人脸性别变换新年活动

import cv2
import numpy as np
from tensorflow.keras.models import load_model

# 加载预训练模型
gender_model = load_model('path_to_gender_model.h5')

def transform_gender(image):
    # 预处理图像
    processed_image = preprocess_image(image)
    
    # 预测性别
    gender_prediction = gender_model.predict(processed_image)
    
    # 根据预测结果进行性别变换
    if gender_prediction[0][0] > 0.5:  # 假设0.5为阈值
        transformed_image = apply_male_features(image)
    else:
        transformed_image = apply_female_features(image)
    
    return transformed_image

def preprocess_image(image):
    # 图像预处理步骤（如缩放、归一化等）
    processed_image = cv2.resize(image, (224, 224))
    processed_image = processed_image / 255.0
    processed_image = np.expand_dims(processed_image, axis=0)
    return processed_image

def apply_male_features(image):
    # 应用男性特征的图像处理逻辑
    pass

def apply_female_features(image):
    # 应用女性特征的图像处理逻辑
    pass

# 示例使用
image = cv2.imread('path_to_image.jpg')
transformed_image = transform_gender(image)
cv2.imshow('Transformed Image', transformed_image)
cv2.waitKey(0)
cv2.destroyAllWindows()