{"id":1011898,"date":"2024-12-27T11:34:04","date_gmt":"2024-12-27T03:34:04","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1011898.html"},"modified":"2024-12-27T11:34:06","modified_gmt":"2024-12-27T03:34:06","slug":"python%e5%a6%82%e4%bd%95%e6%8a%8a%e5%bd%b1%e5%83%8f%e5%88%87%e5%9d%97","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1011898.html","title":{"rendered":"python\u5982\u4f55\u628a\u5f71\u50cf\u5207\u5757"},"content":{"rendered":"

\"python\u5982\u4f55\u628a\u5f71\u50cf\u5207\u5757\"<\/p>\n

Python\u53ef\u4ee5\u901a\u8fc7\u51e0\u79cd\u65b9\u6cd5\u5c06\u5f71\u50cf\u5207\u5757\uff0c\u5305\u62ec\u4f7f\u7528PIL\u5e93\u3001OpenCV\u5e93\u548cNumPy\u6570\u7ec4\u64cd\u4f5c\u3002\u8fd9\u4e9b\u65b9\u6cd5\u5141\u8bb8\u60a8\u6839\u636e\u9700\u8981\u5c06\u5f71\u50cf\u5206\u5272\u4e3a\u5c0f\u5757\uff0c\u4ee5\u4fbf\u8fdb\u884c\u8fdb\u4e00\u6b65\u7684\u5904\u7406\u3001\u5206\u6790\u6216\u5b58\u50a8\u3002\u4ee5\u4e0b\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u65b9\u6cd5\u4e2d\u7684\u4e00\u79cd\uff1aOpenCV\u5e93\uff0c\u901a\u8fc7\u52a0\u8f7d\u5f71\u50cf\u3001\u8ba1\u7b97\u5757\u5927\u5c0f\u5e76\u5faa\u73af\u5207\u5272\u5f71\u50cf\u5b9e\u73b0\u5207\u5757\u3002<\/strong><\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528PIL\u5e93\u5207\u5757<\/p>\n<\/p>\n

PIL\uff08Python Imaging Library\uff09\u662f\u4e00\u4e2a\u7528\u4e8e\u56fe\u50cf\u5904\u7406\u7684\u5e93\uff0c\u867d\u7136\u5b83\u7684\u66f4\u65b0\u5df2\u7ecf\u505c\u6b62\uff0c\u4f46\u5176\u7ee7\u4efb\u8005Pillow\u7ee7\u7eed\u7ef4\u62a4\u548c\u6269\u5c55\u5176\u529f\u80fd\u3002PIL\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u56fe\u50cf\u64cd\u4f5c\u529f\u80fd\uff0c\u5176\u4e2d\u5305\u62ec\u5c06\u5f71\u50cf\u5207\u5757\u7684\u80fd\u529b\u3002<\/p>\n<\/p>\n

    \n
  1. \u52a0\u8f7d\u5f71\u50cf<\/p>\n

    \u9996\u5148\uff0c\u60a8\u9700\u8981\u52a0\u8f7d\u5f71\u50cf\u3002\u53ef\u4ee5\u4f7f\u7528Image.open()<\/code>\u51fd\u6570\u8bfb\u53d6\u5f71\u50cf\u6587\u4ef6\u3002<\/li>\n<\/p>\n<\/ol>\n

    from PIL import Image<\/p>\n
    \u52a0\u8f7d\u5f71\u50cf<\/strong><\/h2>\n
    image = Image.open('path\/to\/your\/image.jpg')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u8ba1\u7b97\u5757\u5927\u5c0f<\/p>\n
      \u6839\u636e\u60a8\u7684\u9700\u6c42\u786e\u5b9a\u5207\u5757\u7684\u5927\u5c0f\u3002\u5047\u8bbe\u60a8\u60f3\u5c06\u5f71\u50cf\u5207\u6210100×100\u50cf\u7d20\u7684\u5757\u3002<\/li>\n<\/p>\n<\/ol>\n
      block_size = (100, 100)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
        \n
      1. \u5207\u5272\u5f71\u50cf<\/p>\n
        \u901a\u8fc7\u5faa\u73af\u904d\u5386\u5f71\u50cf\u7684\u5bbd\u5ea6\u548c\u9ad8\u5ea6\uff0c\u9010\u5757\u5207\u5272\u3002<\/li>\n<\/p>\n<\/ol>\n
        width, height = image.size<\/p>\n
        blocks = []<\/p>\n
        for i in range(0, width, block_size[0]):<\/p>\n
