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[Doc]Add English version of documents in examples (#1070)
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This commit is contained in:
Hu Chuqi
@@ -1,15 +1,16 @@
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# PPOCRv2 Python部署示例
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English | [简体中文](README_CN.md)
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# PPOCRv2 Python Deployment Example
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在部署前，需确认以下两个步骤
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Two steps before deployment
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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本目录下提供`infer.py`快速完成PPOCRv2在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成
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This directory provides examples that `infer.py` fast finishes the deployment of PPOCRv2 on CPU/GPU and GPU accelerated by TensorRT. The script is as follows
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```
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# 下载模型,图片和字典文件
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# Download model, image, and dictionary files
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wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar
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tar -xvf ch_PP-OCRv2_det_infer.tar
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@@ -24,109 +25,107 @@ wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg
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wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt
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#下载部署示例代码
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# Download the example code for deployment
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd examples/vison/ocr/PP-OCRv2/python/
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# CPU推理
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# CPU inference
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python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device cpu
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# GPU推理
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# GPU inference
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python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu
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# GPU上使用TensorRT推理
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# TensorRT inference on GPU
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python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu --backend trt
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# 昆仑芯XPU推理
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# KunlunXin XPU inference
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python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device kunlunxin
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# 华为昇腾推理,需要使用静态shape脚本, 若用户需要连续地预测图片, 输入图片尺寸需要准备为统一尺寸
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python infer_static_shape.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device ascend
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```
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运行完成可视化结果如下图所示
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The visualized result after running is as follows
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<img width="640" src="https://user-images.githubusercontent.com/109218879/185826024-f7593a0c-1bd2-4a60-b76c-15588484fa08.jpg">
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## PPOCRv2 Python接口
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## PPOCRv2 Python Interface
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```
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fd.vision.ocr.PPOCRv2(det_model=det_model, cls_model=cls_model, rec_model=rec_model)
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```
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PPOCRv2的初始化,输入的参数是检测模型，分类模型和识别模型，其中cls_model可选，如无需求，可设置为None
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To initialize PPOCRv2, the input parameters correspond to detection model, classification model, and recognition model. Among them, cls_model is optional. It can be set to None if there is no demand
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**参数**
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**Parameter**
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> * **det_model**(model): OCR中的检测模型
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> * **cls_model**(model): OCR中的分类模型
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> * **rec_model**(model): OCR中的识别模型
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> * **det_model**(model): Detection model in OCR
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> * **cls_model**(model): Classification model in OCR
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> * **rec_model**(model): Recognition model in OCR
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### predict函数
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### predict function
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> ```
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> result = ppocr_v2.predict(im)
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> ```
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>
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> 模型预测接口，输入是一张图片
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> Model prediction interface. Input one image.
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>
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> **参数**
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> **Parameter**
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>
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> > * **im**(np.ndarray): 输入数据，每张图片注意需为HWC，BGR格式
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> > * **im**(np.ndarray): Input data in HWC or BGR format
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> **返回**
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> **Return**
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>
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> > 返回`fastdeploy.vision.OCRResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
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> > Return the `fastdeploy.vision.OCRResult` structure. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for its description.
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## DBDetector Python接口
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## DBDetector Python Interface
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### DBDetector类
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### DBDetector Class
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```
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fastdeploy.vision.ocr.DBDetector(model_file, params_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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```
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DBDetector模型加载和初始化，其中模型为paddle模型格式。
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DBDetector model loading and initialization. The model is in paddle format.
