@mediapipe/tasks-vision/vision.d.ts

MediaPipe Vision Tasks

/**
 * Copyright 2022 The MediaPipe Authors.
 *
 * 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.
 */
/** Options to configure MediaPipe model loading and processing. */
declare interface BaseOptions_2 {
    /**
     * The model path to the model asset file. Only one of `modelAssetPath` or
     * `modelAssetBuffer` can be set.
     */
    modelAssetPath?: string | undefined;
    /**
     * A buffer or stream reader containing the model asset. Only one of
     * `modelAssetPath` or `modelAssetBuffer` can be set.
     */
    modelAssetBuffer?: Uint8Array | ReadableStreamDefaultReader | undefined;
    /** Overrides the default backend to use for the provided model. */
    delegate?: "CPU" | "GPU" | undefined;
}

/**
 * Copyright 2023 The MediaPipe Authors.
 *
 * 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.
 */
/** An integer bounding box, axis aligned. */
export declare interface BoundingBox {
    /** The X coordinate of the top-left corner, in pixels. */
    originX: number;
    /** The Y coordinate of the top-left corner, in pixels. */
    originY: number;
    /** The width of the bounding box, in pixels. */
    width: number;
    /** The height of the bounding box, in pixels. */
    height: number;
    /**
     * Angle of rotation of the original non-rotated box around the top left
     * corner of the original non-rotated box, in clockwise degrees from the
     * horizontal.
     */
    angle: number;
}

/**
 * A user-defined callback to take input data and map it to a custom output
 * value.
 */
export declare type Callback<I, O> = (input: I) => O;

/**
 * Copyright 2022 The MediaPipe Authors.
 *
 * 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.
 */
/** A classification category. */
export declare interface Category {
    /** The probability score of this label category. */
    score: number;
    /** The index of the category in the corresponding label file. */
    index: number;
    /**
     * The label of this category object. Defaults to an empty string if there is
     * no category.
     */
    categoryName: string;
    /**
     * The display name of the label, which may be translated for different
     * locales. For example, a label, "apple", may be translated into Spanish for
     * display purpose, so that the `display_name` is "manzana". Defaults to an
     * empty string if there is no display name.
     */
    displayName: string;
}

/**
 * A category to color mapping that uses either a map or an array to assign
 * category indexes to RGBA colors.
 */
export declare type CategoryToColorMap = Map<number, RGBAColor> | RGBAColor[];

/** Classification results for a given classifier head. */
export declare interface Classifications {
    /**
     * The array of predicted categories, usually sorted by descending scores,
     * e.g., from high to low probability.
     */
    categories: Category[];
    /**
     * The index of the classifier head these categories refer to. This is
     * useful for multi-head models.
     */
    headIndex: number;
    /**
     * The name of the classifier head, which is the corresponding tensor
     * metadata name. Defaults to an empty string if there is no such metadata.
     */
    headName: string;
}

/**
 * Copyright 2022 The MediaPipe Authors.
 *
 * 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.
 */
/** Options to configure a MediaPipe Classifier Task. */
declare interface ClassifierOptions {
    /**
     * The locale to use for display names specified through the TFLite Model
     * Metadata, if any. Defaults to English.
     */
    displayNamesLocale?: string | undefined;
    /** The maximum number of top-scored detection results to return. */
    maxResults?: number | undefined;
    /**
     * Overrides the value provided in the model metadata. Results below this
     * value are rejected.
     */
    scoreThreshold?: number | undefined;
    /**
     * Allowlist of category names. If non-empty, detection results whose category
     * name is not in this set will be filtered out. Duplicate or unknown category
     * names are ignored. Mutually exclusive with `categoryDenylist`.
     */
    categoryAllowlist?: string[] | undefined;
    /**
     * Denylist of category names. If non-empty, detection results whose category
     * name is in this set will be filtered out. Duplicate or unknown category
     * names are ignored. Mutually exclusive with `categoryAllowlist`.
     */
    categoryDenylist?: string[] | undefined;
}

/** A connection between two landmarks. */
declare interface Connection {
    start: number;
    end: number;
}

/** A color map with 22 classes. Used in our demos. */
export declare const DEFAULT_CATEGORY_TO_COLOR_MAP: number[][];

/** Represents one detection by a detection task. */
export declare interface Detection {
    /** A list of `Category` objects. */
    categories: Category[];
    /** The bounding box of the detected objects. */
    boundingBox?: BoundingBox;
    /**
     * List of keypoints associated with the detection. Keypoints represent
     * interesting points related to the detection. For example, the keypoints
     * represent the eye, ear and mouth from face detection model. Or in the
     * template matching detection, e.g. KNIFT, they can represent the feature
     * points for template matching. Contains an empty list if no keypoints are
     * detected.
     */
    keypoints: NormalizedKeypoint[];
}

/** Detection results of a model. */
declare interface DetectionResult {
    /** A list of Detections. */
    detections: Detection[];
}
export { DetectionResult as FaceDetectorResult }
export { DetectionResult as ObjectDetectorResult }

