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前言

坐姿检测可以通过传统机器学习或深度学习方法实现。下面我将详细介绍两种方法，并提供相应的Python实现代码。

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一、基于传统机器学习的方法

方法概述

特征提取

特征提取：从姿势关键点中提取特征（角度、距离等）

分类模型

分类模型：使用机器学习分类器判断坐姿是否正确

实现代码

import cv2
import mediapipe as mp
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import joblib# 数据收集和特征提取
class PostureDataCollector:def __init__(self):self.mp_pose = mp.solutions.poseself.pose = self.mp_pose.Pose()self.dataset = []self.labels = []def extract_features(self, landmarks):"""从关键点中提取特征"""features = []# 获取关键点坐标left_shoulder = landmarks[11][1:3]right_shoulder = landmarks[12][1:3]left_ear = landmarks[7][1:3]right_ear = landmarks[8][1:3]left_hip = landmarks[23][1:3]right_hip = landmarks[24][1:3]# 计算中点shoulder_mid = np.mean([left_shoulder, right_shoulder], axis=0)ear_mid = np.mean([left_ear, right_ear], axis=0)hip_mid = np.mean([left_hip, right_hip], axis=0)# 1. 脊柱角度 (肩膀-髋部-垂直线)spine_angle = self.calculate_angle((shoulder_mid[0], shoulder_mid[1] - 100),shoulder_mid,hip_mid)features.append(spine_angle)# 2. 颈部角度 (耳朵-肩膀-水平线)neck_angle = self.calculate_angle((ear_mid[0] - 100, ear_mid[1]),ear_mid,shoulder_mid)features.append(neck_angle)# 3. 肩膀倾斜度shoulder_slope = (right_shoulder[1] - left_shoulder[1]) / (right_shoulder[0] - left_shoulder[0] + 1e-6)features.append(shoulder_slope)# 4. 耳朵到肩膀的距离ear_to_shoulder = np.linalg.norm(ear_mid - shoulder_mid)features.append(ear_to_shoulder)# 5. 肩膀到髋部的距离shoulder_to_hip = np.linalg.norm(shoulder_mid - hip_mid)features.append(shoulder_to_hip)return np.array(features)def calculate_angle(self, a, b, c):"""计算三个点之间的角度"""a, b, c = np.array(a), np.array(b), 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)return angle if angle <= 180 else 360 - angledef collect_data(self, img, label):"""收集数据并提取特征"""img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)results = self.pose.process(img_rgb)if results.pose_landmarks:landmarks = []for id, lm in enumerate(results.pose_landmarks.landmark):h, w = img.shape[:2]landmarks.append([id, int(lm.x * w), int(lm.y * h), lm.z])features = self.extract_features(landmarks)self.dataset.append(features)self.labels.append(label)return Truereturn False# 训练机器学习模型
def train_model(X, y):X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)# 使用随机森林分类器model = RandomForestClassifier(n_estimators=100, random_state=42)model.fit(X_train, y_train)# 评估模型y_pred = model.predict(X_test)print(f"Accuracy: {accuracy_score(y_test, y_pred):.2f}")# 保存模型joblib.dump(model, 'posture_model.pkl')return model# 使用训练好的模型进行坐姿检测
class MLPostureDetector:def __init__(self, model_path):self.mp_pose = mp.solutions.poseself.pose = self.mp_pose.Pose()self.model = joblib.load(model_path)self.data_collector = PostureDataCollector()def detect(self, img):img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)results = self.pose.process(img_rgb)if results.pose_landmarks:landmarks = []for id, lm in enumerate(results.pose_landmarks.landmark):h, w = img.shape[:2]landmarks.append([id, int(lm.x * w), int(lm.y * h), lm.z])features = self.data_collector.extract_features(landmarks)prediction = self.model.predict([features])[0]# 可视化结果cv2.putText(img, f"Posture: {'Good' if prediction == 1 else 'Bad'}",(10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0) if prediction == 1 else (0, 0, 255), 2)return img, predictionreturn img, None# 数据收集示例
def collect_training_data():collector = PostureDataCollector()cap = cv2.VideoCapture(0)print("Collecting GOOD posture data - Sit straight (Press 'g' to capture)")print("Collecting BAD posture data - Slouch (Press 'b' to capture)")print("Press 'q' to finish and train model")while True:_, img = cap.read()cv2.imshow("Data Collection", img)key = cv2.waitKey(1)if key == ord('g'):  # 收集良好坐姿数据if collector.collect_data(img, 1):print("Good posture sample collected")elif key == ord('b'):  # 收集不良坐姿数据if collector.collect_data(img, 0):print("Bad posture sample collected")elif key == ord('q'):breakcap.release()cv2.destroyAllWindows()# 训练模型if len(collector.dataset) > 0:X = np.array(collector.dataset)y = np.array(collector.labels)train_model(X, y)# 使用模型检测示例
def run_detection():detector = MLPostureDetector('posture_model.pkl')cap = cv2.VideoCapture(0)while True:_, img = cap.read()img, posture = detector.detect(img)cv2.imshow("Posture Detection", img)if cv2.waitKey(1) == ord('q'):breakcap.release()cv2.destroyAllWindows()if __name__ == "__main__":# 第一步: 收集数据# collect_training_data()# 第二步: 使用训练好的模型检测run_detection()

