https://github.com/Sharpiless/PaddleX-Flask-VUE-demo
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
https://github.com/Sharpiless/PaddleX-Flask-VUE-demo
Last synced: about 1 month ago
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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
- Host: GitHub
- URL: https://github.com/Sharpiless/PaddleX-Flask-VUE-demo
- Owner: Sharpiless
- License: gpl-3.0
- Created: 2021-02-03T09:34:21.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2021-03-05T13:35:23.000Z (about 4 years ago)
- Last Synced: 2024-07-31T21:53:32.970Z (9 months ago)
- Language: Vue
- Homepage:
- Size: 4.48 MB
- Stars: 148
- Watchers: 2
- Forks: 50
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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：
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