# installing pixl !pip install pixl

from pixl.utils import Pixl from sklearn.cluster import KMeans import pandas as pd from shutil import unpack_archive import os

# setting directory of sample images category = 'animals' img_dir = './assets/' + category # grabbing file_names filenames = [] for file in os.listdir(img_dir): filenames.append(os.fsdecode(file))

filenames

# instantiate a pixl object p_object = Pixl()

def get_vec(img_dir, filenames): """ Returns a list of vectors and a list of labels. Args: filenames (str): string specifying the name of each file. Returns: vectors (lst): list of vectors representing image files. labels (lst): list of labels taken from file names. """ vectors = [] labels = [] for file in filenames: vec = p_object.get_vec(img_dir + '/' + file) vectors.append(vec) label = file.split('.')[0] labels.append(label) return labels, vectors

labels, vectors = get_vec(img_dir, filenames)

# examining a label and vector print(labels[0], vectors[0])

ids = range(len(vectors)) img_embedd_df = pd.DataFrame(index = ids)

img_embedd_df['Labels'] = labels img_embedd_df['Vectors'] = vectors

print(img_embedd_df)

# setting n_clusters to 2 because we know we only want to cluster 2 classes km = KMeans(n_clusters=2) # fitting to the list of vectors from get_vec func km.fit(vectors) # grabbing a list of clusters clusters = km.labels_.tolist() # column cluter for each cluster img_embedd_df["Cluster"] = clusters # displaying counts per cluster print(img_embedd_df['Cluster'].value_counts())

# displaying our df to see how we did! print(img_embedd_df)