二、基于深度学习的方法

方法概述

端到端学习

端到端学习：直接使用原始图像作为输入

卷积神经网络

卷积神经网络：自动学习坐姿特征

迁移学习

迁移学习：使用预训练模型提高性能

实现代码

import cv2
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers, models, applications
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
from sklearn.model_selection import train_test_split# 数据准备
class PostureDataset:def __init__(self, data_dir, img_size=(224, 224)):self.data_dir = data_dirself.img_size = img_sizeself.classes = ['good', 'bad']self.data = []self.labels = []def load_data(self):for class_idx, class_name in enumerate(self.classes):class_dir = os.path.join(self.data_dir, class_name)for img_name in os.listdir(class_dir):img_path = os.path.join(class_dir, img_name)img = cv2.imread(img_path)img = cv2.resize(img, self.img_size)self.data.append(img)self.labels.append(class_idx)self.data = np.array(self.data)self.labels = np.array(self.labels)return self.data, self.labels# 构建深度学习模型
def build_model(input_shape, num_classes):# 使用预训练的MobileNetV2作为基础模型base_model = applications.MobileNetV2(input_shape=input_shape,include_top=False,weights='imagenet')# 冻结基础模型权重base_model.trainable = False# 添加自定义层model = models.Sequential([base_model,layers.GlobalAveragePooling2D(),layers.Dense(128, activation='relu'),layers.Dropout(0.5),layers.Dense(num_classes, activation='softmax')])model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])return model# 数据增强
def create_datagen():return ImageDataGenerator(rotation_range=15,width_shift_range=0.1,height_shift_range=0.1,shear_range=0.1,zoom_range=0.1,horizontal_flip=True,fill_mode='nearest')# 训练模型
def train_deep_learning_model():# 准备数据dataset = PostureDataset('posture_dataset')X, y = dataset.load_data()X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)# 数据归一化X_train = X_train / 255.0X_test = X_test / 255.0# 构建模型model = build_model(X_train[0].shape, len(dataset.classes))# 数据增强datagen = create_datagen()train_generator = datagen.flow(X_train, y_train, batch_size=32)# 训练模型history = model.fit(train_generator,epochs=20,validation_data=(X_test, y_test))# 保存模型model.save('posture_cnn.h5')return model# 使用深度学习模型进行坐姿检测
class DeepPostureDetector:def __init__(self, model_path):self.model = tf.keras.models.load_model(model_path)self.mp_pose = mp.solutions.poseself.pose = self.mp_pose.Pose()self.img_size = (224, 224)def detect(self, img):# 预处理图像img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)img_resized = cv2.resize(img_rgb, self.img_size)img_normalized = img_resized / 255.0img_expanded = np.expand_dims(img_normalized, axis=0)# 预测prediction = self.model.predict(img_expanded)class_idx = np.argmax(prediction)confidence = np.max(prediction)# 可视化结果label = "Good" if class_idx == 0 else "Bad"color = (0, 255, 0) if class_idx == 0 else (0, 0, 255)cv2.putText(img, f"Posture: {label} ({confidence:.2f})",(10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, color, 2)return img, class_idx# 实时检测示例
def run_deep_detection():detector = DeepPostureDetector('posture_cnn.h5')cap = cv2.VideoCapture(0)while True:_, img = cap.read()img, posture = detector.detect(img)cv2.imshow("Deep Posture Detection", img)if cv2.waitKey(1) == ord('q'):breakcap.release()cv2.destroyAllWindows()if __name__ == "__main__":# 第一步: 训练模型 (需要先准备数据集)# train_deep_learning_model()# 第二步: 使用训练好的模型检测run_deep_detection()

三、方法比较与选择

方法 优点 缺点 适用场景

传统机器学习

优点

1. 需要较少数据
2. 训练速度快
3. 可解释性强

缺点

1. 依赖特征工程
2. 性能有限

应用场景

1. 快速原型开发
2. 资源受限环境

深度学习

优点

1. 自动特征提取
2. 更高的准确率
3. 端到端学习

缺点

1. 需要大量数据
2. 计算资源需求高
3. 训练时间长 高精度要求

复杂姿态检测

复杂姿势检测

四、实际应用建议

从小规模开始

从小规模开始：先尝试传统机器学习方法，验证概念可行性

数据收集

数据收集：收集多样化的坐姿数据（不同体型、光照条件等）

模型优化：

尝试不同的网络架构（ResNet, EfficientNet等）

调整超参数

使用数据增强提高泛化能力

部署考虑：

使用TensorFlow Lite或ONNX优化模型以便在移动端部署
考虑模型量化以减少计算资源需求

五、扩展方向

多类别分类

多类别分类：不仅判断好坏，还能识别具体问题（如头部前倾、驼背等）

时序模型

时序模型：使用LSTM或Transformer处理视频序列，提高检测稳定性

3D姿态估计

3D姿势估计：使用3D卷积网络或点云处理更精确的姿势信息

集成其他传感器

集成其他传感器：结合压力传感器或IMU数据提高准确性
以上两种方法都可以有效实现坐姿检测，选择哪种方法取决于你的具体需求、可用数据和计算资源。

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