surroundings
- windows 10 64bit
- mediapipe 0.8.10.1
foreword
This article uses google ‘s mediapipe machine learning framework, combined with opencv and numpy , to implement a simple system for real-time recognition of four actions: standing , sitting , walking , and waving .
Code Practice
First, you need to install mediapipe
pip install -U mediapipe
Next, look at the code, some comments are added
import cv2 import mediapipe as mp import numpy as np def calculate_angle(a, b, c): '''计算角度:param a: :param b: :param c: :return: ''' a = np.array(a) b = np.array(b) c = np.array(c) radians = np.arctan2(c[1] - b[1], c[0] - b[0]) - np.arctan2(a[1] - b[1], a[0] - b[0]) angle = np.abs(radians * 180.0 / np.pi) if angle > 180.0: angle = 360 - angle return angle def calculate_dist(a, b): '''计算欧式距离:param a: :param b: :return: ''' a = np.array(a) b = np.array(b) dist = np.linalg.norm(a - b) return dist if __name__ == '__main__': mp_drawing = mp.solutions.drawing_utils mp_pose = mp.solutions.pose cap = cv2.VideoCapture('liuruoying.mp4') # 分辨率frame_width = int(cap.get(3)) frame_height = int(cap.get(4)) # 保存结果视频out = cv2.VideoWriter("result.mp4", cv2.VideoWriter_fourcc(*'mp4v'), 30, (frame_width, frame_height)) counter = 0 stage = None with mp_pose.Pose(min_detection_confidence=0.3, min_tracking_confidence=0.8) as pose: while cap.isOpened(): ret, frame = cap.read() if not ret: break # 转换下颜色空间image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # 这里设置为不可写image.flags.writeable = False # 检测results = pose.process(image) # 这里设置为可写，颜色也转换回去image.flags.writeable = True image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # 提取关键点try: landmarks = results.pose_landmarks.landmark # 获取相应关键点的坐标lshoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x, landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y] lelbow = [landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].x, landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].y] lwrist = [landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x, landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y] lhip = [landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].x, landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y] lankle = [landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x, landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].y] lknee = [landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].x, landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y] rshoulder = [landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].y] relbow = [landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value].y] rwrist = [landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].y] rhip = [landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].y] rankle = [landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].y] rknee = [landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].y] # 计算角度langle = calculate_angle(lshoulder, lelbow, lwrist) rangle = calculate_angle(rshoulder, relbow, rwrist) lsangle = calculate_angle(lhip, lshoulder, lelbow) rsangle = calculate_angle(rhip, rshoulder, relbow) ankdist = calculate_dist(lankle, rankle) rwdist = calculate_dist(rhip, rwrist) lwdist = calculate_dist(lhip, lwrist) rhangle = calculate_angle(rshoulder, rhip, rknee) lhangle = calculate_angle(lshoulder, lhip, lknee) rkangle = calculate_angle(rankle, rknee, rhip) lkangle = calculate_angle(lankle, lknee, lhip) # 这块是具体的业务逻辑，各个数值，可根据自己实际情况适当调整if ((rhangle > 80 and lhangle > 80) and (rhangle < 110 and lhangle < 110) and ( lkangle < 100 and rkangle < 100)): stage = 'sitting' elif (langle < 160 and langle > 40) or (rangle < 160 and rangle > 40): if ((lsangle > 20 or rsangle > 20) and (lwdist > 0.3 or rwdist > 0.3)): stage = "wave" elif ((ankdist > 0.084) and (langle > 150) and (rangle > 150)): counter += 1 if counter > 1: stage = 'walking' else: stage = 'standing' counter = 0 except: pass cv2.rectangle(image, (0, 0), (225, 73), (245, 117, 16), -1) cv2.putText(image, 'STAGE', (65, 12), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA) cv2.putText(image, stage, (60, 60), cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 255, 255), 2, cv2.LINE_AA) # 画骨骼关键点mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, mp_drawing.DrawingSpec(color=(245, 117, 66), thickness=2, circle_radius=2), mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=2) ) # 显示结果帧cv2.imshow('mediapipe demo', image) # 保存结果帧out.write(image) # 按q退出if cv2.waitKey(10) & 0xFF == ord('q'): break # 资源释放cap.release() cv2.destroyAllWindows()
This article is reprinted from https://xugaoxiang.com/2022/08/10/mediapipe-action-recognition/
This site is for inclusion only, and the copyright belongs to the original author.