262 lines
12 KiB
Markdown
262 lines
12 KiB
Markdown
English | [简体中文](readme.md)
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- [Service deployment based on PaddleHub Serving](#service-deployment-based-on-paddlehub-serving)
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- [1. Update](#1-update)
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- [2. Quick start service](#2-quick-start-service)
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- [2.1 Prepare the environment](#21-prepare-the-environment)
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- [2.2 Download inference model](#22-download-inference-model)
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- [2.3 Install Service Module](#23-install-service-module)
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- [2.4 Start service](#24-start-service)
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- [2.4.1 Start with command line parameters (CPU only)](#241-start-with-command-line-parameters-cpu-only)
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- [2.4.2 Start with configuration file（CPU、GPU）](#242-start-with-configuration-filecpugpu)
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- [3. Send prediction requests](#3-send-prediction-requests)
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- [4. Returned result format](#4-returned-result-format)
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- [5. User defined service module modification](#5-user-defined-service-module-modification)
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PaddleOCR provides 2 service deployment methods:
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- Based on **PaddleHub Serving**: Code path is "`./deploy/hubserving`". Please follow this tutorial.
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- Based on **PaddleServing**: Code path is "`./deploy/pdserving`". Please refer to the [tutorial](../../deploy/pdserving/README.md) for usage.
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# Service deployment based on PaddleHub Serving
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The hubserving service deployment directory includes seven service packages: text detection, text angle class, text recognition, text detection+text angle class+text recognition three-stage series connection, layout analysis, table recognition and PP-Structure. Please select the corresponding service package to install and start service according to your needs. The directory is as follows:
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```
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deploy/hubserving/
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└─ ocr_det text detection module service package
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└─ ocr_cls text angle class module service package
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└─ ocr_rec text recognition module service package
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└─ ocr_system text detection+text angle class+text recognition three-stage series connection service package
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└─ structure_layout layout analysis service package
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└─ structure_table table recognition service package
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└─ structure_system PP-Structure service package
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```
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Each service pack contains 3 files. Take the 2-stage series connection service package as an example, the directory is as follows:
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```
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deploy/hubserving/ocr_system/
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└─ __init__.py Empty file, required
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└─ config.json Configuration file, optional, passed in as a parameter when using configuration to start the service
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└─ module.py Main module file, required, contains the complete logic of the service
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└─ params.py Parameter file, required, including parameters such as model path, pre- and post-processing parameters
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```
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## 1. Update
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* 2022.05.05 add PP-OCRv3 text detection and recognition models.
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* 2022.03.30 add PP-Structure and table recognition services。
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* 2022.08.23 add layout analysis services。
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## 2. Quick start service
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The following steps take the 2-stage series service as an example. If only the detection service or recognition service is needed, replace the corresponding file path.
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### 2.1 Prepare the environment
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```shell
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# Install paddlehub
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# python>3.6.2 is required bt paddlehub
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pip3 install paddlehub==2.1.0 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
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```
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### 2.2 Download inference model
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Before installing the service module, you need to prepare the inference model and put it in the correct path. By default, the PP-OCRv3 models are used, and the default model path is:
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```
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text detection model: ./inference/ch_PP-OCRv3_det_infer/
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text recognition model: ./inference/ch_PP-OCRv3_rec_infer/
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text angle classifier: ./inference/ch_ppocr_mobile_v2.0_cls_infer/
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layout parse model: ./inference/picodet_lcnet_x1_0_fgd_layout_infer/
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tanle recognition: ./inference/ch_ppstructure_mobile_v2.0_SLANet_infer/
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```
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**The model path can be found and modified in `params.py`.** More models provided by PaddleOCR can be obtained from the [model library](../../doc/doc_en/models_list_en.md). You can also use models trained by yourself.
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### 2.3 Install Service Module
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PaddleOCR provides 5 kinds of service modules, install the required modules according to your needs.
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* On Linux platform, the examples are as follows.
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```shell
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# Install the text detection service module:
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hub install deploy/hubserving/ocr_det/
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# Or, install the text angle class service module:
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hub install deploy/hubserving/ocr_cls/
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# Or, install the text recognition service module:
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hub install deploy/hubserving/ocr_rec/
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# Or, install the 2-stage series service module:
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hub install deploy/hubserving/ocr_system/
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# Or install table recognition service module
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hub install deploy/hubserving/structure_table/
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# Or install PP-Structure service module
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hub install deploy/hubserving/structure_system/
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# Or install layout analysis service module
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hub install deploy/hubserving/structure_layout/
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```
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* On Windows platform, the examples are as follows.
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```shell
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# Install the detection service module:
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hub install deploy\hubserving\ocr_det\
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# Or, install the angle class service module:
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hub install deploy\hubserving\ocr_cls\
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# Or, install the recognition service module:
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hub install deploy\hubserving\ocr_rec\
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# Or, install the 2-stage series service module:
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hub install deploy\hubserving\ocr_system\
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# Or install table recognition service module
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hub install deploy/hubserving/structure_table/
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# Or install PP-Structure service module
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hub install deploy\hubserving\structure_system\
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# Or install layout analysis service module
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hub install deploy\hubserving\structure_layout\
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```
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### 2.4 Start service
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#### 2.4.1 Start with command line parameters (CPU only)
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**start command：**
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```shell
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$ hub serving start --modules [Module1==Version1, Module2==Version2, ...] \
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--port XXXX \
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--use_multiprocess \
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--workers \
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```
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**parameters：**
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|parameters|usage|
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|---|---|
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|--modules/-m|PaddleHub Serving pre-installed model, listed in the form of multiple Module==Version key-value pairs<br>*`When Version is not specified, the latest version is selected by default`*|
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|--port/-p|Service port, default is 8866|
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|--use_multiprocess|Enable concurrent mode, the default is single-process mode, this mode is recommended for multi-core CPU machines<br>*`Windows operating system only supports single-process mode`*|
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|--workers|The number of concurrent tasks specified in concurrent mode, the default is `2*cpu_count-1`, where `cpu_count` is the number of CPU cores|
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For example, start the 2-stage series service:
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```shell
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hub serving start -m ocr_system
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```
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This completes the deployment of a service API, using the default port number 8866.
