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Mediapipe库是一个用于构建基于机器学习的多媒体处理应用的开源框架,主要用于计算机视觉、音频处理和姿势估计等领域。
具体来说,Mediapipe提供了一套易于使用的API,可以帮助开发者在各种平台上实现以下功能:
1、人脸检测与识别:通过预训练模型,Mediapipe能够检测图像或视频中的面部并识别面部关键点。
2、手势识别:识别用户的手部动作和手势,这对于增强现实和游戏等交互式应用非常有用。
3、头像分割:将图像中的人物从背景中分离出来,这在视频通话和图像编辑中很常见。
4、姿态识别:估计人体的姿态,可以用于健身应用、动作捕捉和智能监控等领域。
目标跟踪:在视频序列中追踪移动的目标,如运动员或者动物。
5、3D人体模型:使用统计3D人体模型(如GHUM)来拟合2D姿势数据集,从而获得更精确的3D姿势估计。
以下是一个简单的姿态识别代码:
import cv2 import mediapipe as mp import numpy as np import scipy.ndimage.filters as filters mp_drawing = mp.solutions.drawing_utils mp_pose = mp.solutions.pose history_length = 50 angle_history = { "left_arm_body_x": np.zeros(history_length), "right_arm_body_x": np.zeros(history_length), "left_arm_body_y": np.zeros(history_length), "right_arm_body_y": np.zeros(history_length), "left_arm_forearm": np.zeros(history_length), "right_arm_forearm": np.zeros(history_length), "head_forearm": np.zeros(history_length), } gaussian_filter = filters.gaussian_filter1d def calculate_3d_angle(a, b, c): ba = a - b bc = c - b cosine_angle = np.dot(ba, bc) / (np.linalg.norm(ba) * np.linalg.norm(bc)) angle = np.arccos(cosine_angle) return np.degrees(angle) def calculate_angle(a, b, c): 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 get_landmark_pos(landmark, width, height): return [landmark.x * width, landmark.y * height] def main(): #视频检测 #cap = cv2.VideoCapture('你要识别视频的路径') #widght = int(cap.get(3)) # 在视频流的帧的宽度,3为编号,不能改 #height = int(cap.get(4)) # 在视频流的帧的高度,4为编号,不能改 #size = (width, height) #fps = 30 # 帧率 #fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v') # 为视频编码方式,保存为mp4文 #out = cv2.VideoWriter() # 定义一个视频存储对象,以及视频编码方式,帧率,视频大小格式 #out.open("E:/video.mp4", fourcc, fps, size) #摄像头检测 cap = cv2.VideoCapture(0) width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose: while cap.isOpened(): ret, frame = cap.read() if not ret: continue image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) image.flags.writeable = False results = pose.process(image) image.flags.writeable = True image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) if results.pose_landmarks: landmarks = results.pose_landmarks.landmark # 左臂坐标 left_shoulder = get_landmark_pos( landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value], width, height) left_elbow = get_landmark_pos( landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value], width, height) left_wrist = get_landmark_pos( landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value], width, height) left_hip = get_landmark_pos( landmarks[mp_pose.PoseLandmark.LEFT_HIP.value], width, height) left_knee = get_landmark_pos( landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value], width, height) left_eye = get_landmark_pos( landmarks[mp_pose.PoseLandmark.LEFT_EYE_OUTER.value], width, height) # 右臂坐标 right_shoulder = get_landmark_pos( landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value], width, height) right_elbow = get_landmark_pos( landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value], width, height) right_wrist = get_landmark_pos( landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value], width, height) right_hip = get_landmark_pos( landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value], width, height) right_knee = get_landmark_pos( landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value], width, height) right_eye = get_landmark_pos( landmarks[mp_pose.PoseLandmark.RIGHT_EYE_OUTER.value], width, height) nose = get_landmark_pos( landmarks[mp_pose.PoseLandmark.NOSE.value], width, height) # 获取3D坐标 left_shoulder_3d = np.array([landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x, landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y, landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].z]) left_elbow_3d = np.array([landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].x, landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].y, landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].z]) left_wrist_3d = np.array([landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x, landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y, landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].z]) left_hip_3d = np.array([landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].x, landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y, landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].z]) right_shoulder_3d = np.array([landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].y, landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].z]) right_elbow_3d = np.array([landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value].y, landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value].z]) right_wrist_3d = np.array([landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].y, landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].z]) right_hip_3d = np.array([landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].x, landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].y, landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].z]) # 计算3D角度 angle_left_arm_body_x = calculate_3d_angle( left_shoulder_3d, left_elbow_3d, left_hip_3d) angle_right_arm_body_x = calculate_3d_angle( right_shoulder_3d, right_elbow_3d, right_hip_3d) angle_left_arm_body_y = calculate_3d_angle( left_wrist_3d, left_elbow_3d, left_shoulder_3d) angle_right_arm_body_y = calculate_3d_angle( right_wrist_3d, right_elbow_3d, right_shoulder_3d) angle_left_arm_forearm = calculate_3d_angle( left_shoulder_3d, left_elbow_3d, left_wrist_3d) angle_right_arm_forearm = calculate_3d_angle( right_shoulder_3d, right_elbow_3d, right_wrist_3d) angle_head_forearm = calculate_angle(left_eye, right_eye, nose) # 高斯滤波 angle_history["left_arm_body_x"] = np.append( angle_history["left_arm_body_x"][1:], angle_left_arm_body_x) angle_history["right_arm_body_x"] = np.append( angle_history["right_arm_body_x"][1:], angle_right_arm_body_x) angle_history["left_arm_body_y"] = np.append( angle_history["left_arm_body_y"][1:], angle_left_arm_body_y) angle_history["right_arm_body_y"] = np.append( angle_history["right_arm_body_y"][1:], angle_right_arm_body_y) angle_history["left_arm_forearm"] = np.append( angle_history["left_arm_forearm"][1:], angle_left_arm_forearm) angle_history["right_arm_forearm"] = np.append( angle_history["right_arm_forearm"][1:], angle_right_arm_forearm) angle_history["head_forearm"] = np.append( angle_history["head_forearm"][1:], angle_head_forearm) sigma = 10 angle_left_arm_body_x_filtered = gaussian_filter( angle_history["left_arm_body_x"], sigma=sigma, mode='reflect')[-1] angle_right_arm_body_x_filtered = gaussian_filter( angle_history["right_arm_body_x"], sigma=sigma, mode='reflect')[-1] angle_left_arm_body_y_filtered = gaussian_filter( angle_history["left_arm_body_y"], sigma=sigma, mode='reflect')[-1] angle_right_arm_body_y_filtered = gaussian_filter( angle_history["right_arm_body_y"], sigma=sigma, mode='reflect')[-1] angle_left_arm_forearm_filtered = gaussian_filter( angle_history["left_arm_forearm"], sigma=sigma, mode='reflect')[-1] angle_right_arm_forearm_filtered = gaussian_filter( angle_history["right_arm_forearm"], sigma=sigma, mode='reflect')[-1] angle_head_forearm_filtered = gaussian_filter( angle_history["head_forearm"], sigma=sigma, mode='reflect')[-1] cv2.putText(image, f"X-axis: {int(angle_left_arm_body_x_filtered)}", (int(left_shoulder[0]), int(left_shoulder[1])), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA) cv2.putText(image, f"X-axis: {int(angle_right_arm_body_x_filtered)}", (int(right_shoulder[0]), int(right_shoulder[1])), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA) cv2.putText(image, f"Y-axis: {int(angle_left_arm_body_y_filtered)}", (int(left_shoulder[0]), int(left_shoulder[1] + 30)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA) cv2.putText(image, f"Y-axis: {int(angle_right_arm_body_y_filtered)}", (int(right_shoulder[0]), int(right_shoulder[1] + 30)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA) cv2.putText(image, f"{int(angle_left_arm_forearm_filtered)}", (int(left_elbow[0]), int( left_elbow[1])), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA) cv2.putText(image, f"{int(angle_right_arm_forearm_filtered)}", (int(right_elbow[0]), int( right_elbow[1])), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA) cv2.putText(image, f"Head: {int(angle_head_forearm_filtered)}", (int(nose[0]), int( nose[1])), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA) # 手臂相对于身体的X轴(上举/下垂) # angle_left_arm_body_x_filtered = 左臂身体角度 x (高斯滤波版) # angle_right_arm_body_x_filtered = 右臂身体角度 x (高斯滤波版) # # 手臂相对于身体的Y轴(展开/收紧) # angle_left_arm_body_y_filtered = 左臂身体角度 y (高斯滤波版) # angle_right_arm_body_y_filtered = 右臂身体角度 y (高斯滤波版) # # 前臂(小手臂和大手臂)角度 (展开/收紧) # angle_left_arm_forearm_filtered = 左臂前臂角度 (高斯滤波版) # angle_right_arm_forearm_filtered = 右臂前臂角度 (高斯滤波版) # # 头部角度 # angle_head_forearm_filtered = 头部角度 (高斯滤波版) mp_drawing.draw_landmarks( image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS) #out.write(image)#检测视频保存,默认是保存在代码路径下 cv2.imshow('Pose', image) if cv2.waitKey(5) & 0xFF == 27: break #out.release() cap.release() cv2.destroyAllWindows() if __name__ == "__main__": main()
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