{"id":1081342,"date":"2025-01-08T12:38:46","date_gmt":"2025-01-08T04:38:46","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1081342.html"},"modified":"2025-01-08T12:38:48","modified_gmt":"2025-01-08T04:38:48","slug":"python%e5%a6%82%e4%bd%95%e5%88%a4%e6%96%ad%e4%ba%8c%e5%80%bc%e5%9b%be%e5%83%8f%e5%85%a8%e9%bb%91-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1081342.html","title":{"rendered":"python\u5982\u4f55\u5224\u65ad\u4e8c\u503c\u56fe\u50cf\u5168\u9ed1"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/24183416\/c8ae6034-539a-4282-a3ee-65758053f963.webp\" alt=\"python\u5982\u4f55\u5224\u65ad\u4e8c\u503c\u56fe\u50cf\u5168\u9ed1\" \/><\/p>\n<p><p> <strong>Python\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u5224\u65ad\u4e8c\u503c\u56fe\u50cf\u662f\u5426\u5168\u9ed1\uff1a\u904d\u5386\u56fe\u50cf\u4e2d\u7684\u6bcf\u4e2a\u50cf\u7d20\u70b9\u3001\u4f7f\u7528NumPy\u5e93\u7684\u6570\u7ec4\u8fd0\u7b97\u3001\u4f7f\u7528OpenCV\u5e93\u7684\u51fd\u6570\u3002<\/strong>\u5176\u4e2d\uff0c\u4f7f\u7528NumPy\u5e93\u7684\u6570\u7ec4\u8fd0\u7b97\u662f\u6700\u4e3a\u9ad8\u6548\u548c\u5e38\u7528\u7684\u65b9\u6cd5\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528NumPy\u5e93\u6765\u5224\u65ad\u4e8c\u503c\u56fe\u50cf\u662f\u5426\u5168\u9ed1\u3002<\/p>\n<\/p>\n<p><p>\u4e00\u3001\u4f7f\u7528NumPy\u5e93\u7684\u6570\u7ec4\u8fd0\u7b97<\/p>\n<\/p>\n<p><p>NumPy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684Python\u5e93\uff0c\u4e13\u95e8\u7528\u4e8e\u5904\u7406\u6570\u7ec4\u548c\u77e9\u9635\u8fd0\u7b97\u3002\u6211\u4eec\u53ef\u4ee5\u5c06\u56fe\u50cf\u8bfb\u53d6\u4e3a\u4e00\u4e2aNumPy\u6570\u7ec4\uff0c\u7136\u540e\u5229\u7528NumPy\u7684\u6570\u7ec4\u8fd0\u7b97\u6765\u5224\u65ad\u56fe\u50cf\u662f\u5426\u5168\u9ed1\u3002\u4ee5\u4e0b\u662f\u5177\u4f53\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n<ol>\n<li><strong>\u8bfb\u53d6\u56fe\u50cf\u5e76\u8f6c\u6362\u4e3aNumPy\u6570\u7ec4<\/strong>\uff1a\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u4f7f\u7528\u56fe\u50cf\u5904\u7406\u5e93\uff08\u5982PIL\u6216OpenCV\uff09\u8bfb\u53d6\u56fe\u50cf\uff0c\u5e76\u5c06\u5176\u8f6c\u6362\u4e3aNumPy\u6570\u7ec4\u3002<\/li>\n<li><strong>\u5224\u65ad\u56fe\u50cf\u662f\u5426\u5168\u9ed1<\/strong>\uff1a\u4f7f\u7528NumPy\u7684\u6570\u7ec4\u8fd0\u7b97\u6765\u5224\u65ad\u56fe\u50cf\u4e2d\u662f\u5426\u5b58\u5728\u975e\u96f6\u50cf\u7d20\u3002\u5982\u679c\u56fe\u50cf\u4e2d\u6240\u6709\u50cf\u7d20\u503c\u5747\u4e3a0\uff0c\u5219\u56fe\u50cf\u5168\u9ed1\u3002<\/li>\n<\/ol>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p>from