            for j in range(0, height, block_size[1]):<\/p>\n
                # \u5b9a\u4e49\u5207\u5272\u533a\u57df<\/p>\n
                box = (i, j, i + block_size[0], j + block_size[1])<\/p>\n
                # \u5207\u5272\u5e76\u6dfb\u52a0\u5230\u5217\u8868<\/p>\n
                block = image.crop(box)<\/p>\n
                blocks.append(block)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. \u5904\u7406\u5207\u5757<\/p>\n
          \u6b64\u65f6\uff0c\u60a8\u53ef\u4ee5\u5bf9\u5217\u8868blocks<\/code>\u4e2d\u7684\u6bcf\u4e2a\u5757\u8fdb\u884c\u8fdb\u4e00\u6b65\u5904\u7406\u6216\u5206\u6790\u3002<\/li>\n<\/p>\n<\/ol>\n
          \u4e8c\u3001\u4f7f\u7528OpenCV\u5e93\u5207\u5757<\/p>\n<\/p>\n
          OpenCV\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u652f\u6301\u591a\u79cd\u5f71\u50cf\u5904\u7406\u529f\u80fd\u3002\u5b83\u5728\u5904\u7406\u5927\u578b\u5f71\u50cf\u548c\u590d\u6742\u64cd\u4f5c\u65f6\u8868\u73b0\u51fa\u8272\u3002<\/p>\n<\/p>\n
            \n
          1. \u52a0\u8f7d\u5f71\u50cf<\/p>\n
            \u9996\u5148\uff0c\u4f7f\u7528OpenCV\u7684imread<\/code>\u51fd\u6570\u52a0\u8f7d\u5f71\u50cf\u3002<\/li>\n<\/p>\n<\/ol>\n
            import cv2<\/p>\n
            \u52a0\u8f7d\u5f71\u50cf<\/strong><\/h2>\n
            image = cv2.imread('path\/to\/your\/image.jpg')<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u8ba1\u7b97\u5757\u5927\u5c0f<\/p>\n
              \u540c\u6837\uff0c\u60a8\u9700\u8981\u786e\u5b9a\u5207\u5757\u7684\u5927\u5c0f\u3002<\/li>\n<\/p>\n<\/ol>\n
              block_size = (100, 100)<\/p>\n
              <\/code><\/pre>\n<\/p>\n
                \n
              1. \u5207\u5272\u5f71\u50cf<\/p>\n
                \u901a\u8fc7\u4e24\u4e2a\u5d4c\u5957\u7684\u5faa\u73af\uff0c\u904d\u5386\u5f71\u50cf\u7684\u5bbd\u5ea6\u548c\u9ad8\u5ea6\uff0c\u9010\u5757\u5207\u5272\u3002<\/li>\n<\/p>\n<\/ol>\n
                height, width, _ = image.shape<\/p>\n
                blocks = []<\/p>\n
                for i in range(0, width, block_size[0]):<\/p>\n
                    for j in range(0, height, block_size[1]):<\/p>\n
                        # \u5b9a\u4e49\u5207\u5272\u533a\u57df<\/p>\n
                        block = image[j:j + block_size[1], i:i + block_size[0]]<\/p>\n
                        blocks.append(block)<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                1. \u5904\u7406\u5207\u5757<\/p>\n
                  \u5bf9\u5217\u8868blocks<\/code>\u4e2d\u7684\u6bcf\u4e2a\u5757\u8fdb\u884c\u8fdb\u4e00\u6b65\u5904\u7406\u6216\u4fdd\u5b58\u3002<\/li>\n<\/p>\n<\/ol>\n
                  \u4e09\u3001\u4f7f\u7528NumPy\u6570\u7ec4\u64cd\u4f5c\u5207\u5757<\/p>\n<\/p>\n
                  NumPy\u662fPython\u4e2d\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u7684\u57fa\u7840\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u7ec4\u548c\u77e9\u9635\u64cd\u4f5c\u529f\u80fd\u3002<\/p>\n<\/p>\n
