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import cv2
import numpy as np

# 读取图像文件
img = cv2.imread('image.jpg')

# 转换为灰度图像
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 使用阈值进行二值化
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

# 查找轮廓并获取轮廓的属性
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

# 遍历所有轮廓，检查是否为面部轮廓
faces = []
for contour in contours:
area = cv2.contourArea(contour)
if area / 2000 > 0.5:  # 面部面积大于0.5%时认为是面部
x, y, w, h = cv2.boundingRect(contour)
aspect_ratio = float(w) / h
if aspect_ratio > 0.9 and aspect_ratio < 1.1:  # 判断是否为正面面部
face_descriptor = cv2.face.EigenFaceRecognizer_create()
face_descriptor.train(gray[y:y+h, x:x+w], np.array([]), 1)
face_descriptor.predict(gray[y:y+h, x:x+w])

# 将识别结果存储到 faces 列表中
faces.append((x, y, w, h, face_descriptor.predict(gray[y:y+h, x:x+w])))

# 绘制面部轮廓
for (x, y, w, h, face_score) in faces:
cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)
print("Face score: ", face_score)

# 显示图像
cv2.imshow("img", img)
cv2.waitKey(0)
cv2.destroyAllWindows()