PIL import Image<\/p>\n<h2><strong>\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n<p>image = Image.open(&#39;path_to_your_binary_image.png&#39;)<\/p>\n<p>image_array = np.array(image)<\/p>\n<h2><strong>\u5224\u65ad\u56fe\u50cf\u662f\u5426\u5168\u9ed1<\/strong><\/h2>\n<p>if np.all(image_array == 0):<\/p>\n<p>    print(&quot;\u56fe\u50cf\u662f\u5168\u9ed1\u7684&quot;)<\/p>\n<p>else:<\/p>\n<p>    print(&quot;\u56fe\u50cf\u4e0d\u662f\u5168\u9ed1\u7684&quot;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4e8c\u3001\u4f7f\u7528OpenCV\u5e93\u7684\u51fd\u6570<\/p>\n<\/p>\n<p><p>OpenCV\u662f\u4e00\u4e2a\u5e7f\u6cdb\u4f7f\u7528\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u5177\u6709\u8bb8\u591a\u56fe\u50cf\u5904\u7406\u529f\u80fd\u3002\u6211\u4eec\u53ef\u4ee5\u5229\u7528OpenCV\u5e93\u4e2d\u7684\u51fd\u6570\u6765\u5224\u65ad\u4e8c\u503c\u56fe\u50cf\u662f\u5426\u5168\u9ed1\u3002<\/p>\n<\/p>\n<ol>\n<li><strong>\u8bfb\u53d6\u56fe\u50cf<\/strong>\uff1a\u4f7f\u7528OpenCV\u7684<code>imread<\/code>\u51fd\u6570\u8bfb\u53d6\u56fe\u50cf\u3002<\/li>\n<li><strong>\u5224\u65ad\u56fe\u50cf\u662f\u5426\u5168\u9ed1<\/strong>\uff1a\u4f7f\u7528OpenCV\u7684<code>countNonZero<\/code>\u51fd\u6570\u7edf\u8ba1\u56fe\u50cf\u4e2d\u975e\u96f6\u50cf\u7d20\u7684\u6570\u91cf\u3002\u5982\u679c\u975e\u96f6\u50cf\u7d20\u6570\u91cf\u4e3a0\uff0c\u5219\u56fe\u50cf\u5168\u9ed1\u3002<\/li>\n<\/ol>\n<p><pre><code class=\"language-python\">import cv2<\/p>\n<h2><strong>\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n<p>image = cv2.imread(&#39;path_to_your_binary_image.png&#39;, cv2.IMREAD_GRAYSCALE)<\/p>\n<h2><strong>\u5224\u65ad\u56fe\u50cf\u662f\u5426\u5168\u9ed1<\/strong><\/h2>\n<p>if cv2.countNonZero(image) == 0:<\/p>\n<p>    print(&quot;\u56fe\u50cf\u662f\u5168\u9ed1\u7684&quot;)<\/p>\n<p>else:<\/p>\n<p>    print(&quot;\u56fe\u50cf\u4e0d\u662f\u5168\u9ed1\u7684&quot;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4e09\u3001\u904d\u5386\u56fe\u50cf\u4e2d\u7684\u6bcf\u4e2a\u50cf\u7d20\u70b9<\/p>\n<\/p>\n<p><p>\u904d\u5386\u56fe\u50cf\u4e2d\u7684\u6bcf\u4e2a\u50cf\u7d20\u70b9\u662f\u4e00\u79cd\u8f83\u4e3a\u57fa\u7840\u7684\u65b9\u6cd5\uff0c\u4f46\u5728\u5904\u7406\u5927\u56fe\u50cf\u65f6\u6548\u7387\u8f83\u4f4e\u3002\u6211\u4eec\u53ef\u4ee5\u9010\u4e