ModelScope/modelscope/pipelines/cv/object_detection_pipeline.py

from typing import Any, Dict

import numpy as np

from modelscope.metainfo import Pipelines
from modelscope.outputs import OutputKeys
from modelscope.pipelines.base import Input, Pipeline
from modelscope.pipelines.builder import PIPELINES
from modelscope.preprocessors import LoadImage
from modelscope.utils.constant import Tasks
from modelscope.utils.logger import get_logger


@PIPELINES.register_module(
    Tasks.human_detection, module_name=Pipelines.human_detection)
@PIPELINES.register_module(
    Tasks.object_detection, module_name=Pipelines.object_detection)
class ObjectDetectionPipeline(Pipeline):

    def __init__(self, model: str, **kwargs):
        """
            model: model id on modelscope hub.
        """
        super().__init__(model=model, auto_collate=False, **kwargs)

    def preprocess(self, input: Input) -> Dict[str, Any]:

        img = LoadImage.convert_to_ndarray(input)
        img = img.astype(np.float)
        img = self.model.preprocess(img)
        result = {'img': img}
        return result

    def forward(self, input: Dict[str, Any]) -> Dict[str, Any]:

        outputs = self.model.inference(input['img'])
        result = {'data': outputs}
        return result

    def postprocess(self, inputs: Dict[str, Any]) -> Dict[str, Any]:

        bboxes, scores, labels = self.model.postprocess(inputs['data'])
        if bboxes is None:
            return None
        outputs = {
            OutputKeys.SCORES: scores,
            OutputKeys.LABELS: labels,
            OutputKeys.BOXES: bboxes
        }

        return outputs