基于PaddleX+Flask开发后端，基于VUE开发前端应用，做一个AI医疗的WEB应用.zip

# 基于Paddle+Flask的眼部医疗辅助系统（前后端分离） 视频链接：https://www.bilibili.com/video/BV1k54y1s7j9 # 1. 项目简介： 本项目基于PaddleX提供的FastSCNN语义分割模型，在眼部图像视盘分割数据集上进行训练，并开发了前后端分离项目。 后端代码基于Flask开发，前端WEB界面基于VUE开发。  # 2. 训练语义分割模型： ```python ! pip install paddlex -i https://mirror.baidu.com/pypi/simple ``` done [?25h Created wheel for pycocotools: filename=pycocotools-2.0.2-cp37-cp37m-linux_x86_64.whl size=278368 sha256=69425bf5985e5aa2c1a6c475a0d5e5975b3e5e5a024bda6e4573d07ccae57a21 Stored in directory: /home/aistudio/.cache/pip/wheels/fb/44/67/8baa69040569b1edbd7776ec6f82c387663e724908aaa60963 Successfully built pycocotools Installing collected packages: paddleslim, xlwt, colorama, shapely, paddlehub, pycocotools, paddlex Found existing installation: paddlehub 1.6.0 Uninstalling paddlehub-1.6.0: Successfully uninstalled paddlehub-1.6.0 Successfully installed colorama-0.4.4 paddlehub-1.8.3 paddleslim-1.1.1 paddlex-1.3.4 pycocotools-2.0.2 shapely-1.7.1 xlwt-1.3.0 ```python ! wget https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz ! tar xzf optic_disc_seg.tar.gz ``` --2021-01-23 08:11:17-- https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz Resolving bj.bcebos.com (bj.bcebos.com)... 182.61.200.229, 182.61.200.195, 2409:8c00:6c21:10ad:0:ff:b00e:67d Connecting to bj.bcebos.com (bj.bcebos.com)|182.61.200.229|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 19077558 (18M) [application/octet-stream] Saving to: ‘optic_disc_seg.tar.gz’ optic_disc_seg.tar. 100%[===================>] 18.19M 29.0MB/s in 0.6s 2021-01-23 08:11:18 (29.0 MB/s) - ‘optic_disc_seg.tar.gz’ saved [19077558/19077558] ```python # 设置使用0号GPU卡（如无GPU，执行此代码后仍然会使用CPU训练模型） import matplotlib matplotlib.use('Agg') import os os.environ['CUDA_VISIBLE_DEVICES'] = '0' import paddlex as pdx ``` 2021-01-23 08:11:24,888-INFO: font search path ['/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/ttf', '/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/afm', '/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/pdfcorefonts'] 2021-01-23 08:11:25,242-INFO: generated new fontManager ```python from paddlex.seg import transforms train_transforms = transforms.Compose([ transforms.RandomHorizontalFlip(), transforms.Resize(target_size=512), transforms.RandomPaddingCrop(crop_size=500), transforms.Normalize() ]) eval_transforms = transforms.Compose([ transforms.Resize(512), transforms.Normalize() ]) ``` ```python train_dataset = pdx.datasets.SegDataset( data_dir='optic_disc_seg', file_list='optic_disc_seg/train_list.txt', label_list='optic_disc_seg/labels.txt', transforms=train_transforms, shuffle=True) eval_dataset = pdx.datasets.SegDataset( data_dir='optic_disc_seg', file_list='optic_disc_seg/val_list.txt', label_list='optic_disc_seg/labels.txt', transforms=eval_transforms) ``` 2021-01-23 08:11:39 [INFO] 267 samples in file optic_disc_seg/train_list.txt 2021-01-23 08:11:39 [INFO] 76 samples in file optic_disc_seg/val_list.txt ```python num_classes = len(train_dataset.labels) num_classes ``` 2 ```python num_classes = len(train_dataset.labels) model = pdx.seg.FastSCNN(num_classes=num_classes) model.train( num_epochs=40, train_dataset=train_dataset, train_batch_size=4, eval_dataset=eval_dataset, learning_rate=0.01, save_interval_epochs=1, save_dir='output/', use_vdl=True) ``` ```python !paddlex --export_inference --model_dir=./output/best_model --save_dir=./inference_model --fixed_input_shape=[512,512] ``` W0123 08:16:03.194139 1332 device_context.cc:252] Please NOTE: device: 0, CUDA Capability: 70, Driver API Version: 10.1, Runtime API Version: 9.0 W0123 08:16:03.199599 1332 device_context.cc:260] device: 0, cuDNN Version: 7.6. 2021-01-23 08:16:06 [INFO] Model[FastSCNN] loaded. 2021-01-23 08:16:06 [INFO] Model for inference deploy saved in ./inference_model. # 3. 模型预测 使用模型进行预测，同时使用`pdx.seg.visualize`将结果可视化，可视化结果将保存到`./output/deeplab`下，其中`weight`代表原图的权重，即mask可视化结果与原图权重因子。 ```python import paddlex as pdx model = pdx.deploy.Predictor('inference_model') image_name = 'optic_disc_seg/JPEGImages/H0005.jpg' result = model.predict(image_name) pdx.seg.visualize(image_name, result, weight=0.4, save_dir='./output/deeplab') ``` 2021-01-23 08:16:45 [INFO] The visualized result is saved as ./output/deeplab/visualize_H0005.jpg ```python !zip -r inference_model/ weights.zip ``` zip warning: name not matched: weights.zip zip error: Nothing to do! (try: zip -r inference_model/ . -i weights.zip) # 4. 启动WEB应用： 在 Flask 项目下运行以下代码启动后端： ``` python app.py ``` 在 VUE 项目下运行以下代码安装依赖： ``` npm install ``` 运行以下代码启动前端： ``` npm run serve ``` 然后在浏览器打开Localhost即可：  # 我的公众号： 感兴趣的同学关注我的公众号——可达鸭的深度学习教程：