2a\u50cf\u7d20\u5730\u5224\u65ad\u5176\u503c\u662f\u5426\u4e3a0\u3002<\/p>\n<\/p>\n<ol>\n<li><strong>\u8bfb\u53d6\u56fe\u50cf<\/strong>\uff1a\u4f7f\u7528\u56fe\u50cf\u5904\u7406\u5e93\u8bfb\u53d6\u56fe\u50cf\u3002<\/li>\n<li><strong>\u904d\u5386\u50cf\u7d20\u70b9<\/strong>\uff1a\u904d\u5386\u56fe\u50cf\u4e2d\u7684\u6bcf\u4e2a\u50cf\u7d20\u70b9\uff0c\u5224\u65ad\u5176\u503c\u662f\u5426\u4e3a0\u3002<\/li>\n<\/ol>\n<p><pre><code class=\"language-python\">from PIL import Image<\/p>\n<h2><strong>\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n<p>image = Image.open(&#39;path_to_your_binary_image.png&#39;)<\/p>\n<p>image_array = np.array(image)<\/p>\n<h2><strong>\u904d\u5386\u50cf\u7d20\u70b9<\/strong><\/h2>\n<p>is_all_black = True<\/p>\n<p>for row in image_array:<\/p>\n<p>    for pixel in row:<\/p>\n<p>        if pixel != 0:<\/p>\n<p>            is_all_black = False<\/p>\n<p>            break<\/p>\n<p>    if not is_all_black:<\/p>\n<p>        break<\/p>\n<p>if is_all_black:<\/p>\n<p>    print(&quot;\u56fe\u50cf\u662f\u5168\u9ed1\u7684&quot;)<\/p>\n<p>else:<\/p>\n<p>    print(&quot;\u56fe\u50cf\u4e0d\u662f\u5168\u9ed1\u7684&quot;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4ee5\u4e0a\u4e09\u79cd\u65b9\u6cd5\u90fd\u53ef\u4ee5\u7528\u6765\u5224\u65ad\u4e8c\u503c\u56fe\u50cf\u662f\u5426\u5168\u9ed1\u3002<strong>\u4f7f\u7528NumPy\u5e93\u7684\u6570\u7ec4\u8fd0\u7b97\u548cOpenCV\u5e93\u7684\u51fd\u6570\u662f\u66f4\u4e3a\u9ad8\u6548\u548c\u5e38\u7528\u7684\u65b9\u6cd5<\/strong>\uff0c\u5c24\u5176\u662f\u5728\u5904\u7406\u5927\u56fe\u50cf\u65f6\u3002\u901a\u8fc7\u4ee5\u4e0a\u4ecb\u7ecd\uff0c\u5e0c\u671b\u80fd\u5e2e\u52a9\u60a8\u66f4\u597d\u5730\u7406\u89e3\u548c\u5b9e\u73b0\u8fd9\u4e2a\u4efb\u52a1\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8fdb\u4e00\u6b65\u63a2\u8ba8\u56fe\u50cf\u5904\u7406\u4e2d\u7684\u4e00\u4e9b\u76f8\u5173\u6280\u672f\u548c\u65b9\u6cd5\u3002<\/p>\n<\/p>\n<p><p>\u56db\u3001\u56fe\u50cf\u9884\u5904\u7406\u7684\u91cd\u8981\u6027<\/p>\n<\/p>\n<p><p>\u5728\u8fdb\u884c\u56fe\u50cf\u5904\u7406\u4e4b\u524d\uff0c\u9884\u5904\u7406\u6b65\u9aa4\u662f\u5fc5\u4e0d\u53ef\u5c11\u7684\u3002\u56fe\u50cf\u9884\u5904\u7406\u53ef\u4ee5\u63d0\u9ad8\u56fe\u50cf\u7684\u8d28\u91cf\uff0c\u4f7f\u540e\u7eed\u7684\u5904\u7406\u548c\u5206\u6790\u66f4\u52a0\u51c6\u786e\u3002\u5e38\u89c1\u7684\u56fe\u50cf\u9884\u5904\u7406\u6b65\u9aa4\u5305\u62ec\u7070\u5ea6\u5316\u3001\u4e8c\u503