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* 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>
83 lines
2.4 KiB
Python
Executable File
83 lines
2.4 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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"--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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"--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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return parser.parse_args()
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def build_option(args):
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det_option = fd.RuntimeOption()
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if args.device.lower() == "gpu":
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det_option.use_gpu(args.device_id)
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return det_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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# Set the runtime option
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det_option = build_option(args)
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# Create the det_model
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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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# Set the preporcessing parameters
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det_model.preprocessor.max_side_len = 960
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det_model.postprocessor.det_db_thresh = 0.3
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det_model.postprocessor.det_db_box_thresh = 0.6
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det_model.postprocessor.det_db_unclip_ratio = 1.5
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det_model.postprocessor.det_db_score_mode = "slow"
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det_model.postprocessor.use_dilation = False
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# Read the image
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im = cv2.imread(args.image)
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# Predict and return the results
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result = det_model.predict(im)
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print(result)
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# Visualize the results
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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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