{"id":936969,"date":"2024-12-26T19:40:51","date_gmt":"2024-12-26T11:40:51","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/936969.html"},"modified":"2024-12-26T19:40:54","modified_gmt":"2024-12-26T11:40:54","slug":"python-%e5%a6%82%e4%bd%95%e5%9d%87%e5%80%bc%e6%bb%a4%e6%b3%a2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/936969.html","title":{"rendered":"python \u5982\u4f55\u5747\u503c\u6ee4\u6ce2"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25073023\/1d7388c1-c24f-45fe-bdc2-7e541b18a10a.webp\" alt=\"python \u5982\u4f55\u5747\u503c\u6ee4\u6ce2\" \/><\/p>\n<p><p> \u5f00\u5934\u6bb5\u843d\uff1a<br \/><strong>\u5728Python\u4e2d\u5b9e\u73b0\u5747\u503c\u6ee4\u6ce2\uff0c\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528SciPy\u5e93\u4e2d\u7684ndimage\u6a21\u5757\u3001OpenCV\u5e93\u6216\u8005\u81ea\u5df1\u7f16\u5199\u7b80\u5355\u7684\u4ee3\u7801\u6765\u5b9e\u73b0<\/strong>\u3002\u5747\u503c\u6ee4\u6ce2\u662f\u4e00\u79cd\u7b80\u5355\u4e14\u5e38\u7528\u7684\u56fe\u50cf\u5e73\u6ed1\u6280\u672f\uff0c\u5b83\u901a\u8fc7\u5c06\u56fe\u50cf\u4e2d\u7684\u6bcf\u4e2a\u50cf\u7d20\u503c\u66ff\u6362\u4e3a\u5176\u90bb\u57df\u7684\u5e73\u5747\u503c\u6765\u8fbe\u5230\u53bb\u566a\u7684\u6548\u679c\u3002<strong>\u4f7f\u7528SciPy\u5e93\u7684ndimage\u6a21\u5757\u53ef\u4ee5\u5feb\u901f\u5b9e\u73b0\u5747\u503c\u6ee4\u6ce2<\/strong>\uff0c\u56e0\u4e3a\u5b83\u63d0\u4f9b\u4e86\u591a\u79cd\u56fe\u50cf\u5904\u7406\u51fd\u6570\uff0c\u5305\u62ec\u901a\u7528\u7684\u6ee4\u6ce2\u51fd\u6570\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<code>scipy.ndimage.uniform_filter<\/code>\u51fd\u6570\u6765\u5b9e\u73b0\u5747\u503c\u6ee4\u6ce2\u3002\u8fd9\u4e2a\u51fd\u6570\u5141\u8bb8\u6211\u4eec\u6307\u5b9a\u6ee4\u6ce2\u7684\u5927\u5c0f\uff0c\u4ece\u800c\u63a7\u5236\u5e73\u6ed1\u7684\u7a0b\u5ea6\u3002\u8fd9\u79cd\u65b9\u6cd5\u7b80\u5355\u6613\u7528\uff0c\u5e76\u4e14\u975e\u5e38\u9ad8\u6548\uff0c\u9002\u5408\u5904\u7406\u5927\u89c4\u6a21\u56fe\u50cf\u6570\u636e\u3002<\/p>\n<\/p>\n<p><p>\u6b63\u6587\uff1a<\/p>\n<\/p>\n<p><p>\u4e00\u3001\u5747\u503c\u6ee4\u6ce2\u7684\u57fa\u672c\u6982\u5ff5<br \/>\u5747\u503c\u6ee4\u6ce2\u662f\u4e00\u79cd\u7ebf\u6027\u6ee4\u6ce2\u5668\uff0c\u4e3b\u8981\u7528\u4e8e\u53bb\u9664\u56fe\u50cf\u4e2d\u7684\u9ad8\u9891\u566a\u58f0\u3002\u5176\u57fa\u672c\u601d\u60f3\u662f\u901a\u8fc7\u5c40\u90e8\u90bb\u57df\u7684\u5e73\u5747\u8fd0\u7b97\u6765\u5e73\u6ed1\u56fe\u50cf\uff0c\u51cf\u5c11\u566a\u58f0\u5bf9\u56fe\u50cf\u7684\u5f71\u54cd\u3002\u5747\u503c\u6ee4\u6ce2\u5668\u7684\u5b9e\u73b0\u901a\u5e38\u901a\u8fc7\u5bf9\u76ee\u6807\u50cf\u7d20\u90bb\u57df\u5185\u7684\u50cf\u7d20\u503c\u6c42\u5e73\u5747\uff0c\u8fdb\u800c\u66ff\u4ee3\u76ee\u6807\u50cf\u7d20\u7684\u503c\u3002\u5747\u503c\u6ee4\u6ce2\u7684\u4f18\u70b9\u5728\u4e8e\u5176\u7b80\u5355\u6613\u5b9e\u73b0\uff0c\u5e76\u4e14\u53ef\u4ee5\u6709\u6548\u5730\u53bb\u9664\u56fe\u50cf\u4e2d\u7684\u968f\u673a\u566a\u58f0\u3002\u7136\u800c\uff0c\u5747\u503c\u6ee4\u6ce2\u4e5f\u6709\u7f3a\u70b9\uff0c\u6bd4\u5982\u53ef\u80fd\u4f1a\u6a21\u7cca\u56fe\u50cf\u7684\u8fb9\u7f18\u7ec6\u8282\u3002<\/p>\n<\/p>\n<p><p>\u4e8c\u3001\u4f7f\u7528SciPy\u5b9e\u73b0\u5747\u503c\u6ee4\u6ce2<br \/>SciPy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u5176\u4e2d\u7684ndimage\u6a21\u5757\u63d0\u4f9b\u4e86\u591a\u79cd\u56fe\u50cf\u5904\u7406\u529f\u80fd\u3002\u8981\u4f7f\u7528SciPy\u5b9e\u73b0\u5747\u503c\u6ee4\u6ce2\uff0c\u6211\u4eec\u53ef\u4ee5\u5229\u7528<code>scipy.ndimage.uniform_filter<\/code>\u51fd\u6570\u3002\u8fd9\u4e2a\u51fd\u6570\u53ef\u4ee5\u76f4\u63a5\u5bf9numpy\u6570\u7ec4\u8fdb\u884c\u64cd\u4f5c\uff0c\u4ece\u800c\u5b9e\u73b0\u56fe\u50cf\u7684\u5747\u503c\u6ee4\u6ce2\u5904\u7406\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p>from scipy.ndimage import uniform_filter<\/p>\n<p>import matplotlib.pyplot as plt<\/p>\n<h2><strong>\u521b\u5efa\u4e00\u4e2a\u968f\u673a\u56fe\u50cf<\/strong><\/h2>\n<p>image = np.random.rand(100, 100)<\/p>\n<h2><strong>\u4f7f\u7528SciPy\u7684uniform_filter\u51fd\u6570\u8fdb\u884c\u5747\u503c\u6ee4\u6ce2<\/strong><\/h2>\n<p>filtered_image = uniform_filter(image, size=3)<\/p>\n<h2><strong>\u663e\u793a\u539f\u59cb\u56fe\u50cf\u548c\u6ee4\u6ce2\u540e\u7684\u56fe\u50cf<\/strong><\/h2>\n<p>plt.figure(figsize=(10, 5))<\/p>\n<p>plt.subplot(1, 2, 1)<\/p>\n<p>plt.title(&#39;Original Image&#39;)<\/p>\n<p>plt.imshow(image, cmap=&#39;gray&#39;)<\/p>\n<p>plt.subplot(1, 2, 2)<\/p>\n<p>plt.title(&#39;Filtered