c\u5316\u3001\u566a\u58f0\u53bb\u9664\u7b49\u3002<\/p>\n<\/p>\n<ol>\n<li><strong>\u7070\u5ea6\u5316<\/strong>\uff1a\u5c06\u5f69\u8272\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\uff0c\u4f7f\u5176\u53ea\u6709\u4e00\u4e2a\u901a\u9053\u3002\u8fd9\u53ef\u4ee5\u7b80\u5316\u540e\u7eed\u7684\u5904\u7406\u8fc7\u7a0b\u3002<\/li>\n<li><strong>\u4e8c\u503c\u5316<\/strong>\uff1a\u5c06\u7070\u5ea6\u56fe\u50cf\u8f6c\u6362\u4e3a\u4e8c\u503c\u56fe\u50cf\uff0c\u4f7f\u50cf\u7d20\u503c\u53ea\u67090\u548c255\u3002\u8fd9\u53ef\u4ee5\u7a81\u51fa\u56fe\u50cf\u4e2d\u7684\u76ee\u6807\u5bf9\u8c61\u3002<\/li>\n<li><strong>\u566a\u58f0\u53bb\u9664<\/strong>\uff1a\u4f7f\u7528\u6ee4\u6ce2\u5668\u53bb\u9664\u56fe\u50cf\u4e2d\u7684\u566a\u58f0\uff0c\u63d0\u9ad8\u56fe\u50cf\u7684\u8d28\u91cf\u3002<\/li>\n<\/ol>\n<p><pre><code class=\"language-python\">import cv2<\/p>\n<h2><strong>\u8bfb\u53d6\u5f69\u8272\u56fe\u50cf<\/strong><\/h2>\n<p>image = cv2.imread(&#39;path_to_your_image.png&#39;)<\/p>\n<h2><strong>\u7070\u5ea6\u5316<\/strong><\/h2>\n<p>gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)<\/p>\n<h2><strong>\u4e8c\u503c\u5316<\/strong><\/h2>\n<p>_, binary_image = cv2.threshold(gray_image, 127, 255, cv2.THRESH_BINARY)<\/p>\n<h2><strong>\u566a\u58f0\u53bb\u9664<\/strong><\/h2>\n<p>denoised_image = cv2.medianBlur(binary_image, 5)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4e94\u3001\u56fe\u50cf\u5904\u7406\u4e2d\u7684\u5176\u4ed6\u6280\u672f<\/p>\n<\/p>\n<p><p>\u9664\u4e86\u5224\u65ad\u4e8c\u503c\u56fe\u50cf\u662f\u5426\u5168\u9ed1\u4e4b\u5916\uff0c\u56fe\u50cf\u5904\u7406\u8fd8\u6d89\u53ca\u8bb8\u591a\u5176\u4ed6\u6280\u672f\uff0c\u5982\u8fb9\u7f18\u68c0\u6d4b\u3001\u8f6e\u5ed3\u63d0\u53d6\u3001\u5f62\u6001\u5b66\u64cd\u4f5c\u7b49\u3002\u8fd9\u4e9b\u6280\u672f\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u4ece\u56fe\u50cf\u4e2d\u63d0\u53d6\u6709\u7528\u7684\u4fe1\u606f\uff0c\u5e76\u8fdb\u884c\u8fdb\u4e00\u6b65\u7684\u5206\u6790\u548c\u5904\u7406\u3002<\/p>\n<\/p>\n<ol>\n<li><strong>\u8fb9\u7f18\u68c0\u6d4b<\/strong>\uff1a\u4f7f\u7528\u8fb9\u7f18\u68c0\u6d4b\u7b97\u6cd5\uff08\u5982Canny\u7b97\u6cd5\uff09\u68c0\u6d4b\u56fe\u50cf\u4e2d\u7684\u8fb9\u7f18\u3002\u8fd9\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u8bc6\u522b\u56fe\u50cf\u4e2d\u7684\u5bf9\u8c61\u8f6e\u5ed3\u3002<\/li>\n<li><strong>\u8f6e\u5ed3\u63d0\u53d6<\/strong>\uff1a\u4f7f\u7528\u8f6e\u5ed3\u63d0\u53d6\u7