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from os import PathLike, listdir, remove |
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from os.path import isfile, join, exists |
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from mimetypes import guess_type |
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from base64 import b64encode |
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import pandas as pd |
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import numpy as np |
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from PIL import Image |
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from PIL import ImageFile |
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from tqdm import tqdm |
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from uform import get_model |
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from usearch.index import Index, MetricKind |
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from usearch.io import save_matrix, load_matrix |
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ImageFile.LOAD_TRUNCATED_IMAGES = True |
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def is_image(path: PathLike) -> bool: |
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if not isfile(path): |
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return False |
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try: |
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Image.open(path) |
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return True |
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except Exception: |
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return False |
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def image_to_data(path: PathLike) -> str: |
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"""Convert a file (specified by a path) into a data URI.""" |
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if not exists(path): |
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raise FileNotFoundError |
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mime, _ = guess_type(path) |
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with open(path, 'rb') as fp: |
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data = fp.read() |
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data64 = b64encode(data).decode('utf-8') |
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return f'data:{mime}/jpg;base64,{data64}' |
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def trim_extension(filename: str) -> str: |
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return filename.rsplit('.', 1)[0] |
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names = sorted(f for f in listdir('images') if is_image(join('images', f))) |
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names = [trim_extension(f) for f in names] |
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table = pd.read_table('images.tsv') if exists( |
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'images.tsv') else pd.read_table('images.csv') |
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table = table[table['photo_id'].isin(names)] |
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table = table.sort_values('photo_id') |
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table.reset_index() |
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table.to_csv('images.csv', index=False) |
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names = list(set(table['photo_id']).intersection(names)) |
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names_to_delete = [f for f in listdir( |
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'images') if trim_extension(f) not in names] |
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names = list(table['photo_id']) |
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if len(names_to_delete) > 0: |
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print(f'Plans to delete: {len(names_to_delete)} images without metadata') |
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for name in names_to_delete: |
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remove(join('images', name)) |
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if not exists('images.fbin'): |
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model = get_model('unum-cloud/uform-vl-multilingual') |
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vectors = [] |
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for name in tqdm(names, desc='Vectorizing images'): |
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image = Image.open(join('images', name + '.jpg')) |
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image_data = model.preprocess_image(image) |
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image_embedding = model.encode_image(image_data).detach().numpy() |
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vectors.append(image_embedding) |
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image_mat = np.vstack(vectors) |
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save_matrix(image_mat, 'images.fbin') |
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if not exists('images.txt'): |
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datas = [] |
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for name in tqdm(names, desc='Encoding images'): |
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data = image_to_data(join('images', name + '.jpg')) |
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datas.append(data) |
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with open('images.txt', 'w') as f: |
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f.write('\n'.join(datas)) |
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if not exists('images.usearch'): |
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image_mat = load_matrix('images.fbin') |
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count = image_mat.shape[0] |
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ndim = image_mat.shape[1] |
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index = Index(ndim=ndim, metric=MetricKind.Cos) |
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for idx in tqdm(range(count), desc='Indexing vectors'): |
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index.add(idx, image_mat[idx, :].flatten()) |
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index.save('images.usearch') |
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