/**
 * Options for customizing the drawing routines
 */
export declare interface DrawingOptions {
    /** The color that is used to draw the shape. Defaults to white. */
    color?: string | CanvasGradient | CanvasPattern | Callback<LandmarkData, string | CanvasGradient | CanvasPattern>;
    /**
     * The color that is used to fill the shape. Defaults to `.color` (or black
     * if color is not set).
     */
    fillColor?: string | CanvasGradient | CanvasPattern | Callback<LandmarkData, string | CanvasGradient | CanvasPattern>;
    /** The width of the line boundary of the shape. Defaults to 4. */
    lineWidth?: number | Callback<LandmarkData, number>;
    /** The radius of location marker. Defaults to 6. */
    radius?: number | Callback<LandmarkData, number>;
}

/** Helper class to visualize the result of a MediaPipe Vision task. */
export declare class DrawingUtils {
    /**
     * Creates a new DrawingUtils class.
     *
     * @param gpuContext The WebGL canvas rendering context to render into. If
     *     your Task is using a GPU delegate, the context must be obtained from
     * its canvas (provided via `setOptions({ canvas: .. })`).
     */
    constructor(gpuContext: WebGL2RenderingContext);
    /**
     * Creates a new DrawingUtils class.
     *
     * @param cpuContext The 2D canvas rendering context to render into. If
     *     you are rendering GPU data you must also provide `gpuContext` to allow
     *     for data conversion.
     * @param gpuContext A WebGL canvas that is used for GPU rendering and for
     *     converting GPU to CPU data. If your Task is using a GPU delegate, the
     *     context must be obtained from  its canvas (provided via
     *     `setOptions({ canvas: .. })`).
     */
    constructor(cpuContext: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, gpuContext?: WebGL2RenderingContext);
    /**
     * Restricts a number between two endpoints (order doesn't matter).
     *
     * @export
     * @param x The number to clamp.
     * @param x0 The first boundary.
     * @param x1 The second boundary.
     * @return The clamped value.
     */
    static clamp(x: number, x0: number, x1: number): number;
    /**
     * Linearly interpolates a value between two points, clamping that value to
     * the endpoints.
     *
     * @export
     * @param x The number to interpolate.
     * @param x0 The x coordinate of the start value.
     * @param x1 The x coordinate of the end value.
     * @param y0 The y coordinate of the start value.
     * @param y1 The y coordinate of the end value.
     * @return The interpolated value.
     */
    static lerp(x: number, x0: number, x1: number, y0: number, y1: number): number;
    /**
     * Draws circles onto the provided landmarks.
     *
     * This method can only be used when `DrawingUtils` is initialized with a
     * `CanvasRenderingContext2D`.
     *
     * @export
     * @param landmarks The landmarks to draw.
     * @param style The style to visualize the landmarks.
     */
    drawLandmarks(landmarks?: NormalizedLandmark[], style?: DrawingOptions): void;
    /**
     * Draws lines between landmarks (given a connection graph).
     *
     * This method can only be used when `DrawingUtils` is initialized with a
     * `CanvasRenderingContext2D`.
     *
     * @export
     * @param landmarks The landmarks to draw.
     * @param connections The connections array that contains the start and the
     *     end indices for the connections to draw.
     * @param style The style to visualize the landmarks.
     */
    drawConnectors(landmarks?: NormalizedLandmark[], connections?: Connection[], style?: DrawingOptions): void;
    /**
     * Draws a bounding box.
     *
     * This method can only be used when `DrawingUtils` is initialized with a
     * `CanvasRenderingContext2D`.
     *
     * @export
     * @param boundingBox The bounding box to draw.
     * @param style The style to visualize the bounding box.
     */
    drawBoundingBox(boundingBox: BoundingBox, style?: DrawingOptions): void;
    /**
     * Draws a category mask using the provided category-to-color mapping.
     *
     * @export
     * @param mask A category mask that was returned from a segmentation task.
     * @param categoryToColorMap A map that maps category indices to RGBA
     *     values. You must specify a map entry for each category.
     * @param background A color or image to use as the background. Defaults to
     *     black.
     */
    drawCategoryMask(mask: MPMask, categoryToColorMap: Map<number, RGBAColor>, background?: RGBAColor | ImageSource): void;
    /**
     * Draws a category mask using the provided color array.
     *
     * @export
     * @param mask A category mask that was returned from a segmentation task.
     * @param categoryToColorMap An array that maps indices to RGBA values. The
     *     array's indices must correspond to the category indices of the model
     *     and an entry must be provided for each category.
     * @param background A color or image to use as the background. Defaults to
     *     black.
     */
    drawCategoryMask(mask: MPMask, categoryToColorMap: RGBAColor[], background?: RGBAColor | ImageSource): void;
    /**
     * Blends two images using the provided confidence mask.
     *
     * If you are using an `ImageData` or `HTMLImageElement` as your data source
     * and drawing the result onto a `WebGL2RenderingContext`, this method uploads
     * the image data to the GPU. For still image input that gets re-used every
     * frame, you can reduce the cost of re-uploading these images by passing a
     * `HTMLCanvasElement` instead.
     *
     * @export
     * @param mask A confidence mask that was returned from a segmentation task.
     * @param defaultTexture An image or a four-channel color that will be used
     *     when confidence values are low.
     * @param overlayTexture An image or four-channel color that will be used when
     *     confidence values are high.
     */
    drawConfidenceMask(mask: MPMask, defaultTexture: RGBAColor | ImageSource, overlayTexture: RGBAColor | ImageSource): void;
    /**
     * Frees all WebGL resources held by this class.
     * @export
     */
    close(): void;
}