b97\u6cd5\uff08\u5982findContours\u51fd\u6570\uff09\u63d0\u53d6\u56fe\u50cf\u4e2d\u7684\u8f6e\u5ed3\u3002\u8fd9\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u5206\u6790\u56fe\u50cf\u4e2d\u7684\u5bf9\u8c61\u5f62\u72b6\u3002<\/li>\n<li><strong>\u5f62\u6001\u5b66\u64cd\u4f5c<\/strong>\uff1a\u4f7f\u7528\u5f62\u6001\u5b66\u64cd\u4f5c\uff08\u5982\u81a8\u80c0\u3001\u8150\u8680\u3001\u5f00\u8fd0\u7b97\u3001\u95ed\u8fd0\u7b97\uff09\u5bf9\u56fe\u50cf\u8fdb\u884c\u5904\u7406\u3002\u8fd9\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u53bb\u9664\u566a\u58f0\u3001\u586b\u5145\u7a7a\u6d1e\u7b49\u3002<\/li>\n<\/ol>\n<p><pre><code class=\"language-python\">import cv2<\/p>\n<h2><strong>\u8bfb\u53d6\u4e8c\u503c\u56fe\u50cf<\/strong><\/h2>\n<p>binary_image = cv2.imread(&#39;path_to_your_binary_image.png&#39;, cv2.IMREAD_GRAYSCALE)<\/p>\n<h2><strong>\u8fb9\u7f18\u68c0\u6d4b<\/strong><\/h2>\n<p>edges = cv2.Canny(binary_image, 100, 200)<\/p>\n<h2><strong>\u8f6e\u5ed3\u63d0\u53d6<\/strong><\/h2>\n<p>contours, _ = cv2.findContours(binary_image, cv2.RETR_TREE, cv2.CH<a href=\"https:\/\/docs.pingcode.com\/blog\/59162.html\" target=\"_blank\">AI<\/a>N_APPROX_SIMPLE)<\/p>\n<h2><strong>\u5f62\u6001\u5b66\u64cd\u4f5c<\/strong><\/h2>\n<p>kernel = np.ones((5,5), np.uint8)<\/p>\n<p>dilated_image = cv2.dilate(binary_image, kernel, iterations=1)<\/p>\n<p>eroded_image = cv2.erode(binary_image, kernel, iterations=1)<\/p>\n<p>opened_image = cv2.morphologyEx(binary_image, cv2.MORPH_OPEN, kernel)<\/p>\n<p>closed_image = cv2.morphologyEx(binary_image, cv2.MORPH_CLOSE, kernel)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u516d\u3001\u56fe\u50cf\u5904\u7406\u4e2d\u7684\u5b9e\u8df5\u5e94\u7528<\/p>\n<\/p>\n<p><p>\u56fe\u50cf\u5904\u7406\u6280\u672f\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u6709\u7740\u5e7f\u6cdb\u7684\u5e94\u7528\uff0c\u5982\u533b\u5b66\u56fe\u50cf\u5206\u6790\u3001\u81ea\u52a8\u9a7e\u9a76\u3001\u5b89\u9632\u76d1\u63a7\u7b49\u3002\u901a\u8fc7\u5408\u7406\u8fd0\u7528\u56fe\u50cf\u5904\u7406\u6280\u672f\uff0c\u6211\u4eec\u53ef\u4ee5\u4ece\u56fe\u50cf\u4e2d\u63d0\u53d6\u6709\u7528\u7684\u4fe1\u606f\uff0c\u5e76\u8fdb\u884c\u8fdb\u4e00\u6b65\u7684\u5206\u6790\u548c\u51b3\u7b56\u3002<\/p>\n<\/p>\n<ol>\n<li><strong>\u533b\u5b66\u56fe\u50cf\u5206\u6790<\/strong>\uff1a\u901a\u8fc7\u56fe\u50cf\u5904\u7406\u6280\u672f\uff0c\u53ef\u4ee5\u5bf9\u533b\u5b66\u56fe\u50cf\u8fdb\u884c\u5206\