Support new output format for InteractiveSegmenter

PiperOrigin-RevId: 524940992

mediapipe/tasks/web/vision/core/types.d.ts

@@ -25,17 +25,6 @@ import {NormalizedKeypoint} from '../../../../tasks/web/components/containers/ke
  */
 export type SegmentationMask = Uint8ClampedArray|Float32Array|WebGLTexture;
 
-/**
- * A callback that receives the computed masks from the segmentation tasks. The
- * callback either receives a single element array with a category mask (as a
- * `[Uint8ClampedArray]`) 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;
-
 /**
  * A callback that receives an `ImageData` object from a Vision task. The
  * lifetime of the underlying data is limited to the duration of the callback.

mediapipe/tasks/web/vision/interactive_segmenter/BUILD

@@ -30,7 +30,10 @@ mediapipe_ts_library(
 
 mediapipe_ts_declaration(
     name = "interactive_segmenter_types",
-    srcs = ["interactive_segmenter_options.d.ts"],
+    srcs = [
+        "interactive_segmenter_options.d.ts",
+        "interactive_segmenter_result.d.ts",
+    ],
     deps = [
         "//mediapipe/tasks/web/core",
         "//mediapipe/tasks/web/core:classifier_options",

mediapipe/tasks/web/vision/interactive_segmenter/interactive_segmenter.ts

@@ -21,7 +21,7 @@ import {ImageSegmenterGraphOptions as ImageSegmenterGraphOptionsProto} from '../
 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 {RegionOfInterest, SegmentationMask, SegmentationMaskCallback} from '../../../../tasks/web/vision/core/types';
+import {RegionOfInterest, SegmentationMask} from '../../../../tasks/web/vision/core/types';
 import {VisionGraphRunner, VisionTaskRunner} from '../../../../tasks/web/vision/core/vision_task_runner';
 import {Color as ColorProto} from '../../../../util/color_pb';
 import {RenderAnnotation as RenderAnnotationProto, RenderData as RenderDataProto} from '../../../../util/render_data_pb';
@@ -29,21 +29,35 @@ import {ImageSource, WasmModule} from '../../../../web/graph_runner/graph_runner
 // Placeholder for internal dependency on trusted resource url
 
 import {InteractiveSegmenterOptions} from './interactive_segmenter_options';
+import {InteractiveSegmenterResult} from './interactive_segmenter_result';
 
 export * from './interactive_segmenter_options';
-export {SegmentationMask, SegmentationMaskCallback, RegionOfInterest};
+export * from './interactive_segmenter_result';
+export {SegmentationMask, RegionOfInterest};
 export {ImageSource};
 
 const IMAGE_IN_STREAM = 'image_in';
 const NORM_RECT_IN_STREAM = 'norm_rect_in';
 const ROI_IN_STREAM = 'roi_in';
-const IMAGE_OUT_STREAM = 'image_out';
+const CONFIDENCE_MASKS_STREAM = 'confidence_masks';
+const CATEGORY_MASK_STREAM = 'category_mask';
 const IMAGEA_SEGMENTER_GRAPH =
     'mediapipe.tasks.vision.interactive_segmenter.InteractiveSegmenterGraph';
+const DEFAULT_OUTPUT_CATEGORY_MASK = false;
+const DEFAULT_OUTPUT_CONFIDENCE_MASKS = true;
 
 // The OSS JS API does not support the builder pattern.
 // tslint:disable:jspb-use-builder-pattern
 
+/**
+ * A callback that receives the computed masks from the interactive segmenter.
+ * 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 InteractiveSegmenterCallack =
+    (result: InteractiveSegmenterResult) => void;
+
 /**
  * Performs interactive segmentation on images.
  *
@@ -69,7 +83,9 @@ const IMAGEA_SEGMENTER_GRAPH =
  *   - batch is always 1
  */
 export class InteractiveSegmenter extends VisionTaskRunner {
-  private userCallback: SegmentationMaskCallback = () => {};
+  private result: InteractiveSegmenterResult = {width: 0, height: 0};
+  private outputCategoryMask = DEFAULT_OUTPUT_CATEGORY_MASK;
+  private outputConfidenceMasks = DEFAULT_OUTPUT_CONFIDENCE_MASKS;
   private readonly options: ImageSegmenterGraphOptionsProto;
   private readonly segmenterOptions: SegmenterOptionsProto;
 
