def search_SIFT_BOVW(returnCount=100,
mySIFTmodel=mySIFTmodel,
mySIFTtree=mySIFTtree,
mydataSIFT=mydataSIFT,
txt='',
write=False):
imagematches, searchtime = ImageSearch_Algo_SIFT.SIFT_SEARCH_TREE(
q_path,
mySIFTmodel,
mySIFTtree,
mydataSIFT,
returnCount=returnCount,
kp=100)
if write:
a, d, ind, cnt = accuracy.accuracy_matches(q_path, imagematches,
ACCURRACY_RANGE)
# print ('Accuracy =', a, '%', '| Quality:', d )
# print ('Count', cnt, ' | position', ind)
row_dict['acc_sift_tree' + txt] = a
row_dict['index_sift_tree' + txt] = ind
row_dict['Count_sift_tree' + txt] = cnt
row_dict['quality_sift_tree' + txt] = d
row_dict['time_sift_tree' + txt] = searchtime
return imagematches, searchtime
# ----- Alternative tree search code [Optimized search time ]
# test SIFT TREE SEARCH code
n_clusters = 500
savefile = 'data/' + 'test' + '_SIFT_Tree_Cluster' + str(n_clusters)
SIFTtree, SIFTmodel = ImageSearch_Algo_SIFT.SIFT_CREATE_TREE_MODEL(
mydataSIFT, savefile, n_clusters)
# to load an existing tree
thistree, thismodel = ImageSearch_Algo_SIFT.SIFT_Load_Tree_Model(savefile)
# sample 1 image
q_path = random.sample(imagepaths, 1)[0]
# Run Tree Search
imagematches, searchtime = ImageSearch_Algo_SIFT.SIFT_SEARCH_TREE(
q_path, thismodel, thistree, mydataSIFT, returnCount=100, kp=100)
print('SIFT Tree Search time', searchtime)
a, q, pos, cnt = accuracy.accuracy_matches(q_path, imagematches, 20)
print('Accuracy =', a, '%', '| Quality:', q)
print('Count', cnt, ' | position', pos)
# BF Macher faster
imagematches, searchtime = ImageSearch_Algo_SIFT.SIFT_SEARCH_BF(
mydataSIFT, q_path, sift_features_limit, 0.6, 50)
print(" SIFT BF Search time :", searchtime)
a, q, pos, cnt = accuracy.accuracy_matches(q_path, imagematches, 20)
print('Accuracy =', a, '%', '| Quality:', q)
print('Count', cnt, ' | position', pos)
# FLANN Macher (slower)