{"id":1145836,"date":"2025-01-08T23:11:44","date_gmt":"2025-01-08T15:11:44","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1145836.html"},"modified":"2025-01-08T23:11:48","modified_gmt":"2025-01-08T15:11:48","slug":"python%e5%a6%82%e4%bd%95%e5%b0%86%e9%aa%8c%e8%af%81%e7%a0%81%e7%9a%84%e5%ad%97%e6%89%93%e5%8d%b0%e5%87%ba%e6%9d%a5","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1145836.html","title":{"rendered":"python\u5982\u4f55\u5c06\u9a8c\u8bc1\u7801\u7684\u5b57\u6253\u5370\u51fa\u6765"},"content":{"rendered":"

\"python\u5982\u4f55\u5c06\u9a8c\u8bc1\u7801\u7684\u5b57\u6253\u5370\u51fa\u6765\"<\/p>\n

Python \u5982\u4f55\u5c06\u9a8c\u8bc1\u7801\u7684\u5b57\u6253\u5370\u51fa\u6765<\/strong><\/p>\n<\/p>\n

\u4f7f\u7528Python\u5c06\u9a8c\u8bc1\u7801\u7684\u5b57\u6253\u5370\u51fa\u6765\u7684\u6838\u5fc3\u65b9\u6cd5\u5305\u62ec\u56fe\u50cf\u5904\u7406\u3001\u5b57\u7b26\u8bc6\u522b\uff08OCR\uff09\u3001\u5e93\u9009\u62e9\u3001\u9884\u5904\u7406\u7b49\u3002<\/strong> \u5176\u4e2d\uff0c\u5b57\u7b26\u8bc6\u522b\uff08OCR\uff09\u6700\u4e3a\u5173\u952e\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u65b9\u6cd5\u6765\u5b9e\u73b0\u9a8c\u8bc1\u7801\u7684\u8bc6\u522b\u548c\u6253\u5370\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528Python\u8bfb\u53d6\u9a8c\u8bc1\u7801\u56fe\u50cf<\/h2>\n<\/p>\n

\u5728\u8fdb\u884c\u9a8c\u8bc1\u7801\u8bc6\u522b\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u8bfb\u53d6\u9a8c\u8bc1\u7801\u56fe\u50cf\u3002Python \u63d0\u4f9b\u4e86\u591a\u79cd\u5e93\u6765\u5904\u7406\u56fe\u50cf\uff0c\u5176\u4e2dPIL\uff08Pillow\uff09\u662f\u4e00\u4e2a\u975e\u5e38\u5e38\u7528\u7684\u5e93\u3002<\/p>\n<\/p>\n

1. \u5b89\u88c5Pillow\u5e93<\/h3>\n<\/p>\n

\u5728\u5f00\u59cb\u5904\u7406\u56fe\u50cf\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u5b89\u88c5Pillow\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528pip\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install pillow<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8bfb\u53d6\u9a8c\u8bc1\u7801\u56fe\u50cf<\/h3>\n<\/p>\n
\u4f7f\u7528Pillow\u5e93\u8bfb\u53d6\u9a8c\u8bc1\u7801\u56fe\u50cf\u975e\u5e38\u7b80\u5355\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
from PIL import Image<\/p>\n
\u6253\u5f00\u9a8c\u8bc1\u7801\u56fe\u50cf<\/strong><\/h2>\n
image = Image.open('captcha_image.png')<\/p>\n
\u663e\u793a\u56fe\u50cf<\/strong><\/h2>\n
image.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u4ee3\u7801\uff0c\u53ef\u4ee5\u6210\u529f\u8bfb\u53d6\u5e76\u663e\u793a\u9a8c\u8bc1\u7801\u56fe\u50cf\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u56fe\u50cf\u9884\u5904\u7406<\/h2>\n<\/p>\n
\u5728\u8fdb\u884c\u5b57\u7b26\u8bc6\u522b\u4e4b\u524d\uff0c\u6211\u4eec\u901a\u5e38\u9700\u8981\u5bf9\u56fe\u50cf\u8fdb\u884c\u9884\u5904\u7406\u3002\u9884\u5904\u7406\u6b65\u9aa4\u53ef\u80fd\u5305\u62ec\u7070\u5ea6\u5316\u3001\u4e8c\u503c\u5316\u3001\u964d\u566a\u7b49\uff0c\u4ee5\u63d0\u9ad8OCR\u7684\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n
1. \u7070\u5ea6\u5316<\/h3>\n<\/p>\n
