75 lines
2.3 KiB
Python
75 lines
2.3 KiB
Python
import cv2
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import numpy as np
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import pickle as pickle
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import LFUtilities
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import ImageRecognitionSettings as settings
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#from BEBLIDRescorerDB import BEBLIDRescorerDB
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from BEBLIDRescorer import BEBLIDRescorer
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import SearcherParameters
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from FAISSSearchEngine import FAISSSearchEngine
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import FeatureExtractor as fe
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import BEBLIDExtractorQ as lf
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from line_profiler_pycharm import profile
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class Searcher:
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def __init__(self):
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# self.dataset = h5py.File(settings.dataset_file, 'r')['rmac'][...]
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# np.save('/media/Data/data/beni_culturali/deploy/dataset', self.dataset)
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self.search_engine = FAISSSearchEngine()
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self.rescorer = BEBLIDRescorer()
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def get_id(self, idx):
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return self.search_engine.get_id(idx)
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def add(self, img_file, id):
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self.save(True)
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desc = fe.extract(img_file)
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#orb = lf.extract(img_file)
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self.search_engine.add(desc, id)
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#self.rescorer.add(orb)
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self.save()
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print('added ' + id)
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def remove(self, id):
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self.save(True)
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self.search_engine.remove(id)
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#self.rescorer.remove(idx)
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self.save()
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print('removed ' + id)
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def search_by_id(self, query_id, k=10, search_deep_level=1):
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kq = k
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if search_deep_level > 0:
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kq = SearcherParameters.SEARCH_DEEP_K[search_deep_level]
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res = self.search_engine.search_by_id(query_id, kq)
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if search_deep_level > 0:
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res_lf = self.rescorer.rescore_by_id(query_id, res)
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res = res_lf if res_lf else res[:k]
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return res
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@profile
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def search_by_img(self, query_img, k=10, search_deep_level=1):
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kq = k
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if search_deep_level:
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kq = SearcherParameters.SEARCH_DEEP_K[search_deep_level]
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query_desc = fe.extract(query_img)
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res = self.search_engine.search_by_img(query_desc, kq)
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if search_deep_level > 0:
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query_lf = lf.extract(query_img)
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res_lf = self.rescorer.rescore_by_img(query_lf, res)
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#res = res_lf if res_lf else res[:k]
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res = res_lf if res_lf else res[:k]
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res = [result for result in res if result[1] >= SearcherParameters.GEM_THRESHOLD]
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return res
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def save(self, is_backup=False):
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self.search_engine.save(is_backup)
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#self.rescorer.save(is_backup)
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