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:
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.
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:
| Model | Path |
| ------- | - |
| text detection model | ./inference/ch_PP-OCRv3_det_infer/ |
| text recognition model | ./inference/ch_PP-OCRv3_rec_infer/ |
| text angle classifier | ./inference/ch_ppocr_mobile_v2.0_cls_infer/ |
| layout parse model | ./inference/picodet_lcnet_x1_0_fgd_layout_infer/ |
**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.
|`--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|
- 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
export CUDA_VISIBLE_DEVICES=0
```
- **`use_gpu` and `use_multiprocess` cannot be `true` at the same time.**
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:
```
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`
|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|
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.
If you need to modify the service logic, the following steps are generally required (take the modification of `deploy/hubserving/ocr_system` for example):
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:
