Python开源工具库使用之运动姿势追踪库mediapipe

前言

Mediapipe 是谷歌出品的一种开源框架，旨在为开发者提供一种简单而强大的工具，用于实现各种视觉和感知应用程序。它包括一系列预训练的机器学习模型和用于处理多媒体数据的工具，可以用于姿势估计、手部追踪、人脸检测与跟踪、面部标志、对象检测、图片分割和语言检测等任务

Mediapipe 是支持跨平台的，可以部署在手机端(Android, iOS), web, desktop, edge devices, IoT 等各种平台，编程语言也支持C++, Python, Java, Swift, Objective-C, Javascript等

在本文中，我们将通过Python实现 Mediapipe 在姿势估计和手部追踪不同领域的应用

• github 地址：https://github.com/google/mediapipe

一、姿势估计

1.1 姿态关键点

序号	部位	Pose Landmark
0	鼻子	PoseLandmark.NOSE
1	左眼（内侧）	PoseLandmark.LEFT_EYE_INNER
2	左眼	PoseLandmark.LEFT_EYE
3	左眼（外侧）	PoseLandmark.LEFT_EYE_OUTER
4	右眼（内侧）	PoseLandmark.RIGHT_EYE_INNER
5	右眼	PoseLandmark.RIGHT_EYE
6	右眼（外侧）	PoseLandmark.RIGHT_EYE_OUTER
7	左耳	PoseLandmark.LEFT_EAR
8	右耳	PoseLandmark.RIGHT_EAR
9	嘴巴（左侧）	PoseLandmark.MOUTH_LEFT
10	嘴巴（右侧）	PoseLandmark.MOUTH_RIGHT
11	左肩	PoseLandmark.LEFT_SHOULDER
12	右肩	PoseLandmark.RIGHT_SHOULDER
13	左肘	PoseLandmark.LEFT_ELBOW
14	右肘	PoseLandmark.RIGHT_ELBOW
15	左腕	PoseLandmark.LEFT_WRIST
16	右腕	PoseLandmark.RIGHT_WRIST
17	左小指	PoseLandmark.LEFT_PINKY
18	右小指	PoseLandmark.RIGHT_PINKY
19	左食指	PoseLandmark.LEFT_INDEX
20	右食指	PoseLandmark.RIGHT_INDEX
21	左拇指	PoseLandmark.LEFT_THUMB
22	右拇指	PoseLandmark.RIGHT_THUMB
23	左臀	PoseLandmark.LEFT_HIP
24	右臀	PoseLandmark.RIGHT_HIP
25	左膝	PoseLandmark.LEFT_KNEE
26	右膝	PoseLandmark.RIGHT_KNEE
27	左踝	PoseLandmark.LEFT_ANKLE
28	右踝	PoseLandmark.RIGHT_ANKLE
29	左脚跟	PoseLandmark.LEFT_HEEL
30	右脚跟	PoseLandmark.RIGHT_HEEL
31	左脚趾	PoseLandmark.LEFT_FOOT_INDEX
32	右脚趾	PoseLandmark.RIGHT_FOOT_INDEX

1.2 旧版 solution API

Mediapipe 提供 solution API 来实现快速检测， 不过这种方式在2023年5月10日停止更新了，不过目前还可以使用，可通过 mediapose.solutions.pose.Pose 来实现，配置参数如下

选项	含义	值范围	默认值
static_image_mode	如果设置为 False，会将输入图像视为视频流。它将尝试检测第一张图像中最突出的人，并在成功检测后进一步定位姿势。在随后的图像中，它只是跟踪这些标记，而不调用另一个检测，直到它失去跟踪，从而减少计算和延迟。如果设置为 True，则人员检测将运行每个输入图像，非常适合处理一批静态（可能不相关的）图像	Boolean	False
model_complexity	模型的复杂度，准确性和推理延迟通常随着模型复杂性的增加而增加	{0,1,2}	1
smooth_landmarks	如果设置为 True，则solution 过滤器会在不同的输入图像中设置标记以减少抖动，但如果 static_image_mode 也设置为 True，则忽略该筛选器	Boolean	True
enable_segmentation	如果设置为 True，则除了姿态标记外，还会生成分割蒙版	Boolean	False
smooth_segmentation	如果设置为 True，则会过滤不同输入图像中的分割掩码，以减少抖动。如果enable_segmentation为 false 或 static_image_mode为 True，则忽略	Boolean	True
min_detection_confidence	人员检测模型的最小置信度值 ，用于将检测视为成功	Float [0.0,1.0]	0.5
min_tracking_confidence	来自姿态跟踪模型的最小置信度值 ， 用于将姿态标记视为成功跟踪，否则将在下一个输入图像上自动调用人员检测。将其设置为更高的值可以提高解决方案的可靠性，但代价是延迟更高。如果static_image_mode为 True，则忽略，其中人员检测仅对每个图像运行。	Float [0.0,1.0]	0.5

