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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/google-ai-edge/mediapipe/blob/master/mediapipe/tasks/python/vision/hand_landmarker.py#L332-L614) |

Class that performs hand landmarks detection on images.

    mp.tasks.vision.HandLandmarker(
        lib: serial_dispatcher.SerialDispatcher,
        handle: ctypes.c_void_p,
        dispatcher: _AsyncResultDispatcher,
        async_callback: _C_TYPES_RESULT_CALLBACK
    )

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| ## Args ||
|---|---|
| `lib` | The dispatch library to use for the hand landmarker. |
| `handle` | The C pointer to the hand landmarker. |
| `dispatcher` | The async result handler for the hand landmarker. |
| `async_callback` | The c callback for the hand landmarker. |

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## Methods

### `close`

[View source](https://github.com/google-ai-edge/mediapipe/blob/master/mediapipe/tasks/python/vision/hand_landmarker.py#L587-L593)

    close()

Shuts down the MediaPipe task instance.

### `create_from_model_path`

[View source](https://github.com/google-ai-edge/mediapipe/blob/master/mediapipe/tasks/python/vision/hand_landmarker.py#L360-L383)

    @classmethod
    create_from_model_path(
        model_path: str
    ) -> 'HandLandmarker'

Creates an `HandLandmarker` object from a TensorFlow Lite model and the default `HandLandmarkerOptions`.

Note that the created `HandLandmarker` instance is in image mode, for
detecting hand landmarks on single image inputs.

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| Args ||
|---|---|
| `model_path` | Path to the model. |

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| Returns ||
|---|---|
| `HandLandmarker` object that's created from the model file and the default `HandLandmarkerOptions`. ||

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| Raises ||
|---|---|
| `ValueError` | If failed to create `HandLandmarker` object from the provided file such as invalid file path. |
| `RuntimeError` | If other types of error occurred. |

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### `create_from_options`

[View source](https://github.com/google-ai-edge/mediapipe/blob/master/mediapipe/tasks/python/vision/hand_landmarker.py#L385-L442)

    @classmethod
    create_from_options(
        options: https://developers.google.com/edge/api/mediapipe/python/mp/tasks/vision/HandLandmarkerOptions
    ) -> 'HandLandmarker'

Creates the `HandLandmarker` object from hand landmarker options.

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| Args ||
|---|---|
| `options` | Options for the hand landmarker task. |

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| Returns ||
|---|---|
| `HandLandmarker` object that's created from `options`. ||

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| Raises ||
|---|---|
| `ValueError` | If failed to create `HandLandmarker` object from `HandLandmarkerOptions` such as missing the model. |
| `RuntimeError` | If other types of error occurred. |

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### `detect`

[View source](https://github.com/google-ai-edge/mediapipe/blob/master/mediapipe/tasks/python/vision/hand_landmarker.py#L444-L486)

    detect(
        image: https://developers.google.com/edge/api/mediapipe/python/mp/Image,
        image_processing_options: Optional[https://developers.google.com/edge/api/mediapipe/python/mp/tasks/vision/ImageProcessingOptions] = None
    ) -> https://developers.google.com/edge/api/mediapipe/python/mp/tasks/vision/HandLandmarkerResult

Performs hand landmarks detection on the given image.

Only use this method when the HandLandmarker is created with the image
running mode.

The image can be of any size with format RGB or RGBA.

support is implemented.

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| Args ||
|---|---|
| `image` | MediaPipe Image. |
| `image_processing_options` | Options for image processing. |

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| Returns ||
|---|---|
| The hand landmarks detection results. ||

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| Raises ||
|---|---|
| `ValueError` | If any of the input arguments is invalid. |
| `RuntimeError` | If hand landmarker detection failed to run. |

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### `detect_async`

[View source](https://github.com/google-ai-edge/mediapipe/blob/master/mediapipe/tasks/python/vision/hand_landmarker.py#L536-L585)

    detect_async(
        image: https://developers.google.com/edge/api/mediapipe/python/mp/Image,
        timestamp_ms: int,
        image_processing_options: Optional[https://developers.google.com/edge/api/mediapipe/python/mp/tasks/vision/ImageProcessingOptions] = None
    ) -> None

Sends live image data to perform hand landmarks detection.

The results will be available via the "result_callback" provided in the
HandLandmarkerOptions. Only use this method when the HandLandmarker is
created with the live stream running mode.

Only use this method when the HandLandmarker is created with the live
stream running mode. The input timestamps should be monotonically increasing
for adjacent calls of this method. This method will return immediately after
the input image is accepted. The results will be available via the
`result_callback` provided in the `HandLandmarkerOptions`. The
`detect_async` method is designed to process live stream data such as
camera input. To lower the overall latency, hand landmarker may drop the
input images if needed. In other words, it's not guaranteed to have output
per input image.

The `result_callback` provides:
- The hand landmarks detection results.
- The input image that the hand landmarker runs on.
- The input timestamp in milliseconds.

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| Args ||
|---|---|
| `image` | MediaPipe Image. |
| `timestamp_ms` | The timestamp of the input image in milliseconds. |
| `image_processing_options` | Options for image processing. |

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| Raises ||
|---|---|
| `ValueError` | If the current input timestamp is smaller than what the hand landmarker has already processed. |
| `RuntimeError` | If hand landmarker detection failed to initialize. |

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### `detect_for_video`

[View source](https://github.com/google-ai-edge/mediapipe/blob/master/mediapipe/tasks/python/vision/hand_landmarker.py#L488-L534)

    detect_for_video(
        image: https://developers.google.com/edge/api/mediapipe/python/mp/Image,
        timestamp_ms: int,
        image_processing_options: Optional[https://developers.google.com/edge/api/mediapipe/python/mp/tasks/vision/ImageProcessingOptions] = None
    ) -> https://developers.google.com/edge/api/mediapipe/python/mp/tasks/vision/HandLandmarkerResult

Performs hand landmarks detection on the provided video frame.

Only use this method when the HandLandmarker is created with the video
running mode.

Only use this method when the HandLandmarker is created with the video
running mode. It's required to provide the video frame's timestamp (in
milliseconds) along with the video frame. The input timestamps should be
monotonically increasing for adjacent calls of this method.

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| Args ||
|---|---|
| `image` | MediaPipe Image. |
| `timestamp_ms` | The timestamp of the input video frame in milliseconds. |
| `image_processing_options` | Options for image processing. |

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| Returns ||
|---|---|
| The hand landmarks detection results. ||

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| Raises ||
|---|---|
| `ValueError` | If any of the input arguments is invalid. |
| `RuntimeError` | If hand landmarker detection failed to run. |

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### `__enter__`

[View source](https://github.com/google-ai-edge/mediapipe/blob/master/mediapipe/tasks/python/vision/hand_landmarker.py#L595-L597)

    __enter__()

Returns `self` upon entering the runtime context.

### `__exit__`

[View source](https://github.com/google-ai-edge/mediapipe/blob/master/mediapipe/tasks/python/vision/hand_landmarker.py#L599-L611)

    __exit__(
        exc_type, exc_value, traceback
    )

Shuts down the MediaPipe task instance on exit of the context manager.

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| Args ||
|---|---|
| `exc_type` | The exception type that caused the exit. |
| `exc_value` | The exception value that caused the exit. |
| `traceback` | The exception traceback that caused the exit. |

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| Raises ||
|---|---|
| `RuntimeError` | If the MediaPipe HandLandmarker task failed to close. |

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