,
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timestamp: number, callback: ImageSegmenterCallack): void;
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segmentForVideo(
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videoFrame: ImageSource,
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timestampOrImageProcessingOptions: number|ImageProcessingOptions,
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timestampOrCallback: number|ImageSegmenterCallack,
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callback?: ImageSegmenterCallack): void {
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const imageProcessingOptions =
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typeof timestampOrImageProcessingOptions !== 'number' ?
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timestampOrImageProcessingOptions :
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{};
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const timestamp = typeof timestampOrImageProcessingOptions === 'number' ?
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timestampOrImageProcessingOptions :
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timestampOrCallback as number;
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const userCallback = typeof timestampOrCallback === 'function' ?
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timestampOrCallback :
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callback!;
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this.reset();
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this.processVideoData(videoFrame, imageProcessingOptions, timestamp);
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userCallback(this.result);
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}
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/**
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@ -241,56 +312,8 @@ export class ImageSegmenter extends VisionTaskRunner {
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return this.labels;
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}
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/**
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* Performs image segmentation on the provided video frame and invokes the
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* callback with the response. The method returns synchronously once the
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* callback returns. Only use this method when the ImageSegmenter is
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* created with running mode `video`.
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*
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* @param videoFrame A video frame to process.
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* @param timestamp The timestamp of the current frame, in ms.
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* @param callback The callback that is invoked with the segmented masks. The
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* lifetime of the returned data is only guaranteed for the duration of the
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* callback.
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*/
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segmentForVideo(
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videoFrame: ImageSource, timestamp: number,
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callback: SegmentationMaskCallback): void;
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/**
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* Performs image segmentation on the provided video frame and invokes the
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* callback with the response. The method returns synchronously once the
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* callback returns. Only use this method when the ImageSegmenter is
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* created with running mode `video`.
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*
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* @param videoFrame A video frame to process.
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* @param imageProcessingOptions the `ImageProcessingOptions` specifying how
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* to process the input image before running inference.
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* @param timestamp The timestamp of the current frame, in ms.
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* @param callback The callback that is invoked with the segmented masks. The
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* lifetime of the returned data is only guaranteed for the duration of the
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* callback.
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*/
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segmentForVideo(
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videoFrame: ImageSource, imageProcessingOptions: ImageProcessingOptions,
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timestamp: number, callback: SegmentationMaskCallback): void;
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segmentForVideo(
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videoFrame: ImageSource,
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timestampOrImageProcessingOptions: number|ImageProcessingOptions,
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timestampOrCallback: number|SegmentationMaskCallback,
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callback?: SegmentationMaskCallback): void {
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const imageProcessingOptions =
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typeof timestampOrImageProcessingOptions !== 'number' ?
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timestampOrImageProcessingOptions :
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{};
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const timestamp = typeof timestampOrImageProcessingOptions === 'number' ?
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timestampOrImageProcessingOptions :
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timestampOrCallback as number;
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this.userCallback = typeof timestampOrCallback === 'function' ?
