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[Docs] Pick PPOCR fastdeploy docs from PaddleOCR (#1534)
* Pick PPOCR fastdeploy docs from PaddleOCR * improve ppocr * improve readme * remove old PP-OCRv2 and PP-OCRv3 folfers * rename kunlun to kunlunxin * improve readme * improve readme * improve readme --------- Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
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examples/vision/ocr/PP-OCR/cpu-gpu/python/README.md
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examples/vision/ocr/PP-OCR/cpu-gpu/python/README.md
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[English](README.md) | 简体中文
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# PaddleOCR CPU-GPU Python部署示例
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本目录下提供`infer.py`快速完成PP-OCRv3在CPU/GPU，以及GPU上通过Paddle-TensorRT加速部署的示例.
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## 1. 说明
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PaddleOCR支持利用FastDeploy在NVIDIA GPU、X86 CPU、飞腾CPU、ARM CPU、Intel GPU(独立显卡/集成显卡)硬件上快速部署OCR模型
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## 2. 部署环境准备
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在部署前，需确认软硬件环境，同时下载预编译部署库，参考[FastDeploy安装文档](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#FastDeploy预编译库安装)安装FastDeploy预编译库.
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## 3. 部署模型准备
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在部署前, 请准备好您所需要运行的推理模型, 您可以在[FastDeploy支持的PaddleOCR模型列表](../README.md)中下载所需模型.
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## 4. 运行部署示例
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```bash
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# 安装FastDpeloy python包（详细文档请参考`部署环境准备`）
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pip install fastdeploy-gpu-python -f https://www.paddlepaddle.org.cn/whl/fastdeploy.html
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conda config --add channels conda-forge && conda install cudatoolkit=11.2 cudnn=8.2
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# 下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/ocr/PP-OCR/cpu-gpu/python
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# 如果您希望从PaddleOCR下载示例代码，请运行
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git clone https://github.com/PaddlePaddle/PaddleOCR.git
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# 注意：如果当前分支找不到下面的fastdeploy测试代码，请切换到dygraph分支
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git checkout dygraph
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cd PaddleOCR/deploy/fastdeploy/cpu-gpu/python
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# 下载PP-OCRv3文字检测模型
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wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar
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tar -xvf ch_PP-OCRv3_det_infer.tar
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# 下载文字方向分类器模型
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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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# 下载PP-OCRv3文字识别模型
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wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
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tar -xvf ch_PP-OCRv3_rec_infer.tar
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# 下载预测图片与字典文件
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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
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# 运行部署示例
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# 在CPU上使用Paddle Inference推理
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python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device cpu --backend paddle
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# 在CPU上使用OenVINO推理
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python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device cpu --backend openvino
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# 在CPU上使用ONNX Runtime推理
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python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device cpu --backend ort
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# 在CPU上使用Paddle Lite推理
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python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device cpu --backend pplite
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# 在GPU上使用Paddle Inference推理
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python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu --backend paddle
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# 在GPU上使用Paddle TensorRT推理
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python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu --backend pptrt
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# 在GPU上使用ONNX Runtime推理
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python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu --backend ort
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# 在GPU上使用Nvidia TensorRT推理
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python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu --backend trt
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# 同时, FastDeploy提供文字检测,文字分类,文字识别三个模型的单独推理,
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# 有需要的用户, 请准备合适的图片, 同时根据自己的需求, 参考infer.py来配置自定义硬件与推理后端.
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# 在CPU上,单独使用文字检测模型部署
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python infer_det.py --det_model ch_PP-OCRv3_det_infer --image 12.jpg --device cpu
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# 在CPU上,单独使用文字方向分类模型部署
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python infer_cls.py --cls_model ch_ppocr_mobile_v2.0_cls_infer --image 12.jpg --device cpu
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# 在CPU上,单独使用文字识别模型部署
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python infer_rec.py --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device cpu
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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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<div align="center">
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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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</div>
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## 5. 部署示例选项说明
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|参数|含义|默认值
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|---|---|---|
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|--det_model|指定检测模型文件夹所在的路径|None|
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|--cls_model|指定分类模型文件夹所在的路径|None|
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|--rec_model|指定识别模型文件夹所在的路径|None|
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|--rec_label_file|识别模型所需label所在的路径|None|
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|--image|指定测试图片所在的路径|None|
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|--device|指定即将运行的硬件类型，支持的值为`[cpu, gpu]`，当设置为cpu时，可运行在x86 cpu/arm cpu等cpu上|cpu|
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|--device_id|使用gpu时, 指定设备号|0|
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|--backend|部署模型时使用的后端, 支持的值为`[paddle,pptrt,pplite,ort,openvino,trt]` |paddle|
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关于如何通过FastDeploy使用更多不同的推理后端，以及如何使用不同的硬件，请参考文档：[如何切换模型推理后端引擎](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/how_to_change_backend.md)
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## 6. 更多指南
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### 6.1 如何使用Python部署PP-OCRv2系列模型.
