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"""
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))
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