{"id":104241,"date":"2026-09-12T07:17:02","date_gmt":"2026-09-11T23:17:02","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/104241.html"},"modified":"2026-09-12T07:17:02","modified_gmt":"2026-09-11T23:17:02","slug":"day-01-%ef%bc%9aai-%e6%8a%a0%e5%9b%be%ef%bc%9a3-%e8%a1%8c%e4%bb%a3%e7%a0%81%e5%8e%bb%e6%8e%89%e5%9b%be%e7%89%87%e8%83%8c%e6%99%af","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/104241.html","title":{"rendered":"Day 01 \uff1aAI \u62a0\u56fe\uff1a3 \u884c\u4ee3\u7801\u53bb\u6389\u56fe\u7247\u80cc\u666f"},"content":{"rendered":"

\u4e0a\u5468\u5e2e\u670b\u53cb\u7684\u5c0f\u5e97\u505a\u6dd8\u5b9d\u4e3b\u56fe，50 \u5f20\u5546\u54c1\u7167\u7247\u8981\u628a\u80cc\u666f\u5168\u6362\u6210\u7eaf\u767d\u8272\u3002\u670b\u53cb\u95ee："\u4f60\u4f1a PS \u5427？\u5e2e\u6211\u62a0\u4e2a\u56fe\u5457\u3002"\u6211\u7528\u94a2\u7b14\u5de5\u5177\u8bd5\u4e86\u4e00\u5f20，5 \u5206\u949f\u8d77\u6b65，50 \u5f20\u5c31\u662f 4 \u4e2a\u591a\u5c0f\u65f6\u3002\u540e\u6765\u82b1 3 \u5206\u949f\u5199\u4e86\u6bb5 Python，50 \u5f20\u56fe\u5168\u90e8\u81ea\u52a8\u62a0\u5b8c，\u6548\u679c\u6bd4\u624b\u52a8\u63cf\u7684\u8fd8\u5e72\u51c0\u3002\u8fd9\u7bc7\u6587\u7ae0\u5c31\u628a\u8fd9\u4ef6\u4e8b\u62c6\u5f00\u8bb2：\u5355\u5f20\u62a0\u56fe\u3001\u6279\u91cf\u5904\u7406\u3001\u8bc1\u4ef6\u7167\u6362\u5e95\u3001\u7535\u5546\u767d\u5e95\u56fe，\u4ee5\u53ca\u6bdb\u53d1\u8fb9\u7f18\u7684 Alpha Matting \u6280\u5de7\u548c Segment Anything \u6a21\u578b\u3002\u5168\u7a0b\u672c\u5730\u8fd0\u884c，\u96f6 API \u8d39\u7528\u3002<\/p>\n

\u5148\u770b\u4f20\u7edf\u65b9\u5f0f\u548c rembg \u7684\u5bf9\u6bd4：<\/p>\n\n\u65b9\u5f0f\u5355\u5f20\u8017\u65f6\u6279\u91cf\u80fd\u529b\u8fb9\u7f18\u8d28\u91cf\u95e8\u69db<\/tr>\n\n\n\n\n\n
PS \u94a2\u7b14\u5de5\u5177<\/td>\n3~10 \u5206\u949f<\/td>\n\u65e0<\/td>\n\u53d6\u51b3\u4e8e\u624b\u7a33<\/td>\n\u9ad8<\/td>\n<\/tr>\n
PS \u5feb\u901f\u9009\u62e9\/\u9b54\u68d2<\/td>\n30 \u79d2~2 \u5206\u949f<\/td>\n\u65e0<\/td>\n\u4e00\u822c，\u6bdb\u53d1\u707e\u96be<\/td>\n\u4e2d<\/td>\n<\/tr>\n
remove.bg \u7b49\u5728\u7ebf\u5de5\u5177<\/td>\n5 \u79d2<\/td>\n\u6709（\u6536\u8d39）<\/td>\n\u597d<\/td>\n\u4f4e，\u4f46\u8981\u94b1<\/td>\n<\/tr>\n
Python + rembg<\/td>\n2~5 \u79d2<\/td>\n\u6279\u91cf\u65e0\u4e0a\u9650<\/td>\n\u597d<\/td>\n\u4f1a\u88c5 pip \u5c31\u884c<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u5173\u952e\u533a\u522b：rembg \u5b8c\u5168\u514d\u8d39\u3001\u672c\u5730\u8fd0\u884c\u3001\u6279\u91cf\u65e0\u9650\u5236，\u56fe\u7247\u4e0d\u7528\u4f20\u5230\u522b\u4eba\u7684\u670d\u52a1\u5668\u4e0a，\u5546\u4e1a\u7d20\u6750\u548c\u5ba2\u6237\u7167\u7247\u4e0d\u7528\u62c5\u5fc3\u6cc4\u9732\u3002<\/p>\n

