svm图像瑕疵分类特征提取python代码

SVM (Support Vector Machine) 是一种常用的机器学习算法，在图像瑕疵分类中，可以用于特征提取并训练模型以区分正常图像和有瑕疵的图像。以下是一个简单的Python代码示例，使用Scikit-Learn库来实现SVM图像瑕疵分类，包括特征提取：

import cv2
from skimage.feature import hog
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split

# 加载和预处理数据
def load_data(image_dir):
    # 图像读取、灰度化、大小标准化等操作
    images = [cv2.imread(img_path, cv2.IMREAD_GRAYSCALE).resize((64, 64)) for img_path in image_dir]
    labels = [0] * len(normal_images) + [1] * len(faulty_images)  # 假设normal_images和faulty_images分别存储正常和瑕疵图片路径
    return images, labels

# 提取HOG (Histogram of Oriented Gradients) 特征
def extract_features(images):
    features = []
    for img in images:
        fd, hog_image = hog(img, orientations=9, pixels_per_cell=(8, 8), cells_per_block=(2, 2), transform_sqrt=True, block_norm='L2-Hys')
        features.append(fd)
    return features

# 数据划分
images, labels = load_data('path/to/images')
features = extract_features(images)
X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)

# 训练SVM
svm = SVC(kernel='linear')  # 或者尝试其他内核如'rbf'
svm.fit(X_train, y_train)

# 预测和评估
predictions = svm.predict(X_test)
print("Accuracy:", accuracy_score(y_test, predictions))

请注意，这个例子假设你已经有了正常和故障图像的文件夹，并且`accuracy_score()`函数来自`sklearn.metrics`。实际应用中，你需要根据具体的瑕疵类型和数据集调整参数。