"""
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('-i', '--input', 'opt_fp_files', required=True,
  help='Input ROI CSV')
@click.option('-r', '--rois', 'opt_fp_rois', required=True,
  help='Input ROI CSV')
@click.option('-m', '--media', 'opt_dir_media', required=True,
  help='Input media directory')
@click.option('-o', '--output', 'opt_fp_out', required=True,
  help='Output CSV')
@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_files, opt_fp_rois, opt_dir_media, opt_fp_out, 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.utils import logger_utils, file_utils, im_utils
  from app.processors import face_recognition


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

  log = logger_utils.Logger.getLogger()

  # init face processors
  facerec = face_recognition.RecognitionDLIB()

  # load data
  df_file_meta = pd.read_csv(opt_fp_files)
  df_rois = pd.read_csv(opt_fp_rois)

  if not opt_force and Path(opt_fp_out).exists():
    log.error('File exists. Use "-f / --force" to overwite')
    return
  
  if opt_slice:
    df_rois = df_rois[opt_slice[0]:opt_slice[1]]
  
  # -------------------------------------------------
  # process here

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

  vecs = []

  for image_index, df_img_group in tqdm(df_img_groups):
    # make fp
    roi_index = df_img_group.index.values[0]
    file_meta = df_file_meta.iloc[image_index]  # locate image meta
    fp_im = join(opt_dir_media, file_meta.subdir, '{}.{}'.format(file_meta.fn, file_meta.ext))
    im = cv.imread(fp_im)
    # get bbox
    x = df_img_group.x.values[0]
    y = df_img_group.y.values[0]
    w = df_img_group.w.values[0]
    h = df_img_group.h.values[0]
    imw = df_img_group.image_width.values[0]
    imh = df_img_group.image_height.values[0]
    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, 'image_index': image_index, 'vec': vec_str})


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