Datasets:

diwank
/

lld

+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.hdf5 filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text

lld/__init__.py ADDED Viewed

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1	+ # /usr/bin/env python3
2	+ # llg/__init__.py

lld/crawler.py ADDED Viewed

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+# lld/crawler.py
+import asyncio
+from urllib.parse import urlparse
+import aiohttp
+from extraction import Extractor
+from tqdm.asyncio import tqdm, tqdm_asyncio
+def get_domain(url: str):
+    parsed = urlparse(url)
+    return parsed.netloc
+async def gather_with_concurrency(n: int, tasks):
+    semaphore = asyncio.Semaphore(n)
+    async def sem_task(task):
+        async with semaphore:
+            return await task
+    results = asyncio.as_completed(tasks)
+    return results
+async def scrape_description(
+    url: str, session: aiohttp.ClientSession, timeout: int = 15
+) -> str:
+    """Scrape description of the given url from <head> and <meta> tags."""
+    extractor = Extractor()
+    domain = get_domain(url)
+    try:
+        async with session.get(url, timeout=timeout) as response:
+            html = await response.text()
+            data = extractor.extract(html, source_url=url)
+            collected = [
+                *data.titles,
+                data.description,
+                domain,
+            ]
+            results = "\n".join(filter(bool, collected))
+            return results
+    except:
+        return domain
+async def crawl(urls: list[str], batch_size: int = 100):
+    connector = aiohttp.TCPConnector(limit=None, ttl_dns_cache=300)
+    async with aiohttp.ClientSession(connector=connector) as session:
+        gen_results = await gather_with_concurrency(
+            batch_size, [scrape_description(url, session) for url in urls]
+        )
+        for result in gen_results:
+            yield await result
+async def run(urls: list[str], batch_size: int = 100):
+    crawler = crawl(urls, batch_size)
+    results = [c async for c in tqdm_asyncio(crawler, total=len(urls))]
+    return results

lld/loader.py ADDED Viewed

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+# llg/loader.py
+import os
+import h5py
+import numpy as np
+import PIL.Image as Image
+script_dir = os.path.dirname(__file__)
+datafile_path = os.path.join(script_dir, "../data/LLD-logo.hdf5")
+with h5py.File(datafile_path, "r") as throwaway:
+    samples_count: int = len(throwaway["data"])
+def gen_samples(labels: list[str] = ["data", "meta_data/names"]):
+    # open hdf5 file
+    with h5py.File(datafile_path, "r") as hdf5_file:
+        count = len(hdf5_file["data"])
+        i = 0
+        while i < count:
+            result = {}
+            if "data" in labels:
+                shape = hdf5_file["shapes"][i]
+                images = hdf5_file["data"][i][:, : shape[1], : shape[2]]
+                result["data"] = images.astype(np.uint8)
+            for label in [l for l in labels if l != "data"]:
+                result[label] = hdf5_file[label][i]
+            yield result
+            i += 1
+if __name__ == "__main__":
+    sample = next(gen_samples())
+    name = sample["meta_data/names"]
+    images = sample["data"]
+    print(name)
+    image_pil = Image.fromarray(images[2])
+    image_pil.show()

lld/preprocess.py ADDED Viewed

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+# llg/preprocess.py
+import argparse, asyncio, os
+from itertools import islice
+from aiostream import stream
+import h5py as h5
+import numpy as np
+import pandas as pd
+from tqdm.asyncio import trange
+from .loader import gen_samples, samples_count
+from .crawler import run
+script_dir = os.path.dirname(__file__)
+outfile_path = os.path.join(script_dir, "../data/lld-processed.h5")
+async def gen_processor(batch_size: int, limit: int):
+    count = min(limit, samples_count)
+    batch_size = min(limit, batch_size)
+    samples = gen_samples()
+    steps = count // batch_size
+    for step in trange(steps):
+        batch = list(islice(samples, step * batch_size, (step + 1) * batch_size))
+        urls = [f"http://{sample['meta_data/names'].decode()}.com" for sample in batch]
+        descriptions = await run(urls, batch_size)
+        for sample, description in zip(batch, descriptions):
+            name = (sample["meta_data/names"].decode(),)
+            images = sample["data"]
+            data = (
+                images,
+                description,
+                name,
+            )
+            yield data
+async def preprocess(batch_size: int = 100, limit: int = samples_count + 1):
+    columns = ["images", "description", "name"]
+    processor = gen_processor(batch_size, limit)
+    chunk_size = 10
+    async with stream.chunks(processor, chunk_size).stream() as chunks:
+        async for chunk in chunks:
+            df_chunk = pd.DataFrame(chunk, columns=columns)
+            df_chunk.to_hdf(
+                outfile_path, "data", data_columns=columns, mode="a"
+            )
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--limit",
+        help="Limit to total records processed",
+        type=int,
+        default=samples_count + 1,
+    )
+    parser.add_argument(
+        "--batch_size",
+        help="Batch size",
+        type=int,
+        nargs="?",
+        const=10_000,
+        default=10_000,
+    )
+    args = parser.parse_args()
+    asyncio.run(preprocess(batch_size=args.batch_size, limit=args.limit))

poetry.lock ADDED Viewed

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pyproject.toml ADDED Viewed

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+[tool.poetry]
+name = "lld"
+version = "0.1.0"
+description = "LLD (Large Logo Dataset)"
+authors = ["Diwank Singh Tomer <diwank.singh@gmail.com>"]
+license = "MIT"
+[tool.poetry.dependencies]
+python = "^3.10"
+numpy = "^1.23.1"
+h5py = "^3.7.0"
+datasets = "^2.3.2"
+Pillow = "^9.2.0"
+requests = "^2.28.1"
+bs4 = "^0.0.1"
+html5lib = "^1.1"
+extraction = "^0.3"
+aiohttp = {extras = ["speedups"], version = "^3.8.1"}
+tqdm = "^4.64.0"
+pandas = "^1.4.3"
+tables = "^3.7.0"
+pyarrow = "^8.0.0"
+modin = {extras = ["dask"], version = "^0.15.2"}
+aiostream = "^0.4.4"
+[tool.poetry.dev-dependencies]
+ipython = "^8.4.0"
+black = "^22.6.0"
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"