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- # Copyright 2021-2022 The Alibaba Fundamental Vision Team Authors. All rights reserved.
- from typing import Any, Dict
- import numpy as np
- from modelscope.metainfo import Pipelines
- from modelscope.models.cv.hand_static import hand_model
- 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
- logger = get_logger()
- @PIPELINES.register_module(
- Tasks.hand_static, module_name=Pipelines.hand_static)
- class HandStaticPipeline(Pipeline):
- def __init__(self, model: str, **kwargs):
- """
- use `model` to create hand static pipeline for prediction
- Args:
- model: model id on modelscope hub.
- """
- super().__init__(model=model, **kwargs)
- logger.info('load model done')
- def preprocess(self, input: Input) -> Dict[str, Any]:
- img = LoadImage.convert_to_ndarray(input)
- return img
- def forward(self, input: Dict[str, Any]) -> Dict[str, Any]:
- result = hand_model.infer(input, self.model, self.device)
- return {OutputKeys.OUTPUT: result}
- def postprocess(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
- return inputs
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