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[English](README.md) | 简体中文
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# PP-OCRv3 SOPHGO Python部署示例
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本目录下提供`infer.py`快速完成 PP-OCRv3 在SOPHGO TPU上部署的示例。
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## 1. 部署环境准备
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在部署前，需自行编译基于算能硬件的FastDeploy python wheel包并安装，参考文档[算能硬件部署环境](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#算能硬件部署环境)
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## 2.运行部署示例
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### 2.1 模型准备
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将Paddle模型转换为SOPHGO bmodel模型, 转换步骤参考[文档](../README.md)
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### 2.2 开始部署
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```bash
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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/sophgo/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/sophgo/python
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# 下载图片
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wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg
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#下载字典文件
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wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt
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# 推理
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python3 infer.py --det_model ocr_bmodel/ch_PP-OCRv3_det_1684x_f32.bmodel \
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--cls_model ocr_bmodel/ch_ppocr_mobile_v2.0_cls_1684x_f32.bmodel \
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--rec_model ocr_bmodel/ch_PP-OCRv3_rec_1684x_f32.bmodel \
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--rec_label_file ../ppocr_keys_v1.txt \
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--image ../12.jpg
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# 运行完成后返回结果如下所示
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det boxes: [[42,413],[483,391],[484,428],[43,450]]rec text: 上海斯格威铂尔大酒店 rec score:0.952958 cls label: 0 cls score: 1.000000
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det boxes: [[187,456],[399,448],[400,480],[188,488]]rec text: 打浦路15号 rec score:0.897335 cls label: 0 cls score: 1.000000
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det boxes: [[23,507],[513,488],[515,529],[24,548]]rec text: 绿洲仕格维花园公寓 rec score:0.994589 cls label: 0 cls score: 1.000000
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det boxes: [[74,553],[427,542],[428,571],[75,582]]rec text: 打浦路252935号 rec score:0.900663 cls label: 0 cls score: 1.000000
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可视化结果保存在sophgo_result.jpg中
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```
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## 3. 其它文档
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- [PP-OCRv3 C++部署](../cpp)
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- [转换 PP-OCRv3 SOPHGO模型文档](../README.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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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 label 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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return parser.parse_args()
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args = parse_arguments()
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# 配置runtime，加载模型
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runtime_option = fd.RuntimeOption()
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runtime_option.use_sophgo()
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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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# PPOCR的cls和rec模型现在已经支持推理一个Batch的数据
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# 定义下面两个变量后, 可用于设置trt输入shape, 并在PPOCR模型初始化后, 完成Batch推理设置
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cls_batch_size = 1
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rec_batch_size = 1
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# 当使用TRT时，分别给三个模型的runtime设置动态shape,并完成模型的创建.
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# 注意: 需要在检测模型创建完成后，再设置分类模型的动态输入并创建分类模型, 识别模型同理.
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# 如果用户想要自己改动检测模型的输入shape, 我们建议用户把检测模型的长和高设置为32的倍数.
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det_option = runtime_option
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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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# 用户可以把TRT引擎文件保存至本地
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# det_option.set_trt_cache_file(args.det_model + "/det_trt_cache.trt")
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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=fd.ModelFormat.SOPHGO)
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cls_option = runtime_option
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cls_option.set_trt_input_shape("x", [1, 3, 48, 10],
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[cls_batch_size, 3, 48, 320],
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[cls_batch_size, 3, 48, 1024])
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# 用户可以把TRT引擎文件保存至本地
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# cls_option.set_trt_cache_file(args.cls_model + "/cls_trt_cache.trt")
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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=fd.ModelFormat.SOPHGO)
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rec_option = runtime_option
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rec_option.set_trt_input_shape("x", [1, 3, 48, 10],
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[rec_batch_size, 3, 48, 320],
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[rec_batch_size, 3, 48, 2304])
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# 用户可以把TRT引擎文件保存至本地
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# rec_option.set_trt_cache_file(args.rec_model + "/rec_trt_cache.trt")
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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=fd.ModelFormat.SOPHGO)
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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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# 需要使用下行代码, 来启用rec模型的静态shape推理，这里rec模型的静态输入为[3, 48, 584]
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rec_model.preprocessor.static_shape_infer = True
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rec_model.preprocessor.rec_image_shape = [3, 48, 584]
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# 给cls和rec模型设置推理时的batch size
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# 此值能为-1, 和1到正无穷
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# 当此值为-1时, cls和rec模型的batch size将默认和det模型检测出的框的数量相同
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ppocr_v3.cls_batch_size = cls_batch_size
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ppocr_v3.rec_batch_size = rec_batch_size
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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("sophgo_result.jpg", vis_im)
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print("Visualized result save in ./sophgo_result.jpg")
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