{"id":39607,"date":"2025-05-24T08:46:18","date_gmt":"2025-05-24T00:46:18","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/39607.html"},"modified":"2025-05-24T08:46:18","modified_gmt":"2025-05-24T00:46:18","slug":"%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a0%e6%a8%a1%e5%9e%8b%e9%83%a8%e7%bd%b2%ef%bc%9a%e4%bd%bf%e7%94%a8flask%e5%b0%86%e5%9b%be%e5%83%8f%e5%88%86%e7%b1%bb%ef%bc%885%e7%b1%bb%ef%bc%89%e6%a8%a1%e5%9e%8b","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/39607.html","title":{"rendered":"\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u90e8\u7f72\uff1a\u4f7f\u7528Flask\u5c06\u56fe\u50cf\u5206\u7c7b\uff085\u7c7b\uff09\u6a21\u578b\u90e8\u7f72\u5728\u670d\u52a1\u5668\u4e0a\uff0c\u7136\u540e\u5728\u672c\u5730GUI\u8c03\u7528\u3002\uff08\u5168\u7f51\u6a21\u578b\u90e8\u7f72\u9879\u76ee\u6b65\u9aa4\u8be6\u89e3\uff1a\u4ece\u6a21\u578b\u8bad\u7ec3\u5230\u90e8\u7f72\u518d\u5230\u8c03\u7528\uff09"},"content":{"rendered":"

\u4e2a\u4ebagithub\u5bf9\u5e94\u9879\u76ee\u94fe\u63a5： https:\/\/github.com\/KLWU07\/Image-classification-and-model-deployment 1.\u6d41\u7a0b\u603b\u89c8 2.\u56fe\u50cf\u5206\u7c7b\u7684\u6a21\u578b\u2014Alexnet 3.\u670d\u52a1\u5668\u7aef\u90e8\u7f72\u53ca\u8fd0\u884c 4.\u672c\u5730PyCharm\u8c03\u7528\u2014GUI\u754c\u9762<\/p>\n

\u4e00\u3001\u6d41\u7a0b\u603b\u89c8<\/h2>\n

\u2003\u2003\u672c\u9879\u76ee\u65b9\u6cd5\u8fd8\u662f\u4f7f\u7528Flask \u5e93，\u4e0e\u4e4b\u524d\u4e00\u7bc7\u673a\u5668\u5b66\u4e60\u6a21\u578b\u90e8\u7f72\u7c7b\u4f3c，\u53ea\u4e0d\u8fc7\u8fd9\u91cc\u6362\u4e86\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u4ee5\u53caGUI\u754c\u9762\u8c03\u7528，\u524d\u9762\u4e0d\u77e5\u9053\u53ef\u53c2\u8003\u8fd9\u7bc7\u6587\u7ae0\u673a\u5668\u5b66\u4e60\u6a21\u578b\u90e8\u7f72：\u4f7f\u7528Flask \u5e93\u7684 Python Web \u6846\u67b6\u5c06XGBoost\u6a21\u578b\u90e8\u7f72\u5728\u670d\u52a1\u5668\u4e0a（\u7b80\u5355\u5c0f\u6a21\u578b）\u4ece\u6a21\u578b\u8bad\u7ec3\u5230\u90e8\u7f72\u518d\u5230\u672c\u5730\u8c03\u7528，\u6709\u4e9b\u65b9\u6cd5\u6709\u4fee\u6539，\u81f3\u5c11\u6ca1\u6709\u9047\u5230\u62a5\u9519，\u4e4b\u524d\u662f\u5e93\u7248\u672c\u4e0d\u540c\u62a5\u9519\u3002\u5982\u679c\u4e0d\u77e5\u9053\u600e\u4e48\u8bad\u7ec3\u4e00\u4e2aAlexnet\u7f51\u7edc\u6a21\u578b，\u8bf7\u770b\u5185\u5bb9\u4e8c\u3002\u672c\u6a21\u578b\u5728CPU\u4e0b\u8bad\u7ec3，\u8bad\u7ec3\u73af\u5883python==3.8,\u6846\u67b6pytorch.\u6570\u636e\u96c6\u4e0b\u8f7d\u5730\u5740\u5982\u4e0b： https:\/\/storage.googleapis.com\/download.tensorflow.org\/example_images\/flower_photos.tgz \u2003\u2003\u672c\u9879\u76ee\u76ee\u5f55\u7ed3\u6784\u5982\u4e0b：\u4e3b\u8981\u5f97\u5230server\/\u548cclient\/\u6587\u4ef6\u5939\u3002<\/p>\n

