图片人脸真伪鉴别 怎么搭建

import cv2
import numpy as np

def preprocess_image(image_path):
    img = cv2.imread(image_path)
    img = cv2.resize(img, (160, 160))  # 统一尺寸
    img = img / 255.0  # 归一化
    return np.expand_dims(img, axis=0)

from tensorflow.keras.applications import VGG16
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras.models import Model

base_model = VGG16(weights='imagenet', include_top=False, input_shape=(160, 160, 3))
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(1, activation='sigmoid')(x)
model = Model(inputs=base_model.input, outputs=predictions)

for layer in base_model.layers:
    layer.trainable = False

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(images, labels, test_size=0.2, random_state=42)
model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))

from flask import Flask, request, jsonify

app = Flask(__name__)

@app.route('/predict', methods=['POST'])
def predict():
    file = request.files['file']
    img = preprocess_image(file)
    prediction = model.predict(img)
    return jsonify({'result': 'real' if prediction[0][0] > 0.5 else 'fake'})

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000)