@@ -154,12 +170,14 @@ export class InteractiveSegmenter extends VisionTaskRunner {
    * @return A Promise that resolves when the settings have been applied.
    */
   override setOptions(options: InteractiveSegmenterOptions): Promise<void> {
-    if (options.outputType === 'CONFIDENCE_MASK') {
+    if ('outputCategoryMask' in options) {
-      this.segmenterOptions.setOutputType(
+      this.outputCategoryMask =
-          SegmenterOptionsProto.OutputType.CONFIDENCE_MASK);
+          options.outputCategoryMask ?? DEFAULT_OUTPUT_CATEGORY_MASK;
-    } else {
+    }
-      this.segmenterOptions.setOutputType(
+
-          SegmenterOptionsProto.OutputType.CATEGORY_MASK);
+    if ('outputConfidenceMasks' in options) {
+      this.outputConfidenceMasks =
+          options.outputConfidenceMasks ?? DEFAULT_OUTPUT_CONFIDENCE_MASKS;
     }
 
     return super.applyOptions(options);
@@ -184,7 +202,7 @@ export class InteractiveSegmenter extends VisionTaskRunner {
    */
   segment(
       image: ImageSource, roi: RegionOfInterest,
-      callback: SegmentationMaskCallback): void;
+      callback: InteractiveSegmenterCallack): void;
   /**
    * Performs interactive segmentation on the provided single image and invokes
    * the callback with the response. The `roi` parameter is used to represent a
@@ -213,24 +231,29 @@ export class InteractiveSegmenter extends VisionTaskRunner {
   segment(
       image: ImageSource, roi: RegionOfInterest,
       imageProcessingOptions: ImageProcessingOptions,
-      callback: SegmentationMaskCallback): void;
+      callback: InteractiveSegmenterCallack): void;
   segment(
       image: ImageSource, roi: RegionOfInterest,
       imageProcessingOptionsOrCallback: ImageProcessingOptions|
-      SegmentationMaskCallback,
+      InteractiveSegmenterCallack,
-      callback?: SegmentationMaskCallback): void {
+      callback?: InteractiveSegmenterCallack): void {
     const imageProcessingOptions =
         typeof imageProcessingOptionsOrCallback !== 'function' ?
         imageProcessingOptionsOrCallback :
         {};
-
+    const userCallback =
-    this.userCallback = typeof imageProcessingOptionsOrCallback === 'function' ?
+        typeof imageProcessingOptionsOrCallback === 'function' ?
         imageProcessingOptionsOrCallback :
         callback!;
 
+    this.reset();
     this.processRenderData(roi, this.getSynctheticTimestamp());
     this.processImageData(image, imageProcessingOptions);
-    this.userCallback = () => {};
+    userCallback(this.result);
+  }
+
+  private reset(): void {
+    this.result = {width: 0, height: 0};
   }
 
   /** Updates the MediaPipe graph configuration. */
@@ -239,7 +262,6 @@ export class InteractiveSegmenter extends VisionTaskRunner {
     graphConfig.addInputStream(IMAGE_IN_STREAM);
     graphConfig.addInputStream(ROI_IN_STREAM);
     graphConfig.addInputStream(NORM_RECT_IN_STREAM);
-    graphConfig.addOutputStream(IMAGE_OUT_STREAM);
 
     const calculatorOptions = new CalculatorOptions();
     calculatorOptions.setExtension(
@@ -250,24 +272,47 @@ export class InteractiveSegmenter extends VisionTaskRunner {
     segmenterNode.addInputStream('IMAGE:' + IMAGE_IN_STREAM);
     segmenterNode.addInputStream('ROI:' + ROI_IN_STREAM);
     segmenterNode.addInputStream('NORM_RECT:' + NORM_RECT_IN_STREAM);
-    segmenterNode.addOutputStream('GROUPED_SEGMENTATION:' + IMAGE_OUT_STREAM);
     segmenterNode.setOptions(calculatorOptions);
 
     graphConfig.addNode(segmenterNode);
 
