cassdlcm/mediapipe
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Add MediaPipe Image Segmenter task for Web

Browse Source 
PiperOrigin-RevId: 504912518
This commit is contained in:
Sebastian Schmidt 2023-01-26 12:30:05 -08:00 committed by Copybara-Service
parent 29001234d5
commit 4d38557f11
8 changed files with 636 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
mediapipe/tasks/web/vision/BUILD
View File

@ -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",
]

17
mediapipe/tasks/web/vision/README.md
View File

@ -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

58
mediapipe/tasks/web/vision/image_segmenter/BUILD Normal file
View File

@ -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"],
)

300
mediapipe/tasks/web/vision/image_segmenter/image_segmenter.ts Normal file
View File

@ -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);
}
}

41
mediapipe/tasks/web/vision/image_segmenter/image_segmenter_options.d.ts vendored Normal file
View File

@ -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;
}

215
mediapipe/tasks/web/vision/image_segmenter/image_segmenter_test.ts Normal file
View File

@ -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();
});
});
});
});

3
mediapipe/tasks/web/vision/index.ts
View File

@ -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
};

1
mediapipe/tasks/web/vision/types.ts
View File

@ -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';