154 lines
6.0 KiB
Python
154 lines
6.0 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 Hands."""
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import enum
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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 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.hands_connections import HAND_CONNECTIONS
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# pylint: enable=unused-import
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class HandLandmark(enum.IntEnum):
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"""The 21 hand landmarks."""
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WRIST = 0
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THUMB_CMC = 1
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THUMB_MCP = 2
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THUMB_IP = 3
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THUMB_TIP = 4
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INDEX_FINGER_MCP = 5
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INDEX_FINGER_PIP = 6
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INDEX_FINGER_DIP = 7
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INDEX_FINGER_TIP = 8
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MIDDLE_FINGER_MCP = 9
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MIDDLE_FINGER_PIP = 10
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MIDDLE_FINGER_DIP = 11
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MIDDLE_FINGER_TIP = 12
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RING_FINGER_MCP = 13
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RING_FINGER_PIP = 14
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RING_FINGER_DIP = 15
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RING_FINGER_TIP = 16
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PINKY_MCP = 17
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PINKY_PIP = 18
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PINKY_DIP = 19
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PINKY_TIP = 20
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_BINARYPB_FILE_PATH = 'mediapipe/modules/hand_landmark/hand_landmark_tracking_cpu.binarypb'
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class Hands(SolutionBase):
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"""MediaPipe Hands.
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MediaPipe Hands processes an RGB image and returns the hand landmarks and
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handedness (left v.s. right hand) of each detected hand.
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Note that it determines handedness assuming the input image is mirrored,
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i.e., taken with a front-facing/selfie camera (
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https://en.wikipedia.org/wiki/Front-facing_camera) with images flipped
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horizontally. If that is not the case, use, for instance, cv2.flip(image, 1)
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to flip the image first for a correct handedness output.
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Please refer to https://solutions.mediapipe.dev/hands#python-solution-api for
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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_hands=2,
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model_complexity=1,
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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 Hand 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/hands#static_image_mode.
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max_num_hands: Maximum number of hands to detect. See details in
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https://solutions.mediapipe.dev/hands#max_num_hands.
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model_complexity: Complexity of the hand landmark model: 0 or 1.
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Landmark accuracy as well as inference latency generally go up with the
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model complexity. See details in
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https://solutions.mediapipe.dev/hands#model_complexity.
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min_detection_confidence: Minimum confidence value ([0.0, 1.0]) for hand
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detection to be considered successful. See details in
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https://solutions.mediapipe.dev/hands#min_detection_confidence.
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min_tracking_confidence: Minimum confidence value ([0.0, 1.0]) for the
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hand landmarks to be considered tracked successfully. See details in
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https://solutions.mediapipe.dev/hands#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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'model_complexity': model_complexity,
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'num_hands': max_num_hands,
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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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'palmdetectioncpu__TensorsToDetectionsCalculator.min_score_thresh':
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min_detection_confidence,
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'handlandmarkcpu__ThresholdingCalculator.threshold':
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min_tracking_confidence,
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},
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outputs=[
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'multi_hand_landmarks', 'multi_hand_world_landmarks',
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'multi_handedness'
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])
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def process(self, image: np.ndarray) -> NamedTuple:
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"""Processes an RGB image and returns the hand landmarks and handedness of each detected hand.
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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 the following fields:
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1) a "multi_hand_landmarks" field that contains the hand landmarks on
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each detected hand.
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2) a "multi_hand_world_landmarks" field that contains the hand landmarks
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on each detected hand in real-world 3D coordinates that are in meters
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with the origin at the hand's approximate geometric center.
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3) a "multi_handedness" field that contains the handedness (left v.s.
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right hand) of the detected hand.
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"""
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return super().process(input_data={'image': image})
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