import functools

from ketrface.util import *
from ketrface.dbscan import *
from ketrface.db import *
from ketrface.config import *

config = read_config()

html_path = merge_config_path(config['path'], 'frontend')
pictures_path = merge_config_path(config['path'], config['picturesPath'])
faces_path = merge_config_path(config['path'], config['facesPath'])
db_path = merge_config_path(config['path'], config["db"]["photos"]["host"])
html_base = config['basePath']
if html_base == "/":
  html_base = "."

print(f'Connecting to database: {db_path}')
conn = create_connection(db_path)
with conn:
  cur = conn.cursor()
  res = cur.execute('''
    SELECT identities.descriptors,
    GROUP_CONCAT(faces.id) AS relatedFaceIds,
    GROUP_CONCAT(faces.descriptorId) AS relatedFaceDescriptorIds,
    GROUP_CONCAT(faces.photoId) AS relatedFacePhotoIds 
    FROM identities 
    INNER JOIN faces ON identities.id=faces.identityId
    WHERE identities.id=7
    GROUP BY identities.id
    ''')
  for identity in res.fetchall():
    relatedFaceDescriptorIds = identity[2].split(',')
  
    res2 = cur.execute(
      'SELECT descriptors FROM facedescriptors WHERE id IN (%s)' %
      ','.join('?'*len(relatedFaceDescriptorIds)), relatedFaceDescriptorIds)

    descriptors = []
    for row2 in res2.fetchall():
      descriptors.append(np.frombuffer(row2[0]))

    distances = []

    relatedFaceIds = identity[2].split(',')
    for i, face in enumerate(relatedFaceIds):
      distance = findEuclideanDistance(
        descriptors[i],
        np.frombuffer(identity[0])
      )
      distances.append(distance)

    distances.sort()
    print(distances)