{"id":980640,"date":"2024-12-27T06:56:04","date_gmt":"2024-12-26T22:56:04","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/980640.html"},"modified":"2024-12-27T06:56:06","modified_gmt":"2024-12-26T22:56:06","slug":"python%e5%a6%82%e4%bd%95%e8%b0%83%e6%95%b4%e7%85%a7%e7%89%87%e4%ba%ae%e5%ba%a6","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/980640.html","title":{"rendered":"python\u5982\u4f55\u8c03\u6574\u7167\u7247\u4eae\u5ea6"},"content":{"rendered":"

\"python\u5982\u4f55\u8c03\u6574\u7167\u7247\u4eae\u5ea6\"<\/p>\n

\u5f00\u5934\u6bb5\u843d\uff1a
\u8c03\u6574\u7167\u7247\u4eae\u5ea6\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528Python\u5e93\u5982PIL\u3001OpenCV\u3001NumPy\u3001\u8c03\u6574\u4eae\u5ea6\u7684\u57fa\u672c\u539f\u7406\u662f\u6539\u53d8\u6bcf\u4e2a\u50cf\u7d20\u7684\u4eae\u5ea6\u503c<\/strong>\u3002Python\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u8c03\u6574\u56fe\u50cf\u7684\u4eae\u5ea6\uff0c\u5176\u4e2d\u6700\u5e38\u7528\u7684\u5e93\u5305\u62ecPIL\uff08Pillow\uff09\u3001OpenCV\u548cNumPy\u3002\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\uff0c\u4f7f\u5f97\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u5bf9\u56fe\u50cf\u8fdb\u884c\u5404\u79cd\u64cd\u4f5c\u3002\u901a\u8fc7\u6539\u53d8\u6bcf\u4e2a\u50cf\u7d20\u7684\u4eae\u5ea6\u503c\uff0c\u6211\u4eec\u53ef\u4ee5\u5b9e\u73b0\u5bf9\u56fe\u50cf\u6574\u4f53\u4eae\u5ea6\u7684\u589e\u52a0\u6216\u51cf\u5c11\u3002\u4e0b\u9762\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u5de5\u5177\u6765\u8c03\u6574\u7167\u7247\u7684\u4eae\u5ea6\u3002<\/p>\n<\/p>\n

\u4e00\u3001PIL\uff08Pillow\uff09\u5e93\u8c03\u6574\u7167\u7247\u4eae\u5ea6 <\/p>\n<\/p>\n

PIL\u5e93\u662fPython\u5904\u7406\u56fe\u50cf\u7684\u4e00\u4e2a\u7ecf\u5178\u5e93\uff0cPillow\u662f\u5176\u66f4\u73b0\u4ee3\u7684\u5206\u652f\u3002Pillow\u63d0\u4f9b\u4e86\u7b80\u5355\u6613\u7528\u7684\u63a5\u53e3\u6765\u8fdb\u884c\u56fe\u50cf\u64cd\u4f5c\uff0c\u5305\u62ec\u8c03\u6574\u4eae\u5ea6\u3002<\/p>\n<\/p>\n

    \n
  1. \u5b89\u88c5Pillow\u5e93
    \u5728\u5f00\u59cb\u4f7f\u7528Pillow\u4e4b\u524d\uff0c\u9700\u8981\u5148\u5b89\u88c5\u8fd9\u4e2a\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7pip\u547d\u4ee4\u6765\u5b89\u88c5\uff1a<\/li>\n<\/ol>\n

    pip install pillow<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u4f7f\u7528Pillow\u8c03\u6574\u4eae\u5ea6
      \u8c03\u6574\u56fe\u50cf\u4eae\u5ea6\u7684\u5173\u952e\u662fImageEnhance\u6a21\u5757\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/li>\n<\/ol>\n
      from PIL import Image<\/p>\n
      from PIL import ImageEnhance<\/p>\n
      \u6253\u5f00\u56fe\u50cf<\/strong><\/h2>\n
      image = Image.open("example.jpg")<\/p>\n
      \u521b\u5efa\u4eae\u5ea6\u589e\u5f3a\u5bf9\u8c61<\/strong><\/h2>\n
