人脸搜索购买

import cv2
import dlib
import numpy as np

# 加载预训练的人脸检测器和特征点预测器
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")

# 假设我们有一个已知人脸的特征向量列表 known_face_encodings 和对应的名字 known_face_names
known_face_encodings = [...]
known_face_names = [...]

# 读取待搜索的人脸图像
image = cv2.imread('test_image.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 检测人脸
faces = detector(gray)

for face in faces:
    # 获取面部特征点
    landmarks = predictor(gray, face)
    # 计算人脸特征向量
    face_encoding = face_rec_model.compute_face_descriptor(image, landmarks)
    # 将特征向量转换为numpy数组
    face_encoding = np.array(face_encoding)
    
    # 计算与已知人脸的相似度
    matches = np.linalg.norm(known_face_encodings - face_encoding, axis=1)
    min_index = np.argmin(matches)
    
    if matches[min_index] < 0.6:  # 设置一个阈值
        name = known_face_names[min_index]
    else:
        name = "Unknown"

    # 在图像上标记人脸
    x, y, w, h = face.left(), face.top(), face.width(), face.height()
    cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
    cv2.putText(image, name, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)

# 显示结果
cv2.imshow('Face Search', image)
cv2.waitKey(0)
cv2.destroyAllWindows()