def step_06_third_clustering_by_hamming_distance(self):
from uuid import UUID
def distance_hamming_group(g1, g2):
distances = [
img1.pd_hamming_distance(img2) for img1 in g1.members
for img2 in g2.members
]
return min(distances)
group_cnt = 0
for group1 in self.result:
img = Image()
img.phash = UUID(b'0' * 32)
img.dhash = 0
m_value = Group()
m_value.members = [img]
cluster = Clustering(group1, IMG_PD_HDIST_THREASHOLD,
distance_hamming_group, m_value, False)
cluster.run()
group1.members = cluster.result
group_cnt += len(cluster.result)
print('step_06:group_cnt == %d' % group_cnt)
return True
