{"id":65233,"date":"2026-01-24T17:25:02","date_gmt":"2026-01-24T09:25:02","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/65233.html"},"modified":"2026-01-24T17:25:02","modified_gmt":"2026-01-24T09:25:02","slug":"%e5%b0%8f%e7%99%bd%e4%bb%8e%e9%9b%b6%e5%bc%80%e5%a7%8b%e5%8b%87%e9%97%af%e4%ba%ba%e5%b7%a5%e6%99%ba%e8%83%bd%ef%bc%9a%e8%ae%a1%e7%ae%97%e6%9c%ba%e8%a7%86%e8%a7%89%e5%88%9d%e7%ba%a7%e7%af%87%ef%bc%88","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/65233.html","title":{"rendered":"\u5c0f\u767d\u4ece\u96f6\u5f00\u59cb\u52c7\u95ef\u4eba\u5de5\u667a\u80fd\uff1a\u8ba1\u7b97\u673a\u89c6\u89c9\u521d\u7ea7\u7bc7\uff08\u521d\u8bc6Opencv\u4e0b\uff09"},"content":{"rendered":"

\u5f15\u8a00<\/h2>\n

\u00a0\u00a0\u5728\u8ba1\u7b97\u673a\u89c6\u89c9\u4e2d，\u8f6e\u5ed3\u68c0\u6d4b\u4e0e\u6a21\u677f\u5339\u914d\u662f\u4e24\u9879\u57fa\u7840\u7684\u56fe\u50cf\u5206\u6790\u6280\u672f\u3002\u8f6e\u5ed3\u68c0\u6d4b\u901a\u8fc7\u63d0\u53d6\u76ee\u6807\u7684\u5f62\u72b6\u8fb9\u754c，\u5c06\u56fe\u50cf\u7b80\u5316\u4e3a\u51e0\u4f55\u8f6e\u5ed3，\u662f\u5b9e\u73b0\u7269\u4f53\u5f62\u72b6\u5206\u6790\u3001\u5c3a\u5bf8\u6d4b\u91cf\u7684\u5173\u952e\u6b65\u9aa4\u3002\u6a21\u677f\u5339\u914d\u5219\u901a\u8fc7\u5728\u56fe\u50cf\u4e2d\u6ed1\u52a8\u5df2\u77e5\u6a21\u677f\u8fdb\u884c\u6bd4\u5bf9，\u76f4\u63a5\u5b9a\u4f4d\u7279\u5b9a\u76ee\u6807，\u9002\u7528\u4e8e\u5143\u4ef6\u5b9a\u4f4d\u3001\u56fa\u5b9a\u56fe\u6848\u8bc6\u522b\u7b49\u4efb\u52a1\u3002\u5b83\u4eec\u5e38\u534f\u540c\u5de5\u4f5c，\u5982\u7528\u8f6e\u5ed3\u5feb\u901f\u7b5b\u9009\u533a\u57df，\u518d\u7528\u6a21\u677f\u7cbe\u786e\u5339\u914d，\u5171\u540c\u4e3a\u66f4\u590d\u6742\u7684\u89c6\u89c9\u4efb\u52a1\u63d0\u4f9b\u53ef\u9760\u652f\u6301\u3002\u63a5\u4e0b\u6765\u5c31\u8ba9\u6211\u4eec\u4e00\u8d77\u5b66\u4e60\u8fd9\u4e24\u6837\u6280\u672f\u3002<\/p>\n

\u4e00\u3001\u8f6e\u5ed3\u68c0\u6d4b：\u56fe\u50cf\u7684\u201c\u63cf\u8fb9\u201d\u827a\u672f<\/h3>\n

