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"""
Crop images to prepare for training
"""
import click
# from PIL import Image, ImageOps, ImageFilter, ImageDraw
from app.settings import types
from app.utils import click_utils
from app.settings import app_cfg as cfg
color_filters = {'color': 1, 'gray': 2, 'all': 3}
@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=(480, 480),
help='Output image size')
@click.option('-d', '--detector', 'opt_detector_type',
type=cfg.FaceDetectNetVar,
default=click_utils.get_default(types.FaceDetectNet.CVDNN),
help=click_utils.show_help(types.FaceDetectNet))
@click.option('-g', '--gpu', 'opt_gpu', default=0,
help='GPU index')
@click.option('--conf', 'opt_conf_thresh', default=0.85, type=click.FloatRange(0,1),
help='Confidence minimum threshold')
@click.option('-p', '--pyramids', 'opt_pyramids', default=0, type=click.IntRange(0,4),
help='Number pyramids to upscale for DLIB detectors')
@click.option('--slice', 'opt_slice', type=(int, int), default=(None, None),
help='Slice list of files')
@click.option('--display/--no-display', 'opt_display', is_flag=True, default=False,
help='Display detections to debug')
@click.option('-f', '--force', 'opt_force', is_flag=True,
help='Force overwrite file')
@click.option('--color', 'opt_color_filter',
type=click.Choice(color_filters.keys()), default='color',
help='Filter to keep color or grayscale images (color = keep color')
@click.option('--keep', 'opt_largest', type=click.Choice(['largest', 'all']), default='largest',
help='Only keep largest face')
@click.option('--zone', 'opt_zone', default=(0.0, 0.0), type=(float, float),
help='Face center must be located within zone region (0.5 = half width/height)')
@click.pass_context
def cli(ctx, opt_fp_in, opt_dir_media, opt_fp_out, opt_data_store, opt_dataset, opt_size, opt_detector_type,
opt_gpu, opt_conf_thresh, opt_pyramids, opt_slice, opt_display, opt_force, opt_color_filter,
opt_largest, opt_zone):
"""Converts frames with faces to CSV of ROIs"""
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.utils import logger_utils, file_utils, im_utils, display_utils, draw_utils
from app.processors import face_detector
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_ROI) 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
# set detector
if opt_detector_type == types.FaceDetectNet.CVDNN:
detector = face_detector.DetectorCVDNN()
elif opt_detector_type == types.FaceDetectNet.DLIB_CNN:
detector = face_detector.DetectorDLIBCNN(gpu=opt_gpu)
elif opt_detector_type == types.FaceDetectNet.DLIB_HOG:
detector = face_detector.DetectorDLIBHOG()
elif opt_detector_type == types.FaceDetectNet.MTCNN_TF:
detector = face_detector.DetectorMTCNN_TF(gpu=opt_gpu)
elif opt_detector_type == types.FaceDetectNet.HAAR:
log.error('{} not yet implemented'.format(opt_detector_type.name))
return
# get list of files to process
fp_record = data_store.metadata(types.Metadata.FILE_RECORD) if opt_fp_in is None else opt_fp_in
df_record = pd.read_csv(fp_record, dtype=cfg.FILE_RECORD_DTYPES).set_index('index')
if opt_slice:
df_record = df_record[opt_slice[0]:opt_slice[1]]
log.debug('processing {:,} files'.format(len(df_record)))
# filter out grayscale
color_filter = color_filters[opt_color_filter]
# set largest flag, to keep all or only largest
opt_largest = (opt_largest == 'largest')
data = []
skipped_files = []
processed_files = []
for df_record in tqdm(df_record.itertuples(), total=len(df_record)):
fp_im = data_store.face(str(df_record.subdir), str(df_record.fn), str(df_record.ext))
try:
im = cv.imread(fp_im)
im_resized = im_utils.resize(im, width=opt_size[0], height=opt_size[1])
except Exception as e:
log.debug(f'could not read: {fp_im}')
return
# filter out color or grayscale iamges
if color_filter != color_filters['all']:
try:
is_gray = im_utils.is_grayscale(im)
if is_gray and color_filter != color_filters['gray']:
log.debug('Skipping grayscale image: {}'.format(fp_im))
continue
except Exception as e:
log.error('Could not check grayscale: {}'.format(fp_im))
continue
try:
bboxes_norm = detector.detect(im_resized, pyramids=opt_pyramids, largest=opt_largest,
zone=opt_zone, conf_thresh=opt_conf_thresh)
except Exception as e:
log.error('could not detect: {}'.format(fp_im))
log.error('{}'.format(e))
continue
if len(bboxes_norm) == 0:
skipped_files.append(fp_im)
log.warn(f'no faces in: {fp_im}')
log.warn(f'skipped: {len(skipped_files)}. found:{len(processed_files)} files')
else:
processed_files.append(fp_im)
for bbox in bboxes_norm:
roi = {
'record_index': int(df_record.Index),
'x': bbox.x,
'y': bbox.y,
'w': bbox.w,
'h': bbox.h
}
data.append(roi)
# if display optined
if opt_display and len(bboxes_norm):
# draw each box
for bbox_norm in bboxes_norm:
dim = im_resized.shape[:2][::-1]
bbox_dim = bbox.to_dim(dim)
if dim[0] > 1000:
im_resized = im_utils.resize(im_resized, width=1000)
im_resized = draw_utils.draw_bbox(im_resized, bbox_norm)
# display and wait
cv.imshow('', im_resized)
display_utils.handle_keyboard()
# create DataFrame and save to CSV
file_utils.mkdirs(fp_out)
df = pd.DataFrame.from_dict(data)
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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