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f36f9324de
* Pick PPOCR fastdeploy docs from PaddleOCR * improve ppocr * improve readme * remove old PP-OCRv2 and PP-OCRv3 folfers * rename kunlun to kunlunxin * improve readme * improve readme * improve readme --------- Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
104 lines
3.1 KiB
Python
Executable File
104 lines
3.1 KiB
Python
Executable File
# 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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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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det_option.use_ascend()
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cls_option.use_ascend()
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rec_option.use_ascend()
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return det_option, cls_option, rec_option
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args = parse_arguments()
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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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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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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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det_option, cls_option, rec_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=det_option)
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cls_model = fd.vision.ocr.Classifier(
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cls_model_file, cls_params_file, runtime_option=cls_option)
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rec_model = fd.vision.ocr.Recognizer(
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rec_model_file, rec_params_file, rec_label_file, runtime_option=rec_option)
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# Rec model enable static shape infer.
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# When deploy on Ascend, it must be true.
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rec_model.preprocessor.static_shape_infer = True
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# Create PP-OCRv3, if cls_model is not needed,
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# just set cls_model=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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# The batch size must be set to 1, when enable static shape infer.
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ppocr_v3.cls_batch_size = 1
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ppocr_v3.rec_batch_size = 1
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# Prepare image.
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im = cv2.imread(args.image)
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# Print the results.
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result = ppocr_v3.predict(im)
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print(result)
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# Visuliaze the output.
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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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