\u539f\u7406：U\u00b2-Net \u600e\u4e48\u628a\u524d\u666f"\u770b"\u51fa\u6765<\/h3>\n

\u4e0d\u5c55\u5f00\u8bba\u6587，\u53ea\u8bf4\u601d\u8def\u3002rembg \u7684\u6838\u5fc3\u662f\u4e00\u5957\u53eb U\u00b2-Net \u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b：<\/p>\n

  • \u8bad\u7ec3\u9636\u6bb5（\u5df2\u7ecf\u66ff\u4f60\u505a\u5b8c\u4e86）：\u7ed9\u6a21\u578b\u770b\u4e86\u51e0\u767e\u4e07\u5f20\u56fe，\u544a\u8bc9\u5b83"\u8fd9\u4e9b\u50cf\u7d20\u5c5e\u4e8e\u524d\u666f\u7269\u4f53，\u90a3\u4e9b\u5c5e\u4e8e\u80cc\u666f"，\u6a21\u578b\u6162\u6162\u5b66\u4f1a\u5206\u8fa8\u7269\u4f53\u8fb9\u754c\u3002<\/li>\n
  • \u63a8\u7406\u9636\u6bb5（\u4f60\u7528\u7684\u90e8\u5206）：\u628a\u56fe\u7247\u5582\u7ed9\u6a21\u578b，\u6a21\u578b\u7ed9\u6bcf\u4e2a\u50cf\u7d20\u6253\u4e00\u4e2a\u5206\u2014\u2014\u201c\u8fd9\u4e2a\u50cf\u7d20\u5c5e\u4e8e\u524d\u666f\u7684\u6982\u7387\u662f\u591a\u5c11\u201d，\u6700\u540e\u751f\u6210\u4e00\u5f20 Alpha \u8499\u7248（\u767d\u8272=\u524d\u666f，\u9ed1\u8272=\u80cc\u666f，\u7070\u8272=\u8fb9\u7f18\u8fc7\u6e21\u533a\u57df）\u3002<\/li>\n
  • \u5408\u6210\u9636\u6bb5：\u7528\u8fd9\u5f20\u8499\u7248\u505a\u900f\u660e\u5904\u7406，\u80cc\u666f\u53d8\u900f\u660e，\u524d\u666f\u4fdd\u7559\u3002<\/li>\n

    \u6574\u4e2a\u8fc7\u7a0b\u5c31\u662f：\u8f93\u5165\u4e00\u5f20\u56fe \u2192 \u6a21\u578b\u9884\u6d4b\u8499\u7248 \u2192 \u8f93\u51fa\u900f\u660e\u80cc\u666f\u56fe\u3002<\/p>\n

    rembg \u5185\u7f6e\u4e86\u597d\u51e0\u4e2a\u6a21\u578b，\u6700\u5e38\u7528\u8fd9\u51e0\u4e2a：<\/p>\n\n\u6a21\u578b\u5927\u5c0f\u7279\u70b9<\/tr>\n\n\n\n\n\n
    u2net<\/td>\n170MB<\/td>\n\u901a\u7528\u4e4b\u738b，\u6548\u679c\u6700\u5747\u8861（\u9ed8\u8ba4）<\/td>\n<\/tr>\n
    u2netp<\/td>\n4MB<\/td>\n\u8f7b\u91cf\u5feb\u901f，\u6548\u679c\u7a0d\u5dee<\/td>\n<\/tr>\n
    u2net_human_seg<\/td>\n170MB<\/td>\n\u4e13\u4e3a\u4eba\u50cf\u4f18\u5316，\u5934\u53d1\u8fb9\u7f18\u66f4\u597d<\/td>\n<\/tr>\n
    isnet-general-use<\/td>\n\u7ea6 180MB<\/td>\nISNet \u6a21\u578b，\u7ec6\u8282\u4fdd\u7559\u5f3a<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u9996\u6b21\u4f7f\u7528\u67d0\u4e2a\u6a21\u578b\u65f6\u4f1a\u81ea\u52a8\u4e0b\u8f7d，\u4e4b\u540e\u5c31\u5b58\u5728\u672c\u5730\u4e86，\u4e0d\u9700\u8981\u8054\u7f51\u3002\u4ee3\u7801\u548c\u6a21\u578b\u5206\u522b\u6765\u81ea rembg \u7684 GitHub \u4ed3\u5e93 \u548c U\u00b2-Net \u8bba\u6587（arXiv: 2005.09007）\u3002<\/p>\n

