# 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. """MediaPipe face detector task.""" import ctypes import dataclasses from typing import Callable, Optional, Tuple from mediapipe.tasks.python.components.containers import detections as detections_module from mediapipe.tasks.python.components.containers import detections_c as detections_c_module from mediapipe.tasks.python.core import async_result_dispatcher from mediapipe.tasks.python.core import base_options as base_options_module from mediapipe.tasks.python.core import base_options_c as base_options_c_module from mediapipe.tasks.python.core import mediapipe_c_bindings from mediapipe.tasks.python.core import mediapipe_c_utils from mediapipe.tasks.python.core import serial_dispatcher from mediapipe.tasks.python.core.optional_dependencies import doc_controls from mediapipe.tasks.python.vision.core import image as image_module from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module from mediapipe.tasks.python.vision.core import image_processing_options_c as image_processing_options_c_module from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module FaceDetectorResult = detections_module.DetectionResult _RunningMode = running_mode_module.VisionTaskRunningMode _BaseOptions = base_options_module.BaseOptions _ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions _C_TYPES_RESULT_CALLBACK = ctypes.CFUNCTYPE( None, ctypes.c_int32, # MpStatus ctypes.POINTER(detections_c_module.DetectionResultC), ctypes.c_void_p, # MpImage ctypes.c_int64, # timestamp_ms ) class FaceDetectorOptionsC(ctypes.Structure): """C types for FaceDetectorOptions.""" _fields_ = [ ('base_options', base_options_c_module.BaseOptionsC), ('running_mode', ctypes.c_int), ('min_detection_confidence', ctypes.c_float), ('min_suppression_threshold', ctypes.c_float), ('result_callback', _C_TYPES_RESULT_CALLBACK), ] @classmethod @doc_controls.do_not_generate_docs def from_c_options( cls, base_options: base_options_c_module.BaseOptionsC, running_mode: _RunningMode, min_detection_confidence: float, min_suppression_threshold: float, result_callback: _C_TYPES_RESULT_CALLBACK, ) -> 'FaceDetectorOptionsC': """Creates a FaceDetectorOptionsC object from the given options.""" return cls( base_options=base_options, running_mode=running_mode.ctype, min_detection_confidence=min_detection_confidence, min_suppression_threshold=min_suppression_threshold, result_callback=result_callback, ) _CTYPES_SIGNATURES = ( mediapipe_c_utils.CStatusFunction( 'MpFaceDetectorCreate', ( ctypes.POINTER(FaceDetectorOptionsC), ctypes.POINTER(ctypes.c_void_p), ), ), mediapipe_c_utils.CStatusFunction( 'MpFaceDetectorDetectImage', ( ctypes.c_void_p, ctypes.c_void_p, ctypes.POINTER( image_processing_options_c_module.ImageProcessingOptionsC ), ctypes.POINTER(detections_c_module.DetectionResultC), ), ), mediapipe_c_utils.CStatusFunction( 'MpFaceDetectorDetectForVideo', ( ctypes.c_void_p, ctypes.c_void_p, ctypes.POINTER( image_processing_options_c_module.ImageProcessingOptionsC ), ctypes.c_int64, ctypes.POINTER(detections_c_module.DetectionResultC), ), ), mediapipe_c_utils.CStatusFunction( 'MpFaceDetectorDetectAsync', ( ctypes.c_void_p, ctypes.c_void_p, ctypes.POINTER( image_processing_options_c_module.ImageProcessingOptionsC ), ctypes.c_int64, ), ), mediapipe_c_utils.CFunction( 'MpFaceDetectorCloseResult', [ctypes.POINTER(detections_c_module.DetectionResultC)], None, ), mediapipe_c_utils.CStatusFunction( 'MpFaceDetectorClose', (ctypes.c_void_p,), ), ) @dataclasses.dataclass class FaceDetectorOptions: """Options for the face detector task. Attributes: base_options: Base options for the face detector task. running_mode: The running mode of the task. Default to the image mode. Face detector task has three running modes: 1) The image mode for detecting faces on single image inputs. 2) The video mode for detecting faces on the decoded frames of a video. 