image_classification\/
\n\u251c\u2500\u2500 server\/
\n\u2502 \u251c\u2500\u2500 app.py # Flask\u670d\u52a1\u7aef\u4ee3\u7801<\/span>
\n\u2502 \u251c\u2500\u2500 model.py # AlexNet\u6a21\u578b\u5b9a\u4e49，\u7531train\/\u91cc\u590d\u5236\u8fc7\u6765<\/span>
\n\u2502 \u251c\u2500\u2500 requirements.txt # \u670d\u52a1\u7aef\u4f9d\u8d56<\/span>
\n\u2502 \u2514\u2500\u2500 model\/
\n\u2502 \u251c\u2500\u2500 AlexNet-flower.pth # \u8bad\u7ec3\u597d\u7684\u6a21\u578b\u6743\u91cd，\u7531train\/\u91cc\u590d\u5236\u8fc7\u6765<\/span>
\n\u2502 \u2514\u2500\u2500 class_indices.json # \u7c7b\u522b\u6807\u7b7e\u6587\u4ef6，\u7531train\/\u91cc\u590d\u5236\u8fc7\u6765<\/span>
\n\u2514\u2500\u2500 client\/
\n\u2502 \u251c\u2500\u2500 gui_app.py # \u5ba2\u6237\u7aefGUI\u4ee3\u7801<\/span>
\n\u2502 \u2514\u2500\u2500 requirements.txt # \u5ba2\u6237\u7aef\u4f9d\u8d56<\/span>
\n\u251c\u2500\u2500 train\/ # \u8bad\u7ec3\u6a21\u578b\u6587\u4ef6\u5939<\/span>
\n\u2502 \u251c\u2500\u2500 model.py # AlexNet\u6a21\u578b\u5b9a\u4e49<\/span>
\n\u2502 \u251c\u2500\u2500 train.py # \u8bad\u7ec3\u6a21\u578b\u6587\u4ef6<\/span>
\n\u2502 \u251c\u2500\u2500 predict.py # \u8bad\u7ec3\u597d\u7684\u6a21\u578b\u4f7f\u7528\u4e00\u5f20\u56fe\u7247\u9884\u6d4b\u6587\u4ef6<\/span>
\n\u2502 \u2514\u2500\u2500 class_indices.json # \u8bad\u7ec3\u6a21\u578b\u65f6\u751f\u6210\u6587\u4ef6<\/span>
\n\u2514\u2500\u2500 data_set\/
\n\u2502 \u251c\u2500\u2500 flower_data # \u6570\u636e\u96c6<\/span>
\n\u2502 \u2502 \u251c\u2500\u2500 flower_photos\/ # \u5b98\u7f51\u516c\u5f00\u6570\u636e\u96c6<\/span>
\n\u2502 \u2502 \u251c\u2500\u2500 daisy\/ # \u91cc\u9762\u662f\u81ea\u5df1\u79cd\u7c7b\u56fe\u7247<\/span>
\n\u2502 \u2502 \u251c\u2500\u2500 dandelion\/ # \u56fe\u7247<\/span>
\n\u2502 \u2502 \u251c\u2500\u2500 roses\/ # \u56fe\u7247<\/span>
\n\u2502 \u2502 \u251c\u2500\u2500 sunflowers\/ # \u56fe\u7247<\/span>
\n\u2502 \u2502 \u2514\u2500\u2500tulips\/ # \u56fe\u7247<\/span>
\n\u2502 \u2502 \u251c\u2500\u2500train\/ # \u8bad\u7ec3\u96c6\u56fe\u7247,\u91cc\u9762\u7ed3\u6784\u548cflower_photos\u4e00\u6837<\/span>
\n\u2502 \u2502 \u2514\u2500\u2500val\/ # \u9a8c\u8bc1\u96c6\u56fe\u7247,\u91cc\u9762\u7ed3\u6784\u548cflower_photos\u4e00\u6837<\/span>
\n\u2502 \u2514\u2500\u2500 split_data.py # \u5212\u5206\u6570\u636e\u96c6\u6587\u4ef6，\u4e89\u5bf9flower_photos\u6587\u4ef6\u5939\u91cc\u6570\u636e\u5212\u5206\u6210train\u548cval\u4e24\u4e2a\u6587\u4ef6\u5939<\/span>
\n\u2514\u2500\u2500 requirements.txt # \u5728\u672c\u5730\u7aefpycharm\u7ec8\u7aef\u751f\u6210\u7684\u6587\u4ef6，\u65b9\u9762\u5728\u670d\u52a1\u5668\u7aef\u521b\u5efa\u76f8\u540c\u73af\u5883，\u5b89\u88c5\u76f8\u540c\u7248\u672c\u7684\u5e93，\u4e09\u4e2arequirements.txt\u6587\u4ef6\u76f8\u540c\u3002<\/span><\/p>\n

\u4e8c\u3001\u8bad\u7ec3\u56fe\u50cf\u5206\u7c7b\u6a21\u578b\u2014Alexnet<\/h2>\n

\u2003\u2003 \u8bf4\u660e\u8fd9\u91cc\u4e0d\u4ecb\u7ecdAlexnet\u6a21\u578b\u7ed3\u6784\u4ee5\u53ca\u600e\u4e48\u8bad\u7ec3\u4f60\u81ea\u5df1\u7684\u5206\u7c7b\u6a21\u578b，\u8bf7\u53c2\u8003\u4e4b\u524d\u4e24\u7bc7\u6587\u7ae0 \u3002\u5377\u79ef\u795e\u7ecf\u7f51\u7edcCNN\u91cc\u7ecf\u5178\u7f51\u7edc\u6a21\u578b\u4e4b AlexNet\u5168\u7f51\u6700\u8be6\u89e3（\u7406\u8bba\u7bc7）\u548c \u6df1\u5ea6\u5b66\u4e60pytorch\u5b9e\u6218\u4e8c：AlexNet\u56fe\u50cf\u5206\u7c7b\u7bc7\u4e14\u5b98\u7f51\u63d0\u4f9b\u82b1\u6570\u636e\u96c6\u5206\u4e94\u7c7b\u3002<\/p>\n

