PaddleX/paddlex/inference/pipelines/object_detection/pipeline.py

# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import Any, Dict, Optional, Union, Tuple, List
import numpy as np

from ...utils.pp_option import PaddlePredictorOption
from ..base import BasePipeline

from ...models.object_detection.result import DetResult


class ObjectDetectionPipeline(BasePipeline):
    """Object Detection Pipeline"""

    entities = "object_detection"

    def __init__(
        self,
        config: Dict,
        device: str = None,
        pp_option: PaddlePredictorOption = None,
        use_hpip: bool = False,
    ) -> None:
        """
        Initializes the class with given configurations and options.

        Args:
            config (Dict): Configuration dictionary containing model and other parameters.
            device (str): The device to run the prediction on. Default is None.
            pp_option (PaddlePredictorOption): Options for PaddlePaddle predictor. Default is None.
            use_hpip (bool): Whether to use high-performance inference (hpip) for prediction. Defaults to False.
        """
        super().__init__(device=device, pp_option=pp_option, use_hpip=use_hpip)
        model_cfg = config["SubModules"]["ObjectDetection"]
        model_kwargs = {}
        if "threshold" in model_cfg:
            model_kwargs["threshold"] = model_cfg["threshold"]
        if "img_size" in model_cfg:
            model_kwargs["img_size"] = model_cfg["img_size"]
        if "layout_nms" in model_cfg:
            model_kwargs["layout_nms"] = model_cfg["layout_nms"]
        if "layout_unclip_ratio" in model_cfg:
            model_kwargs["layout_unclip_ratio"] = model_cfg["layout_unclip_ratio"]
        if "layout_merge_bboxes_mode" in model_cfg:
            model_kwargs["layout_merge_bboxes_mode"] = model_cfg[
                "layout_merge_bboxes_mode"
            ]
        self.det_model = self.create_model(model_cfg, **model_kwargs)

    def predict(
        self,
        input: Union[str, List[str], np.ndarray, List[np.ndarray]],
        threshold: Optional[Union[float, dict]] = None,
        layout_nms: Optional[bool] = None,
        layout_unclip_ratio: Optional[Union[float, Tuple[float, float]]] = None,
        layout_merge_bboxes_mode: Optional[str] = None,
        **kwargs,
    ) -> DetResult:
        """Predicts object detection results for the given input.

        Args:
            input (Union[str, list[str], np.ndarray, list[np.ndarray]]): The input image(s) or path(s) to the images.
            img_size (Optional[Union[int, Tuple[int, int]]]): The size of the input image. Default is None.
            threshold (Optional[float]): The threshold value to filter out low-confidence predictions. Default is None.
            layout_nms (bool, optional): Whether to use layout-aware NMS. Defaults to False.
            layout_unclip_ratio (Optional[Union[float, Tuple[float, float]]], optional): The ratio of unclipping the bounding box.
                Defaults to None.
                If it's a single number, then both width and height are used.
                If it's a tuple of two numbers, then they are used separately for width and height respectively.
                If it's None, then no unclipping will be performed.
            layout_merge_bboxes_mode (Optional[str], optional): The mode for merging bounding boxes. Defaults to None.
            **kwargs: Additional keyword arguments that can be passed to the function.
        Returns:
            DetResult: The predicted detection results.
        """
        yield from self.det_model(
            input,
            threshold=threshold,
            layout_nms=layout_nms,
            layout_unclip_ratio=layout_unclip_ratio,
            layout_merge_bboxes_mode=layout_merge_bboxes_mode,
            **kwargs,
        )