"""
Crop images to prepare for training
"""

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', required=True,
  help='Input directory')
@click.option('-r', '--records', 'opt_fp_records', required=True,
  help='Input directory')
@click.option('-m', '--media', 'opt_fp_media', required=True,
  help='Image 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('-g', '--gpu', 'opt_gpu', default=0,
  help='GPU index')
@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('-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.pass_context
def cli(ctx, opt_fp_in, opt_fp_records, opt_fp_out, opt_fp_media, opt_size, opt_gpu,
  opt_slice, opt_jitters, opt_padding, opt_force):
  """Converts frames with faces to CSV of rows"""
  
  import sys
  import os
  from os.path import join
  from pathlib import Path
  
  from tqdm import tqdm
  import numpy as np
  import dlib  # must keep a local reference for dlib
  import cv2 as cv
  import dlib
  import pandas as pd

  from app.utils import logger_utils, file_utils, im_utils
  from app.models.bbox import BBox
  from app.processors import face_recognition

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

  log = logger_utils.Logger.getLogger()

  if not opt_force and Path(opt_fp_out).exists():
    log.error('File exists. Use "-f / --force" to overwite')
    return
  
  # init dlib FR
  facerec = face_recognition.RecognitionDLIB()

  # load data
  df_rois = pd.read_csv(opt_fp_in)
  df_records = pd.read_csv(opt_fp_records)

  if opt_slice:
    df_rois = df_rois[opt_slice[0]:opt_slice[1]]
  log.info('Processing {:,} rows'.format(len(df_rois)))
  nrows = len(df_rois)

  # face vecs
  vecs = []

  for roi_idx, row in tqdm(df_rois.iterrows(), total=nrows):
    # make image path
    record_id = int(row['id'])
    df = df_records.iloc[record_id]
    fp_im = join(opt_fp_media, df['subdir'], '{}.{}'.format(df['fn'], df['ext'])) 
    # load image
    im = cv.imread(fp_im)
    # make bbox
    xywh = [row['x'], row['y'], row['w'] , row['h']]
    bbox = BBox.from_xywh(*xywh)
    # scale to actual image size
    dim = (row['image_width'], row['image_height'])
    bbox_dim = bbox.to_dim(dim)
    # compute vec
    vec = facerec.vec(im, bbox_dim, jitters=opt_jitters, padding=opt_padding)
    vec_str = ','.join([repr(x) for x in vec])
    vecs.append( {'id': row['id'], 'vec': vec_str})
  
  # save data
  file_utils.mkdirs(opt_fp_out)
  df_vecs = pd.DataFrame.from_dict(vecs)
  df_vecs.to_csv(opt_fp_out, index=False)
  log.info('saved {:,} lines to {}'.format(len(df_vecs), opt_fp_out))