+    if (this.outputConfidenceMasks) {
+      graphConfig.addOutputStream(CONFIDENCE_MASKS_STREAM);
+      segmenterNode.addOutputStream(
+          'CONFIDENCE_MASKS:' + CONFIDENCE_MASKS_STREAM);
+
       this.graphRunner.attachImageVectorListener(
-        IMAGE_OUT_STREAM, (masks, timestamp) => {
+          CONFIDENCE_MASKS_STREAM, (masks, timestamp) => {
-          if (masks.length === 0) {
+            this.result.confidenceMasks = masks.map(m => m.data);
-            this.userCallback([], 0, 0);
+            if (masks.length >= 0) {
-          } else {
+              this.result.width = masks[0].width;
-            this.userCallback(
+              this.result.height = masks[0].height;
-                masks.map(m => m.data), masks[0].width, masks[0].height);
             }
+
             this.setLatestOutputTimestamp(timestamp);
           });
-    this.graphRunner.attachEmptyPacketListener(IMAGE_OUT_STREAM, timestamp => {
+      this.graphRunner.attachEmptyPacketListener(
+          CONFIDENCE_MASKS_STREAM, timestamp => {
             this.setLatestOutputTimestamp(timestamp);
           });
+    }
+
+    if (this.outputCategoryMask) {
+      graphConfig.addOutputStream(CATEGORY_MASK_STREAM);
+      segmenterNode.addOutputStream('CATEGORY_MASK:' + CATEGORY_MASK_STREAM);
+
+      this.graphRunner.attachImageListener(
+          CATEGORY_MASK_STREAM, (mask, timestamp) => {
+            this.result.categoryMask = mask.data;
+            this.result.width = mask.width;
+            this.result.height = mask.height;
+            this.setLatestOutputTimestamp(timestamp);
+          });
+      this.graphRunner.attachEmptyPacketListener(
+          CATEGORY_MASK_STREAM, timestamp => {
+            this.setLatestOutputTimestamp(timestamp);
+          });
+    }
 
     const binaryGraph = graphConfig.serializeBinary();
     this.setGraph(new Uint8Array(binaryGraph), /* isBinary= */ true);

mediapipe/tasks/web/vision/interactive_segmenter/interactive_segmenter_options.d.ts

@@ -19,18 +19,9 @@ import {TaskRunnerOptions} from '../../../../tasks/web/core/task_runner_options'
 
 /** Options to configure the MediaPipe Interactive Segmenter Task */
 export interface InteractiveSegmenterOptions extends TaskRunnerOptions {
-  /**
+  /** Whether to output confidence masks. Defaults to true. */
-   * The output type of segmentation results.
+  outputConfidenceMasks?: boolean|undefined;
-   *
+
-   * The two supported modes are:
+  /** Whether to output the category masks. Defaults to false. */
-   * - Category Mask:   Gives a single output mask where each pixel represents
+  outputCategoryMask?: boolean|undefined;
-   *                    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/interactive_segmenter/interactive_segmenter_result.d.ts

@@ -0,0 +1,37 @@
+/**
+ * Copyright 2023 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.
+ */
+
+/** The output result of InteractiveSegmenter. */
+export declare interface InteractiveSegmenterResult {
+  /**
+   * Multiple masks as Float32Arrays or WebGLTextures where, for each mask, each
+   * pixel represents the prediction confidence, usually in the [0, 1] range.
+   */
+  confidenceMasks?: Float32Array[]|WebGLTexture[];
+
+  /**
+   * A category mask as a Uint8ClampedArray or WebGLTexture where each
+   * pixel represents the class which the pixel in the original image was
+   * predicted to belong to.
+   */
+  categoryMask?: Uint8ClampedArray|WebGLTexture;
+
+  /** The width of the masks. */
+  width: number;
+
+  /** The height of the masks. */
+  height: number;
+}

mediapipe/tasks/web/vision/interactive_segmenter/interactive_segmenter_test.ts

@@ -18,7 +18,7 @@ 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 {addJasmineCustomFloatEqualityTester, createSpyWasmModule, MediapipeTasksFake, SpyWasmModule, verifyGraph} from '../../../../tasks/web/core/task_runner_test_utils';
 import {RenderData as RenderDataProto} from '../../../../util/render_data_pb';
 import {WasmImage} from '../../../../web/graph_runner/graph_runner_image_lib';
 