Image&#39;)<\/p>\n<p>plt.imshow(filtered_image, cmap=&#39;gray&#39;)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p><strong>\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u521b\u5efa\u4e00\u4e2a\u968f\u673a\u7684100&#215;100\u56fe\u50cf\uff0c\u7136\u540e\u4f7f\u7528<code>uniform_filter<\/code>\u51fd\u6570\u8fdb\u884c\u5747\u503c\u6ee4\u6ce2\u3002\u53c2\u6570<code>size<\/code>\u63a7\u5236\u6ee4\u6ce2\u5668\u7684\u5927\u5c0f\u3002<\/strong><\/p>\n<\/p>\n<p><p>\u4e09\u3001\u4f7f\u7528OpenCV\u5b9e\u73b0\u5747\u503c\u6ee4\u6ce2<br \/>OpenCV\u662f\u4e00\u4e2a\u5e7f\u6cdb\u4f7f\u7528\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\u3002\u4f7f\u7528OpenCV\u5b9e\u73b0\u5747\u503c\u6ee4\u6ce2\u975e\u5e38\u7b80\u5355\uff0c\u501f\u52a9\u5176\u5185\u7f6e\u7684<code>cv2.blur<\/code>\u51fd\u6570\u5373\u53ef\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import cv2<\/p>\n<p>import numpy as np<\/p>\n<p>import matplotlib.pyplot as plt<\/p>\n<h2><strong>\u521b\u5efa\u4e00\u4e2a\u968f\u673a\u56fe\u50cf<\/strong><\/h2>\n<p>image = np.random.rand(100, 100).astype(np.float32)<\/p>\n<h2><strong>\u4f7f\u7528OpenCV\u7684blur\u51fd\u6570\u8fdb\u884c\u5747\u503c\u6ee4\u6ce2<\/strong><\/h2>\n<p>filtered_image = cv2.blur(image, (5, 5))<\/p>\n<h2><strong>\u663e\u793a\u539f\u59cb\u56fe\u50cf\u548c\u6ee4\u6ce2\u540e\u7684\u56fe\u50cf<\/strong><\/h2>\n<p>plt.figure(figsize=(10, 5))<\/p>\n<p>plt.subplot(1, 2, 1)<\/p>\n<p>plt.title(&#39;Original Image&#39;)<\/p>\n<p>plt.imshow(image, cmap=&#39;gray&#39;)<\/p>\n<p>plt.subplot(1, 2, 2)<\/p>\n<p>plt.title(&#39;Filtered Image&#39;)<\/p>\n<p>plt.imshow(filtered_image, cmap=&#39;gray&#39;)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p><strong>\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c<code>cv2.blur<\/code>\u51fd\u6570\u5bf9\u8f93\u5165\u7684\u56fe\u50cf\u8fdb\u884c\u5747\u503c\u6ee4\u6ce2\uff0c\u7b2c\u4e8c\u4e2a\u53c2\u6570\u6307\u5b9a\u4e86\u6ee4\u6ce2\u5668\u7684\u5c3a\u5bf8\u3002\u4f7f\u7528OpenCV\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u4e0d\u540c\u7c7b\u578b\u7684\u56fe\u50cf\u6ee4\u6ce2\u64cd\u4f5c\u3002<\/strong><\/p>\n<\/p>\n<p><p>\u56db\u3001\u624b\u52a8\u5b9e\u73b0\u5747\u503c\u6ee4\u6ce2<br \/>\u9664\u4e86\u4f7f\u7528\u73b0\u6709\u7684\u5e93\uff0c\u6211\u4eec\u4e5f\u53ef\u4ee5\u624b\u52a8\u5b9e\u73b0\u5747\u503c\u6ee4\u6ce2\uff0c\u8fd9\u6709\u52a9\u4e8e\u7406\u89e3\u5176\u5de5\u4f5c\u539f\u7406\u3002\u624b\u52a8\u5b9e\u73b0\u9700\u8981\u904d\u5386\u56fe\u50cf\u7684\u6bcf\u4e2a\u50cf\u7d20\uff0c\u5e76\u8ba1\u7b97\u5176\u90bb\u57df\u7684\u5e73\u5747\u503c\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p>def