Upload 3 files

images.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cb6a68e6bace132ee827d922adb88554da67d5a7806cf0ca9e39545aa924abc2
+size 406969617

main.py

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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')

requirements.txt

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+uform
+usearch
+
+numpy
+Pillow
+tqdm