Add MediaPipe Image Segmenter task for Web

PiperOrigin-RevId: 504912518

mediapipe/tasks/web/vision/BUILD

@@ -23,6 +23,7 @@ VISION_LIBS = [
     "//mediapipe/tasks/web/vision/hand_landmarker",
     "//mediapipe/tasks/web/vision/image_classifier",
     "//mediapipe/tasks/web/vision/image_embedder",
+    "//mediapipe/tasks/web/vision/image_segmenter",
     "//mediapipe/tasks/web/vision/object_detector",
 ]
 

mediapipe/tasks/web/vision/README.md

@@ -39,6 +39,23 @@ const classifications = imageClassifier.classify(image);
 
 For more information, refer to the [Image Classification](https://developers.google.com/mediapipe/solutions/vision/image_classifier/web_js) documentation.
 
+## Image Segmentation
+
+The MediaPipe Image Segmenter lets you segment an image into categories.
+
+```
+const vision = await FilesetResolver.forVisionTasks(
+    "https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@latest/wasm"
+);
+const imageSegmenter = await ImageSegmenter.createFromModelPath(vision,
+    "model.tflite"
+);
+const image = document.getElementById("image") as HTMLImageElement;
+imageSegmenter.segment(image, (masks, width, height) => {
+  ...
+});
+```
+
 ## Gesture Recognition
 
 The MediaPipe Gesture Recognizer task lets you recognize hand gestures in real

mediapipe/tasks/web/vision/image_segmenter/BUILD

@@ -0,0 +1,58 @@
+# This contains the MediaPipe Image Segmenter Task.
+
+load("//mediapipe/framework/port:build_config.bzl", "mediapipe_ts_declaration", "mediapipe_ts_library")
+load("@npm//@bazel/jasmine:index.bzl", "jasmine_node_test")
+
+package(default_visibility = ["//mediapipe/tasks:internal"])
+
+licenses(["notice"])
+
+mediapipe_ts_library(
+    name = "image_segmenter",
+    srcs = ["image_segmenter.ts"],
+    deps = [
+        ":image_segmenter_types",
+        "//mediapipe/framework:calculator_jspb_proto",
+        "//mediapipe/framework:calculator_options_jspb_proto",
+        "//mediapipe/tasks/cc/core/proto:base_options_jspb_proto",
+        "//mediapipe/tasks/cc/vision/image_segmenter/proto:image_segmenter_graph_options_jspb_proto",
+        "//mediapipe/tasks/cc/vision/image_segmenter/proto:segmenter_options_jspb_proto",
+        "//mediapipe/tasks/web/core",
+        "//mediapipe/tasks/web/vision/core:image_processing_options",
+        "//mediapipe/tasks/web/vision/core:vision_task_runner",
+        "//mediapipe/web/graph_runner:graph_runner_image_lib_ts",
+        "//mediapipe/web/graph_runner:graph_runner_ts",
+    ],
+)
+
+mediapipe_ts_declaration(
+    name = "image_segmenter_types",
+    srcs = ["image_segmenter_options.d.ts"],
+    deps = [
+        "//mediapipe/tasks/web/core",
+        "//mediapipe/tasks/web/core:classifier_options",
+        "//mediapipe/tasks/web/vision/core:vision_task_options",
+    ],
+)
+
+mediapipe_ts_library(
+    name = "image_segmenter_test_lib",
+    testonly = True,
+    srcs = [
+        "image_segmenter_test.ts",
+    ],
+    deps = [
+        ":image_segmenter",
+        ":image_segmenter_types",
+        "//mediapipe/framework:calculator_jspb_proto",
+        "//mediapipe/tasks/web/core",
+        "//mediapipe/tasks/web/core:task_runner_test_utils",
+        "//mediapipe/web/graph_runner:graph_runner_image_lib_ts",
+    ],
+)
+
+jasmine_node_test(
+    name = "image_segmenter_test",
+    tags = ["nomsan"],
+    deps = [":image_segmenter_test_lib"],
+)