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本目录下的`infer.py`代码是以PP-OCRv3模型为例, 如果用户有使用PP-OCRv2的需求, 只需要按照下面所示的方式, 来创建PP-OCRv2并使用.
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```python
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# 此行为创建PP-OCRv3模型的代码
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ppocr_v3 = fd.vision.ocr.PPOCRv3(det_model=det_model, cls_model=cls_model, rec_model=rec_model)
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# 只需要将PPOCRv3改为PPOCRv2,即可创造PPOCRv2模型, 同时, 后续的接口均使用ppocr_v2来调用
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ppocr_v2 = fd.vision.ocr.PPOCRv2(det_model=det_model, cls_model=cls_model, rec_model=rec_model)
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# 如果用户在部署PP-OCRv2时, 需要使用TensorRT推理, 还需要改动Rec模型的TensorRT的输入shape.
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# 建议如下修改, 需要把 H 维度改为32, W 纬度按需修改.
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rec_option.set_trt_input_shape("x", [1, 3, 32, 10],
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[args.rec_bs, 3, 32, 320],
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[args.rec_bs, 3, 32, 2304])
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```
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### 6.2 如何在PP-OCRv2/v3系列模型中, 关闭文字方向分类器的使用.
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在PP-OCRv3/v2中, 文字方向分类器是可选的, 用户可以按照以下方式, 来决定自己是否使用方向分类器.
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```python
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# 使用 Cls 模型
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ppocr_v3 = fd.vision.ocr.PPOCRv3(det_model=det_model, cls_model=cls_model, rec_model=rec_model)
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# 不使用 Cls 模型
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ppocr_v3 = fd.vision.ocr.PPOCRv3(det_model=det_model, cls_model=None, rec_model=rec_model)
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```
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### 6.3 如何修改前后处理超参数.
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在示例代码中, 我们展示出了修改前后处理超参数的接口,并设置为默认值,其中, FastDeploy提供的超参数的含义与文档[PaddleOCR推理模型参数解释](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/inference_args.md)是相同的. 如果用户想要进行更多定制化的开发, 请阅读[PP-OCR系列 Python API查阅](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/python/html/ocr.html)
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```python
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# 设置检测模型的max_side_len
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det_model.preprocessor.max_side_len = 960
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# 其他...
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```
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### 6.4 其他指南
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- [FastDeploy部署PaddleOCR模型概览](../../)
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- [PP-OCRv3 C++部署](../cpp)
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- [PP-OCRv3 C 部署](../c)
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- [PP-OCRv3 C# 部署](../csharp)
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## 7. 常见问题
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- PaddleOCR能在FastDeploy支持的多种后端上推理,支持情况如下表所示, 如何切换后端, 详见文档[如何切换模型推理后端引擎](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/how_to_change_backend.md)
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|硬件类型|支持的后端|
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|:---:|:---:|
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|X86 CPU| Paddle Inference, ONNX Runtime, OpenVINO |
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|ARM CPU| Paddle Lite |
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|飞腾 CPU| ONNX Runtime |
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|NVIDIA GPU| Paddle Inference, ONNX Runtime, TensorRT |
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- [如何将模型预测结果转为numpy格式](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/vision_result_related_problems.md)
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- [Intel GPU(独立显卡/集成显卡)的使用](https://github.com/PaddlePaddle/FastDeploy/blob/develop/tutorials/intel_gpu/README.md)
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- [编译CPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/cpu.md)
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- [编译GPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/gpu.md)
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- [编译Jetson部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/jetson.md)
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218
examples/vision/ocr/PP-OCR/cpu-gpu/python/infer.py
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examples/vision/ocr/PP-OCR/cpu-gpu/python/infer.py
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
|
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# you may not use this file except in compliance with the License.
|
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# You may obtain a copy of the License at
|
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#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
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# limitations under the License.
|
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import fastdeploy as fd
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import cv2
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import os
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def parse_arguments():
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import argparse
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import ast
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--det_model", required=True, help="Path of Detection model of PPOCR.")