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#### 2.4.2 Start with configuration file（CPU、GPU）
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**start command：**
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```shell
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hub serving start --config/-c config.json
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```
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Wherein, the format of `config.json` is as follows:
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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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"use_gpu": true
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},
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"predict_args": {
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}
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}
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},
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"port": 8868,
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"use_multiprocess": false,
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"workers": 2
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}
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```
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- The configurable parameters in `init_args` are consistent with the `_initialize` function interface in `module.py`. Among them, **when `use_gpu` is `true`, it means that the GPU is used to start the service**.
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- The configurable parameters in `predict_args` are consistent with the `predict` function interface in `module.py`.
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**Note:**
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- When using the configuration file to start the service, other parameters will be ignored.
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- If you use GPU prediction (that is, `use_gpu` is set to `true`), you need to set the environment variable CUDA_VISIBLE_DEVICES before starting the service, such as: ```export CUDA_VISIBLE_DEVICES=0```, otherwise you do not need to set it.
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- **`use_gpu` and `use_multiprocess` cannot be `true` at the same time.**
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For example, use GPU card No. 3 to start the 2-stage series service:
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```shell
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export CUDA_VISIBLE_DEVICES=3
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hub serving start -c deploy/hubserving/ocr_system/config.json
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```
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## 3. Send prediction requests
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After the service starts, you can use the following command to send a prediction request to obtain the prediction result:
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```shell
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python tools/test_hubserving.py --server_url=server_url --image_dir=image_path
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```
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Two parameters need to be passed to the script:
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- **server_url**：service address，format of which is
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`http://[ip_address]:[port]/predict/[module_name]`
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For example, if using the configuration file to start the text angle classification, text detection, text recognition, detection+classification+recognition 3 stages, table recognition and PP-Structure service, then the `server_url` to send the request will be:
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`http://127.0.0.1:8865/predict/ocr_det`
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`http://127.0.0.1:8866/predict/ocr_cls`
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`http://127.0.0.1:8867/predict/ocr_rec`
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`http://127.0.0.1:8868/predict/ocr_system`
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`http://127.0.0.1:8869/predict/structure_table`
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`http://127.0.0.1:8870/predict/structure_system`
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`http://127.0.0.1:8870/predict/structure_layout`
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- **image_dir**：Test image path, can be a single image path or an image directory path
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- **visualize**：Whether to visualize the results, the default value is False
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- **output**：The floder to save Visualization result, default value is `./hubserving_result`
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**Eg.**
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```shell
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python tools/test_hubserving.py --server_url=http://127.0.0.1:8868/predict/ocr_system --image_dir=./doc/imgs/ --visualize=false`
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```
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## 4. Returned result format
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The returned result is a list. Each item in the list is a dict. The dict may contain three fields. The information is as follows:
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|field name|data type|description|
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|----|----|----|
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|angle|str|angle|
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|text|str|text content|
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|confidence|float|text recognition confidence|
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|text_region|list|text location coordinates|
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|html|str|table html str|
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|regions|list|The result of layout analysis + table recognition + OCR, each item is a list, including `bbox` indicating area coordinates, `type` of area type and `res` of area results|
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|layout|list|The result of layout analysis, each item is a dict, including `bbox` indicating area coordinates, `label` of area type|
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The fields returned by different modules are different. For example, the results returned by the text recognition service module do not contain `text_region`. The details are as follows:
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| field name/module name | ocr_det | ocr_cls | ocr_rec | ocr_system | structure_table | structure_system | structure_layout |
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| --- | --- | --- | --- | --- | --- |--- |--- |
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|angle| | ✔ | | ✔ | || |
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|text| | |✔|✔| | ✔ | |
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|confidence| |✔ |✔| | | ✔| |
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|text_region| ✔| | |✔ | | ✔| |
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|html| | | | |✔ |✔| |
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|regions| | | | |✔ |✔ | |
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|layout| | | | | | |✔ |
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**Note：** If you need to add, delete or modify the returned fields, you can modify the file `module.py` of the corresponding module. For the complete process, refer to the user-defined modification service module in the next section.
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## 5. User defined service module modification
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If you need to modify the service logic, the following steps are generally required (take the modification of `ocr_system` for example):
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- 1. Stop service
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```shell
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hub serving stop --port/-p XXXX
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```
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- 2. Modify the code in the corresponding files, like `module.py` and `params.py`, according to the actual needs.
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For example, if you need to replace the model used by the deployed service, you need to modify model path parameters `det_model_dir` and `rec_model_dir` in `params.py`. If you want to turn off the text direction classifier, set the parameter `use_angle_cls` to `False`. Of course, other related parameters may need to be modified at the same time. Please modify and debug according to the actual situation. It is suggested to run `module.py` directly for debugging after modification before starting the service test.
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**Note** The image input shape used by the PPOCR-v3 recognition model is `3, 48, 320`, so you need to modify `cfg.rec_image_shape = "3, 48, 320"` in `params.py`, if you do not use the PPOCR-v3 recognition model, then there is no need to modify this parameter.
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- 3. Uninstall old service module
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```shell
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hub uninstall ocr_system
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```
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- 4. Install modified service module
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```shell
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hub install deploy/hubserving/ocr_system/
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```
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- 5. Restart service
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```shell
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hub serving start -m ocr_system
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```
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