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https://huggingface.co/datasets/Qdrant/google-landmark-geo/resolve/main/code/main.py
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2.53 kB
| import requests | |
| from bs4 import BeautifulSoup | |
| from csv import DictReader | |
| from pathlib import Path | |
| import re | |
| import uuid | |
| import json | |
| import db_upload | |
| import make_embeddings_upload | |
| #TODO this is definitely not efficient as it generates vectors for all the images, not just the ones with geo | |
| # to fix we would somehow copy all the original images in the payloads_non_list into the directory we want to use in the vector making | |
| #TODO there is also repeated code everywhere | |
| # And hard coded values for too many things | |
| image_path = Path("../images_000") | |
| metadata_path = "../train_attribution.csv" | |
| image_names = {} | |
| for path in image_path.rglob('*.*'): | |
| match = re.search('.*(\w{16})\.jpg$', str(path)) | |
| image_names[match.group(1)] = path | |
| csvfile = open(metadata_path, "r", encoding='utf-8') | |
| csvlines = DictReader(csvfile) | |
| payloads_non_list = {} | |
| i= 1 | |
| for line in csvlines: | |
| if line['id'] in image_names: | |
| try: | |
| page = requests.get(line['url']) | |
| html = BeautifulSoup(page.text, features="html.parser") | |
| our_tag = html.find('a', {"data-style": "osm-intl"}) | |
| if our_tag is not None: | |
| if "data-lat" in our_tag.attrs and "data-lon" in our_tag.attrs: | |
| lat = float(our_tag.attrs["data-lat"]) | |
| lon = float(our_tag.attrs["data-lon"]) | |
| # We have our payload at this point | |
| print("found one " + line['id'] + " : " + line['url'] + " coords: " + str(lat) + ", " + str(lon)) | |
| payloads_non_list[line['id']] = {"picture": line['id'], "filename": str(image_names[line['id']]), "url": line['url'], "location": {"lon": lon, "lat": lat}} | |
| except: | |
| print("Threw an exception on: " + line['id']) | |
| csvfile.close() | |
| # Write our payloads out to file | |
| with open('../train_attribution_geo.json', 'w') as out_file: | |
| json.dump(payloads_non_list, out_file, sort_keys=True, indent=4, | |
| ensure_ascii=False) | |
| # now create our vector | |
| vectors_non_list = make_embeddings_upload.get_features() | |
| ids, vectors, payloads = [], [], [] | |
| # Put them together - need to do this because of the sorting problem - need to get them to line up | |
| for key, payload in payloads_non_list.items(): | |
| payloads.append(payload) | |
| vectors.append(vectors_non_list[key]) | |
| ids.append(str(uuid.uuid3(uuid.NAMESPACE_DNS,payload["url"]))) | |
| # now insert into the collection | |
| uploader = db_upload.DBUpload(512, "images") | |
| uploader.upsert_vectors(ids, vectors, payloads) | |
| print("finished") | |