import cv2
import numpy as np
import mediapipe as mpdef main():FILE_PATH = 'data/1.png'img = cv2.imread(FILE_PATH)mp_pose = mp.solutions.posepose = mp_pose.Pose(static_image_mode=True,min_detection_confidence=0.5, min_tracking_confidence=0.5)res = pose.process(img)img_copy = img.copy()if res.pose_landmarks is not None:mp_drawing = mp.solutions.drawing_utils# mp_drawing.draw_landmarks(#     img_copy, res.pose_landmarks, mp.solutions.pose.POSE_CONNECTIONS)mp_drawing.draw_landmarks(img_copy,res.pose_landmarks,mp_pose.POSE_CONNECTIONS,  # frozenset，定义了哪些关键点要连接mp_drawing.DrawingSpec(color=(255, 255, 255),  # 姿态关键点thickness=2,circle_radius=2),mp_drawing.DrawingSpec(color=(174, 139, 45),   # 连线颜色thickness=2,circle_radius=2),)cv2.imshow('MediaPipe Pose Estimation', img_copy)cv2.waitKey(0)if __name__ == '__main__':main()

import cv2
import numpy as np
import mediapipe as mpdef video():# 读取摄像头# cap = cv2.VideoCapture(0)# 读取视频cap = cv2.VideoCapture('data/1.mp4')mp_pose = mp.solutions.posepose = mp_pose.Pose(static_image_mode=False,min_detection_confidence=0.5, min_tracking_confidence=0.5)while cap.isOpened():ret, frame = cap.read()if not ret:break# 摄像头# continue# 将 BGR 图像转换为 RGBrgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)# 进行姿势估计results = pose.process(rgb_frame)if results.pose_landmarks is not None:# 绘制关键点和连接线mp_drawing = mp.solutions.drawing_utilsmp_drawing.draw_landmarks(frame, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)# 显示结果cv2.imshow('MediaPipe Pose Estimation', frame)if cv2.waitKey(1) & 0xFF == ord('q'):break# 释放资源cap.release()cv2.destroyAllWindows()if __name__ == '__main__':video()

1.3 新版 solution API

旧版 API 并不能检测多个姿态，新版 API 可以实现多个姿态检测

选项	含义	值范围	默认值
running_mode	设置任务的运行模式，有三种模式可选: IMAGE: 单一照片输入. VIDEO: 视频. LIVE_STREAM: 输入数据（例如来自摄像机）为实时流。在此模式下，必须调用 resultListener 来设置侦听器以异步接收结果.	{IMAGE, VIDEO, LIVE_STREAM}	IMAGE
num_poses	姿势检测器可以检测到的最大姿势数	Integer > 0	1
min_pose_detection_confidence	姿势检测被认为是成功的最小置信度得分	Float [0.0,1.0]	0.5
min_pose_presence_confidence	姿态检测中的姿态存在分数的最小置信度分数	Float [0.0,1.0]	0.5
min_tracking_confidence	姿势跟踪被视为成功的最小置信度分数	Float [0.0,1.0]	0.5
output_segmentation_masks	是否为检测到的姿势输出分割掩码	Boolean	False
result_callback	将结果侦听器设置为在Pose Landmark处于LIVE_STREAM模式时异步接收Landmark结果。仅当运行模式设置为LIVE_STREAM时才能使用	ResultListener	N/A

