{"id":1089329,"date":"2025-01-08T13:52:35","date_gmt":"2025-01-08T05:52:35","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1089329.html"},"modified":"2025-01-08T13:52:38","modified_gmt":"2025-01-08T05:52:38","slug":"python%e5%a6%82%e4%bd%95%e5%b0%86%e5%9b%be%e7%89%87%e5%a4%96%e9%9d%a2%e6%96%b9%e6%a1%86%e5%8e%bb%e6%8e%89-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1089329.html","title":{"rendered":"python\u5982\u4f55\u5c06\u56fe\u7247\u5916\u9762\u65b9\u6846\u53bb\u6389"},"content":{"rendered":"

\"python\u5982\u4f55\u5c06\u56fe\u7247\u5916\u9762\u65b9\u6846\u53bb\u6389\"<\/p>\n

Python\u5982\u4f55\u5c06\u56fe\u7247\u5916\u9762\u65b9\u6846\u53bb\u6389<\/strong><\/p>\n<\/p>\n

Python\u53ef\u4ee5\u4f7f\u7528Pillow\u5e93\u3001OpenCV\u5e93\u3001NumPy\u5e93\u7b49\u5de5\u5177\u53bb\u6389\u56fe\u7247\u5916\u9762\u7684\u65b9\u6846\uff0c\u53ef\u4ee5\u901a\u8fc7\u56fe\u50cf\u5904\u7406\u6280\u672f\u5b9e\u73b0<\/strong>\u3002\u5176\u4e2d\uff0cPillow\u5e93\u662f\u4e00\u79cd\u9ad8\u7ea7\u56fe\u50cf\u5904\u7406\u5de5\u5177\uff0cOpenCV\u5e93\u5219\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u529f\u80fd\uff0cNumPy\u5e93\u7528\u6765\u8fdb\u884c\u6570\u7ec4\u64cd\u4f5c\u3002\u4ee5\u4e0b\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Pillow\u5e93\u53bb\u9664\u56fe\u7247\u5916\u9762\u7684\u65b9\u6846\u3002<\/p>\n<\/p>\n

\u4e00\u3001Pillow\u5e93\u7684\u4f7f\u7528<\/h3>\n<\/p>\n

1\u3001\u5b89\u88c5Pillow\u5e93<\/h4>\n<\/p>\n

\u5728\u5f00\u59cb\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5Pillow\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install pillow<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u52a0\u8f7d\u56fe\u7247<\/h4>\n<\/p>\n
\u5728\u5904\u7406\u56fe\u7247\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u52a0\u8f7d\u56fe\u7247\u3002\u53ef\u4ee5\u4f7f\u7528Pillow\u5e93\u4e2d\u7684Image<\/code>\u6a21\u5757\u6765\u5b8c\u6210\uff1a<\/p>\n<\/p>\n
from PIL import Image<\/p>\n
\u52a0\u8f7d\u56fe\u7247<\/strong><\/h2>\n
image = Image.open('path_to_image.jpg')<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u53bb\u9664\u65b9\u6846\u7684\u6b65\u9aa4<\/h4>\n<\/p>\n
\u53bb\u9664\u65b9\u6846\u7684\u6b65\u9aa4\u5305\u62ec\u4ee5\u4e0b\u51e0\u4e2a\u65b9\u9762\uff1a <\/p>\n<\/p>\n
    \n
  1. \u5c06\u56fe\u7247\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\uff0c<\/li>\n
  2. \u8fdb\u884c\u4e8c\u503c\u5316\u5904\u7406\uff0c<\/li>\n
  3. \u627e\u5230\u65b9\u6846\u7684\u8fb9\u754c\uff0c<\/li>\n
  4. \u526a\u5207\u51fa\u4e0d\u5305\u542b\u65b9\u6846\u7684\u533a\u57df\u3002<\/li>\n<\/ol>\n
    \u5177\u4f53\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
    from PIL import Image, ImageOps<\/p>\n
    import numpy as np<\/p>\n
    \u52a0\u8f7d\u56fe\u7247<\/strong><\/h2>\n
    image = Image.open('path_to_image.jpg')<\/p>\n
    \u5c06\u56fe\u7247\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/strong><\/h2>\n
    gray_image = ImageOps.grayscale(image)<\/p>\n
    \u5c06\u7070\u5ea6\u56fe\u50cf\u8f6c\u6362\u4e3aNumPy\u6570\u7ec4<\/strong><\/h2>\n
    image_array = np.array(gray_image)<\/p>\n
    \u4e8c\u503c\u5316\u5904\u7406<\/strong><\/h2>\n
    threshold = 128<\/p>\n
    binary_image = (image_array > threshold) * 255<\/p>\n
    \u627e\u5230\u65b9\u6846\u7684\u8fb9\u754c<\/strong><\/h2>\n
    rows = np.any(binary_image, axis=1)<\/p>\n
