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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, required=True,
help='Image filepath')
@click.option('-o', '--output', 'opt_fp_out', default=None,
help='GIF output path')
@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('-f', '--force', 'opt_force', is_flag=True,
help='Force overwrite file')
@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_fp_out, opt_gpu, opt_size, opt_force, opt_display):
"""Face detector demo"""
import sys
import os
from os.path import join
from pathlib import Path
import time
from tqdm import tqdm
import numpy as np
import pandas as pd
import cv2 as cv
import dlib
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, face_beauty
from app.models.data_store import DataStore
log = logger_utils.Logger.getLogger()
# -------------------------------------------------
# load image
im = cv.imread(opt_fp_in)
# im = cv.cvtColor(im, cv.COLOR_BGR2RGB)
if im.shape[0] > 1280:
new_shape = (1280, im.shape[1] * 1280 / im.shape[0])
elif im.shape[1] > 1280:
new_shape = (im.shape[0] * 1280 / im.shape[1], 1280)
elif im.shape[0] < 640 or im.shape[1] < 640:
new_shape = (im.shape[0] * 2, im.shape[1] * 2)
else:
new_shape = im.shape[0:2]
im_resized = cv.resize(im, (int(new_shape[1]), int(new_shape[0])))
#im_resized = im_utils.resize(im, width=opt_size[0], height=opt_size[1])
# ----------------------------------------------------------------------------
# detect face
face_detector = face_detector.DetectorDLIBCNN() # -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
else:
log.info(f'face detected: {bbox_dim.to_xyxy()}')
# ----------------------------------------------------------------------------
# beauty
beauty_predictor = face_beauty.FaceBeauty()
beauty_score = beauty_predictor.beauty(im_resized, bbox_dim)
# ----------------------------------------------------------------------------
# output
log.info(f'Face coords: {bbox_dim} face')
log.info(f'beauty score: {(100*beauty_score):.2f}')
# ----------------------------------------------------------------------------
# draw
# draw 2d landmarks
im_beauty = im_resized.copy()
draw_utils.draw_bbox(im_beauty, bbox_dim)
txt = f'Beauty score: {(100*beauty_score):.2f}'
draw_utils.draw_text(im_beauty, bbox_dim.pt_tl, txt)
# ----------------------------------------------------------------------------
# save
if opt_fp_out:
# save pose only
cv.imwrite(opt_fp_out, im_beauty)
# ----------------------------------------------------------------------------
# display
if opt_display:
# show all images here
cv.imshow('Beauty', im_beauty)
display_utils.handle_keyboard()
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