mean_filter_manual(image, filter_size):<\/p>\n<p>    padded_image = np.pad(image, pad_width=filter_size\/\/2, mode=&#39;constant&#39;, constant_values=0)<\/p>\n<p>    filtered_image = np.zeros_like(image)<\/p>\n<p>    for i in range(image.shape[0]):<\/p>\n<p>        for j in range(image.shape[1]):<\/p>\n<p>            neighborhood = padded_image[i:i+filter_size, j:j+filter_size]<\/p>\n<p>            filtered_image[i, j] = np.mean(neighborhood)<\/p>\n<p>    return filtered_image<\/p>\n<h2><strong>\u521b\u5efa\u4e00\u4e2a\u968f\u673a\u56fe\u50cf<\/strong><\/h2>\n<p>image = np.random.rand(10, 10)<\/p>\n<h2><strong>\u4f7f\u7528\u624b\u52a8\u5b9e\u73b0\u7684\u5747\u503c\u6ee4\u6ce2<\/strong><\/h2>\n<p>filtered_image = mean_filter_manual(image, 3)<\/p>\n<p>print(&quot;Original Image:\\n&quot;, image)<\/p>\n<p>print(&quot;Filtered Image:\\n&quot;, filtered_image)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p><strong>\u5728\u8fd9\u4e2a\u624b\u52a8\u5b9e\u73b0\u7684\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5bf9\u56fe\u50cf\u8fdb\u884c\u586b\u5145\uff0c\u4ee5\u4fbf\u5904\u7406\u8fb9\u754c\u50cf\u7d20\u3002\u7136\u540e\u904d\u5386\u6bcf\u4e2a\u50cf\u7d20\uff0c\u8ba1\u7b97\u5176\u90bb\u57df\u7684\u5e73\u5747\u503c\u5e76\u66ff\u6362\u539f\u50cf\u7d20\u503c\u3002<\/strong>\u8fd9\u79cd\u65b9\u6cd5\u5c3d\u7ba1\u8f83\u4e3a\u7e41\u7410\uff0c\u4f46\u6709\u52a9\u4e8e\u7406\u89e3\u5747\u503c\u6ee4\u6ce2\u7684\u6838\u5fc3\u64cd\u4f5c\u3002<\/p>\n<\/p>\n<p><p>\u4e94\u3001\u5747\u503c\u6ee4\u6ce2\u7684\u5e94\u7528\u573a\u666f<br \/>\u5747\u503c\u6ee4\u6ce2\u5728\u56fe\u50cf\u5904\u7406\u9886\u57df\u6709\u7740\u5e7f\u6cdb\u7684\u5e94\u7528\uff0c\u7279\u522b\u662f\u5728\u9884\u5904\u7406\u9636\u6bb5\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5178\u578b\u7684\u5e94\u7528\u573a\u666f\uff1a<\/p>\n<\/p>\n<ol>\n<li>\n<p><strong>\u53bb\u566a\u5904\u7406<\/strong>\uff1a\u5747\u503c\u6ee4\u6ce2\u53ef\u4ee5\u6709\u6548\u53bb\u9664\u56fe\u50cf\u4e2d\u7684\u968f\u673a\u566a\u58f0\uff0c\u7279\u522b\u662f\u5728\u56fe\u50cf\u6570\u636e\u91c7\u96c6\u8fc7\u7a0b\u4e2d\u5e38\u89c1\u7684\u9ad8\u65af\u566a\u58f0\u3002\u901a\u8fc7\u53bb\u566a\uff0c\u53ef\u4ee5\u63d0\u9ad8\u540e\u7eed\u56fe\u50cf\u5904\u7406\u548c\u5206\u6790\u7684\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