English | [简体中文](readme.md) PaddleOCR provides 2 service deployment methods: - 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](../../deploy/pdserving/README.md) for usage. # Service deployment based on PaddleHub Serving The hubserving service deployment directory includes three service packages: detection, recognition, and two-stage series connection. Please select the corresponding service package to install and start service according to your needs. The directory is as follows: ``` deploy/hubserving/ └─ ocr_det detection module service package └─ ocr_cls angle class module service package └─ ocr_rec recognition module service package └─ ocr_system two-stage series connection service package ``` Each service pack contains 3 files. Take the 2-stage series connection service package as an example, the directory is as follows: ``` 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 ``` ## 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. ### 1. Prepare the environment ```shell # Install paddlehub # python>3.6.2 is required bt paddlehub pip3 install paddlehub==2.1.0 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple ``` ### 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-OCRv2 models are used, and the default model path is: ``` detection model: ./inference/ch_PP-OCRv2_det_infer/ recognition model: ./inference/ch_PP-OCRv2_rec_infer/ text direction classifier: ./inference/ch_ppocr_mobile_v2.0_cls_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. ### 3. Install Service Module PaddleOCR provides 3 kinds of service modules, install the required modules according to your needs. * On Linux platform, the examples are as follows. ```shell # Install the detection service module: hub install deploy/hubserving/ocr_det/ # Or, install the angle class service module: hub install deploy/hubserving/ocr_cls/ # Or, install the recognition service module: hub install deploy/hubserving/ocr_rec/ # Or, install the 2-stage series service module: hub install deploy/hubserving/ocr_system/ ``` * On Windows platform, the examples are as follows. ```shell # Install the detection service module: hub install deploy\hubserving\ocr_det\ # Or, install the angle class service module: hub install deploy\hubserving\ocr_cls\ # Or, install the recognition service module: hub install deploy\hubserving\ocr_rec\ # Or, install the 2-stage series service module: hub install deploy\hubserving\ocr_system\ ``` ### 4. Start service #### Way 1. Start with command line parameters (CPU only) **start command:** ```shell $ hub serving start --modules [Module1==Version1, Module2==Version2, ...] \ --port XXXX \ --use_multiprocess \ --workers \ ``` **parameters:** |parameters|usage| |-|-| |--modules/-m|PaddleHub Serving pre-installed model, listed in the form of multiple Module==Version key-value pairs
*`When Version is not specified, the latest version is selected by default`*| |--port/-p|Service port, default is 8866| |--use_multiprocess|Enable concurrent mode, the default is single-process mode, this mode is recommended for multi-core CPU machines
*`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: ```shell hub serving start -m ocr_system ``` This completes the deployment of a service API, using the default port number 8866. #### Way 2. Start with configuration file(CPU、GPU) **start command:** ```shell hub serving start --config/-c config.json ``` Wherein, the format of `config.json` is as follows: ```python { "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`. Among them, **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: ```export CUDA_VISIBLE_DEVICES=0```, otherwise you do not need to set it. - **`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: ```shell export CUDA_VISIBLE_DEVICES=3 hub serving start -c deploy/hubserving/ocr_system/config.json ``` ## Send prediction requests After the service starts, you can use the following command to send a prediction request to obtain the prediction result: ```shell python tools/test_hubserving.py server_url image_path ``` Two parameters need to be passed to the script: - **server_url**:service address,format of which is `http://[ip_address]:[port]/predict/[module_name]` For example, if the detection, recognition and 2-stage serial services are started with provided configuration files, the respective `server_url` would 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` - **image_path**:Test image path, can be a single image path or an image directory path **Eg.** ```shell python tools/test_hubserving.py http://127.0.0.1:8868/predict/ocr_system ./doc/imgs/ ``` ## Returned result format 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: |field name|data type|description| |----|----|----| |angle|str|angle| |text|str|text content| |confidence|float|text recognition confidence| |text_region|list|text location coordinates| 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: | field name/module name | ocr_det | ocr_cls | ocr_rec | ocr_system | | ---- | ---- | ---- | ---- | ---- | |angle| | ✔ | | ✔ | |text| | |✔|✔| |confidence| |✔ |✔|✔| |text_region| ✔| | |✔ | **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. ## User defined service module modification If you need to modify the service logic, the following steps are generally required (take the modification of `ocr_system` for example): - 1. Stop service ```shell hub serving stop --port/-p XXXX ``` - 2. Modify the code in the corresponding files, like `module.py` and `params.py`, according to the 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. - 3. Uninstall old service module ```shell hub uninstall ocr_system ``` - 4. Install modified service module ```shell hub install deploy/hubserving/ocr_system/ ``` - 5. Restart service ```shell hub serving start -m ocr_system ```