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from os import listdir, path, PathLike
from os.path import isfile, join
import pandas as pd
import numpy as np
from PIL import Image
from PIL import ImageFile
from tqdm import tqdm
from uform import get_model
from usearch.index import Index
from usearch.io import save_matrix, load_matrix
ImageFile.LOAD_TRUNCATED_IMAGES = True
def is_image(path: PathLike) -> bool:
if not isfile(path):
return False
try:
Image.open(path)
return True
except:
return False
names = sorted(f for f in listdir('images') if is_image(join('images', f)))
names = [filename.rsplit('.', 1)[0] for filename in names]
table = pd.read_table('images.tsv') if path.exists(
'images.tsv') else pd.read_table('images.csv')
table = table[table['photo_id'].isin(names)]
table = table.sort_values('photo_id')
table.reset_index()
table.to_csv('images.csv', index=False)
names = list(set(table['photo_id']).intersection(names))
model = get_model('unum-cloud/uform-vl-english')
vectors = []
for name in tqdm(names, desc='Vectorizing images'):
image = Image.open(join('images', name + '.jpg'))
image_data = model.preprocess_image(image)
image_embedding = model.encode_image(image_data).detach().numpy()
vectors.append(image_embedding)
image_mat = np.concatenate(vectors)
save_matrix(image_mat, 'images.fbin')
index = Index(ndim=256, metric='cos')
image_mat = load_matrix('images.fbin')
for idx, vector in tqdm(enumerate(vectors), desc='Indexing vectors'):
index.add(idx, vector.flatten())
index.save('images.usearch')
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