from mediapipe import solutions
from mediapipe.framework.formats import landmark_pb2
import cv2
import numpy as np
import mediapipe as mpmp_drawing = mp.solutions.drawing_utils
mp_pose = mp.solutions.posedef draw_landmarks_on_image(rgb_image, detection_result):pose_landmarks_list = detection_result.pose_landmarksannotated_image = np.copy(rgb_image)# Loop through the detected poses to visualize.for idx in range(len(pose_landmarks_list)):pose_landmarks = pose_landmarks_list[idx]# Draw the pose landmarks.pose_landmarks_proto = landmark_pb2.NormalizedLandmarkList()pose_landmarks_proto.landmark.extend([landmark_pb2.NormalizedLandmark(x=landmark.x, y=landmark.y, z=landmark.z) for landmark in pose_landmarks])solutions.drawing_utils.draw_landmarks(annotated_image,pose_landmarks_proto,solutions.pose.POSE_CONNECTIONS,solutions.drawing_styles.get_default_pose_landmarks_style())return annotated_imagedef newSolution():BaseOptions = mp.tasks.BaseOptionsPoseLandmarker = mp.tasks.vision.PoseLandmarkerPoseLandmarkerOptions = mp.tasks.vision.PoseLandmarkerOptionsVisionRunningMode = mp.tasks.vision.RunningModemodel_path = 'data/pose_landmarker_heavy.task'options = PoseLandmarkerOptions(base_options=BaseOptions(model_asset_path=model_path),running_mode=VisionRunningMode.IMAGE,num_poses=10)FILE_PATH = 'data/4.jpg'image = cv2.imread(FILE_PATH)img = mp.Image.create_from_file(FILE_PATH)with PoseLandmarker.create_from_options(options) as detector:res = detector.detect(img)image = draw_landmarks_on_image(image, res)cv2.imshow('MediaPipe Pose Estimation', image)cv2.waitKey(0)if __name__ == '__main__':newSolution()

1.4 俯卧撑计数

通过计算胳膊弯曲角度来判断状态，并计算俯卧撑个数

import cv2
import mediapipe as mp
import numpy as npmp_drawing = mp.solutions.drawing_utils
mp_pose = mp.solutions.posedef calculate_angle(a, b, c):radians = np.arctan2(c.y - b.y, c.x - b.x) - \np.arctan2(a.y - b.y, a.x - b.x)angle = np.abs(np.degrees(radians))return angle if angle <= 180 else 360 - angledef angle_of_arm(landmarks, shoulder, elbow, wrist):shoulder_coord = landmarks[mp_pose.PoseLandmark[shoulder].value]elbow_coord = landmarks[mp_pose.PoseLandmark[elbow].value]wrist_coord = landmarks[mp_pose.PoseLandmark[wrist].value]return calculate_angle(shoulder_coord, elbow_coord, wrist_coord)def count_push_up(landmarks, counter, status):left_arm_angle = angle_of_arm(landmarks, "LEFT_SHOULDER", "LEFT_ELBOW", "LEFT_WRIST")right_arm_angle = angle_of_arm(landmarks, "RIGHT_SHOULDER", "RIGHT_ELBOW", "RIGHT_WRIST")avg_arm_angle = (left_arm_angle + right_arm_angle) // 2if status:if avg_arm_angle < 70:counter += 1status = Falseelse:if avg_arm_angle > 160:status = Truereturn counter, statusdef main():cap = cv2.VideoCapture('data/test.mp4')counter = 0status = Falsewith mp_pose.Pose(min_detection_confidence=0.7, min_tracking_confidence=0.7) as pose:while cap.isOpened():success, image = cap.read()if not success:print("empty camera")breakresult = pose.process(image)if result.pose_landmarks:mp_drawing.draw_landmarks(image, result.pose_landmarks, mp_pose.POSE_CONNECTIONS)counter, status = count_push_up(result.pose_landmarks.landmark, counter, status)cv2.putText(image, text=str(counter), org=(100, 100), fontFace=cv2.FONT_HERSHEY_SIMPLEX,fontScale=4, color=(255, 255, 255), thickness=2, lineType=cv2.LINE_AA)cv2.imshow("push-up counter", image)key = cv2.waitKey(1)if key == ord('q'):breakcap.release()if __name__ == '__main__':main()

