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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, b64decode |
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from io import BytesIO |
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import re |
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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_onnx |
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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 data_to_image(data_uri: str) -> Image: |
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"""Convert a base64-encoded data URI to a Pillow Image.""" |
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base64_str = re.search(r"base64,(.*)", data_uri).group(1) |
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image_data = b64decode(base64_str) |
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image = Image.open(BytesIO(image_data)) |
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return image |
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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 = ( |
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pd.read_table("images.tsv") if exists("images.tsv") else pd.read_table("images.csv") |
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) |
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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("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") and 0: |
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model, processor = get_model_onnx( |
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"unum-cloud/uform-vl-english-small", |
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device="cpu", |
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dtype="fp32", |
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) |
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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 = processor.preprocess_image(image) |
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image_embedding = model.encode_image(image_data) |
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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.base64.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.base64.txt", "w") as f: |
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f.write("\n".join(datas)) |
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if not exists("images.names.txt"): |
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with open("images.names.txt", "w") as f: |
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f.write("\n".join(names)) |
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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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