import cv2 import os import numpy as np from sklearn import svm import joblib def read_images(folder): images = [] labels = [] for filename in os.listdir(folder): label = filename.split('.')[0] img = cv2.imread(os.path.join(folder,filename)) if img is not None: images.append(img) labels.append(label) return images, labels # 提取特征向量 def extract_features(images): features = [] for img in images: gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) hist = cv2.calcHist([gray],[0],None,[256],[0,256]) features.append(hist.flatten()) return features # 读取图像和标签 images, labels = read_images('C:/Users/Administrator/Desktop/111') # 提取特征向量 features = extract_features(images) print(features) # 训练模型 clf = svm.SVC() clf.fit(features, labels) # 保存模型到文件 joblib.dump(clf, 'model.pkl') clf = joblib.load('E:/xiangmu/measure/model.pkl') print(clf) # 预测新图像 img = cv2.imread('C:/Users/Administrator/Downloads/092b08c53b49d92254db7874c0a7b073.jpg') gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) hist = cv2.calcHist([gray],[0],None,[256],[0,256]) features = np.array(hist.flatten()).reshape(1, -1) print(clf.predict(features)) label = clf.predict(features)[0] print('识别结果：', label) 帮我把这写代码，打印识别率

这段代码中没有计算识别率的部分，需要添加一些代码来实现。以下是修改过的代码：

import cv2
import os
import numpy as np
from sklearn import svm
from sklearn.metrics import accuracy_score
import joblib

def read_images(folder):
    images = []
    labels = []
    for filename in os.listdir(folder):
        label = filename.split('.')[0]
        img = cv2.imread(os.path.join(folder,filename))
        if img is not None:
            images.append(img)
            labels.append(label)
    return images, labels

def extract_features(images):
    features = []
    for img in images:
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        hist = cv2.calcHist([gray],[0],None,[256],[0,256])
        features.append(hist.flatten())
    return features

images, labels = read_images('C:/Users/Administrator/Desktop/111')
features = extract_features(images)

clf = svm.SVC()
clf.fit(features, labels)

joblib.dump(clf, 'model.pkl')

clf = joblib.load('model.pkl')

test_images, test_labels = read_images('C:/Users/Administrator/Desktop/222')
test_features = extract_features(test_images)

pred_labels = clf.predict(test_features)
accuracy = accuracy_score(test_labels, pred_labels)

print('识别率：', accuracy)

这段代码会读取一个文件夹中的图片作为训练数据，另一个文件夹中的图片作为测试数据，然后计算识别率并打印出来。注意修改文件夹的路径和文件名的格式。