二、手部追踪

2.1 手部姿态

2.2 API 使用

照片

选项	含义	值范围	默认值
static_image_mode	如果设置为 False，会将输入图像视为视频流。它将尝试在第一个输入图像中检测手，并在成功检测后进一步定位手部标志。在随后的图像中，一旦检测到所有 max_num_hands 手并定位了相应的手部标志，它就会简单地跟踪这些标志，而不会调用其他检测，直到它失去对任何手的跟踪。这减少了延迟，是处理视频帧的理想选择。如果设置为 True，则对每个输入图像运行手动检测，非常适合处理一批静态（可能不相关的）图像	Boolean	False
max_num_hands	要检测的最大手数	Integer	2
model_complexity	模型的复杂度，准确性和推理延迟通常随着模型复杂性的增加而增加	{0,1}	1
min_detection_confidence	检测模型的最小置信度值 ，用于将检测视为成功	Float [0.0,1.0]	0.5
min_tracking_confidence	来自手部跟踪模型的最小置信度值 ， 用于将手部标记视为成功跟踪，否则将在下一个输入图像上自动调用检测。将其设置为更高的值可以提高解决方案的可靠性，但代价是延迟更高。如果static_image_mode为 True，则忽略，其中手部检测仅对每个图像运行。	Float [0.0,1.0]	0.5

import cv2
import mediapipe as mpmp_hands = mp.solutions.handsdef main():cv2.namedWindow("MediaPipe Hand", cv2.WINDOW_NORMAL)hands = mp_hands.Hands(static_image_mode=False, max_num_hands=2,min_detection_confidence=0.5, min_tracking_confidence=0.5)img = cv2.imread('data/finger/1.jpg')rgb_frame = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)# 进行手部追踪results = hands.process(rgb_frame)if results.multi_hand_landmarks:# 绘制手部关键点和连接线for hand_landmarks in results.multi_hand_landmarks:mp_drawing = mp.solutions.drawing_utilsmp_drawing.draw_landmarks(img, hand_landmarks, mp_hands.HAND_CONNECTIONS)# 显示结果cv2.imshow('MediaPipe Hand', img)cv2.waitKey(0)if __name__ == '__main__':main()

import cv2
import mediapipe as mpmp_hands = mp.solutions.handsdef video():hands = mp_hands.Hands(static_image_mode=False, max_num_hands=2,min_detection_confidence=0.4, min_tracking_confidence=0.4)# 读取视频cap = cv2.VideoCapture('data/hand.mp4')while cap.isOpened():ret, frame = cap.read()if not ret:break# 将 BGR 图像转换为 RGBrgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)# 进行手部追踪results = hands.process(rgb_frame)if results.multi_hand_landmarks:# 绘制手部关键点和连接线for hand_landmarks in results.multi_hand_landmarks:mp_drawing = mp.solutions.drawing_utilsmp_drawing.draw_landmarks(frame, hand_landmarks, mp_hands.HAND_CONNECTIONS)# 显示结果cv2.imshow('MediaPipe Hand Tracking', frame)if cv2.waitKey(1) & 0xFF == ord('q'):break# 释放资源cap.release()cv2.destroyAllWindows()if __name__ == '__main__':video()