      enhancer = ImageEnhance.Brightness(image)<\/p>\n
      \u8c03\u6574\u4eae\u5ea6<\/strong><\/h2>\n
      factor = 1.5  # \u4eae\u5ea6\u56e0\u5b50\uff0c\u5927\u4e8e1\u589e\u52a0\u4eae\u5ea6\uff0c\u5c0f\u4e8e1\u51cf\u5c11\u4eae\u5ea6<\/p>\n
      image_enhanced = enhancer.enhance(factor)<\/p>\n
      \u4fdd\u5b58\u8c03\u6574\u540e\u7684\u56fe\u50cf<\/strong><\/h2>\n
      image_enhanced.save("enhanced_image.jpg")<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0cImageEnhance.Brightness<\/code>\u7528\u6765\u521b\u5efa\u4e00\u4e2a\u4eae\u5ea6\u589e\u5f3a\u5bf9\u8c61\uff0c\u800cenhance<\/code>\u65b9\u6cd5\u7528\u4e8e\u8bbe\u7f6e\u4eae\u5ea6\u56e0\u5b50\u3002\u901a\u8fc7\u8c03\u6574\u56e0\u5b50\u7684\u503c\uff0c\u53ef\u4ee5\u5b9e\u73b0\u4e0d\u540c\u7a0b\u5ea6\u7684\u4eae\u5ea6\u8c03\u6574\u3002<\/p>\n<\/p>\n
      \u4e8c\u3001OpenCV\u8c03\u6574\u7167\u7247\u4eae\u5ea6  <\/p>\n<\/p>\n
      OpenCV\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u5e7f\u6cdb\u7528\u4e8e\u5b9e\u65f6\u56fe\u50cf\u5904\u7406\u3002\u5b83\u4e5f\u53ef\u4ee5\u7528\u4e8e\u8c03\u6574\u56fe\u50cf\u7684\u4eae\u5ea6\u3002<\/p>\n<\/p>\n
        \n
      1. \u5b89\u88c5OpenCV\u5e93
        \u9996\u5148\uff0c\u9700\u8981\u5b89\u88c5OpenCV\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5\uff1a<\/li>\n<\/ol>\n
        pip install opencv-python<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. \u4f7f\u7528OpenCV\u8c03\u6574\u4eae\u5ea6
          \u5728OpenCV\u4e2d\uff0c\u8c03\u6574\u4eae\u5ea6\u53ef\u4ee5\u901a\u8fc7\u7b80\u5355\u5730\u52a0\u51cf\u50cf\u7d20\u503c\u6765\u5b9e\u73b0\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/li>\n<\/ol>\n
          import cv2<\/p>\n
          \u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n
          image = cv2.imread("example.jpg")<\/p>\n
          \u521b\u5efa\u4e00\u4e2a\u7a7a\u767d\u56fe\u50cf\uff0c\u5927\u5c0f\u548c\u539f\u56fe\u76f8\u540c<\/strong><\/h2>\n
          brightness_matrix = cv2.add(image, (50, 50, 50, 0))<\/p>\n
          \u663e\u793a\u8c03\u6574\u540e\u7684\u56fe\u50cf<\/strong><\/h2>\n
          cv2.imshow("Brightness Adjusted", brightness_matrix)<\/p>\n
          cv2.wAI<\/a>tKey(0)<\/p>\n
          cv2.destroyAllWindows()<\/p>\n
          \u4fdd\u5b58\u8c03\u6574\u540e\u7684\u56fe\u50cf<\/strong><\/h2>\n
          cv2.imwrite("brightness_adjusted.jpg", brightness_matrix)<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u5728\u8fd9\u4e2a\u4ee3\u7801\u4e2d\uff0ccv2.add<\/code>\u51fd\u6570\u7528\u4e8e\u5c06\u4e00\u4e2a\u56fa\u5b9a\u7684\u4eae\u5ea6\u503c\u6dfb\u52a0\u5230\u6bcf\u4e2a\u50cf\u7d20\uff0c\u4ece\u800c\u63d0\u9ad8\u56fe\u50cf\u7684\u4eae\u5ea6\u3002\u901a\u8fc7\u8c03\u6574\u8fd9\u4e2a\u503c\uff0c\u53ef\u4ee5\u5b9e\u73b0\u4e0d\u540c\u7a0b\u5ea6\u7684\u4eae\u5ea6\u8c03\u6574\u3002<\/p>\n<\/p>\n
