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PaddleOcr_v4/deploy/fastdeploy/rockchip/cpp
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README.md
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PP-OCRv3 RKNPU2 C++部署示例

本目录下提供infer.cc, 供用户完成PP-OCRv3在RKNPU2的部署.

1. 部署环境准备

在部署前，需确认以下两个步骤

2.部署模型准备

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模型列表中下载所需模型. 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. 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.

# 下载PP-OCRv3文字检测模型
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar
tar -xvf ch_PP-OCRv3_det_infer.tar
# 下载文字方向分类器模型
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar
# 下载PP-OCRv3文字识别模型
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
tar -xvf ch_PP-OCRv3_rec_infer.tar

# 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
paddle2onnx --model_dir ch_PP-OCRv3_det_infer \
            --model_filename inference.pdmodel \
            --params_filename inference.pdiparams \
            --save_file ch_PP-OCRv3_det_infer/ch_PP-OCRv3_det_infer.onnx \
            --enable_dev_version True
paddle2onnx --model_dir ch_ppocr_mobile_v2.0_cls_infer \
            --model_filename inference.pdmodel \
            --params_filename inference.pdiparams \
            --save_file ch_ppocr_mobile_v2.0_cls_infer/ch_ppocr_mobile_v2.0_cls_infer.onnx \
            --enable_dev_version True
paddle2onnx --model_dir ch_PP-OCRv3_rec_infer \
            --model_filename inference.pdmodel \
            --params_filename inference.pdiparams \
            --save_file ch_PP-OCRv3_rec_infer/ch_PP-OCRv3_rec_infer.onnx \
            --enable_dev_version True

# 固定模型的输入shape
python -m paddle2onnx.optimize --input_model ch_PP-OCRv3_det_infer/ch_PP-OCRv3_det_infer.onnx \
                               --output_model ch_PP-OCRv3_det_infer/ch_PP-OCRv3_det_infer.onnx \
                               --input_shape_dict "{'x':[1,3,960,960]}"
python -m paddle2onnx.optimize --input_model ch_ppocr_mobile_v2.0_cls_infer/ch_ppocr_mobile_v2.0_cls_infer.onnx \
                               --output_model ch_ppocr_mobile_v2.0_cls_infer/ch_ppocr_mobile_v2.0_cls_infer.onnx \
                               --input_shape_dict "{'x':[1,3,48,192]}"
python -m paddle2onnx.optimize --input_model ch_PP-OCRv3_rec_infer/ch_PP-OCRv3_rec_infer.onnx \
                               --output_model ch_PP-OCRv3_rec_infer/ch_PP-OCRv3_rec_infer.onnx \
                               --input_shape_dict "{'x':[1,3,48,320]}"

# 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
python rockchip/rknpu2_tools/export.py --config_path tools/rknpu2/config/ppocrv3_det.yaml \
                              --target_platform rk3588
python rockchip/rknpu2_tools/export.py --config_path tools/rknpu2/config/ppocrv3_rec.yaml \
                              --target_platform rk3588
python rockchip/rknpu2_tools/export.py --config_path tools/rknpu2/config/ppocrv3_cls.yaml \
                              --target_platform rk3588

3.运行部署示例

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。

# 下载部署示例代码
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd  FastDeploy/examples/vision/ocr/PP-OCR/rockchip/cpp

# 如果您希望从PaddleOCR下载示例代码，请运行
git clone https://github.com/PaddlePaddle/PaddleOCR.git
# 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
git checkout dygraph
cd PaddleOCR/deploy/fastdeploy/rockchip/cpp

mkdir build
cd build
# 使用编译完成的FastDeploy库编译infer_demo
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-rockchip
make -j

# 下载图片和字典文件
wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg
wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt

# 拷贝RKNN模型到build目录

# CPU推理
./infer_demo ./ch_PP-OCRv3_det_infer/ch_PP-OCRv3_det_infer.onnx \
                          ./ch_ppocr_mobile_v2.0_cls_infer/ch_ppocr_mobile_v2.0_cls_infer.onnx \
                          ./ch_PP-OCRv3_rec_infer/ch_PP-OCRv3_rec_infer.onnx \
                          ./ppocr_keys_v1.txt \
                          ./12.jpg \
                          0
# RKNPU推理
./infer_demo ./ch_PP-OCRv3_det_infer/ch_PP-OCRv3_det_infer_rk3588_unquantized.rknn \
                            ./ch_ppocr_mobile_v2.0_cls_infer/ch_ppocr_mobile_v20_cls_infer_rk3588_unquantized.rknn \
                             ./ch_PP-OCRv3_rec_infer/ch_PP-OCRv3_rec_infer_rk3588_unquantized.rknn \
                              ./ppocr_keys_v1.txt \
                              ./12.jpg \
                              1

运行完成可视化结果如下图所示:

结果输出如下:

det boxes: [[276,174],[285,173],[285,178],[276,179]]rec text:  rec score:0.000000 cls label: 1 cls score: 0.766602
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
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
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
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
Visualized result saved in ./vis_result.jpg

4. 更多指南