mediapipe/tasks/web/vision/image_segmenter/image_segmenter.ts

@@ -0,0 +1,300 @@
+/**
+ * Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+import {CalculatorGraphConfig} from '../../../../framework/calculator_pb';
+import {CalculatorOptions} from '../../../../framework/calculator_options_pb';
+import {BaseOptions as BaseOptionsProto} from '../../../../tasks/cc/core/proto/base_options_pb';
+import {ImageSegmenterGraphOptions as ImageSegmenterGraphOptionsProto} from '../../../../tasks/cc/vision/image_segmenter/proto/image_segmenter_graph_options_pb';
+import {SegmenterOptions as SegmenterOptionsProto} from '../../../../tasks/cc/vision/image_segmenter/proto/segmenter_options_pb';
+import {WasmFileset} from '../../../../tasks/web/core/wasm_fileset';
+import {ImageProcessingOptions} from '../../../../tasks/web/vision/core/image_processing_options';
+import {VisionGraphRunner, VisionTaskRunner} from '../../../../tasks/web/vision/core/vision_task_runner';
+import {ImageSource, WasmModule} from '../../../../web/graph_runner/graph_runner';
+// Placeholder for internal dependency on trusted resource url
+
+import {ImageSegmenterOptions} from './image_segmenter_options';
+
+export * from './image_segmenter_options';
+export {ImageSource};  // Used in the public API
+
+/**
+ * The ImageSegmenter returns the segmentation result as a Uint8Array (when
+ * the default mode of `CATEGORY_MASK` is used) or as a Float32Array (for
+ * output type `CONFIDENCE_MASK`). The `WebGLTexture` output type is reserved
+ * for future usage.
+ */
+export type SegmentationMask = Uint8Array|Float32Array|WebGLTexture;
+
+/**
+ * A callback that receives the computed masks from the image segmenter. The
+ * callback either receives a single element array with a category mask (as a
+ * `[Uint8Array]`) or multiple confidence masks (as a `Float32Array[]`).
+ * The returned data is only valid for the duration of the callback. If
+ * asynchronous processing is needed, all data needs to be copied before the
+ * callback returns.
+ */
+export type SegmentationMaskCallback =
+    (masks: SegmentationMask[], width: number, height: number) => void;
+
+const IMAGE_STREAM = 'image_in';
+const NORM_RECT_STREAM = 'norm_rect';
+const GROUPED_SEGMENTATIONS_STREAM = 'segmented_masks';
+const IMAGEA_SEGMENTER_GRAPH =
+    'mediapipe.tasks.vision.image_segmenter.ImageSegmenterGraph';
+
+// The OSS JS API does not support the builder pattern.
+// tslint:disable:jspb-use-builder-pattern
+
+/** Performs image segmentation on images. */
+export class ImageSegmenter extends VisionTaskRunner {
+  private userCallback: SegmentationMaskCallback = () => {};
+  private readonly options: ImageSegmenterGraphOptionsProto;
+  private readonly segmenterOptions: SegmenterOptionsProto;
+
+  /**
+   * Initializes the Wasm runtime and creates a new image segmenter from the
+   * provided options.
+   * @param wasmFileset A configuration object that provides the location of
+   *     the Wasm binary and its loader.
+   * @param imageSegmenterOptions The options for the Image Segmenter. Note
+   *     that either a path to the model asset or a model buffer needs to be
+   *     provided (via `baseOptions`).
+   */
+  static createFromOptions(
+      wasmFileset: WasmFileset,
+      imageSegmenterOptions: ImageSegmenterOptions): Promise<ImageSegmenter> {
+    return VisionTaskRunner.createInstance(
+        ImageSegmenter, /* initializeCanvas= */ true, wasmFileset,
+        imageSegmenterOptions);
+  }
+
+  /**
+   * Initializes the Wasm runtime and creates a new image segmenter based on
+   * the provided model asset buffer.
+   * @param wasmFileset A configuration object that provides the location of
+   *     the Wasm binary and its loader.
+   * @param modelAssetBuffer A binary representation of the model.
+   */
+  static createFromModelBuffer(
+      wasmFileset: WasmFileset,
+      modelAssetBuffer: Uint8Array): Promise<ImageSegmenter> {
+    return VisionTaskRunner.createInstance(
+        ImageSegmenter, /* initializeCanvas= */ true, wasmFileset,
+        {baseOptions: {modelAssetBuffer}});
+  }
+
+  /**
