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PaddleOCR provides 2 service deployment methods:
- Based on PaddleHub Serving: Code path is "
./deploy/hubserving
". Please follow this tutorial. - (coming soon)Based on PaddleServing: Code path is "
./deploy/pdserving
". Please refer to the tutorial for usage.
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
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.
# Install paddlehub
pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
Before installing the service module, you need to prepare the inference model and put it in the correct path. By default, the ultra lightweight model of v2.0 is used, and the default model path is:
detection model: ./inference/ch_ppocr_mobile_v2.0_det_infer/
recognition model: ./inference/ch_ppocr_mobile_v2.0_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. You can also use models trained by yourself.
PaddleOCR provides 3 kinds of service modules, install the required modules according to your needs.
- On Linux platform, the examples are as follows.
# 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.
# 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\
start command:
$ 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 pairsWhen 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 machinesWindows 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:
hub serving start -m ocr_system
This completes the deployment of a service API, using the default port number 8866.
start command:
hub serving start --config/-c config.json
Wherein, the format of config.json
is as follows:
{
"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 inmodule.py
. Among them, whenuse_gpu
istrue
, it means that the GPU is used to start the service. - The configurable parameters in
predict_args
are consistent with thepredict
function interface inmodule.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 totrue
), 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
anduse_multiprocess
cannot betrue
at the same time.
For example, use GPU card No. 3 to start the 2-stage series service:
export CUDA_VISIBLE_DEVICES=3
hub serving start -c deploy/hubserving/ocr_system/config.json
After the service starts, you can use the following command to send a prediction request to obtain the prediction result:
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 respectiveserver_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.
python tools/test_hubserving.py http://127.0.0.1:8868/predict/ocr_system ./doc/imgs/
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.
If you need to modify the service logic, the following steps are generally required (take the modification of ocr_system
for example):
-
- Stop service
hub serving stop --port/-p XXXX
-
- Modify the code in the corresponding files, like
module.py
andparams.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 parametersdet_model_dir
andrec_model_dir
inparams.py
. If you want to turn off the text direction classifier, set the parameteruse_angle_cls
toFalse
. 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 runmodule.py
directly for debugging after modification before starting the service test.
- Modify the code in the corresponding files, like
-
- Uninstall old service module
hub uninstall ocr_system
-
- Install modified service module
hub install deploy/hubserving/ocr_system/
-
- Restart service
hub serving start -m ocr_system