/**
 * Copyright 2022 The MediaPipe Authors.
 *
 * 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.
 */
/** Options to configure a MediaPipe Embedder Task */
declare interface EmbedderOptions {
    /**
     * Whether to normalize the returned feature vector with L2 norm. Use this
     * option only if the model does not already contain a native L2_NORMALIZATION
     * TF Lite Op. In most cases, this is already the case and L2 norm is thus
     * achieved through TF Lite inference.
     */
    l2Normalize?: boolean | undefined;
    /**
     * Whether the returned embedding should be quantized to bytes via scalar
     * quantization. Embeddings are implicitly assumed to be unit-norm and
     * therefore any dimension is guaranteed to have a value in [-1.0, 1.0]. Use
     * the l2_normalize option if this is not the case.
     */
    quantize?: boolean | undefined;
}

/**
 * Copyright 2022 The MediaPipe Authors.
 *
 * 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.
 */
/**
 * List of embeddings with an optional timestamp.
 *
 * One and only one of the two 'floatEmbedding' and 'quantizedEmbedding' will
 * contain data, based on whether or not the embedder was configured to perform
 * scalar quantization.
 */
export declare interface Embedding {
    /**
     *  Floating-point embedding. Empty if the embedder was configured to perform
     * scalar-quantization.
     */
    floatEmbedding?: number[];
    /**
     * Scalar-quantized embedding. Empty if the embedder was not configured to
     * perform scalar quantization.
     */
    quantizedEmbedding?: Uint8Array;
    /**
     * The index of the classifier head these categories refer to. This is
     * useful for multi-head models.
     */
    headIndex: number;
    /**
     * The name of the classifier head, which is the corresponding tensor
     * metadata name.
     */
    headName: string;
}

/** Performs face detection on images. */
export declare class FaceDetector extends VisionTaskRunner {
    /**
     * Initializes the Wasm runtime and creates a new face detector from the
     * provided options.
     *
     * @export
     * @param wasmFileset A configuration object that provides the location of the
     *     Wasm binary and its loader.
     * @param faceDetectorOptions The options for the FaceDetector. Note that
     *     either a path to the model asset or a model buffer needs to be
     *     provided (via `baseOptions`).
     */
    static createFromOptions(wasmFileset: WasmFileset, faceDetectorOptions: FaceDetectorOptions): Promise<FaceDetector>;
    /**
     * Initializes the Wasm runtime and creates a new face detector based on the
     * provided model asset buffer.
     *
     * @export
     * @param wasmFileset A configuration object that provides the location of the
     *     Wasm binary and its loader.
     * @param modelAssetBuffer An array or a stream containing a binary
     *    representation of the model.
     */
    static createFromModelBuffer(wasmFileset: WasmFileset, modelAssetBuffer: Uint8Array | ReadableStreamDefaultReader): Promise<FaceDetector>;
    /**
     * Initializes the Wasm runtime and creates a new face detector based on the
     * path to the model asset.
     *
     * @export
     * @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<FaceDetector>;
    private constructor();
    /**
     * Sets new options for the FaceDetector.
     *
     * 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`.
     *
     * @export
     * @param options The options for the FaceDetector.
     */
    setOptions(options: FaceDetectorOptions): Promise<void>;
    /**
     * Performs face detection on the provided single image and waits
     * synchronously for the response. Only use this method when the
     * FaceDetector is created with running mode `image`.
     *
     * @export
     * @param image An image to process.
     * @param imageProcessingOptions the `ImageProcessingOptions` specifying how
     *    to process the input image before running inference.
     * @return A result containing the list of detected faces.
     */
    detect(image: ImageSource, imageProcessingOptions?: ImageProcessingOptions): DetectionResult;
    /**
     * Performs face detection on the provided video frame and waits
     * synchronously for the response. Only use this method when the
     * FaceDetector is created with running mode `video`.
     *
     * @export
     * @param videoFrame A video frame to process.
     * @param timestamp The timestamp of the current frame, in ms.
     * @param imageProcessingOptions the `ImageProcessingOptions` specifying how
     *    to process the input image before running inference.
     * @return A result containing the list of detected faces.
     */
    detectForVideo(videoFrame: ImageSource, timestamp: number, imageProcessingOptions?: ImageProcessingOptions): DetectionResult;
}