                    \n
                  1. \u52a0\u8f7d\u5f71\u50cf<\/p>\n
                    \u53ef\u4ee5\u4f7f\u7528OpenCV\u6216\u5176\u4ed6\u5e93\u5c06\u5f71\u50cf\u52a0\u8f7d\u4e3aNumPy\u6570\u7ec4\u3002<\/li>\n<\/p>\n<\/ol>\n
                    import cv2<\/p>\n
                    \u52a0\u8f7d\u5f71\u50cf\u5e76\u8f6c\u6362\u4e3aNumPy\u6570\u7ec4<\/strong><\/h2>\n
                    image = cv2.imread('path\/to\/your\/image.jpg')<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                      \n
                    1. \u8ba1\u7b97\u5757\u5927\u5c0f<\/p>\n
                      \u786e\u5b9a\u6bcf\u4e2a\u5757\u7684\u5927\u5c0f\u3002<\/li>\n<\/p>\n<\/ol>\n
                      block_size = (100, 100)<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                        \n
                      1. \u5207\u5272\u5f71\u50cf<\/p>\n
                        \u4f7f\u7528NumPy\u7684\u6570\u7ec4\u5207\u7247\u529f\u80fd\u8fdb\u884c\u5207\u5272\u3002<\/li>\n<\/p>\n<\/ol>\n
                        height, width, _ = image.shape<\/p>\n
                        blocks = []<\/p>\n
                        for i in range(0, width, block_size[0]):<\/p>\n
                            for j in range(0, height, block_size[1]):<\/p>\n
                                block = image[j:j + block_size[1], i:i + block_size[0]]<\/p>\n
                                blocks.append(block)<\/p>\n
                        <\/code><\/pre>\n<\/p>\n
                          \n
                        1. \u5904\u7406\u5207\u5757<\/p>\n
                          \u5bf9\u5207\u5272\u540e\u7684\u5757\u8fdb\u884c\u5904\u7406\u6216\u5b58\u50a8\u3002<\/li>\n<\/p>\n<\/ol>\n
                          \u56db\u3001\u5207\u5757\u540e\u7684\u5e94\u7528<\/p>\n<\/p>\n
                            \n
                          1. \n
                            \u6570\u636e\u589e\u5f3a<\/strong>\uff1a\u5728\u673a\u5668\u5b66\u4e60<\/a>\u4e2d\uff0c\u5c06\u5f71\u50cf\u5207\u5757\u53ef\u4ee5\u4f5c\u4e3a\u4e00\u79cd\u6570\u636e\u589e\u5f3a\u6280\u672f\uff0c\u4ee5\u589e\u52a0\u8bad\u7ec3\u6570\u636e\u7684\u591a\u6837\u6027\u3002<\/p>\n<\/p>\n<\/li>\n
                          2. \n
                            \u9ad8\u6548\u5b58\u50a8\u548c\u4f20\u8f93<\/strong>\uff1a\u5c06\u5927\u5f71\u50cf\u5207\u5757\u540e\uff0c\u53ef\u4ee5\u9009\u62e9\u6027\u5730\u5b58\u50a8\u6216\u4f20\u8f93\u91cd\u8981\u533a\u57df\uff0c\u51cf\u5c11\u5b58\u50a8\u7a7a\u95f4\u548c\u4f20\u8f93\u5e26\u5bbd\u3002<\/p>\n<\/p>\n<\/li>\n
                          3. \n
                            \u5c40\u90e8\u5904\u7406<\/strong>\uff1a\u5728\u4e00\u4e9b\u5e94\u7528\u4e2d\uff0c\u53ef\u80fd\u53ea\u9700\u8981\u5904\u7406\u5f71\u50cf\u7684\u5c40\u90e8\u533a\u57df\u3002\u5207\u5757\u53ef\u4ee5\u8ba9\u5904\u7406\u66f4\u52a0\u9ad8\u6548\u3002<\/p>\n<\/p>\n<\/li>\n
