python --face_recognition(人脸识别,检测,标题,特征提取)

dlib 安装方法 之前博文 https://blog.csdn.net/weixin_44634704/article/details/141332644

环境:

python==3.8
opencv-python==4.11.0.86
face_recognition==1.3.0
dlib==19.24.6

人脸检测

import cv2
import face_recognition

# 读取人脸图片
img = cv2.imread(r"C:\Users\123\Desktop\1.jpg")
face_List = face_recognition.face_locations(img) # 检测人脸，返回人脸坐标信息
print(face_List)

for x in face_List: # 画框
    cv2.rectangle(img, (x[3], x[0]), (x[1], x[2]), (0, 255, 0), 2)
cv2.imshow("a", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
# 输出: [(116, 306, 223, 199)]

人脸分割(切割)

import cv2
import face_recognition

# 读取人脸图片
img = cv2.imread(r"C:\Users\123\Desktop\1.jpg")
face_List = face_recognition.face_locations(img) # 检测人脸，返回人脸坐标信息
print(face_List)

for x in face_List: # 画框
    cv2.rectangle(img, (x[3], x[0]), (x[1], x[2]), (0, 255, 0), 2)
    qie_img = img[x[0]:x[2], x[3]:x[1]]
    
cv2.imshow("a", qie_img)
cv2.waitKey(0)
cv2.destroyAllWindows()

提取人脸特征向量

img = cv2.imread(r"C:\Users\123\Desktop\1.jpg")
# 提取人脸特征向量
face01 = face_recognition.face_encodings(img)[0]
print(face01)

人脸比对

import cv2
import face_recognition
import numpy as np

# 读取人脸图片
img = cv2.imread(r"C:\Users\123\Desktop\1.jpg")
# 提取人脸特征向量
face01 = face_recognition.face_encodings(img)[0]
# 读取人脸原图的图片
img2 = cv2.imread(r"C:\Users\123\Desktop\1.jpg")
face02 = face_recognition.face_encodings(img2)[0]
#
# 计算欧几里得距离
v = np.linalg.norm(face01 - face02)
if v < 0.8:
    print("是一个人")
else:
    print("不是一个人")

转为置信度

import cv2
import face_recognition
import numpy as np

def euclidean_distance_to_confidence(distance, max_distance):
    # 确保距离在合理范围内
    distance = min(distance, max_distance)
    # 计算置信度
    confidence = 1 - (distance / max_distance)
    return confidence

# 读取人脸图片
img = cv2.imread(r"C:\Users\123\Desktop\1.jpg")
# 提取人脸特征向量
face01 = face_recognition.face_encodings(img)[0]
# 读取人脸原图的图片
img2 = cv2.imread(r"C:\Users\123\Desktop\1.jpg")
face02 = face_recognition.face_encodings(img2)[0]
#
# 计算欧几里得距离
v = np.linalg.norm(face01 - face02)
w = euclidean_distance_to_confidence(v, 1) # 置信度最大阈值为1
print(w)