    cols = np.any(binary_image, axis=0)<\/p>\n
    top, bottom = np.where(rows)[0][[0, -1]]<\/p>\n
    left, right = np.where(cols)[0][[0, -1]]<\/p>\n
    \u526a\u5207\u51fa\u4e0d\u5305\u542b\u65b9\u6846\u7684\u533a\u57df<\/strong><\/h2>\n
    cropped_image = image.crop((left, top, right, bottom))<\/p>\n
    \u4fdd\u5b58\u5904\u7406\u540e\u7684\u56fe\u7247<\/strong><\/h2>\n
    cropped_image.save('cropped_image.jpg')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u53ef\u4ee5\u4f7f\u7528Pillow\u5e93\u53bb\u9664\u56fe\u7247\u5916\u9762\u7684\u65b9\u6846\u3002<\/p>\n<\/p>\n
    \u4e8c\u3001OpenCV\u5e93\u7684\u4f7f\u7528<\/h3>\n<\/p>\n
    1\u3001\u5b89\u88c5OpenCV\u5e93<\/h4>\n<\/p>\n
    \u540c\u6837\u5730\uff0c\u9700\u8981\u5148\u5b89\u88c5OpenCV\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
    pip install opencv-python<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2\u3001\u52a0\u8f7d\u56fe\u7247<\/h4>\n<\/p>\n
    \u5728\u5904\u7406\u56fe\u7247\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u52a0\u8f7d\u56fe\u7247\u3002\u53ef\u4ee5\u4f7f\u7528OpenCV\u5e93\u4e2d\u7684cv2<\/code>\u6a21\u5757\u6765\u5b8c\u6210\uff1a<\/p>\n<\/p>\n
    import cv2<\/p>\n
    \u52a0\u8f7d\u56fe\u7247<\/strong><\/h2>\n
    image = cv2.imread('path_to_image.jpg')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    3\u3001\u53bb\u9664\u65b9\u6846\u7684\u6b65\u9aa4<\/h4>\n<\/p>\n
    \u53bb\u9664\u65b9\u6846\u7684\u6b65\u9aa4\u5305\u62ec\u4ee5\u4e0b\u51e0\u4e2a\u65b9\u9762\uff1a<\/p>\n<\/p>\n
      \n
    1. \u5c06\u56fe\u7247\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\uff0c<\/li>\n
    2. \u8fdb\u884c\u4e8c\u503c\u5316\u5904\u7406\uff0c<\/li>\n
    3. \u627e\u5230\u65b9\u6846\u7684\u8f6e\u5ed3\uff0c<\/li>\n
    4. \u526a\u5207\u51fa\u4e0d\u5305\u542b\u65b9\u6846\u7684\u533a\u57df\u3002<\/li>\n<\/ol>\n
      \u5177\u4f53\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
      import cv2<\/p>\n
      import numpy as np<\/p>\n
      \u52a0\u8f7d\u56fe\u7247<\/strong><\/h2>\n
      image = cv2.imread('path_to_image.jpg')<\/p>\n
      \u5c06\u56fe\u7247\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/strong><\/h2>\n
      gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)<\/p>\n
      \u4e8c\u503c\u5316\u5904\u7406<\/strong><\/h2>\n
      _, binary_image = cv2.threshold(gray_image, 128, 255, cv2.THRESH_BINARY)<\/p>\n
      \u627e\u5230\u65b9\u6846\u7684\u8f6e\u5ed3<\/strong><\/h2>\n
      contours, _ = cv2.findContours(binary_image, cv2.RETR_EXTERNAL, cv2.CHAI<\/a>N_APPROX_SIMPLE)<\/p>\n
      contour = max(contours, key=cv2.contourArea)<\/p>\n
      \u83b7\u53d6\u65b9\u6846\u7684\u8fb9\u754c<\/strong><\/h2>\n
      x, y, w, h = cv2.boundingRect(contour)<\/p>\n
      \u526a\u5207\u51fa\u4e0d\u5305\u542b\u65b9\u6846\u7684\u533a\u57df<\/strong><\/h2>\n
      cropped_image = image[y:y+h, x:x+w]<\/p>\n
      \u4fdd\u5b58\u5904\u7406\u540e\u7684\u56fe\u7247<\/strong><\/h2>\n
      cv2.imwrite('cropped_image.jpg', cropped_image)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u53ef\u4ee5\u4f7f\u7528OpenCV\u5e93\u53bb\u9664\u56fe\u7247\u5916\u9762\u7684\u65b9\u6846\u3002<\/p>\n<\/p>\n
      \u4e09\u3001NumPy\u5e93\u7684\u4f7f\u7528<\/h3>\n<\/p>\n
      1\u3001\u5b89\u88c5NumPy\u5e93<\/h4>\n<\/p>\n
      NumPy\u5e93\u901a\u5e38\u4f1a\u4e0e\u5176\u4ed6\u5e93\u4e00\u8d77\u5b89\u88c5\u3002\u5982\u679c\u5c1a\u672a\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