u6790\uff0c\u5e2e\u52a9\u533b\u751f\u8fdb\u884c\u8bca\u65ad\u548c\u6cbb\u7597\u3002\u4f8b\u5982\uff0c\u901a\u8fc7\u8fb9\u7f18\u68c0\u6d4b\u548c\u8f6e\u5ed3\u63d0\u53d6\uff0c\u53ef\u4ee5\u8bc6\u522b\u548c\u5206\u5272\u51fa\u80bf\u7624\u533a\u57df\u3002<\/li>\n<li><strong>\u81ea\u52a8\u9a7e\u9a76<\/strong>\uff1a\u56fe\u50cf\u5904\u7406\u6280\u672f\u5728\u81ea\u52a8\u9a7e\u9a76\u4e2d\u8d77\u7740\u91cd\u8981\u4f5c\u7528\u3002\u4f8b\u5982\uff0c\u901a\u8fc7\u8fb9\u7f18\u68c0\u6d4b\u548c\u5f62\u6001\u5b66\u64cd\u4f5c\uff0c\u53ef\u4ee5\u8bc6\u522b\u9053\u8def\u6807\u7ebf\u548c\u4ea4\u901a\u6807\u5fd7\u3002<\/li>\n<li><strong>\u5b89\u9632\u76d1\u63a7<\/strong>\uff1a\u901a\u8fc7\u56fe\u50cf\u5904\u7406\u6280\u672f\uff0c\u53ef\u4ee5\u5bf9\u76d1\u63a7\u89c6\u9891\u8fdb\u884c\u5206\u6790\uff0c\u68c0\u6d4b\u5f02\u5e38\u884c\u4e3a\u548c\u4e8b\u4ef6\u3002\u4f8b\u5982\uff0c\u901a\u8fc7\u8f6e\u5ed3\u63d0\u53d6\u548c\u8fd0\u52a8\u68c0\u6d4b\uff0c\u53ef\u4ee5\u8bc6\u522b\u548c\u8ddf\u8e2a\u76ee\u6807\u5bf9\u8c61\u3002<\/li>\n<\/ol>\n<p><pre><code class=\"language-python\">import cv2<\/p>\n<h2><strong>\u533b\u5b66\u56fe\u50cf\u5206\u6790\u793a\u4f8b<\/strong><\/h2>\n<h2><strong>\u8bfb\u53d6\u533b\u5b66\u56fe\u50cf<\/strong><\/h2>\n<p>medical_image = cv2.imread(&#39;path_to_your_medical_image.png&#39;, cv2.IMREAD_GRAYSCALE)<\/p>\n<h2><strong>\u8fb9\u7f18\u68c0\u6d4b<\/strong><\/h2>\n<p>edges = cv2.Canny(medical_image, 100, 200)<\/p>\n<h2><strong>\u8f6e\u5ed3\u63d0\u53d6<\/strong><\/h2>\n<p>contours, _ = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)<\/p>\n<h2><strong>\u81ea\u52a8\u9a7e\u9a76\u793a\u4f8b<\/strong><\/h2>\n<h2><strong>\u8bfb\u53d6\u9a7e\u9a76\u56fe\u50cf<\/strong><\/h2>\n<p>driving_image = cv2.imread(&#39;path_to_your_driving_image.png&#39;)<\/p>\n<h2><strong>\u7070\u5ea6\u5316<\/strong><\/h2>\n<p>gray_image = cv2.cvtColor(driving_image, cv2.COLOR_BGR2GRAY)<\/p>\n<h2><strong>\u8fb9\u7f18\u68c0\u6d4b<\/strong><\/h2>\n<p>edges = cv2.Canny(gray_image, 100, 200)<\/p>\n<h2><strong>\u5f62\u6001\u5b66\u64cd\u4f5c<\/strong><\/h2>\n<p>kernel = np.ones((5,5), np.uint8)<\/p>\n<p>opened_image = cv2.morphologyEx(edges, cv2.MORPH_OPEN, kernel)<\/p>\n<h2><strong>\u5b89\u9632\u76d1\u63a7\u793a\u4f8b<\/strong><\/h2>\n<h2><strong>\u8bfb\u53d6\u76d1\u63a7\u89c6\u9891<\/strong><\/h2>\n<p>cap = cv2.VideoCapture(&#39;path_to_your_video.mp4&#39;)<\/p>\n<p>while cap.isOpened():<\/p>\n<p>    ret, frame = cap.read()<\/p>\n<p>    if not ret:<\/p>\n<p>        break<\/p>\n<p>    # \u7070\u5ea6\u5316<\/p>\n<p>    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)<\/p>\n<p>    # \u8fd0\u52a8\u68c0\u6d4b<\/p>\n<p>    motion_mask = cv2.absdiff(gray_frame, cv2.GaussianBlur(gray_frame, (21, 21), 0))<\/p>\n<p>    # \u8f6e\u5ed3\u63d0\u53d6<\/p>\n<p>    contours, _ = cv2.findContours(motion_mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)<\/p>\n<p>    # \u7ed8\u5236\u8f6e\u5ed3<\/p>\n<p>    for contour in contours:<\/p>\n<p>        if cv2.contourArea(contour) &lt; 500:<\/p>\n<p>            continue<\/p>\n<p>        (x, y, w, h) = cv2.boundingRect(contour)<\/p>\n<p>        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)<\/p>\n<p>    # \u663e\u793a\u7ed3\u679c<\/p>\n<p>    cv2.imshow(&#39;Frame&#39;, frame)<\/p>\n<p>    if cv2.waitKey(1) &amp; 0xFF == ord(&#39;q&#39;):<\/p>\n<p>        break<\/p>\n<p>cap.release()<\/p>\n<p>cv2.destroyAllWindows()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u901a\u8fc7\u4ee5\u4e0a\u8be6\u7ec6\u7684\u4ecb\u7ecd\u548c\u793a\u4f8b\u4ee3\u7801\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0c<strong>\u56fe\u50cf\u5904\u7406\u6280\u672f\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u5177\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\u573a\u666f\u548c\u91cd\u8981\u6027<\/strong>\u3002\u5e0c\u671b\u672c\u6587\u80fd\u591f\u5e2e\u52a9\u60a8\u66f4\u597d\u5730\u7406\u89e3\u548c\u638c\u63e1\u8fd9\u4e9b\u6280\u672f\uff0c\u5e76\u5728\u5b9e\u9645\u9879\u76ee\u4e2d\u52a0\u4ee5\u5e94\u7528\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u5728Python\u4e2d\u8bfb\u53d6\u4e8c\u503c\u56fe\u50cf\u4ee5\u5224\u65ad\u5176\u662f\u5426\u5168\u9ed1\uff1f<\/strong><br \/>\u8981\u5224\u65ad\u4e8c\u503c\u56fe\u50cf\u662f\u5426\u5168\u9ed1\uff0c\u9996\u5148\u9700\u8981\u4f7f\u7528Python\u4e2d\u7684\u56fe\u50cf\u5904\u7406\u5e93\uff0c\u5982OpenCV\u6216PIL\uff08Pillow\uff09\u6765\u8bfb\u53d6\u56fe\u50cf\u3002\u8bfb\u53d6\u56fe\u50cf\u540e\uff0c\u53ef\u4ee5\u68c0\u67e5\u6240\u6709\u50cf\u7d20\u503c\uff0c\u5982\u679c\u6240\u6709\u503c\u5747\u4e3a0\uff0c\u5219\u8be5\u56fe\u50cf\u4e3a\u5168\u9ed1\u3002\u4f8b\u5982\uff0c\u5728OpenCV\u4e2d\u53ef\u4ee5\u4f7f\u7528<code>cv2.imread()<\/code>\u8bfb\u53d6\u56fe\u50cf\uff0c\u5e76\u901a\u8fc7<code>numpy<\/code>\u7684<code>np.all()<\/code>\u51fd\u6570\u5224\u65ad\u50cf\u7d20\u503c\u3002<\/p>\n<p><strong>\u662f\u5426\u6709\u5176\u4ed6\u65b9\u6cd5\u53ef\u4ee5\u5224\u65ad\u4e8c\u503c\u56fe\u50cf\u662f\u5426\u5168\u9ed1\uff1f<\/strong><br \/>\u9664\u4e86\u4f7f\u7528\u56fe\u50cf\u5904\u7406\u5e93\u5916\uff0c\u4e5f\u53ef\u4ee5\u901a\u8fc7\u8ba1\u7b97\u56fe\u50cf\u7684\u603b\u50cf\u7d20\u503c\u3002\u53ef\u4ee5\u5148\u5c06\u56fe\u50cf\u8f6c\u4e3a\u4e00\u7ef4\u6570\u7ec4\uff0c\u7136\u540e\u8ba1\u7b97\u6570\u7ec4\u7684\u603b\u548c\uff0c\u5982\u679c\u603b\u548c\u4e3a0\uff0c\u5219\u8bf4\u660e\u56fe\u50cf\u5168\u9ed1\u3002\u8fd9\u79cd\u65b9\u6cd5\u5728\u5904\u7406\u5927\u578b\u56fe\u50cf\u65f6\u53ef\u80fd\u4f1a\u6bd4\u8f83\u9ad8\u6548\u3002<\/p>\n<p><strong>\u5728\u5904\u7406\u4e8c\u503c\u56fe\u50cf\u65f6\uff0c\u6709\u54ea\u4e9b\u5e38\u89c1\u7684\u95ee\u9898\u9700\u8981\u6ce8\u610f\uff1f<\/strong><br \/>\u5728\u5904\u7406\u4e8c\u503c\u56fe\u50cf\u65f6\uff0c\u5e38\u89c1\u95ee\u9898\u5305\u62ec\u56fe\u50cf\u683c\u5f0f\u4e0d\u6b63\u786e\u3001\u56fe\u50cf\u8bfb\u53d6\u5931\u8d25\u4ee5\u53ca\u56fe\u50cf\u4e2d\u5305\u542b\u610f\u5916\u7684\u7070\u5ea6\u503c\u3002\u786e\u4fdd\u56fe\u50cf\u4e3a\u6b63\u786e\u7684\u4e8c\u503c\u683c\u5f0f\uff08\u9ed1\u767d\uff09\uff0c\u5e76\u5728\u8bfb\u53d6\u65f6\u4f7f\u7528\u6b63\u786e\u7684\u53c2\u6570\uff0c\u907f\u514d\u51fa\u73b0\u9519\u8bef\u3002\u6b64\u5916\uff0c\u4f7f\u7528\u56fe\u50cf\u9884\u5904\u7406\u6b65\u9aa4\uff0c\u6bd4\u5982\u4e8c\u503c\u5316\u5904\u7406\uff0c\u53ef\u4ee5\u786e\u4fdd\u56fe\u50cf\u7b26\u5408\u9884\u671f\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u5224\u65ad\u4e8c\u503c\u56fe\u50cf\u662f\u5426\u5168\u9ed1\uff1a\u904d\u5386\u56fe\u50cf\u4e2d\u7684\u6bcf\u4e2a\u50cf\u7d20\u70b9\u3001\u4f7f\u7528NumPy\u5e93\u7684\u6570\u7ec4\u8fd0\u7b97\u3001\u4f7f\u7528Op [&hellip;]","protected":false},"author":3,"featured_media":1081352,"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\/1081342"}],"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=1081342"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1081342\/revisions"}],"predecessor-version":[{"id":1081354,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1081342\/revisions\/1081354"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1081352"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1081342"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1081342"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1081342"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}