{"id":975509,"date":"2024-12-27T06:13:50","date_gmt":"2024-12-26T22:13:50","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/975509.html"},"modified":"2024-12-27T06:13:52","modified_gmt":"2024-12-26T22:13:52","slug":"python%e5%a6%82%e4%bd%95%e6%8f%90%e5%8f%96%e5%9b%be%e7%89%87%e8%be%b9%e6%a1%86","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/975509.html","title":{"rendered":"python\u5982\u4f55\u63d0\u53d6\u56fe\u7247\u8fb9\u6846"},"content":{"rendered":"

\"python\u5982\u4f55\u63d0\u53d6\u56fe\u7247\u8fb9\u6846\"<\/p>\n

\u5728Python\u4e2d\u63d0\u53d6\u56fe\u7247\u8fb9\u6846\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u4e3b\u8981\u5305\u62ec\uff1a\u4f7f\u7528OpenCV\u8fdb\u884c\u8fb9\u7f18\u68c0\u6d4b\u548c\u8f6e\u5ed3\u63d0\u53d6\u3001\u5229\u7528PIL\u5e93\u8fdb\u884c\u56fe\u50cf\u5904\u7406\u3001\u4ee5\u53ca\u901a\u8fc7scikit-image\u5e93\u8fdb\u884c\u56fe\u50cf\u7684\u8fb9\u7f18\u68c0\u6d4b\u3002OpenCV\u662f\u6700\u5e38\u7528\u7684\u65b9\u6cd5\uff0c\u56e0\u4e3a\u5b83\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u529f\u80fd\u3001\u6613\u4e8e\u4f7f\u7528\u548c\u9ad8\u6548\u3002<\/strong>\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528OpenCV\u8fdb\u884c\u8fb9\u7f18\u68c0\u6d4b\u548c\u8f6e\u5ed3\u63d0\u53d6\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528OpenCV\u8fdb\u884c\u8fb9\u7f18\u68c0\u6d4b<\/p>\n<\/p>\n

OpenCV\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\u3002\u5728\u63d0\u53d6\u56fe\u7247\u8fb9\u6846\u65f6\uff0c\u8fb9\u7f18\u68c0\u6d4b\u662f\u4e00\u4e2a\u91cd\u8981\u7684\u6b65\u9aa4\u3002\u53ef\u4ee5\u4f7f\u7528Canny\u8fb9\u7f18\u68c0\u6d4b\u7b97\u6cd5\u6765\u8fdb\u884c\u8fb9\u7f18\u68c0\u6d4b\u3002<\/p>\n<\/p>\n

    \n
  1. \u5b89\u88c5\u548c\u5bfc\u5165OpenCV<\/strong><\/li>\n<\/ol>\n

    \u9996\u5148\u9700\u8981\u5b89\u88c5OpenCV\u5e93\uff0c\u53ef\u4ee5\u901a\u8fc7pip\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

    pip install opencv-python<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165OpenCV\u5e93\uff1a<\/p>\n<\/p>\n
    import cv2<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u8bfb\u53d6\u548c\u9884\u5904\u7406\u56fe\u50cf<\/strong><\/li>\n<\/ol>\n
      \u4f7f\u7528OpenCV\u8bfb\u53d6\u56fe\u50cf\uff0c\u5e76\u8fdb\u884c\u7070\u5ea6\u8f6c\u6362\u548c\u9ad8\u65af\u6a21\u7cca\u5904\u7406\uff0c\u4ee5\u51cf\u5c11\u566a\u58f0\u5bf9\u8fb9\u7f18\u68c0\u6d4b\u7684\u5f71\u54cd\uff1a<\/p>\n<\/p>\n
      # \u8bfb\u53d6\u56fe\u50cf<\/p>\n
      image = cv2.imread('image.jpg')<\/p>\n
      \u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf<\/strong><\/h2>\n
      gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)<\/p>\n
      \u5e94\u7528\u9ad8\u65af\u6a21\u7cca<\/strong><\/h2>\n
      blurred = cv2.GaussianBlur(gray, (5, 5), 0)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
        \n
      1. Canny\u8fb9\u7f18\u68c0\u6d4b<\/strong><\/li>\n<\/ol>\n
        \u4f7f\u7528Canny\u8fb9\u7f18\u68c0\u6d4b\u7b97\u6cd5\u6765\u68c0\u6d4b\u56fe\u50cf\u7684\u8fb9\u7f18\uff1a<\/p>\n<\/p>\n
        # \u5e94\u7528Canny\u8fb9\u7f18\u68c0\u6d4b<\/p>\n