      pip install numpy<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      2\u3001\u52a0\u8f7d\u56fe\u7247<\/h4>\n<\/p>\n
      \u5728\u5904\u7406\u56fe\u7247\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u52a0\u8f7d\u56fe\u7247\u3002\u53ef\u4ee5\u4f7f\u7528Pillow\u5e93\u4e2d\u7684Image<\/code>\u6a21\u5757\u5e76\u5c06\u5176\u8f6c\u6362\u4e3aNumPy\u6570\u7ec4\uff1a<\/p>\n<\/p>\n
      from PIL import Image<\/p>\n
      import numpy as np<\/p>\n
      \u52a0\u8f7d\u56fe\u7247<\/strong><\/h2>\n
      image = Image.open('path_to_image.jpg')<\/p>\n
      image_array = np.array(image)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      3\u3001\u53bb\u9664\u65b9\u6846\u7684\u6b65\u9aa4<\/h4>\n<\/p>\n
      \u53bb\u9664\u65b9\u6846\u7684\u6b65\u9aa4\u5305\u62ec\u4ee5\u4e0b\u51e0\u4e2a\u65b9\u9762\uff1a<\/p>\n<\/p>\n
        \n
      1. \u5c06\u56fe\u7247\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\uff0c<\/li>\n
      2. \u8fdb\u884c\u4e8c\u503c\u5316\u5904\u7406\uff0c<\/li>\n
      3. \u627e\u5230\u65b9\u6846\u7684\u8fb9\u754c\uff0c<\/li>\n
      4. \u526a\u5207\u51fa\u4e0d\u5305\u542b\u65b9\u6846\u7684\u533a\u57df\u3002<\/li>\n<\/ol>\n
        \u5177\u4f53\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
        from PIL import Image, ImageOps<\/p>\n
        import numpy as np<\/p>\n
        \u52a0\u8f7d\u56fe\u7247<\/strong><\/h2>\n
        image = Image.open('path_to_image.jpg')<\/p>\n
        image_array = np.array(image)<\/p>\n
        \u5c06\u56fe\u7247\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/strong><\/h2>\n
        gray_image = ImageOps.grayscale(image)<\/p>\n
        gray_image_array = np.array(gray_image)<\/p>\n
        \u4e8c\u503c\u5316\u5904\u7406<\/strong><\/h2>\n
        threshold = 128<\/p>\n
        binary_image = (gray_image_array > threshold) * 255<\/p>\n
        \u627e\u5230\u65b9\u6846\u7684\u8fb9\u754c<\/strong><\/h2>\n
        rows = np.any(binary_image, axis=1)<\/p>\n
        cols = np.any(binary_image, axis=0)<\/p>\n
        top, bottom = np.where(rows)[0][[0, -1]]<\/p>\n
        left, right = np.where(cols)[0][[0, -1]]<\/p>\n
        \u526a\u5207\u51fa\u4e0d\u5305\u542b\u65b9\u6846\u7684\u533a\u57df<\/strong><\/h2>\n
        cropped_image_array = image_array[top:bottom+1, left:right+1]<\/p>\n
        \u5c06NumPy\u6570\u7ec4\u8f6c\u6362\u4e3a\u56fe\u7247<\/strong><\/h2>\n
        cropped_image = Image.fromarray(cropped_image_array)<\/p>\n
        \u4fdd\u5b58\u5904\u7406\u540e\u7684\u56fe\u7247<\/strong><\/h2>\n
        cropped_image.save('cropped_image.jpg')<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u53bb\u9664\u56fe\u7247\u5916\u9762\u7684\u65b9\u6846\u3002<\/p>\n<\/p>\n
        \u56db\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
        \u5728\u5904\u7406\u56fe\u7247\u65f6\uff0c\u53bb\u9664\u56fe\u7247\u5916\u9762\u7684\u65b9\u6846\u662f\u4e00\u9879\u5e38\u89c1\u7684\u4efb\u52a1\u3002\u4f7f\u7528Pillow\u5e93\u3001OpenCV\u5e93\u548cNumPy\u5e93\u90fd\u53ef\u4ee5\u5b9e\u73b0\u8fd9\u4e00\u529f\u80fd<\/strong>\u3002\u5176\u4e2d\uff0cPillow\u5e93\u548cOpenCV\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\uff0c\u800cNumPy\u5e93\u5219\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u7ec4\u64cd\u4f5c\u529f\u80fd\u3002\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\u548c\u65b9\u6cd5\uff0c\u53ef\u4ee5\u9ad8\u6548\u5730\u5b8c\u6210\u56fe\u7247\u5904\u7406\u4efb\u52a1\u3002<\/p>\n<\/p>\n
