| import requests |
| from bs4 import BeautifulSoup |
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| from csv import DictReader |
| from pathlib import Path |
| import re |
| import uuid |
| import json |
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| import db_upload |
| import make_embeddings_upload |
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| image_path = Path("../images_000") |
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| metadata_path = "../train_attribution.csv" |
| image_names = {} |
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| for path in image_path.rglob('*.*'): |
| match = re.search('.*(\w{16})\.jpg$', str(path)) |
| image_names[match.group(1)] = path |
|
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| 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"]) |
|
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| |
| 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']) |
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| csvfile.close() |
| |
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| 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) |
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| |
| vectors_non_list = make_embeddings_upload.get_features() |
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| ids, vectors, payloads = [], [], [] |
| |
| 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"]))) |
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| |
| uploader = db_upload.DBUpload(512, "images") |
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| uploader.upsert_vectors(ids, vectors, payloads) |
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| print("finished") |
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