/** Options to configure the MediaPipe Face Detector Task */
export declare interface FaceDetectorOptions extends VisionTaskOptions {
    /**
     * The minimum confidence score for the face detection to be considered
     * successful. Defaults to 0.5.
     */
    minDetectionConfidence?: number | undefined;
    /**
     * The minimum non-maximum-suppression threshold for face detection to be
     * considered overlapped. Defaults to 0.3.
     */
    minSuppressionThreshold?: number | undefined;
}

/**
 * Performs face landmarks detection on images.
 *
 * This API expects a pre-trained face landmarker model asset bundle.
 */
export declare class FaceLandmarker extends VisionTaskRunner {
    /**
     * Initializes the Wasm runtime and creates a new `FaceLandmarker` from the
     * provided options.
     * @export
     * @param wasmFileset A configuration object that provides the location of the
     *     Wasm binary and its loader.
     * @param faceLandmarkerOptions The options for the FaceLandmarker.
     *     Note that either a path to the model asset or a model buffer needs to
     *     be provided (via `baseOptions`).
     */
    static createFromOptions(wasmFileset: WasmFileset, faceLandmarkerOptions: FaceLandmarkerOptions): Promise<FaceLandmarker>;
    /**
     * Initializes the Wasm runtime and creates a new `FaceLandmarker` based on
     * the provided model asset buffer.
     * @export
     * @param wasmFileset A configuration object that provides the location of the
     *     Wasm binary and its loader.
     * @param modelAssetBuffer An array or a stream containing a binary
     *    representation of the model.
     */
    static createFromModelBuffer(wasmFileset: WasmFileset, modelAssetBuffer: Uint8Array | ReadableStreamDefaultReader): Promise<FaceLandmarker>;
    /**
     * Initializes the Wasm runtime and creates a new `FaceLandmarker` based on
     * the path to the model asset.
     * @export
     * @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<FaceLandmarker>;
    /**
     * Landmark connections to draw the connection between a face's lips.
     * @export
     * @nocollapse
     */
    static FACE_LANDMARKS_LIPS: Connection[];
    /**
     * Landmark connections to draw the connection between a face's left eye.
     * @export
     * @nocollapse
     */
    static FACE_LANDMARKS_LEFT_EYE: Connection[];
    /**
     * Landmark connections to draw the connection between a face's left eyebrow.
     * @export
     * @nocollapse
     */
    static FACE_LANDMARKS_LEFT_EYEBROW: Connection[];
    /**
     * Landmark connections to draw the connection between a face's left iris.
     * @export
     * @nocollapse
     */
    static FACE_LANDMARKS_LEFT_IRIS: Connection[];
    /**
     * Landmark connections to draw the connection between a face's right eye.
     * @export
     * @nocollapse
     */
    static FACE_LANDMARKS_RIGHT_EYE: Connection[];
    /**
     * Landmark connections to draw the connection between a face's right
     * eyebrow.
     * @export
     * @nocollapse
     */
    static FACE_LANDMARKS_RIGHT_EYEBROW: Connection[];
    /**
     * Landmark connections to draw the connection between a face's right iris.
     * @export
     * @nocollapse
     */
    static FACE_LANDMARKS_RIGHT_IRIS: Connection[];
    /**
     * Landmark connections to draw the face's oval.
     * @export
     * @nocollapse
     */
    static FACE_LANDMARKS_FACE_OVAL: Connection[];
    /**
     * Landmark connections to draw the face's contour.
     * @export
     * @nocollapse
     */
    static FACE_LANDMARKS_CONTOURS: Connection[];
    /**
     * Landmark connections to draw the face's tesselation.
     * @export
     * @nocollapse
     */
    static FACE_LANDMARKS_TESSELATION: Connection[];
    private constructor();
    /**
     * Sets new options for this `FaceLandmarker`.
     *
     * 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`.
     *
     * @export
     * @param options The options for the face landmarker.
     */
    setOptions(options: FaceLandmarkerOptions): Promise<void>;
    /**
     * Performs face landmarks detection on the provided single image and waits
     * synchronously for the response. Only use this method when the
     * FaceLandmarker is created with running mode `image`.
     *
     * @export
     * @param image An image to process.
     * @param imageProcessingOptions the `ImageProcessingOptions` specifying how
     *    to process the input image before running inference.
     * @return The detected face landmarks.
     */
    detect(image: ImageSource, imageProcessingOptions?: ImageProcessingOptions): FaceLandmarkerResult;
    /**
     * Performs face landmarks detection on the provided video frame and waits
     * synchronously for the response. Only use this method when the
     * FaceLandmarker is created with running mode `video`.
     *
     * @export
     * @param videoFrame A video frame to process.
     * @param timestamp The timestamp of the current frame, in ms.
     * @param imageProcessingOptions the `ImageProcessingOptions` specifying how
     *    to process the input image before running inference.
     * @return The detected face landmarks.
     */
    detectForVideo(videoFrame: ImageSource, timestamp: number, imageProcessingOptions?: ImageProcessingOptions): FaceLandmarkerResult;
}

/** Options to configure the MediaPipe FaceLandmarker Task */
export declare interface FaceLandmarkerOptions extends VisionTaskOptions {
    /**
     * The maximum number of faces can be detected by the FaceLandmarker.
     * Defaults to 1.
     */
    numFaces?: number | undefined;
    /**
     * The minimum confidence score for the face detection to be considered
     * successful. Defaults to 0.5.
     */
    minFaceDetectionConfidence?: number | undefined;
    /**
     * The minimum confidence score of face presence score in the face landmark
     * detection. Defaults to 0.5.
     */
    minFacePresenceConfidence?: number | undefined;
    /**
     * The minimum confidence score for the face tracking to be considered
     * successful. Defaults to 0.5.
     */
    minTrackingConfidence?: number | undefined;
    /**
     * Whether FaceLandmarker outputs face blendshapes classification. Face
     * blendshapes are used for rendering the 3D face model.
     */
    outputFaceBlendshapes?: boolean | undefined;
    /**
     * Whether FaceLandmarker outputs facial transformation_matrix. Facial
     * transformation matrix is used to transform the face landmarks in canonical
     * face to the detected face, so that users can apply face effects on the
     * detected landmarks.
     */
    outputFacialTransformationMatrixes?: boolean | undefined;
}

/**
 * Represents the face landmarks deection results generated by `FaceLandmarker`.
 */
export declare interface FaceLandmarkerResult {
    /** Detected face landmarks in normalized image coordinates. */
    faceLandmarks: NormalizedLandmark[][];
    /** Optional face blendshapes results. */
    faceBlendshapes: Classifications[];
    /** Optional facial transformation matrix. */
    facialTransformationMatrixes: Matrix[];
}

/**
 * Resolves the files required for the MediaPipe Task APIs.
 *
 * This class verifies whether SIMD is supported in the current environment and
 * loads the SIMD files only if support is detected. The returned filesets
 * require that the Wasm files are published without renaming. If this is not
 * possible, you can invoke the MediaPipe Tasks APIs using a manually created
 * `WasmFileset`.
 */
export declare class FilesetResolver {
    /**
     * Returns whether SIMD is supported in the current environment.
     *
     * If your environment requires custom locations for the MediaPipe Wasm files,
     * you can use `isSimdSupported()` to decide whether to load the SIMD-based
     * assets. Note that for ES6 Modules, SIMD is assumed to be always supported.
     *
     * @param useModule Whether to use ES6 Modules for the Wasm files.
     * @export
     * @return Whether SIMD support was detected in the current environment.
     */
    static isSimdSupported(useModule?: boolean): Promise<boolean>;
    /**
     * Creates a fileset for the MediaPipe Audio tasks.
     *
     * @export
     * @param basePath An optional base path to specify the directory the Wasm
     *    files should be loaded from. If not specified, the Wasm files are
     *    loaded from the host's root directory.
     * @param useModule Whether to use ES6 Modules for the Wasm files.
     * @return A `WasmFileset` that can be used to initialize MediaPipe Audio
     *    tasks.
     */
    static forAudioTasks(basePath?: string, useModule?: boolean): Promise<WasmFileset>;
    /**
     * Creates a fileset for the MediaPipe GenAI tasks.
     *
     * @export
     * @param basePath An optional base path to specify the directory the Wasm
     *    files should be loaded from. If not specified, the Wasm files are
     *    loaded from the host's root directory.
     * @param useModule Whether to use ES6 Modules for the Wasm files.
     * @return A `WasmFileset` that can be used to initialize MediaPipe GenAI
     *    tasks.
     */
    static forGenAiTasks(basePath?: string, useModule?: boolean): Promise<WasmFileset>;
    /**
     * Creates a fileset for the MediaPipe Text tasks.
     *
     * @export
     * @param basePath An optional base path to specify the directory the Wasm
     *    files should be loaded from. If not specified, the Wasm files are
     *    loaded from the host's root directory.
     * @param useModule Whether to use ES6 Modules for the Wasm files.
     * @return A `WasmFileset` that can be used to initialize MediaPipe Text
     *    tasks.
     */
    static forTextTasks(basePath?: string, useModule?: boolean): Promise<WasmFileset>;
    /**
     * Creates a fileset for the MediaPipe Vision tasks.
     *
     * @export
     * @param basePath An optional base path to specify the directory the Wasm
     *    files should be loaded from. If not specified, the Wasm files are
     *    loaded from the host's root directory.
     * @param useModule Whether to use ES6 Modules for the Wasm files.
     * @return A `WasmFileset` that can be used to initialize MediaPipe Vision
     *    tasks.
     */
    static forVisionTasks(basePath?: string, useModule?: boolean): Promise<WasmFileset>;
}