1.\u8bad\u7ec3\u6a21\u578b\u548c\u9884\u6d4b<\/h3>\n

\u603b\u7ed3\u4ee5\u4e0b\u51e0\u6b65： 1.\u81ea\u5efa\u51e0\u7c7b\u6570\u636e\u96c6，train\u548cval\u653e\u597d\u5bf9\u5e94\u51e0\u7c7b\u6587\u4ef6\u5939，\u5982\u679c\u7ec3\u4e60\u5c31\u91c7\u7528\u5b98\u7f51\u63d0\u4f9b\u6570\u636e\u96c6\u3002 2.split_data.py\u6587\u4ef6\u5212\u5206\u6570\u636e\u96c6，\u5f97\u5230\u5f53\u524d\u6587\u4ef6\u5939\u4e0b\u4ea7\u751ftrain\u548cval\u6587\u4ef6\u5939（\u6a21\u578b\u8bad\u7ec3\u8bad\u7ec3\u96c6\u548c\u9a8c\u8bc1\u96c6）\u3002 3.model.py\u548ctrain.py\u6587\u4ef6（\u4ee3\u7801\u76f4\u63a5\u5728\u90a3\u7bc7\u6587\u7ae0\u590d\u5236）. 4.\u6a21\u578b\u8bad\u7ec3\u7ed3\u675f\u6709\u4e24\u4e2a\u6587\u4ef6AlexNet-flower.pth\u548cclass_indices.json\u3002 5.\u590d\u5236\u79fb\u52a8\u76f8\u5e94\u7684\u6587\u4ef6\u5230\u6307\u5b9a\u76ee\u5f55<\/p>\n

2.\u751f\u6210requirements.txt<\/h3>\n

\u65b9\u6cd51.\u4e0d\u5efa\u8bae\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801，\u5f53\u524d\u73af\u5883\u4e2d\u6240\u6709\u5df2\u5b89\u88c5\u7684\u5305\u53ca\u5176\u7248\u672c\u5199\u5165requirements.txt，\u4f7f\u7528\u65b9\u6cd52<\/p>\n

pip freeze ><\/span> requirements.<\/span>txt<\/p>\n

# \u56e0\u4e3a\u6587\u672c\u4e2d\u6709\u542b\u5176\u4ed6\u5185\u5bb9，\u5982<\/span>
\nFlask==<\/span>3.1<\/span>.1<\/span>
\nmatplotlib==<\/span>3.7<\/span>.2<\/span>
\ngmpy2 @ file<\/span>:<\/span>\/\/<\/span>\/<\/span>C:<\/span>\/<\/span>ci\/<\/span>gmpy2_1645456279018\/<\/span>work
\nnumpy==<\/span>1.24<\/span>.4<\/span>
\nidna @ file<\/span>:<\/span>\/\/<\/span>\/<\/span>C:<\/span>\/<\/span>b\/<\/span>abs_aad84bnnw5\/<\/span>croot\/<\/span>idna_1714398896795\/<\/span>work<\/p>\n

\u65b9\u6cd52.\u4f7f\u7528pipreqs\u5de5\u5177\u81ea\u52a8\u5206\u6790\u4ee3\u7801\u4e2d\u7684\u5bfc\u5165\u8bed\u53e5，\u751f\u6210\u66f4\u7cbe\u51c6\u7684\u4f9d\u8d56\u5217\u8868<\/p>\n

# \u5b89\u88c5\u5de5\u5177<\/span>
\npip install pipreqs<\/p>\n

# \u751f\u6210requirements.txt（\u5728\u9879\u76ee\u6839\u76ee\u5f55\u4e0b\u6267\u884c）<\/span>
\npipreqs .<\/span><\/p>\n

# –force\u53c2\u6570\u7684\u4f5c\u7528\u662f\u8986\u76d6\u5df2\u6709\u7684requirements.txt\u6587\u4ef6\u3002\u8fd0\u884c\u4ee3\u7801\u8fc7\u7a0b\u4e2d\u4e5f\u4f1a\u8b66\u544a\u62a5\u9519\u4e00\u4e9b\u5185\u5bb9，\u4e0d\u5f71\u54cd\u7b49\u5f85\u751f\u6210requirements.txt\u3002<\/span>
\npipreqs .<\/span> –<\/span>–<\/span>force<\/p>\n

\u65b9\u6cd53:\u6bd4\u8f83\u9ebb\u70e6，\u9700\u8981\u4e00\u4e2a\u4e2a\u53bb\u641c\u7248\u672c\u5e93\u5728\u5b89\u88c5\u3002 \u5f53\u7136\u8fd9\u91cc\u53ef\u4ee5\u68c0\u67e5\u5bf9\u5e94\u5e93\u7248\u672c，\u4ee5\u53ca\u66f4\u65b0\u5bf9\u5e94\u7248\u672c\u5e93\u3002<\/p>\n

(<\/span>pytorchcpu)<\/span> :<\/span>pip show torch #\u793a\u4f8b\u4e00\u4e2a<\/span><\/p>\n

Name:<\/span> torch
\nVersion:<\/span> 2.4<\/span>.1<\/span>
\nSummary:<\/span> Tensors and<\/span> Dynamic neural networks in<\/span> Python with<\/span> strong GPU acceleration
\nHome–<\/span>page:<\/span> https:<\/span>\/\/<\/span>pytorch.<\/span>org\/<\/span>
\nAuthor:<\/span> PyTorch Team
\nAuthor–<\/span>email:<\/span> packages@pytorch.<\/span>org
\nLicense:<\/span> BSD–<\/span>3<\/span>
\nLocation:<\/span> c:<\/span>\\\\users\\\\.<\/span>conda\\\\envs\\\\pytorchcpu\\\\lib\\\\site–<\/span>packages
\nRequires:<\/span> filelock,<\/span> fsspec,<\/span> jinja2,<\/span> networkx,<\/span> sympy,<\/span> typing–<\/span>extensions
\nRequired–<\/span>by:<\/span> torchaudio,<\/span> torchvision<\/p>\n

pip install torch==<\/span>2.4<\/span>.1<\/span> # \u5b89\u88c5 torch 2.4.1 \u7248\u672c<\/span><\/p>\n

