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88 lines
2.6 KiB
88 lines
2.6 KiB
# Copyright (c) 2025 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 abc
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from .._utils.cli import (
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add_simple_inference_args,
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get_subcommand_args,
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perform_simple_inference,
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str2bool,
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)
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from .base import PaddleXPredictorWrapper, PredictorCLISubcommandExecutor
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class ObjectDetection(PaddleXPredictorWrapper):
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def __init__(
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self,
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*,
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img_size=None,
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threshold=None,
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layout_nms=None,
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layout_unclip_ratio=None,
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layout_merge_bboxes_mode=None,
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**kwargs,
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):
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self._extra_init_args = {
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"img_size": img_size,
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"threshold": threshold,
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"layout_nms": layout_nms,
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"layout_unclip_ratio": layout_unclip_ratio,
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"layout_merge_bboxes_mode": layout_merge_bboxes_mode,
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}
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super().__init__(**kwargs)
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def _get_extra_paddlex_predictor_init_args(self):
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return self._extra_init_args
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class ObjectDetectionSubcommandExecutor(PredictorCLISubcommandExecutor):
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def _update_subparser(self, subparser):
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add_simple_inference_args(subparser)
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subparser.add_argument(
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"--img_size",
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type=int,
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help="The input image size (w, h).",
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)
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subparser.add_argument(
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"--threshold",
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type=float,
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help="The threshold for filtering out low-confidence predictions.",
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)
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subparser.add_argument(
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"--layout_nms",
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type=str2bool,
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help="Whether to use layout-aware NMS.",
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)
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subparser.add_argument(
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"--layout_unclip_ratio",
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type=float,
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help="The ratio of unclipping the bounding box.",
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)
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subparser.add_argument(
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"--layout_merge_bboxes_mode",
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type=str,
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help="The mode for merging bounding boxes.",
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)
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@property
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@abc.abstractmethod
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def wrapper_cls(self):
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raise NotImplementedError
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def execute_with_args(self, args):
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params = get_subcommand_args(args)
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perform_simple_inference(self.wrapper_cls, params)
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