import cv2 import numpy as np def extract_features(image): # 将图像转换为HSV颜色空间 hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # 计算颜色直方图 hist = cv2.calcHist([hsv], [0, 1], None, [180, 256], [0, 180, 0, 256]) hist = cv2.normalize(hist, hist) # 展开直方图为一维数组 features = hist.flatten() return features from sklearn.model_selection import train_test_split from sklearn.svm import SVC # 加载数据集和标签 data = [] labels = [] for i in range(10): for j in range(100): image = cv2.imread(f"data/{i}_{j}.jpg") feature = extract_features(image) data.append(feature)w labels.append(i) # 划分数据集为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2) # 训练SVM分类器 model = SVC(kernel="linear", C=1.0) model.fit(X_train, y_train) # 在测试集上进行测试 accuracy = model.score(X_test, y_test) pr# 加载新的测试图像 test_image = cv2.imread("test.jpg") test_feature = extract_features(test_image) # 对图像进行分类 prediction = model.predict([test_feature]) print(f"Prediction: {prediction}") print(f"Accuracy: {accuracy}")

这段代码主要是训练一个基于HSV颜色直方图特征的SVM分类器，并对新的测试图像进行分类。

其中`extract_features()`函数用于提取图像的特征，采用的是将图像转换为HSV颜色空间，并计算其颜色直方图的方法。`train_test_split()`函数用于将数据集划分为训练集和测试集，`SVC`类构建了一个基于线性核函数的SVM分类器，并用训练集进行训练，`score()`函数用于计算分类器在测试集上的准确率。

你遇到的问题可能是因为在第12行和第13行多了一个"w"字符，需要将其删除。修改后的代码如下：

import cv2
import numpy as np

def extract_features(image):
    # 将图像转换为HSV颜色空间
    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
    
    # 计算颜色直方图
    hist = cv2.calcHist([hsv], [0, 1], None, [180, 256], [0, 180, 0, 256])
    hist = cv2.normalize(hist, hist)
    
    # 展开直方图为一维数组
    features = hist.flatten()
    
    return features

from sklearn.model_selection import train_test_split
from sklearn.svm import SVC

# 加载数据集和标签
data = []
labels = []
for i in range(10):
    for j in range(100):
        image = cv2.imread(f"data/{i}_{j}.jpg")
        feature = extract_features(image)
        data.append(feature)
        labels.append(i)

# 划分数据集为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2)

# 训练SVM分类器
model = SVC(kernel="linear", C=1.0)
model.fit(X_train, y_train)

# 在测试集上进行测试
accuracy = model.score(X_test, y_test)

# 加载新的测试图像
test_image = cv2.imread("test.jpg")
test_feature = extract_features(test_image)

# 对图像进行分类
prediction = model.predict([test_feature])
print(f"Prediction: {prediction}")
print(f"Accuracy: {accuracy}")