3) The live stream mode for detecting faces on a live stream of input data, such as from camera. min_detection_confidence: The minimum confidence score for the face detection to be considered successful. min_suppression_threshold: The minimum non-maximum-suppression threshold for face detection to be considered overlapped. result_callback: The user-defined result callback for processing live stream data. The result callback should only be specified when the running mode is set to the live stream mode. """ base_options: _BaseOptions running_mode: _RunningMode = _RunningMode.IMAGE min_detection_confidence: float = 0.5 min_suppression_threshold: float = 0.3 result_callback: Optional[ Callable[ [detections_module.DetectionResult, image_module.Image, int], None ] ] = None class FaceDetector: """Class that performs face detection on images.""" _lib: serial_dispatcher.SerialDispatcher _handle: ctypes.c_void_p _dispatcher: async_result_dispatcher.AsyncResultDispatcher _async_callback: _C_TYPES_RESULT_CALLBACK def __init__( self, lib: serial_dispatcher.SerialDispatcher, handle: ctypes.c_void_p, dispatcher: async_result_dispatcher.AsyncResultDispatcher, async_callback: _C_TYPES_RESULT_CALLBACK, ): """Initializes the face detector. Args: lib: The dispatch library to use for the face detector. handle: The C pointer to the face detector. dispatcher: The async result handler for the face detector. async_callback: The c callback for the face detector. """ self._lib = lib self._handle = handle self._dispatcher = dispatcher self._async_callback = async_callback @classmethod def create_from_model_path(cls, model_path: str) -> 'FaceDetector': """Creates an `FaceDetector` object from a TensorFlow Lite model and the default `FaceDetectorOptions`. Note that the created `FaceDetector` instance is in image mode, for detecting faces on single image inputs. Args: model_path: Path to the model. Returns: `FaceDetector` object that's created from the model file and the default `FaceDetectorOptions`. Raises: ValueError: If failed to create `FaceDetector` object from the provided file such as invalid file path. RuntimeError: If other types of error occurred. """ base_options = _BaseOptions(model_asset_path=model_path) options = FaceDetectorOptions( base_options=base_options, running_mode=_RunningMode.IMAGE ) return cls.create_from_options(options) @classmethod def create_from_options(cls, options: FaceDetectorOptions) -> 'FaceDetector': """Creates the `FaceDetector` object from face detector options. Args: options: Options for the face detector task. Returns: `FaceDetector` object that's created from `options`. Raises: ValueError: If failed to create `FaceDetector` object from `FaceDetectorOptions` such as missing the model. RuntimeError: If other types of error occurred. """ running_mode_module.validate_running_mode( options.running_mode, options.result_callback ) lib = mediapipe_c_bindings.load_shared_library(_CTYPES_SIGNATURES) def convert_result( c_result_ptr: ctypes.POINTER(detections_c_module.DetectionResultC), image_ptr: ctypes.c_void_p, timestamp_ms: int, ) -> Tuple[FaceDetectorResult, image_module.Image, int]: c_result = c_result_ptr[0] py_result = FaceDetectorResult.from_ctypes(c_result) py_image = image_module.Image.create_from_ctypes(image_ptr) return (py_result, py_image, timestamp_ms) dispatcher = async_result_dispatcher.AsyncResultDispatcher( converter=convert_result ) c_callback = dispatcher.wrap_callback( options.result_callback, _C_TYPES_RESULT_CALLBACK ) ctypes_options = FaceDetectorOptionsC.from_c_options( base_options=options.base_options.to_ctypes(), running_mode=options.running_mode, min_detection_confidence=options.min_detection_confidence, min_suppression_threshold=options.min_suppression_threshold, result_callback=c_callback, ) detector_handle = ctypes.c_void_p() lib.MpFaceDetectorCreate( ctypes.byref(ctypes_options), ctypes.byref(detector_handle), ) return FaceDetector( lib=lib, handle=detector_handle, dispatcher=dispatcher, async_callback=c_callback, ) def detect( self, image: image_module.Image, image_processing_options: Optional[_ImageProcessingOptions] = None, ) -> FaceDetectorResult: """Performs face detection on the provided MediaPipe Image. Only use this method when the FaceDetector is created with the image running mode. Args: image: MediaPipe Image. image_processing_options: Options for image processing. Returns: A face detection result object that contains a list of face detections, each detection has a bounding box that is expressed in the unrotated input frame of reference coordinates system, i.e. in `[0,image_width) x [0, image_height)`, which are the