写一个姿势识别的SVM算法
时间: 2024-05-13 22:16:05 浏览: 82
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:
```python
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:
```python
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:
```python
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:
```python
# 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。
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