          \u4e09\u3001NumPy\u8c03\u6574\u7167\u7247\u4eae\u5ea6  <\/p>\n<\/p>\n
          NumPy\u662fPython\u4e2d\u5904\u7406\u5927\u89c4\u6a21\u77e9\u9635\u7684\u5e93\uff0c\u5b83\u53ef\u4ee5\u7528\u4e8e\u5904\u7406\u56fe\u50cf\u6570\u636e\u3002\u56fe\u50cf\u53ef\u4ee5\u770b\u4f5c\u662f\u4e00\u4e2a\u4e09\u7ef4\u7684\u77e9\u9635\uff0c\u5176\u4e2d\u6bcf\u4e2a\u5143\u7d20\u4ee3\u8868\u4e00\u4e2a\u50cf\u7d20\u7684RGB\u503c\u3002<\/p>\n<\/p>\n
            \n
          1. \u4f7f\u7528NumPy\u8c03\u6574\u4eae\u5ea6
            NumPy\u63d0\u4f9b\u4e86\u76f4\u63a5\u5bf9\u6570\u7ec4\u8fdb\u884c\u64cd\u4f5c\u7684\u80fd\u529b\uff0c\u53ef\u4ee5\u7528\u6765\u8c03\u6574\u56fe\u50cf\u7684\u4eae\u5ea6\u3002<\/li>\n<\/ol>\n
            import cv2<\/p>\n
            import numpy as np<\/p>\n
            \u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n
            image = cv2.imread("example.jpg")<\/p>\n
            \u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u6d6e\u70b9\u578b<\/strong><\/h2>\n
            image_float = image.astype(np.float64)<\/p>\n
            \u8c03\u6574\u4eae\u5ea6<\/strong><\/h2>\n
            factor = 1.2  # \u4eae\u5ea6\u56e0\u5b50<\/p>\n
            image_float *= factor<\/p>\n
            \u5c06\u56fe\u50cf\u8f6c\u6362\u4e3auint8<\/strong><\/h2>\n
            image_bright = np.clip(image_float, 0, 255).astype(np.uint8)<\/p>\n
            \u663e\u793a\u8c03\u6574\u540e\u7684\u56fe\u50cf<\/strong><\/h2>\n
            cv2.imshow("Brightness Adjusted", image_bright)<\/p>\n
            cv2.waitKey(0)<\/p>\n
            cv2.destroyAllWindows()<\/p>\n
            \u4fdd\u5b58\u8c03\u6574\u540e\u7684\u56fe\u50cf<\/strong><\/h2>\n
            cv2.imwrite("brightness_adjusted_numpy.jpg", image_bright)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u9996\u5148\u5c06\u56fe\u50cf\u6570\u636e\u8f6c\u6362\u4e3a\u6d6e\u70b9\u578b\uff0c\u7136\u540e\u4e58\u4ee5\u4e00\u4e2a\u4eae\u5ea6\u56e0\u5b50\u3002np.clip<\/code>\u51fd\u6570\u7528\u4e8e\u786e\u4fdd\u8c03\u6574\u540e\u7684\u503c\u57280\u5230255\u4e4b\u95f4\uff0c\u907f\u514d\u6ea2\u51fa\u3002<\/p>\n<\/p>\n
            \u56db\u3001\u8c03\u6574\u4eae\u5ea6\u7684\u57fa\u672c\u539f\u7406  <\/p>\n<\/p>\n
            \u8c03\u6574\u56fe\u50cf\u4eae\u5ea6\u7684\u57fa\u672c\u539f\u7406\u662f\u6539\u53d8\u6bcf\u4e2a\u50cf\u7d20\u7684\u4eae\u5ea6\u503c\u3002\u4eae\u5ea6\u503c\u901a\u5e38\u4e0e\u50cf\u7d20\u7684RGB\u503c\u76f8\u5173\uff0c\u901a\u8fc7\u8c03\u6574\u8fd9\u4e9b\u503c\uff0c\u6211\u4eec\u53ef\u4ee5\u63a7\u5236\u56fe\u50cf\u7684\u4eae\u5ea6\u3002<\/p>\n<\/p>\n
              \n
            1. \n
              \u589e\u52a0\u4eae\u5ea6