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**参数**
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**Parameter**
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> * **model_file**(str): 模型文件路径
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> * **params_file**(str): 参数文件路径，当模型格式为ONNX时，此参数传入空字符串即可
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> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
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> * **model_format**(ModelFormat): 模型格式，默认为PADDLE格式
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> * **model_file**(str): Model file path
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> * **params_file**(str): Parameter file path. Merely passing an empty string when the model is in ONNX format
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> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
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> * **model_format**(ModelFormat): Model format. PADDLE format by default
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### Classifier类与DBDetector类相同
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### The same applies to the Classifier class
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### Recognizer类
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### Recognizer Class
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```
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fastdeploy.vision.ocr.Recognizer(rec_model_file,rec_params_file,rec_label_file,
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runtime_option=rec_runtime_option,model_format=ModelFormat.PADDLE)
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```
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Recognizer类初始化时,需要在rec_label_file参数中,输入识别模型所需的label文件路径，其他参数均与DBDetector类相同
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To initialize the Recognizer class, users need to input the label file path required by the recognition model in the rec_label_file parameter. Other parameters are the same as those of DBDetector class
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**参数**
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> * **label_path**(str): 识别模型的label文件路径
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**Parameter**
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> * **label_path**(str): The label path of the recognition model
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### 类成员变量
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### Class Member Variable
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#### DBDetector预处理参数
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用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果
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#### DBDetector Pre-processing Parameter
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Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results
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> > * **max_side_len**(int): 检测算法前向时图片长边的最大尺寸，当长边超出这个值时会将长边resize到这个大小，短边等比例缩放,默认为960
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> > * **det_db_thresh**(double): DB模型输出预测图的二值化阈值，默认为0.3
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> > * **det_db_box_thresh**(double): DB模型输出框的阈值，低于此值的预测框会被丢弃，默认为0.6
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> > * **det_db_unclip_ratio**(double): DB模型输出框扩大的比例，默认为1.5
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> > * **det_db_score_mode**(string):DB后处理中计算文本框平均得分的方式,默认为slow，即求polygon区域的平均分数的方式
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> > * **use_dilation**(bool):是否对检测输出的feature map做膨胀处理,默认为Fasle
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> > * **max_side_len**(int): The long side’s maximum size of the oriented view before detection. The long side will be resized to this size when exceeding the value. And the short side will be scaled in equal proportion. Default 960
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> > * **det_db_thresh**(double): The binarization threshold of the prediction image from DB models. Default 0.3
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> > * **det_db_box_thresh**(double): The threshold for the output box of DB models, below which the predicted box is discarded. Default 0.6
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> > * **det_db_unclip_ratio**(double): The expansion ratio of the DB model output box. Default 1.5
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> > * **det_db_score_mode**(string): The way to calculate the average score of the text box in DB post-processing. Default slow, which is identical to the calculation of the polygon area’s average score
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> > * **use_dilation**(bool): Whether to expand the feature map from the detection. Default False
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#### Classifier预处理参数
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用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果
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#### Classifier Pre-processing Parameter
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Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results
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> > * **cls_thresh**(double): 当分类模型输出的得分超过此阈值，输入的图片将被翻转，默认为0.9
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> > * **cls_thresh**(double): The input image will be flipped when the score output by the classification model exceeds this threshold. Default 0.9
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## 其它文档
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## Other Documents
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- [PPOCR 系列模型介绍](../../)
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- [PPOCRv2 C++部署](../cpp)