        edges = cv2.Canny(blurred, 50, 150)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. \u663e\u793a\u8fb9\u7f18\u56fe\u50cf<\/strong><\/li>\n<\/ol>\n
          \u4f7f\u7528OpenCV\u663e\u793a\u8fb9\u7f18\u68c0\u6d4b\u540e\u7684\u56fe\u50cf\uff1a<\/p>\n<\/p>\n
          cv2.imshow('Edges', edges)<\/p>\n
          cv2.wAI<\/a>tKey(0)<\/p>\n
          cv2.destroyAllWindows()<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u4e8c\u3001\u4f7f\u7528OpenCV\u8fdb\u884c\u8f6e\u5ed3\u63d0\u53d6<\/p>\n<\/p>\n
          \u5728\u5b8c\u6210\u8fb9\u7f18\u68c0\u6d4b\u540e\uff0c\u53ef\u4ee5\u63d0\u53d6\u56fe\u50cf\u4e2d\u7684\u8f6e\u5ed3\uff0c\u8fdb\u4e00\u6b65\u8bc6\u522b\u548c\u63d0\u53d6\u8fb9\u6846\u3002<\/p>\n<\/p>\n
            \n
          1. \u67e5\u627e\u8f6e\u5ed3<\/strong><\/li>\n<\/ol>\n
            \u4f7f\u7528OpenCV\u7684findContours<\/code>\u51fd\u6570\u6765\u67e5\u627e\u56fe\u50cf\u4e2d\u7684\u8f6e\u5ed3\uff1a<\/p>\n<\/p>\n
            contours, hierarchy = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u7ed8\u5236\u8f6e\u5ed3<\/strong><\/li>\n<\/ol>\n
              \u4f7f\u7528OpenCV\u7684drawContours<\/code>\u51fd\u6570\u6765\u7ed8\u5236\u68c0\u6d4b\u5230\u7684\u8f6e\u5ed3\uff1a<\/p>\n<\/p>\n
              # \u5728\u539f\u59cb\u56fe\u50cf\u4e0a\u7ed8\u5236\u8f6e\u5ed3<\/p>\n
              cv2.drawContours(image, contours, -1, (0, 255, 0), 2)<\/p>\n
              \u663e\u793a\u7ed8\u5236\u8f6e\u5ed3\u7684\u56fe\u50cf<\/strong><\/h2>\n
              cv2.imshow('Contours', image)<\/p>\n
              cv2.waitKey(0)<\/p>\n
              cv2.destroyAllWindows()<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u63d0\u53d6\u548c\u663e\u793a\u56fe\u50cf\u7684\u8fb9\u6846\u3002<\/strong><\/p>\n<\/p>\n
              \u4e09\u3001\u4f7f\u7528PIL\u5e93\u8fdb\u884c\u56fe\u50cf\u5904\u7406<\/p>\n<\/p>\n
              \u867d\u7136OpenCV\u662f\u63d0\u53d6\u8fb9\u6846\u7684\u5e38\u7528\u5de5\u5177\uff0c\u4f46PIL\u5e93\u4e5f\u53ef\u4ee5\u7528\u4e8e\u57fa\u672c\u7684\u56fe\u50cf\u5904\u7406\u4efb\u52a1\u3002<\/p>\n<\/p>\n
                \n
              1. \u5b89\u88c5\u548c\u5bfc\u5165PIL<\/strong><\/li>\n<\/ol>\n
                \u9996\u5148\u9700\u8981\u5b89\u88c5PIL\u5e93\uff0c\u53ef\u4ee5\u901a\u8fc7Pillow\u6765\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
                pip install Pillow<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                \u7136\u540e\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165PIL\u5e93\uff1a<\/p>\n<\/p>\n
                from PIL import Image, ImageFilter<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                1. \u6253\u5f00\u548c\u5904\u7406\u56fe\u50cf<\/strong><\/li>\n<\/ol>\n
                  \u4f7f\u7528PIL\u5e93\u6253\u5f00\u56fe\u50cf\u5e76\u5e94\u7528\u8fb9\u7f18\u589e\u5f3a\u8fc7\u6ee4\u5668\uff1a<\/p>\n<\/p>\n
                  # \u6253\u5f00\u56fe\u50cf<\/p>\n
                  image = Image.open('image.jpg')<\/p>\n
                  \u5e94\u7528\u8fb9\u7f18\u589e\u5f3a<\/strong><\/h2>\n
                  edges = image.filter(ImageFilter.FIND_EDGES)<\/p>\n
                  \u663e\u793a\u8fb9\u7f18\u56fe\u50cf<\/strong><\/h2>\n
