45 lines
1.4 KiB
Python
45 lines
1.4 KiB
Python
from dataclasses import dataclass
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from typing import List
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import random
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import numpy as np
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from vision.model import Image
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import vision.maths.precision_recall as pr
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import logging
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logger = logging.getLogger(__name__)
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@dataclass
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class QueryResult:
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sorted_images: List[Image]
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query_image: Image
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precision_recall: pr.PrecisionRecall
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def run_query(images: List[Image], distance_measure=None, query_index=None):
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logger.info(f'running query on {len(images)} images, query index {query_index}')
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if query_index is not None:
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query_image = images[query_index]
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else:
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query_image = random.choice(images)
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if any(i for i in images if i.descriptor is None):
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raise ValueError('descriptors required for all images')
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for image in images:
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if distance_measure is None:
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image.distance = np.linalg.norm(image.descriptor-query_image.descriptor)
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else:
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image.distance = distance_measure(image.descriptor - query_image.descriptor)
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images = [i for i in images if not (i.category == query_image.category and i.name == query_image.name)]
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query_pr = pr.get_pr(images, query=query_image)
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results = QueryResult(sorted_images=images,
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query_image=query_image,
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precision_recall=query_pr)
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logger.info(f'query finished AP: {results.precision_recall.ap}')
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return results
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