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2c5fd91a7f
* first draft * update api name * fix bug * fix bug and * fix bug in c api * fix bug in c_api --------- Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
176 lines
6.0 KiB
Python
Executable File
176 lines
6.0 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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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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"--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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"--table_model",
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required=True,
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help="Path of Table recognition 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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"--table_char_dict_path",
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type=str,
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required=True,
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help="tabel recognition dict path.")
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parser.add_argument(
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"--rec_bs",
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type=int,
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default=6,
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help="Recognition model inference batch size")
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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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"--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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"--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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return parser.parse_args()
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def build_option(args):
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det_option = fd.RuntimeOption()
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rec_option = fd.RuntimeOption()
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table_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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rec_option.use_gpu(args.device_id)
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table_option.use_gpu(args.device_id)
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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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det_option.use_trt_backend()
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rec_option.use_trt_backend()
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table_option.use_trt_backend()
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# If use TRT backend, the dynamic shape will be set as follow.
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# We recommend that users set the length and height of the detection model to a multiple of 32.
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# We also recommend that users set the Trt input shape as follow.
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det_option.set_trt_input_shape("x", [1, 3, 64, 64], [1, 3, 640, 640],
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[1, 3, 960, 960])
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rec_option.set_trt_input_shape("x", [1, 3, 48, 10],
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[args.rec_bs, 3, 48, 320],
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[args.rec_bs, 3, 48, 2304])
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table_option.set_trt_input_shape("x", [1, 3, 488, 488])
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# Users could save TRT cache file to disk as follow.
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det_option.set_trt_cache_file(args.det_model + "/det_trt_cache.trt")
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rec_option.set_trt_cache_file(args.rec_model + "/rec_trt_cache.trt")
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table_option.set_trt_cache_file(args.table_model +
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"/table_trt_cache.trt")
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elif args.backend.lower() == "ort":
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det_option.use_ort_backend()
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rec_option.use_ort_backend()
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table_option.use_ort_backend()
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elif args.backend.lower() == "paddle":
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det_option.use_paddle_infer_backend()
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rec_option.use_paddle_infer_backend()
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table_option.use_paddle_infer_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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det_option.use_openvino_backend()
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rec_option.use_openvino_backend()
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table_option.use_openvino_backend()
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return det_option, rec_option, table_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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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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table_model_file = os.path.join(args.table_model, "inference.pdmodel")
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table_params_file = os.path.join(args.table_model, "inference.pdiparams")
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table_char_dict_path = args.table_char_dict_path
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# Set the runtime option
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det_option, rec_option, table_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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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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table_model = fd.vision.ocr.StructureV2Table(
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table_model_file,
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table_params_file,
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table_char_dict_path,
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runtime_option=table_option)
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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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ppstructurev2_table = fd.vision.ocr.PPStructureV2Table(
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det_model=det_model, rec_model=rec_model, table_model=table_model)
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ppstructurev2_table.rec_batch_size = args.rec_bs
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# Read the input image
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im = cv2.imread(args.image)
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# Predict and reutrn the results
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result = ppstructurev2_table.predict(im)
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print(result)
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# Visuliaze 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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