#\u5982\u679c<\/span>
\npip install –<\/span>–<\/span>upgrade flask==<\/span>2.2<\/span>.5<\/span> # \u5347\u7ea7\u6216\u964d\u7ea7\u5230 2.2.5 \u7248\u672c<\/span><\/p>\n

#conda\u4ee3\u7801<\/span>
\nconda install flask=<\/span>2.2<\/span>.5<\/span> # \u6ce8\u610f\u662f\u7b49\u53f7（=）\u800c\u975e\u53cc\u7b49\u53f7（==）<\/span>
\nconda update flask=<\/span>2.2<\/span>.5<\/span><\/p>\n

\u5f53\u7136\u8fd8\u6709\u5176\u4ed6\u5b89\u88c5\u5e93\u548c\u5305，\u53c2\u8003\u8fd9\u7bc7\u6587\u7ae0\u4e89\u5bf9\u673a\u5668\u5b66\u4e60\u548c\u6df1\u5ea6\u5b66\u4e60\u91ccPython\u9879\u76ee\u5f00\u53d1\u7ba1\u7406\u9879\u76ee\u4f9d\u8d56\u7684\u5de5\u5177\u4e2d\u65b9\u4fbf\u7b2c\u4e09\u65b9\u5e93\u548c\u5305\u7684\u5b89\u88c5<\/p>\n

\u4e09\u3001\u670d\u52a1\u5668\u7aef\u90e8\u7f72\u53ca\u8fd0\u884c<\/h2>\n

1.\u670d\u52a1\u5668\u7aef\u4ee3\u7801 (server\/app.py)<\/h3>\n

import<\/span> os
\nfrom<\/span> flask import<\/span> Flask,<\/span> request,<\/span> jsonify
\nfrom<\/span> werkzeug.<\/span>utils import<\/span> secure_filename
\nimport<\/span> torch
\nfrom<\/span> torchvision import<\/span> transforms
\nfrom<\/span> PIL import<\/span> Image
\nimport<\/span> json
\nimport<\/span> numpy as<\/span> np
\nfrom<\/span> model import<\/span> AlexNet # \u786e\u4fdd\u60a8\u7684\u6a21\u578b\u5b9a\u4e49\u53ef\u7528<\/span><\/p>\n

app =<\/span> Flask(<\/span>__name__)<\/span><\/p>\n

# \u914d\u7f6e<\/span>
\nUPLOAD_FOLDER =<\/span> 'uploads'<\/span>
\nALLOWED_EXTENSIONS =<\/span> {<\/span>'png'<\/span>,<\/span> 'jpg'<\/span>,<\/span> 'jpeg'<\/span>}<\/span>
\napp.<\/span>config[<\/span>'UPLOAD_FOLDER'<\/span>]<\/span> =<\/span> UPLOAD_FOLDER<\/p>\n

# \u52a0\u8f7d\u6a21\u578b\u548c\u7c7b\u522b\u4fe1\u606f<\/span>
\ndevice =<\/span> torch.<\/span>device(<\/span>"cuda:0"<\/span> if<\/span> torch.<\/span>cuda.<\/span>is_available(<\/span>)<\/span> else<\/span> "cpu"<\/span>)<\/span>
\nmodel =<\/span> AlexNet(<\/span>num_classes=<\/span>5<\/span>,<\/span> init_weights=<\/span>True<\/span>)<\/span>
\nmodel.<\/span>load_state_dict(<\/span>torch.<\/span>load(<\/span>'model\/AlexNet-flower.pth'<\/span>,<\/span> map_location=<\/span>device,<\/span> weights_only=<\/span>True<\/span>)<\/span>)<\/span>
\nmodel.<\/span>to(<\/span>device)<\/span>
\nmodel.<\/span>eval<\/span>(<\/span>)<\/span><\/p>\n

# \u52a0\u8f7d\u7c7b\u522b\u4fe1\u606f<\/span>
\nwith<\/span> open<\/span>(<\/span>'model\/class_indices.json'<\/span>,<\/span> 'r'<\/span>)<\/span> as<\/span> f:<\/span>
\n class_indices =<\/span> json.<\/span>load(<\/span>f)<\/span><\/p>\n

# \u56fe\u50cf\u9884\u5904\u7406<\/span>
\ndata_transform =<\/span> transforms.<\/span>Compose(<\/span>[<\/span>
\n transforms.<\/span>Resize(<\/span>(<\/span>224<\/span>,<\/span> 224<\/span>)<\/span>)<\/span>,<\/span>
\n transforms.<\/span>ToTensor(<\/span>)<\/span>,<\/span>
\n transforms.<\/span>Normalize(<\/span>(<\/span>0.5<\/span>,<\/span> 0.5<\/span>,<\/span> 0.5<\/span>)<\/span>,<\/span> (<\/span>0.5<\/span>,<\/span> 0.5<\/span>,<\/span> 0.5<\/span>)<\/span>)<\/span>
\n]<\/span>)<\/span><\/p>\n

def<\/span> allowed_file<\/span>(<\/span>filename)<\/span>:<\/span>
\n return<\/span> '.'<\/span> in<\/span> filename and<\/span> filename.<\/span>rsplit(<\/span>'.'<\/span>,<\/span> 1<\/span>)<\/span>[<\/span>1<\/span>]<\/span>.<\/span>lower(<\/span>)<\/span> in<\/span> ALLOWED_EXTENSIONS<\/p>\n

