For more software version requirements, please refer to the instructions in [Installation Document](https://www.paddlepaddle.org.cn/install/quick) for operation.
This chapter introduces the C++ deployment method of the PaddleOCR model, and the corresponding python predictive deployment method refers to [document](../../doc/doc_ch/inference.md).
C++ is better than python in terms of performance calculation. Therefore, in most CPU and GPU deployment scenarios, C++ deployment is mostly used.
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@@ -6,14 +6,14 @@ This section will introduce how to configure the C++ environment and complete it
PaddleOCR model deployment.
## 1. Prepare the environment
## 1. Prepare the Environment
### Environment
- Linux, docker is recommended.
### 1.1 Compile opencv
### 1.1 Compile OpenCV
* First of all, you need to download the source code compiled package in the Linux environment from the opencv official website. Taking opencv3.4.7 as an example, the download command is as follows.
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@@ -73,7 +73,7 @@ opencv3/
|-- share
```
### 1.2 Compile or download or the Paddle inference library
### 1.2 Compile or Download or the Paddle Inference Library
* There are 2 ways to obtain the Paddle inference library, described in detail below.
Among them, `paddle` is the Paddle library required for C++ prediction later, and `version.txt` contains the version information of the current inference library.
## 2. Compile and run the demo
## 2. Compile and Run the Demo
### 2.1 Export the inference model
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@@ -183,7 +183,7 @@ or the generated Paddle inference library path (`build/paddle_inference_install_
Execute the built executable file:
```shell
./build/ppocr <mode> [--param1][--param2][...]
```
```
Here, `mode` is a required parameter，and the value range is ['det', 'rec', 'system'], representing using detection only, using recognition only and using the end-to-end system respectively. Specifically,
The angle classification is used in the scene where the image is not 0 degrees. In this scene, it is necessary to perform a correction operation on the text line detected in the picture. In the PaddleOCR system,
The text line image obtained after text detection is sent to the recognition model after affine transformation. At this time, only a 0 and 180 degree angle classification of the text is required, so the built-in PaddleOCR text angle classifier **only supports 0 and 180 degree classification**. If you want to support more angles, you can modify the algorithm yourself to support.
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@@ -16,7 +17,7 @@ Example of 0 and 180 degree data samples：
<aname="data-preparation"></a>
## Data Preparation
## 2. Data Preparation
Please organize the dataset as follows:
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@@ -72,7 +73,7 @@ containing all images (test) and a cls_gt_test.txt. The structure of the test se
| ...
```
<aname="training"></a>
## Training
## 3. Training
Write the prepared txt file and image folder path into the configuration file under the `Train/Eval.dataset.label_file_list` and `Train/Eval.dataset.data_dir` fields, the absolute path of the image consists of the `Train/Eval.dataset.data_dir` field and the image name recorded in the txt file.
PaddleOCR provides training scripts, evaluation scripts, and prediction scripts.
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@@ -117,7 +118,7 @@ If the evaluation set is large, the test will be time-consuming. It is recommend
**Note that the configuration file for prediction/evaluation must be consistent with the training.**
<aname="evaluation"></a>
## Evaluation
## 4. Evaluation
The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/cls/cls_mv3.yml` file.
@@ -7,7 +15,9 @@ The following list can be viewed through `--help`
| -c | ALL | Specify configuration file to use | None | **Please refer to the parameter introduction for configuration file usage** |
| -o | ALL | set configuration options | None | Configuration using -o has higher priority than the configuration file selected with -c. E.g: -o Global.use_gpu=false |
## INTRODUCTION TO GLOBAL PARAMETERS OF CONFIGURATION FILE
## 2. Intorduction to Global Parameters of Configuration File
Take rec_chinese_lite_train_v2.0.yml as an example
### Global
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@@ -121,8 +131,9 @@ In PaddleOCR, the network is divided into four stages: Transform, Backbone, Neck
| drop_last | Whether to discard the last incomplete mini-batch because the number of samples in the data set cannot be divisible by batch_size | True | \ |
| num_workers | The number of sub-processes used to load data, if it is 0, the sub-process is not started, and the data is loaded in the main process | 8 | \ |
PaddleOCR currently supports 80 (except Chinese) language recognition. A multi-language configuration file template is
provided under the path `configs/rec/multi_languages`: [rec_multi_language_lite_train.yml](../../configs/rec/multi_language/rec_multi_language_lite_train.yml)。
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@@ -187,21 +198,21 @@ Italian is made up of Latin letters, so after executing the command, you will ge
...