/** Performs hand gesture recognition on images. */
export declare class GestureRecognizer extends VisionTaskRunner {
    /**
     * An array containing the pairs of hand landmark indices to be rendered with
     * connections.
     * @export
     * @nocollapse
     */
    static HAND_CONNECTIONS: Connection[];
    /**
     * Initializes the Wasm runtime and creates a new gesture recognizer from the
     * provided options.
     * @export
     * @param wasmFileset A configuration object that provides the location of the
     *     Wasm binary and its loader.
     * @param gestureRecognizerOptions The options for the gesture recognizer.
     *     Note that either a path to the model asset or a model buffer needs to
     *     be provided (via `baseOptions`).
     */
    static createFromOptions(wasmFileset: WasmFileset, gestureRecognizerOptions: GestureRecognizerOptions): Promise<GestureRecognizer>;
    /**
     * Initializes the Wasm runtime and creates a new gesture recognizer based on
     * the provided model asset buffer.
     * @export
     * @param wasmFileset A configuration object that provides the location of the
     *     Wasm binary and its loader.
     * @param modelAssetBuffer An array or a stream containing a binary
     *    representation of the model.
     */
    static createFromModelBuffer(wasmFileset: WasmFileset, modelAssetBuffer: Uint8Array | ReadableStreamDefaultReader): Promise<GestureRecognizer>;
    /**
     * Initializes the Wasm runtime and creates a new gesture recognizer based on
     * the path to the model asset.
     * @export
     * @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<GestureRecognizer>;
    private constructor();
    /**
     * Sets new options for the gesture recognizer.
     *
     * 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`.
     *
     * @export
     * @param options The options for the gesture recognizer.
     */
    setOptions(options: GestureRecognizerOptions): Promise<void>;
    /**
     * Performs gesture recognition on the provided single image and waits
     * synchronously for the response. Only use this method when the
     * GestureRecognizer is created with running mode `image`.
     *
     * @export
     * @param image A single image to process.
     * @param imageProcessingOptions the `ImageProcessingOptions` specifying how
     *    to process the input image before running inference.
     * @return The detected gestures.
     */
    recognize(image: ImageSource, imageProcessingOptions?: ImageProcessingOptions): GestureRecognizerResult;
    /**
     * Performs gesture recognition on the provided video frame and waits
     * synchronously for the response. Only use this method when the
     * GestureRecognizer is created with running mode `video`.
     *
     * @export
     * @param videoFrame A video frame to process.
     * @param timestamp The timestamp of the current frame, in ms.
     * @param imageProcessingOptions the `ImageProcessingOptions` specifying how
     *    to process the input image before running inference.
     * @return The detected gestures.
     */
    recognizeForVideo(videoFrame: ImageSource, timestamp: number, imageProcessingOptions?: ImageProcessingOptions): GestureRecognizerResult;
}

/** Options to configure the MediaPipe Gesture Recognizer Task */
export declare interface GestureRecognizerOptions extends VisionTaskOptions {
    /**
     * The maximum number of hands can be detected by the GestureRecognizer.
     * Defaults to 1.
     */
    numHands?: number | undefined;
    /**
     * The minimum confidence score for the hand detection to be considered
     * successful. Defaults to 0.5.
     */
    minHandDetectionConfidence?: number | undefined;
    /**
     * The minimum confidence score of hand presence score in the hand landmark
     * detection. Defaults to 0.5.
     */
    minHandPresenceConfidence?: number | undefined;
    /**
     * The minimum confidence score for the hand tracking to be considered
     * successful. Defaults to 0.5.
     */
    minTrackingConfidence?: number | undefined;
    /**
     * Sets the optional `ClassifierOptions` controlling the canned gestures
     * classifier, such as score threshold, allow list and deny list of gestures.
     * The categories for canned gesture
     * classifiers are: ["None", "Closed_Fist", "Open_Palm", "Pointing_Up",
     * "Thumb_Down", "Thumb_Up", "Victory", "ILoveYou"]
     */
    cannedGesturesClassifierOptions?: ClassifierOptions | undefined;
    /**
     * Options for configuring the custom gestures classifier, such as score
     * threshold, allow list and deny list of gestures.
     */
    customGesturesClassifierOptions?: ClassifierOptions | undefined;
}

/**
 * Represents the gesture recognition results generated by `GestureRecognizer`.
 */
export declare interface GestureRecognizerResult {
    /** Hand landmarks of detected hands. */
    landmarks: NormalizedLandmark[][];
    /** Hand landmarks in world coordinates of detected hands. */
    worldLandmarks: Landmark[][];
    /** Handedness of detected hands. */
    handedness: Category[][];
    /**
     * Handedness of detected hands.
     * @deprecated Use `.handedness` instead.
     */
    handednesses: Category[][];
    /**
     * Recognized hand gestures of detected hands. Note that the index of the
     * gesture is always -1, because the raw indices from multiple gesture
     * classifiers cannot consolidate to a meaningful index.
     */
    gestures: Category[][];
}

