写一个姿势识别的SVM算法

姿势识别可以使用SVM（支持向量机）算法进行分类。以下是基于OpenCV和Scikit-learn库实现的SVM姿势识别算法的示例代码：

首先，我们需要定义一个函数来提取姿势特征。这里使用了手部和肩部的位置信息作为特征：

import cv2
import numpy as np

def extract_features(img):
    # Convert image to grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # Detect keypoints using Shi-Tomasi corner detection
    corners = cv2.goodFeaturesToTrack(gray, 25, 0.01, 10)
    corners = np.int0(corners)
    # Calculate average x and y coordinates of keypoints
    x_sum = 0
    y_sum = 0
    for corner in corners:
        x, y = corner.ravel()
        x_sum += x
        y_sum += y
    x_avg = x_sum / len(corners)
    y_avg = y_sum / len(corners)
    # Extract shoulder and hand positions relative to center of image
    shoulders = [0, 0]
    hands = [0, 0]
    if len(corners) >= 2:
        shoulders = corners[0].ravel() - [x_avg, y_avg]
        hands = corners[1].ravel() - [x_avg, y_avg]
    # Return feature vector
    return np.concatenate([shoulders, hands])

然后，我们可以使用该函数提取训练数据和测试数据的特征：

import os

def load_data(directory):
    # Load images from directory
    X = []
    y = []
    for filename in os.listdir(directory):
        if filename.endswith('.jpg'):
            # Load image
            img = cv2.imread(os.path.join(directory, filename))
            # Extract features
            features = extract_features(img)
            # Add to dataset
            X.append(features)
            y.append(int(filename[0]))
    # Convert lists to numpy arrays
    X = np.array(X)
    y = np.array(y)
    # Return dataset
    return X, y

# Load training and testing data
X_train, y_train = load_data('train')
X_test, y_test = load_data('test')

接下来，我们可以使用Scikit-learn库来训练SVM分类器：

from sklearn import svm

# Train SVM classifier
clf = svm.SVC(kernel='linear', C=1)
clf.fit(X_train, y_train)

最后，我们可以使用训练好的分类器来预测测试数据的姿势：

# Predict posture of test data
y_pred = clf.predict(X_test)
# Print accuracy
accuracy = np.mean(y_pred == y_test)
print('Accuracy:', accuracy)

这里使用了线性SVM分类器，但是你也可以尝试其他类型的SVM分类器，例如多项式或高斯核SVM。