              \u589e\u52a0\u4eae\u5ea6\u7684\u6700\u7b80\u5355\u65b9\u6cd5\u662f\u5c06\u6bcf\u4e2a\u50cf\u7d20\u7684RGB\u503c\u52a0\u4e0a\u4e00\u4e2a\u5e38\u91cf\u3002\u8fd9\u76f8\u5f53\u4e8e\u589e\u52a0\u6bcf\u4e2a\u50cf\u7d20\u7684\u4eae\u5ea6\u503c\uff0c\u4f7f\u56fe\u50cf\u770b\u8d77\u6765\u66f4\u4eae\u3002<\/p>\n<\/p>\n<\/li>\n
            2. \n
              \u51cf\u5c11\u4eae\u5ea6
              \u51cf\u5c11\u4eae\u5ea6\u5219\u662f\u5c06\u6bcf\u4e2a\u50cf\u7d20\u7684RGB\u503c\u51cf\u53bb\u4e00\u4e2a\u5e38\u91cf\u3002\u8fd9\u76f8\u5f53\u4e8e\u51cf\u5c11\u6bcf\u4e2a\u50cf\u7d20\u7684\u4eae\u5ea6\u503c\uff0c\u4f7f\u56fe\u50cf\u770b\u8d77\u6765\u66f4\u6697\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
              \u4e94\u3001\u4eae\u5ea6\u8c03\u6574\u7684\u5b9e\u8df5\u5e94\u7528  <\/p>\n<\/p>\n
              \u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u8c03\u6574\u56fe\u50cf\u4eae\u5ea6\u53ef\u4ee5\u7528\u4e8e\u591a\u79cd\u573a\u666f\uff0c\u4f8b\u5982\u56fe\u50cf\u589e\u5f3a\u3001\u56fe\u50cf\u98ce\u683c\u5316\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u5e94\u7528\u573a\u666f\uff1a<\/p>\n<\/p>\n
                \n
              1. \n
                \u56fe\u50cf\u589e\u5f3a
                \u5728\u4e00\u4e9b\u5149\u7ebf\u4e0d\u8db3\u7684\u73af\u5883\u4e2d\uff0c\u62cd\u6444\u7684\u7167\u7247\u53ef\u80fd\u663e\u5f97\u6bd4\u8f83\u6697\u6de1\u3002\u901a\u8fc7\u8c03\u6574\u4eae\u5ea6\uff0c\u53ef\u4ee5\u4f7f\u56fe\u50cf\u66f4\u52a0\u6e05\u6670\u3001\u660e\u4eae\u3002<\/p>\n<\/p>\n<\/li>\n
              2. \n
                \u56fe\u50cf\u98ce\u683c\u5316
                \u5728\u67d0\u4e9b\u827a\u672f\u521b\u4f5c\u4e2d\uff0c\u8c03\u6574\u56fe\u50cf\u7684\u4eae\u5ea6\u53ef\u4ee5\u7528\u4e8e\u6539\u53d8\u56fe\u50cf\u7684\u6574\u4f53\u98ce\u683c\u3002\u4f8b\u5982\uff0c\u901a\u8fc7\u589e\u52a0\u4eae\u5ea6\uff0c\u53ef\u4ee5\u4f7f\u56fe\u50cf\u663e\u5f97\u66f4\u52a0\u68a6\u5e7b\u548c\u67d4\u548c\u3002<\/p>\n<\/p>\n<\/li>\n
              3. \n
                \u6570\u636e\u9884\u5904\u7406
                \u5728\u673a\u5668\u5b66\u4e60<\/a>\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u9886\u57df\uff0c\u8c03\u6574\u56fe\u50cf\u4eae\u5ea6\u53ef\u4ee5\u7528\u4e8e\u6570\u636e\u9884\u5904\u7406\uff0c\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u8bad\u7ec3\u6548\u679c\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
                \u516d\u3001\u4eae\u5ea6\u8c03\u6574\u7684\u6ce8\u610f\u4e8b\u9879  <\/p>\n<\/p>\n
                \u5728\u8c03\u6574\u56fe\u50cf\u4eae\u5ea6\u65f6\uff0c\u6709\u4e00\u4e9b\u6ce8\u610f\u4e8b\u9879\u9700\u8981\u8003\u8651\uff0c\u4ee5\u786e\u4fdd\u56fe\u50cf\u8d28\u91cf\u548c\u6548\u679c\u3002<\/p>\n<\/p>\n
                  \n
                1. \n
                  \u907f\u514d\u8fc7\u5ea6\u8c03\u6574
