"""

"""

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('-i', '--input', 'opt_fp_in', default=None,
  help='Override enum input filename CSV')
@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('-d', '--detector', 'opt_detector_type',
  type=cfg.FaceLandmark2D_68Var,
  default=click_utils.get_default(types.FaceLandmark2D_68.DLIB),
  help=click_utils.show_help(types.FaceLandmark2D_68))
@click.option('--size', 'opt_size', 
  type=(int, int), default=(300, 300),
  help='Output image size')
@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('-d', '--display', 'opt_display', is_flag=True,
  help='Display image for debugging')
@click.pass_context
def cli(ctx, opt_fp_in, opt_fp_out, opt_dir_media, opt_data_store, opt_dataset, opt_detector_type,
  opt_size, opt_slice, opt_force, opt_display):
  """Creates 2D 68-point landmarks"""
  
  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 cv2 as cv
  import pandas as pd

  from app.utils import logger_utils, file_utils, im_utils, display_utils, draw_utils
  from app.processors import face_landmarks
  from app.models.data_store import DataStore
  from app.models.bbox import BBox

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

  log = logger_utils.Logger.getLogger()
  # init filepaths
  data_store = DataStore(opt_data_store, opt_dataset)
  # set file output path
  metadata_type = types.Metadata.FACE_LANDMARK_2D_68
  fp_out = data_store.metadata(metadata_type) 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 landmark processors
  if opt_detector_type == types.FaceLandmark2D_68.DLIB:
    # use dlib 68 point detector
    landmark_detector = face_landmarks.Dlib2D_68()
  elif opt_detector_type == types.FaceLandmark2D_68.FACE_ALIGNMENT:
    # use dlib 5 point detector
    landmark_detector = face_landmarks.FaceAlignment2D_68()
  else:
    log.error('{} not yet implemented'.format(opt_detector_type.name))
    return

  log.info(f'Using landmark detector: {opt_detector_type.name}')  

  # -------------------------------------------------------------------------
  # load filepath data
  fp_record = data_store.metadata(types.Metadata.FILE_RECORD)
  df_record = pd.read_csv(fp_record).set_index('index')
  # load ROI data
  fp_roi = data_store.metadata(types.Metadata.FACE_ROI)
  df_roi = pd.read_csv(fp_roi).set_index('index')
  # slice if you want
  if opt_slice:
    df_roi = df_roi[opt_slice[0]:opt_slice[1]]
  # group by image index (speedup if multiple faces per image)
  df_img_groups = df_roi.groupby('record_index')
  log.debug('processing {:,} groups'.format(len(df_img_groups)))

  # store landmarks in list
  results = []

  # -------------------------------------------------------------------------
  # iterate groups with file/record index as key

  for record_index, df_img_group in tqdm(df_img_groups):
    
    # access file_record DataSeries
    file_record = df_record.iloc[record_index]

    # load image
    fp_im = data_store.face(file_record.subdir, file_record.fn, file_record.ext)
    im = cv.imread(fp_im)
    im_resized = im_utils.resize(im, width=opt_size[0], height=opt_size[1])
    dim = im_resized.shape[:2][::-1]
    
    # iterate ROIs in this image
    for roi_index, df_img in df_img_group.iterrows():
      
      # find landmarks
      x, y, w, h = df_img.x, df_img.y, df_img.w, df_img.h  # normalized values
      #dim = (file_record.width, file_record.height)  # original w,h
      bbox = BBox.from_xywh(x, y, w, h).to_dim(dim)
      points = landmark_detector.landmarks(im_resized, bbox)
      points_norm = landmark_detector.normalize(points, dim)
      points_str = landmark_detector.to_str(points_norm)

      # display if optioned
      if opt_display:
        dst = im_resized.copy()
        draw_utils.draw_landmarks2D(dst, points)
        draw_utils.draw_bbox(dst, bbox)
        cv.imshow('', dst)
        display_utils.handle_keyboard()

      # add to results for CSV
      results.append({'vec': points_str, 'roi_index':roi_index})


  # create DataFrame and save to CSV
  file_utils.mkdirs(fp_out)
  df = pd.DataFrame.from_dict(results)
  df.index.name = 'index'
  df.to_csv(fp_out)
  
  # save script
  file_utils.write_text(' '.join(sys.argv), '{}.sh'.format(fp_out))