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import faiss
from sklearn.metrics import pairwise_distances_argmin_min
import random
import numpy as np
from utils import *
def kmeans(number_of_clusters, features):
# Cluster the frames using K-Means
# K-means from sklearn
#kmeans = KMeans(n_clusters=number_of_clusters, random_state=0).fit(features)
# K-means from faiss
ncentroids = number_of_clusters
niter = 10
verbose = True
x = features
# Take the first dimension of the first element of the list
dimension = x[0].shape[0]
kmeans = faiss.Kmeans(dimension, ncentroids, niter=niter, verbose=verbose)
kmeans.train(x)
#closest, _ = pairwise_distances_argmin_min(kmeans.cluster_centers_, features)
closest, _ = pairwise_distances_argmin_min(kmeans.centroids, x)
closest_clips_frames = []
for i in sorted(closest):
for idx in range(i*8, (i+1)*8):
closest_clips_frames.append(idx)
return closest_clips_frames
def tt01(features, threshold):
i = 0
clips = []
# compare the sum of squared difference between clips i and j
for j in range(1, len(features)):
if sum_of_squared_difference(features[i], features[j]) > threshold:
clip = []
# add frames from clip i to j-1 to the clip list
for b in range(i*8, j*8):
clip.append(b)
# randomly select 15% of the frames from the clip list
random_num = round(len(clip)*0.15)
# sort the frames in the clip list to ensure the order of the frames
random_Frames = sorted(random.sample(clip, random_num))
i = j
clips.extend(random_Frames)
# add the last clip to the clip list
clip = []
if i==j:
for c in range(j*8, j*8+8):
clip.append(c)
random_num = round(len(clip)*0.15)
random_Frames = sorted(random.sample(clip, random_num))
#print("i == j")
else: # (i<j)
for c in range(i*8, (j+1)*8):
clip.append(c)
random_num = round(len(clip)*0.15)
random_Frames = sorted(random.sample(clip, random_num))
#print(f"{i} with {j}")
clips.extend(random_Frames)
return clips
def tt02(features, threshold):
i = 0
previous = i
clips = []
#compare the sum of squared difference between clips j and previous
for j in range(1, len(features)):
if sum_of_squared_difference(features[previous], features[j]) > threshold:
clip = []
# add frames from clip i to j-1 to the clip list
for b in range(i*8, j*8):
clip.append(b)
# randomly select 15% of the frames from the clip list
random_num = round(len(clip)*0.15)
# sort the frames in the clip list to ensure the order of the frames
random_Frames = sorted(random.sample(clip, random_num))
i = j
clips.extend(random_Frames)
previous = j
# add the last clip to the clip list
clip = []
if i==j:
for c in range(j*8, j*8+8):
clip.append(c)
random_num = round(len(clip)*0.15)
random_Frames = sorted(random.sample(clip, random_num))
else: # (i<j)
for c in range(i*8, (j+1)*8):
clip.append(c)
random_num = round(len(clip)*0.15)
random_Frames = sorted(random.sample(clip, random_num))
clips.extend(random_Frames)
return clips
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