1\u3001\u4ec0\u4e48\u662f\u8f6e\u5ed3\u68c0\u6d4b？<\/h4>\n

\u00a0\u00a0\u00a0\u8f6e\u5ed3\u68c0\u6d4b\u7684\u6838\u5fc3\u76ee\u6807\u662f\u4ece\u6570\u5b57\u56fe\u50cf\u4e2d\u8bc6\u522b\u5e76\u63d0\u53d6\u51fa\u5176\u4e2d\u7269\u4f53\u6216\u533a\u57df\u7684\u8fb9\u754c\u7ebf\u3002\u5176\u76ee\u7684\u662f\u627e\u51fa\u8fde\u7eed\u4e14\u901a\u5e38\u95ed\u5408\u7684\u8f6e\u5ed3\u66f2\u7ebf，\u4ece\u800c\u52fe\u52d2\u51fa\u7269\u4f53\u7684\u5b8c\u6574\u5916\u90e8\u5f62\u72b6\u6216\u5185\u90e8\u7ed3\u6784\u8fb9\u754c\u3002\u8fd9\u4e2a\u8fc7\u7a0b\u7c7b\u4f3c\u4e8e\u4e3a\u56fe\u50cf\u4e2d\u611f\u5174\u8da3\u7684\u7269\u4f53\u8fdb\u884c\u201c\u63cf\u8fb9\u201d\u6216\u201c\u52fe\u52d2\u5916\u5f62\u201d，\u5176\u7ed3\u679c\u662f\u7531\u4e00\u7cfb\u5217\u8fde\u7eed\u70b9\u6784\u6210\u7684\u3001\u53ef\u7528\u4e8e\u8fdb\u4e00\u6b65\u5206\u6790\u7684\u5c01\u95ed\u6216\u5f00\u653e\u8f6e\u5ed3\u96c6\u5408\u3002\u5b83\u4f7f\u7a0b\u5e8f\u80fd\u591f\u5bf9\u56fe\u50cf\u4e2d\u7684\u76ee\u6807\u8fdb\u884c\u5b9a\u4f4d\u3001\u6d4b\u91cf\u548c\u5206\u79bb\u64cd\u4f5c\u3002\u5728OpenCV\u7b49\u5e38\u7528\u5e93\u4e2d，\u901a\u5e38\u901a\u8fc7cv2.findContours()\u7b49\u6838\u5fc3\u51fd\u6570\u6765\u5b9e\u73b0\u8fd9\u4e00\u529f\u80fd\u3002<\/p>\n

image, contours, hierarchy = cv2.findContours(img, mode, method)
\n\u53c2\u6570：
\nimg：\u8f93\u5165\u56fe\u50cf，\u5fc5\u987b\u662f\u4e8c\u503c\u56fe\u50cf（\u53ea\u67090\u548c255\u4e24\u79cd\u50cf\u7d20\u503c）\u3002<\/p>\n

mode：\u8f6e\u5ed3\u68c0\u7d22\u6a21\u5f0f，\u5e38\u7528\u6709\u4ee5\u4e0b\u51e0\u79cd：<\/p>\n

cv2.RETR_EXTERNAL：\u53ea\u68c0\u6d4b\u6700\u5916\u5c42\u8f6e\u5ed3<\/p>\n

cv2.RETR_TREE：\u68c0\u6d4b\u6240\u6709\u8f6e\u5ed3\u5e76\u5efa\u7acb\u5b8c\u6574\u7684\u5c42\u6b21\u7ed3\u6784（\u6700\u5e38\u7528）<\/p>\n

method：\u8f6e\u5ed3\u8fd1\u4f3c\u65b9\u6cd5：<\/p>\n

cv2.CHAIN_APPROX_NONE：\u4fdd\u5b58\u8f6e\u5ed3\u4e0a\u6240\u6709\u70b9<\/p>\n

cv2.CHAIN_APPROX_SIMPLE：\u53ea\u4fdd\u5b58\u8f6e\u5ed3\u7684\u62d0\u70b9（\u5982\u77e9\u5f62\u53ea\u4fdd\u5b584\u4e2a\u9876\u70b9）<\/p>\n