        \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
         \u5982\u4f55\u4f7f\u7528Python\u53bb\u6389\u56fe\u7247\u5916\u9762\u7684\u65b9\u6846\uff1f<\/strong>
        \u8981\u53bb\u6389\u56fe\u7247\u5916\u9762\u7684\u65b9\u6846\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u56fe\u50cf\u5904\u7406\u5e93\uff0c\u5982OpenCV\u6216PIL\uff08Pillow\uff09\u3002\u9996\u5148\uff0c\u4f7f\u7528\u8fd9\u4e9b\u5e93\u52a0\u8f7d\u56fe\u7247\uff0c\u7136\u540e\u901a\u8fc7\u56fe\u50cf\u5904\u7406\u6280\u672f\uff08\u5982\u88c1\u526a\u3001\u9608\u503c\u5904\u7406\u548c\u8fb9\u7f18\u68c0\u6d4b\uff09\u6765\u8bc6\u522b\u548c\u53bb\u9664\u65b9\u6846\u3002\u5177\u4f53\u6b65\u9aa4\u53ef\u4ee5\u5305\u62ec\u8bfb\u53d6\u56fe\u7247\u3001\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u3001\u5e94\u7528\u8fb9\u7f18\u68c0\u6d4b\u7b97\u6cd5\uff0c\u7136\u540e\u8bc6\u522b\u5e76\u88c1\u526a\u51fa\u60f3\u8981\u7684\u533a\u57df\u3002<\/p>\n
        \u53bb\u6389\u56fe\u7247\u65b9\u6846\u540e\uff0c\u5982\u4f55\u4fdd\u5b58\u5904\u7406\u7ed3\u679c\uff1f<\/strong>
        \u5904\u7406\u5b8c\u56fe\u7247\u540e\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528PIL\u6216OpenCV\u63d0\u4f9b\u7684\u4fdd\u5b58\u529f\u80fd\u3002\u4f7f\u7528PIL\u65f6\uff0c\u8c03\u7528save()<\/code>\u65b9\u6cd5\u5e76\u6307\u5b9a\u6587\u4ef6\u540d\u548c\u683c\u5f0f\uff1b\u4f7f\u7528OpenCV\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528cv2.imwrite()<\/code>\u51fd\u6570\u3002\u786e\u4fdd\u9009\u62e9\u5408\u9002\u7684\u6587\u4ef6\u683c\u5f0f\uff08\u5982PNG\u6216JPEG\uff09\uff0c\u4ee5\u4fdd\u6301\u56fe\u50cf\u8d28\u91cf\u3002<\/p>\n
        \u662f\u5426\u53ef\u4ee5\u6279\u91cf\u5904\u7406\u591a\u5f20\u56fe\u7247\uff1f<\/strong>
        \u786e\u5b9e\u53ef\u4ee5\u901a\u8fc7\u7f16\u5199\u5faa\u73af\u6765\u6279\u91cf\u5904\u7406\u591a\u5f20\u56fe\u7247\u3002\u60a8\u53ef\u4ee5\u5c06\u6240\u6709\u5f85\u5904\u7406\u7684\u56fe\u7247\u8def\u5f84\u653e\u5165\u4e00\u4e2a\u5217\u8868\u4e2d\uff0c\u4f7f\u7528\u5faa\u73af\u904d\u5386\u6bcf\u5f20\u56fe\u7247\uff0c\u5e94\u7528\u76f8\u540c\u7684\u53bb\u65b9\u6846\u5904\u7406\u903b\u8f91\uff0c\u5e76\u5c06\u7ed3\u679c\u4fdd\u5b58\u5230\u6307\u5b9a\u76ee\u5f55\u3002\u8fd9\u6837\u53ef\u4ee5\u8282\u7701\u65f6\u95f4\uff0c\u7279\u522b\u662f\u5f53\u9700\u8981\u5904\u7406\u5927\u91cf\u56fe\u7247\u65f6\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u5982\u4f55\u5c06\u56fe\u7247\u5916\u9762\u65b9\u6846\u53bb\u6389 Python\u53ef\u4ee5\u4f7f\u7528Pillow\u5e93\u3001OpenCV\u5e93\u3001NumPy\u5e93\u7b49\u5de5\u5177\u53bb\u6389 […]","protected":false},"author":3,"featured_media":1089338,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1089329"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1089329"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1089329\/revisions"}],"predecessor-version":[{"id":1089341,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1089329\/revisions\/1089341"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1089338"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1089329"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1089329"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1089329"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}