{"id":1131563,"date":"2025-01-08T20:49:36","date_gmt":"2025-01-08T12:49:36","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1131563.html"},"modified":"2025-01-08T20:49:39","modified_gmt":"2025-01-08T12:49:39","slug":"python%e5%a6%82%e4%bd%95%e5%b0%86%e6%8c%87%e5%ae%9a%e5%8c%ba%e5%9f%9f%e4%ba%8c%e5%80%bc%e5%8c%96%e4%b8%ba1","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1131563.html","title":{"rendered":"python\u5982\u4f55\u5c06\u6307\u5b9a\u533a\u57df\u4e8c\u503c\u5316\u4e3a1"},"content":{"rendered":"

\"python\u5982\u4f55\u5c06\u6307\u5b9a\u533a\u57df\u4e8c\u503c\u5316\u4e3a1\"<\/p>\n

Python\u5c06\u6307\u5b9a\u533a\u57df\u4e8c\u503c\u5316\u4e3a1\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528OpenCV\u5e93\u3001\u5229\u7528NumPy\u6570\u7ec4\u64cd\u4f5c\u3001\u9009\u62e9\u7279\u5b9a\u533a\u57df\u3001\u5e94\u7528\u9608\u503c\u4e8c\u503c\u5316\u3002<\/strong> \u4e0b\u9762\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u5176\u4e2d\u4e00\u79cd\u65b9\u6cd5\uff0c\u5373\u5229\u7528OpenCV\u548cNumPy\u6765\u8fdb\u884c\u56fe\u50cf\u5904\u7406\u3002<\/p>\n<\/p>\n

\n

Python\u5982\u4f55\u5c06\u6307\u5b9a\u533a\u57df\u4e8c\u503c\u5316\u4e3a1<\/strong><\/h2>\n

\u5728\u8ba1\u7b97\u673a\u89c6\u89c9\u548c\u56fe\u50cf\u5904\u7406\u9886\u57df\uff0c\u4e8c\u503c\u5316\u662f\u4e00\u79cd\u5e38\u7528\u7684\u6280\u672f\uff0c\u5b83\u80fd\u591f\u5c06\u56fe\u50cf\u4e2d\u7684\u50cf\u7d20\u503c\u8f6c\u5316\u4e3a\u9ed1\u767d\u4e24\u79cd\u989c\u8272\u3002\u672c\u6587\u5c06\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Python\u5c06\u56fe\u50cf\u4e2d\u6307\u5b9a\u533a\u57df\u7684\u50cf\u7d20\u503c\u4e8c\u503c\u5316\u4e3a1\u3002\u6211\u4eec\u5c06\u501f\u52a9OpenCV\u548cNumPy\u5e93\u6765\u5b9e\u73b0\u8fd9\u4e00\u76ee\u6807\u3002<\/p>\n<\/p>\n

\u4e00\u3001OpenCV\u548cNumPy\u7684\u5b89\u88c5\u548c\u57fa\u7840\u4ecb\u7ecd<\/h2>\n<\/p>\n

1\u3001\u5b89\u88c5OpenCV\u548cNumPy<\/h3>\n<\/p>\n

\u5728\u5f00\u59cb\u4e4b\u524d\uff0c\u8bf7\u786e\u4fdd\u4f60\u5df2\u7ecf\u5b89\u88c5\u4e86OpenCV\u548cNumPy\u5e93\u3002\u4f60\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u6765\u5b89\u88c5\u8fd9\u4e9b\u5e93\uff1a<\/p>\n<\/p>\n

