Add: Script to generate data URIs in `.txt`

main.py

@@ -1,6 +1,8 @@
 #!/usr/bin/env python3
-from os import listdir, path, PathLike, remove
+from os import PathLike, listdir, remove
 from os.path import isfile, join, exists
+from mimetypes import guess_type
+from base64 import b64encode
 
 import pandas as pd
 import numpy as np
@@ -25,6 +27,17 @@ def is_image(path: PathLike) -> bool:
         return False
 
 
+def image_to_data(path: PathLike) -> str:
+    """Convert a file (specified by a path) into a data URI."""
+    if not exists(path):
+        raise FileNotFoundError
+    mime, _ = guess_type(path)
+    with open(path, 'rb') as fp:
+        data = fp.read()
+        data64 = b64encode(data).decode('utf-8')
+        return f'data:{mime}/jpg;base64,{data64}'
+
+
 def trim_extension(filename: str) -> str:
     return filename.rsplit('.', 1)[0]
 
@@ -32,7 +45,7 @@ def trim_extension(filename: str) -> str:
 names = sorted(f for f in listdir('images') if is_image(join('images', f)))
 names = [trim_extension(f) for f in names]
 
-table = pd.read_table('images.tsv') if path.exists(
+table = pd.read_table('images.tsv') if exists(
     'images.tsv') else pd.read_table('images.csv')
 table = table[table['photo_id'].isin(names)]
 table = table.sort_values('photo_id')
@@ -42,6 +55,7 @@ table.to_csv('images.csv', index=False)
 names = list(set(table['photo_id']).intersection(names))
 names_to_delete = [f for f in listdir(
     'images') if trim_extension(f) not in names]
+names = list(table['photo_id'])
 
 if len(names_to_delete) > 0:
     print(f'Plans to delete: {len(names_to_delete)} images without metadata')
@@ -52,7 +66,7 @@ if not exists('images.fbin'):
     model = get_model('unum-cloud/uform-vl-english')
     vectors = []
 
-    for name in tqdm(list(table["photo_id"]), desc='Vectorizing images'):
+    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()
@@ -61,6 +75,17 @@ if not exists('images.fbin'):
     image_mat = np.vstack(vectors)
     save_matrix(image_mat, 'images.fbin')
 
+if not exists('images.txt'):
+
+    datas = []
+    for name in tqdm(names, desc='Encoding images'):
+        data = image_to_data(join('images', name + '.jpg'))
+        datas.append(data)
+
+    with open('images.txt', 'w') as f:
+        f.write('\n'.join(datas))
+
+
 if not exists('images.usearch'):
     image_mat = load_matrix('images.fbin')
     count = image_mat.shape[0]