/** Performs hand landmarks detection on images. */
export declare class HandLandmarker extends VisionTaskRunner {
    /**
     * An array containing the pairs of hand landmark indices to be rendered with
     * connections.
     * @export
     * @nocollapse
     */
    static HAND_CONNECTIONS: Connection[];
    /**
     * Initializes the Wasm runtime and creates a new `HandLandmarker` from the
     * provided options.
     * @export
     * @param wasmFileset A configuration object that provides the location of the
     *     Wasm binary and its loader.
     * @param handLandmarkerOptions The options for the HandLandmarker.
     *     Note that either a path to the model asset or a model buffer needs to
     *     be provided (via `baseOptions`).
     */
    static createFromOptions(wasmFileset: WasmFileset, handLandmarkerOptions: HandLandmarkerOptions): Promise<HandLandmarker>;
    /**
     * Initializes the Wasm runtime and creates a new `HandLandmarker` based on
     * the provided model asset buffer.
     * @export
     * @param wasmFileset A configuration object that provides the location of the
     *     Wasm binary and its loader.
     * @param modelAssetBuffer An array or a stream containing a binary
     *    representation of the model.
     */
    static createFromModelBuffer(wasmFileset: WasmFileset, modelAssetBuffer: Uint8Array | ReadableStreamDefaultReader): Promise<HandLandmarker>;
    /**
     * Initializes the Wasm runtime and creates a new `HandLandmarker` based on
     * the path to the model asset.
     * @export
     * @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<HandLandmarker>;
    private constructor();
    /**
     * Sets new options for this `HandLandmarker`.
     *
     * 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`.
     *
     * @export
     * @param options The options for the hand landmarker.
     */
    setOptions(options: HandLandmarkerOptions): Promise<void>;
    /**
     * Performs hand landmarks detection on the provided single image and waits
     * synchronously for the response. Only use this method when the
     * HandLandmarker is created with running mode `image`.
     *
     * @export
     * @param image An image to process.
     * @param imageProcessingOptions the `ImageProcessingOptions` specifying how
     *    to process the input image before running inference.
     * @return The detected hand landmarks.
     */
    detect(image: ImageSource, imageProcessingOptions?: ImageProcessingOptions): HandLandmarkerResult;
    /**
     * Performs hand landmarks detection on the provided video frame and waits
     * synchronously for the response. Only use this method when the
     * HandLandmarker is created with running mode `video`.
     *
     * @export
     * @param videoFrame A video frame to process.
     * @param timestamp The timestamp of the current frame, in ms.
     * @param imageProcessingOptions the `ImageProcessingOptions` specifying how
     *    to process the input image before running inference.
     * @return The detected hand landmarks.
     */
    detectForVideo(videoFrame: ImageSource, timestamp: number, imageProcessingOptions?: ImageProcessingOptions): HandLandmarkerResult;
}

/** Options to configure the MediaPipe HandLandmarker Task */
export declare interface HandLandmarkerOptions extends VisionTaskOptions {
    /**
     * The maximum number of hands can be detected by the HandLandmarker.
     * Defaults to 1.
     */
    numHands?: number | undefined;
    /**
     * The minimum confidence score for the hand detection to be considered
     * successful. Defaults to 0.5.
     */
    minHandDetectionConfidence?: number | undefined;
    /**
     * The minimum confidence score of hand presence score in the hand landmark
     * detection. Defaults to 0.5.
     */
    minHandPresenceConfidence?: number | undefined;
    /**
     * The minimum confidence score for the hand tracking to be considered
     * successful. Defaults to 0.5.
     */
    minTrackingConfidence?: number | undefined;
}

/**
 * Represents the hand landmarks deection results generated by `HandLandmarker`.
 */
export declare interface HandLandmarkerResult {
    /** Hand landmarks of detected hands. */
    landmarks: NormalizedLandmark[][];
    /** Hand landmarks in world coordinates of detected hands. */
    worldLandmarks: Landmark[][];
    /**
     * Handedness of detected hands.
     * @deprecated Use `.handedness` instead.
     */
    handednesses: Category[][];
    /** Handedness of detected hands. */
    handedness: Category[][];
}