u56fe\u50cf\u5e73\u6ed1<\/strong>\uff1a\u5728\u4e00\u4e9b\u89c6\u89c9\u4efb\u52a1\u4e2d\uff0c\u4e3a\u4e86\u66f4\u597d\u5730\u63d0\u53d6\u76ee\u6807\u7279\u5f81\uff0c\u53ef\u80fd\u9700\u8981\u5bf9\u56fe\u50cf\u8fdb\u884c\u5e73\u6ed1\u5904\u7406\u3002\u5747\u503c\u6ee4\u6ce2\u901a\u8fc7\u6d88\u9664\u7ec6\u8282\u566a\u58f0\uff0c\u4f7f\u5f97\u56fe\u50cf\u66f4\u4e3a\u5e73\u6ed1\u3002<\/p>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u89c6\u9891\u53bb\u566a<\/strong>\uff1a\u5728\u89c6\u9891\u5904\u7406\u8fc7\u7a0b\u4e2d\uff0c\u5747\u503c\u6ee4\u6ce2\u53ef\u4ee5\u7528\u4e8e\u5e27\u95f4\u53bb\u566a\uff0c\u964d\u4f4e\u7531\u4e8e\u4f20\u8f93\u6216\u7f16\u7801\u5f15\u5165\u7684\u566a\u58f0\u5f71\u54cd\uff0c\u6539\u5584\u89c6\u9891\u8d28\u91cf\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n<p><p>\u516d\u3001\u5747\u503c\u6ee4\u6ce2\u7684\u5c40\u9650\u6027<br \/>\u867d\u7136\u5747\u503c\u6ee4\u6ce2\u7b80\u5355\u6613\u7528\uff0c\u4f46\u5728\u67d0\u4e9b\u5e94\u7528\u4e2d\u4e5f\u5b58\u5728\u4e00\u5b9a\u7684\u5c40\u9650\u6027\uff1a<\/p>\n<\/p>\n<ol>\n<li>\n<p><strong>\u8fb9\u7f18\u6a21\u7cca<\/strong>\uff1a\u7531\u4e8e\u5747\u503c\u6ee4\u6ce2\u5bf9\u90bb\u57df\u50cf\u7d20\u8fdb\u884c\u5e73\u5747\u8fd0\u7b97\uff0c\u56e0\u6b64\u5bb9\u6613\u5bfc\u81f4\u56fe\u50cf\u8fb9\u7f18\u7684\u6a21\u7cca\u3002\u8fd9\u5728\u4e00\u4e9b\u9700\u8981\u4fdd\u7559\u8fb9\u7f18\u7ec6\u8282\u7684\u5e94\u7528\u4e2d\u53ef\u80fd\u4e0d\u592a\u9002\u7528\u3002<\/p>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u5bf9\u5927\u566a\u58f0\u4e0d\u654f\u611f<\/strong>\uff1a\u5747\u503c\u6ee4\u6ce2\u5bf9\u5927\u5e45\u5ea6\u7684\u566a\u58f0\u53ef\u80fd\u6548\u679c\u4e0d\u4f73\uff0c\u56e0\u4e3a\u5927\u566a\u58f0\u53ef\u80fd\u4f1a\u663e\u8457\u5f71\u54cd\u90bb\u57df\u5e73\u5747\u503c\uff0c\u4ece\u800c\u4e0d\u80fd\u6709\u6548\u53bb\u9664\u566a\u58f0\u3002<\/p>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u975e\u81ea\u9002\u5e94\u6027<\/strong>\uff1a\u5747\u503c\u6ee4\u6ce2\u7684\u53c2\u6570\uff08\u5982\u7a97\u53e3\u5927\u5c0f\uff09\u9700\u8981\u624b\u52a8\u8bbe\u5b9a\uff0c\u4e0d\u80fd\u6839\u636e\u56fe\u50cf\u5185\u5bb9\u81ea\u9002\u5e94\u8c03\u6574\uff0c\u8fd9\u53ef\u80fd\u5bfc\u81f4\u5728\u4e0d\u540c\u56fe\u50cf\u573a\u666f\u4e0b\u6548\u679c\u4e0d\u4e00\u81f4\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n<p><p>\u4e03\u3001\u5747\u503c\u6ee4\u6ce2\u4e0e\u5176\u4ed6\u6ee4\u6ce2\u5668\u7684\u6bd4\u8f83<br \/>\u5728\u56fe\u50cf\u5904\u7406\u4e2d\uff0c\u9664\u4e86\u5747\u503c\u6ee4\u6ce2\uff0c\u8fd8\u6709\u8bb8\u591a\u5176\u4ed6\u7c7b\u578b\u7684\u6ee4\u6ce2\u5668\uff0c\u5982\u4e2d\u503c\u6ee4\u6ce2\u3001\u9ad8\u65af\u6ee4\u6ce2\u3001\u53cc\u8fb9\u6ee4\u6ce2\u7b49\u3002\u6bcf\u79cd\u6ee4\u6ce2\u5668\u90fd\u6709\u5176\u72ec\u7279\u7684\u7279\u6027\u548c\u9002\u7528\u573a\u666f\uff1a<\/p>\n<\/p>\n<ol>\n<li>\n<p><strong>\u4e2d\u503c\u6ee4\u6ce2<\/strong>\uff1a\u4e2d\u503c\u6ee4\u6ce2\u901a\u8fc7\u53d6\u90bb\u57df\u50cf\u7d20\u7684\u4e2d\u503c\u6765\u4ee3\u66ff\u76ee\u6807\u50cf\u7d20\u503c\uff0c\u80fd\u591f\u6709\u6548\u53bb\u9664\u6912\u76d0\u566a\u58f0\uff0c\u5e76\u4e14\u80fd\u8f83\u597d\u5730\u4fdd\u7559\u8fb9\u7f18\u4fe1\u606f\u3002<\/p>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u9ad8\u65af\u6ee4\u6ce2<\/strong>\uff1a\u9ad8\u65af\u6ee4\u6ce2\u5668\u662f\u4e00\u79cd\u57fa\u4e8e\u9ad8\u65af\u5206\u5e03\u7684\u5e73\u6ed1\u6ee4\u6ce2\u5668\uff0c\u53ef\u4ee5\u6709\u6548\u53bb\u9664\u9ad8\u65af\u566a\u58f0\uff0c\u5e76\u4e14\u901a\u8fc7\u8c03\u6574\u9ad8\u65af\u6838\u7684\u6807\u51c6\u5dee\u53c2\u6570\uff0c\u53ef\u4ee5\u63a7\u5236\u5e73\u6ed1\u7a0b\u5ea6\u3002<\/p>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u53cc\u8fb9\u6ee4\u6ce2<\/strong>\uff1a\u53cc\u8fb9\u6ee4\u6ce2\u4e0d\u4ec5\u8003\u8651\u7a7a\u95f4\u90bb\u57df\uff0c\u8fd8\u8003\u8651\u50cf\u7d20\u503c\u76f8\u4f3c\u6027\uff0c\u80fd\u591f\u5728\u53bb\u566a\u7684\u540c\u65f6\u4fdd\u7559\u66f4\u591a\u7684\u8fb9\u7f18\u7ec6\u8282\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n<p><p><strong>\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u6ee4\u6ce2\u5668\u5c24\u4e3a\u91cd\u8981\u3002\u5747\u503c\u6ee4\u6ce2\u7531\u4e8e\u5176\u7b80\u5355\u6027\u548c\u8ba1\u7b97\u6548\u7387\uff0c\u5e38\u88ab\u7528\u4e8e\u5b9e\u65f6\u6027\u8981\u6c42\u8f83\u9ad8\u7684\u5e94\u7528\u573a\u666f\uff0c\u4f46\u5728\u9700\u8981\u4fdd\u7559\u7ec6\u8282\u7684\u573a\u666f\u4e0b\uff0c\u5176\u4ed6\u6ee4\u6ce2\u5668\u53ef\u80fd\u66f4\u4e3a\u5408\u9002\u3002<\/strong><\/p>\n<\/p>\n<p><p>\u516b\u3001\u603b\u7ed3<br \/>\u5747\u503c\u6ee4\u6ce2\u662f\u4e00\u79cd\u57fa\u7840\u7684\u56fe\u50cf\u5904\u7406\u6280\u672f\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u5404\u79cd\u53bb\u566a\u548c\u5e73\u6ed1\u4efb\u52a1\u4e2d\u3002\u901a\u8fc7SciPy\u548cOpenCV\u7b49\u5e93\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u5728Python\u4e2d\u5b9e\u73b0\u5747\u503c\u6ee4\u6ce2\u3002\u5c3d\u7ba1\u5747\u503c\u6ee4\u6ce2\u6709\u5176\u5c40\u9650\u6027\uff0c\u4f46\u7ed3\u5408\u5177\u4f53\u5e94\u7528\u573a\u666f\u548c\u5176\u4ed6\u6ee4\u6ce2\u5668\uff0c\u5747\u503c\u6ee4\u6ce2\u4ecd\u7136\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u5de5\u5177\u3002\u5728\u56fe\u50cf\u5904\u7406\u9886\u57df\uff0c\u7406\u89e3\u548c\u638c\u63e1\u4e0d\u540c\u6ee4\u6ce2\u5668\u7684\u7279\u6027\uff0c\u5bf9\u4e8e\u5f00\u53d1\u9ad8\u6548\u7684\u56fe\u50cf\u5904\u7406\u7b97\u6cd5\u81f3\u5173\u91cd\u8981\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u4ec0\u4e48\u662f\u5747\u503c\u6ee4\u6ce2\uff0c\u4e3a\u4ec0\u4e48\u5728\u56fe\u50cf\u5904\u7406\u4e2d\u7684\u5e94\u7528\u5982\u6b64\u91cd\u8981\uff1f<\/strong><br \/>\u5747\u503c\u6ee4\u6ce2\u662f\u4e00\u79cd\u5e38\u7528\u7684\u56fe\u50cf\u5e73\u6ed1\u6280\u672f\uff0c\u901a\u8fc7\u8ba1\u7b97\u56fe\u50cf\u4e2d\u6bcf\u4e2a\u50cf\u7d20\u7684\u5468\u56f4\u50cf\u7d20\u7684\u5e73\u5747\u503c\u6765\u51cf\u5c11\u566a\u58f0\u3002\u8fd9\u79cd\u65b9\u6cd5\u5728\u56fe\u50cf\u5904\u7406\u9886\u57df\u975e\u5e38\u91cd\u8981\uff0c\u56e0\u4e3a\u5b83\u80fd\u591f\u6709\u6548\u53bb\u9664\u968f\u673a\u566a\u58f0\uff0c\u4f7f\u56fe\u50cf\u66f4\u52a0\u6e05\u6670\uff0c\u4fbf\u4e8e\u540e\u7eed\u7684\u5206\u6790\u548c\u5904\u7406\u3002\u5747\u503c\u6ee4\u6ce2\u5728\u56fe\u50cf\u589e\u5f3a\u3001\u8fb9\u7f18\u68c0\u6d4b\u7b49\u591a\u79cd\u5e94\u7528\u4e2d\u90fd\u53d1\u6325\u7740\u91cd\u8981\u4f5c\u7528\u3002<\/p>\n<p><strong>\u5728Python\u4e2d\u5982\u4f55\u5b9e\u73b0\u5747\u503c\u6ee4\u6ce2\uff1f<\/strong><br \/>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528OpenCV\u5e93\u6765\u5b9e\u73b0\u5747\u503c\u6ee4\u6ce2\u3002\u5177\u4f53\u6b65\u9aa4\u5305\u62ec\uff1a\u9996\u5148\uff0c\u5b89\u88c5OpenCV\u5e93\uff1b\u7136\u540e\uff0c\u8bfb\u53d6\u56fe\u50cf\u5e76\u8c03\u7528<code>cv2.blur()<\/code>\u6216<code>cv2.boxFilter()<\/code>\u65b9\u6cd5\u8fdb\u884c\u5747\u503c\u6ee4\u6ce2\u3002\u8fd9\u4e9b\u65b9\u6cd5\u90fd\u5141\u8bb8\u8bbe\u7f6e\u6ee4\u6ce2\u5668\u7684\u5927\u5c0f\uff0c\u4ece\u800c\u63a7\u5236\u5e73\u6ed1\u6548\u679c\u7684\u5f3a\u5ea6\u3002\u4ee3\u7801\u793a\u4f8b\u5982\u4e0b\uff1a<\/p>\n<pre><code