AI证件照生成中的表情控制方法

import cv2
import dlib

# 加载预训练的人脸检测器和表情识别模型
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
face_rec_model = dlib.face_recognition_model_v1("dlib_face_recognition_resnet_model_v1.dat")

# 读取图像
image = cv2.imread("user_image.jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 检测人脸
faces = detector(gray)

for face in faces:
    # 检测面部 landmark
    landmarks = predictor(gray, face)

    # 识别表情
    expression = recognize_expression(landmarks)

    # 根据识别结果调整表情
    if expression == "mouth_open":
        adjust_mouth(image, landmarks)

    # 保存调整后的图像
    cv2.imwrite("adjusted_image.jpg", image)

from PyQt5.QtWidgets import QApplication, QWidget, QSlider, QVBoxLayout, QLabel
from PyQt5.QtGui import QPixmap

class ExpressionControlApp(QWidget):
    def __init__(self):
        super().__init__()
        self.initUI()

    def initUI(self):
        self.setWindowTitle('表情控制')

        self.label = QLabel(self)
        self.pixmap = QPixmap('default_image.jpg')
        self.label.setPixmap(self.pixmap)

        self.slider = QSlider()
        self.slider.setOrientation(Qt.Horizontal)
        self.slider.setMinimum(0)
        self.slider.setMaximum(100)
        self.slider.setValue(50)
        self.slider.valueChanged.connect(self.updateExpression)

        layout = QVBoxLayout()
        layout.addWidget(self.label)
        layout.addWidget(self.slider)

        self.setLayout(layout)

    def updateExpression(self, value):
        # 根据滑块值调整表情
        adjusted_image = adjust_expression('default_image.jpg', value)
        self.pixmap = QPixmap(adjusted_image)
        self.label.setPixmap(self.pixmap)

if __name__ == '__main__':
    app = QApplication([])
    ex = ExpressionControlApp()
    ex.show()
    app.exec_()

import tensorflow as tf
import numpy as np

# 加载表情检测模型
expression_detector = tf.keras.models.load_model('expression_detector_model.h5')

# 加载表情调整模型
expression_adjuster = tf.keras.models.load_model('expression_adjuster_model.h5')

def generate_certificate_photo(image):
    # 检测表情
    detected_expression = expression_detector.predict(image)

    # 根据检测结果调整表情
    adjusted_image = expression_adjuster.predict([image, detected_expression])

    return adjusted_image