                          4. \n
                            \u5e76\u884c\u8ba1\u7b97<\/strong>\uff1a\u5728\u591a\u5904\u7406\u5668\u7cfb\u7edf\u4e2d\uff0c\u53ef\u4ee5\u5c06\u5f71\u50cf\u5207\u5757\u540e\u5206\u914d\u7ed9\u4e0d\u540c\u7684\u5904\u7406\u5668\u8fdb\u884c\u5e76\u884c\u5904\u7406\uff0c\u63d0\u9ad8\u5904\u7406\u901f\u5ea6\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
                            \u4e94\u3001\u6ce8\u610f\u4e8b\u9879<\/p>\n<\/p>\n
                              \n
                            1. \n
                              \u5757\u5927\u5c0f\u7684\u9009\u62e9<\/strong>\uff1a\u9009\u62e9\u5408\u9002\u7684\u5757\u5927\u5c0f\u53d6\u51b3\u4e8e\u5177\u4f53\u5e94\u7528\u3002\u592a\u5c0f\u7684\u5757\u53ef\u80fd\u5bfc\u81f4\u4fe1\u606f\u4e22\u5931\uff0c\u592a\u5927\u7684\u5757\u53ef\u80fd\u4f7f\u5904\u7406\u53d8\u5f97\u4e0d\u591f\u7075\u6d3b\u3002<\/p>\n<\/p>\n<\/li>\n
                            2. \n
                              \u8fb9\u754c\u5904\u7406<\/strong>\uff1a\u5728\u5207\u5272\u5f71\u50cf\u65f6\uff0c\u6ce8\u610f\u5904\u7406\u8fb9\u754c\u5757\u3002\u5982\u679c\u5f71\u50cf\u5c3a\u5bf8\u4e0d\u662f\u5757\u5927\u5c0f\u7684\u6574\u6570\u500d\uff0c\u53ef\u80fd\u4f1a\u51fa\u73b0\u8fb9\u754c\u5757\u4e0d\u5b8c\u6574\u7684\u95ee\u9898\u3002<\/p>\n<\/p>\n<\/li>\n
                            3. \n
                              \u683c\u5f0f\u517c\u5bb9\u6027<\/strong>\uff1a\u786e\u4fdd\u5f71\u50cf\u683c\u5f0f\u517c\u5bb9\u60a8\u4f7f\u7528\u7684\u5e93\u3002\u4e0d\u540c\u5e93\u5bf9\u5f71\u50cf\u683c\u5f0f\u7684\u652f\u6301\u53ef\u80fd\u6709\u6240\u4e0d\u540c\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
                              \u901a\u8fc7\u4ee5\u4e0a\u65b9\u6cd5\uff0cPython\u53ef\u4ee5\u9ad8\u6548\u5730\u5c06\u5f71\u50cf\u5207\u5757\uff0c\u4e3a\u8fdb\u4e00\u6b65\u7684\u56fe\u50cf\u5904\u7406\u548c\u5206\u6790\u63d0\u4f9b\u57fa\u7840\u3002\u65e0\u8bba\u662f\u4f7f\u7528PIL\u3001OpenCV\u8fd8\u662fNumPy\uff0c\u6bcf\u79cd\u65b9\u6cd5\u90fd\u6709\u5176\u72ec\u7279\u7684\u4f18\u52bf\uff0c\u9009\u62e9\u9002\u5408\u60a8\u9700\u6c42\u7684\u65b9\u6cd5\u662f\u5b9e\u73b0\u9ad8\u6548\u5f71\u50cf\u5207\u5757\u7684\u5173\u952e\u3002<\/p>\n<\/p>\n
                              \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                               \u5982\u4f55\u5728Python\u4e2d\u5b9e\u73b0\u5f71\u50cf\u5207\u5757\u7684\u529f\u80fd\uff1f<\/strong>
                              \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u5229\u7528\u56fe\u50cf\u5904\u7406\u5e93\u5982PIL\uff08Pillow\uff09\u6216\u8005OpenCV\u6765\u5b9e\u73b0\u5f71\u50cf\u5207\u5757\u3002\u9996\u5148\uff0c\u4f7f\u7528\u8fd9\u4e9b\u5e93\u52a0\u8f7d\u5f71\u50cf\uff0c\u7136\u540e\u6839\u636e\u6307\u5b9a\u7684\u5207\u5757\u5927\u5c0f\u8ba1\u7b97\u51fa\u5207\u5757\u7684\u6570\u91cf\uff0c\u901a\u8fc7\u5faa\u73af\u5c06\u5f71\u50cf\u5207\u5272\u6210\u5c0f\u5757\u5e76\u4fdd\u5b58\u6216\u5904\u7406\u3002\u5177\u4f53\u4ee3\u7801\u793a\u4f8b\u53ef\u4ee5\u5728\u76f8\u5173\u6587\u6863\u4e2d\u627e\u5230\uff0c\u786e\u4fdd\u5b89\u88c5\u5e93\u540e\u8fdb\u884c\u6d4b\u8bd5\u3002<\/p>\n
