{"id":850,"date":"2025-05-07T19:51:45","date_gmt":"2025-05-07T11:51:45","guid":{"rendered":"https:\/\/www.ddosidc.com\/wp\/?p=850"},"modified":"2025-05-07T19:51:45","modified_gmt":"2025-05-07T11:51:45","slug":"%e5%bd%a2%e7%8a%b6%e8%af%86%e5%88%ab%e6%8a%80%e6%9c%af%e7%9a%84%e6%97%a0%e9%99%90%e5%8f%af%e8%83%bd%e6%80%a7%ef%bc%81","status":"publish","type":"post","link":"https:\/\/www.ddosidc.com\/wp\/850.html","title":{"rendered":"\u5f62\u72b6\u8bc6\u522b\u6280\u672f\u7684\u65e0\u9650\u53ef\u80fd\u6027\uff01"},"content":{"rendered":"<p><h2>\u5f62\u72b6\u8bc6\u522b\u6280\u672f\u6982\u8ff0<\/h2>\n<\/p>\n<p style=\"text-align:center\"><img decoding=\"async\" src=\"https:\/\/www.ddosidc.com\/wp\/wp-content\/uploads\/2025\/05\/G3j179GAu4.jpg\" alt=\"\u5f62\u72b6\u8bc6\u522b\u6280\u672f\u7684\u65e0\u9650\u53ef\u80fd\u6027\uff01\" title=\"\u5f62\u72b6\u8bc6\u522b\u6280\u672f\u7684\u65e0\u9650\u53ef\u80fd\u6027\uff01\"><\/p>\n<p><p>\u5f62\u72b6\u8bc6\u522b\u662f\u4e00\u79cd\u8ba1\u7b97\u673a\u89c6\u89c9\u6280\u672f\uff0c\u65e8\u5728\u4ece\u56fe\u50cf\u4e2d\u8bc6\u522b\u548c\u5206\u7c7b\u5404\u79cd\u5f62\u72b6\u3002\u8be5\u6280\u672f\u5728\u8bb8\u591a\u9886\u57df\u4e2d\u90fd\u6709\u5e94\u7528\uff0c\u5982\u673a\u5668\u4eba\u89c6\u89c9\u3001\u81ea\u52a8\u9a7e\u9a76\u3001\u56fe\u50cf\u5904\u7406\u7b49\u3002\u672c\u6587\u5c06\u6df1\u5165\u63a2\u8ba8\u5f62\u72b6\u8bc6\u522b\u7684\u57fa\u672c\u64cd\u4f5c\u6b65\u9aa4\u3001\u6240\u9700\u547d\u4ee4\u793a\u4f8b\u4ee5\u53ca\u5b9e\u7528\u6280\u5de7\uff0c\u5e2e\u52a9\u8bfb\u8005\u66f4\u597d\u5730\u7406\u89e3\u548c\u5e94\u7528\u8fd9\u4e00\u6280\u672f\u3002<\/p>\n<\/p>\n<p><h2>\u5f62\u72b6\u8bc6\u522b\u7684\u57fa\u672c\u6b65\u9aa4<\/h2>\n<\/p>\n<p><h3>\u6b65\u9aa4\u4e00\uff1a\u56fe\u50cf\u9884\u5904\u7406<\/h3>\n<\/p>\n<p><p>\u4e3a\u4e86\u63d0\u9ad8\u5f62\u72b6\u8bc6\u522b\u7684\u51c6\u786e\u6027\uff0c\u9996\u5148\u9700\u8981\u5bf9\u8f93\u5165\u56fe\u50cf\u8fdb\u884c\u9884\u5904\u7406\u3002\u8fd9\u5305\u62ec\u8f6c\u7070\u5ea6\u56fe\u50cf\u3001\u53bb\u566a\u548c\u4e8c\u503c\u5316\u7b49\u6b65\u9aa4\u3002<\/p>\n<\/p>\n<ul>\n<li><strong>\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\uff1a<\/strong>\u5c06\u5f69\u8272\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\uff0c\u51cf\u5c11\u8ba1\u7b97\u91cf\u3002<\/li>\n<li><strong>\u53bb\u566a\uff1a<\/strong>\u4f7f\u7528\u9ad8\u65af\u6ee4\u6ce2\u6216\u4e2d\u503c\u6ee4\u6ce2\u53bb\u9664\u56fe\u50cf\u4e2d\u7684\u566a\u58