    \u5b89\u88c5\u4e0e\u7b2c\u4e00\u6bb5\u4ee3\u7801<\/h3>\n

    \u88c5\u4e09\u4e2a\u5305，\u6ca1\u6709\u522b\u7684\u4f9d\u8d56：<\/p>\n

    pip install<\/span> rembg pillow onnxruntime<\/p>\n

      \n
    • rembg：\u62a0\u56fe\u6838\u5fc3\u5e93<\/li>\n
    • pillow：Python \u56fe\u7247\u5904\u7406\u6807\u51c6\u5e93<\/li>\n
    • onnxruntime：\u6a21\u578b\u63a8\u7406\u5f15\u64ce<\/li>\n<\/ul>\n

      \u6700\u7b80\u7248\u672c\u771f\u7684\u53ea\u8981 3 \u884c：<\/p>\n

      from<\/span> PIL import<\/span> Image
      \nfrom<\/span> rembg import<\/span> remove<\/p>\n

      # \u6253\u5f00\u56fe\u7247 \u2192 \u53bb\u9664\u80cc\u666f \u2192 \u4fdd\u5b58<\/span>
      \nimg =<\/span> Image.<\/span>open<\/span>(<\/span>"input.jpg"<\/span>)<\/span>
      \nresult =<\/span> remove(<\/span>img)<\/span>
      \nresult.<\/span>save(<\/span>"output.png"<\/span>)<\/span><\/p>\n

      \u4e09\u884c\u3002\u6ca1\u7701\u7565\u3002\u6ca1 trick\u3002\u8dd1\u5b8c\u4f60\u5c31\u80fd\u62ff\u5230\u4e00\u5f20\u900f\u660e\u80cc\u666f\u7684 PNG\u3002\u8fd9\u91cc remove() \u5185\u90e8\u4f1a\u81ea\u5df1\u52a0\u8f7d\u9ed8\u8ba4\u7684 u2net \u6a21\u578b，\u5355\u5f20\u56fe\u65e0\u6240\u8c13；\u6279\u91cf\u5904\u7406\u5c31\u522b\u8fd9\u4e48\u5199\u4e86，\u89c1\u4e0b\u4e00\u8282\u3002<\/p>\n

      \u6279\u91cf\u5904\u7406：50 \u5f20\u56fe\u51e0\u5206\u949f<\/h3>\n

      \u6279\u91cf\u573a\u666f\u4e0b\u6700\u91cd\u8981\u7684\u662f\u9884\u52a0\u8f7d\u6a21\u578b\u2014\u2014\u5982\u679c\u6bcf\u5f20\u56fe\u90fd\u8ba9 remove() \u91cd\u65b0\u52a0\u8f7d\u4e00\u6b21，170MB \u7684\u6a21\u578b\u5149\u52a0\u8f7d\u5c31\u5f97\u597d\u51e0\u79d2，\u603b\u65f6\u95f4\u80fd\u7ffb 3 \u500d：<\/p>\n

      import<\/span> os
      \nfrom<\/span> pathlib import<\/span> Path
      \nfrom<\/span> PIL import<\/span> Image
      \nfrom<\/span> rembg import<\/span> remove,<\/span> new_session<\/p>\n

      input_dir =<\/span> ".\/product_photos"<\/span>
      \noutput_dir =<\/span> ".\/product_photos_cutout"<\/span>
      \nos.<\/span>makedirs(<\/span>output_dir,<\/span> exist_ok=<\/span>True<\/span>)<\/span><\/p>\n

      # \u652f\u6301\u7684\u56fe\u7247\u683c\u5f0f<\/span>
      \nvalid_ext =<\/span> {<\/span>".jpg"<\/span>,<\/span> ".jpeg"<\/span>,<\/span> ".png"<\/span>,<\/span> ".bmp"<\/span>,<\/span> ".webp"<\/span>}<\/span><\/p>\n