pip install opencv-python numpy<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001OpenCV\u548cNumPy\u7b80\u4ecb<\/h3>\n<\/p>\n
OpenCV\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u5b83\u5305\u542b\u4e86\u5927\u91cf\u7684\u56fe\u50cf\u5904\u7406\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u7b97\u6cd5\u3002NumPy\u662f\u4e00\u4e2a\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u7684\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u652f\u6301\u5927\u591a\u6570\u7ec4\u5904\u7406\u7684\u9ad8\u6548\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u8bfb\u53d6\u548c\u663e\u793a\u56fe\u50cf<\/h2>\n<\/p>\n
1\u3001\u8bfb\u53d6\u56fe\u50cf<\/h3>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u8bfb\u53d6\u4e00\u5f20\u56fe\u50cf\u3002\u53ef\u4ee5\u4f7f\u7528OpenCV\u7684imread<\/code>\u51fd\u6570\u6765\u5b9e\u73b0\uff1a<\/p>\n<\/p>\n
import cv2<\/p>\n
\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n
image = cv2.imread('path_to_image.jpg')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u663e\u793a\u56fe\u50cf<\/h3>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528OpenCV\u7684imshow<\/code>\u51fd\u6570\u663e\u793a\u56fe\u50cf\uff1a<\/p>\n<\/p>\n
cv2.imshow('Original Image', image)<\/p>\n
cv2.wAI<\/a>tKey(0)<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u9009\u62e9\u6307\u5b9a\u533a\u57df<\/h2>\n<\/p>\n
1\u3001\u5b9a\u4e49\u533a\u57df<\/h3>\n<\/p>\n
\u4e3a\u4e86\u4e8c\u503c\u5316\u6307\u5b9a\u533a\u57df\uff0c\u6211\u4eec\u9700\u8981\u5148\u5b9a\u4e49\u8be5\u533a\u57df\u3002\u5047\u8bbe\u6211\u4eec\u8981\u9009\u62e9\u4e00\u4e2a\u77e9\u5f62\u533a\u57df\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u6307\u5b9a\u5de6\u4e0a\u89d2\u548c\u53f3\u4e0b\u89d2\u7684\u5750\u6807\u6765\u5b9a\u4e49\uff1a<\/p>\n<\/p>\n
# \u5b9a\u4e49\u77e9\u5f62\u533a\u57df<\/p>\n
x1, y1 = 50, 50  # \u5de6\u4e0a\u89d2\u5750\u6807<\/p>\n
x2, y2 = 150, 150  # \u53f3\u4e0b\u89d2\u5750\u6807<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u63d0\u53d6\u533a\u57df<\/h3>\n<\/p>\n
\u4f7f\u7528NumPy\u7684\u5207\u7247\u529f\u80fd\u53ef\u4ee5\u63d0\u53d6\u8be5\u533a\u57df\uff1a<\/p>\n<\/p>\n
# \u63d0\u53d6\u533a\u57df<\/p>\n
region = image[y1:y2, x1:x2]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u5e94\u7528\u4e8c\u503c\u5316<\/h2>\n<\/p>\n
1\u3001\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/h3>\n<\/p>\n
\u4e3a\u4e86\u7b80\u5316\u5904\u7406\uff0c\u901a\u5e38\u5148\u5c06\u5f69\u8272\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\uff1a<\/p>\n<\/p>\n
# \u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/p>\n
gray_region = cv2.cvtColor(region, cv2.COLOR_BGR2GRAY)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5e94\u7528\u9608\u503c\u4e8c\u503c\u5316<\/h3>\n<\/p>\n
\u4f7f\u7528OpenCV\u7684threshold<\/code>\u51fd\u6570\u53ef\u4ee5\u5e94\u7528\u4e8c\u503c\u5316\uff1a<\/p>\n<\/p>\n
_, binary_region = cv2.threshold(gray_region, 127, 255, cv2.THRESH_BINARY)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u5c06\u4e8c\u503c\u5316\u7ed3\u679c\u5e94\u7528\u56de\u539f\u56fe<\/h3>\n<\/p>\n