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parser.add_argument(
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"--cls_model",
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required=True,
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help="Path of Classification model of PPOCR.")
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parser.add_argument(
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"--rec_model",
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required=True,
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help="Path of Recognization model of PPOCR.")
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parser.add_argument(
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"--rec_label_file",
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required=True,
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help="Path of Recognization model of PPOCR.")
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parser.add_argument(
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"--image", type=str, required=True, help="Path of test image file.")
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parser.add_argument(
|
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"--device",
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type=str,
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default='cpu',
|
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help="Type of inference device, support 'cpu' or 'gpu'.")
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parser.add_argument(
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"--device_id",
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type=int,
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default=0,
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help="Define which GPU card used to run model.")
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parser.add_argument(
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"--cls_bs",
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type=int,
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default=1,
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help="Classification model inference batch size.")
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parser.add_argument(
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"--rec_bs",
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type=int,
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default=6,
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help="Recognition model inference batch size")
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parser.add_argument(
|
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"--backend",
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type=str,
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default="default",
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help="Type of inference backend, support ort/trt/paddle/openvino, default 'openvino' for cpu, 'tensorrt' for gpu"
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)
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return parser.parse_args()
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def build_option(args):
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det_option = fd.RuntimeOption()
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cls_option = fd.RuntimeOption()
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rec_option = fd.RuntimeOption()
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if args.device.lower() == "gpu":
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det_option.use_gpu(args.device_id)
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cls_option.use_gpu(args.device_id)
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rec_option.use_gpu(args.device_id)
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|
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if args.backend.lower() == "trt":
|
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assert args.device.lower(
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) == "gpu", "TensorRT backend require inference on device GPU."
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det_option.use_trt_backend()
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cls_option.use_trt_backend()
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rec_option.use_trt_backend()
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|
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# If use TRT backend, the dynamic shape will be set as follow.
|
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# We recommend that users set the length and height of the detection model to a multiple of 32.
|
||||
# We also recommend that users set the Trt input shape as follow.
|
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det_option.set_trt_input_shape("x", [1, 3, 64, 64], [1, 3, 640, 640],
|
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[1, 3, 960, 960])
|
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cls_option.set_trt_input_shape("x", [1, 3, 48, 10],
|
||||
[args.cls_bs, 3, 48, 320],
|
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[args.cls_bs, 3, 48, 1024])
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rec_option.set_trt_input_shape("x", [1, 3, 48, 10],
|
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[args.rec_bs, 3, 48, 320],
|
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[args.rec_bs, 3, 48, 2304])
|
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|
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# Users could save TRT cache file to disk as follow.
|
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det_option.set_trt_cache_file(args.det_model + "/det_trt_cache.trt")
|
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cls_option.set_trt_cache_file(args.cls_model + "/cls_trt_cache.trt")
|
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rec_option.set_trt_cache_file(args.rec_model + "/rec_trt_cache.trt")
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|
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elif args.backend.lower() == "pptrt":
|
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assert args.device.lower(
|
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) == "gpu", "Paddle-TensorRT backend require inference on device GPU."
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det_option.use_paddle_infer_backend()
|
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det_option.paddle_infer_option.collect_trt_shape = True
|
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det_option.paddle_infer_option.enable_trt = True
|
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|
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cls_option.use_paddle_infer_backend()
|
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cls_option.paddle_infer_option.collect_trt_shape = True
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cls_option.paddle_infer_option.enable_trt = True
|
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|
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rec_option.use_paddle_infer_backend()
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rec_option.paddle_infer_option.collect_trt_shape = True
|
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rec_option.paddle_infer_option.enable_trt = True
|
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|
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# If use TRT backend, the dynamic shape will be set as follow.