f0\u3002<\/li>\n<li><strong>\u4e8c\u503c\u5316\uff1a<\/strong>\u4f7f\u7528\u9608\u503c\u5904\u7406\u5c06\u7070\u5ea6\u56fe\u50cf\u8f6c\u6362\u4e3a\u4e8c\u503c\u56fe\u50cf\uff0c\u4ee5\u4fbf\u4e8e\u540e\u7eed\u7684\u5f62\u72b6\u68c0\u6d4b\u3002<\/li>\n<\/ul>\n<p><h3>\u547d\u4ee4\u793a\u4f8b<\/h3>\n<\/p>\n<p><p>\u4ee5\u4e0b\u662f\u4f7f\u7528Python\u7684OpenCV\u5e93\u8fdb\u884c\u56fe\u50cf\u9884\u5904\u7406\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n<p><pre><code>import cv2<\/p>\r\n\r\n<p># \u8bfb\u53d6\u56fe\u50cf<\/p>\r\n<p>image = cv2.imread('input.jpg')<\/p>\r\n\r\n<p># \u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/p>\r\n<p>gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)<\/p>\r\n\r\n<p># \u4f7f\u7528\u9ad8\u65af\u6ee4\u6ce2\u53bb\u566a<\/p>\r\n<p>blurred_image = cv2.GaussianBlur(gray_image, (5, 5), 0)<\/p>\r\n\r\n<p># \u4e8c\u503c\u5316\u5904\u7406<\/p>\r\n<p>_, binary_image = cv2.threshold(blurred_image, 127, 255, cv2.THRESH_BINARY)<\/p>\r\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u6b65\u9aa4\u4e8c\uff1a\u5f62\u72b6\u68c0\u6d4b<\/h2>\n<\/p>\n<p><p>\u5f62\u72b6\u68c0\u6d4b\u662f\u8bc6\u522b\u8fc7\u7a0b\u4e2d\u6700\u5173\u952e\u7684\u4e00\u6b65\uff0c\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\u8f6e\u5ed3\u68c0\u6d4b\u548cHough\u53d8\u6362\u3002<\/p>\n<\/p>\n<ul>\n<li><strong>\u8f6e\u5ed3\u68c0\u6d4b\uff1a<\/strong>\u5229\u7528OpenCV\u7684findContours\u51fd\u6570\u68c0\u6d4b\u56fe\u50cf\u4e2d\u7684\u8f6e\u5ed3\u3002<\/li>\n<li><strong>Hough\u53d8\u6362\uff1a<\/strong>\u5bf9\u4e8e\u7279\u5b9a\u5f62\u72b6\uff08\u5982\u5706\u5f62\u548c\u76f4\u7ebf\uff09\uff0c\u53ef\u4ee5\u4f7f\u7528Hough\u53d8\u6362\u8fdb\u884c\u68c0\u6d4b\u3002<\/li>\n<\/ul>\n<p><h3>\u547d\u4ee4\u793a\u4f8b<\/h3>\n<\/p>\n<p><p>\u4ee5\u4e0b\u662f\u8f6e\u5ed3\u68c0\u6d4b\u7684\u4ee3\u7801\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code># \u8f6e\u5ed3\u68c0\u6d4b<\/p>\r\n<p>contours, _ = cv2.findContours(binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)<\/p>\r\n\r\n<p># \u7ed8\u5236\u68c0\u6d4b\u5230\u7684\u8f6e\u5ed3<\/p>\r\n<p>output_image = cv2.drawContours(image.copy(), contours, -1, (0, 255, 0), 