character_type: it # language
character_dict_path: {path/of/dict} # path of dict
Train:
dataset:
name: SimpleDataSet
data_dir: train_data/ # root directory of training data
The inference model (the model saved by `paddle.jit.save`) is generally a solidified model saved after the model training is completed, and is mostly used to give prediction in deployment.
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@@ -10,21 +10,21 @@ For more details, please refer to the document [Classification Framework](https:
Next, we first introduce how to convert a trained model into an inference model, and then we will introduce text detection, text recognition, angle class, and the concatenation of them based on inference model.
-[CONVERT TRAINING MODEL TO INFERENCE MODEL](#CONVERT)
-[Convert detection model to inference model](#Convert_detection_model)
-[Convert recognition model to inference model](#Convert_recognition_model)
-[Convert angle classification model to inference model](#Convert_angle_class_model)
-[1. Convert Training Model to Inference Model](#CONVERT)
-[1.1 Convert Detection Model to Inference Model](#Convert_detection_model)
-[1.2 Convert Recognition Model to Inference Model](#Convert_recognition_model)
-[1.3 Convert Angle Classification Model to Inference Model](#Convert_angle_class_model)
-[TEXT DETECTION MODEL INFERENCE](#DETECTION_MODEL_INFERENCE)
-[1. LIGHTWEIGHT CHINESE DETECTION MODEL INFERENCE](#LIGHTWEIGHT_DETECTION)
-[2. DB TEXT DETECTION MODEL INFERENCE](#DB_DETECTION)
-[3. EAST TEXT DETECTION MODEL INFERENCE](#EAST_DETECTION)
-[4. SAST TEXT DETECTION MODEL INFERENCE](#SAST_DETECTION)
-[2. Text Detection Model Inference](#DETECTION_MODEL_INFERENCE)
-[2.1 Lightweight Chinese Detection Model Inference](#LIGHTWEIGHT_DETECTION)
-[2.2 DB Text Detection Model Inference](#DB_DETECTION)
-[2.3 East Text Detection Model Inference](#EAST_DETECTION)
-[2.4 Sast Text Detection Model Inference](#SAST_DETECTION)
-[5. Multilingual model inference](#Multilingual model inference)
-[TEXT RECOGNITION MODEL INFERENCE](#RECOGNITION_MODEL_INFERENCE)
-[1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_RECOGNITION)
-[3. Text Recognition Model Inference](#RECOGNITION_MODEL_INFERENCE)
-[3.1 Lightweight Chinese Text Recognition Model Reference](#LIGHTWEIGHT_RECOGNITION)
-[2. CTC-BASED TEXT RECOGNITION MODEL INFERENCE](#CTC-BASED_RECOGNITION)
-[3. SRN-BASED TEXT RECOGNITION MODEL INFERENCE](#SRN-BASED_RECOGNITION)
-[3. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY](#USING_CUSTOM_CHARACTERS)
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@@ -38,9 +38,9 @@ Next, we first introduce how to convert a trained model into an inference model,
-[2. OTHER MODELS](#OTHER_MODELS)
<aname="CONVERT"></a>
## CONVERT TRAINING MODEL TO INFERENCE MODEL
## 1. Convert Training Model to Inference Model
<aname="Convert_detection_model"></a>
### Convert detection model to inference model
### 1.1 Convert Detection Model to Inference Model
Download the lightweight Chinese detection model:
```
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@@ -67,7 +67,7 @@ inference/det_db/
```
<aname="Convert_recognition_model"></a>
### Convert recognition model to inference model
### 1.2 Convert Recognition Model to Inference Model
Download the lightweight Chinese recognition model:
```
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@@ -95,7 +95,7 @@ inference/det_db/
```
<aname="Convert_angle_class_model"></a>
### Convert angle classification model to inference model
### 1.3 Convert Angle Classification Model to Inference Model
Download the angle classification model:
```
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@@ -122,13 +122,13 @@ inference/det_db/
<aname="DETECTION_MODEL_INFERENCE"></a>
## TEXT DETECTION MODEL INFERENCE
## 2. Text Detection Model Inference
The following will introduce the lightweight Chinese detection model inference, DB text detection model inference and EAST text detection model inference. The default configuration is based on the inference setting of the DB text detection model.
