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82 lines
2.2 KiB
Python
82 lines
2.2 KiB
Python
# 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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"--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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"--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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)
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return parser.parse_args()
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def build_option(args):
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rec_option = fd.RuntimeOption()
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if args.device.lower() == "gpu":
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rec_option.use_gpu(args.device_id)
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return rec_option
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args = parse_arguments()
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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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# Set the runtime option
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rec_option = build_option(args)
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# Create the rec_model
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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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)
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# Read the image
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im = cv2.imread(args.image)
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# Predict and return the result
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result = rec_model.predict(im)
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# User can infer a batch of images by following code.
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# result = rec_model.batch_predict([im])
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print(result)
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