import os
import re
import time
import dlib
import numpy as np
from flask import Blueprint, request, jsonify
from PIL import Image  # todo: try to remove PIL dependency

from app.processors import face_recognition
from app.processors import face_detector
from app.processors.faiss import load_faiss_databases
from app.models.sql_factory import load_sql_datasets, list_datasets, get_dataset, get_table
from app.utils.im_utils import pil2np

sanitize_re = re.compile('[\W]+')
valid_exts = ['.gif', '.jpg', '.jpeg', '.png']

LIMIT = 9
THRESHOLD = 0.3

api = Blueprint('api', __name__)

faiss_datasets = load_faiss_databases()

@api.route('/')
def index():
  """List the datasets and their fields"""
  return jsonify({ 'datasets': list_datasets() })


@api.route('/dataset/<name>')
def show(name):
  """Show the data that a dataset will return"""
  dataset = get_dataset(name)
  if dataset:
    return jsonify(dataset.describe())
  else:
    return jsonify({ 'status': 404 })


@api.route('/dataset/<name>/face/', methods=['POST'])
def upload(name):
  """Query an image against FAISS and return the matching identities"""
  start = time.time()
  dataset = get_dataset(name)
  if name not in faiss_datasets:
    return jsonify({
      'error': 'invalid dataset'  
    })
  faiss_dataset = faiss_datasets[name]
  file = request.files['query_img']
  fn = file.filename
  if fn.endswith('blob'):
    fn = 'filename.jpg'

  basename, ext = os.path.splitext(fn)
  # print("got {}, type {}".format(basename, ext))
  if ext.lower() not in valid_exts:
    return jsonify({ 'error': 'not an image' })

  im = Image.open(file.stream).convert('RGB')
  im_np = pil2np(im)

  # Face detection
  detector = face_detector.DetectorDLIBHOG()

  # get detection as BBox object
  bboxes = detector.detect(im_np, largest=True)
  if not bboxes or not len(bboxes):
    return jsonify({
      'error': 'bbox'
    })
  bbox = bboxes[0]
  if not bbox:
    return jsonify({
      'error': 'bbox'
    })

  dim = im_np.shape[:2][::-1]
  bbox = bbox.to_dim(dim)  # convert back to real dimensions
  # print("got bbox")
  if not bbox:
    return jsonify({
      'error': 'bbox'
    })

  # extract 128-D vector
  recognition = face_recognition.RecognitionDLIB(gpu=-1)
  vec = recognition.vec(im_np, bbox)
  query = np.array([ vec ]).astype('float32')
  
  # query FAISS
  distances, indexes = faiss_dataset.search(query, LIMIT)

  if len(indexes) == 0 or len(indexes[0]) == 0:
    return jsonify({
      'error': 'nomatch'
    })

  # get the results for this single query...
  distances = distances[0]
  indexes = indexes[0]

  dists = []
  ids = []
  for _d, _i in zip(distances, indexes):
    if _d <= THRESHOLD:
      dists.append(round(float(_d), 2))
      ids.append(_i+1)

  results = [ dataset.get_identity(int(_i)) for _i in ids ]

  # print(distances)
  # print(ids)

  query = {
    'bbox': bboxes[0],
    'bbox_dim': bbox,
    'timing': round(time.time() - start, 3),
  }
  # print(results)
  return jsonify({
    'query': query,
    'results': results,
    'distances': dists,
  })


@api.route('/dataset/<name>/name', methods=['GET'])
def name_lookup(dataset):
  """Find a name in the dataset"""
  start = time.time()
  dataset = get_dataset(name)

  # we have a query from the request query string...
  # use this to do a like* query on the identities_meta table

  query = {
    'timing': time.time() - start,
  }
  results = []

  # print(results)
  return jsonify({
    'query': query,
    'results': results,
  })