\u7070\u5ea6\u5316\u662f\u5c06\u5f69\u8272\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\u3002\u53ef\u4ee5\u4f7f\u7528Pillow\u5e93\u7684convert<\/code>\u65b9\u6cd5\u6765\u5b9e\u73b0\uff1a<\/p>\n<\/p>\n
# \u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/p>\n
gray_image = image.convert('L')<\/p>\n
gray_image.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u4e8c\u503c\u5316<\/h3>\n<\/p>\n
\u4e8c\u503c\u5316\u662f\u5c06\u7070\u5ea6\u56fe\u50cf\u8f6c\u6362\u4e3a\u9ed1\u767d\u56fe\u50cf\u3002\u53ef\u4ee5\u4f7f\u7528Pillow\u5e93\u7684point<\/code>\u65b9\u6cd5\u6765\u5b9e\u73b0\uff1a<\/p>\n<\/p>\n
# \u8bbe\u7f6e\u9608\u503c<\/p>\n
threshold = 140<\/p>\n
\u5c06\u7070\u5ea6\u56fe\u50cf\u4e8c\u503c\u5316<\/strong><\/h2>\n
binary_image = gray_image.point(lambda x: 0 if x < threshold else 255, '1')<\/p>\n
binary_image.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u964d\u566a<\/h3>\n<\/p>\n
\u964d\u566a\u662f\u53bb\u9664\u56fe\u50cf\u4e2d\u7684\u566a\u70b9\uff0c\u4ee5\u63d0\u9ad8OCR\u7684\u51c6\u786e\u6027\u3002\u53ef\u4ee5\u4f7f\u7528\u81ea\u5b9a\u4e49\u51fd\u6570\u6765\u5b9e\u73b0\u964d\u566a\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
def denoise(image):<\/p>\n
    pixels = np.array(image)<\/p>\n
    width, height = image.size<\/p>\n
    for x in range(1, width - 1):<\/p>\n
        for y in range(1, height - 1):<\/p>\n
            if pixels[y, x] == 0:<\/p>\n
                neighbors = [<\/p>\n
                    pixels[y - 1, x],<\/p>\n
                    pixels[y + 1, x],<\/p>\n
                    pixels[y, x - 1],<\/p>\n
                    pixels[y, x + 1]<\/p>\n
                ]<\/p>\n
                if sum(neighbors) > 3 * 255:<\/p>\n
                    pixels[y, x] = 255<\/p>\n
    return Image.fromarray(pixels)<\/p>\n
\u964d\u566a\u5904\u7406<\/strong><\/h2>\n
clean_image = denoise(binary_image)<\/p>\n
clean_image.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u5b57\u7b26\u8bc6\u522b\uff08OCR\uff09<\/h2>\n<\/p>\n
\u5b57\u7b26\u8bc6\u522b\uff08OCR, Optical Character Recognition\uff09\u662f\u8bc6\u522b\u9a8c\u8bc1\u7801\u7684\u6838\u5fc3\u6b65\u9aa4\u3002Tesseract\u662f\u4e00\u4e2a\u5f00\u6e90\u7684OCR\u5f15\u64ce\uff0c\u7ed3\u5408Python\u7684Tesseract\u5e93\uff08pytesseract\uff09\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u5b57\u7b26\u8bc6\u522b\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5Tesseract\u548cpytesseract<\/h3>\n<\/p>\n
\u9996\u5148\u9700\u8981\u5b89\u88c5Tesseract OCR\u5f15\u64ce\u548cpytesseract\u5e93\uff1a<\/p>\n<\/p>\n
sudo apt-get install tesseract-ocr<\/p>\n
pip install pytesseract<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u4f7f\u7528pytesseract\u8fdb\u884c\u5b57\u7b26\u8bc6\u522b<\/h3>\n<\/p>\n
\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u4ee3\u7801\uff0c\u4f7f\u7528pytesseract\u8fdb\u884c\u5b57\u7b26\u8bc6\u522b\uff1a<\/p>\n<\/p>\n
import pytesseract<\/p>\n
\u8fdb\u884c\u5b57\u7b26\u8bc6\u522b<\/strong><\/h2>\n
text = pytesseract.image_to_string(clean_image)<\/p>\n
\u6253\u5370\u8bc6\u522b\u7ed3\u679c<\/strong><\/h2>\n
print(text)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u4ee3\u7801\uff0c\u53ef\u4ee5\u6210\u529f\u8bc6\u522b\u5e76\u6253\u5370\u9a8c\u8bc1\u7801\u4e2d\u7684\u5b57\u7b26\u3002<\/p>\n<\/p>\n
