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"""
Reads in CSV of ROIs and extracts facial regions with padding
"""
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 CSV')
@click.option('-m', '--media', 'opt_dir_media', required=True,
help='Input image/video directory')
@click.option('-o', '--output', 'opt_dir_out', required=True,
help='Output directory for extracted ROI images')
@click.option('--slice', 'opt_slice', type=(int, int), default=(None, None),
help='Slice list of files')
@click.option('--padding', 'opt_padding', default=0.25,
help='Facial padding as percentage of face width')
@click.option('--ext', 'opt_ext_out', default='png', type=click.Choice(['jpg', 'png']),
help='Output image type')
@click.option('--min', 'opt_min', default=(60, 60),
help='Minimum original face size')
@click.pass_context
def cli(ctx, opt_fp_in, opt_dir_media, opt_dir_out, opt_slice,
opt_padding, opt_ext_out, opt_min):
"""Converts ROIs to images"""
import os
from os.path import join
from pathlib import Path
from glob import glob
from tqdm import tqdm
import numpy as np
from PIL import Image, ImageOps, ImageFilter, ImageDraw
import cv2 as cv
import pandas as pd
from app.utils import logger_utils, file_utils, im_utils
from app.models.bbox import BBox
# -------------------------------------------------
# process here
log = logger_utils.Logger.getLogger()
df_rois = pd.read_csv(opt_fp_in, dtype={'subdir': str, 'fn': str})
if opt_slice:
df_rois = df_rois[opt_slice[0]:opt_slice[1]]
log.info('Processing {:,} rows'.format(len(df_rois)))
file_utils.mkdirs(opt_dir_out)
df_rois_grouped = df_rois.groupby(['fn']) # group by fn/filename
groups = df_rois_grouped.groups
skipped = []
for group in tqdm(groups):
# get image
group_rows = df_rois_grouped.get_group(group)
row = group_rows.iloc[0]
fp_im = join(opt_dir_media, str(row['subdir']), '{fn}.{ext}'.format(**row)) # TODO change to ext
try:
im = Image.open(fp_im).convert('RGB')
im.verify()
except Exception as e:
log.warn('Could not open: {}'.format(fp_im))
log.error(e)
continue
for idx, roi in group_rows.iterrows():
# get bbox to im dimensions
xywh = [roi['x'], roi['y'], roi['w'] , roi['h']]
bbox = BBox.from_xywh(*xywh)
dim = im.size
bbox_dim = bbox.to_dim(dim)
# expand
opt_padding_px = int(opt_padding * bbox_dim.width)
bbox_dim_exp = bbox_dim.expand_dim(opt_padding_px, dim)
# crop
x1y2 = bbox_dim_exp.pt_tl + bbox_dim_exp.pt_br
im_crop = im.crop(box=x1y2)
# strip exif, create new image and paste data
im_crop_data = list(im_crop.getdata())
im_crop_no_exif = Image.new(im_crop.mode, im_crop.size)
im_crop_no_exif.putdata(im_crop_data)
# save
idx_zpad = file_utils.zpad(idx, zeros=3)
subdir = '' if roi['subdir'] == '.' else '{}_'.format(roi['subdir'])
subdir = subdir.replace('/', '_')
fp_im_out = join(opt_dir_out, '{}{}{}.{}'.format(subdir, roi['fn'], idx_zpad, opt_ext_out))
# threshold size and save
if im_crop_no_exif.size[0] < opt_min[0] or im_crop_no_exif.size[1] < opt_min[1]:
skipped.append(fp_im_out)
log.info('Face too small: {}, idx: {}'.format(fp_im, idx))
else:
im_crop_no_exif.save(fp_im_out)
log.info('Skipped {:,} images'.format(len(skipped)))
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