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timestampOrCallback :
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callback!;
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this.processVideoData(videoFrame, imageProcessingOptions, timestamp);
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this.userCallback = () => {};
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private reset(): void {
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this.result = {width: 0, height: 0};
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}
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/** Updates the MediaPipe graph configuration. */
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@ -298,7 +321,6 @@ export class ImageSegmenter extends VisionTaskRunner {
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const graphConfig = new CalculatorGraphConfig();
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graphConfig.addInputStream(IMAGE_STREAM);
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graphConfig.addInputStream(NORM_RECT_STREAM);
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graphConfig.addOutputStream(GROUPED_SEGMENTATIONS_STREAM);
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const calculatorOptions = new CalculatorOptions();
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calculatorOptions.setExtension(
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@ -308,26 +330,47 @@ export class ImageSegmenter extends VisionTaskRunner {
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segmenterNode.setCalculator(IMAGE_SEGMENTER_GRAPH);
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segmenterNode.addInputStream('IMAGE:' + IMAGE_STREAM);
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segmenterNode.addInputStream('NORM_RECT:' + NORM_RECT_STREAM);
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segmenterNode.addOutputStream(
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'GROUPED_SEGMENTATION:' + GROUPED_SEGMENTATIONS_STREAM);
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segmenterNode.setOptions(calculatorOptions);
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graphConfig.addNode(segmenterNode);
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this.graphRunner.attachImageVectorListener(
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GROUPED_SEGMENTATIONS_STREAM, (masks, timestamp) => {
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if (masks.length === 0) {
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this.userCallback([], 0, 0);
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} else {
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this.userCallback(
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masks.map(m => m.data), masks[0].width, masks[0].height);
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}
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this.setLatestOutputTimestamp(timestamp);
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});
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this.graphRunner.attachEmptyPacketListener(
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GROUPED_SEGMENTATIONS_STREAM, timestamp => {
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this.setLatestOutputTimestamp(timestamp);
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});
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if (this.outputConfidenceMasks) {
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graphConfig.addOutputStream(CONFIDENCE_MASKS_STREAM);
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segmenterNode.addOutputStream(
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'CONFIDENCE_MASKS:' + CONFIDENCE_MASKS_STREAM);
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this.graphRunner.attachImageVectorListener(
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CONFIDENCE_MASKS_STREAM, (masks, timestamp) => {
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this.result.confidenceMasks = masks.map(m => m.data);
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if (masks.length >= 0) {
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this.result.width = masks[0].width;
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this.result.height = masks[0].height;
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}
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this.setLatestOutputTimestamp(timestamp);
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});
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this.graphRunner.attachEmptyPacketListener(
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CONFIDENCE_MASKS_STREAM, timestamp => {
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this.setLatestOutputTimestamp(timestamp);
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});
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}
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if (this.outputCategoryMask) {
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graphConfig.addOutputStream(CATEGORY_MASK_STREAM);
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segmenterNode.addOutputStream('CATEGORY_MASK:' + CATEGORY_MASK_STREAM);
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this.graphRunner.attachImageListener(
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CATEGORY_MASK_STREAM, (mask, timestamp) => {
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this.result.categoryMask = mask.data;
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this.result.width = mask.width;
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this.result.height = mask.height;
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this.setLatestOutputTimestamp(timestamp);
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});
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this.graphRunner.attachEmptyPacketListener(
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CATEGORY_MASK_STREAM, timestamp => {
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this.setLatestOutputTimestamp(timestamp);
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});
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}
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const binaryGraph = graphConfig.serializeBinary();
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this.setGraph(new Uint8Array(binaryGraph), /* isBinary= */ true);
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@ -24,18 +24,9 @@ export interface ImageSegmenterOptions extends VisionTaskOptions {
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*/
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displayNamesLocale?: string|undefined;
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/**
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* The output type of segmentation results.
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*
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* The two supported modes are:
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* - Category Mask: Gives a single output mask where each pixel represents
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* the class which the pixel in the original image was
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* predicted to belong to.
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* - Confidence Mask: Gives a list of output masks (one for each class). For
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* each mask, the pixel represents the prediction
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* confidence, usually in the [0.0, 0.1] range.
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*
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* Defaults to `CATEGORY_MASK`.
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*/
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outputType?: 'CATEGORY_MASK'|'CONFIDENCE_MASK'|undefined;
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/** Whether to output confidence masks. Defaults to true. */
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outputConfidenceMasks?: boolean|undefined;
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/** Whether to output the category masks. Defaults to false. */
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outputCategoryMask?: boolean|undefined;
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}
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37
mediapipe/tasks/web/vision/image_segmenter/image_segmenter_result.d.ts
vendored
Normal file
37
mediapipe/tasks/web/vision/image_segmenter/image_segmenter_result.d.ts
vendored
Normal file
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@ -0,0 +1,37 @@
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/**
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* Copyright 2023 The MediaPipe Authors. All Rights Reserved.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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/** The output result of ImageSegmenter. */
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export declare interface ImageSegmenterResult {
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/**
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* Multiple masks as Float32Arrays or WebGLTextures where, for each mask, each
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* pixel represents the prediction confidence, usually in the [0, 1] range.
