python语言基于utkface数据库的年龄和性别识别的facenet模型代码
时间: 2024-02-13 21:07:27 浏览: 26
以下是基于utkface数据库的年龄和性别识别的facenet模型代码,使用的编程语言为Python:
```
# 导入所需的库
import os
import cv2
import numpy as np
import tensorflow as tf
from keras.models import load_model
from keras.preprocessing.image import load_img, img_to_array
from keras.models import Model
# 加载facenet模型
model = load_model('facenet_keras.h5')
# 加载年龄和性别识别模型
age_gender_model = load_model('utk_age_gender.h5')
# 定义函数,用于进行人脸检测和裁剪
def face_detector(img):
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
if faces is None:
return None
(x, y, w, h) = faces[0]
face = img[y:y+w, x:x+h]
return face
# 定义函数,用于将图像转换为facenet模型所需的格式
def preprocess_image(image_path):
img = load_img(image_path, target_size=(160, 160))
img = img_to_array(img)
img = np.expand_dims(img, axis=0)
img = (img - 127.5) / 128.0
return img
# 定义函数,用于获取图像的年龄和性别
def get_age_gender(image_path):
img = cv2.imread(image_path)
face = face_detector(img)
if face is None:
return None
img = preprocess_image(face)
# 获取facenet模型的输出
embedding = model.predict(img)[0]
# 获取年龄和性别识别模型的输出
age_gender = age_gender_model.predict(np.array([embedding]))
age = int(age_gender[0][0])
gender = "Male" if age_gender[1][0] < 0.5 else "Female"
return age, gender
```
注:这段代码需要使用已经训练好的facenet模型和年龄和性别识别模型。