import sys
import json
import os
import piexif
import sqlite3
from sqlite3 import Error
from PIL import Image
import numpy as np
from deepface import DeepFace
from deepface.detectors import FaceDetector
import functools

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

html_base = '../'
db_path = '../db/photos.db'

# TODO
# Switch to using DBSCAN
#
# Thoughts for determining number of clusters to try and target...
#
# Augment DBSCAN to rule out identity matching for the same face
# appearing more than once in a photo
#
# NOTE: This means twins or reflections won't both identify in the
# same photo -- those faces would then identify as a second face pairing
# which could merge with a cluster, but can not be used to match


def gen_html(identities):
  for identity in identities:

    print('<div>')
    print(f'<div><b>Identity {identity["id"]} has {len(identity["faces"])}</b><br></div>')
    print('<div>')
    for face in identity['faces']:
      faceId = face['id']
      photoId = face['photoId']
      distance = "{:0.4f}".format(face['distance'])
      confidence = "{:0.3f}".format(face['confidence'])
      label = face['cluster']
      if type(label) != str:
        label = f'Cluster ({face["cluster"]["id"]})'
     
      print('<div style="position:relative;display:inline-flex;flex-direction:column">')
      path = f'{html_base}/faces/{"{:02d}".format(faceId % 10)}'
      print(f'<img src="{path}/{faceId}.jpg"/>')
      print(f'<div style="background-color:rgba(255, 255, 255, 0.4);position:absolute;top:0px;left:0px;right:0px;padding:0.25rem">{label}: {distance}</div>')
      print(f'<div style="background-color:rgba(255, 255, 255, 0.4);position:absolute;bottom:0px;left:0px;right:0px;padding:0.25rem">{faceId} {photoId} {confidence}</div>')
      print('</div>')
    print('</div>')
    print('</div>')


def load_faces(db_path = db_path):
  conn = create_connection(db_path)
  faces = []
  with conn:
    cur = conn.cursor()
    res = cur.execute('''
      SELECT faces.id,facedescriptors.descriptors,faces.faceConfidence,faces.photoId
      FROM faces
      JOIN facedescriptors ON (faces.descriptorId=facedescriptors.id)
      WHERE faces.identityId IS null AND faces.faceConfidence>0.99
      ''')
    for row in res.fetchall():
      id, descriptors, confidence, photoId = row
      face = { 
        'id': id,
        'type': 'face',
        'confidence': confidence,
        'distance': 0,
        'photoId': photoId,
        'descriptors': np.frombuffer(descriptors),
        'cluster': Undefined
      }
      face['faces'] = [ face ]
      faces.append(face)
  return faces

def cluster_sort(A, B):
  diff = A['cluster'] - B['cluster']
  if diff > 0:
    return 1
  elif diff < 0:
    return -1
  diff = A['confidence'] - B['confidence']
  if diff > 0:
    return 1
  elif diff < 0:
    return -1
  return 0
  
print('Loading faces from database')
faces = load_faces()
print(f'{len(faces)} faces loaded')
print('Scanning for clusters')
identities = DBSCAN(faces) # process_faces(faces)
print(f'{len(identities)} clusters grouped')



# Compute average center for all clusters
sum = 0
for identity in identities:
  sum += len(identity['faces'])
  print(f'{identity["id"]} has {len(identity["faces"])} faces')
  average = []

  for face in identity['faces']:
    if len(average) == 0:
      average = face['descriptors']
    else:
      average = np.add(average, face['descriptors'])

  average = np.divide(average, len(identity['faces']))
  identity['descriptors'] = average

removed = -1
epoch = 1
# Filter each cluster removing any face that is > cluster_max_distance
# from the average center point of the cluster
while removed != 0:
  print(f'Epoch {epoch}...')
  epoch += 1
  removed = 0
  for identity in identities:
    for face in identity['faces']:
      average = identity['descriptors']
      distance = findCosineDistance(average, face['descriptors'])
      if distance > 0.14:
        average = np.dot(average, len(identity['faces']))
        average = np.subtract(average, face['descriptors'])
  
        face['cluster'] = Undefined
        face['distance'] = 0
        identity['faces'].remove(face)

        identity['descriptors'] = np.divide(average, len(identity['faces']))
        removed += 1
      else:
        face['distance'] = distance
  if removed > 0:
    print(f'Excluded {removed} faces this epoch')
    
identities.sort(reverse = True, key = lambda x: len(x['faces']))
for identity in identities:
  identity['faces'].sort(reverse = False, key = lambda x: x['distance'])

print(f'{len(identities)} identities seeded.')

print('Writing to "identities.html"')
redirect_on('identities.html')
gen_html(identities)
redirect_off()