      # \u9884\u52a0\u8f7d\u6a21\u578b（\u91cd\u8981！\u907f\u514d\u6bcf\u5f20\u56fe\u7247\u91cd\u590d\u52a0\u8f7d）<\/span>
      \nsession =<\/span> new_session(<\/span>"u2net"<\/span>)<\/span><\/p>\n

      image_files =<\/span> [<\/span>
      \n f for<\/span> f in<\/span> Path(<\/span>input_dir)<\/span>.<\/span>iterdir(<\/span>)<\/span>
      \n if<\/span> f.<\/span>suffix.<\/span>lower(<\/span>)<\/span> in<\/span> valid_ext
      \n]<\/span><\/p>\n

      for<\/span> i,<\/span> img_path in<\/span> enumerate<\/span>(<\/span>image_files,<\/span> 1<\/span>)<\/span>:<\/span>
      \n img =<\/span> Image.<\/span>open<\/span>(<\/span>img_path)<\/span>.<\/span>convert(<\/span>"RGB"<\/span>)<\/span>
      \n result =<\/span> remove(<\/span>img,<\/span> session=<\/span>session)<\/span><\/p>\n

      out_name =<\/span> img_path.<\/span>stem +<\/span> "_nobg.png"<\/span>
      \n result.<\/span>save(<\/span>os.<\/span>path.<\/span>join(<\/span>output_dir,<\/span> out_name)<\/span>)<\/span>
      \n print<\/span>(<\/span>f"[<\/span>{<\/span>i}<\/span><\/span>\/<\/span>{<\/span>len<\/span>(<\/span>image_files)<\/span>}<\/span><\/span>] \u2705 <\/span>{<\/span>img_path.<\/span>name}<\/span><\/span>"<\/span><\/span>)<\/span><\/p>\n

      print<\/span>(<\/span>f"🎉 \u5168\u90e8\u5b8c\u6210！\u8f93\u51fa\u76ee\u5f55：<\/span>{<\/span>output_dir}<\/span><\/span>"<\/span><\/span>)<\/span><\/p>\n

      \u914d\u5957\u811a\u672c day01_remove_bg.py \u628a\u5355\u5f20\u3001\u6279\u91cf\u3001\u7cbe\u7ec6\u62a0\u56fe\u3001\u8bc1\u4ef6\u7167\u6362\u5e95\u3001\u767d\u5e95\u56fe\u90fd\u6574\u5408\u597d\u4e86，\u5e26\u6ce8\u91ca，\u76f4\u63a5\u8fd0\u884c\u5373\u53ef\u3002<\/p>\n

      \u8fdb\u9636：\u7cbe\u7ec6\u8fb9\u7f18\u3001\u6362\u5e95\u8272\u3001\u767d\u5e95\u56fe<\/h3>\n

      Alpha Matting：\u6bdb\u53d1\u6551\u661f<\/h4>\n

      \u5904\u7406\u732b\u54aa\u3001\u957f\u53d1\u4eba\u50cf\u3001\u6bdb\u7ed2\u73a9\u5177\u8fd9\u7c7b\u6709"\u6a21\u7cca\u8fb9\u7f18"\u7684\u7269\u4f53\u65f6，\u666e\u901a\u7684\u4e8c\u503c\u8499\u7248\u4f1a\u8ba9\u8fb9\u7f18\u770b\u8d77\u6765\u786c\u90a6\u90a6\u7684\u3002\u5f00\u542f alpha_matting=True \u540e，rembg \u4f1a\u5728\u8fb9\u7f18\u533a\u57df\u505a\u66f4\u7cbe\u7ec6\u7684\u534a\u900f\u660e\u8ba1\u7b97，\u8ba9\u6bdb\u53d1\u4e1d\u4e00\u6839\u6839\u81ea\u7136\u5730\u4fdd\u7559\u4e0b\u6765：<\/p>\n

      result =<\/span> remove(<\/span>
      \n img,<\/span>
      \n alpha_matting=<\/span>True<\/span>,<\/span> # \u5f00\u542f！<\/span>
      \n alpha_matting_foreground_threshold=<\/span>240<\/span>,<\/span> # \u524d\u666f\u5224\u5b9a\u9608\u503c<\/span>
      \n alpha_matting_background_threshold=<\/span>10<\/span>,<\/span> # \u80cc\u666f\u5224\u5b9a\u9608\u503c<\/span>
      \n alpha_matting_erode_size=<\/span>10<\/span>,<\/span> # \u8fb9\u7f18\u8150\u8680\u534a\u5f84<\/span>
      \n)<\/span><\/p>\n