2.3 识别手势含义

使用 KNN 对手势进行预测

import mediapipe as mp
import numpy as np
import cv2
from mediapipe.framework.formats.landmark_pb2 import NormalizedLandmarkList
from sklearn.neighbors import KNeighborsClassifiermp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_hands = mp.solutions.hands# 压缩特征点
class Embedder(object):def __init__(self):self._landmark_names = mp.solutions.hands.HandLandmarkdef __call__(self, landmarks):# modify the call func can both handle a 3-dim dataset and a single referencing result.if isinstance(landmarks, np.ndarray):if landmarks.ndim == 3:  # for datasetembeddings = []for lmks in landmarks:embedding = self.__call__(lmks)embeddings.append(embedding)return np.array(embeddings)elif landmarks.ndim == 2:  # for inferenceassert landmarks.shape[0] == len(list(self._landmark_names)), 'Unexpected number of landmarks: {}'.format(landmarks.shape[0])# Normalize landmarks.landmarks = self._normalize_landmarks(landmarks)# Get embedding.embedding = self._get_embedding(landmarks)return embeddingelse:print('ERROR: Can NOT embedding the data you provided !')else:if isinstance(landmarks, list):  # for datasetembeddings = []for lmks in landmarks:embedding = self.__call__(lmks)embeddings.append(embedding)return np.array(embeddings)elif isinstance(landmarks, NormalizedLandmarkList):  # for inference# Normalize landmarks.landmarks = np.array([[lmk.x, lmk.y, lmk.z]for lmk in landmarks.landmark], dtype=np.float32)assert landmarks.shape[0] == len(list(self._landmark_names)), 'Unexpected number of landmarks: {}'.format(landmarks.shape[0])landmarks = self._normalize_landmarks(landmarks)# Get embedding.embedding = self._get_embedding(landmarks)return embeddingelse:print('ERROR: Can NOT embedding the data you provided !')def _get_center(self, landmarks):# MIDDLE_FINGER_MCP:9return landmarks[9]def _get_size(self, landmarks):landmarks = landmarks[:, :2]max_dist = np.max(np.linalg.norm(landmarks - self._get_center(landmarks), axis=1))return max_dist * 2def _normalize_landmarks(self, landmarks):landmarks = np.copy(landmarks)# Normalizecenter = self._get_center(landmarks)size = self._get_size(landmarks)landmarks = (landmarks - center) / sizelandmarks *= 100  # optional, but makes debugging easier.return landmarksdef _get_embedding(self, landmarks):# we can add and delete any embedding featurestest = np.array([np.dot((landmarks[2]-landmarks[0]),(landmarks[3]-landmarks[4])),   # thumb bentnp.dot((landmarks[5]-landmarks[0]), (landmarks[6]-landmarks[7])),np.dot((landmarks[9]-landmarks[0]), (landmarks[10]-landmarks[11])),np.dot((landmarks[13]-landmarks[0]),(landmarks[14]-landmarks[15])),np.dot((landmarks[17]-landmarks[0]), (landmarks[18]-landmarks[19]))]).flatten()return testdef init_knn(file='data/dataset_embedded.npz'):npzfile = np.load(file)X = npzfile['X']y = npzfile['y']neigh = KNeighborsClassifier(n_neighbors=5)neigh.fit(X, y)return neighdef hand_pose_recognition(stream_img):# For static images:stream_img = cv2.cvtColor(stream_img, cv2.COLOR_BGR2RGB)embedder = Embedder()neighbors = init_knn()with mp_hands.Hands(static_image_mode=True,max_num_hands=2,min_detection_confidence=0.5) as hands:results = hands.process(stream_img)if not results.multi_hand_landmarks:return ['no_gesture'], stream_imgelse:annotated_image = stream_img.copy()multi_landmarks = results.multi_hand_landmarks# KNN inferenceembeddings = embedder(multi_landmarks)hand_class = neighbors.predict(embeddings)# hand_class_prob = neighbors.predict_proba(embeddings)# print(hand_class_prob)for landmarks in results.multi_hand_landmarks:mp_drawing.draw_landmarks(annotated_image,landmarks,mp_hands.HAND_CONNECTIONS,mp_drawing_styles.get_default_hand_landmarks_style(),mp_drawing_styles.get_default_hand_connections_style())return hand_class, annotated_image# 手势有10种，数字有8种，1-10之间7和9没有，还有两种是OK手势，和蜘蛛侠spide手势
# `eight_sign`, `five_sign`, `four_sign`, `ok`, `one_sign`, `six_sign`, `spider`, `ten_sign`, `three_sign`, `two_sign`def image():FILE_PATH = 'data/ok.png'img = cv2.imread(FILE_PATH)handclass, img_final = hand_pose_recognition(img)cv2.putText(img_final, text=handclass[0], org=(200, 50), fontFace=cv2.FONT_HERSHEY_SIMPLEX,fontScale=2, color=(255, 255, 255), thickness=2, lineType=cv2.LINE_AA)cv2.imshow('test', cv2.cvtColor(img_final, cv2.COLOR_RGB2BGR))cv2.waitKey(0)def video():cap = cv2.VideoCapture('data/ok.mp4')while cap.isOpened():ret, frame = cap.read()if not ret:breakhandclass, img_final = hand_pose_recognition(frame)cv2.putText(img_final, text=handclass[0], org=(50, 50), fontFace=cv2.FONT_HERSHEY_SIMPLEX,fontScale=2, color=(255, 0, 0), thickness=2, lineType=cv2.LINE_AA)cv2.imshow('test', cv2.cvtColor(img_final, cv2.COLOR_RGB2BGR))if cv2.waitKey(1) & 0xFF == ord('q'):breakif __name__ == '__main__':video()

参考

1. https://developers.google.cn/mediapipe/solutions/
2. https://github.com/googlesamples/mediapipe
3. https://github.com/Furkan-Gulsen/Sport-With-AI
4. https://github.com/Chuanfang-Neptune/DLAV-G9