|
||||
# We recommend that users set the length and height of the detection model to a multiple of 32.
|
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# We also recommend that users set the Trt input shape as follow.
|
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det_option.set_trt_input_shape("x", [1, 3, 64, 64], [1, 3, 640, 640],
|
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[1, 3, 960, 960])
|
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cls_option.set_trt_input_shape("x", [1, 3, 48, 10],
|
||||
[args.cls_bs, 3, 48, 320],
|
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[args.cls_bs, 3, 48, 1024])
|
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rec_option.set_trt_input_shape("x", [1, 3, 48, 10],
|
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[args.rec_bs, 3, 48, 320],
|
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[args.rec_bs, 3, 48, 2304])
|
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|
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# Users could save TRT cache file to disk as follow.
|
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det_option.set_trt_cache_file(args.det_model)
|
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cls_option.set_trt_cache_file(args.cls_model)
|
||||
rec_option.set_trt_cache_file(args.rec_model)
|
||||
|
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elif args.backend.lower() == "ort":
|
||||
det_option.use_ort_backend()
|
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cls_option.use_ort_backend()
|
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rec_option.use_ort_backend()
|
||||
|
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elif args.backend.lower() == "paddle":
|
||||
det_option.use_paddle_infer_backend()
|
||||
cls_option.use_paddle_infer_backend()
|
||||
rec_option.use_paddle_infer_backend()
|
||||
|
||||
elif args.backend.lower() == "openvino":
|
||||
assert args.device.lower(
|
||||
) == "cpu", "OpenVINO backend require inference on device CPU."
|
||||
det_option.use_openvino_backend()
|
||||
cls_option.use_openvino_backend()
|
||||
rec_option.use_openvino_backend()
|
||||
|
||||
elif args.backend.lower() == "pplite":
|
||||
assert args.device.lower(
|
||||
) == "cpu", "Paddle Lite backend require inference on device CPU."
|
||||
det_option.use_lite_backend()
|
||||
cls_option.use_lite_backend()
|
||||
rec_option.use_lite_backend()
|
||||
|
||||
return det_option, cls_option, rec_option
|
||||
|
||||
|
||||
args = parse_arguments()
|
||||
|
||||
det_model_file = os.path.join(args.det_model, "inference.pdmodel")
|
||||
det_params_file = os.path.join(args.det_model, "inference.pdiparams")
|
||||
|
||||
cls_model_file = os.path.join(args.cls_model, "inference.pdmodel")
|
||||
cls_params_file = os.path.join(args.cls_model, "inference.pdiparams")
|
||||
|
||||
rec_model_file = os.path.join(args.rec_model, "inference.pdmodel")
|
||||
rec_params_file = os.path.join(args.rec_model, "inference.pdiparams")
|
||||
rec_label_file = args.rec_label_file
|
||||
|
||||
det_option, cls_option, rec_option = build_option(args)
|
||||
|
||||
det_model = fd.vision.ocr.DBDetector(
|
||||
det_model_file, det_params_file, runtime_option=det_option)
|
||||
|
||||
cls_model = fd.vision.ocr.Classifier(
|
||||
cls_model_file, cls_params_file, runtime_option=cls_option)
|
||||
|
||||
rec_model = fd.vision.ocr.Recognizer(
|
||||
rec_model_file, rec_params_file, rec_label_file, runtime_option=rec_option)
|
||||
|
||||
# Parameters settings for pre and post processing of Det/Cls/Rec Models.
|
||||
# All parameters are set to default values.
|
||||
det_model.preprocessor.max_side_len = 960
|
||||
det_model.postprocessor.det_db_thresh = 0.3
|
||||
det_model.postprocessor.det_db_box_thresh = 0.6
|
||||
det_model.postprocessor.det_db_unclip_ratio = 1.5
|
||||
det_model.postprocessor.det_db_score_mode = "slow"
|
||||
det_model.postprocessor.use_dilation = False
|
||||
cls_model.postprocessor.cls_thresh = 0.9
|
||||
|
||||
# Create PP-OCRv3, if cls_model is not needed, just set cls_model=None .
|
||||
ppocr_v3 = fd.vision.ocr.PPOCRv3(
|
||||
det_model=det_model, cls_model=cls_model, rec_model=rec_model)
