python骨架点 python骨骼检测

环境：

 

python

pip --version

pip install --upgrade opencv-python==4.5.4.58

pip install mediapipe

 

代码：

骨架检测：

import cv2
import mediapipe as mp
 
#mp.solutions.drawing_utils用于绘制
mp_drawing = mp.solutions.drawing_utils
 
#参数：1、颜色，2、线条粗细，3、点的半径
DrawingSpec_point = mp_drawing.DrawingSpec((0, 255, 0), 1 , 1)
DrawingSpec_line = mp_drawing.DrawingSpec((0, 0, 255), 1, 1)
 
#mp.solutions.pose，是人的骨架
mp_pose = mp.solutions.pose
 
#参数：1、是否检测静态图片，2、姿态模型的复杂度，3、结果看起来平滑（用于video有效），4、检测阈值，5、跟踪阈值
pose_mode = mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5)

cap = cap = cv2.VideoCapture(0) # 0表示第一个摄像头
#cap = cv2.VideoCapture('input.mp4')
while cap.isOpened():
    success, image = cap.read()
    if not success:
        print("Ignoring empty camera frame.")
        continue
    image1 = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 
    # 处理RGB图像
    results = pose_mode.process(image1)
 
    '''
    mp_holistic.PoseLandmark类中共33个人体骨骼点
    '''
 
    # 绘制
    mp_drawing.draw_landmarks(
        image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, DrawingSpec_point, DrawingSpec_line)
 
    cv2.imshow('MediaPipe Pose', image)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
 
pose_mode.close()
cv2.destroyAllWindows()
cap.release()

指关节检测：

import cv2
import mediapipe as mp
 
#mp.solutions.drawing_utils用于绘制
mp_drawing = mp.solutions.drawing_utils
 
#参数：1、颜色，2、线条粗细，3、点的半径
DrawingSpec_point = mp_drawing.DrawingSpec((0, 255, 0), 1 , 1)
DrawingSpec_line = mp_drawing.DrawingSpec((0, 0, 255), 1, 1)
 
#mp.solutions.hands，是人的手
mp_hands = mp.solutions.hands
 
#参数：1、是否检测静态图片，2、手的数量，3、检测阈值，4、跟踪阈值
hands_mode = mp_hands.Hands(max_num_hands=2)
 
cap = cv2.VideoCapture(0)
while cap.isOpened():
    success, image = cap.read()
    if not success:
        print("Ignoring empty camera frame.")
        continue
    image1 = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 
    # 处理RGB图像
    results = hands_mode.process(image1)
 
    # 绘制
    if results.multi_hand_landmarks:
        for hand_landmarks in results.multi_hand_landmarks:
            mp_drawing.draw_landmarks(
                image, hand_landmarks, mp_hands.HAND_CONNECTIONS, DrawingSpec_point, DrawingSpec_line)
 
    cv2.imshow('MediaPipe Hands', image)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
 
hands_mode.close()
cv2.destroyAllWindows()
cap.release()

 

关节连线：

import cv2
import mediapipe as mp
import math
#mp.solutions.drawing_utils用于绘制
mp_drawing = mp.solutions.drawing_utils
 
#参数：1、颜色，2、线条粗细，3、点的半径
DrawingSpec_point = mp_drawing.DrawingSpec((0, 255, 0), 1 , 1)
DrawingSpec_line = mp_drawing.DrawingSpec((0, 0, 255), 1, 1)

resize_w = 640 # 宽
resize_h = 480 # 高
#mp.solutions.hands，是人的手
mp_hands = mp.solutions.hands
 
#参数：1、是否检测静态图片，2、手的数量，3、检测阈值，4、跟踪阈值
hands_mode = mp_hands.Hands(max_num_hands=2)
 
cap = cv2.VideoCapture(0)
while cap.isOpened():
    success, image = cap.read()
    if not success:
        print("Ignoring empty camera frame.")
        continue
    image1 = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 
    # 处理RGB图像
    results = hands_mode.process(image1)
 