\u8fd4\u56de\u503c：
\nimage：\u5904\u7406\u540e\u7684\u56fe\u50cf<\/p>\n

contours：\u68c0\u6d4b\u5230\u7684\u8f6e\u5ed3\u5217\u8868，\u6bcf\u4e2a\u8f6e\u5ed3\u7531\u4e00\u7cfb\u5217\u70b9\u7ec4\u6210<\/p>\n

hierarchy：\u8f6e\u5ed3\u7684\u5c42\u6b21\u5173\u7cfb\u4fe1\u606f<\/p>\n

2\u3001\u56fe\u50cf\u9884\u5904\u7406<\/h4>\n

\u00a0\u00a0\u5728\u8fdb\u884c\u8f6e\u5ed3\u68c0\u6d4b\u4e4b\u524d，\u56fe\u50cf\u5fc5\u987b\u7ecf\u8fc7\u9884\u5904\u7406\u8f6c\u6362\u4e3a\u4e8c\u503c\u56fe\u50cf，\u8fd9\u662f\u56e0\u4e3a\u8f6e\u5ed3\u68c0\u6d4b\u7b97\u6cd5\u9700\u8981\u660e\u786e\u533a\u5206\u524d\u666f\u76ee\u6807\u4e0e\u80cc\u666f\u3002\u5904\u7406\u6d41\u7a0b\u5305\u542b\u4e09\u4e2a\u5173\u952e\u6b65\u9aa4：\u9996\u5148，\u4f7f\u7528cv2.imread()\u51fd\u6570\u8bfb\u53d6\u539f\u59cb\u5f69\u8272\u56fe\u50cf，\u63a5\u7740，\u901a\u8fc7cv2.cvtColor()\u51fd\u6570\u5e76\u5c06\u8f6c\u6362\u4ee3\u7801\u8bbe\u4e3aCOLOR_BGR2GRAY，\u5c06\u5f69\u8272\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf，\u8fd9\u91cc\u6d88\u9664\u4e86\u989c\u8272\u4fe1\u606f，\u5c06\u4e09\u7ef4\u7684\u50cf\u7d20\u77e9\u9635\u7b80\u5316\u4e3a\u4ec5\u5305\u542b\u4eae\u5ea6\u4fe1\u606f\u7684\u4e8c\u7ef4\u77e9\u9635，\u6700\u540e，\u7528cv2.threshold()\u51fd\u6570\u5bf9\u7070\u5ea6\u56fe\u8fdb\u884c\u4e8c\u503c\u5316\u64cd\u4f5c，\u901a\u8fc7\u8bbe\u5b9a\u4e00\u4e2a\u9608\u503c（\u4f8b\u5982\u4f8b\u5b50\u4e2d\u7684120），\u5c06\u7070\u5ea6\u56fe\u4e2d\u6240\u6709\u4f4e\u4e8e\u6b64\u9608\u503c\u7684\u50cf\u7d20\u70b9\u7f6e\u4e3a0（\u9ed1\u8272），\u9ad8\u4e8e\u6216\u7b49\u4e8e\u9608\u503c\u7684\u50cf\u7d20\u70b9\u7f6e\u4e3a255（\u767d\u8272），\u4ece\u800c\u751f\u6210\u4e00\u4e2a\u9ed1\u767d\u5206\u660e\u3001\u53ea\u6709\u524d\u666f\u548c\u80cc\u666f\u7684\u4e24\u503c\u56fe\u50cf\u4e3a\u540e\u7eed\u51c6\u786e\u7684\u8f6e\u5ed3\u67e5\u627e\u5960\u5b9a\u57fa\u7840\u3002<\/p>\n

import cv2<\/p>\n

# \u8bfb\u53d6\u56fe\u50cf
\nphone = cv2.imread('phone.png')
\n# \u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe
\nphone_gray = cv2.cvtColor(phone, cv2.COLOR_BGR2GRAY)
\n# \u4e8c\u503c\u5316\u5904\u7406
\nret, phone_binary = cv2.threshold(phone_gray, 120, 255, cv2.THRESH_BINARY)<\/p>\n