                  edges.show()<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u867d\u7136PIL\u5e93\u7684\u8fb9\u7f18\u68c0\u6d4b\u80fd\u529b\u4e0d\u5982OpenCV\u5f3a\u5927\uff0c\u4f46\u5bf9\u4e8e\u7b80\u5355\u7684\u56fe\u50cf\u5904\u7406\u4efb\u52a1\u6765\u8bf4\u5df2\u7ecf\u8db3\u591f\u3002<\/p>\n<\/p>\n
                  \u56db\u3001\u4f7f\u7528scikit-image\u5e93\u8fdb\u884c\u8fb9\u7f18\u68c0\u6d4b<\/p>\n<\/p>\n
                  scikit-image\u662f\u4e00\u4e2a\u7528\u4e8e\u56fe\u50cf\u5904\u7406\u7684Python\u5e93\uff0c\u63d0\u4f9b\u4e86\u591a\u79cd\u56fe\u50cf\u5904\u7406\u7b97\u6cd5\u3002<\/p>\n<\/p>\n
                    \n
                  1. \u5b89\u88c5\u548c\u5bfc\u5165scikit-image<\/strong><\/li>\n<\/ol>\n
                    \u9996\u5148\u9700\u8981\u5b89\u88c5scikit-image\u5e93\uff1a<\/p>\n<\/p>\n
                    pip install scikit-image<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                    \u7136\u540e\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165scikit-image\u5e93\uff1a<\/p>\n<\/p>\n
                    from skimage import io, filters<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                      \n
                    1. \u8bfb\u53d6\u548c\u5904\u7406\u56fe\u50cf<\/strong><\/li>\n<\/ol>\n
                      \u4f7f\u7528scikit-image\u8bfb\u53d6\u56fe\u50cf\u5e76\u5e94\u7528Sobel\u6ee4\u6ce2\u5668\u8fdb\u884c\u8fb9\u7f18\u68c0\u6d4b\uff1a<\/p>\n<\/p>\n
                      # \u8bfb\u53d6\u56fe\u50cf<\/p>\n
                      image = io.imread('image.jpg', as_gray=True)<\/p>\n
                      \u5e94\u7528Sobel\u6ee4\u6ce2\u5668<\/strong><\/h2>\n
                      edges = filters.sobel(image)<\/p>\n
                      \u663e\u793a\u8fb9\u7f18\u56fe\u50cf<\/strong><\/h2>\n
                      io.imshow(edges)<\/p>\n
                      io.show()<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                      \u901a\u8fc7\u4f7f\u7528scikit-image\u5e93\uff0c\u53ef\u4ee5\u5b9e\u73b0\u8f83\u4e3a\u7ec6\u81f4\u7684\u8fb9\u7f18\u68c0\u6d4b\u6548\u679c\u3002<\/strong><\/p>\n<\/p>\n
                      \u4e94\u3001\u603b\u7ed3<\/p>\n<\/p>\n
                      \u5728Python\u4e2d\u63d0\u53d6\u56fe\u7247\u8fb9\u6846\u7684\u65b9\u6cd5\u591a\u79cd\u591a\u6837\uff0c\u6bcf\u79cd\u65b9\u6cd5\u90fd\u6709\u5176\u72ec\u7279\u7684\u4f18\u52bf\u3002OpenCV\u51ed\u501f\u5176\u5f3a\u5927\u7684\u529f\u80fd\u548c\u9ad8\u6548\u6027\uff0c\u662f\u56fe\u50cf\u5904\u7406\u7684\u9996\u9009\u5de5\u5177\u3002PIL\u548cscikit-image\u5219\u63d0\u4f9b\u4e86\u7b80\u5316\u7684\u56fe\u50cf\u5904\u7406\u63a5\u53e3\uff0c\u9002\u7528\u4e8e\u8f7b\u91cf\u7ea7\u7684\u56fe\u50cf\u5904\u7406\u4efb\u52a1\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5de5\u5177\u548c\u65b9\u6cd5\u6765\u5b9e\u73b0\u56fe\u7247\u8fb9\u6846\u7684\u63d0\u53d6\u3002<\/p>\n<\/p>\n
                      \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                       \u5982\u4f55\u4f7f\u7528Python\u63d0\u53d6\u56fe\u7247\u7684\u8fb9\u6846\uff1f<\/strong>