      \u4e09\u4e2a\u53c2\u6570\u7684\u76f4\u89c9\u7406\u89e3：<\/p>\n

        \n
      • \u524d\u666f\u9608\u503c：\u8d8a\u9ad8\u8d8a\u4e25\u683c，\u53ea\u6709"\u975e\u5e38\u786e\u5b9a\u662f\u524d\u666f"\u7684\u50cf\u7d20\u624d\u4fdd\u7559<\/li>\n
      • \u80cc\u666f\u9608\u503c：\u8d8a\u4f4e\u8d8a\u4e25\u683c，\u53ea\u6709"\u975e\u5e38\u786e\u5b9a\u662f\u80cc\u666f"\u7684\u50cf\u7d20\u624d\u53bb\u9664<\/li>\n
      • \u8150\u8680\u534a\u5f84：\u8d8a\u5927，\u8fb9\u7f18\u8d8a\u5411\u5185\u6536\u7f29，\u9002\u5408\u53bb\u80cc\u666f\u8272\u6e17\u5165\u7684\u60c5\u51b5<\/li>\n<\/ul>\n

        \u8bc1\u4ef6\u7167\u6362\u5e95\u8272<\/h4>\n

        \u62a0\u51fa\u4eba\u50cf\u540e\u586b\u4e00\u5c42\u7eaf\u8272\u80cc\u666f，\u5c31\u662f\u8bc1\u4ef6\u7167\u6362\u5e95\u3002\u914d\u5957\u811a\u672c\u91cc\u7684 id_photo_change_bg() \u5c31\u662f\u8fd9\u4e2a\u601d\u8def：\u7528 u2net_human_seg \u4eba\u50cf\u6a21\u578b\u62a0\u56fe \u2192 \u65b0\u5efa\u7eaf\u8272 RGBA \u80cc\u666f \u2192 bg.paste(result, (0, 0), result) \u900f\u660e\u6df7\u5408\u3002\u5e38\u7528\u5e95\u8272：\u84dd\u8272 (0, 102, 204)\u3001\u7ea2\u8272 (255, 0, 0)\u3001\u767d\u8272 (255, 255, 255)\u3002<\/p>\n

        \u60f3\u7ec3\u624b\u7684\u8bdd，\u628a\u5b83\u6539\u6210\u547d\u4ee4\u884c\u5de5\u5177：\u8f93\u5165\u56fe\u7247\u8def\u5f84\u548c\u989c\u8272（r g b），\u8f93\u51fa\u6362\u597d\u5e95\u8272\u7684\u8bc1\u4ef6\u7167；\u518d\u52a0\u4e2a\u53c2\u6570\u652f\u6301\u81ea\u5b9a\u4e49\u8f93\u51fa\u5c3a\u5bf8（\u4e00\u5bf8 295\u00d7413\u3001\u4e8c\u5bf8 413\u00d7579）\u3002<\/p>\n

        \u7535\u5546\u767d\u5e95\u56fe<\/h4>\n

        \u540c\u6837\u7684\u5957\u8def，\u80cc\u666f\u6362\u6210\u7eaf\u767d，\u4fdd\u5b58\u6210\u9ad8\u8d28\u91cf JPEG（quality=95），\u5c31\u662f\u6dd8\u5b9d\/\u62fc\u591a\u591a\/1688 \u7684\u5546\u54c1\u4e3b\u56fe\u683c\u5f0f\u3002\u914d\u5957\u811a\u672c\u7684 product_white_bg() \u76f4\u63a5\u53ef\u7528\u3002<\/p>\n

        CPU \u591f\u7528，GPU \u66f4\u5feb<\/h4>\n

        rembg \u9ed8\u8ba4\u7528 CPU \u63a8\u7406，\u5355\u5f20 2~5 \u79d2\u5b8c\u5168\u591f\u7528；\u6279\u91cf\u51e0\u767e\u5f20\u6216\u5bf9\u901f\u5ea6\u8981\u6c42\u6781\u9ad8\u65f6，GPU \u80fd\u5feb 5~10 \u500d：<\/p>\n

        # \u5148\u786e\u4fdd\u88c5\u4e86 CUDA \u7248 onnxruntime<\/span>
        \npip install<\/span> rembg[<\/span>gpu]<\/span> onnxruntime-gpu<\/p>\n

        \u4ee3\u7801\u5b8c\u5168\u4e0d\u7528\u6539，rembg \u4f1a\u81ea\u52a8\u68c0\u6d4b\u5e76\u4f7f\u7528 GPU\u3002<\/p>\n