\u56db\u3001\u5904\u7406\u590d\u6742\u9a8c\u8bc1\u7801<\/h2>\n<\/p>\n
\u6709\u4e9b\u9a8c\u8bc1\u7801\u53ef\u80fd\u4f1a\u66f4\u52a0\u590d\u6742\uff0c\u5305\u62ec\u5e72\u6270\u7ebf\u3001\u53d8\u5f62\u5b57\u7b26\u7b49\u3002\u5904\u7406\u8fd9\u4e9b\u9a8c\u8bc1\u7801\u9700\u8981\u66f4\u590d\u6742\u7684\u9884\u5904\u7406\u548c\u8bc6\u522b\u6b65\u9aa4\u3002<\/p>\n<\/p>\n
1. \u53bb\u9664\u5e72\u6270\u7ebf<\/h3>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528OpenCV\u5e93\u6765\u53bb\u9664\u5e72\u6270\u7ebf\u3002\u9996\u5148\u9700\u8981\u5b89\u88c5OpenCV\uff1a<\/p>\n<\/p>\n
pip install opencv-python<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff0c\u4f7f\u7528OpenCV\u53bb\u9664\u5e72\u6270\u7ebf\uff1a<\/p>\n<\/p>\n
import cv2<\/p>\n
import numpy as np<\/p>\n
\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n
image = cv2.imread('captcha_image.png', cv2.IMREAD_GRAYSCALE)<\/p>\n
\u4e8c\u503c\u5316<\/strong><\/h2>\n
_, binary_image = cv2.threshold(image, 140, 255, cv2.THRESH_BINARY)<\/p>\n
\u5b9a\u4e49\u6838<\/strong><\/h2>\n
kernel = np.ones((2, 2), np.uint8)<\/p>\n
\u8fdb\u884c\u5f62\u6001\u5b66\u64cd\u4f5c\u53bb\u9664\u5e72\u6270\u7ebf<\/strong><\/h2>\n
clean_image = cv2.morphologyEx(binary_image, cv2.MORPH_CLOSE, kernel)<\/p>\n
\u663e\u793a\u56fe\u50cf<\/strong><\/h2>\n
cv2.imshow('Clean Image', clean_image)<\/p>\n
cv2.wAI<\/a>tKey(0)<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u5904\u7406\u53d8\u5f62\u5b57\u7b26<\/h3>\n<\/p>\n
\u5bf9\u4e8e\u53d8\u5f62\u5b57\u7b26\uff0c\u53ef\u4ee5\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u8fdb\u884c\u8bc6\u522b\u3002\u8bad\u7ec3\u4e00\u4e2a\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09\u53ef\u4ee5\u6709\u6548\u5730\u8bc6\u522b\u53d8\u5f62\u5b57\u7b26\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u8981\u7684\u793a\u4f8b\u4ee3\u7801\uff0c\u4f7f\u7528Keras\u8bad\u7ec3CNN\u6a21\u578b\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
from keras.models import Sequential<\/p>\n
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense<\/p>\n
from keras.utils import to_categorical<\/p>\n
from sklearn.model_selection import train_test_split<\/p>\n
\u52a0\u8f7d\u6570\u636e\u96c6<\/strong><\/h2>\n
def load_data():<\/p>\n
    # \u52a0\u8f7d\u4f60\u7684\u9a8c\u8bc1\u7801\u6570\u636e\u96c6<\/p>\n
    pass<\/p>\n
\u51c6\u5907\u6570\u636e<\/strong><\/h2>\n
X, y = load_data()<\/p>\n
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)<\/p>\n
y_train = to_categorical(y_train)<\/p>\n
y_test = to_categorical(y_test)<\/p>\n
\u6784\u5efa\u6a21\u578b<\/strong><\/h2>\n
model = Sequential()<\/p>\n
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(60, 160, 1)))<\/p>\n
model.add(MaxPooling2D((2, 2)))<\/p>\n
model.add(Conv2D(64, (3, 3), activation='relu'))<\/p>\n
model.add(MaxPooling2D((2, 2)))<\/p>\n
model.add(Flatten())<\/p>\n
model.add(Dense(128, activation='relu'))<\/p>\n
model.add(Dense(10, activation='softmax'))<\/p>\n
\u7f16\u8bd1\u6a21\u578b<\/strong><\/h2>\n
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])<\/p>\n