class=\"language-python\">import cv2\n\n# \u8bfb\u53d6\u56fe\u50cf\nimage = cv2.imread(&#39;image.jpg&#39;)\n\n# \u5e94\u7528\u5747\u503c\u6ee4\u6ce2\nfiltered_image = cv2.blur(image, (5, 5))\n\n# \u663e\u793a\u7ed3\u679c\ncv2.imshow(&#39;Filtered Image&#39;, filtered_image)\ncv2.w<a href=\"https:\/\/docs.pingcode.com\/blog\/59162.html\" target=\"_blank\">AI<\/a>tKey(0)\ncv2.destroyAllWindows()\n<\/code><\/pre>\n<p><strong>\u5747\u503c\u6ee4\u6ce2\u4e0e\u5176\u4ed6\u6ee4\u6ce2\u65b9\u6cd5\u76f8\u6bd4\u6709\u4ec0\u4e48\u4f18\u7f3a\u70b9\uff1f<\/strong><br \/>\u5747\u503c\u6ee4\u6ce2\u7684\u4f18\u70b9\u5728\u4e8e\u5176\u7b80\u5355\u6613\u61c2\uff0c\u8ba1\u7b97\u91cf\u76f8\u5bf9\u8f83\u5c0f\uff0c\u9002\u5408\u5904\u7406\u5747\u5300\u566a\u58f0\u3002\u7136\u800c\uff0c\u5b83\u7684\u7f3a\u70b9\u4e5f\u5f88\u660e\u663e\uff0c\u5c24\u5176\u662f\u5728\u5904\u7406\u8fb9\u7f18\u65f6\uff0c\u5747\u503c\u6ee4\u6ce2\u53ef\u80fd\u4f1a\u6a21\u7cca\u8fb9\u7f18\uff0c\u4f7f\u5f97\u56fe\u50cf\u7ec6\u8282\u4e22\u5931\u3002\u4e0e\u4e2d\u503c\u6ee4\u6ce2\u7b49\u5176\u4ed6\u65b9\u6cd5\u76f8\u6bd4\uff0c\u5747\u503c\u6ee4\u6ce2\u5728\u53bb\u9664\u5c16\u9510\u566a\u58f0\u65b9\u9762\u7684\u6548\u679c\u8f83\u5dee\uff0c\u56e0\u6b64\u5728\u9009\u62e9\u6ee4\u6ce2\u65b9\u6cd5\u65f6\u9700\u6839\u636e\u5177\u4f53\u9700\u6c42\u8fdb\u884c\u5224\u65ad\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5f00\u5934\u6bb5\u843d\uff1a\u5728Python\u4e2d\u5b9e\u73b0\u5747\u503c\u6ee4\u6ce2\uff0c\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528SciPy\u5e93\u4e2d\u7684ndimage\u6a21\u5757\u3001OpenCV\u5e93\u6216\u8005\u81ea\u5df1 [&hellip;]","protected":false},"author":3,"featured_media":936974,"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\/936969"}],"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=936969"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/936969\/revisions"}],"predecessor-version":[{"id":936975,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/936969\/revisions\/936975"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/936974"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=936969"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=936969"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=936969"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}