+   * Initializes the Wasm runtime and creates a new image segmenter based on
+   * the path to the model asset.
+   * @param wasmFileset A configuration object that provides the location of
+   *     the Wasm binary and its loader.
+   * @param modelAssetPath The path to the model asset.
+   */
+  static createFromModelPath(
+      wasmFileset: WasmFileset,
+      modelAssetPath: string): Promise<ImageSegmenter> {
+    return VisionTaskRunner.createInstance(
+        ImageSegmenter, /* initializeCanvas= */ true, wasmFileset,
+        {baseOptions: {modelAssetPath}});
+  }
+
+  /** @hideconstructor */
+  constructor(
+      wasmModule: WasmModule,
+      glCanvas?: HTMLCanvasElement|OffscreenCanvas|null) {
+    super(
+        new VisionGraphRunner(wasmModule, glCanvas), IMAGE_STREAM,
+        NORM_RECT_STREAM, /* roiAllowed= */ false);
+    this.options = new ImageSegmenterGraphOptionsProto();
+    this.segmenterOptions = new SegmenterOptionsProto();
+    this.options.setSegmenterOptions(this.segmenterOptions);
+    this.options.setBaseOptions(new BaseOptionsProto());
+  }
+
+
+  protected override get baseOptions(): BaseOptionsProto {
+    return this.options.getBaseOptions()!;
+  }
+
+  protected override set baseOptions(proto: BaseOptionsProto) {
+    this.options.setBaseOptions(proto);
+  }
+
+  /**
+   * Sets new options for the image segmenter.
+   *
+   * Calling `setOptions()` with a subset of options only affects those
+   * options. You can reset an option back to its default value by
+   * explicitly setting it to `undefined`.
+   *
+   * @param options The options for the image segmenter.
+   */
+  override setOptions(options: ImageSegmenterOptions): Promise<void> {
+    // Note that we have to support both JSPB and ProtobufJS, hence we
+    // have to expliclity clear the values instead of setting them to
+    // `undefined`.
+    if (options.displayNamesLocale !== undefined) {
+      this.options.setDisplayNamesLocale(options.displayNamesLocale);
+    } else if ('displayNamesLocale' in options) {  // Check for undefined
+      this.options.clearDisplayNamesLocale();
+    }
+
+    if (options.outputType === 'CONFIDENCE_MASK') {
+      this.segmenterOptions.setOutputType(
+          SegmenterOptionsProto.OutputType.CONFIDENCE_MASK);
+    } else {
+      this.segmenterOptions.setOutputType(
+          SegmenterOptionsProto.OutputType.CATEGORY_MASK);
+    }
+
+    return super.applyOptions(options);
+  }
+
+  /**
+   * Performs image segmentation on the provided single image and invokes the
+   * callback with the response. The method returns synchronously once the
+   * callback returns. Only use this method when the ImageSegmenter is
+   * created with running mode `image`.
+   *
+   * @param image An image to process.
+   * @param callback The callback that is invoked with the segmented masks. The
+   *    lifetime of the returned data is only guaranteed for the duration of the
+   *    callback.
+   */
+  segment(image: ImageSource, callback: SegmentationMaskCallback): void;
+  /**
+   * Performs image segmentation on the provided single image and invokes the
+   * callback with the response. The method returns synchronously once the
+   * callback returns. Only use this method when the ImageSegmenter is
+   * created with running mode `image`.
+   *
+   * @param image An image to process.
+   * @param imageProcessingOptions the `ImageProcessingOptions` specifying how
+   *    to process the input image before running inference.
+   * @param callback The callback that is invoked with the segmented masks. The
+   *    lifetime of the returned data is only guaranteed for the duration of the
+   *    callback.
+   */
+  segment(
+      image: ImageSource, imageProcessingOptions: ImageProcessingOptions,
+      callback: SegmentationMaskCallback): void;
+  segment(
+      image: ImageSource,
+      imageProcessingOptionsOrCallback: ImageProcessingOptions|
+      SegmentationMaskCallback,
+      callback?: SegmentationMaskCallback): void {
+    const imageProcessingOptions =
+        typeof imageProcessingOptionsOrCallback !== 'function' ?
+        imageProcessingOptionsOrCallback :