\u5c06\u4e8c\u503c\u5316\u540e\u7684\u533a\u57df\u5e94\u7528\u56de\u539f\u56fe\uff1a<\/p>\n<\/p>\n
# \u5c06\u4e8c\u503c\u5316\u7ed3\u679c\u5e94\u7528\u56de\u539f\u56fe<\/p>\n
image[y1:y2, x1:x2, 0] = binary_region<\/p>\n
image[y1:y2, x1:x2, 1] = binary_region<\/p>\n
image[y1:y2, x1:x2, 2] = binary_region<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u663e\u793a\u6700\u7ec8\u56fe\u50cf<\/h2>\n<\/p>\n
\u6700\u540e\uff0c\u663e\u793a\u5904\u7406\u540e\u7684\u56fe\u50cf\uff1a<\/p>\n<\/p>\n
cv2.imshow('Binary Region Image', image)<\/p>\n
cv2.waitKey(0)<\/p>\n
cv2.destroyAllWindows()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u603b\u7ed3\u4e0e\u6269\u5c55<\/h2>\n<\/p>\n
1\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u672c\u6587\u4ecb\u7ecd\u4e86\u5982\u4f55\u4f7f\u7528Python\u5c06\u56fe\u50cf\u4e2d\u6307\u5b9a\u533a\u57df\u7684\u50cf\u7d20\u503c\u4e8c\u503c\u5316\u4e3a1\u3002\u4e3b\u8981\u6b65\u9aa4\u5305\u62ec\uff1a\u8bfb\u53d6\u56fe\u50cf\u3001\u9009\u62e9\u6307\u5b9a\u533a\u57df\u3001\u5e94\u7528\u4e8c\u503c\u5316\u3001\u5c06\u7ed3\u679c\u5e94\u7528\u56de\u539f\u56fe\u5e76\u663e\u793a\u3002\u901a\u8fc7\u8fd9\u4e9b\u6b65\u9aa4\uff0c\u53ef\u4ee5\u5b9e\u73b0\u5bf9\u56fe\u50cf\u4e2d\u611f\u5174\u8da3\u533a\u57df\u7684\u4e8c\u503c\u5316\u5904\u7406\u3002<\/p>\n<\/p>\n
2\u3001\u6269\u5c55<\/h3>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u4f60\u53ef\u80fd\u9700\u8981\u5904\u7406\u66f4\u52a0\u590d\u6742\u7684\u56fe\u50cf\u6216\u533a\u57df\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u53ef\u80fd\u7684\u6269\u5c55\uff1a<\/p>\n<\/p>\n
    \n
  • \u5904\u7406\u4e0d\u89c4\u5219\u533a\u57df<\/strong>\uff1a\u53ef\u4ee5\u4f7f\u7528\u56fe\u50cf\u63a9\u6a21\uff08mask\uff09\u6765\u5904\u7406\u4e0d\u89c4\u5219\u533a\u57df\u3002<\/li>\n
  • \u81ea\u9002\u5e94\u9608\u503c<\/strong>\uff1a\u5bf9\u4e8e\u5149\u7167\u4e0d\u5747\u7684\u56fe\u50cf\uff0c\u53ef\u4ee5\u4f7f\u7528\u81ea\u9002\u5e94\u9608\u503c\u65b9\u6cd5\uff08\u5982cv2.adaptiveThreshold<\/code>\uff09\u6765\u63d0\u9ad8\u4e8c\u503c\u5316\u6548\u679c\u3002<\/li>\n
  • \u591a\u533a\u57df\u5904\u7406<\/strong>\uff1a\u5982\u679c\u9700\u8981\u5904\u7406\u591a\u4e2a\u533a\u57df\uff0c\u53ef\u4ee5\u5728\u5faa\u73af\u4e2d\u4f9d\u6b21\u5bf9\u6bcf\u4e2a\u533a\u57df\u8fdb\u884c\u5904\u7406\u3002<\/li>\n<\/ul>\n
    \u901a\u8fc7\u7075\u6d3b\u8fd0\u7528OpenCV\u548cNumPy\u5e93\uff0c\u4f60\u53ef\u4ee5\u5b9e\u73b0\u66f4\u591a\u590d\u6742\u7684\u56fe\u50cf\u5904\u7406\u4efb\u52a1\u3002\u5e0c\u671b\u672c\u6587\u5bf9\u4f60\u6709\u6240\u5e2e\u52a9\uff0c\u795d\u4f60\u5728\u56fe\u50cf\u5904\u7406\u9886\u57df\u53d6\u5f97\u66f4\u5927\u7684\u8fdb\u6b65\uff01<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u5982\u4f55\u5728Python\u4e2d\u5b9e\u73b0\u56fe\u50cf\u7684\u533a\u57df\u4e8c\u503c\u5316\uff1f<\/strong>
    \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528OpenCV\u5e93\u6765\u5b9e\u73b0\u56fe\u50cf\u7684\u533a\u57df\u4e8c\u503c\u5316\u3002\u9996\u5148\uff0c\u52a0\u8f7d\u56fe\u50cf\u5e76\u5b9a\u4e49\u9700\u8981\u4e8c\u503c\u5316\u7684\u533a\u57df\u7684\u5750\u6807\u3002\u63a5\u7740\uff0c\u4f7f\u7528numpy\u6570\u7ec4\u64cd\u4f5c\uff0c\u5c06\u8be5\u533a\u57df\u7684\u50cf\u7d20\u503c\u8bbe\u4e3a1\uff0c\u5176\u4f59\u90e8\u5206\u4fdd\u6301\u4e0d\u53d8\u3002\u5177\u4f53\u6b65\u9aa4\u5305\u62ec\u8bfb\u53d6\u56fe\u50cf\u3001\u521b\u5efa\u63a9\u7801\u5e76\u5e94\u7528\u4e8c\u503c\u5316\u3002<\/p>\n