3\u3001\u68c0\u6d4b\u5e76\u7ed8\u5236\u8f6e\u5ed3<\/h4>\n

\u00a0\u00a0\u63a5\u4e0b\u6765\u6211\u4eec\u8981\u68c0\u6d4b\u5e76\u7ed8\u5236\u8f6e\u5ed3，\u8fd9\u4e2a\u64cd\u4f5c\u9700\u8981\u4e24\u4e2a\u5173\u952e\u51fd\u6570\u3002\u9996\u5148，\u8c03\u7528cv2.findContours()\u51fd\u6570\u5728\u4e8c\u503c\u56fe\u50cf\u4e0a\u67e5\u627e\u8f6e\u5ed3\u3002\u5176\u4e2d，\u53c2\u6570cv2.RETR_TREE\u8868\u793a\u68c0\u7d22\u6240\u6709\u8f6e\u5ed3\u5e76\u5efa\u7acb\u5b8c\u6574\u7684\u5c42\u7ea7\u5173\u7cfb\u6811，cv2.CHAIN_APPROX_NONE\u5219\u5b58\u50a8\u8f6e\u5ed3\u4e0a\u6240\u6709\u7684\u70b9，\u4e0d\u8fdb\u884c\u8fd1\u4f3c\u538b\u7f29\u3002\u8be5\u51fd\u6570\u8fd4\u56de\u8f6e\u5ed3\u5217\u8868\u548c\u5c42\u7ea7\u4fe1\u606f\u3002\u6211\u4eec\u901a\u5e38\u5148\u521b\u5efa\u4e00\u4e2a\u526f\u672c\u4ee5\u907f\u514d\u4fee\u6539\u539f\u56fe\u3002\u63a5\u7740，\u4f7f\u7528cv2.drawContours()\u51fd\u6570\u8fdb\u884c\u7ed8\u5236，\u5c06contours\u5217\u8868\u4f20\u5165，\u8bbe\u5b9acontourIdx=-1\u4ee5\u753b\u51fa\u6240\u6709\u8f6e\u5ed3，\u5e76\u6307\u5b9a\u7ed8\u5236\u989c\u8272\u4e3a\u7eff\u8272(0, 255, 0)，\u7ebf\u6761\u7c97\u7ec6\u4e3a2\u50cf\u7d20\u3002\u6700\u540e，\u901a\u8fc7cv2.imshow()\u663e\u793a\u7ed8\u5236\u7ed3\u679c\u3002<\/p>\n

# \u68c0\u6d4b\u8f6e\u5ed3
\ncontours, hierarchy = cv2.findContours(phone_binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)[-2:]<\/p>\n

# \u5728\u539f\u56fe\u4e0a\u7ed8\u5236\u6240\u6709\u8f6e\u5ed3
\nimage_copy = phone.copy()
\ncv2.drawContours(image=image_copy, contours=contours,
\n contourIdx=-1, color=(0, 255, 0), thickness=2)
\ncv2.imshow('Contours_show', image_copy)
\ncv2.waitKey(0)<\/p>\n

\"\"<\/p>\n

\u4e8c\u3001\u8f6e\u5ed3\u7279\u5f81：\u4e86\u89e3\u4f60\u7684\u201c\u8f6e\u5ed3\u201d<\/h3>\n

\u00a0 \u5728\u6211\u4eec\u83b7\u53d6\u8f6e\u5ed3\u540e，\u6211\u4eec\u9700\u8981\u5206\u6790\u5b83\u4eec\u7684\u7279\u5f81，\u6bd4\u5982\u9762\u79ef\u3001\u5468\u957f\u7b49，\u4ee5\u4fbf\u8fdb\u4e00\u6b65\u7b5b\u9009\u548c\u5904\u7406\u3002<\/p>\n

1\u3001\u8f6e\u5ed3\u9762\u79ef<\/h4>\n

\u00a0\u00a0\u5728OpenCV\u5e93\u4e2d，\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528cv2.contourArea()\u51fd\u6570\u8ba1\u7b97\u8f6e\u5ed3\u7684\u9762\u79ef\u3002\u9762\u79ef\u662f\u8f6e\u5ed3\u7684\u4e00\u4e2a\u57fa\u7840\u51e0\u4f55\u7279\u5f81，\u7528\u4e8e\u6839\u636e\u5b9e\u9645\u5c3a\u5bf8\u9700\u6c42\u4ece\u56fe\u50cf\u4e2d\u7b5b\u9009\u51fa\u7b26\u5408\u6761\u4ef6\u7684\u8f6e\u5ed3，\u4f8b\u5982\u8fc7\u6ee4\u6389\u566a\u70b9\u6216\u9009\u4e2d\u7279\u5b9a\u5927\u5c0f\u7684\u76ee\u6807\u7269\u4f53\u3002<\/p>\n