+        {};
+
+    this.userCallback = typeof imageProcessingOptionsOrCallback === 'function' ?
+        imageProcessingOptionsOrCallback :
+        callback!;
+    this.processImageData(image, imageProcessingOptions);
+    this.userCallback = () => {};
+  }
+
+  /**
+   * Performs image segmentation on the provided video frame and invokes the
+   * callback with the response. The method returns synchronously once the
+   * callback returns. Only use this method when the ImageSegmenter is
+   * created with running mode `video`.
+   *
+   * @param videoFrame A video frame to process.
+   * @param timestamp The timestamp of the current frame, in ms.
+   * @param callback The callback that is invoked with the segmented masks. The
+   *    lifetime of the returned data is only guaranteed for the duration of the
+   *    callback.
+   */
+  segmentForVideo(
+      videoFrame: ImageSource, timestamp: number,
+      callback: SegmentationMaskCallback): void;
+  /**
+   * Performs image segmentation on the provided video frame and invokes the
+   * callback with the response. The method returns synchronously once the
+   * callback returns. Only use this method when the ImageSegmenter is
+   * created with running mode `video`.
+   *
+   * @param videoFrame A video frame to process.
+   * @param imageProcessingOptions the `ImageProcessingOptions` specifying how
+   *    to process the input image before running inference.
+   * @param timestamp The timestamp of the current frame, in ms.
+   * @param callback The callback that is invoked with the segmented masks. The
+   *    lifetime of the returned data is only guaranteed for the duration of the
+   *    callback.
+   */
+  segmentForVideo(
+      videoFrame: ImageSource, imageProcessingOptions: ImageProcessingOptions,
+      timestamp: number, callback: SegmentationMaskCallback): void;
+  segmentForVideo(
+      videoFrame: ImageSource,
+      timestampOrImageProcessingOptions: number|ImageProcessingOptions,
+      timestampOrCallback: number|SegmentationMaskCallback,
+      callback?: SegmentationMaskCallback): void {
+    const imageProcessingOptions =
+        typeof timestampOrImageProcessingOptions !== 'number' ?
+        timestampOrImageProcessingOptions :
+        {};
+    const timestamp = typeof timestampOrImageProcessingOptions === 'number' ?
+        timestampOrImageProcessingOptions :
+        timestampOrCallback as number;
+
+    this.userCallback = typeof timestampOrCallback === 'function' ?
+        timestampOrCallback :
+        callback!;
+    this.processVideoData(videoFrame, imageProcessingOptions, timestamp);
+    this.userCallback = () => {};
+  }
+
+  /** Updates the MediaPipe graph configuration. */
+  protected override refreshGraph(): void {
+    const graphConfig = new CalculatorGraphConfig();
+    graphConfig.addInputStream(IMAGE_STREAM);
+    graphConfig.addInputStream(NORM_RECT_STREAM);
+    graphConfig.addOutputStream(GROUPED_SEGMENTATIONS_STREAM);
+
+    const calculatorOptions = new CalculatorOptions();
+    calculatorOptions.setExtension(
+        ImageSegmenterGraphOptionsProto.ext, this.options);
+
+    const segmenterNode = new CalculatorGraphConfig.Node();
+    segmenterNode.setCalculator(IMAGEA_SEGMENTER_GRAPH);
+    segmenterNode.addInputStream('IMAGE:' + IMAGE_STREAM);
+    segmenterNode.addInputStream('NORM_RECT:' + NORM_RECT_STREAM);
+    segmenterNode.addOutputStream(
+        'GROUPED_SEGMENTATION:' + GROUPED_SEGMENTATIONS_STREAM);
+    segmenterNode.setOptions(calculatorOptions);
+
+    graphConfig.addNode(segmenterNode);
+
+    this.graphRunner.attachImageVectorListener(
+        GROUPED_SEGMENTATIONS_STREAM, (masks, timestamp) => {
+          if (masks.length === 0) {
+            this.userCallback([], 0, 0);
+          } else {
+            this.userCallback(
+                masks.map(m => m.data), masks[0].width, masks[0].height);
+          }
+          this.setLatestOutputTimestamp(timestamp);
+        });
+
+    const binaryGraph = graphConfig.serializeBinary();
+    this.setGraph(new Uint8Array(binaryGraph), /* isBinary= */ true);
+  }
+}
+
+