        \u6a21\u578b\u9009\u62e9\u7b56\u7565：<\/p>\n\n\u573a\u666f\u63a8\u8350\u6a21\u578b\u7406\u7531<\/tr>\n\n\n\n\n\n
        \u65e5\u5e38\u62a0\u56fe\u3001\u793e\u4ea4\u5a92\u4f53\u7d20\u6750<\/td>\nu2net（\u9ed8\u8ba4）<\/td>\n\u5747\u8861<\/td>\n<\/tr>\n
        \u5b9e\u65f6\u9884\u89c8\u3001\u79fb\u52a8\u7aef\u90e8\u7f72<\/td>\nu2netp<\/td>\n\u5feb，4MB \u5c31\u80fd\u8dd1<\/td>\n<\/tr>\n
        \u4eba\u50cf\u3001\u8bc1\u4ef6\u7167<\/td>\nu2net_human_seg<\/td>\n\u5934\u53d1\u8fb9\u7f18\u66f4\u81ea\u7136<\/td>\n<\/tr>\n
        \u590d\u6742\u80cc\u666f\u3001\u5546\u54c1<\/td>\nisnet-general-use<\/td>\n\u7ec6\u8282\u4fdd\u7559\u597d<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        \u8fd0\u884c\u6548\u679c<\/h3>\n

        \u4ee5 input.jpg \u4e3a\u4f8b，\u914d\u5957\u811a\u672c\u8dd1\u5b8c\u4f1a\u5f97\u5230\u8fd9\u51e0\u7c7b\u4ea7\u7269（\u4e0b\u56fe\u4e3a\u672c\u673a\u5b9e\u8dd1\u7ed3\u679c）：<\/p>\n

        !\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0<\/p>\n

        \u8fd0\u884c\u65e5\u5fd7：<\/p>\n

        !\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0<\/p>\n

        \u900f\u660e\u80cc\u666f\u62a0\u56fe：<\/p>\n

        !\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0<\/p>\n

        Alpha Matting \u7cbe\u7ec6\u62a0\u56fe（\u9002\u5408\u6bdb\u53d1\u3001\u534a\u900f\u660e\u8fb9\u7f18）：<\/p>\n

        !\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0<\/p>\n

        \u8bc1\u4ef6\u7167\u6362\u84dd\u5e95：<\/p>\n

        !\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0<\/p>\n

        \u7535\u5546\u767d\u5e95\u56fe：<\/p>\n

        !\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0<\/p>\n

        \u8d39\u7528\u4e0e\u8bb8\u53ef<\/h3>\n\n\u9879\u76ee\u8d39\u7528<\/tr>\n\n\n\n\n\n\n
        rembg \u5e93<\/td>\n\u514d\u8d39，MIT \u5f00\u6e90\u534f\u8bae<\/td>\n<\/tr>\n
        \u6a21\u578b\u6743\u91cd<\/td>\n\u514d\u8d39，\u9996\u6b21\u81ea\u52a8\u4e0b\u8f7d<\/td>\n<\/tr>\n
        GPU \u63a8\u7406<\/td>\n\u672c\u5730\u663e\u5361，\u65e0\u989d\u5916\u8d39\u7528<\/td>\n<\/tr>\n
        \u5728\u7ebf API（remove.bg \u7b49）<\/td>\n\u7ea6 \u00a50.1~0.5\/\u5f20（\u5bf9\u6bd4\u7528）<\/td>\n<\/tr>\n
        \u6bcf\u6708\u6279\u91cf 1000 \u5f20\u6210\u672c<\/td>\n\u00a50<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        \u5546\u7528\u5408\u89c4\u6027：rembg \u662f MIT \u534f\u8bae，\u5546\u7528\u65e0\u9650\u5236；U\u00b2-Net \u6a21\u578b\u662f Apache 2.0 \u534f\u8bae，\u5546\u7528\u65e0\u9650\u5236；ISNet \u6a21\u578b\u662f\u5b66\u672f\u7814\u7a76\u7528\u9014，\u5546\u7528\u9700\u8981\u786e\u8ba4\u8bb8\u53ef\u3002\u5bf9\u6bd4\u4e00\u4e0b：\u7528 remove.bg \u7684 API \u5904\u7406 1000 \u5f20\u56fe\u5927\u7ea6 \u00a5100~500，\u7528 rembg，\u6210\u672c\u662f\u96f6\u3002<\/p>\n

