265c61df1a
replaced by blank images in the stitching process and completly broken images are also replaced by blank images which are passed through and are classified as 'other' with all_pased == False. This should be changed in the future by introducing a new key to the response, indicating that the image is not valid.
43 lines
1.7 KiB
Python
43 lines
1.7 KiB
Python
from enum import Enum
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from operator import itemgetter
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from typing import Mapping, Iterable, Union
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import numpy as np
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from funcy import rcompose, rpartial
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from image_prediction.exceptions import UnexpectedLabelFormat
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from image_prediction.label_mapper.mapper import LabelMapper
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class ProbabilityMapperKeys(Enum):
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LABEL = "label"
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PROBABILITIES = "probabilities"
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class ProbabilityMapper(LabelMapper):
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def __init__(self, labels: Mapping[int, str]):
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self.__labels = labels
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# String conversion in the middle due to floating point precision issues.
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# See: https://stackoverflow.com/questions/56820/round-doesnt-seem-to-be-rounding-properly
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self.__rounder = rcompose(rpartial(round, 4), str, float)
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def __validate_array_label_format(self, probabilities: np.ndarray) -> None:
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if not len(probabilities) == len(self.__labels):
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raise UnexpectedLabelFormat(
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f"Received fewer probabilities ({len(probabilities)}) than labels were passed ({len(self.__labels)})."
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)
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def __map_array(self, probabilities: Union[np.ndarray, None]) -> Union[dict, None]:
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if not isinstance(probabilities, np.ndarray) and not probabilities:
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return None
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self.__validate_array_label_format(probabilities)
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cls2prob = dict(
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sorted(zip(self.__labels, list(map(self.__rounder, probabilities))), key=itemgetter(1), reverse=True)
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)
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most_likely = [*cls2prob][0]
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return {ProbabilityMapperKeys.LABEL: most_likely, ProbabilityMapperKeys.PROBABILITIES: cls2prob}
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def map_labels(self, probabilities: Iterable[np.ndarray]) -> Iterable[dict]:
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return map(self.__map_array, probabilities)
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