"""
Converts ROIs to face vector
NB: the VGG Face2 extractor should be used with MTCNN ROIs (not square)
  the DLIB face extractor should be used with DLIB ROIs (square)
see https://github.com/ox-vgg/vgg_face2 for TAR@FAR
"""

import click

from app.settings import types
from app.utils import click_utils
from app.settings import app_cfg as cfg

@click.command()
@click.option('-o', '--output', 'opt_fp_out', default=None,
  help='Override enum output filename CSV')
@click.option('-m', '--media', 'opt_dir_media', default=None,
  help='Override enum media directory')
@click.option('--store', 'opt_data_store',
  type=cfg.DataStoreVar,
  default=click_utils.get_default(types.DataStore.HDD),
  show_default=True,
  help=click_utils.show_help(types.Dataset))
@click.option('--dataset', 'opt_dataset',
  type=cfg.DatasetVar,
  required=True,
  show_default=True,
  help=click_utils.show_help(types.Dataset))
@click.option('--size', 'opt_size', 
  type=(int, int), default=cfg.DEFAULT_SIZE_FACE_DETECT,
  help='Output image size')
@click.option('-e', '--extractor', 'opt_extractor',
  default=click_utils.get_default(types.FaceExtractor.VGG),
  type=cfg.FaceExtractorVar,
  help='Type of extractor framework/network to use')
@click.option('-j', '--jitters', 'opt_jitters', default=cfg.DLIB_FACEREC_JITTERS,
  help='Number of jitters (only for dlib')
@click.option('-p', '--padding', 'opt_padding', default=cfg.FACEREC_PADDING,
  help='Percentage ROI padding')
@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 file')
@click.option('-g', '--gpu', 'opt_gpu', default=0,
  help='GPU index')
@click.pass_context
def cli(ctx, opt_fp_out, opt_dir_media, opt_data_store, opt_dataset, opt_size, 
  opt_extractor, opt_slice, opt_force, opt_gpu, opt_jitters, opt_padding):
  """Converts face ROIs to vectors"""
  
  import sys
  import os
  from os.path import join
  from pathlib import Path
  from glob import glob
  
  from tqdm import tqdm
  import numpy as np
  import dlib  # must keep a local reference for dlib
  import cv2 as cv
  import pandas as pd

  from app.models.bbox import BBox
  from app.models.data_store import DataStore
  from app.utils import logger_utils, file_utils, im_utils
  from app.processors import face_extractor


  # -------------------------------------------------
  # init here

  log = logger_utils.Logger.getLogger()
  # set data_store
  data_store = DataStore(opt_data_store, opt_dataset)

  # get filepath out
  fp_out = data_store.metadata(types.Metadata.FACE_VECTOR) if opt_fp_out is None else opt_fp_out
  if not opt_force and Path(fp_out).exists():
    log.error('File exists. Use "-f / --force" to overwite')
    return

  # init face processors
  if opt_extractor == types.FaceExtractor.DLIB:
    log.debug('set dlib')
    extractor = face_extractor.ExtractorDLIB(gpu=opt_gpu, jitters=opt_jitters)
  elif opt_extractor == types.FaceExtractor.VGG:
    extractor = face_extractor.ExtractorVGG()

  # load data
  fp_record = data_store.metadata(types.Metadata.FILE_RECORD)
  df_record = pd.read_csv(fp_record, dtype={'fn':str}).set_index('index')
  fp_roi = data_store.metadata(types.Metadata.FACE_ROI)
  df_roi = pd.read_csv(fp_roi).set_index('index')

  if opt_slice:
    df_roi = df_roi[opt_slice[0]:opt_slice[1]]
  
  # -------------------------------------------------
  # process images

  df_img_groups = df_roi.groupby('record_index')
  log.debug('processing {:,} groups'.format(len(df_img_groups)))

  vecs = []
  for record_index, df_img_group in tqdm(df_img_groups):
    # make fp
    ds_record = df_record.iloc[record_index]
    fp_im = data_store.face(ds_record.subdir, ds_record.fn, ds_record.ext)
    im = cv.imread(fp_im)
    for roi_index, df_img in df_img_group.iterrows():
      # get bbox
      x, y, w, h = df_img.x, df_img.y, df_img.w, df_img.h
      dim = (ds_record.width, ds_record.height)
      # get face vector
      bbox = BBox.from_xywh(x, y, w, h)  # norm
      # compute vec
      vec = extractor.extract(im, bbox)  # use normalized BBox
      vec_str = extractor.to_str(vec)
      vec_obj = {'vec':vec_str, 'roi_index': roi_index, 'record_index':record_index}
      vecs.append(vec_obj)

  # -------------------------------------------------
  # save data

  # create DataFrame and save to CSV
  df = pd.DataFrame.from_dict(vecs)
  df.index.name = 'index'
  file_utils.mkdirs(fp_out)
  df.to_csv(fp_out)

  # save script
  file_utils.write_text(' '.join(sys.argv), '{}.sh'.format(fp_out))