mediapipe/tasks/web/vision/image_segmenter/image_segmenter_options.d.ts

@@ -0,0 +1,41 @@
+/**
+ * Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+import {VisionTaskOptions} from '../../../../tasks/web/vision/core/vision_task_options';
+
+/** Options to configure the MediaPipe Image Segmenter Task */
+export interface ImageSegmenterOptions extends VisionTaskOptions {
+  /**
+   * The locale to use for display names specified through the TFLite Model
+   * Metadata, if any. Defaults to English.
+   */
+  displayNamesLocale?: string|undefined;
+
+  /**
+   * The output type of segmentation results.
+   *
+   * The two supported modes are:
+   * - Category Mask:   Gives a single output mask where each pixel represents
+   *                    the class which the pixel in the original image was
+   *                    predicted to belong to.
+   * - Confidence Mask: Gives a list of output masks (one for each class). For
+   *                    each mask, the pixel represents the prediction
+   *                    confidence, usually in the [0.0, 0.1] range.
+   *
+   * Defaults to `CATEGORY_MASK`.
+   */
+  outputType?: 'CATEGORY_MASK'|'CONFIDENCE_MASK'|undefined;
+}

mediapipe/tasks/web/vision/image_segmenter/image_segmenter_test.ts

@@ -0,0 +1,215 @@
+/**
+ * Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+import 'jasmine';
+
+// Placeholder for internal dependency on encodeByteArray
+import {CalculatorGraphConfig} from '../../../../framework/calculator_pb';
+import {addJasmineCustomFloatEqualityTester, createSpyWasmModule, MediapipeTasksFake, SpyWasmModule, verifyGraph, verifyListenersRegistered} from '../../../../tasks/web/core/task_runner_test_utils';
+import {WasmImage} from '../../../../web/graph_runner/graph_runner_image_lib';
+
+import {ImageSegmenter} from './image_segmenter';
+import {ImageSegmenterOptions} from './image_segmenter_options';
+
+class ImageSegmenterFake extends ImageSegmenter implements MediapipeTasksFake {
+  calculatorName = 'mediapipe.tasks.vision.image_segmenter.ImageSegmenterGraph';
+  attachListenerSpies: jasmine.Spy[] = [];
+  graph: CalculatorGraphConfig|undefined;
+
+  fakeWasmModule: SpyWasmModule;
+  imageVectorListener:
+      ((images: WasmImage[], timestamp: number) => void)|undefined;
+
+  constructor() {
+    super(createSpyWasmModule(), /* glCanvas= */ null);
+    this.fakeWasmModule =
+        this.graphRunner.wasmModule as unknown as SpyWasmModule;
+
+    this.attachListenerSpies[0] =
+        spyOn(this.graphRunner, 'attachImageVectorListener')
+            .and.callFake((stream, listener) => {
+              expect(stream).toEqual('segmented_masks');
+              this.imageVectorListener = listener;
+            });
+    spyOn(this.graphRunner, 'setGraph').and.callFake(binaryGraph => {
+      this.graph = CalculatorGraphConfig.deserializeBinary(binaryGraph);
+    });
+    spyOn(this.graphRunner, 'addGpuBufferAsImageToStream');
+  }
+}
+
+describe('ImageSegmenter', () => {
+  let imageSegmenter: ImageSegmenterFake;
+
+  beforeEach(async () => {
+    addJasmineCustomFloatEqualityTester();
+    imageSegmenter = new ImageSegmenterFake();
+    await imageSegmenter.setOptions(
+        {baseOptions: {modelAssetBuffer: new Uint8Array([])}});
+  });
+
+  it('initializes graph', async () => {
+    verifyGraph(imageSegmenter);
+    verifyListenersRegistered(imageSegmenter);
+  });
+
+  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 () => {
+    const newModel = new Uint8Array([0, 1, 2, 3, 4]);
+    const newModelBase64 = Buffer.from(newModel).toString('base64');
+    await imageSegmenter.setOptions({
+      baseOptions: {
+        modelAssetBuffer: newModel,
+      }
+    });
+
+    verifyGraph(
+        imageSegmenter,
+        /* expectedCalculatorOptions= */ undefined,
+        /* expectedBaseOptions= */
+        [
+          'modelAsset', {
+            fileContent: newModelBase64,
+            fileName: undefined,
+            fileDescriptorMeta: undefined,
+            filePointerMeta: undefined
+          }
+        ]);
+  });
+
+  it('merges options', async () => {
+    await imageSegmenter.setOptions({outputType: 'CATEGORY_MASK'});
+    await imageSegmenter.setOptions({displayNamesLocale: 'en'});
+    verifyGraph(imageSegmenter, [['segmenterOptions', 'outputType'], 1]);
+    verifyGraph(imageSegmenter, ['displayNamesLocale', 'en']);
+  });
+
+  describe('setOptions()', () => {
+    interface TestCase {
+      optionName: keyof ImageSegmenterOptions;
+      fieldPath: string[];
+      userValue: unknown;
+      graphValue: unknown;
+      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
+      },
+    ];
+
+    for (const testCase of testCases) {
+      it(`can set ${testCase.optionName}`, async () => {
+        await imageSegmenter.setOptions(
+            {[testCase.optionName]: testCase.userValue});
+        verifyGraph(imageSegmenter, [testCase.fieldPath, testCase.graphValue]);
+      });
+
+      it(`can clear ${testCase.optionName}`, async () => {
+        await imageSegmenter.setOptions(
+            {[testCase.optionName]: testCase.userValue});
+        verifyGraph(imageSegmenter, [testCase.fieldPath, testCase.graphValue]);
+        await imageSegmenter.setOptions({[testCase.optionName]: undefined});
+        verifyGraph(
+            imageSegmenter, [testCase.fieldPath, testCase.defaultValue]);
+      });
+    }
+  });
+
+  it('doesn\'t support region of interest', () => {
+    expect(() => {
+      imageSegmenter.segment(
+          {} as HTMLImageElement,
+          {regionOfInterest: {left: 0, right: 0, top: 0, bottom: 0}}, () => {});
+    }).toThrowError('This task doesn\'t support region-of-interest.');
+  });
+
+  it('supports category masks', (done) => {
+    const mask = new Uint8Array([1, 2, 3, 4]);
+
+    // Pass the test data to our listener
+    imageSegmenter.fakeWasmModule._waitUntilIdle.and.callFake(() => {
+      verifyListenersRegistered(imageSegmenter);
+      imageSegmenter.imageVectorListener!(
+          [
+            {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();
+    });
+  });
+
+  it('supports confidence masks', async () => {
+    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'});
+
+    // Pass the test data to our listener
+    imageSegmenter.fakeWasmModule._waitUntilIdle.and.callFake(() => {
+      verifyListenersRegistered(imageSegmenter);
+      imageSegmenter.imageVectorListener!(
+          [
+            {data: mask1, width: 2, height: 2},
+            {data: mask2, width: 2, height: 2},
+          ],
+          1337);
+    });
+
+    return new Promise<void>(resolve => {
+      // Invoke the image segmenter
+      imageSegmenter.segment({} as HTMLImageElement, (masks, width, height) => {
+        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);
+        resolve();
+      });
+    });
+  });
+});