Because EAST and DB algorithms are very different, when inference, it is necessary to **adapt the EAST text detection algorithm by passing in corresponding parameters**.
<aname="LIGHTWEIGHT_DETECTION"></a>
### 1. LIGHTWEIGHT CHINESE DETECTION MODEL INFERENCE
### 2.1 Lightweight Chinese Detection Model Inference
For lightweight Chinese detection model inference, you can execute the following commands:
First, convert the model saved in the DB text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)), you can use the following command to convert:
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@@ -184,7 +184,7 @@ The visualized text detection results are saved to the `./inference_results` fol
**Note**: Since the ICDAR2015 dataset has only 1,000 training images, mainly for English scenes, the above model has very poor detection result on Chinese text images.
<aname="EAST_DETECTION"></a>
### 3. EAST TEXT DETECTION MODEL INFERENCE
### 2.3 EAST TEXT DETECTION MODEL INFERENCE
First, convert the model saved in the EAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)), you can use the following command to convert:
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@@ -205,7 +205,7 @@ The visualized text detection results are saved to the `./inference_results` fol
<aname="SAST_DETECTION"></a>
### 4. SAST TEXT DETECTION MODEL INFERENCE
### 2.4 Sast Text Detection Model Inference
#### (1). Quadrangle text detection model (ICDAR2015)
First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)), you can use the following command to convert:
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@@ -243,13 +243,13 @@ The visualized text detection results are saved to the `./inference_results` fol
**Note**: SAST post-processing locality aware NMS has two versions: Python and C++. The speed of C++ version is obviously faster than that of Python version. Due to the compilation version problem of NMS of C++ version, C++ version NMS will be called only in Python 3.5 environment, and python version NMS will be called in other cases.
<aname="RECOGNITION_MODEL_INFERENCE"></a>
## TEXT RECOGNITION MODEL INFERENCE
## 3. Text Recognition Model Inference
The following will introduce the lightweight Chinese recognition model inference, other CTC-based and Attention-based text recognition models inference. For Chinese text recognition, it is recommended to choose the recognition model based on CTC loss. In practice, it is also found that the result of the model based on Attention loss is not as good as the one based on CTC loss. In addition, if the characters dictionary is modified during training, make sure that you use the same characters set during inferencing. Please check below for details.
<aname="LIGHTWEIGHT_RECOGNITION"></a>
### 1. LIGHTWEIGHT CHINESE TEXT RECOGNITION MODEL REFERENCE
### 3.1 Lightweight Chinese Text Recognition Model Reference
For lightweight Chinese recognition model inference, you can execute the following commands:
@@ -39,7 +39,7 @@ pip install "paddleocr>=2.0.1" # Recommend to use version 2.0.1+
<aname="21-use-by-command-line"></a>
### 2.1 Use by command line
### 2.1 Use by Command Line
PaddleOCR provides a series of test images, click [here](https://paddleocr.bj.bcebos.com/dygraph_v2.1/ppocr_img.zip) to download, and then switch to the corresponding directory in the terminal
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@@ -95,7 +95,7 @@ If you do not use the provided test image, you can replace the following `--imag
['PAIN', 0.990372]
```
If you need to use the 2.0 model, please specify the parameter `--version 2.0`, paddleocr uses the 2.1 model by default. More whl package usage can be found in [whl package](./whl_en.md)
If you need to use the 2.0 model, please specify the parameter `--version PP-OCR`, paddleocr uses the 2.1 model by default(`--versioin PP-OCRv2`). More whl package usage can be found in [whl package](./whl_en.md)