                  \u8fc7\u5ea6\u589e\u52a0\u6216\u51cf\u5c11\u4eae\u5ea6\u53ef\u80fd\u4f1a\u5bfc\u81f4\u56fe\u50cf\u7ec6\u8282\u7684\u635f\u5931\u6216\u989c\u8272\u5931\u771f\u3002\u56e0\u6b64\uff0c\u5728\u8c03\u6574\u4eae\u5ea6\u65f6\uff0c\u9700\u8981\u6839\u636e\u5177\u4f53\u60c5\u51b5\u5408\u7406\u8bbe\u7f6e\u4eae\u5ea6\u56e0\u5b50\u3002<\/p>\n<\/p>\n<\/li>\n
                2. \n
                  \u8003\u8651\u56fe\u50cf\u7684\u52a8\u6001\u8303\u56f4
                  \u56fe\u50cf\u7684\u52a8\u6001\u8303\u56f4\u6307\u7684\u662f\u56fe\u50cf\u4e2d\u6700\u4eae\u548c\u6700\u6697\u90e8\u5206\u7684\u5dee\u5f02\u3002\u5728\u8c03\u6574\u4eae\u5ea6\u65f6\uff0c\u9700\u8981\u6ce8\u610f\u4fdd\u6301\u56fe\u50cf\u7684\u52a8\u6001\u8303\u56f4\uff0c\u4ee5\u907f\u514d\u8fc7\u66dd\u6216\u6b20\u66dd\u3002<\/p>\n<\/p>\n<\/li>\n
                3. \n
                  \u4f7f\u7528\u7ebf\u6027\u8c03\u6574\u65b9\u6cd5
                  \u5728\u8c03\u6574\u4eae\u5ea6\u65f6\uff0c\u5c3d\u91cf\u4f7f\u7528\u7ebf\u6027\u8c03\u6574\u65b9\u6cd5\uff0c\u5373\u5bf9\u6bcf\u4e2a\u50cf\u7d20\u8fdb\u884c\u76f8\u540c\u7684\u4eae\u5ea6\u8c03\u6574\u3002\u8fd9\u53ef\u4ee5\u786e\u4fdd\u56fe\u50cf\u7684\u6574\u4f53\u8272\u8c03\u548c\u98ce\u683c\u4fdd\u6301\u4e00\u81f4\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
                  \u4e03\u3001\u603b\u7ed3  <\/p>\n<\/p>\n
                  \u8c03\u6574\u7167\u7247\u4eae\u5ea6\u662f\u56fe\u50cf\u5904\u7406\u4e2d\u7684\u4e00\u4e2a\u57fa\u672c\u64cd\u4f5c\uff0c\u901a\u8fc7\u4f7f\u7528Python\u7684Pillow\u3001OpenCV\u3001NumPy\u7b49\u5e93\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u8fd9\u4e00\u529f\u80fd\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u9700\u8981\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u548c\u4eae\u5ea6\u56e0\u5b50\uff0c\u4ee5\u5b9e\u73b0\u6700\u4f73\u6548\u679c\u3002\u901a\u8fc7\u5408\u7406\u7684\u4eae\u5ea6\u8c03\u6574\uff0c\u53ef\u4ee5\u63d0\u9ad8\u56fe\u50cf\u7684\u89c6\u89c9\u6548\u679c\uff0c\u4f7f\u5176\u66f4\u52a0\u6e05\u6670\u3001\u660e\u4eae\u3002<\/p>\n<\/p>\n
                  \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                   \u5982\u4f55\u5728Python\u4e2d\u8bfb\u53d6\u548c\u5904\u7406\u7167\u7247\u7684\u4eae\u5ea6\uff1f<\/strong>
                  \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528PIL\uff08Pillow\uff09\u5e93\u6765\u8bfb\u53d6\u548c\u5904\u7406\u56fe\u50cf\u3002\u901a\u8fc7\u52a0\u8f7d\u56fe\u50cf\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528ImageEnhance<\/code>\u6a21\u5757\u4e2d\u7684Brightness<\/code>\u7c7b\u6765\u8c03\u6574\u4eae\u5ea6\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a  <\/p>\n
