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"""
Copyright (c) 2022, salesforce.com, inc.
All rights reserved.
SPDX-License-Identifier: BSD-3-Clause
For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""
import time
from PIL import Image
from lavis.common.utils import get_abs_path, get_cache_path
from multiprocessing import Pool
from omegaconf import OmegaConf
from pathlib import Path
from torchvision.transforms import functional as TF
from tqdm import tqdm
import glob
import io
import json
import magic # pip install python-magic
import numpy as np
import os
import pandas as pd
import requests
import shelve
import zlib
headers = {
#'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36',
"User-Agent": "Googlebot-Image/1.0", # Pretend to be googlebot
"X-Forwarded-For": "64.18.15.200",
}
def _df_split_apply(tup_arg):
split_ind, subset, func = tup_arg
r = subset.apply(func, axis=1)
return (split_ind, r)
def df_multiprocess(df, processes, chunk_size, func, dataset_name):
print("Generating parts...")
with shelve.open(
"%s_%s_%s_results.tmp" % (dataset_name, func.__name__, chunk_size)
) as results:
pbar = tqdm(total=len(df), position=0)
# Resume:
finished_chunks = set([int(k) for k in results.keys()])
pbar.desc = "Resuming"
for k in results.keys():
pbar.update(len(results[str(k)][1]))
pool_data = (
(index, df[i : i + chunk_size], func)
for index, i in enumerate(range(0, len(df), chunk_size))
if index not in finished_chunks
)
print(
int(len(df) / chunk_size),
"parts.",
chunk_size,
"per part.",
"Using",
processes,
"processes",
)
pbar.desc = "Downloading"
with Pool(processes) as pool:
for i, result in enumerate(
pool.imap_unordered(_df_split_apply, pool_data, 2)
):
results[str(result[0])] = result
pbar.update(len(result[1]))
pbar.close()
print("Finished Downloading.")
return
# Unique name based on url
def _file_name(row):
name = (
"%s/%s_%s"
% (
# row["folder"],
storage_dir,
row.name,
(zlib.crc32(row["url"].encode("utf-8")) & 0xFFFFFFFF),
)
+ ".jpg"
)
return name
# For checking mimetypes separately without download
def check_mimetype(row):
if os.path.isfile(str(row["file"])):
row["mimetype"] = magic.from_file(row["file"], mime=True)
row["size"] = os.stat(row["file"]).st_size
return row
# Don't download image, just check with a HEAD request, can't resume.
# Can use this instead of download_image to get HTTP status codes.
def check_download(row):
fname = _file_name(row)
try:
# not all sites will support HEAD
response = requests.head(
row["url"], stream=False, timeout=5, allow_redirects=True, headers=headers
)
row["status"] = response.status_code
row["headers"] = dict(response.headers)
except:
# log errors later, set error as 408 timeout
row["status"] = 408
return row
if response.ok:
row["file"] = fname
return row
def resize_img(req):
image = Image.open(req).convert("RGB")
image = TF.resize(
# image, size=(resize_size, resize_size)
image,
size=resize_size,
) # , interpolation=Image.LANCZOS)
return image
def download_image(row):
fname = _file_name(row)
# Skip Already downloaded, retry others later
if os.path.isfile(fname):
row["status"] = 200
row["file"] = fname
row["mimetype"] = magic.from_file(row["file"], mime=True)
row["size"] = os.stat(row["file"]).st_size
return row
try:
# use smaller timeout to skip errors, but can result in failed downloads
response = requests.get(
row["url"], stream=False, timeout=5, allow_redirects=True, headers=headers
)
row["status"] = response.status_code
# row['headers'] = dict(response.headers)
except Exception as e:
# log errors later, set error as 408 timeout
row["status"] = 408
return row
if response.ok:
try:
# some sites respond with gzip transport encoding
response.raw.decode_content = True
img = resize_img(io.BytesIO(response.content))
img.save(fname)
row["mimetype"] = magic.from_file(fname, mime=True)
row["size"] = os.stat(fname).st_size
except Exception as e:
# # This is if it times out during a download or decode
row["status"] = 408
row["file"] = fname
return row
def open_tsv(fname, folder):
print("Opening %s Data File..." % fname)
df = pd.read_csv(
fname, sep="\t", names=["url", "caption"]
) # , usecols=range(1, 2))
df["folder"] = folder
print("Processing", len(df), " Images:")
return df
def df_from_shelve(chunk_size, func, dataset_name):
print("Generating Dataframe from results...")
with shelve.open(
"%s_%s_%s_results.tmp" % (dataset_name, func.__name__, chunk_size)
) as results:
keylist = sorted([int(k) for k in results.keys()])
df = pd.concat([results[str(k)][1] for k in keylist], sort=True)
return df
resize_size = 384
config_path = get_abs_path("configs/datasets/conceptual_caption/defaults_12m.yaml")
storage_dir = OmegaConf.load(
config_path
).datasets.conceptual_caption_12m.build_info.images.storage
storage_dir = Path(get_cache_path(storage_dir))
os.makedirs(storage_dir, exist_ok=True)
# number of processes in the pool can be larger than cores
num_processes = 96
# num_processes = 1
# chunk_size is how many images per chunk per process - changing this resets progress when restarting.
images_per_part = 100
data_name = "cc12m"
# os.makedirs(data_name, exist_ok=True)
df = open_tsv("cc12m.tsv", data_name)
df_multiprocess(
df=df,
processes=num_processes,
chunk_size=images_per_part,
func=download_image,
dataset_name=data_name,
)
df = df_from_shelve(
chunk_size=images_per_part, func=download_image, dataset_name=data_name
)
df.to_csv(
"downloaded_%s_report.tsv.gz" % data_name,
compression="gzip",
sep="\t",
header=False,
index=False,
)
print("Saved.")
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