基于mediapipe的姿态估计

关键点说明

mediapipe姿态估计可以识别出身体的33个关键点

获取关键点坐标

模型给出的结果是归一化的坐标比例，要获取在图像中的坐标需要乘以图像的宽高

x = results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE].x * image_width
y = results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE].y * image_height

示例代码

import cv2
import mediapipe as mp
import numpy as np
Key_Esc = 27
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_pose = mp.solutions.pose
# For webcam input:
cap = cv2.VideoCapture(0)
with mp_pose.Pose(
        min_detection_confidence=0.5,
        min_tracking_confidence=0.5) as pose:
    while cap.isOpened():
        success, image = cap.read()
        if not success:
            print("Ignoring empty camera frame.")
            # If loading a video, use 'break' instead of 'continue'.
            continue
        # To improve performance, optionally mark the image as not writeable to
        # pass by reference.
        image.flags.writeable = False
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        results = pose.process(image)
        # Draw the pose annotation on the image.
        image.flags.writeable = True
        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
        mp_drawing.draw_landmarks(
            image,
            results.pose_landmarks,
            mp_pose.POSE_CONNECTIONS,
            landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style())
        # Flip the image horizontally for a selfie-view display.
        cv2.imshow('MediaPipe Pose', np.rot90(
            cv2.resize(cv2.flip(image, 1), (320, 240))))
        if cv2.waitKey(5) == Key_Esc:
            break
cap.release()