\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))<\/p>\n
\u4fdd\u5b58\u6a21\u578b<\/strong><\/h2>\n
model.save('captcha_model.h5')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/h2>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u6210\u529f\u4f7f\u7528Python\u5c06\u9a8c\u8bc1\u7801\u7684\u5b57\u6253\u5370\u51fa\u6765\u3002\u603b\u7ed3\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
    \n
  1. \u8bfb\u53d6\u9a8c\u8bc1\u7801\u56fe\u50cf<\/strong>\uff1a\u4f7f\u7528Pillow\u5e93\u8bfb\u53d6\u9a8c\u8bc1\u7801\u56fe\u50cf\uff1b<\/li>\n
  2. \u56fe\u50cf\u9884\u5904\u7406<\/strong>\uff1a\u5305\u62ec\u7070\u5ea6\u5316\u3001\u4e8c\u503c\u5316\u3001\u964d\u566a\u7b49\uff1b<\/li>\n
  3. \u5b57\u7b26\u8bc6\u522b\uff08OCR\uff09<\/strong>\uff1a\u4f7f\u7528Tesseract OCR\u5f15\u64ce\u548cpytesseract\u5e93\u8fdb\u884c\u5b57\u7b26\u8bc6\u522b\uff1b<\/li>\n
  4. \u5904\u7406\u590d\u6742\u9a8c\u8bc1\u7801<\/strong>\uff1a\u5305\u62ec\u53bb\u9664\u5e72\u6270\u7ebf\u3001\u5904\u7406\u53d8\u5f62\u5b57\u7b26\u7b49\u3002<\/li>\n<\/ol>\n
    \u901a\u8fc7\u8fd9\u4e9b\u6b65\u9aa4\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u8bc6\u522b\u5e76\u6253\u5370\u9a8c\u8bc1\u7801\u4e2d\u7684\u5b57\u7b26\uff0c\u5e2e\u52a9\u6211\u4eec\u5b9e\u73b0\u9a8c\u8bc1\u7801\u8bc6\u522b\u7684\u76ee\u6807\u3002<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u5982\u4f55\u4f7f\u7528Python\u8bc6\u522b\u548c\u6253\u5370\u9a8c\u8bc1\u7801\u4e2d\u7684\u5b57\u7b26\uff1f<\/strong>
    \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u56fe\u50cf\u5904\u7406\u5e93\u5982PIL\uff08Pillow\uff09\u548cOCR\uff08\u5149\u5b66\u5b57\u7b26\u8bc6\u522b\uff09\u5e93\u5982Tesseract\u6765\u8bc6\u522b\u9a8c\u8bc1\u7801\u3002\u9996\u5148\uff0c\u4f7f\u7528PIL\u5e93\u52a0\u8f7d\u9a8c\u8bc1\u7801\u56fe\u50cf\u5e76\u8fdb\u884c\u9884\u5904\u7406\uff0c\u4f8b\u5982\u8c03\u6574\u5927\u5c0f\u3001\u53bb\u566a\u7b49\u3002\u63a5\u7740\uff0c\u5229\u7528Tesseract\u5bf9\u5904\u7406\u540e\u7684\u56fe\u50cf\u8fdb\u884c\u5b57\u7b26\u8bc6\u522b\uff0c\u5c06\u9a8c\u8bc1\u7801\u4e2d\u7684\u5b57\u63d0\u53d6\u51fa\u6765\u3002\u6700\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u7684\u6253\u5370\u529f\u80fd\u5c06\u8bc6\u522b\u51fa\u7684\u5b57\u7b26\u8f93\u51fa\u3002<\/p>\n
    \u6709\u54ea\u4e9b\u5e38\u7528\u7684Python\u5e93\u53ef\u4ee5\u5e2e\u52a9\u8bc6\u522b\u9a8c\u8bc1\u7801\uff1f<\/strong>
    \u5728Python\u4e2d\uff0c\u5e38\u7528\u7684\u5e93\u5305\u62ecPIL\uff08Pillow\uff09\u7528\u4e8e\u56fe\u50cf\u5904\u7406\uff0cTesseract\u7528\u4e8eOCR\u8bc6\u522b\u3002\u6b64\u5916\uff0cOpenCV\u4e5f\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u56fe\u50cf\u5904\u7406\u5e93\uff0c\u53ef\u4ee5\u7528\u4e8e\u9a8c\u8bc1\u7801\u7684\u9884\u5904\u7406\u3002\u901a\u8fc7\u7ed3\u5408\u8fd9\u4e9b\u5e93\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u63d0\u5347\u9a8c\u8bc1\u7801\u8bc6\u522b\u7684\u51c6\u786e\u6027\u548c\u6548\u7387\u3002<\/p>\n
    \u9a8c\u8bc1\u7801\u8bc6\u522b\u7684\u51c6\u786e\u7387\u5982\u4f55\u63d0\u9ad8\uff1f<\/strong>
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