@app.<\/span>route<\/span>(<\/span>'\/predict'<\/span>,<\/span> methods=<\/span>[<\/span>'POST'<\/span>]<\/span>)<\/span>
\ndef<\/span> predict<\/span>(<\/span>)<\/span>:<\/span>
\n if<\/span> 'file'<\/span> not<\/span> in<\/span> request.<\/span>files:<\/span>
\n return<\/span> jsonify(<\/span>{<\/span>'error'<\/span>:<\/span> 'No file part'<\/span>}<\/span>)<\/span>,<\/span> 400<\/span><\/p>\n

file<\/span> =<\/span> request.<\/span>files[<\/span>'file'<\/span>]<\/span>
\n if<\/span> file<\/span>.<\/span>filename ==<\/span> ''<\/span>:<\/span>
\n return<\/span> jsonify(<\/span>{<\/span>'error'<\/span>:<\/span> 'No selected file'<\/span>}<\/span>)<\/span>,<\/span> 400<\/span><\/p>\n

if<\/span> file<\/span> and<\/span> allowed_file(<\/span>file<\/span>.<\/span>filename)<\/span>:<\/span>
\n filename =<\/span> secure_filename(<\/span>file<\/span>.<\/span>filename)<\/span>
\n filepath =<\/span> os.<\/span>path.<\/span>join(<\/span>app.<\/span>config[<\/span>'UPLOAD_FOLDER'<\/span>]<\/span>,<\/span> filename)<\/span>
\n file<\/span>.<\/span>save(<\/span>filepath)<\/span><\/p>\n

try<\/span>:<\/span>
\n # \u9884\u5904\u7406\u56fe\u50cf<\/span>
\n img =<\/span> Image.<\/span>open<\/span>(<\/span>filepath)<\/span>
\n if<\/span> img.<\/span>mode !=<\/span> 'RGB'<\/span>:<\/span>
\n img =<\/span> img.<\/span>convert(<\/span>'RGB'<\/span>)<\/span>
\n img =<\/span> data_transform(<\/span>img)<\/span>
\n img =<\/span> torch.<\/span>unsqueeze(<\/span>img,<\/span> dim=<\/span>0<\/span>)<\/span><\/p>\n

# \u9884\u6d4b<\/span>
\n with<\/span> torch.<\/span>no_grad(<\/span>)<\/span>:<\/span>
\n output =<\/span> model(<\/span>img.<\/span>to(<\/span>device)<\/span>)<\/span>
\n predict =<\/span> torch.<\/span>softmax(<\/span>output,<\/span> dim=<\/span>1<\/span>)<\/span>
\n prob,<\/span> classes =<\/span> torch.<\/span>max<\/span>(<\/span>predict,<\/span> 1<\/span>)<\/span><\/p>\n

class_name =<\/span> class_indices[<\/span>str<\/span>(<\/span>classes.<\/span>item(<\/span>)<\/span>)<\/span>]<\/span>
\n confidence =<\/span> prob.<\/span>item(<\/span>)<\/span><\/p>\n

# \u6e05\u7406\u4e0a\u4f20\u7684\u6587\u4ef6<\/span>
\n os.<\/span>remove(<\/span>filepath)<\/span><\/p>\n

return<\/span> jsonify(<\/span>{<\/span>
\n 'class'<\/span>:<\/span> class_name,<\/span>
\n 'confidence'<\/span>:<\/span> confidence,<\/span>
\n 'class_id'<\/span>:<\/span> classes.<\/span>item(<\/span>)<\/span>
\n }<\/span>)<\/span>
\n except<\/span> Exception as<\/span> e:<\/span>
\n return<\/span> jsonify(<\/span>{<\/span>'error'<\/span>:<\/span> f'Prediction failed: <\/span>{<\/span>str<\/span>(<\/span>e)<\/span>}<\/span><\/span>'<\/span><\/span>}<\/span>)<\/span>,<\/span> 500<\/span><\/p>\n

return<\/span> jsonify(<\/span>{<\/span>'error'<\/span>:<\/span> 'File type not allowed'<\/span>}<\/span>)<\/span>,<\/span> 400<\/span><\/p>\n

if<\/span> __name__ ==<\/span> '__main__'<\/span>:<\/span>
\n # \u786e\u4fdd\u4e0a\u4f20\u6587\u4ef6\u5939\u5b58\u5728<\/span>
\n os.<\/span>makedirs(<\/span>UPLOAD_FOLDER,<\/span> exist_ok=<\/span>True<\/span>)<\/span>
\n app.<\/span>run(<\/span>host=<\/span>'0.0.0.0'<\/span>,<\/span> port=<\/span>5000<\/span>,<\/span> debug=<\/span>True<\/span>)<\/span><\/p>\n

2.\u521b\u5efa\u73af\u5883\u548c\u5b89\u88c5\u5bf9\u5e94\u5e93<\/h3>\n

conda create –name<\/span> WY python<\/span>=<\/span>3.8<\/span> #\u521b\u5efa\u73af\u5883<\/span><\/p>\n

\u5b89\u88c5requirements.txt\u95ee\u4ef7\u91cc\u5e93\u3002<\/p>\n

Flask==<\/span>3.0<\/span>.3<\/span>
\nmatplotlib==<\/span>3.7<\/span>.2<\/span>
\nnumpy==<\/span>1.24<\/span>.4<\/span>
\nPillow==<\/span>10.4<\/span>.0<\/span>
\nRequests==<\/span>2.32<\/span>.3<\/span>
\ntorch==<\/span>2.4<\/span>.1<\/span>
\ntorchvision==<\/span>0.20<\/span>.0<\/span>
\ntqdm==<\/span>4.67<\/span>.1<\/span>
\nWerkzeug==<\/span>3.0<\/span>.3<\/span><\/p>\n