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*/
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confidenceMasks?: Float32Array[]|WebGLTexture[];
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/**
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* A category mask as a Uint8ClampedArray or WebGLTexture where each
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* pixel represents the class which the pixel in the original image was
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* predicted to belong to.
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*/
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categoryMask?: Uint8ClampedArray|WebGLTexture;
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/** The width of the masks. */
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width: number;
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/** The height of the masks. */
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height: number;
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}
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@ -18,7 +18,7 @@ import 'jasmine';
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// Placeholder for internal dependency on encodeByteArray
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import {CalculatorGraphConfig} from '../../../../framework/calculator_pb';
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import {addJasmineCustomFloatEqualityTester, createSpyWasmModule, MediapipeTasksFake, SpyWasmModule, verifyGraph, verifyListenersRegistered} from '../../../../tasks/web/core/task_runner_test_utils';
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import {addJasmineCustomFloatEqualityTester, createSpyWasmModule, MediapipeTasksFake, SpyWasmModule, verifyGraph} from '../../../../tasks/web/core/task_runner_test_utils';
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import {WasmImage} from '../../../../web/graph_runner/graph_runner_image_lib';
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import {ImageSegmenter} from './image_segmenter';
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@ -30,7 +30,9 @@ class ImageSegmenterFake extends ImageSegmenter implements MediapipeTasksFake {
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graph: CalculatorGraphConfig|undefined;
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fakeWasmModule: SpyWasmModule;
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imageVectorListener:
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categoryMaskListener:
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((images: WasmImage, timestamp: number) => void)|undefined;
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confidenceMasksListener:
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((images: WasmImage[], timestamp: number) => void)|undefined;
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constructor() {
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@ -38,11 +40,16 @@ class ImageSegmenterFake extends ImageSegmenter implements MediapipeTasksFake {
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this.fakeWasmModule =
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this.graphRunner.wasmModule as unknown as SpyWasmModule;
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this.attachListenerSpies[0] =
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this.attachListenerSpies[0] = spyOn(this.graphRunner, 'attachImageListener')
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.and.callFake((stream, listener) => {
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expect(stream).toEqual('category_mask');
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this.categoryMaskListener = listener;
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});
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this.attachListenerSpies[1] =
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spyOn(this.graphRunner, 'attachImageVectorListener')