area = cv2.contourArea(contour)<\/p>\n

2\u3001\u8f6e\u5ed3\u5468\u957f<\/h4>\n

\u00a0 \u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528cv2.arcLength()\u51fd\u6570\u53ef\u4ee5\u8ba1\u7b97\u8f6e\u5ed3\u7684\u5468\u957f\u3002\u5176closed\u53c2\u6570\u7528\u4e8e\u6307\u5b9a\u8f6e\u5ed3\u662f\u5426\u4e3a\u5c01\u95ed\u66f2\u7ebf\u3002\u5468\u957f\u662f\u53e6\u4e00\u4e2a\u6838\u5fc3\u7684\u8f6e\u5ed3\u7279\u5f81，\u5e38\u4e0e\u9762\u79ef\u7ed3\u5408\u7528\u4e8e\u5f62\u72b6\u5206\u6790，\u4f8b\u5982\u8ba1\u7b97\u8f6e\u5ed3\u7684\u5706\u5ea6，\u6216\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u4f30\u7b97\u7269\u4f53\u7684\u8fb9\u754c\u957f\u5ea6\u3002<\/p>\n

perimeter = cv2.arcLength(contour, closed=True)<\/p>\n

\u00a0 \u90a3\u63a5\u4e0b\u6765\u8ba9\u6211\u4eec\u6309\u9762\u79ef\u7b5b\u9009\u8f6e\u5ed3：\u6211\u4eec\u53ea\u60f3\u663e\u793a\u9762\u79ef\u5927\u4e8e10000\u7684\u8f6e\u5ed3<\/p>\n

large_contours = []
\nfor cnt in contours:
\n if cv2.contourArea(cnt) > 10000:
\n large_contours.append(cnt)<\/p>\n

image_copy = phone.copy()
\ncv2.drawContours(image_copy, large_contours, -1, (0, 255, 0), 3)
\ncv2.imshow('Large_Contours', image_copy)
\ncv2.waitKey(0)<\/p>\n

\"\"<\/p>\n

\u4e09\u3001\u8f6e\u5ed3\u8fd1\u4f3c：\u7b80\u5316\u8f6e\u5ed3\u5f62\u72b6<\/h3>\n

1\u3001\u4e3a\u4ec0\u4e48\u8981\u8fd1\u4f3c？<\/h4>\n

\u00a0\u00a0\u56e0\u4e3a\u4ece\u5b9e\u9645\u56fe\u50cf\u4e2d\u68c0\u6d4b\u51fa\u7684\u8f6e\u5ed3\u5f80\u5f80\u5305\u542b\u8fc7\u591a\u7684\u5197\u4f59\u70b9，\u5bfc\u81f4\u5f62\u72b6\u63cf\u8ff0\u8fc7\u4e8e\u590d\u6742\u4e14\u8ba1\u7b97\u6548\u7387\u4f4e\u4e0b\u3002\u8fd9\u65f6\u6211\u4eec\u901a\u8fc7\u7528\u66f4\u5c11\u7684\u70b9\u6765\u903c\u8fd1\u539f\u59cb\u8f6e\u5ed3，\u53ef\u4ee5\u5728\u57fa\u672c\u4fdd\u6301\u5176\u6838\u5fc3\u51e0\u4f55\u5f62\u72b6\u7684\u524d\u63d0\u4e0b，\u7b80\u5316\u6570\u636e\u3002\u8fd9\u79cd\u7b80\u5316\u5bf9\u4e8e\u540e\u7eed\u7684\u5f62\u72b6\u8bc6\u522b\u3001\u5339\u914d\u548c\u5b58\u50a8\u81f3\u5173\u91cd\u8981，\u5b83\u80fd\u63d0\u9ad8\u7b97\u6cd5\u6548\u7387\u5e76\u51cf\u5c11\u566a\u58f0\u5e26\u6765\u7684\u5e72\u6270\u3002<\/p>\n