2)<\/p>\r\n<p>cv2.imshow('Contours', output_image)<\/p>\r\n<p>cv2.waitKey(0)<\/p>\r\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u6b65\u9aa4\u4e09\uff1a\u5f62\u72b6\u7279\u5f81\u63d0\u53d6<\/h2>\n<\/p>\n<p><p>\u63d0\u53d6\u5f62\u72b6\u7684\u7279\u5f81\u662f\u4e3a\u4e86\u66f4\u597d\u5730\u8fdb\u884c\u5206\u7c7b\u548c\u8bc6\u522b\u3002\u5e38\u89c1\u7684\u7279\u5f81\u5305\u62ec\u5468\u957f\u3001\u9762\u79ef\u3001\u5f62\u72b6\u77e9\u548cHu\u77e9\u7b49\u3002<\/p>\n<\/p>\n<ul>\n<li><strong>\u5468\u957f\uff1a<\/strong>\u4f7f\u7528cv2.arcLength\u51fd\u6570\u8ba1\u7b97\u5f62\u72b6\u7684\u5468\u957f\u3002<\/li>\n<li><strong>\u9762\u79ef\uff1a<\/strong>\u4f7f\u7528cv2.contourArea\u51fd\u6570\u8ba1\u7b97\u5f62\u72b6\u7684\u9762\u79ef\u3002<\/li>\n<li><strong>\u5f62\u72b6\u77e9\uff1a<\/strong>\u4f7f\u7528cv2.moments\u51fd\u6570\u8ba1\u7b97\u5f62\u72b6\u7684\u77e9\uff0c\u5e76\u53ef\u8fdb\u4e00\u6b65\u8ba1\u7b97Hu\u77e9\u3002<\/li>\n<\/ul>\n<p><h3>\u547d\u4ee4\u793a\u4f8b<\/h3>\n<\/p>\n<p><p>\u4ee5\u4e0b\u662f\u63d0\u53d6\u5f62\u72b6\u7279\u5f81\u7684\u4ee3\u7801\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code># \u63d0\u53d6\u7279\u5f81<\/p>\r\n<p>for contour in contours:<\/p>\r\n<p>    area = cv2.contourArea(contour)<\/p>\r\n<p>    perimeter = cv2.arcLength(contour, True)<\/p>\r\n<p>    moments = cv2.moments(contour)<\/p>\r\n<p>    hu_moments = cv2.HuMoments(moments)<\/p>\r\n<p>    print(f'Area: {area}, Perimeter: {perimeter}, Hu Moments: {hu_moments.flatten()}')<\/p>\r\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u6b65\u9aa4\u56db\uff1a\u5f62\u72b6\u5206\u7c7b<\/h2>\n<\/p>\n<p><p>\u5f62\u72b6\u5206\u7c7b\u662f\u6839\u636e\u63d0\u53d6\u7684\u7279\u5f81\u5c06\u5f62\u72b6\u5f52\u7c7b\u3002\u53ef\u4ee5\u4f7f\u7528\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff08\u5982K\u8fd1\u90bb\u3001\u652f\u6301\u5411\u91cf\u673a\u7b49\uff09\u8fdb\u884c\u5206\u7c7b\u3002<\/p>\n<\/p>\n<ul>\n<li><strong>\u6570\u636e\u96c6\u51c6\u5907\uff1a<\/strong>\u51c6\u5907\u5305\u542b\u6807\u8bb0\u7684\u5f62\u72b6\u6570\u636e\u96c6\uff0c\u7528\u4e8e\u8bad\u7ec3\u5206\u7c7b\u5668\u3002<\/li>\n<li><strong>\u6a21\u578b\u8bad\u7ec3\uff1a<\/strong>\u9009\u62e9\u5408\u9002\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff0c\u5e76\u4f7f\u7528\u63d0\u53d6\u7684\u7279\u5f81\u8fdb\u884c\u8bad\u7ec3\u3002<\/li>\n<li><strong>\u6a21\u578b\u8bc4\u4f30\uff1a<\/strong>\u4f7f\u7528\u6d4b\u8bd5\u96c6\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\uff0c\u5e76\u6839\u636e\u9700\u8981\u8c03\u6574\u6a21\u578b\u53c2\u6570\u3002<\/li>\n<\/ul>\n<p><h3>\u547d\u4ee4\u793a\u4f8b<\/h3>\n<\/p>\n<p><p>\u4ee5\u4e0b\u662f\u4f7f\u7528K\u8fd1\u90bb\u7b97\u6cd5\u8fdb\u884c\u5f62\u72b6\u5206\u7c7b\u7684\u4ee3\u7801\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code>from sklearn.neighbors import KNeighborsClassifier<\/p>\r\n<p>import numpy as np<\/p>\r\n\r\n<p># \u5047\u8bbefeatures\u662f\u63d0\u53d6\u7684\u7279\u5f81\uff0clabels\u662f\u5bf9\u5e94\u7684\u5f62\u72b6\u7c7b\u578b<\/p>\r\n<p>features = np.array([...])  # \u7279\u5f81\u6570\u7ec4<\/p>\r\n<p>labels = np.array([...])  # \u6807\u7b7e\u6570\u7ec4<\/p>\r\n\r\n<p># \u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/p>\r\n<p>from sklearn.model_selection import train_test_split<\/p>\r\n<p>X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2)<\/p>\r\n\r\n<p># \u521b\u5efaK\u8fd1\u90bb\u5206\u7c7b\u5668\u5e76\u8bad\u7ec3<\/p>\r\n<p>knn = KNeighborsClassifier(n_neighbors=3)<\/p>\r\n<p>knn.fit(X_train, y_train)<\/p>\r\n\r\n<p># \u8fdb\u884c\u9884\u6d4b<\/p>\r\n<p>predictions = knn.predict(X_test)<\/p>\r\n<p><\/code><\/pre>\n<\/p>\n<p><h2>\u6ce8\u610f\u4e8b\u9879\u4e0e\u5b9e\u7528\u6280\u5de7<\/h2>\n<\/p>\n<ul>\n<li><strong>\u56fe\u50cf\u8d28\u91cf\uff1a<\/strong>\u9ad8\u8d28\u91cf\u7684\u8f93\u5165\u56fe\u50cf\u4f1a\u663e\u8457\u63d0\u9ad8\u8bc6\u522b\u7684\u51c6\u786e\u6027\uff0c\u56e0\u6b64\u5728\u91c7\u96c6\u6570\u636e\u65f6\u5e94\u5c3d\u91cf\u4fdd\u8bc1\u56fe\u50cf\u6e05\u6670\u3002<\/li>\n<li><strong>\u53c2\u6570\u8c03\u6574\uff1a<\/strong>\u8bb8\u591a\u7b97\u6cd5\u7684\u6548\u679c\u4f9d\u8d56\u4e8e\u53c2\u6570\u7684\u8bbe\u7f6e\uff0c\u4f8b\u5982\u9ad8\u65af\u6ee4\u6ce2\u7684\u6838\u5927\u5c0f\u3001\u9608\u503c\u5904\u7406\u7684\u9608\u503c\u7b49\uff0c\u9700\u6839\u636e\u5177\u4f53\u60c5\u51b5\u8fdb\u884c\u8c03\u6574\u3002<\/li>\n<li><strong>\u6570\u636e\u589e\u5f3a\uff1a<\/strong>\u5982\u679c\u6570\u636e\u96c6\u8f83\u5c0f\uff0c\u53ef\u4ee5\u901a\u8fc7\u65cb\u8f6c\u3001\u7f29\u653e\u7b49\u65b9\u5f0f\u8fdb\u884c\u6570\u636e\u589e\u5f3a\uff0c\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u3002<\/li>\n<li><strong>\u6a21\u578b\u9009\u62e9\uff1a<\/strong>\u5728\u591a\u4e2a\u6a21\u578b\u4e2d\u8fdb\u884c\u6bd4\u8f83\uff0c\u6311\u9009\u51fa\u5bf9\u7279\u5b9a\u4efb\u52a1\u6548\u679c\u6700\u597d\u7684\u6a21\u578b\u3002<\/li>\n<\/ul>\n<p><h2>\u5b9e\u4f8b\u5e94\u7528<\/h2>\n<\/p>\n<p><p>\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u5f62\u72b6\u8bc6\u522b\u6280\u672f\u88ab\u5e7f\u6cdb\u7528\u4e8e\u5de5\u4e1a\u81ea\u52a8\u5316\u3001\u4ea4\u901a\u76d1\u63a7\u3001\u4eba\u673a\u4ea4\u4e92\u7b49\u9886\u57df\u3002\u901a\u8fc7\u4e0a\u6587\u7684\u6b65\u9aa4\uff0c\u8bfb\u8005\u53ef\u4ee5\u57fa\u4e8ePython\u548cOpenCV\u5b9e\u73b0\u7b80\u5355\u7684\u5f62\u72b6\u8bc6\u522b\u5e94\u7528\uff0c\u8fdb\u884c\u56fe\u50cf\u5206\u7c7b\u53ca\u76ee\u6807\u8bc6\u522b\u3002<\/p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u5f62\u72b6\u8bc6\u522b\u6280\u672f\u6982\u8ff0 \u5f62\u72b6\u8bc6\u522b\u662f\u4e00\u79cd\u8ba1\u7b97\u673a\u89c6\u89c9\u6280\u672f\uff0c\u65e8\u5728\u4ece\u56fe\u50cf\u4e2d\u8bc6\u522b\u548c\u5206\u7c7b\u5404\u79cd\u5f62\u72b6\u3002\u8be5\u6280\u672f\u5728\u8bb8\u591a\u9886\u57df\u4e2d\u90fd\u6709\u5e94\u7528\uff0c\u5982\u673a\u5668\u4eba\u89c6\u89c9\u3001\u81ea\u52a8\u9a7e\u9a76\u3001\u56fe\u50cf\u5904\u7406\u7b49\u3002\u672c\u6587\u5c06\u6df1\u5165\u63a2\u8ba8\u5f62\u72b6\u8bc6\u522b\u7684\u57fa\u672c\u64cd\u4f5c\u6b65\u9aa4\u3001\u6240\u9700\u547d\u4ee4\u793a\u4f8b\u4ee5\u53ca\u5b9e\u7528\u6280\u5de7\uff0c\u5e2e\u52a9\u8bfb\u8005\u66f4\u597d\u5730\u7406\u89e3\u548c\u5e94\u7528\u8fd9\u4e00\u6280\u672f&#8230;<\/p>\n","protected":false},"author":1,"featured_media":851,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"topic":[],"class_list":["post-850","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-baike"],"_links":{"self":[{"href":"https:\/\/www.ddosidc.com\/wp\/wp-json\/wp\/v2\/posts\/850","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.ddosidc.com\/wp\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.ddosidc.com\/wp\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.ddosidc.com\/wp\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ddosidc.com\/wp\/wp-json\/wp\/v2\/comments?post=850"}],"version-history":[{"count":1,"href":"https:\/\/www.ddosidc.com\/wp\/wp-json\/wp\/v2\/posts\/850\/revisions"}],"predecessor-version":[{"id":853,"href":"https:\/\/www.ddosidc.com\/wp\/wp-json\/wp\/v2\/posts\/850\/revisions\/853"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.ddosidc.com\/wp\/wp-json\/wp\/v2\/media\/851"}],"wp:attachment":[{"href":"https:\/\/www.ddosidc.com\/wp\/wp-json\/wp\/v2\/media?parent=850"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.ddosidc.com\/wp\/wp-json\/wp\/v2\/categories?post=850"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.ddosidc.com\/wp\/wp-json\/wp\/v2\/tags?post=850"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/www.ddosidc.com\/wp\/wp-json\/wp\/v2\/topic?post=850"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}