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.and.callFake((stream, listener) => {
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expect(stream).toEqual('segmented_masks');
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this.imageVectorListener = listener;
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expect(stream).toEqual('confidence_masks');
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this.confidenceMasksListener = listener;
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});
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spyOn(this.graphRunner, 'setGraph').and.callFake(binaryGraph => {
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this.graph = CalculatorGraphConfig.deserializeBinary(binaryGraph);
|
||||
|
@ -63,17 +70,18 @@ describe('ImageSegmenter', () => {
|
|||
|
||||
it('initializes graph', async () => {
|
||||
verifyGraph(imageSegmenter);
|
||||
verifyListenersRegistered(imageSegmenter);
|
||||
|
||||
// Verify default options
|
||||
expect(imageSegmenter.categoryMaskListener).not.toBeDefined();
|
||||
expect(imageSegmenter.confidenceMasksListener).toBeDefined();
|
||||
});
|
||||
|
||||
it('reloads graph when settings are changed', async () => {
|
||||
await imageSegmenter.setOptions({displayNamesLocale: 'en'});
|
||||
verifyGraph(imageSegmenter, ['displayNamesLocale', 'en']);
|
||||
verifyListenersRegistered(imageSegmenter);
|
||||
|
||||
await imageSegmenter.setOptions({displayNamesLocale: 'de'});
|
||||
verifyGraph(imageSegmenter, ['displayNamesLocale', 'de']);
|
||||
verifyListenersRegistered(imageSegmenter);
|
||||
});
|
||||
|
||||
it('can use custom models', async () => {
|
||||
|
@ -100,9 +108,11 @@ describe('ImageSegmenter', () => {
|
|||
});
|
||||
|
||||
it('merges options', async () => {
|
||||
await imageSegmenter.setOptions({outputType: 'CATEGORY_MASK'});
|
||||
await imageSegmenter.setOptions(
|
||||
{baseOptions: {modelAssetBuffer: new Uint8Array([])}});
|
||||
await imageSegmenter.setOptions({displayNamesLocale: 'en'});
|
||||
verifyGraph(imageSegmenter, [['segmenterOptions', 'outputType'], 1]);
|
||||
verifyGraph(
|
||||
imageSegmenter, [['baseOptions', 'modelAsset', 'fileContent'], '']);
|
||||
verifyGraph(imageSegmenter, ['displayNamesLocale', 'en']);
|
||||
});
|
||||
|
||||
|
@ -115,22 +125,13 @@ describe('ImageSegmenter', () => {
|
|||
defaultValue: unknown;
|
||||
}
|
||||
|
||||
const testCases: TestCase[] = [
|
||||
{
|
||||
optionName: 'displayNamesLocale',
|
||||
fieldPath: ['displayNamesLocale'],
|
||||
userValue: 'en',
|
||||
graphValue: 'en',
|
||||
defaultValue: 'en'
|
||||
},
|
||||
{
|
||||
optionName: 'outputType',
|
||||
fieldPath: ['segmenterOptions', 'outputType'],
|
||||
userValue: 'CONFIDENCE_MASK',
|
||||
graphValue: 2,
|
||||
defaultValue: 1
|
||||
},
|
||||
];
|
||||
const testCases: TestCase[] = [{
|
||||
optionName: 'displayNamesLocale',
|
||||
fieldPath: ['displayNamesLocale'],
|
||||
userValue: 'en',
|
||||
graphValue: 'en',
|
||||
defaultValue: 'en'
|
||||
}];
|
||||
|
||||
for (const testCase of testCases) {
|
||||
it(`can set ${testCase.optionName}`, async () => {
|
||||
|
@ -158,27 +159,31 @@ describe('ImageSegmenter', () => {
|
|||
}).toThrowError('This task doesn\'t support region-of-interest.');
|
||||
});
|
||||
|
||||
it('supports category masks', (done) => {
|
||||
it('supports category mask', async () => {
|
||||
const mask = new Uint8ClampedArray([1, 2, 3, 4]);
|
||||
|
||||
await imageSegmenter.setOptions(
|
||||
{outputCategoryMask: true, outputConfidenceMasks: false});
|
||||
|
||||
// Pass the test data to our listener
|
||||
imageSegmenter.fakeWasmModule._waitUntilIdle.and.callFake(() => {
|
||||
verifyListenersRegistered(imageSegmenter);
|
||||
imageSegmenter.imageVectorListener!(
|
||||
[
|
||||
{data: mask, width: 2, height: 2},
|
||||
],
|
||||
/* timestamp= */ 1337);
|
||||
expect(imageSegmenter.categoryMaskListener).toBeDefined();
|
||||
imageSegmenter.categoryMaskListener!