@@ -37,7 +37,9 @@ class InteractiveSegmenterFake extends InteractiveSegmenter implements
   graph: CalculatorGraphConfig|undefined;
 
   fakeWasmModule: SpyWasmModule;
-  imageVectorListener:
+  categoryMaskListener:
+      ((images: WasmImage, timestamp: number) => void)|undefined;
+  confidenceMasksListener:
       ((images: WasmImage[], timestamp: number) => void)|undefined;
   lastRoi?: RenderDataProto;
 
@@ -46,11 +48,16 @@ class InteractiveSegmenterFake extends InteractiveSegmenter implements
     this.fakeWasmModule =
         this.graphRunner.wasmModule as unknown as SpyWasmModule;
 
-    this.attachListenerSpies[0] =
+    this.attachListenerSpies[0] = spyOn(this.graphRunner, 'attachImageListener')
+                                      .and.callFake((stream, listener) => {
+                                        expect(stream).toEqual('category_mask');
+                                        this.categoryMaskListener = listener;
+                                      });
+    this.attachListenerSpies[1] =
         spyOn(this.graphRunner, 'attachImageVectorListener')
             .and.callFake((stream, listener) => {
-              expect(stream).toEqual('image_out');
+              expect(stream).toEqual('confidence_masks');
-              this.imageVectorListener = listener;
+              this.confidenceMasksListener = listener;
             });
     spyOn(this.graphRunner, 'setGraph').and.callFake(binaryGraph => {
       this.graph = CalculatorGraphConfig.deserializeBinary(binaryGraph);
@@ -79,17 +86,21 @@ describe('InteractiveSegmenter', () => {
 
   it('initializes graph', async () => {
     verifyGraph(interactiveSegmenter);
-    verifyListenersRegistered(interactiveSegmenter);
+
+    // Verify default options
+    expect(interactiveSegmenter.categoryMaskListener).not.toBeDefined();
+    expect(interactiveSegmenter.confidenceMasksListener).toBeDefined();
   });
 
   it('reloads graph when settings are changed', async () => {
-    await interactiveSegmenter.setOptions({outputType: 'CATEGORY_MASK'});
+    await interactiveSegmenter.setOptions(
-    verifyGraph(interactiveSegmenter, [['segmenterOptions', 'outputType'], 1]);
+        {outputConfidenceMasks: true, outputCategoryMask: false});
-    verifyListenersRegistered(interactiveSegmenter);
+    expect(interactiveSegmenter.categoryMaskListener).not.toBeDefined();
+    expect(interactiveSegmenter.confidenceMasksListener).toBeDefined();
 
-    await interactiveSegmenter.setOptions({outputType: 'CONFIDENCE_MASK'});
+    await interactiveSegmenter.setOptions(
-    verifyGraph(interactiveSegmenter, [['segmenterOptions', 'outputType'], 2]);
+        {outputConfidenceMasks: false, outputCategoryMask: true});
-    verifyListenersRegistered(interactiveSegmenter);
+    expect(interactiveSegmenter.categoryMaskListener).toBeDefined();
   });
 
   it('can use custom models', async () => {
@@ -115,23 +126,6 @@ describe('InteractiveSegmenter', () => {
         ]);
   });
 
-
-  describe('setOptions()', () => {
-    const fieldPath = ['segmenterOptions', 'outputType'];
-
-    it(`can set outputType`, async () => {
-      await interactiveSegmenter.setOptions({outputType: 'CONFIDENCE_MASK'});
-      verifyGraph(interactiveSegmenter, [fieldPath, 2]);
-    });
-
-    it(`can clear outputType`, async () => {
-      await interactiveSegmenter.setOptions({outputType: 'CONFIDENCE_MASK'});
-      verifyGraph(interactiveSegmenter, [fieldPath, 2]);
-      await interactiveSegmenter.setOptions({outputType: undefined});
-      verifyGraph(interactiveSegmenter, [fieldPath, 1]);
-    });
-  });
-
   it('doesn\'t support region of interest', () => {
     expect(() => {
       interactiveSegmenter.segment(
@@ -153,29 +147,31 @@ describe('InteractiveSegmenter', () => {
     interactiveSegmenter.segment({} as HTMLImageElement, ROI, () => {});
   });
 
-  it('supports category masks', (done) => {
+  it('supports category mask', async () => {
     const mask = new Uint8ClampedArray([1, 2, 3, 4]);
 