3.\u4e0a\u4f20server\u6587\u4ef6\u5939\u5230\u670d\u52a1\u5668<\/h3>\n

\u8bd5\u7740\u9a8c\u8bc1\u662f\u5426\u80fd\u8fd0\u884c-modle.py<\/p>\n

conda activate WY #\u6fc0\u6d3b\u73af\u5883<\/span>
\npython modle.py #\u662f\u5426\u62a5\u9519，\u6ca1\u6709\u62a5\u9519\u5c31\u53ef\u4ee5，\u8fd0\u884capp.py\u6587\u4ef6<\/span><\/p>\n

\u5982\u679c\u62a5\u9519\u5982\u4e0b<\/p>\n

(<\/span>WY)<\/span> $ python model.py
\nTraceback (<\/span>most recent call last)<\/span>:
\n File "model.py"<\/span>, line 1<\/span>, in<\/span> <<\/span>module><\/span>
\n import<\/span> torch.nn as nn
\n File "\/data\/home\/sgt_wkl\/software\/miniconda3\/envs\/WY\/lib\/python3.8\/site-packages\/torch\/__init__.py"<\/span>, line 289<\/span>, in<\/span> <<\/span>module><\/span>
\n _load_global_deps(<\/span>)<\/span>
\n File "\/data\/home\/sgt_wkl\/software\/miniconda3\/envs\/WY\/lib\/python3.8\/site-packages\/torch\/__init__.py"<\/span>, line 245<\/span>, in<\/span> _load_global_deps
\n raise err
\n File "\/data\/home\/sgt_wkl\/software\/miniconda3\/envs\/WY\/lib\/python3.8\/site-packages\/torch\/__init__.py"<\/span>, line 226<\/span>, in<\/span> _load_global_deps
\n ctypes.CDLL(<\/span>global_deps_lib_path, mode<\/span>=<\/span>ctypes.RTLD_GLOBAL)<\/span>
\n File "\/data\/home\/sgt_wkl\/software\/miniconda3\/envs\/WY\/lib\/python3.8\/ctypes\/__init__.py"<\/span>, line 373<\/span>, in<\/span> __init__
\n self._handle =<\/span> _dlopen(<\/span>self._name, mode)<\/span>
\nOSError: libstdc++.so.6: wrong ELF class: ELFCLASS32<\/p>\n

\u89e3\u6790：\u95ee\u9898\u7684\u6839\u672c\u539f\u56e0\u662f libstdc++.so.6 \u6587\u4ef6\u7684 ELF \u7c7b\u578b\u4e0e\u7cfb\u7edf\u8981\u6c42\u4e0d\u5339\u914d\u3002\u5177\u4f53\u6765\u8bf4，\u62a5\u9519\u4fe1\u606f\u4e2d\u63d0\u5230 wrong ELF class: ELFCLASS32，\u8fd9\u610f\u5473\u7740\u52a0\u8f7d\u7684 libstdc++.so.6 \u662f 32<\/span> \u4f4d\u7684，\u800c\u4f60\u7684\u7cfb\u7edf\u6216\u7a0b\u5e8f\u53ef\u80fd\u9700\u8981 64<\/span> \u4f4d\u7684\u7248\u672c\u3002Miniconda \u73af\u5883\u4e2d\u53ef\u80fd\u5b89\u88c5\u4e86\u9519\u8bef\u7248\u672c\u7684 libstdc++，<\/p>\n

# \u89e3\u51b3<\/span>
\nconda install<\/span> -c<\/span> conda-forge libstdcxx-ng # \u5728 Miniconda \u73af\u5883\u4e2d\u5b89\u88c5\u6b63\u786e\u7684 libstdc++<\/span>
\nconda list libstdc<\/p>\n

4.\u8fd0\u884capp.py<\/h3>\n

python app.py<\/p>\n

\u8fd0\u884c\u540e\u751f\u6210\u4e00\u4e9b\u5185\u5bb9<\/p>\n

(<\/span>WY)<\/span> $ python app.py<\/p>\n

* Serving Flask app 'app'<\/span>
\n * Debug mode: on
\nWARNING: This is a development server. Do not use it in<\/span> a production deployment. Use a production WSGI server instead.
\n * Running on all addresses (<\/span>0.0<\/span>.0.0)<\/span>
\n * Running on http:\/\/111.1.1.1:5000 # IP\u6211\u6539\u62101\u66ff\u6362<\/span>
\n * Running on http:\/\/111.111.111.1:5000 # IP\u6211\u6539\u62101\u66ff\u6362<\/span><\/p>\n

Press CTRL+C to quit
\n * Restarting with stat<\/span>
\n * Debugger is active!<\/span>
\n * Debugger PIN: 111<\/span>-111-111 # \u6211\u6539\u62101\u66ff\u6362<\/span><\/p>\n

\u6bcf\u4e2a\u8bbe\u5907\u751f\u6210\u7684IP\u5730\u5740\u4e0d\u540c，\u8bb0\u5f97\u4fee\u6539\u3002<\/p>\n

