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[English](README_CN.md) | 简体中文
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# PP-OCRv3 RKNPU2 C++部署示例
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本目录下提供`infer.cc`, 供用户完成PP-OCRv3在RKNPU2的部署.
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## 1. 部署环境准备
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在部署前，需确认以下两个步骤
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- 1. 在部署前，需自行编译基于RKNPU2的预测库，参考文档[RKNPU2部署环境编译](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#自行编译安装)
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- 2. 同时请用户参考[FastDeploy RKNPU2资源导航](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/rknpu2.md)
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## 2.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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同时, 在RKNPU2上部署PP-OCR系列模型时，我们需要把Paddle的推理模型转为RKNN模型.
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由于rknn_toolkit2工具暂不支持直接从Paddle直接转换为RKNN模型，因此我们需要先将Paddle推理模型转为ONNX模型, 最后转为RKNN模型, 示例如下.
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```bash
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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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# 请用户自行安装最新发布版本的paddle2onnx, 转换模型到ONNX格式的模型
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paddle2onnx --model_dir ch_PP-OCRv3_det_infer \
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--model_filename inference.pdmodel \
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--params_filename inference.pdiparams \
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--save_file ch_PP-OCRv3_det_infer/ch_PP-OCRv3_det_infer.onnx \
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--enable_dev_version True
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paddle2onnx --model_dir ch_ppocr_mobile_v2.0_cls_infer \
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--model_filename inference.pdmodel \
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--params_filename inference.pdiparams \
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--save_file ch_ppocr_mobile_v2.0_cls_infer/ch_ppocr_mobile_v2.0_cls_infer.onnx \
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--enable_dev_version True
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paddle2onnx --model_dir ch_PP-OCRv3_rec_infer \
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--model_filename inference.pdmodel \
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--params_filename inference.pdiparams \
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--save_file ch_PP-OCRv3_rec_infer/ch_PP-OCRv3_rec_infer.onnx \
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--enable_dev_version True
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# 固定模型的输入shape
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python -m paddle2onnx.optimize --input_model ch_PP-OCRv3_det_infer/ch_PP-OCRv3_det_infer.onnx \
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--output_model ch_PP-OCRv3_det_infer/ch_PP-OCRv3_det_infer.onnx \
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--input_shape_dict "{'x':[1,3,960,960]}"
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python -m paddle2onnx.optimize --input_model ch_ppocr_mobile_v2.0_cls_infer/ch_ppocr_mobile_v2.0_cls_infer.onnx \
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--output_model ch_ppocr_mobile_v2.0_cls_infer/ch_ppocr_mobile_v2.0_cls_infer.onnx \
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--input_shape_dict "{'x':[1,3,48,192]}"
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python -m paddle2onnx.optimize --input_model ch_PP-OCRv3_rec_infer/ch_PP-OCRv3_rec_infer.onnx \
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--output_model ch_PP-OCRv3_rec_infer/ch_PP-OCRv3_rec_infer.onnx \
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--input_shape_dict "{'x':[1,3,48,320]}"
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# 在rockchip/rknpu2_tools/目录下, 我们为用户提供了转换ONNX模型到RKNN模型的工具
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python rockchip/rknpu2_tools/export.py --config_path tools/rknpu2/config/ppocrv3_det.yaml \
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--target_platform rk3588
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python rockchip/rknpu2_tools/export.py --config_path tools/rknpu2/config/ppocrv3_rec.yaml \
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--target_platform rk3588
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python rockchip/rknpu2_tools/export.py --config_path tools/rknpu2/config/ppocrv3_cls.yaml \
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--target_platform rk3588
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```
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## 3.运行部署示例
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在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本1.0.3以上(x.x.x>1.0.3), RKNN版本在1.4.1b22以上。
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```
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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/rockchip/cpp
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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/rockchip/cpp
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mkdir build
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cd build
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# 使用编译完成的FastDeploy库编译infer_demo
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-rockchip
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make -j
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# 下载图片和字典文件
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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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# 拷贝RKNN模型到build目录
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# CPU推理
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./infer_demo ./ch_PP-OCRv3_det_infer/ch_PP-OCRv3_det_infer.onnx \
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./ch_ppocr_mobile_v2.0_cls_infer/ch_ppocr_mobile_v2.0_cls_infer.onnx \
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./ch_PP-OCRv3_rec_infer/ch_PP-OCRv3_rec_infer.onnx \
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./ppocr_keys_v1.txt \
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./12.jpg \
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0
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# RKNPU推理
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./infer_demo ./ch_PP-OCRv3_det_infer/ch_PP-OCRv3_det_infer_rk3588_unquantized.rknn \
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./ch_ppocr_mobile_v2.0_cls_infer/ch_ppocr_mobile_v20_cls_infer_rk3588_unquantized.rknn \
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./ch_PP-OCRv3_rec_infer/ch_PP-OCRv3_rec_infer_rk3588_unquantized.rknn \
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./ppocr_keys_v1.txt \
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./12.jpg \
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1
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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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<img width="640" src="https://user-images.githubusercontent.com/109218879/185826024-f7593a0c-1bd2-4a60-b76c-15588484fa08.jpg">
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结果输出如下:
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```text
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det boxes: [[276,174],[285,173],[285,178],[276,179]]rec text: rec score:0.000000 cls label: 1 cls score: 0.766602
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det boxes: [[43,408],[483,390],[483,431],[44,449]]rec text: 上海斯格威铂尔曼大酒店 rec score:0.888450 cls label: 0 cls score: 1.000000
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det boxes: [[186,456],[399,448],[399,480],[186,488]]rec text: 打浦路15号 rec score:0.988769 cls label: 0 cls score: 1.000000
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det boxes: [[18,501],[513,485],[514,537],[18,554]]rec text: 绿洲仕格维花园公寓 rec score:0.992730 cls label: 0 cls score: 1.000000
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det boxes: [[78,553],[404,541],[404,573],[78,585]]rec text: 打浦路252935号 rec score:0.983545 cls label: 0 cls score: 1.000000
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Visualized result saved in ./vis_result.jpg
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```
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## 4. 更多指南
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- [PP-OCR系列 C++ API查阅](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/cpp/html/namespacefastdeploy_1_1vision_1_1ocr.html)
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- [FastDeploy部署PaddleOCR模型概览](../../)
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- [PP-OCRv3 Python部署](../python)
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- [FastDeploy RKNPU2资源导航](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/rknpu2.md)
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- 如果用户想要调整前后处理超参数、单独使用文字检测识别模型、使用其他模型等，更多详细文档与说明请参考[PP-OCR系列在CPU/GPU上的部署](../../cpu-gpu/cpp/README.md)
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