/** Performs holistic landmarks detection on images. */
export declare class HolisticLandmarker extends VisionTaskRunner {
    /**
     * An array containing the pairs of hand landmark indices to be rendered with
     * connections.
     * @export
     * @nocollapse
     */
    static HAND_CONNECTIONS: Connection[];
    /**
     * An array containing the pairs of pose landmark indices to be rendered with
     * connections.
     * @export
     * @nocollapse
     */
    static POSE_CONNECTIONS: Connection[];
    /**
     * Landmark connections to draw the connection between a face's lips.
     * @export
     * @nocollapse
     */
    static FACE_LANDMARKS_LIPS: Connection[];
    /**
     * Landmark connections to draw the connection between a face's left eye.
     * @export
     * @nocollapse
     */
    static FACE_LANDMARKS_LEFT_EYE: Connection[];
    /**
     * Landmark connections to draw the connection between a face's left eyebrow.
     * @export
     * @nocollapse
     */
    static FACE_LANDMARKS_LEFT_EYEBROW: Connection[];
    /**
     * Landmark connections to draw the connection between a face's left iris.
     * @export
     * @nocollapse
     */
    static FACE_LANDMARKS_LEFT_IRIS: Connection[];
    /**
     * Landmark connections to draw the connection between a face's right eye.
     * @export
     * @nocollapse
     */
    static FACE_LANDMARKS_RIGHT_EYE: Connection[];
    /**
     * Landmark connections to draw the connection between a face's right
     * eyebrow.
     * @export
     * @nocollapse
     */
    static FACE_LANDMARKS_RIGHT_EYEBROW: Connection[];
    /**
     * Landmark connections to draw the connection between a face's right iris.
     * @export
     * @nocollapse
     */
    static FACE_LANDMARKS_RIGHT_IRIS: Connection[];
    /**
     * Landmark connections to draw the face's oval.
     * @export
     * @nocollapse
     */
    static FACE_LANDMARKS_FACE_OVAL: Connection[];
    /**
     * Landmark connections to draw the face's contour.
     * @export
     * @nocollapse
     */
    static FACE_LANDMARKS_CONTOURS: Connection[];
    /**
     * Landmark connections to draw the face's tesselation.
     * @export
     * @nocollapse
     */
    static FACE_LANDMARKS_TESSELATION: Connection[];
    /**
     * Initializes the Wasm runtime and creates a new `HolisticLandmarker` from
     * the provided options.
     * @export
     * @param wasmFileset A configuration object that provides the location of the
     *     Wasm binary and its loader.
     * @param holisticLandmarkerOptions The options for the HolisticLandmarker.
     *     Note that either a path to the model asset or a model buffer needs to
     *     be provided (via `baseOptions`).
     */
    static createFromOptions(wasmFileset: WasmFileset, holisticLandmarkerOptions: HolisticLandmarkerOptions): Promise<HolisticLandmarker>;
    /**
     * Initializes the Wasm runtime and creates a new `HolisticLandmarker` based
     * on the provided model asset buffer.
     * @export
     * @param wasmFileset A configuration object that provides the location of the
     *     Wasm binary and its loader.
     * @param modelAssetBuffer An array or a stream containing a binary
     *    representation of the model.
     */
    static createFromModelBuffer(wasmFileset: WasmFileset, modelAssetBuffer: Uint8Array | ReadableStreamDefaultReader): Promise<HolisticLandmarker>;
    /**
     * Initializes the Wasm runtime and creates a new `HolisticLandmarker` based
     * on the path to the model asset.
     * @export
     * @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<HolisticLandmarker>;
    private constructor();
    /**
     * Sets new options for this `HolisticLandmarker`.
     *
     * 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`.
     *
     * @export
     * @param options The options for the holistic landmarker.
     */
    setOptions(options: HolisticLandmarkerOptions): Promise<void>;
    /**
     * Performs holistic landmarks detection 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 HolisticLandmarker
     * is created with running mode `image`.
     *
     * @export
     * @param image An image to process.
     * @param callback The callback that is invoked with the result. The
     *    lifetime of the returned masks is only guaranteed for the duration of
     *    the callback.
     */
    detect(image: ImageSource, callback: HolisticLandmarkerCallback): void;
    /**
     * Performs holistic landmarks detection 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 HolisticLandmarker
     * is created with running mode `image`.
     *
     * @export
     * @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 result. The
     *    lifetime of the returned masks is only guaranteed for the duration of
     *    the callback.
     */
    detect(image: ImageSource, imageProcessingOptions: ImageProcessingOptions, callback: HolisticLandmarkerCallback): void;
    /**
     * Performs holistic landmarks detection on the provided single image and
     * waits synchronously for the response. This method creates a copy of the
     * resulting masks and should not be used in high-throughput applications.
     * Only use this method when the HolisticLandmarker is created with running
     * mode `image`.
     *
     * @export
     * @param image An image to process.
     * @return The landmarker result. Any masks are copied to avoid lifetime
     *     limits.
     * @return The detected pose landmarks.
     */
    detect(image: ImageSource): HolisticLandmarkerResult;
    /**
     * Performs holistic landmarks detection on the provided single image and
     * waits synchronously for the response. This method creates a copy of the
     * resulting masks and should not be used in high-throughput applications.
     * Only use this method when the HolisticLandmarker is created with running
     * mode `image`.
     *
     * @export
     * @param image An image to process.
     * @return The landmarker result. Any masks are copied to avoid lifetime
     *     limits.
     * @return The detected pose landmarks.
     */
    detect(image: ImageSource, imageProcessingOptions: ImageProcessingOptions): HolisticLandmarkerResult;
    /**
     * Performs holistic landmarks detection on the provided video frame and
     * invokes the callback with the response. The method returns synchronously
     * once the callback returns. Only use