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- [模型预测结果说明](../../../../../docs/api/vision_results/)
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- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
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- [PPOCR Model Description](../../)
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- [PPOCRv2 C++ Deployment](../cpp)
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- [Model Prediction Results](../../../../../docs/api/vision_results/)
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- [How to switch the model inference backend engine](../../../../../docs/cn/faq/how_to_change_backend.md)
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133
examples/vision/ocr/PP-OCRv2/python/README_CN.md
Normal file
133
examples/vision/ocr/PP-OCRv2/python/README_CN.md
Normal file
@@ -0,0 +1,133 @@
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[English](README.md) | 简体中文
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# PPOCRv2 Python部署示例
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在部署前，需确认以下两个步骤
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||||
- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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本目录下提供`infer.py`快速完成PPOCRv2在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成
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```
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# 下载模型,图片和字典文件
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wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar
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tar -xvf ch_PP-OCRv2_det_infer.tar
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wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
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tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar
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wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar
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tar -xvf ch_PP-OCRv2_rec_infer.tar
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wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg
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wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt
|
||||
|
||||
|
||||
#下载部署示例代码
|
||||
git clone https://github.com/PaddlePaddle/FastDeploy.git
|
||||
cd examples/vison/ocr/PP-OCRv2/python/
|
||||
|
||||
# CPU推理
|
||||
python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device cpu
|
||||
# GPU推理
|
||||
python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu
|
||||
# GPU上使用TensorRT推理
|
||||
python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu --backend trt
|
||||
# 昆仑芯XPU推理
|
||||
python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device kunlunxin
|
||||
# 华为昇腾推理,需要使用静态shape脚本, 若用户需要连续地预测图片, 输入图片尺寸需要准备为统一尺寸
|
||||
python infer_static_shape.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device ascend
|
||||
```
|
||||
|
||||
运行完成可视化结果如下图所示
|
||||
<img width="640" src="https://user-images.githubusercontent.com/109218879/185826024-f7593a0c-1bd2-4a60-b76c-15588484fa08.jpg">
|
||||
|
||||
## PPOCRv2 Python接口
|
||||
|
||||
```
|
||||
fd.vision.ocr.PPOCRv2(det_model=det_model, cls_model=cls_model, rec_model=rec_model)
|
||||
```
|
||||
PPOCRv2的初始化,输入的参数是检测模型，分类模型和识别模型，其中cls_model可选，如无需求，可设置为None
|
||||
|
||||
**参数**
|
||||
|
||||
> * **det_model**(model): OCR中的检测模型
|
||||
> * **cls_model**(model): OCR中的分类模型
|
||||
> * **rec_model**(model): OCR中的识别模型
|
||||
|
||||
### predict函数
|
||||
|
||||
> ```
|
||||
> result = ppocr_v2.predict(im)
|
||||
> ```
|
||||
>
|
||||
> 模型预测接口，输入是一张图片
|
||||
>
|
||||
> **参数**
|
||||
>
|
||||
> > * **im**(np.ndarray): 输入数据，每张图片注意需为HWC，BGR格式
|
||||
|
||||
> **返回**
|
||||
>
|
||||
> > 返回`fastdeploy.vision.OCRResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
|
||||
|
||||
|
||||
|
||||
## DBDetector Python接口
|
||||
|
||||
### DBDetector类
|
||||
|
||||
```
|
||||
fastdeploy.vision.ocr.DBDetector(model_file, params_file, runtime_option=None, model_format=ModelFormat.PADDLE)
|
||||
```
|
||||
|
||||
DBDetector模型加载和初始化，其中模型为paddle模型格式。
|
||||
|
||||
**参数**
|
||||
|
||||
> * **model_file**(str): 模型文件路径
|
||||
> * **params_file**(str): 参数文件路径，当模型格式为ONNX时，此参数传入空字符串即可
|
||||
> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
|
||||
> * **model_format**(ModelFormat): 模型格式，默认为PADDLE格式
|
||||
|
||||
### Classifier类与DBDetector类相同
|
||||
|
||||
### Recognizer类
|
||||
```
|
||||
fastdeploy.vision.ocr.Recognizer(rec_model_file,rec_params_file,rec_label_file,
|
||||
runtime_option=rec_runtime_option,model_format=ModelFormat.PADDLE)
|
||||
```
|
||||
Recognizer类初始化时,需要在rec_label_file参数中,输入识别模型所需的label文件路径，其他参数均与DBDetector类相同
|
||||
|
||||
**参数**
|
||||
> * **label_path**(str): 识别模型的label文件路径
|
||||
|
||||
|
||||
|
||||
### 类成员变量
|
||||
|
||||
#### DBDetector预处理参数
|
||||
用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果
|
||||
|
||||
> > * **max_side_len**(int): 检测算法前向时图片长边的最大尺寸，当长边超出这个值时会将长边resize到这个大小，短边等比例缩放,默认为960
|
||||
> > * **det_db_thresh**(double): DB模型输出预测图的二值化阈值，默认为0.3
|
||||
> > * **det_db_box_thresh**(double): DB模型输出框的阈值，低于此值的预测框会被丢弃，默认为0.6
|
||||
> > * **det_db_unclip_ratio**(double): DB模型输出框扩大的比例，默认为1.5
|
||||
> > * **det_db_score_mode**(string):DB后处理中计算文本框平均得分的方式,默认为slow，即求polygon区域的平均分数的方式
|
||||
> > * **use_dilation**(bool):是否对检测输出的feature map做膨胀处理,默认为Fasle
|
||||
|
||||
#### Classifier预处理参数
|
||||
用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果
|
||||
|
||||
> > * **cls_thresh**(double): 当分类模型输出的得分超过此阈值，输入的图片将被翻转，默认为0.9
|
||||
|
||||
|
||||
|
||||
## 其它文档
|
||||
|
||||
- [PPOCR 系列模型介绍](../../)
|
||||
- [PPOCRv2 C++部署](../cpp)
|
||||
- [模型预测结果说明](../../../../../docs/api/vision_results/)
|
||||
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
|
||||
Reference in New Issue
Block a user