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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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)
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parser.add_argument(
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"--cls_model", required=True, help="Path of Classification model of PPOCR."
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)
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parser.add_argument(
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"--rec_model", required=True, help="Path of Recognization model of PPOCR."
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)
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parser.add_argument(
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"--rec_label_file", required=True, help="Path of Recognization model of PPOCR."
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)
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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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)
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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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)
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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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)
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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, det_params_file, runtime_option=det_option, model_format=det_format
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)
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cls_model = fd.vision.ocr.Classifier(
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cls_model_file, cls_params_file, runtime_option=cls_option, model_format=cls_format
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)
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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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)
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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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)
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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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