2\u3001\u8fd1\u4f3c\u65b9\u6cd5<\/h4>\n

\u00a0\u00a0cv2.approxPolyDP() \u662f\u5b9e\u73b0\u8f6e\u5ed3\u8fd1\u4f3c\u7684\u6838\u5fc3\u51fd\u6570\u3002\u5176\u5173\u952e\u53c2\u6570 epsilon \u4e3a\u8fd1\u4f3c\u7cbe\u5ea6，\u901a\u5e38\u8bbe\u7f6e\u4e3a\u8f6e\u5ed3\u5468\u957f\u7684\u4e00\u4e2a\u767e\u5206\u6bd4\u3002\u901a\u8fc7\u8c03\u6574\u8be5\u503c，\u53ef\u4ee5\u63a7\u5236\u8fd1\u4f3c\u7684\u7a0b\u5ea6：epsilon \u503c\u8d8a\u5c0f，\u5f97\u5230\u7684\u591a\u8fb9\u5f62\u9876\u70b9\u8d8a\u591a，\u5f62\u72b6\u8d8a\u63a5\u8fd1\u539f\u59cb\u8f6e\u5ed3，\u503c\u8d8a\u5927，\u5219\u9876\u70b9\u8d8a\u5c11，\u5f62\u72b6\u8d8a\u7b80\u5316，\u76f4\u81f3\u5931\u53bb\u5173\u952e\u7279\u5f81\u3002<\/p>\n

epsilon = 0.01 * cv2.arcLength(contour, True) # \u8fd1\u4f3c\u7cbe\u5ea6
\napprox = cv2.approxPolyDP(contour, epsilon, True) # \u8fdb\u884c\u8fd1\u4f3c<\/p>\n

\u00a0 \u8f6e\u5ed3\u8fd1\u4f3c\u793a\u4f8b：<\/p>\n

# \u83b7\u53d6\u6700\u5927\u9762\u79ef\u7684\u8f6e\u5ed3
\ncontours_with_area = [(cnt, cv2.contourArea(cnt)) for cnt in contours]
\nsorted_contours = sorted(contours_with_area, key=lambda x: x[1], reverse=True)
\nlargest_contour = sorted_contours[0][0]<\/p>\n

# \u5bf9\u6700\u5927\u8f6e\u5ed3\u8fdb\u884c\u8fd1\u4f3c
\nepsilon = 0.01 * cv2.arcLength(largest_contour, True)
\napprox = cv2.approxPolyDP(largest_contour, epsilon, True)<\/p>\n

# \u7ed8\u5236\u8fd1\u4f3c\u540e\u7684\u8f6e\u5ed3
\nphone_new = phone.copy()
\ncv2.drawContours(phone_new, [approx], -1, (0, 255, 0), 3)
\ncv2.imshow('Approximated_Contour', phone_new)
\ncv2.waitKey(0)<\/p>\n

\"\"<\/p>\n

\u56db\u3001\u8f6e\u5ed3\u7684\u5916\u63a5\u5f62\u72b6<\/h3>\n

1\u3001\u5916\u63a5\u5706<\/h4>\n

\u00a0\u00a0cv2.minEnclosingCircle() \u51fd\u6570\u7528\u4e8e\u8ba1\u7b97\u8f6e\u5ed3\u7684\u6700\u5c0f\u5916\u63a5\u5706，\u8fd4\u56de\u5706\u5fc3\u5750\u6807\u548c\u534a\u5f84\u3002\u8be5\u5706\u662f\u80fd\u5b8c\u5168\u5305\u56f4\u8f6e\u5ed3\u7684\u6700\u5c0f\u7684\u5706，\u5e38\u7528\u4e8e\u5b9a\u4f4d\u548c\u6d4b\u91cf\u3002\u6211\u4eec\u5728\u4ee3\u7801\u91cc\u83b7\u53d6\u7684\u6d6e\u70b9\u6570\u7ed3\u679c\u9700\u8f6c\u6362\u4e3a\u6574\u6570，\u4ee5\u4fbf\u4f7f\u7528 cv2.circle() \u51fd\u6570\u5728\u56fe\u50cf\u4e0a\u7ed8\u5236\u51fa\u8be5\u5706\u5f62，\u4ece\u800c\u76f4\u89c2\u5730\u53ef\u89c6\u5316\u8f6e\u5ed3\u7684\u5916\u63a5\u8303\u56f4\u3002<\/p>\n

