126 lines
5.7 KiB
Python
126 lines
5.7 KiB
Python
# Copyright 2020 The MediaPipe Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""MediaPipe Face Mesh."""
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from typing import NamedTuple
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import numpy as np
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# pylint: disable=unused-import
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from mediapipe.calculators.core import constant_side_packet_calculator_pb2
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from mediapipe.calculators.core import gate_calculator_pb2
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from mediapipe.calculators.core import split_vector_calculator_pb2
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from mediapipe.calculators.tensor import image_to_tensor_calculator_pb2
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from mediapipe.calculators.tensor import inference_calculator_pb2
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from mediapipe.calculators.tensor import tensors_to_classification_calculator_pb2
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from mediapipe.calculators.tensor import tensors_to_detections_calculator_pb2
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from mediapipe.calculators.tensor import tensors_to_landmarks_calculator_pb2
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from mediapipe.calculators.tflite import ssd_anchors_calculator_pb2
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from mediapipe.calculators.util import association_calculator_pb2
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from mediapipe.calculators.util import detections_to_rects_calculator_pb2
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from mediapipe.calculators.util import landmarks_refinement_calculator_pb2
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from mediapipe.calculators.util import logic_calculator_pb2
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from mediapipe.calculators.util import non_max_suppression_calculator_pb2
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from mediapipe.calculators.util import rect_transformation_calculator_pb2
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from mediapipe.calculators.util import thresholding_calculator_pb2
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# pylint: enable=unused-import
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from mediapipe.python.solution_base import SolutionBase
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# pylint: disable=unused-import
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from mediapipe.python.solutions.face_mesh_connections import FACEMESH_CONTOURS
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from mediapipe.python.solutions.face_mesh_connections import FACEMESH_FACE_OVAL
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from mediapipe.python.solutions.face_mesh_connections import FACEMESH_IRISES
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from mediapipe.python.solutions.face_mesh_connections import FACEMESH_LEFT_EYE
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from mediapipe.python.solutions.face_mesh_connections import FACEMESH_LEFT_EYEBROW
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from mediapipe.python.solutions.face_mesh_connections import FACEMESH_LEFT_IRIS
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from mediapipe.python.solutions.face_mesh_connections import FACEMESH_LIPS
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from mediapipe.python.solutions.face_mesh_connections import FACEMESH_NOSE
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from mediapipe.python.solutions.face_mesh_connections import FACEMESH_RIGHT_EYE
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from mediapipe.python.solutions.face_mesh_connections import FACEMESH_RIGHT_EYEBROW
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from mediapipe.python.solutions.face_mesh_connections import FACEMESH_RIGHT_IRIS
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from mediapipe.python.solutions.face_mesh_connections import FACEMESH_TESSELATION
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# pylint: enable=unused-import
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FACEMESH_NUM_LANDMARKS = 468
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FACEMESH_NUM_LANDMARKS_WITH_IRISES = 478
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_BINARYPB_FILE_PATH = 'mediapipe/modules/face_landmark/face_landmark_front_cpu.binarypb'
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class FaceMesh(SolutionBase):
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"""MediaPipe Face Mesh.
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MediaPipe Face Mesh processes an RGB image and returns the face landmarks on
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each detected face.
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Please refer to https://solutions.mediapipe.dev/face_mesh#python-solution-api
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for usage examples.
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"""
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def __init__(self,
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static_image_mode=False,
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max_num_faces=1,
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refine_landmarks=False,
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min_detection_confidence=0.5,
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min_tracking_confidence=0.5):
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"""Initializes a MediaPipe Face Mesh object.
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Args:
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static_image_mode: Whether to treat the input images as a batch of static
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and possibly unrelated images, or a video stream. See details in
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https://solutions.mediapipe.dev/face_mesh#static_image_mode.
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max_num_faces: Maximum number of faces to detect. See details in
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https://solutions.mediapipe.dev/face_mesh#max_num_faces.
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refine_landmarks: Whether to further refine the landmark coordinates
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around the eyes and lips, and output additional landmarks around the
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irises. Default to False. See details in
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https://solutions.mediapipe.dev/face_mesh#refine_landmarks.
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min_detection_confidence: Minimum confidence value ([0.0, 1.0]) for face
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detection to be considered successful. See details in
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https://solutions.mediapipe.dev/face_mesh#min_detection_confidence.
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min_tracking_confidence: Minimum confidence value ([0.0, 1.0]) for the
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face landmarks to be considered tracked successfully. See details in
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https://solutions.mediapipe.dev/face_mesh#min_tracking_confidence.
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"""
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super().__init__(
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binary_graph_path=_BINARYPB_FILE_PATH,
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side_inputs={
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'num_faces': max_num_faces,
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'with_attention': refine_landmarks,
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'use_prev_landmarks': not static_image_mode,
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},
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calculator_params={
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'facedetectionshortrangecpu__facedetectionshortrange__facedetection__TensorsToDetectionsCalculator.min_score_thresh':
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min_detection_confidence,
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'facelandmarkcpu__ThresholdingCalculator.threshold':
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min_tracking_confidence,
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},
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outputs=['multi_face_landmarks'])
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def process(self, image: np.ndarray) -> NamedTuple:
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"""Processes an RGB image and returns the face landmarks on each detected face.
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Args:
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image: An RGB image represented as a numpy ndarray.
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Raises:
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RuntimeError: If the underlying graph throws any error.
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ValueError: If the input image is not three channel RGB.
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Returns:
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A NamedTuple object with a "multi_face_landmarks" field that contains the
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face landmarks on each detected face.
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"""
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return super().process(input_data={'image': image})
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