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import sys
import os
from os.path import join
from pathlib import Path
import math
import cv2 as cv
import numpy as np
import imutils
import pickle
os.environ['CUDA_VISIBLE_DEVICES'] = ''
import keras
from keras.layers import Conv2D, Input, MaxPool2D,Flatten, Dense, Permute, GlobalAveragePooling2D
from keras.models import Model
from keras.optimizers import adam
import os.path
from keras.models import Sequential
from keras.applications.resnet50 import ResNet50
#from keras.applications.resnet50 import Dense
from keras.layers import Dense
from keras.optimizers import Adam
from keras.layers import Dropout
from app.utils import im_utils, logger_utils
from app.models.bbox import BBox
from app.settings import app_cfg as cfg
from app.settings import types
class FaceBeauty:
# Estimates beauty using CNN
def __init__(self, gpu=-1):
# don't really need GPU, CPU is quick enough
self.log = logger_utils.Logger.getLogger()
resnet = ResNet50(include_top=False, pooling='avg')
self.model = Sequential()
self.model.add(resnet)
self.model.add(Dense(5, activation='softmax'))
self.model.layers[0].trainable = False
fp_model = join(cfg.DIR_MODELS_KERAS, 'model-ldl-resnet.h5')
self.model.load_weights(fp_model)
def beauty(self, im, bbox_dim):
'''Predicts facial "beauty" score based on SCUT-FBP attractiveness labels
:param im: (numpy.ndarray) BGR image
:param bbox_dim: (BBox) dimensioned BBox
:returns (float) 0.0-1.0 with 1 being most attractive
'''
face = bbox_dim.to_xyxy()
self.log.debug(f'face: {face}')
cropped_im = im[face[1]:face[3], face[0]:face[2]]
im_resized = cv.resize(cropped_im, (224, 224)) # force size
im_norm = np.array([(im_resized - 127.5) / 127.5]) # subtract mean
# forward pass
pred = self.model.predict(im_norm)
# combines score to make final estimate?
ldList = pred[0]
score = 1 * ldList[0] + 2 * ldList[1] + 3 * ldList[2] + 4 * ldList[3] + 5 * ldList[4]
return score/5.0
def score_mapping(self, score):
'''(deprecated)
'''
# if score <= 1.9:
# score_mapped = ((4 - 2.5) / (1.9 - 1.0)) * (score-1.0) + 2.5
# elif score <= 2.8:
# score_mapped = ((5.5 - 4) / (2.8 - 1.9)) * (score-1.9) + 4
# elif score <= 3.4:
# score_mapped = ((6.5 - 5.5) / (3.4 - 2.8)) * (score-2.8) + 5.5
# elif score <= 4:
# score_mapped = ((8 - 6.5) / (4 - 3.4)) * (score-3.4) + 6.5
# elif score < 5:
# score_mapped = ((9 - 8) / (5 - 4)) * (score-4) + 8
# return score_mapped
return False
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