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Correct PP-Series models' name
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jiangjiajun
128
examples/vision/ocr/PP-OCRv2/python/README.md
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examples/vision/ocr/PP-OCRv2/python/README.md
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# PPOCRSystemv2 Python部署示例
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在部署前，需确认以下两个步骤
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/environment.md)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/quick_start)
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本目录下提供`infer.py`快速完成PPOCRSystemv2在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成
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```
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# 下载模型,图片和字典文件
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wget https://bj.bcebos.com/paddlehub/fastdeploy/ch_PP-OCRv2_det_infer.tar.gz
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tar -xvf ch_PP-OCRv2_det_infer.tar.gz
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wget https://bj.bcebos.com/paddlehub/fastdeploy/ch_ppocr_mobile_v2.0_cls_infer.tar.gz
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tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar.gz
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wget https://bj.bcebos.com/paddlehub/fastdeploy/ch_PP-OCRv2_rec_infer.tar.gz
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tar -xvf ch_PP-OCRv2_rec_infer.tar.gz
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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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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd examples/vison/ocr/PP-OCRv2/python/
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# CPU推理
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python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device cpu
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# GPU推理
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python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu
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# GPU上使用TensorRT推理
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python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu --backend trt
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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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## PPOCRSystemv2 Python接口
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```
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fd.vision.ocr.PPOCRSystemv2(det_model=det_model, cls_model=cls_model, rec_model=rec_model)
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```
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PPOCRSystemv2的初始化,输入的参数是检测模型，分类模型和识别模型，其中cls_model可选，如无需求，可设置为None
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**参数**
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> * **det_model**(model): OCR中的检测模型
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> * **cls_model**(model): OCR中的分类模型
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> * **rec_model**(model): OCR中的识别模型
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### predict函数
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> ```
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> result = ocr_system.predict(im)
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> ```
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>
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> 模型预测接口，输入是一张图片
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>
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> **参数**
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>
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> > * **im**(np.ndarray): 输入数据，每张图片注意需为HWC，BGR格式
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> **返回**
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>
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> > 返回`fastdeploy.vision.OCRResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
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## DBDetector Python接口
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### DBDetector类
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```
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fastdeploy.vision.ocr.DBDetector(model_file, params_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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```
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DBDetector模型加载和初始化，其中模型为paddle模型格式。
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**参数**
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> * **model_file**(str): 模型文件路径
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> * **params_file**(str): 参数文件路径，当模型格式为ONNX时，此参数传入空字符串即可
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> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
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> * **model_format**(ModelFormat): 模型格式，默认为PADDLE格式
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### Classifier类与DBDetector类相同
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### Recognizer类
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```
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fastdeploy.vision.ocr.Recognizer(rec_model_file,rec_params_file,rec_label_file,
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runtime_option=rec_runtime_option,model_format=ModelFormat.PADDLE)
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```
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Recognizer类初始化时,需要在rec_label_file参数中,输入识别模型所需的label文件路径，其他参数均与DBDetector类相同
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**参数**
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> * **label_path**(str): 识别模型的label文件路径
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### 类成员变量
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#### DBDetector预处理参数
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用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果
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> > * **max_side_len**(int): 检测算法前向时图片长边的最大尺寸，当长边超出这个值时会将长边resize到这个大小，短边等比例缩放,默认为960
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> > * **det_db_thresh**(double): DB模型输出预测图的二值化阈值，默认为0.3
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> > * **det_db_box_thresh**(double): DB模型输出框的阈值，低于此值的预测框会被丢弃，默认为0.6
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> > * **det_db_unclip_ratio**(double): DB模型输出框扩大的比例，默认为1.5
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> > * **det_db_score_mode**(string):DB后处理中计算文本框平均得分的方式,默认为slow，即求polygon区域的平均分数的方式
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> > * **use_dilation**(bool):是否对检测输出的feature map做膨胀处理,默认为Fasle
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#### Classifier预处理参数
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用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果
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> > * **cls_thresh**(double): 当分类模型输出的得分超过此阈值，输入的图片将被翻转，默认为0.9
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## 其它文档
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- [PPOCR 系列模型介绍](../../)
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- [PPOCRv2 C++部署](../cpp)
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- [模型预测结果说明](../../../../../docs/api/vision_results/)
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- [如何切换模型推理后端引擎](../../../../../docs/runtime/how_to_change_backend.md)
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128
examples/vision/ocr/PP-OCRv2/python/infer.py
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128
examples/vision/ocr/PP-OCRv2/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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#
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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' or 'gpu'.")
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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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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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"--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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option = fd.RuntimeOption()
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if args.device.lower() == "gpu":
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option.use_gpu(0)
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option.set_cpu_thread_num(args.cpu_thread_num)
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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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option.use_trt_backend()
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elif args.backend.lower() == "ort":
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option.use_ort_backend()
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elif args.backend.lower() == "paddle":
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option.use_paddle_backend()
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elif args.backend.lower() == "openvino":
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assert args.device.lower(
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) == "cpu", "OpenVINO backend require inference on device CPU."
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option.use_openvino_backend()
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return option
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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 = os.path.join(args.det_model, "inference.pdmodel")
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det_params_file = os.path.join(args.det_model, "inference.pdiparams")
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# Classification模型，方向分类，可选
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cls_model_file = os.path.join(args.cls_model, "inference.pdmodel")
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cls_params_file = os.path.join(args.cls_model, "inference.pdiparams")
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# Recognition模型，文字识别模型
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rec_model_file = os.path.join(args.rec_model, "inference.pdmodel")
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rec_params_file = os.path.join(args.rec_model, "inference.pdiparams")
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rec_label_file = args.rec_label_file
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# 对于三个模型，均采用同样的部署配置
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# 用户也可根据自行需求分别配置
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runtime_option = build_option(args)
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det_model = fd.vision.ocr.DBDetector(
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det_model_file, det_params_file, runtime_option=runtime_option)
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cls_model = fd.vision.ocr.Classifier(
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cls_model_file, cls_params_file, runtime_option=runtime_option)
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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=runtime_option)
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# 创建OCR系统，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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ocr_system = fd.vision.ocr.PPOCRSystemv2(
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det_model=det_model, cls_model=cls_model, rec_model=rec_model)
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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 = ocr_system.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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