- Based on **PaddleHub Serving**: Code path is `./deploy/hubserving`. Please follow this tutorial.
- Based on **PaddleServing**: Code path is `./deploy/pdserving`. Please refer to the [tutorial](../../ppocr/infer_deploy/paddle_server.en.md) for usage.
# Service deployment based on PaddleHub Serving
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 the service according to your needs. The directory is as follows:
```text linenums="1"
deploy/hubserving/
└─ ocr_det text detection module service package
└─ ocr_cls text angle class module service package
└─ ocr_rec text recognition module service package
└─ ocr_system text detection+text angle class+text recognition three-stage series connection service package
└─ structure_layout layout analysis service package
└─ structure_table table recognition service package
└─ structure_system PP-Structure service package
└─ kie_ser KIE(SER) service package
└─ kie_ser_re KIE(SER+RE) service package
```
Each service pack contains 3 files. Take the 2-stage series connection service package as an example, the directory is as follows:
```text linenums="1"
deploy/hubserving/ocr_system/
└─ __init__.py Empty file, required
└─ config.json Configuration file, optional, passed in as a parameter when using configuration to start the service
└─ module.py Main module file, required, contains the complete logic of the service
└─ params.py Parameter file, required, including parameters such as model path, pre and post-processing parameters
```
## 1. Update
- 2022.10.09 add KIE services.
- 2022.08.23 add layout analysis services.
- 2022.03.30 add PP-Structure and table recognition services.
- 2022.05.05 add PP-OCRv3 text detection and recognition services.
## 2. Quick start service
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.
### 2.1 Install PaddleHub
```bash linenums="1"
pip3 install paddlehub==2.1.0 --upgrade
```
### 2.2 Download inference model
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:
|`--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**|
|`--port`/`-p`|Service port, default is 8866|
|`--use_multiprocess`|Enable concurrent mode, by default using the single-process mode, this mode is recommended for multi-core CPU machines<br>**Windows operating system only supports single-process mode**|
|`--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|
For example, start the 2-stage series service:
```bash linenums="1"
hub serving start -m ocr_system
```
This completes the deployment of a service API, using the default port number 8866.
#### 2.4.2 Start with configuration file（CPU and GPU）
**start command:**
```bash linenums="1"
hub serving start --config/-c config.json
```
In which the format of `config.json` is as follows:
```json
{
"modules_info": {
"ocr_system": {
"init_args": {
"version": "1.0.0",
"use_gpu": true
},
"predict_args": {
}
}
},
"port": 8868,
"use_multiprocess": false,
"workers": 2
}
```
- The configurable parameters in `init_args` are consistent with the `_initialize` function interface in `module.py`.
**When `use_gpu` is `true`, it means that the GPU is used to start the service**.
- The configurable parameters in `predict_args` are consistent with the `predict` function interface in `module.py`.
**Note:**
- When using the configuration file to start the service, other parameters will be ignored.
- 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:
```bash linenums="1"
export CUDA_VISIBLE_DEVICES=0
```
- **`use_gpu` and `use_multiprocess` cannot be `true` at the same time.**
For example, use GPU card No. 3 to start the 2-stage series service:
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,
also modified the port for each service, then the `server_url` to send the request will be:
```text linenums="1"
http://127.0.0.1:8865/predict/ocr_det
http://127.0.0.1:8866/predict/ocr_cls
http://127.0.0.1:8867/predict/ocr_rec
http://127.0.0.1:8868/predict/ocr_system
http://127.0.0.1:8869/predict/structure_table
http://127.0.0.1:8870/predict/structure_system
http://127.0.0.1:8870/predict/structure_layout
http://127.0.0.1:8871/predict/kie_ser
http://127.0.0.1:8872/predict/kie_ser_re
```
- **image_dir**:Test image path, which can be a single image path or an image directory path
- **visualize**:Whether to visualize the results, the default value is False
- **output**:The folder to save the Visualization result, the default value is `./hubserving_result`
The returned result is a list. Each item in the list is a dictionary which may contain three fields. The information is as follows:
|field name|data type|description|
|----|----|----|
|angle|str|angle|
|text|str|text content|
|confidence|float|text recognition confidence|
|text_region|list|text location coordinates|
|html|str|table HTML string|
|regions|list|The result of layout analysis + table recognition + OCR, each item is a list<br>including `bbox` indicating area coordinates, `type` of area type and `res` of area results|
|layout|list|The result of layout analysis, each item is a dict, including `bbox` indicating area coordinates, `label` of area type|
The fields returned by different modules are different. For example, the results returned by the text recognition service module do not contain `text_region`, detailed table is as follows:
**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.
## 5. User-defined service module modification
If you need to modify the service logic, the following steps are generally required (take the modification of `deploy/hubserving/ocr_system` for example):
1. Stop service:
```bash linenums="1"
hub serving stop --port/-p XXXX
```
2. Modify the code in the corresponding files under `deploy/hubserving/ocr_system`, such as `module.py` and `params.py`, to your actual needs.
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.**
**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.
3. (Optional) If you want to rename the module, the following lines should be modified:
- [`ocr_system` within `from deploy.hubserving.ocr_system.params import read_params`](https://github.com/PaddlePaddle/PaddleOCR/blob/a923f35de57b5e378f8dd16e54d0a3e4f51267fd/deploy/hubserving/ocr_system/module.py#L35)
- [`ocr_system` within `name="ocr_system",`](https://github.com/PaddlePaddle/PaddleOCR/blob/a923f35de57b5e378f8dd16e54d0a3e4f51267fd/deploy/hubserving/ocr_system/module.py#L39)
4. (Optional) It may require you to delete the directory `__pycache__` to force flush build cache of CPython:
