def knn_visual(k, locationid, model, database, this_vector=None):
nearest = {}
if not this_vector:
this_vector = Vectorizer.visual_vector(locationid, database, model)
others = database.get_location_ids()
others.remove(locationid)
# Get distance to each other vecctor and add to nearest if it is less than the
# distance to an existing vector.
for other in others:
other_vector = Vectorizer.visual_vector(other, database, model)
distance = Distance.l_p_distance(3,
this_vector,
other_vector,
positional=True)
if len(nearest) < k:
largest_key, largest_best = None, inf
else:
largest_key, largest_best = max(nearest.items(),
key=itemgetter(1))
if distance < largest_best:
if largest_key:
nearest.pop(largest_key)
nearest[other] = distance
return nearest
def visual_sim_contribution(this_vector, ids, database, model, k=3):
other_vectors = [
Vectorizer.visual_vector(locid, database, model) for locid in ids
]
return Neighbor.similarity_contribution(this_vector,
other_vectors,
k,
positional=True)
def nearest_visual(self, *args):
"""
arg[0] = locationid
arg[1] = model (CM, CM3x3, CN, CN3x3, CSD, GLRLM, GLRLM3x3, HOG, LBP, LBP3x3, ALL)
arg[2] = k
"""
if not self.database:
print("[ERROR] The database must be loaded for this action.")
return
if not len(args) == 3:
print("[ERROR] Expected three arguments but got " + str(len(args)) + ".")
print("\targ = " + str(args))
# Get the first argument
try:
k = int(args[2])
except:
print("[ERROR] K Value provided is invalid.")
print("\tk = " + str(args[2]))
return
# Get the model to use. We do this before the item as it is easier
# to differentiate valid from invalid
model = args[1]
if not model in self.valid_vis_models:
print("[ERROR] Model Type value provided was invalid.")
print("\tModel Type = " + str(args[1]))
return
try:
locationid = int(args[0])
except:
print("[ERROR] The ID specified was not valid")
print("\tID = " + str(locationid) + "; Model = " + model)
return
start = time()
if model == 'ALL':
this_vector = Vectorizer.visual_vector_multimodel(locationid, self.database, self.valid_vis_models)
nearest = Neighbor.knn_visual_all(k, locationid, self.valid_vis_models, self.database, this_vector = this_vector)
else:
# get vector representing item associated w/ id
this_vector = Vectorizer.visual_vector(locationid, self.database, model)
nearest = Neighbor.knn_visual(k, locationid, model, self.database, this_vector=this_vector)
contribs = Neighbor.visual_sim_contribution(this_vector, nearest.keys(), self.database,
model, k=3)
print("The Visual Descriptor Nearest Neighbor took " + str(time() - start))
print(str(k) + " Nearest Neighbors:")
for i, (an_id, distance) in enumerate(nearest.items()):
print('\t' + str(i) + ". " + str(an_id) + "; Distance = " + str(distance))
print('Top 3 Image Pairs:')
for i, item in enumerate(contribs):
print('\t' + str(i) + '. ' + str(item))