|
||||
|
||||
# Set inference batch size for cls model and rec model, the value could be -1 and 1 to positive infinity.
|
||||
# When inference batch size is set to -1, it means that the inference batch size
|
||||
# of the cls and rec models will be the same as the number of boxes detected by the det model.
|
||||
ppocr_v3.cls_batch_size = args.cls_bs
|
||||
ppocr_v3.rec_batch_size = args.rec_bs
|
||||
|
||||
# Read the input image
|
||||
im = cv2.imread(args.image)
|
||||
|
||||
# Predict and reutrn the results
|
||||
result = ppocr_v3.predict(im)
|
||||
|
||||
print(result)
|
||||
|
||||
# Visuliaze the results.
|
||||
vis_im = fd.vision.vis_ppocr(im, result)
|
||||
cv2.imwrite("visualized_result.jpg", vis_im)
|
||||
print("Visualized result save in ./visualized_result.jpg")
|
||||
77
examples/vision/ocr/PP-OCR/cpu-gpu/python/infer_cls.py
Executable file
77
examples/vision/ocr/PP-OCR/cpu-gpu/python/infer_cls.py
Executable file
@@ -0,0 +1,77 @@
|
||||
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import fastdeploy as fd
|
||||
import cv2
|
||||
import os
|
||||
|
||||
|
||||
def parse_arguments():
|
||||
import argparse
|
||||
import ast
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"--cls_model",
|
||||
required=True,
|
||||
help="Path of Classification model of PPOCR.")
|
||||
parser.add_argument(
|
||||
"--image", type=str, required=True, help="Path of test image file.")
|
||||
parser.add_argument(
|
||||
"--device",
|
||||
type=str,
|
||||
default='cpu',
|
||||
help="Type of inference device, support 'cpu', 'kunlunxin' or 'gpu'.")
|
||||
parser.add_argument(
|
||||
"--device_id",
|
||||
type=int,
|
||||
default=0,
|
||||
help="Define which GPU card used to run model.")
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def build_option(args):
|
||||
|
||||
cls_option = fd.RuntimeOption()
|
||||
|
||||
if args.device.lower() == "gpu":
|
||||
cls_option.use_gpu(args.device_id)
|
||||
|
||||
return cls_option
|
||||
|
||||
|
||||
args = parse_arguments()
|
||||
|
||||
cls_model_file = os.path.join(args.cls_model, "inference.pdmodel")
|
||||
cls_params_file = os.path.join(args.cls_model, "inference.pdiparams")
|
||||
|
||||
# Set the runtime option
|
||||
cls_option = build_option(args)
|
||||
|
||||
# Create the cls_model
|
||||
cls_model = fd.vision.ocr.Classifier(
|
||||
cls_model_file, cls_params_file, runtime_option=cls_option)
|
||||
|
||||
# Set the postprocessing parameters
|
||||
cls_model.postprocessor.cls_thresh = 0.9
|
||||
|
||||
# Read the image
|
||||
im = cv2.imread(args.image)
|
||||
|
||||
# Predict and return the results
|
||||
result = cls_model.predict(im)
|
||||
|
||||
# User can infer a batch of images by following code.
|
||||
# result = cls_model.batch_predict([im])
|
||||
|
||||
print(result)
|
||||
82
examples/vision/ocr/PP-OCR/cpu-gpu/python/infer_det.py
Executable file
82
examples/vision/ocr/PP-OCR/cpu-gpu/python/infer_det.py
Executable file
@@ -0,0 +1,82 @@
|
||||
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import fastdeploy as fd
|
||||
import cv2
|
||||
import os
|
||||
|
||||
|
||||
def parse_arguments():
|
||||
import argparse
|
||||
import ast
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"--det_model", required=True, help="Path of Detection model of PPOCR.")
|
||||
parser.add_argument(
|
||||
"--image", type=str, required=True, help="Path of test image file.")
|
||||
parser.add_argument(
|
||||
"--device",
|
||||
type=str,
|
||||
default='cpu',
|
||||
help="Type of inference device, support 'cpu', 'kunlunxin' or 'gpu'.")
|
||||
parser.add_argument(
|
||||
"--device_id",
|
||||
type=int,
|
||||
default=0,
|
||||
help="Define which GPU card used to run model.")
