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"""
"""
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=cfg.DEFAULT_SIZE_FACE_DETECT,
help='Processing size for detection')
@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):
"""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_age_gender
from app.models.data_store import DataStore
from app.models.bbox import BBox
# -------------------------------------------------------------------------
# init here
log = logger_utils.Logger.getLogger()
# init face processors
age_estimator_apnt = face_age_gender.FaceAgeApparent()
age_estimator_real = face_age_gender.FaceAgeReal()
gender_estimator = face_age_gender.FaceGender()
# init filepaths
data_store = DataStore(opt_data_store, opt_dataset)
# set file output path
metadata_type = types.Metadata.FACE_ATTRIBUTES
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
# -------------------------------------------------------------------------
# load filepath data
fp_record = data_store.metadata(types.Metadata.FILE_RECORD)
df_record = pd.read_csv(fp_record, dtype=cfg.FILE_RECORD_DTYPES).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
bbox_norm = BBox.from_xywh(df_img.x, df_img.y, df_img.w, df_img.h)
bbox_dim = bbox_norm.to_dim(dim)
age_apnt = age_estimator_apnt.predict(im_resized, bbox_norm)
age_real = age_estimator_real.predict(im_resized, bbox_norm)
gender = gender_estimator.predict(im_resized, bbox_norm)
attr_obj = {
'age_real':float(f'{age_real:.2f}'),
'age_apparent': float(f'{age_apnt:.2f}'),
'm': float(f'{gender["m"]:.4f}'),
'f': float(f'{gender["f"]:.4f}'),
'roi_index': roi_index
}
results.append(attr_obj)
# 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))
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