+    await interactiveSegmenter.setOptions(
+        {outputCategoryMask: true, outputConfidenceMasks: false});
+
     // Pass the test data to our listener
     interactiveSegmenter.fakeWasmModule._waitUntilIdle.and.callFake(() => {
-      verifyListenersRegistered(interactiveSegmenter);
+      expect(interactiveSegmenter.categoryMaskListener).toBeDefined();
-      interactiveSegmenter.imageVectorListener!(
+      interactiveSegmenter.categoryMaskListener!
-          [
+          ({data: mask, width: 2, height: 2},
-            {data: mask, width: 2, height: 2},
-          ],
            /* timestamp= */ 1337);
     });
 
     // Invoke the image segmenter
-    interactiveSegmenter.segment(
+    return new Promise<void>(resolve => {
-        {} as HTMLImageElement, ROI, (masks, width, height) => {
+      interactiveSegmenter.segment({} as HTMLImageElement, ROI, result => {
         expect(interactiveSegmenter.fakeWasmModule._waitUntilIdle)
             .toHaveBeenCalled();
-          expect(masks).toHaveSize(1);
+        expect(result.categoryMask).toEqual(mask);
-          expect(masks[0]).toEqual(mask);
+        expect(result.confidenceMasks).not.toBeDefined();
-          expect(width).toEqual(2);
+        expect(result.width).toEqual(2);
-          expect(height).toEqual(2);
+        expect(result.height).toEqual(2);
-          done();
+        resolve();
+      });
     });
   });
 
@@ -183,30 +179,67 @@ describe('InteractiveSegmenter', () => {
     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 interactiveSegmenter.setOptions({outputType: 'CONFIDENCE_MASK'});
+    await interactiveSegmenter.setOptions(
+        {outputCategoryMask: false, outputConfidenceMasks: true});
 
     // Pass the test data to our listener
     interactiveSegmenter.fakeWasmModule._waitUntilIdle.and.callFake(() => {
-      verifyListenersRegistered(interactiveSegmenter);
+      expect(interactiveSegmenter.confidenceMasksListener).toBeDefined();
-      interactiveSegmenter.imageVectorListener!(
+      interactiveSegmenter.confidenceMasksListener!(
           [
             {data: mask1, width: 2, height: 2},
             {data: mask2, width: 2, height: 2},
           ],
           1337);
     });
+    return new Promise<void>(resolve => {
+      // Invoke the image segmenter
+      interactiveSegmenter.segment({} as HTMLImageElement, ROI, result => {
+        expect(interactiveSegmenter.fakeWasmModule._waitUntilIdle)
+            .toHaveBeenCalled();
+        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 interactiveSegmenter.setOptions(
+        {outputCategoryMask: true, outputConfidenceMasks: true});
+
+    // Pass the test data to our listener
+    interactiveSegmenter.fakeWasmModule._waitUntilIdle.and.callFake(() => {
+      expect(interactiveSegmenter.categoryMaskListener).toBeDefined();
+      expect(interactiveSegmenter.confidenceMasksListener).toBeDefined();
+      interactiveSegmenter.categoryMaskListener!
+          ({data: categoryMask, width: 1, height: 1}, 1337);
+      interactiveSegmenter.confidenceMasksListener!(
+          [
+            {data: confidenceMask1, width: 1, height: 1},
+            {data: confidenceMask2, width: 1, height: 1},
+          ],
+          1337);
+    });
 
     return new Promise<void>(resolve => {
       // Invoke the image segmenter
       interactiveSegmenter.segment(
-          {} as HTMLImageElement, ROI, (masks, width, height) => {
+          {} as HTMLImageElement, ROI, result => {
             expect(interactiveSegmenter.fakeWasmModule._waitUntilIdle)
                 .toHaveBeenCalled();
-            expect(masks).toHaveSize(2);
+            expect(result.categoryMask).toEqual(categoryMask);
-            expect(masks[0]).toEqual(mask1);
+            expect(result.confidenceMasks).toEqual([
-            expect(masks[1]).toEqual(mask2);
+              confidenceMask1, confidenceMask2
-            expect(width).toEqual(2);
+            ]);
-            expect(height).toEqual(2);
+            expect(result.width).toEqual(1);
+            expect(result.height).toEqual(1);
             resolve();
           });
     });