modify hubserving
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# 服务部署
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PaddleOCR提供2种服务部署方式：
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- 基于HubServing的部署：已集成到PaddleOCR中（[code](https://github.com/PaddlePaddle/PaddleOCR/tree/develop/deploy/ocr_hubserving)），按照本教程使用；
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- 基于PaddleServing的部署：详见PaddleServing官网[demo](https://github.com/PaddlePaddle/Serving/tree/develop/python/examples/ocr)，后续也将集成到PaddleOCR。
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服务部署目录下包括检测、识别、2阶段串联三种服务包，根据需求选择相应的服务包进行安装和启动。目录如下：
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```
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deploy/hubserving/
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└─ ocr_det 检测模块服务包
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└─ ocr_rec 识别模块服务包
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└─ ocr_system 检测+识别串联服务包
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```
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每个服务包下包含3个文件。以2阶段串联服务包为例，目录如下：
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```
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deploy/hubserving/ocr_system/
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└─ __init__.py 空文件
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└─ config.json 配置文件，启动服务时作为参数传入
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└─ module.py 主模块，包含服务的完整逻辑
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```
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## 启动服务
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以下步骤以检测+识别2阶段串联服务为例，如果只需要检测服务或识别服务，替换相应文件路径即可。
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### 1. 安装paddlehub
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```pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple```
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### 2. 安装服务模块
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PaddleOCR提供3种服务模块，根据需要安装所需模块。如：
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安装检测服务模块：
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```hub install deploy/hubserving/ocr_det/```
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或，安装识别服务模块：
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```hub install deploy/hubserving/ocr_rec/```
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或，安装检测+识别串联服务模块：
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```hub install deploy/hubserving/ocr_system/```
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### 3. 修改配置文件
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在config.json中指定模型路径、是否使用GPU、是否对结果做可视化等参数，如，串联服务ocr_system的配置：
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```python
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{
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"modules_info": {
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"ocr_system": {
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"init_args": {
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"version": "1.0.0",
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"det_model_dir": "./inference/det/",
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"rec_model_dir": "./inference/rec/",
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"use_gpu": true
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},
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"predict_args": {
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"visualization": false
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}
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}
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}
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}
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```
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其中，模型路径对应的模型为```inference模型```。
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### 4. 运行启动命令
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```hub serving start -m ocr_system --config hubserving/ocr_det/config.json```
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这样就完成了一个服务化API的部署，默认端口号为8866。
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**NOTE:** 如使用GPU预测(即，config中use_gpu置为true)，则需要在启动服务之前，设置CUDA_VISIBLE_DEVICES环境变量，如：```export CUDA_VISIBLE_DEVICES=0```，否则不用设置。
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## 发送预测请求
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配置好服务端，以下数行代码即可实现发送预测请求，获取预测结果:
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```python
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import requests
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import json
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import cv2
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import base64
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def cv2_to_base64(image):
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return base64.b64encode(image).decode('utf8')
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# 发送HTTP请求
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data = {'images':[cv2_to_base64(open("./doc/imgs/11.jpg", 'rb').read())]}
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headers = {"Content-type": "application/json"}
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# url = "http://127.0.0.1:8866/predict/ocr_det"
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# url = "http://127.0.0.1:8866/predict/ocr_rec"
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url = "http://127.0.0.1:8866/predict/ocr_system"
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r = requests.post(url=url, headers=headers, data=json.dumps(data))
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# 打印预测结果
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print(r.json()["results"])
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```
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你可能需要根据实际情况修改```url```字符串中的端口号和服务模块名称。
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上面所示代码都已写入测试脚本，可直接运行命令：```python tools/test_hubserving.py```
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## 自定义修改服务模块
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如果需要修改服务逻辑，你一般需要操作以下步骤：
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1、 停止服务
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```hub serving stop -m ocr_system```
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2、 到相应的module.py文件中根据实际需求修改代码
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3、 卸载旧服务包
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```hub uninstall ocr_system```
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4、 安装修改后的新服务包
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```hub install deploy/hubserving/ocr_system/```
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@ -1,41 +0,0 @@
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# -*- coding:utf-8 -*-
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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class Config(object):
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pass
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def read_params():
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cfg = Config()
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#params for text detector
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cfg.det_algorithm = "DB"
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# cfg.det_model_dir = "./inference/ch_det_mv3_db/"
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cfg.det_model_dir = "./inference/det/"
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cfg.det_max_side_len = 960
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#DB parmas
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cfg.det_db_thresh =0.3
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cfg.det_db_box_thresh =0.5
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cfg.det_db_unclip_ratio =2.0
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# #EAST parmas
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# cfg.det_east_score_thresh = 0.8
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# cfg.det_east_cover_thresh = 0.1
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# cfg.det_east_nms_thresh = 0.2
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# #params for text recognizer
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# cfg.rec_algorithm = "CRNN"
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# # cfg.rec_model_dir = "./inference/ch_det_mv3_crnn/"
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# cfg.rec_model_dir = "./inference/rec/"
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# cfg.rec_image_shape = "3, 32, 320"
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# cfg.rec_char_type = 'ch'
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# cfg.rec_batch_num = 30
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# cfg.rec_char_dict_path = "./ppocr/utils/ppocr_keys_v1.txt"
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# cfg.use_space_char = True
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return cfg
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