        \u5ef6\u4f38：Segment Anything<\/h3>\n

        \u5982\u679c\u4f60\u9700\u8981\u7684\u662f"\u5206\u5272\u56fe\u4e2d\u67d0\u4e2a\u7269\u4f53"\u800c\u4e0d\u662f"\u4e00\u952e\u53bb\u80cc\u666f"，\u53ef\u4ee5\u770b\u770b Meta \u5728 2023 \u5e74\u5f00\u6e90\u7684 Segment Anything Model（SAM）\u3002\u4e24\u8005\u601d\u8def\u4e0d\u540c：rembg \u81ea\u52a8\u5224\u65ad"\u4ec0\u4e48\u662f\u524d\u666f"，\u4e00\u952e\u51fa\u7ed3\u679c；SAM \u7528\u70b9\u3001\u6846\u3001\u6587\u5b57\u63d0\u793a\u6307\u5b9a"\u5206\u5272\u8fd9\u4e2a\u533a\u57df"，\u518d\u7cbe\u786e\u5206\u5272\u51fa\u6765\u3002\u7c7b\u6bd4：rembg \u50cf\u81ea\u52a8\u6321\u6c7d\u8f66，SAM \u50cf\u624b\u52a8\u6321\u2014\u2014\u66f4\u7075\u6d3b，\u4f46\u9700\u8981\u66f4\u591a\u64cd\u4f5c\u3002<\/p>\n

        # pip install segment-anything opencv-python<\/span>
        \nfrom<\/span> segment_anything import<\/span> sam_model_registry,<\/span> SamAutomaticMaskGenerator
        \nimport<\/span> cv2<\/p>\n

        # \u52a0\u8f7d SAM \u6a21\u578b<\/span>
        \nsam =<\/span> sam_model_registry[<\/span>"vit_h"<\/span>]<\/span>(<\/span>checkpoint=<\/span>"sam_vit_h_4b8939.pth"<\/span>)<\/span>
        \nmask_generator =<\/span> SamAutomaticMaskGenerator(<\/span>sam)<\/span><\/p>\n

        # \u751f\u6210\u6240\u6709\u53ef\u80fd\u7684\u5206\u5272\u533a\u57df<\/span>
        \nimage =<\/span> cv2.<\/span>imread(<\/span>"input.jpg"<\/span>)<\/span>
        \nmasks =<\/span> mask_generator.<\/span>generate(<\/span>image)<\/span><\/p>\n

        print<\/span>(<\/span>f"\u68c0\u6d4b\u5230 <\/span>{<\/span>len<\/span>(<\/span>masks)<\/span>}<\/span><\/span> \u4e2a\u5206\u5272\u533a\u57df"<\/span><\/span>)<\/span>
        \n# \u6bcf\u4e2a mask \u5305\u542b：segmentation（\u5e03\u5c14\u77e9\u9635）\u3001area（\u9762\u79ef）\u3001bbox（\u8fb9\u754c\u6846）\u7b49<\/span><\/p>\n

        SAM \u7684\u6a21\u578b\u6587\u4ef6\u6bd4\u8f83\u5927（ViT-H \u7ea6 2.4GB），\u9996\u6b21\u4f7f\u7528\u9700\u8981\u4e0b\u8f7d，\u9002\u5408\u9700\u8981\u66f4\u7cbe\u7ec6\u63a7\u5236\u7684\u573a\u666f；\u65e5\u5e38\u7b80\u5355\u62a0\u56fe\u7528 rembg \u5c31\u591f\u4e86\u3002\u76f8\u5173\u8d44\u6599：SAM \u5b98\u7f51\u3001SAM GitHub\u30022024 \u5e74 Meta \u53c8\u5f00\u6e90\u4e86 SAM 2，\u6269\u5c55\u5230\u4e86\u89c6\u9891\u5206\u5272，\u53ef\u4ee5\u8ffd\u8e2a\u89c6\u9891\u4e2d\u7684\u7269\u4f53，\u5bf9\u89c6\u9891\u5206\u5272\u611f\u5174\u8da3\u53ef\u4ee5\u5173\u6ce8\u3002<\/p>\n