dimensions of the underlying image data. Raises: ValueError: If any of the input arguments is invalid. RuntimeError: If face detection failed to run. """ c_image = image._image_ptr # pylint: disable=protected-access c_result = detections_c_module.DetectionResultC() c_image_processing_options = ( ctypes.byref(image_processing_options.to_ctypes()) if image_processing_options else None ) self._lib.MpFaceDetectorDetectImage( self._handle, c_image, c_image_processing_options, ctypes.byref(c_result), ) py_result = FaceDetectorResult.from_ctypes(c_result) self._lib.MpFaceDetectorCloseResult(ctypes.byref(c_result)) return py_result def detect_for_video( self, image: image_module.Image, timestamp_ms: int, image_processing_options: Optional[_ImageProcessingOptions] = None, ) -> detections_module.DetectionResult: """Performs face detection on the provided video frames. Only use this method when the FaceDetector 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. Args: image: MediaPipe Image. timestamp_ms: The timestamp of the input video frame in milliseconds. image_processing_options: Options for image processing. Returns: A face detection result object that contains a list of face detections, each detection has a bounding box that is expressed in the unrotated input frame of reference coordinates system, i.e. in `[0,image_width) x [0, image_height)`, which are the dimensions of the underlying image data. Raises: ValueError: If any of the input arguments is invalid. RuntimeError: If face detection failed to run. """ c_image = image._image_ptr # pylint: disable=protected-access c_result = detections_c_module.DetectionResultC() c_image_processing_options = ( ctypes.byref(image_processing_options.to_ctypes()) if image_processing_options else None ) self._lib.MpFaceDetectorDetectForVideo( self._handle, c_image, c_image_processing_options, timestamp_ms, ctypes.byref(c_result), ) py_result = FaceDetectorResult.from_ctypes(c_result) self._lib.MpFaceDetectorCloseResult(ctypes.byref(c_result)) return py_result def detect_async( self, image: image_module.Image, timestamp_ms: int, image_processing_options: Optional[_ImageProcessingOptions] = None, ) -> None: """Sends live image data (an Image with a unique timestamp) to perform face detection. Only use this method when the FaceDetector 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 `FaceDetectorOptions`. The `detect_async` method is designed to process live stream data such as camera input. To lower the overall latency, face detector may drop the input images if needed. In other words, it's not guaranteed to have output per input image. The `result_callback` provides: - A face detection result object that contains a list of face detections, each detection has a bounding box that is expressed in the unrotated input frame of reference coordinates system, i.e. in `[0,image_width) x [0,image_height)`, which are the dimensions of the underlying image data. - The input image that the face detector runs on. - The input timestamp in milliseconds. Args: image: MediaPipe Image. timestamp_ms: The timestamp of the input image in milliseconds. image_processing_options: Options for image processing. Raises: ValueError: If the current input timestamp is smaller than what the face detector has already processed. RuntimeError: If face detection failed to initialize. """ c_image = image._image_ptr # pylint: disable=protected-access c_image_processing_options = ( ctypes.byref(image_processing_options.to_ctypes()) if image_processing_options else None ) self._lib.MpFaceDetectorDetectAsync( self._handle, c_image, c_image_processing_options, timestamp_ms, ) def close(self): """Shuts down the MediaPipe task instance.""" if not self._handle: return self._lib.MpFaceDetectorClose(self._handle) self._handle = None self._dispatcher.close() self._lib.close() def __enter__(self): """Returns `self` upon entering the runtime context.""" return self def __exit__(self, exc_type, exc_value, traceback): """Shuts down the MediaPipe task instance on exit of the context manager. 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. Raises: RuntimeError: If the MediaPipe FaceDetector task failed to close. """ del exc_type, exc_value, traceback # Unused. self.close() def __del__(self): self.close()