"""
Converts ROIs to face vector
"""

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('--data_store', 'opt_data_store',
  type=cfg.DataStoreVar,
  default=click_utils.get_default(types.DataStore.SSD),
  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=(300, 300),
  help='Output image size')
@click.option('-j', '--jitters', 'opt_jitters', default=cfg.DLIB_FACEREC_JITTERS,
  help='Number of jitters')
@click.option('-p', '--padding', 'opt_padding', default=cfg.DLIB_FACEREC_PADDING,
  help='Percentage 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_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_recognition


  # -------------------------------------------------
  # 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
  facerec = face_recognition.RecognitionDLIB()

  # load data
  fp_record = data_store.metadata(types.Metadata.FILE_RECORD)
  df_record = pd.read_csv(fp_record).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 here
  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
      imw = df_img.image_width
      imh = df_img.image_height
      dim = im.shape[:2][::-1]
      # get face vector
      dim = (imw, imh)
      bbox_dim = BBox.from_xywh(x, y, w, h).to_dim(dim)  # convert to int real dimensions
      # compute vec
      # padding=opt_padding not yet implemented in 19.16 but merged in master
      vec = facerec.vec(im, bbox_dim, jitters=opt_jitters)
      vec_str = ','.join([repr(x) for x in vec])  # convert to string for CSV
      vecs.append( {'roi_index': roi_index, 'record_index': record_index, 'vec': vec_str})


  # save date
  df = pd.DataFrame.from_dict(vecs)
  df.index.name = 'index'
  file_utils.mkdirs(fp_out)
  df.to_csv(fp_out)