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[English](README_CN.md) | 简体中文
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# PP-OCRv3 RKNPU2 Python部署示例
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本目录下提供`infer.py`, 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Python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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# 下载部署示例代码
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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/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/rockchip/python
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# CPU推理
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python3 infer.py \
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--det_model ./ch_PP-OCRv3_det_infer/ch_PP-OCRv3_det_infer.onnx \
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--cls_model ./ch_ppocr_mobile_v2.0_cls_infer/ch_ppocr_mobile_v2.0_cls_infer.onnx \
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--rec_model ./ch_PP-OCRv3_rec_infer/ch_PP-OCRv3_rec_infer.onnx \
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--rec_label_file ./ppocr_keys_v1.txt \
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--image 12.jpg \
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--device cpu
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# NPU推理
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python3 infer.py \
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--det_model ./ch_PP-OCRv3_det_infer/ch_PP-OCRv3_det_infer_rk3588_unquantized.rknn \
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--cls_model ./ch_ppocr_mobile_v2.0_cls_infer/ch_ppocr_mobile_v20_cls_infer_rk3588_unquantized.rknn \
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--rec_model ./ch_PP-OCRv3_rec_infer/ch_PP-OCRv3_rec_infer_rk3588_unquantized.rknn \
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--rec_label_file ppocr_keys_v1.txt \
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--image 12.jpg \
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--device npu
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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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## 4. 更多指南
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- [PP-OCR系列 Python API查阅](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/python/html/ocr.html)
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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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- [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/python/README.md)
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# 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', 'kunlunxin' or 'gpu'.")
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parser.add_argument(
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"--cpu_thread_num",
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type=int,
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default=9,
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help="Number of threads while inference on CPU.")
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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 == "npu":
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det_option.use_rknpu2()
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cls_option.use_rknpu2()
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rec_option.use_rknpu2()
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return det_option, cls_option, rec_option
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def build_format(args):
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det_format = fd.ModelFormat.ONNX
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cls_format = fd.ModelFormat.ONNX
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rec_format = fd.ModelFormat.ONNX
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if args.device == "npu":
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det_format = fd.ModelFormat.RKNN
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cls_format = fd.ModelFormat.RKNN
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rec_format = fd.ModelFormat.RKNN
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return det_format, cls_format, rec_format
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args = parse_arguments()
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# Detection模型, 检测文字框
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det_model_file = args.det_model
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det_params_file = ""
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# Classification模型，方向分类，可选
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cls_model_file = args.cls_model
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cls_params_file = ""
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# Recognition模型，文字识别模型
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rec_model_file = args.rec_model
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rec_params_file = ""
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rec_label_file = args.rec_label_file
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det_option, cls_option, rec_option = build_option(args)
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det_format, cls_format, rec_format = build_format(args)
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det_model = fd.vision.ocr.DBDetector(
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det_model_file,
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det_params_file,
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runtime_option=det_option,
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model_format=det_format)
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cls_model = fd.vision.ocr.Classifier(
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cls_model_file,
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cls_params_file,
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runtime_option=cls_option,
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model_format=cls_format)
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rec_model = fd.vision.ocr.Recognizer(
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rec_model_file,
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rec_params_file,
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rec_label_file,
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runtime_option=rec_option,
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model_format=rec_format)
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# Det,Rec模型启用静态shape推理
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det_model.preprocessor.static_shape_infer = True
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rec_model.preprocessor.static_shape_infer = True
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if args.device == "npu":
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det_model.preprocessor.disable_normalize()
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det_model.preprocessor.disable_permute()
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cls_model.preprocessor.disable_normalize()
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cls_model.preprocessor.disable_permute()
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rec_model.preprocessor.disable_normalize()
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rec_model.preprocessor.disable_permute()
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# 创建PP-OCR，串联3个模型，其中cls_model可选，如无需求，可设置为None
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ppocr_v3 = fd.vision.ocr.PPOCRv3(
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det_model=det_model, cls_model=cls_model, rec_model=rec_model)
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# Cls模型和Rec模型的batch size 必须设置为1, 开启静态shape推理
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ppocr_v3.cls_batch_size = 1
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ppocr_v3.rec_batch_size = 1
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# 预测图片准备
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im = cv2.imread(args.image)
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#预测并打印结果
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result = ppocr_v3.predict(im)
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print(result)
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# 可视化结果
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vis_im = fd.vision.vis_ppocr(im, result)
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cv2.imwrite("visualized_result.jpg", vis_im)
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print("Visualized result save in ./visualized_result.jpg")
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