mediapipe/tasks/web/vision/index.ts

@@ -19,6 +19,7 @@ import {GestureRecognizer as GestureRecognizerImpl} from '../../../tasks/web/vis
 import {HandLandmarker as HandLandmarkerImpl} from '../../../tasks/web/vision/hand_landmarker/hand_landmarker';
 import {ImageClassifier as ImageClassifierImpl} from '../../../tasks/web/vision/image_classifier/image_classifier';
 import {ImageEmbedder as ImageEmbedderImpl} from '../../../tasks/web/vision/image_embedder/image_embedder';
+import {ImageSegmenter as ImageSegementerImpl} from '../../../tasks/web/vision/image_segmenter/image_segmenter';
 import {ObjectDetector as ObjectDetectorImpl} from '../../../tasks/web/vision/object_detector/object_detector';
 
 // Declare the variables locally so that Rollup in OSS includes them explicitly
@@ -28,6 +29,7 @@ const GestureRecognizer = GestureRecognizerImpl;
 const HandLandmarker = HandLandmarkerImpl;
 const ImageClassifier = ImageClassifierImpl;
 const ImageEmbedder = ImageEmbedderImpl;
+const ImageSegmenter = ImageSegementerImpl;
 const ObjectDetector = ObjectDetectorImpl;
 
 export {
@@ -36,5 +38,6 @@ export {
   HandLandmarker,
   ImageClassifier,
   ImageEmbedder,
+  ImageSegmenter,
   ObjectDetector
 };

mediapipe/tasks/web/vision/types.ts

@@ -19,4 +19,5 @@ export * from '../../../tasks/web/vision/gesture_recognizer/gesture_recognizer';
 export * from '../../../tasks/web/vision/hand_landmarker/hand_landmarker';
 export * from '../../../tasks/web/vision/image_classifier/image_classifier';
 export * from '../../../tasks/web/vision/image_embedder/image_embedder';
+export * from '../../../tasks/web/vision/image_segmenter/image_segmenter';
 export * from '../../../tasks/web/vision/object_detector/object_detector';