# \u8ba1\u7b97\u6700\u5c0f\u5916\u63a5\u5706\u5e76\u7ed8\u5236（\u4f7f\u7528\u6700\u5927\u8f6e\u5ed3）
\n(x, y), radius = cv2.minEnclosingCircle(largest_contour)
\ncircle_image = phone.copy()
\ncv2.circle(circle_image, (int(x), int(y)), int(radius), (0, 255, 0), 2)
\ncv2.imshow('Min_Enclosing_Circle', circle_image)<\/p>\n

\"\"<\/p>\n

2\u3001\u5916\u63a5\u77e9\u5f62<\/h4>\n

\u00a0\u00a0cv2.boundingRect()\u51fd\u6570\u7528\u4e8e\u8ba1\u7b97\u8f6e\u5ed3\u7684\u76f4\u7acb\u5916\u63a5\u77e9\u5f62，\u8fd4\u56de\u5176\u5de6\u4e0a\u89d2\u9876\u70b9\u5750\u6807\u3001\u5bbd\u5ea6\u548c\u9ad8\u5ea6\u3002\u8be5\u77e9\u5f62\u662f\u80fd\u5b8c\u5168\u5305\u56f4\u8f6e\u5ed3\u7684\u6700\u5c0f\u6b63\u77e9\u5f62，\u5e38\u7528\u4e8e\u76ee\u6807\u7684\u5feb\u901f\u5b9a\u4f4d\u548c\u533a\u57df\u63d0\u53d6\u3002\u968f\u540e，\u7528cv2.rectangle()\u5e76\u4f20\u5165\u77e9\u5f62\u7684\u4e24\u4e2a\u5bf9\u89d2\u70b9，\u5373\u53ef\u5728\u56fe\u50cf\u4e0a\u7ed8\u5236\u51fa\u8be5\u7eff\u8272\u8fb9\u6846，\u4ece\u800c\u76f4\u89c2\u5730\u6807\u5b9a\u8f6e\u5ed3\u7684\u8303\u56f4\u3002<\/p>\n

# \u8ba1\u7b97\u5916\u63a5\u77e9\u5f62\u5e76\u7ed8\u5236
\nx, y, w, h = cv2.boundingRect(largest_contour)
\nrect_image = phone.copy()
\ncv2.rectangle(rect_image, (x, y), (x + w, y + h), (0, 255, 0), 2)
\ncv2.imshow('Bounding_Rectangle', rect_image)<\/p>\n

\"\"<\/p>\n

\u4e94\u3001\u6a21\u677f\u5339\u914d：\u56fe\u50cf\u4e2d\u7684\u201c\u627e\u832c\u201d\u6e38\u620f<\/h3>\n

1\u3001\u4ec0\u4e48\u662f\u6a21\u677f\u5339\u914d？<\/h4>\n

\u00a0\u00a0\u6a21\u677f\u5339\u914d\u662f\u5728\u6e90\u56fe\u50cf\u4e2d\u5bfb\u627e\u4e0e\u7ed9\u5b9a\u6a21\u677f\u56fe\u50cf\u6700\u76f8\u4f3c\u7684\u533a\u57df\u3002\u5176\u6838\u5fc3\u601d\u60f3\u662f\u5c06\u6a21\u677f\u4f5c\u4e3a\u6ed1\u52a8\u7a97\u53e3，\u904d\u5386\u6e90\u56fe\u50cf\u8fdb\u884c\u6bd4\u8f83，\u901a\u8fc7\u7279\u5b9a\u7684\u65b9\u6cd5\u8ba1\u7b97\u76f8\u4f3c\u5ea6\u3002\u4f7f\u7528\u7684\u6838\u5fc3\u51fd\u6570\u662fcv2.matchTemplate()，\u5b83\u8f93\u5165\u6e90\u56fe\u50cf\u3001\u6a21\u677f\u56fe\u50cf\u548c\u5339\u914d\u65b9\u6cd5，\u8f93\u51fa\u4e00\u4e2a\u7ed3\u679c\u77e9\u9635，\u5176\u4e2d\u7684\u6781\u503c\u70b9\u4f4d\u7f6e\u5373\u6307\u793a\u4e86\u6700\u4f73\u5339\u914d\u533a\u57df\u3002<\/p>\n

