70 lines
2.4 KiB
Python
Executable File
70 lines
2.4 KiB
Python
Executable File
import cv2
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import numpy as np
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import LFUtilities
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import BEBLIDParameters
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import WebAppSettings as settings
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class BEBLIDSearcher:
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def __init__(self):
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self.lf = LFUtilities.load(settings.DATASET_BEBLID)
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self.ids = np.loadtxt(settings.DATASET_IDS, dtype=str).tolist()
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#self.bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
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self.bf = cv2.DescriptorMatcher_create(cv2.DescriptorMatcher_BRUTEFORCE_HAMMING)
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def get_id(self, idx):
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return self.ids[idx]
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def search_by_id(self, query_id):
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query_idx = self.ids.index(query_id)
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return self.search_by_img(self.lf[query_idx])
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def search_by_img(self, query):
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max_inliers = -1
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res = []
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for img_id, features in zip(self.ids, self.lf):
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try:
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nn_matches = self.bf.knnMatch(query[1], features[1], 2)
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good = [m for m, n in nn_matches if m.distance < BEBLIDParameters.NN_MATCH_RATIO * n.distance]
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if len(good) > BEBLIDParameters.MIN_GOOD_MATCHES:
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src_pts = np.float32([query[0][m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
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dst_pts = np.float32([features[0][m.trainIdx].pt for m in good]).reshape(-1, 1, 2)
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M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 1.0)
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matches_mask = mask.ravel().tolist()
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# print(len(good))
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inliers = np.count_nonzero(matches_mask)
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# print(inliers)
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if (inliers >= BEBLIDParameters.MIN_INLIERS and inliers > max_inliers):
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max_inliers = inliers
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class_name = img_id.split('/')[0]
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res.append((class_name, round(inliers/len(good), 3)))
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except:
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print('search error evaluating ' + img_id)
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pass
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if res:
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res.sort(key=lambda result: result[1], reverse=True)
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return res
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def add(self, lf):
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self.lf.append(lf)
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def remove(self, idx):
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self.descs = np.delete(self.descs, idx, axis=0)
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def save(self, is_backup=False):
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lf_save_file = settings.DATASET_LF
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ids_file = settings.DATASET_IDS_LF
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if lf_save_file != "None":
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if is_backup:
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lf_save_file += '.bak'
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ids_file += '.bak'
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LFUtilities.save(lf_save_file, self.lf)
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np.savetxt(ids_file, self.ids, fmt='%s')
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