                  from PIL import Image\nfrom PIL import ImageEnhance\n\n# \u8bfb\u53d6\u56fe\u50cf\nimage = Image.open('your_image.jpg')\n\n# \u521b\u5efa\u4eae\u5ea6\u589e\u5f3a\u5bf9\u8c61\nenhancer = ImageEnhance.Brightness(image)\n\n# \u8c03\u6574\u4eae\u5ea6\uff0c1.0\u8868\u793a\u539f\u59cb\u4eae\u5ea6\uff0c0.0\u4e3a\u9ed1\u8272\uff0c2.0\u4e3a\u4e24\u500d\u4eae\u5ea6\nbrightened_image = enhancer.enhance(1.5)  # \u8c03\u6574\u4e3a1.5\u500d\u4eae\u5ea6\n\n# \u4fdd\u5b58\u8c03\u6574\u540e\u7684\u56fe\u50cf\nbrightened_image.save('brightened_image.jpg')\n<\/code><\/pre>\n
                  \u53ef\u4ee5\u4f7f\u7528\u54ea\u4e9b\u5e93\u6765\u8c03\u6574\u56fe\u50cf\u7684\u4eae\u5ea6\uff1f<\/strong>
                  Python\u4e2d\u6709\u591a\u4e2a\u5e93\u53ef\u4ee5\u7528\u6765\u8c03\u6574\u56fe\u50cf\u4eae\u5ea6\uff0c\u6700\u5e38\u7528\u7684\u5305\u62ecPillow\u3001OpenCV\u548cMatplotlib\u3002Pillow\u9002\u5408\u7b80\u5355\u7684\u56fe\u50cf\u5904\u7406\uff0cOpenCV\u5219\u63d0\u4f9b\u4e86\u66f4\u5f3a\u5927\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u529f\u80fd\uff0c\u800cMatplotlib\u9002\u5408\u4e8e\u6570\u636e\u53ef\u89c6\u5316\u548c\u56fe\u50cf\u5c55\u793a\u3002\u6839\u636e\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\uff0c\u53ef\u4ee5\u66f4\u6709\u6548\u5730\u5b8c\u6210\u56fe\u50cf\u5904\u7406\u4efb\u52a1\u3002<\/p>\n
                  \u8c03\u6574\u7167\u7247\u4eae\u5ea6\u65f6\u9700\u8981\u6ce8\u610f\u54ea\u4e9b\u4e8b\u9879\uff1f<\/strong>
                  \u5728\u8c03\u6574\u7167\u7247\u4eae\u5ea6\u65f6\uff0c\u9700\u8003\u8651\u5230\u56fe\u50cf\u7684\u6574\u4f53\u6548\u679c\u3002\u8fc7\u5ea6\u7684\u4eae\u5ea6\u8c03\u6574\u53ef\u80fd\u5bfc\u81f4\u56fe\u50cf\u7ec6\u8282\u4e22\u5931\uff0c\u7279\u522b\u662f\u5728\u9ad8\u5149\u533a\u57df\u3002\u5efa\u8bae\u5728\u8c03\u6574\u65f6\u9010\u6b65\u9884\u89c8\u6548\u679c\uff0c\u786e\u4fdd\u56fe\u50cf\u7684\u81ea\u7136\u611f\u3002\u540c\u65f6\uff0c\u4fdd\u5b58\u539f\u59cb\u56fe\u50cf\u7684\u5907\u4efd\uff0c\u4ee5\u4fbf\u9700\u8981\u65f6\u8fdb\u884c\u8fd8\u539f\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5f00\u5934\u6bb5\u843d\uff1a\u8c03\u6574\u7167\u7247\u4eae\u5ea6\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528Python\u5e93\u5982PIL\u3001OpenCV\u3001NumPy\u3001\u8c03\u6574\u4eae\u5ea6\u7684\u57fa\u672c\u539f\u7406\u662f\u6539\u53d8 […]","protected":false},"author":3,"featured_media":980647,"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\/980640"}],"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=980640"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/980640\/revisions"}],"predecessor-version":[{"id":980651,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/980640\/revisions\/980651"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/980647"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=980640"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=980640"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=980640"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}