    \u4f7f\u7528\u4ec0\u4e48\u5de5\u5177\u6216\u5e93\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u56fe\u50cf\u4e8c\u503c\u5316\u64cd\u4f5c\uff1f<\/strong>
    \u5728Python\u4e2d\uff0cOpenCV\u548cPIL\uff08Pillow\uff09\u662f\u4e24\u4e2a\u5e38\u7528\u7684\u5e93\uff0c\u9002\u5408\u8fdb\u884c\u56fe\u50cf\u5904\u7406\u4efb\u52a1\u3002OpenCV\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\uff0c\u5305\u62ec\u4e8c\u503c\u5316\u3001\u8fb9\u7f18\u68c0\u6d4b\u7b49\u3002\u800cPIL\u5219\u66f4\u9002\u5408\u7b80\u5355\u7684\u56fe\u50cf\u64cd\u4f5c\uff0c\u7528\u6237\u53ef\u4ee5\u6839\u636e\u81ea\u5df1\u7684\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\u3002<\/p>\n
    \u5982\u4f55\u5b9a\u4e49\u4e8c\u503c\u5316\u533a\u57df\u7684\u5750\u6807\uff1f<\/strong>
    \u5b9a\u4e49\u4e8c\u503c\u5316\u533a\u57df\u7684\u5750\u6807\u901a\u5e38\u9700\u8981\u6839\u636e\u56fe\u50cf\u7684\u5c3a\u5bf8\u6765\u8fdb\u884c\u3002\u53ef\u4ee5\u4f7f\u7528\u5750\u6807\u5143\u7ec4\uff08x_start, y_start, x_end, y_end\uff09\u6765\u8868\u793a\u533a\u57df\u7684\u5de6\u4e0a\u89d2\u548c\u53f3\u4e0b\u89d2\u3002\u901a\u8fc7\u8fd9\u4e9b\u5750\u6807\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u63d0\u53d6\u51fa\u9700\u8981\u5904\u7406\u7684\u533a\u57df\uff0c\u5e76\u8fdb\u884c\u4e8c\u503c\u5316\u64cd\u4f5c\u3002<\/p>\n
    \u4e8c\u503c\u5316\u5904\u7406\u540e\uff0c\u5982\u4f55\u4fdd\u5b58\u5904\u7406\u540e\u7684\u56fe\u50cf\uff1f<\/strong>
    \u5904\u7406\u5b8c\u56fe\u50cf\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528OpenCV\u7684cv2.imwrite()<\/code>\u51fd\u6570\u5c06\u7ed3\u679c\u4fdd\u5b58\u5230\u672c\u5730\u3002\u6307\u5b9a\u6587\u4ef6\u540d\u548c\u8def\u5f84\u540e\uff0c\u5904\u7406\u540e\u7684\u56fe\u50cf\u5c06\u88ab\u4fdd\u5b58\uff0c\u7528\u6237\u53ef\u4ee5\u65b9\u4fbf\u5730\u67e5\u770b\u548c\u4f7f\u7528\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u5c06\u6307\u5b9a\u533a\u57df\u4e8c\u503c\u5316\u4e3a1\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528OpenCV\u5e93\u3001\u5229\u7528NumPy\u6570\u7ec4\u64cd\u4f5c\u3001\u9009\u62e9\u7279\u5b9a\u533a\u57df\u3001\u5e94\u7528\u9608\u503c […]","protected":false},"author":3,"featured_media":1131574,"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\/1131563"}],"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=1131563"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1131563\/revisions"}],"predecessor-version":[{"id":1131576,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1131563\/revisions\/1131576"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1131574"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1131563"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1131563"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1131563"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}