\u56db\u3001\u672c\u5730PyCharm\u8c03\u7528\u2014GUI\u754c\u9762<\/h2>\n

1.gui_app.py\u4ee3\u7801<\/h3>\n

import<\/span> tkinter as<\/span> tk
\nfrom<\/span> tkinter import<\/span> filedialog,<\/span> messagebox
\nfrom<\/span> PIL import<\/span> Image,<\/span> ImageTk
\nimport<\/span> requests
\nimport<\/span> io<\/p>\n

class<\/span> ImageClassifierApp<\/span>:<\/span>
\n def<\/span> __init__<\/span>(<\/span>self,<\/span> root)<\/span>:<\/span>
\n self.<\/span>root =<\/span> root
\n self.<\/span>root.<\/span>title(<\/span>"\u82b1\u5349\u56fe\u50cf\u5206\u7c7b\u5668"<\/span>)<\/span>
\n self.<\/span>root.<\/span>geometry(<\/span>"800×600"<\/span>)<\/span><\/p>\n

# \u670d\u52a1\u5668URL<\/span>
\n self.<\/span>server_url =<\/span> "http:\/\/192.168.168.5:5000\/predict"<\/span><\/p>\n

# \u521b\u5efaUI\u7ec4\u4ef6<\/span>
\n self.<\/span>create_widgets(<\/span>)<\/span><\/p>\n

def<\/span> create_widgets<\/span>(<\/span>self)<\/span>:<\/span>
\n # \u9876\u90e8\u6846\u67b6<\/span>
\n top_frame =<\/span> tk.<\/span>Frame(<\/span>self.<\/span>root)<\/span>
\n top_frame.<\/span>pack(<\/span>pady=<\/span>20<\/span>)<\/span><\/p>\n

# \u9009\u62e9\u56fe\u50cf\u6309\u94ae<\/span>
\n self.<\/span>select_btn =<\/span> tk.<\/span>Button(<\/span>top_frame,<\/span> text=<\/span>"\u9009\u62e9\u82b1\u5349\u56fe\u50cf"<\/span>,<\/span> command=<\/span>self.<\/span>select_image)<\/span>
\n self.<\/span>select_btn.<\/span>pack(<\/span>side=<\/span>tk.<\/span>LEFT,<\/span> padx=<\/span>10<\/span>)<\/span><\/p>\n

# \u9884\u6d4b\u6309\u94ae<\/span>
\n self.<\/span>predict_btn =<\/span> tk.<\/span>Button(<\/span>top_frame,<\/span> text=<\/span>"\u9884\u6d4b"<\/span>,<\/span> command=<\/span>self.<\/span>predict_image,<\/span> state=<\/span>tk.<\/span>DISABLED)<\/span>
\n self.<\/span>predict_btn.<\/span>pack(<\/span>side=<\/span>tk.<\/span>LEFT,<\/span> padx=<\/span>10<\/span>)<\/span><\/p>\n

# \u56fe\u50cf\u663e\u793a\u533a\u57df<\/span>
\n self.<\/span>image_frame =<\/span> tk.<\/span>Frame(<\/span>self.<\/span>root)<\/span>
\n self.<\/span>image_frame.<\/span>pack(<\/span>pady=<\/span>20<\/span>)<\/span>
\n self.<\/span>image_label =<\/span> tk.<\/span>Label(<\/span>self.<\/span>image_frame)<\/span>
\n self.<\/span>image_label.<\/span>pack(<\/span>)<\/span><\/p>\n

# \u7ed3\u679c\u663e\u793a\u533a\u57df<\/span>
\n self.<\/span>result_frame =<\/span> tk.<\/span>Frame(<\/span>self.<\/span>root)<\/span>
\n self.<\/span>result_frame.<\/span>pack(<\/span>pady=<\/span>20<\/span>)<\/span><\/p>\n

self.<\/span>class_label =<\/span> tk.<\/span>Label(<\/span>self.<\/span>result_frame,<\/span> text=<\/span>"\u82b1\u5349\u79cd\u7c7b: "<\/span>,<\/span> font=<\/span>(<\/span>'Arial'<\/span>,<\/span> 14<\/span>)<\/span>)<\/span>
\n self.<\/span>class_label.<\/span>pack(<\/span>)<\/span><\/p>\n

self.<\/span>confidence_label =<\/span> tk.<\/span>Label(<\/span>self.<\/span>result_frame,<\/span> text=<\/span>"\u7f6e\u4fe1\u5ea6: "<\/span>,<\/span> font=<\/span>(<\/span>'Arial'<\/span>,<\/span> 14<\/span>)<\/span>)<\/span>
\n self.<\/span>confidence_label.<\/span>pack(<\/span>)<\/span><\/p>\n

# \u6dfb\u52a0\u4e00\u4e2a\u663e\u793a\u7c7b\u522b\u5bf9\u5e94\u5173\u7cfb\u7684\u6587\u672c\u533a\u57df<\/span>
\n self.<\/span>class_info =<\/span> tk.<\/span>Text(<\/span>self.<\/span>root,<\/span> height=<\/span>6<\/span>,<\/span> width=<\/span>50<\/span>,<\/span> font=<\/span>(<\/span>'Arial'<\/span>,<\/span> 10<\/span>)<\/span>)<\/span>
\n self.<\/span>class_info.<\/span>pack(<\/span>pady=<\/span>10<\/span>)<\/span>
\n self.<\/span>class_info.<\/span>insert(<\/span>tk.<\/span>END,<\/span> "\u7c7b\u522b\u5bf9\u5e94\u5173\u7cfb:\\\\n0: daisy\\\\n1: dandelion\\\\n2: roses\\\\n3: sunflower\\\\n4: tulips"<\/span>)<\/span>
\n self.<\/span>class_info.<\/span>config(<\/span>state=<\/span>tk.<\/span>DISABLED)<\/span><\/p>\n

