就是做一个knn识别手写字符的程序 有没有懂传统cv的技术老师 ，要用传统cv提特征值Python。请把代码写出来

以下是一个简单的KNN手写字符识别程序，使用传统CV技术提取特征值： ```python import cv2 import numpy as np from sklearn.neighbors import KNeighborsClassifier # 加载数据集 train_data = np.load('train_data.npy') train_labels = np.load('train_labels.npy') test_data = np.load('test_data.npy') test_labels = np.load('test_labels.npy') # 提取特征值 sift = cv2.xfeatures2d.SIFT_create() train_features = [] for img in train_data: kp, des = sift.detectAndCompute(img, None) train_features.append(des) # 将特征值转换为numpy数组 train_features = np.array(train_features) # 将特征值reshape成二维数组 train_features = train_features.reshape(train_features.shape[0], -1) # 训练KNN模型 knn = KNeighborsClassifier(n_neighbors=3) knn.fit(train_features, train_labels) # 测试模型 test_features = [] for img in test_data: kp, des = sift.detectAndCompute(img, None) test_features.append(des) # 将特征值转换为numpy数组 test_features = np.array(test_features) # 将特征值reshape成二维数组 test_features = test_features.reshape(test_features.shape[0], -1) # 预测测试数据的标签 predictions = knn.predict(test_features) # 计算准确率 accuracy = np.mean(predictions == test_labels) * 100 print("Accuracy:", accuracy) ``` 请注意，这只是一个简单的示例代码，您可能需要根据您的数据集和需求进行修改。