-[4. Returned result format](#4-returned-result-format)
-[5. User defined service module modification](#5-user-defined-service-module-modification)
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:
The hubserving service deployment directory includes six service packages: text detection, text angle class, text recognition, text detection+text angle class+text recognition three-stage series connection, 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:
```
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
└─ 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_table table recognition service package
└─ structure_system PP-Structure service package
```
Each service pack contains 3 files. Take the 2-stage series connection service package as an example, the directory is as follows:
...
...
@@ -23,43 +39,54 @@ deploy/hubserving/ocr_system/
└─ 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
* 2022.03.30 add PP-Structure and table recognition services。
## Quick start service
## 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.
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:
**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.
### 2.3 Install Service Module
PaddleOCR provides 5 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:
# Install the text detection service module:
hub install deploy/hubserving/ocr_det/
# Or, install the angle class service module:
# Or, install the text angle class service module:
hub install deploy/hubserving/ocr_cls/
# Or, install the recognition service module:
# Or, install the text recognition service module:
hub install deploy/hubserving/ocr_rec/
# Or, install the 2-stage series service module:
hub install deploy/hubserving/ocr_system/
# Or install table recognition service module
hub install deploy/hubserving/structure_table/
# Or install PP-Structure service module
hub install deploy/hubserving/structure_system/
```
* On Windows platform, the examples are as follows.
|--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, the default is single-process mode, this mode is recommended for multi-core CPU machines<br>*`Windows operating system only supports single-process mode`*|
For example, if the detection, recognition and 2-stage serial services are started with provided configuration files, the respective `server_url` would be:
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:
`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
`http://127.0.0.1:8869/predict/structure_table`
`http://127.0.0.1:8870/predict/structure_system`
-**image_dir**：Test image path, can be a single image path or an image directory path
-**visualize**：Whether to visualize the results, the default value is False
-**output**：The floder to save Visualization result, default value is `./hubserving_result`
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|
...
...
@@ -170,19 +208,23 @@ The returned result is a list. Each item in the list is a dict. The dict may con
|text|str|text content|
|confidence|float|text recognition confidence|
|text_region|list|text location coordinates|
|html|str|table html str|
|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|
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| ✔| | |✔ |
| field name/module name | ocr_det | ocr_cls | ocr_rec | ocr_system | structure_table | structure_system |
| --- | --- | --- | --- | --- | --- |--- |
|angle| | ✔ | | ✔ | ||
|text| | |✔|✔| | ✔ |
|confidence| |✔ |✔| | | ✔|
|text_region| ✔| | |✔ | | ✔|
|html| | | | |✔ |✔|
|regions| | | | |✔ |✔ |
**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
## 5. 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):
"Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES via export CUDA_VISIBLE_DEVICES=cuda_device_id."
)
cfg.ir_optim=True
cfg.enable_mkldnn=enable_mkldnn
self.table_sys=PPStructureSystem(cfg)
defmerge_configs(self):
# deafult cfg
backup_argv=copy.deepcopy(sys.argv)
sys.argv=sys.argv[:1]
cfg=parse_args()
update_cfg_map=vars(read_params())
forkeyinupdate_cfg_map:
cfg.__setattr__(key,update_cfg_map[key])
sys.argv=copy.deepcopy(backup_argv)
returncfg
defread_images(self,paths=[]):
images=[]
forimg_pathinpaths:
assertos.path.isfile(
img_path),"The {} isn't a valid file.".format(img_path)
img=cv2.imread(img_path)
ifimgisNone:
logger.info("error in loading image:{}".format(img_path))
continue
images.append(img)
returnimages
defpredict(self,images=[],paths=[]):
"""
Get the chinese texts in the predicted images.
Args:
images (list(numpy.ndarray)): images data, shape of each is [H, W, C]. If images not paths
paths (list[str]): The paths of images. If paths not images
Returns:
res (list): The result of chinese texts and save path of images.
"Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES via export CUDA_VISIBLE_DEVICES=cuda_device_id."
)
cfg.ir_optim=True
cfg.enable_mkldnn=enable_mkldnn
self.table_sys=_TableSystem(cfg)
defmerge_configs(self):
# deafult cfg
backup_argv=copy.deepcopy(sys.argv)
sys.argv=sys.argv[:1]
cfg=parse_args()
update_cfg_map=vars(read_params())
forkeyinupdate_cfg_map:
cfg.__setattr__(key,update_cfg_map[key])
sys.argv=copy.deepcopy(backup_argv)
returncfg
defread_images(self,paths=[]):
images=[]
forimg_pathinpaths:
assertos.path.isfile(
img_path),"The {} isn't a valid file.".format(img_path)
img=cv2.imread(img_path)
ifimgisNone:
logger.info("error in loading image:{}".format(img_path))
continue
images.append(img)
returnimages
defpredict(self,images=[],paths=[]):
"""
Get the chinese texts in the predicted images.
Args:
images (list(numpy.ndarray)): images data, shape of each is [H, W, C]. If images not paths
paths (list[str]): The paths of images. If paths not images
Returns:
res (list): The result of chinese texts and save path of images.