def<\/span> select_image<\/span>(<\/span>self)<\/span>:<\/span>
\n file_path =<\/span> filedialog.<\/span>askopenfilename(<\/span>
\n title=<\/span>"\u9009\u62e9\u82b1\u5349\u56fe\u50cf"<\/span>,<\/span>
\n filetypes=<\/span>(<\/span>(<\/span>"JPEG files"<\/span>,<\/span> "*.jpg"<\/span>)<\/span>,<\/span> (<\/span>"PNG files"<\/span>,<\/span> "*.png"<\/span>)<\/span>,<\/span> (<\/span>"All files"<\/span>,<\/span> "*.*"<\/span>)<\/span>)<\/span>)<\/span><\/p>\n

if<\/span> file_path:<\/span>
\n try<\/span>:<\/span>
\n self.<\/span>image_path =<\/span> file_path
\n self.<\/span>display_image(<\/span>file_path)<\/span>
\n self.<\/span>predict_btn.<\/span>config(<\/span>state=<\/span>tk.<\/span>NORMAL)<\/span>
\n except<\/span> Exception as<\/span> e:<\/span>
\n messagebox.<\/span>showerror(<\/span>"\u9519\u8bef"<\/span>,<\/span> f"\u65e0\u6cd5\u52a0\u8f7d\u56fe\u50cf: <\/span>{<\/span>str<\/span>(<\/span>e)<\/span>}<\/span><\/span>"<\/span><\/span>)<\/span><\/p>\n

def<\/span> display_image<\/span>(<\/span>self,<\/span> image_path)<\/span>:<\/span>
\n img =<\/span> Image.<\/span>open<\/span>(<\/span>image_path)<\/span>
\n img.<\/span>thumbnail(<\/span>(<\/span>400<\/span>,<\/span> 400<\/span>)<\/span>)<\/span><\/p>\n

img_tk =<\/span> ImageTk.<\/span>PhotoImage(<\/span>img)<\/span><\/p>\n

self.<\/span>image_label.<\/span>config(<\/span>image=<\/span>img_tk)<\/span>
\n self.<\/span>image_label.<\/span>image =<\/span> img_tk<\/p>\n

def<\/span> predict_image<\/span>(<\/span>self)<\/span>:<\/span>
\n if<\/span> not<\/span> hasattr<\/span>(<\/span>self,<\/span> 'image_path'<\/span>)<\/span>:<\/span>
\n messagebox.<\/span>showwarning(<\/span>"\u8b66\u544a"<\/span>,<\/span> "\u8bf7\u5148\u9009\u62e9\u56fe\u50cf"<\/span>)<\/span>
\n return<\/span><\/p>\n

try<\/span>:<\/span>
\n with<\/span> open<\/span>(<\/span>self.<\/span>image_path,<\/span> 'rb'<\/span>)<\/span> as<\/span> img_file:<\/span>
\n files =<\/span> {<\/span>'file'<\/span>:<\/span> img_file}<\/span>
\n response =<\/span> requests.<\/span>post(<\/span>self.<\/span>server_url,<\/span> files=<\/span>files)<\/span><\/p>\n

if<\/span> response.<\/span>status_code ==<\/span> 200<\/span>:<\/span>
\n result =<\/span> response.<\/span>json(<\/span>)<\/span>
\n self.<\/span>show_result(<\/span>result)<\/span>
\n else<\/span>:<\/span>
\n messagebox.<\/span>showerror(<\/span>"\u9519\u8bef"<\/span>,<\/span> f"\u9884\u6d4b\u5931\u8d25: <\/span>{<\/span>response.<\/span>text}<\/span><\/span>"<\/span><\/span>)<\/span>
\n except<\/span> Exception as<\/span> e:<\/span>
\n messagebox.<\/span>showerror(<\/span>"\u9519\u8bef"<\/span>,<\/span> f"\u53d1\u751f\u9519\u8bef: <\/span>{<\/span>str<\/span>(<\/span>e)<\/span>}<\/span><\/span>"<\/span><\/span>)<\/span><\/p>\n

def<\/span> show_result<\/span>(<\/span>self,<\/span> result)<\/span>:<\/span>
\n self.<\/span>class_label.<\/span>config(<\/span>text=<\/span>f"\u82b1\u5349\u79cd\u7c7b: <\/span>{<\/span>result[<\/span>'class'<\/span>]<\/span>}<\/span><\/span> (ID: <\/span>{<\/span>result[<\/span>'class_id'<\/span>]<\/span>}<\/span><\/span>)"<\/span><\/span>)<\/span>
\n self.<\/span>confidence_label.<\/span>config(<\/span>text=<\/span>f"\u7f6e\u4fe1\u5ea6: <\/span>{<\/span>result[<\/span>'confidence'<\/span>]<\/span>:<\/span>.2%<\/span>}<\/span><\/span>"<\/span><\/span>)<\/span><\/p>\n

if<\/span> __name__ ==<\/span> "__main__"<\/span>:<\/span>
\n root =<\/span> tk.<\/span>Tk(<\/span>)<\/span>
\n app =<\/span> ImageClassifierApp(<\/span>root)<\/span>
\n root.<\/span>mainloop(<\/span>)<\/span><\/p>\n

2.\u8fd0\u884capp.py\u7684GUI\u754c\u9762\u4ee5\u53ca\u64cd\u4f5c<\/h3>\n

1.\u9009\u62e9\u82b1\u5349\u56fe\u50cf：\u7535\u8111\u672c\u5730\u6587\u4ef6\u5939 2.\u9884\u6d4b：\u4ea7\u751f\u7ed3\u679c \"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

3.\u670d\u52a1\u5668\u7aef\u8c03\u7528\u4e00\u6b21\u751f\u6210\u4e00\u6b21\u8bb0\u5f55<\/h3>\n

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