import cv2 import mediapipe as mp import math  class poseDetector() :  def __init__(self, mode=False, complexity=1, smooth_landmarks=True, enable_segmentation=False, smooth_segmentation=True, detectionCon=0.5, trackCon=0.5):  self.mode = mode  self.complexity = complexity self.smooth_landmarks = smooth_landmarks self.enable_segmentation = enable_segmentation self.smooth_segmentation = smooth_segmentation self.detectionCon = detectionCon self.trackCon = trackCon  self.mpDraw = mp.solutions.drawing_utils self.mpPose = mp.solutions.pose self.pose = self.mpPose.Pose(self.mode, self.complexity, self.smooth_landmarks, self.enable_segmentation, self.smooth_segmentation, self.detectionCon, self.trackCon)   def findPose (self, img, draw=True): imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) self.results = self.pose.process(imgRGB)  if self.results.pose_landmarks: if draw: self.mpDraw.draw_landmarks(img,self.results.pose_landmarks, self.mpPose.POSE_CONNECTIONS)  return img  def findPosition(self, img, draw=True): self.lmList = [] if self.results.pose_landmarks: for id, lm in enumerate(self.results.pose_landmarks.landmark): #finding height, width of the image printed h, w, c = img.shape #Determining the pixels of the landmarks cx, cy = int(lm.x * w), int(lm.y * h) self.lmList.append([id, cx, cy]) if draw: cv2.circle(img, (cx, cy), 5, (255,0,0), cv2.FILLED) return self.lmList  def findAngle(self, img, p1, p2, p3, draw=True):  #Get the landmarks x1, y1 = self.lmList[p1][1:] x2, y2 = self.lmList[p2][1:] x3, y3 = self.lmList[p3][1:]  #Calculate Angle angle = math.degrees(math.atan2(y3-y2, x3-x2) -  math.atan2(y1-y2, x1-x2)) if angle < 0: angle += 360 if angle > 180: angle = 360 - angle elif angle > 180: angle = 360 - angle # print(angle)  #Draw if draw: cv2.line(img, (x1, y1), (x2, y2), (255,255,255), 3) cv2.line(img, (x3, y3), (x2, y2), (255,255,255), 3)   cv2.circle(img, (x1, y1), 5, (0,0,255), cv2.FILLED) cv2.circle(img, (x1, y1), 15, (0,0,255), 2) cv2.circle(img, (x2, y2), 5, (0,0,255), cv2.FILLED) cv2.circle(img, (x2, y2), 15, (0,0,255), 2) cv2.circle(img, (x3, y3), 5, (0,0,255), cv2.FILLED) cv2.circle(img, (x3, y3), 15, (0,0,255), 2)  cv2.putText(img, str(int(angle)), (x2-50, y2+50),  cv2.FONT_HERSHEY_PLAIN, 2, (0,0,255), 2) return angle   def main(): detector = poseDetector() cap = cv2.VideoCapture(0) while cap.isOpened(): ret, img = cap.read() #ret is just the return variable, not much in there that we will use.  if ret:  img = detector.findPose(img) cv2.imshow('Pose Detection', img) if cv2.waitKey(10) & 0xFF == ord('q'): break  cap.release() cv2.destroyAllWindows()  if __name__ == "__main__": main()

以上是這個程序的代碼。

上面的代碼來源于PoseModule.py，有以下幾個功能：

• 激活mediapipe的姿勢檢測模塊。

• 檢測人體。

• 根據模型找到人體上不同肢體關節的位置。(肢體顯示在上面的圖片中）。

• 查找關節之間的角度（取決于你選擇的關節）。對于我的俯臥撐程序，我選擇找到肘部、肩部和臀部的角度，因為這些對俯臥撐動作的標準至關重要。

接下來是實際的俯臥撐計數的代碼。我們使用PoseModule并確定一個俯臥撐合格與否的標準。