                      \u8981\u63d0\u53d6\u56fe\u7247\u7684\u8fb9\u6846\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u7684\u56fe\u50cf\u5904\u7406\u5e93\uff0c\u5982OpenCV\u548cPillow\u3002\u9996\u5148\uff0c\u52a0\u8f7d\u56fe\u7247\u5e76\u5c06\u5176\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\uff0c\u7136\u540e\u5e94\u7528\u8fb9\u7f18\u68c0\u6d4b\u7b97\u6cd5\uff08\u5982Canny\u7b97\u6cd5\uff09\u6765\u8bc6\u522b\u8fb9\u7f18\uff0c\u6700\u540e\u901a\u8fc7\u8f6e\u5ed3\u63d0\u53d6\u6216\u5176\u4ed6\u65b9\u6cd5\u5c06\u8fb9\u6846\u5206\u79bb\u51fa\u6765\u3002\u5177\u4f53\u6b65\u9aa4\u5305\u62ec\u8c03\u6574\u9608\u503c\u548c\u4f7f\u7528\u81a8\u80c0\u4e0e\u8150\u8680\u7b49\u6280\u672f\u6765\u6e05\u6670\u5730\u5b9a\u4e49\u8fb9\u6846\u3002<\/p>\n
                      \u63d0\u53d6\u8fb9\u6846\u65f6\u9700\u8981\u6ce8\u610f\u54ea\u4e9b\u53c2\u6570\u8bbe\u7f6e\uff1f<\/strong>
                      \u5728\u63d0\u53d6\u8fb9\u6846\u65f6\uff0c\u8fb9\u7f18\u68c0\u6d4b\u7b97\u6cd5\u7684\u53c2\u6570\u8bbe\u7f6e\u975e\u5e38\u91cd\u8981\u3002\u4f8b\u5982\uff0cCanny\u7b97\u6cd5\u7684\u4f4e\u9608\u503c\u548c\u9ad8\u9608\u503c\u4f1a\u5f71\u54cd\u8fb9\u7f18\u7684\u68c0\u6d4b\u6548\u679c\u3002\u540c\u65f6\uff0c\u56fe\u50cf\u7684\u5206\u8fa8\u7387\u548c\u5bf9\u6bd4\u5ea6\u4e5f\u4f1a\u5f71\u54cd\u63d0\u53d6\u7ed3\u679c\u3002\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u56fe\u7247\u7684\u7279\u70b9\u8fdb\u884c\u5b9e\u9a8c\uff0c\u627e\u5230\u6700\u4f73\u53c2\u6570\u7ec4\u5408\u3002<\/p>\n
                      \u63d0\u53d6\u8fb9\u6846\u540e\u5982\u4f55\u4fdd\u5b58\u5904\u7406\u8fc7\u7684\u56fe\u7247\uff1f<\/strong>
                      \u5904\u7406\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528Pillow\u5e93\u4e2d\u7684save()<\/code>\u65b9\u6cd5\u5c06\u63d0\u53d6\u7684\u8fb9\u6846\u56fe\u50cf\u4fdd\u5b58\u4e3a\u65b0\u6587\u4ef6\u3002\u53ef\u4ee5\u9009\u62e9\u4e0d\u540c\u7684\u6587\u4ef6\u683c\u5f0f\uff0c\u5982JPEG\u6216PNG\uff0c\u4ee5\u6ee1\u8db3\u4e0d\u540c\u7684\u9700\u6c42\u3002\u5728\u4fdd\u5b58\u65f6\uff0c\u786e\u4fdd\u6587\u4ef6\u8def\u5f84\u6b63\u786e\uff0c\u5e76\u9009\u62e9\u5408\u9002\u7684\u538b\u7f29\u8bbe\u7f6e\uff0c\u4ee5\u4fdd\u6301\u56fe\u50cf\u8d28\u91cf\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u63d0\u53d6\u56fe\u7247\u8fb9\u6846\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u4e3b\u8981\u5305\u62ec\uff1a\u4f7f\u7528OpenCV\u8fdb\u884c\u8fb9\u7f18\u68c0\u6d4b\u548c\u8f6e\u5ed3\u63d0\u53d6\u3001\u5229\u7528PIL\u5e93\u8fdb\u884c\u56fe […]","protected":false},"author":3,"featured_media":975515,"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\/975509"}],"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=975509"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/975509\/revisions"}],"predecessor-version":[{"id":975518,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/975509\/revisions\/975518"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/975515"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=975509"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=975509"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=975509"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}