"""
NB: This only works with the DLIB 68-point landmarks.

Converts ROIs to pose: yaw, roll, pitch
pitch: looking down or up in yes gesture
roll: tilting head towards shoulder
yaw: twisting head left to right in no gesture

"""

"""
TODO
- check compatibility with MTCNN 68 point detector
- improve accuracy by using MTCNN 5-point
- refer to https://github.com/jerryhouuu/Face-Yaw-Roll-Pitch-from-Pose-Estimation-using-OpenCV/
"""

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('--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_size, 
  opt_slice, opt_force, opt_display):
  """Converts ROIs to pose: roll, yaw, pitch"""
  
  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.utils import logger_utils, file_utils, im_utils, display_utils, draw_utils
  from app.processors.face_landmarks import Dlib2D_68
  from app.processors.face_pose import FacePoseDLIB
  from app.models.data_store import DataStore

  # -------------------------------------------------
  # 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_POSE) 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
  face_pose = FacePoseDLIB()
  face_landmarks = Dlib2D_68()

  # -------------------------------------------------
  # load 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 poses and convert to DataFrame
  results = []

  # -------------------------------------------------
  # iterate groups with file/record index as key
  for record_index, df_img_group in tqdm(df_img_groups):

    # access the file_record
    file_record = df_record.iloc[record_index]  # pands.DataSeries
    
    # 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])

    # iterate image group dataframe with roi index as key
    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 = (file_record.width, file_record.height)
      dim = im_resized.shape[:2][::-1]
      bbox = BBox.from_xywh(x, y, w, h).to_dim(dim)

      # get pose
      landmarks = face_landmarks.landmarks(im_resized, bbox)
      pose_data = face_pose.pose(landmarks, dim)
      #pose_degrees = pose_data['degrees']  # only keep the degrees data
      #pose_degrees['points_nose'] = pose_data
      
      # draw landmarks if optioned
      if opt_display:
        draw_utils.draw_pose(im_resized, pose_data['point_nose'], pose_data['points'])
        draw_utils.draw_degrees(im_resized, pose_data)
        cv.imshow('', im_resized)
        display_utils.handle_keyboard()

      # add image index and append to result CSV data
      pose_data['roi_index'] = roi_index
      for k, v in pose_data['points'].items():
        pose_data[f'point_{k}_x'] = v[0][0] / dim[0]
        pose_data[f'point_{k}_y'] = v[0][1] / dim[1]

      # rearrange data structure for DataFrame
      pose_data.pop('points')
      pose_data['point_nose_x'] = pose_data['point_nose'][0] / dim[0]
      pose_data['point_nose_y'] = pose_data['point_nose'][1] / dim[1]
      pose_data.pop('point_nose')
      results.append(pose_data)

  # 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))