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from os.path import join
import click
from app.utils.logger_utils import Logger
log = Logger.getLogger()
# datasets
dataset_keys = ['pipa', 'megaface', 'helen', 'ibm_dif', 'adience', 'who_goes_there', 'vgg_face']
@click.command()
@click.option('-i', '--input', 'opt_fp_in', required=True,
help='Input file for embassies')
@click.option('-o', '--output', 'opt_fp_out', required=True,
help='Output file')
@click.option('--slice', 'opt_slice', type=(int, int), default=(None, None),
help='Slice list of files')
@click.option('-f', '--force', 'opt_force', is_flag=True,
help='Force overwrite')
@click.pass_context
def cli(ctx, opt_fp_in, opt_fp_out, opt_slice, opt_force):
"""Cross reference"""
"""
# data input example
first_name last_name nsid path_alias url username skip
Query_01 98022916@N00 range_of_light https://www.flickr.com/photos/range_of_light/ range_of_light
"""
import sys
from os.path import join
from glob import glob
from pathlib import Path
import time
import pandas as pd
from tqdm import tqdm
log = Logger.getLogger()
log.info('Cross reference embassy list')
fp_counts = {}
fp_filepaths = {}
fp_dataset_base = '/data_store/datasets/people/'
for dk in dataset_keys:
fp_counts[dk] = join(fp_dataset_base, dk, f'metadata/{dk}_counts.csv')
fp_filepaths[dk] = join(fp_dataset_base, dk, f'metadata/{dk}_filepaths.csv')
df_queries = pd.read_csv(opt_fp_in)
df_queries.fillna('', inplace=True)
len_orig = len(df_queries)
df_queries = df_queries[df_queries.skip != True]
log.debug(f'Skipping {len_orig - len(df_queries)} embassies')
query_nsids = list(df_queries['nsid'])
match_items = []
images = []
for dataset_key, fp_dataset in tqdm(fp_counts.items()):
df_counts = pd.read_csv(fp_dataset)
log.debug(f'loading: {fp_filepaths[dataset_key]}')
df_filepaths = pd.read_csv(fp_filepaths[dataset_key])
nsids = list(df_counts['nsid'])
for nsid in nsids:
if nsid in query_nsids:
# add to matches, and count
count = df_counts[df_counts['nsid'] == nsid]['count'].values[0]
first_name = df_queries[df_queries['nsid'] == nsid]['first_name'].values[0]
last_name = df_queries[df_queries['nsid'] == nsid]['last_name'].values[0]
path_alias = df_queries[df_queries['nsid'] == nsid]['path_alias'].values[0]
page_url = f'https://flickr.com/photos/{path_alias}'
name = f'{first_name} {last_name}'
meta = df_queries[df_queries['nsid'] == nsid].iloc[0]
match_obj = {
'count': count,
'path_alias': path_alias,
'name': name,
'dataset_key': dataset_key,
'nsid': nsid,
'page_url': page_url,
'username': meta.username
}
match_items.append(match_obj)
# add photo ids or url
df_nsids = df_filepaths[df_filepaths['nsid'] == nsid]
nsid_records = df_nsids.to_dict('records')
for nsid_record in nsid_records:
photo_id = nsid_record.get('photo_id')
im_obj = {
'nsid': nsid,
'url': nsid_record.get('url'),
'photo_id': photo_id,
'dataset_key': dataset_key,
'path_alias': path_alias,
'name': name,
'page_url': page_url,
'username': meta.username,
'filepath': f'{photo_id}.jpg'
}
images.append(im_obj)
# Save embassy matches
df_matches = pd.DataFrame.from_dict(match_items)
df_matches.to_csv(opt_fp_out, index=False)
total = df_matches['count'].sum()
# Save image matches
df_images = pd.DataFrame.from_dict(images)
fp_out = opt_fp_out.replace('.csv', '_images.csv')
df_images.to_csv(fp_out, index=False)
total = len(images)
log.debug(f'Found {total:,} embassy images')
# save summary count per dataset
groups_datasets = df_matches.groupby('dataset_key')
summary_counts = []
for group_dataset, df_dataset in groups_datasets:
log.debug(f'{group_dataset}')
summary_counts.append({'dataset': group_dataset, 'images': df_dataset['count'].sum()})
df_dataset_counts = pd.DataFrame.from_dict(summary_counts)
fp_out = opt_fp_out.replace('.csv', '_counts_summary_dataset.csv')
df_dataset_counts.to_csv(fp_out, index=False)
log.debug(f'wrote {fp_out}')
log.debug(f'Found {len(images):,} embassy images')
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