                              \u5207\u5757\u7684\u5c3a\u5bf8\u5982\u4f55\u9009\u62e9\uff1f<\/strong>
                              \u9009\u62e9\u5207\u5757\u5c3a\u5bf8\u65f6\uff0c\u9700\u8003\u8651\u5f71\u50cf\u7684\u5b9e\u9645\u5e94\u7528\u573a\u666f\u548c\u540e\u7eed\u5904\u7406\u9700\u6c42\u3002\u4f8b\u5982\uff0c\u5728\u8fdb\u884c\u673a\u5668\u5b66\u4e60\u65f6\uff0c\u5207\u5757\u5c3a\u5bf8\u5e94\u4e0e\u6a21\u578b\u8f93\u5165\u8981\u6c42\u4e00\u81f4\u3002\u5728\u5b9e\u9645\u64cd\u4f5c\u4e2d\uff0c\u53ef\u4ee5\u6839\u636e\u5185\u5b58\u9650\u5236\u548c\u5904\u7406\u6548\u7387\u6765\u8c03\u6574\u5207\u5757\u7684\u5927\u5c0f\uff0c\u786e\u4fdd\u5728\u4e0d\u635f\u5931\u91cd\u8981\u4fe1\u606f\u7684\u524d\u63d0\u4e0b\uff0c\u4f18\u5316\u5904\u7406\u901f\u5ea6\u548c\u5b58\u50a8\u6548\u7387\u3002<\/p>\n
                              \u5207\u5757\u540e\u5982\u4f55\u5904\u7406\u6bcf\u4e2a\u5c0f\u5757\uff1f<\/strong>
                              \u6bcf\u4e2a\u5c0f\u5757\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u8fdb\u884c\u4e0d\u540c\u7684\u5904\u7406\u3002\u5e38\u89c1\u7684\u64cd\u4f5c\u5305\u62ec\u56fe\u50cf\u589e\u5f3a\u3001\u7279\u5f81\u63d0\u53d6\u3001\u5206\u7c7b\u6216\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u8fdb\u884c\u9884\u6d4b\u3002\u5904\u7406\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u9009\u62e9\u5c06\u7ed3\u679c\u5408\u5e76\u56de\u539f\u5f71\u50cf\u6216\u5355\u72ec\u4fdd\u5b58\u6bcf\u4e2a\u5c0f\u5757\u3002\u5177\u4f53\u5904\u7406\u65b9\u5f0f\u53d6\u51b3\u4e8e\u9879\u76ee\u9700\u6c42\uff0c\u5efa\u8bae\u5728\u5b9e\u65bd\u524d\u8fdb\u884c\u8be6\u7ec6\u89c4\u5212\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u53ef\u4ee5\u901a\u8fc7\u51e0\u79cd\u65b9\u6cd5\u5c06\u5f71\u50cf\u5207\u5757\uff0c\u5305\u62ec\u4f7f\u7528PIL\u5e93\u3001OpenCV\u5e93\u548cNumPy\u6570\u7ec4\u64cd\u4f5c\u3002\u8fd9\u4e9b\u65b9\u6cd5\u5141\u8bb8\u60a8\u6839 […]","protected":false},"author":3,"featured_media":1011906,"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\/1011898"}],"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=1011898"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1011898\/revisions"}],"predecessor-version":[{"id":1011907,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1011898\/revisions\/1011907"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1011906"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1011898"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1011898"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1011898"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}