        \u53e6\u5916\u4e24\u4e2a\u7ec3\u624b\u65b9\u5411：\u4e00\u662f\u6279\u91cf\u6c34\u5370\u6dfb\u52a0\u5668，\u5728\u62a0\u56fe\u57fa\u7840\u4e0a\u7528 Pillow \u7684 ImageDraw \u548c ImageFont \u7ed9\u6bcf\u5f20\u56fe\u53f3\u4e0b\u89d2\u52a0\u534a\u900f\u660e\u6587\u5b57\u6c34\u5370（\u5982\u5e97\u94fa\u540d），\u652f\u6301\u81ea\u5b9a\u4e49\u4f4d\u7f6e\u3001\u5b57\u53f7\u3001\u900f\u660e\u5ea6；\u4e8c\u662f\u89c6\u9891\u62a0\u56fe，pip install opencv-python \u540e\u7528 cv2.VideoCapture \u9010\u5e27\u8bfb\u53d6\u3001\u5bf9\u6bcf\u5e27\u8c03\u7528 remove() \u751f\u6210\u7eff\u5e55\u6548\u679c\u77ed\u89c6\u9891，\u52a0\u5206\u9879\u662f\u7528\u591a\u7ebf\u7a0b\/\u591a\u8fdb\u7a0b\u52a0\u901f\u3001\u52a0\u8fdb\u5ea6\u6761\u663e\u793a\u3002<\/p>\n

        \u6536\u5c3e<\/h3>\n

        \u6838\u5fc3\u5c31\u4e00\u53e5\u8bdd：<\/p>\n

        from<\/span> rembg import<\/span> remove
        \nresult =<\/span> remove(<\/span>Image.<\/span>open<\/span>(<\/span>"input.jpg"<\/span>)<\/span>)<\/span>
        \nresult.<\/span>save(<\/span>"output.png"<\/span>)<\/span><\/p>\n

        \u8bb0\u4f4f\u56db\u6761：\u6279\u91cf\u5904\u7406\u4e00\u5b9a\u8981\u9884\u52a0\u8f7d session，\u522b\u6bcf\u6b21\u91cd\u65b0\u52a0\u8f7d\u6a21\u578b；\u6bdb\u53d1\/\u534a\u900f\u660e\u573a\u666f\u5f00 alpha_matting；\u4eba\u50cf\u7528 u2net_human_seg；\u5b8c\u5168\u672c\u5730\u8fd0\u884c，\u96f6\u6210\u672c，MIT \u5546\u7528\u65e0\u5fe7\u3002\u628a input.jpg \u6362\u6210\u4f60\u81ea\u5df1\u7684\u56fe\u8dd1\u4e00\u904d，\u5269\u4e0b\u7684\u5c31\u662f\u770b\u6548\u679c\u4e86\u3002<\/p>\n

        \u53c2\u8003\u6587\u6863：Pillow（Pillow \u5b98\u65b9\u6587\u6863）\u3001onnxruntime（onnxruntime \u5b98\u65b9\u6587\u6863）\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

        \u4e0a\u5468\u5e2e\u670b\u53cb\u7684\u5c0f\u5e97\u505a\u6dd8\u5b9d\u4e3b\u56fe，50 \u5f20\u5546\u54c1\u7167\u7247\u8981\u628a\u80cc\u666f\u5168\u6362\u6210\u7eaf\u767d\u8272\u3002\u670b\u53cb\u95ee：\\”\u4f60\u4f1a PS \u5427？\u5e2e\u6211\u62a0\u4e2a\u56fe\u5457\u3002\\”\u6211\u7528\u94a2\u7b14\u5de5\u5177\u8bd5\u4e86\u4e00\u5f20，5 \u5206\u949f\u8d77\u6b65，50 \u5f20\u5c31\u662f 4 \u4e2a\u591a\u5c0f\u65f6\u3002\u540e\u6765\u82b1 3 \u5206\u949f\u5199\u4e86\u6bb5 Python，50 \u5f20\u56fe\u5168\u90e8\u81ea\u52a8\u62a0\u5b8c，\u6548\u679c\u6bd4\u624b\u52a8\u63cf\u7684\u8fd8\u5e72\u51c0\u3002\u8fd9\u7bc7\u6587\u7ae0\u5c31\u628a\u8fd9\u4ef6\u4e8b\u62c6\u5f00\u8bb2：\u5355\u5f20\u62a0\u56fe\u3001\u6279\u91cf\u5904\u7406\u3001\u8bc1\u4ef6\u7167\u6362\u5e95\u3001\u7535\u5546\u767d\u5e95\u56fe，\u4ee5\u53ca\u6bdb\u53d1\u8fb9\u7f18\u7684 Alpha <\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[9755,11863,11864,11721,81],"topic":[],"class_list":["post-104241","post","type-post","status-publish","format-standard","hentry","category-server","tag-ai","tag-rembg","tag-u2net","tag-11721","tag-python"],"yoast_head":"\nDay 01 \uff1aAI \u62a0\u56fe\uff1a3 \u884c\u4ee3\u7801\u53bb\u6389\u56fe\u7247\u80cc\u666f - 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