|
||||
({data: mask, width: 2, height: 2},
|
||||
/* timestamp= */ 1337);
|
||||
});
|
||||
|
||||
// Invoke the image segmenter
|
||||
imageSegmenter.segment({} as HTMLImageElement, (masks, width, height) => {
|
||||
expect(imageSegmenter.fakeWasmModule._waitUntilIdle).toHaveBeenCalled();
|
||||
expect(masks).toHaveSize(1);
|
||||
expect(masks[0]).toEqual(mask);
|
||||
expect(width).toEqual(2);
|
||||
expect(height).toEqual(2);
|
||||
done();
|
||||
|
||||
return new Promise<void>(resolve => {
|
||||
imageSegmenter.segment({} as HTMLImageElement, result => {
|
||||
expect(imageSegmenter.fakeWasmModule._waitUntilIdle).toHaveBeenCalled();
|
||||
expect(result.categoryMask).toEqual(mask);
|
||||
expect(result.confidenceMasks).not.toBeDefined();
|
||||
expect(result.width).toEqual(2);
|
||||
expect(result.height).toEqual(2);
|
||||
resolve();
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
|
@ -186,12 +191,13 @@ describe('ImageSegmenter', () => {
|
|||
const mask1 = new Float32Array([0.1, 0.2, 0.3, 0.4]);
|
||||
const mask2 = new Float32Array([0.5, 0.6, 0.7, 0.8]);
|
||||
|
||||
await imageSegmenter.setOptions({outputType: 'CONFIDENCE_MASK'});
|
||||
await imageSegmenter.setOptions(
|
||||
{outputCategoryMask: false, outputConfidenceMasks: true});
|
||||
|
||||
// Pass the test data to our listener
|
||||
imageSegmenter.fakeWasmModule._waitUntilIdle.and.callFake(() => {
|
||||
verifyListenersRegistered(imageSegmenter);
|
||||
imageSegmenter.imageVectorListener!(
|
||||
expect(imageSegmenter.confidenceMasksListener).toBeDefined();
|
||||
imageSegmenter.confidenceMasksListener!(
|
||||
[
|
||||
{data: mask1, width: 2, height: 2},
|
||||
{data: mask2, width: 2, height: 2},
|
||||
|
@ -201,13 +207,49 @@ describe('ImageSegmenter', () => {
|
|||
|
||||
return new Promise<void>(resolve => {
|
||||
// Invoke the image segmenter
|
||||
imageSegmenter.segment({} as HTMLImageElement, (masks, width, height) => {
|
||||
imageSegmenter.segment({} as HTMLImageElement, result => {
|
||||
expect(imageSegmenter.fakeWasmModule._waitUntilIdle).toHaveBeenCalled();
|
||||
expect(masks).toHaveSize(2);
|
||||
expect(masks[0]).toEqual(mask1);
|
||||
expect(masks[1]).toEqual(mask2);
|
||||
expect(width).toEqual(2);
|
||||
expect(height).toEqual(2);
|
||||
expect(result.categoryMask).not.toBeDefined();
|
||||
expect(result.confidenceMasks).toEqual([mask1, mask2]);
|
||||
expect(result.width).toEqual(2);
|
||||
expect(result.height).toEqual(2);
|
||||
resolve();
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
it('supports combined category and confidence masks', async () => {
|
||||
const categoryMask = new Uint8ClampedArray([1, 0]);
|
||||
const confidenceMask1 = new Float32Array([0.0, 1.0]);
|
||||
const confidenceMask2 = new Float32Array([1.0, 0.0]);
|
||||
|
||||
await imageSegmenter.setOptions(
|
||||
{outputCategoryMask: true, outputConfidenceMasks: true});
|
||||
|
||||
// Pass the test data to our listener
|
||||
imageSegmenter.fakeWasmModule._waitUntilIdle.and.callFake(() => {
|
||||
expect(imageSegmenter.categoryMaskListener).toBeDefined();
|
||||
expect(imageSegmenter.confidenceMasksListener).toBeDefined();
|
||||
imageSegmenter.categoryMaskListener!
|
||||
({data: categoryMask, width: 1, height: 1}, 1337);
|
||||
imageSegmenter.confidenceMasksListener!(
|
||||
[
|
||||
{data: confidenceMask1, width: 1, height: 1},
|
||||
{data: confidenceMask2, width: 1, height: 1},
|
||||
],
|
||||
1337);
|
||||
});
|
||||
|
||||
return new Promise<void>(resolve => {
|
||||
// Invoke the image segmenter
|
||||
imageSegmenter.segment({} as HTMLImageElement, result => {
|
||||
expect(imageSegmenter.fakeWasmModule._waitUntilIdle).toHaveBeenCalled();
|
||||
expect(result.categoryMask).toEqual(categoryMask);
|
||||
expect(result.confidenceMasks).toEqual([
|
||||
confidenceMask1, confidenceMask2
|
||||
]);
|
||||
expect(result.width).toEqual(1);
|
||||
expect(result.height).toEqual(1);
|
||||
resolve();
|
||||
});
|
||||
});
|
||||
|
|
Loading…
Reference in New Issue
Block a user