res = cv2.matchTemplate(image, templ, method)<\/p>\n

\u5339\u914d\u65b9\u6cd5：
\ncv2.TM_SQDIFF_NORMED：\u5f52\u4e00\u5316\u5e73\u65b9\u5dee\u5339\u914d，\u503c\u8d8a\u5c0f\u5339\u914d\u8d8a\u597d<\/p>\n

cv2.TM_CCORR_NORMED：\u5f52\u4e00\u5316\u76f8\u5173\u5339\u914d，\u503c\u8d8a\u5927\u5339\u914d\u8d8a\u597d<\/p>\n

cv2.TM_CCOEFF_NORMED：\u5f52\u4e00\u5316\u76f8\u5173\u7cfb\u6570\u5339\u914d，\u6700\u5e38\u7528，\u503c\u8d8a\u5927\u5339\u914d\u8d8a\u597d<\/p>\n

\u00a0 \u6a21\u677f\u5339\u914d\u7684\u5de5\u4f5c\u539f\u7406：\u9996\u5148\u5c06\u6a21\u677f\u56fe\u50cf\u653e\u5728\u539f\u56fe\u7684\u6bcf\u4e2a\u53ef\u80fd\u4f4d\u7f6e，\u63a5\u7740\u5728\u6bcf\u4e2a\u4f4d\u7f6e\u8ba1\u7b97\u76f8\u4f3c\u5ea6\u5206\u6570，\u627e\u5230\u5206\u6570\u6700\u9ad8\u7684\u4f4d\u7f6e，\u8be5\u4f4d\u7f6e\u5c31\u662f\u6700\u4f73\u5339\u914d\u533a\u57df\u3002<\/p>\n

\u6848\u4f8b：\u5728\u53ef\u4e50\u7f50\u56fe\u7247\u4e2d\u627e\u5546\u6807<\/h4>\n

import cv2<\/p>\n

# \u8bfb\u53d6\u56fe\u50cf\u548c\u6a21\u677f
\nkele = cv2.imread('kele.png') # \u539f\u56fe
\ntemplate = cv2.imread('template.png') # \u6a21\u677f<\/p>\n

# \u83b7\u53d6\u6a21\u677f\u5c3a\u5bf8
\nh, w = template.shape[:2]<\/p>\n

# \u8fdb\u884c\u6a21\u677f\u5339\u914d
\nres = cv2.matchTemplate(kele, template, cv2.TM_CCOEFF_NORMED)<\/p>\n

# \u627e\u5230\u6700\u4f73\u5339\u914d\u4f4d\u7f6e
\nmin_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
\ntop_left = max_loc
\nbottom_right = (top_left[0] + w, top_left[1] + h)<\/p>\n

# \u5728\u539f\u56fe\u4e0a\u6807\u8bb0\u5339\u914d\u533a\u57df
\nkele_template = cv2.rectangle(kele, top_left, bottom_right, (0, 255, 0), 2)<\/p>\n

# \u663e\u793a\u7ed3\u679c
\ncv2.imshow('Template_Matching_Result', kele_template)
\ncv2.waitKey(0)
\ncv2.destroyAllWindows()<\/p>\n

\"\"<\/p>\n

\u00a0 \u5230\u8fd9\u91cc\u6211\u4eec\u5c31\u5b66\u5b8c\u4e86opencv\u4e2d\u7684\u57fa\u7840\u5185\u5bb9\u4e86，\u63a5\u4e0b\u6765\u5c06\u4f1a\u5728\u540e\u9762\u7684\u6587\u7ae0\u91cc\u5f00\u59cb\u5b66\u4e60\u8fdb\u9636\u7684\u66f4\u590d\u6742\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u64cd\u4f5c\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

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