Python进行图片验证码识别方法步骤

import cv2
from PIL import Image
def preprocess_image(image_path):
    # 读取图片并灰度化
    image = cv2.imread(image_path)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # 图片二值化
    _, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
    # 图像去噪
    denoised = cv2.medianBlur(binary, 3)
    return denoised
image_path = 'captcha.png'
preprocessed_image = preprocess_image(image_path)

import cv2
def extract_features(image):
    contours, _ = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    features = []
    for contour in contours:
        # 计算轮廓的矩
        moments = cv2.moments(contour)
        # 提取特征并添加到列表中
        features.append(moments['m00'])
    return features
features = extract_features(preprocessed_image)

from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# 准备训练数据集（假设我们有已标记好的数据集 X 和相应的标签 y）
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# 创建 SVM 分类器并拟合训练数据
classifier = svm.SVC()
classifier.fit(X_train, y_train)
# 在测试集上进行预测并计算准确率
predictions = classifier.predict(X_test)
accuracy = accuracy_score(y_test, predictions)

# 读取待识别的验证码图片并进行预处理
captcha_image = preprocess_image('captcha.png')
# 提取特征
features = extract_features(captcha_image)
# 使用训练好的模型进行预测
prediction = classifier.predict([features])
print(f"验证码识别结果: {prediction}")