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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
@click.command()
@click.option('-i', '--input', 'opt_fp_in', default=None, required=True,
help='Image filepath')
@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('--display/--no-display', 'opt_display', is_flag=True, default=False,
help='Display detections to debug')
@click.pass_context
def cli(ctx, opt_fp_in, opt_gpu, opt_size, opt_display):
"""Demo generating face vector"""
import sys
import os
from os.path import join
from pathlib import Path
import time
import numpy as np
import cv2 as cv
import dlib # NB: keep a reference in main file if using dlib detector processors
from app.utils import logger_utils, file_utils, im_utils, display_utils, draw_utils
from app.utils import plot_utils
from app.processors import face_detector
from app.models.data_store import DataStore
# -------------------------------------------------
# init here
log = logger_utils.Logger.getLogger()
# ----------------------------------------------------------------------------
# load image
im = cv.imread(opt_fp_in)
im_resized = im_utils.resize(im, width=opt_size[0], height=opt_size[1])
# ----------------------------------------------------------------------------
# detect face
face_detector = face_detector.DetectorDLIBCNN(gpu=opt_gpu) # -1 for CPU
bboxes = face_detector.detect(im_resized, largest=True)
bbox = bboxes[0]
dim = im_resized.shape[:2][::-1]
bbox_dim = bbox.to_dim(dim)
if not bbox:
log.error('no face detected')
return
# ----------------------------------------------------------------------------
# generate face vectors, only to test if feature extraction works
from app.processors import face_recognition
facerec = face_recognition.RecognitionDLIB()
vec = facerec.vec(im_resized, bbox_dim)
vec_flat = facerec.flatten(vec)
log.info(f'generated vector. showing vec[0:10]:')
log.info(f'\n{vec_flat}')
if opt_display:
draw_utils.draw_bbox(im_resized, bbox_dim)
cv.imshow('Original', im_resized)
display_utils.handle_keyboard()
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