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def build_option(args):
|
||||
|
||||
det_option = fd.RuntimeOption()
|
||||
|
||||
if args.device.lower() == "gpu":
|
||||
det_option.use_gpu(args.device_id)
|
||||
|
||||
return det_option
|
||||
|
||||
|
||||
args = parse_arguments()
|
||||
|
||||
det_model_file = os.path.join(args.det_model, "inference.pdmodel")
|
||||
det_params_file = os.path.join(args.det_model, "inference.pdiparams")
|
||||
|
||||
# Set the runtime option
|
||||
det_option = build_option(args)
|
||||
|
||||
# Create the det_model
|
||||
det_model = fd.vision.ocr.DBDetector(
|
||||
det_model_file, det_params_file, runtime_option=det_option)
|
||||
|
||||
# Set the preporcessing parameters
|
||||
det_model.preprocessor.max_side_len = 960
|
||||
det_model.postprocessor.det_db_thresh = 0.3
|
||||
det_model.postprocessor.det_db_box_thresh = 0.6
|
||||
det_model.postprocessor.det_db_unclip_ratio = 1.5
|
||||
det_model.postprocessor.det_db_score_mode = "slow"
|
||||
det_model.postprocessor.use_dilation = False
|
||||
|
||||
# Read the image
|
||||
im = cv2.imread(args.image)
|
||||
|
||||
# Predict and return the results
|
||||
result = det_model.predict(im)
|
||||
|
||||
print(result)
|
||||
|
||||
# Visualize the results
|
||||
vis_im = fd.vision.vis_ppocr(im, result)
|
||||
cv2.imwrite("visualized_result.jpg", vis_im)
|
||||
print("Visualized result save in ./visualized_result.jpg")
|
||||
79
examples/vision/ocr/PP-OCR/cpu-gpu/python/infer_rec.py
Executable file
79
examples/vision/ocr/PP-OCR/cpu-gpu/python/infer_rec.py
Executable file
@@ -0,0 +1,79 @@
|
||||
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import fastdeploy as fd
|
||||
import cv2
|
||||
import os
|
||||
|
||||
|
||||
def parse_arguments():
|
||||
import argparse
|
||||
import ast
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"--rec_model",
|
||||
required=True,
|
||||
help="Path of Recognization model of PPOCR.")
|
||||
parser.add_argument(
|
||||
"--rec_label_file",
|
||||
required=True,
|
||||
help="Path of Recognization model of PPOCR.")
|
||||
parser.add_argument(
|
||||
"--image", type=str, required=True, help="Path of test image file.")
|
||||
parser.add_argument(
|
||||
"--device",
|
||||
type=str,
|
||||
default='cpu',
|
||||
help="Type of inference device, support 'cpu', 'kunlunxin' or 'gpu'.")
|
||||
parser.add_argument(
|
||||
"--device_id",
|
||||
type=int,
|
||||
default=0,
|
||||
help="Define which GPU card used to run model.")
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def build_option(args):
|
||||
|
||||
rec_option = fd.RuntimeOption()
|
||||
|
||||
if args.device.lower() == "gpu":
|
||||
rec_option.use_gpu(args.device_id)
|
||||
|
||||
return rec_option
|
||||
|
||||
|
||||
args = parse_arguments()
|
||||
|
||||
rec_model_file = os.path.join(args.rec_model, "inference.pdmodel")
|
||||
rec_params_file = os.path.join(args.rec_model, "inference.pdiparams")
|
||||
rec_label_file = args.rec_label_file
|
||||
|
||||
# Set the runtime option
|
||||
rec_option = build_option(args)
|
||||
|
||||
# Create the rec_model
|
||||
rec_model = fd.vision.ocr.Recognizer(
|
||||
rec_model_file, rec_params_file, rec_label_file, runtime_option=rec_option)
|
||||
|
||||
# Read the image
|
||||
im = cv2.imread(args.image)
|
||||
|
||||
# Predict and return the result
|
||||
result = rec_model.predict(im)
|
||||
|
||||
# User can infer a batch of images by following code.
|
||||
# result = rec_model.batch_predict([im])
|
||||
|
||||
print(result)
|
||||
Reference in New Issue
Block a user