    # 绘制
    if results.multi_hand_landmarks:
        for hand_landmarks in results.multi_hand_landmarks:
            mp_drawing.draw_landmarks(
                image, hand_landmarks, mp_hands.HAND_CONNECTIONS, DrawingSpec_point, DrawingSpec_line)
        # 解析手指，存入各个手指坐标
        landmark_list = []#初始化一个列表来存储
        for landmark_id, finger_axis in enumerate(
                hand_landmarks.landmark):#便利某个手的每个关节
            landmark_list.append([
                landmark_id, finger_axis.x, finger_axis.y,
                finger_axis.z
            ])#将手指序号，像素点横、纵、深度坐标打包为一个列表，共同存入列表中
        if landmark_list:
            # 获取大拇指指尖坐标，序号为4
            thumb_finger_tip = landmark_list[4]
            # 向上取整，得到手指坐标的整数
            thumb_finger_tip_x = math.ceil(thumb_finger_tip[1] * resize_w)#thumb_finger_tip[1]里存储的x值范围是0-1，乘以分辨率宽，便得到在图像上的位置
            thumb_finger_tip_y = math.ceil(thumb_finger_tip[2] * resize_h) #thumb_finger_tip[2]里存储的x值范围是0-1，乘以分辨率高，便得到在图像上的位置
            # 获取食指指尖坐标，序号为4，操作同理
            index_finger_tip = landmark_list[8]
            index_finger_tip_x = math.ceil(index_finger_tip[1] * resize_w)
            index_finger_tip_y = math.ceil(index_finger_tip[2] * resize_h)
            # 得到食指和拇指的中间点
            finger_middle_point = (thumb_finger_tip_x + index_finger_tip_x) // 2, (
                    thumb_finger_tip_y + index_finger_tip_y) // 2
            # print(thumb_finger_tip_x)
            thumb_finger_point = (thumb_finger_tip_x, thumb_finger_tip_y)
            index_finger_point = (index_finger_tip_x, index_finger_tip_y)
            # 用opencv的circle函数画图，将食指、拇指和中间点画出
            image = cv2.circle(image, thumb_finger_point, 10, (255, 0, 255), -1)
            image = cv2.circle(image, index_finger_point, 10, (255, 0, 255), -1)
            image = cv2.circle(image, finger_middle_point, 10, (255, 0, 255), -1)
            # 用opencv的line函数将食指和拇指连接在一起
            image = cv2.line(image, thumb_finger_point, index_finger_point, (255, 0, 255), 5)
            # math.hypot为勾股定理计算两点长度的函数，得到食指和拇指的距离
            line_len = math.hypot((index_finger_tip_x - thumb_finger_tip_x),
                                    (index_finger_tip_y - thumb_finger_tip_y))
            cv2.imshow('MediaPipe Hands',image)


    
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
 
hands_mode.close()
cv2.destroyAllWindows()
cap.release()

节点序号：

 

脸部检测：

import cv2
import mediapipe as mp
 
#mp.solutions.drawing_utils用于绘制
mp_drawing = mp.solutions.drawing_utils
 
#参数：1、颜色，2、线条粗细，3、点的半径
DrawingSpec_point = mp_drawing.DrawingSpec((0, 255, 0), 1 , 1)
DrawingSpec_line = mp_drawing.DrawingSpec((0, 0, 255), 1, 1)
 
#mp.solutions.holistic是一个类别，是人的整体
mp_holistic = mp.solutions.holistic
 
#参数：1、是否检测静态图片，2、姿态模型的复杂度，3、结果看起来平滑（用于video有效），4、检测阈值，5、跟踪阈值
holistic = mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5)
 
cap = cv2.VideoCapture(0)
while cap.isOpened():
    success, image = cap.read()
    if not success:
        print("Ignoring empty camera frame.")
        continue
    image1 = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 
    # 处理RGB图像
    results = holistic.process(image1)
 
    '''
    mp_holistic.PoseLandmark类中共33个人体骨骼点
    mp_holistic.HandLandmark类中共21个手部关键点
    脸部有468个关键点
    '''
 
    # 绘制
    mp_drawing.draw_landmarks(
        image, results.face_landmarks, mp_holistic.FACEMESH_TESSELATION, DrawingSpec_point, DrawingSpec_line)
    mp_drawing.draw_landmarks(
        image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS, DrawingSpec_point, DrawingSpec_line)
    mp_drawing.draw_landmarks(
        image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS, DrawingSpec_point, DrawingSpec_line)
    mp_drawing.draw_landmarks(
        image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS, DrawingSpec_point, DrawingSpec_line)
 
    cv2.imshow('MediaPipe Holistic', image)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
 
holistic.close()
cv2.destroyAllWindows()
cap.release()