Python中提取人脸特征的三种方法详解

import dlib,numpy
import cv2
# 人脸关键点检测器
predictor_path = "shape_predictor_68_face_landmarks.dat"
# 人脸识别模型、提取特征值
face_rec_model_path = "dlib_face_recognition_resnet_model_v1.dat"

# 加载模型
detector = dlib.get_frontal_face_detector() #人脸检测
sp = dlib.shape_predictor(predictor_path) #关键点检测
facerec = dlib.face_recognition_model_v1(face_rec_model_path)# 编码

image_path='train_images/11.jpg'
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# 人脸检测
dets = detector(image, 1)
if len(dets)==1:
    print('检测到人脸')
shape = sp(image, dets[0])# 关键点
# 提取特征
face_descriptor = facerec.compute_face_descriptor(image, shape)#获取到128位的编码
v = numpy.array(face_descriptor)
print(v)

import dlib,numpy
import cv2

# 人脸关键点检测器
predictor_path = "shape_predictor_68_face_landmarks.dat"
# 人脸识别模型、提取特征值
face_rec_model_path = "dlib_face_recognition_resnet_model_v1.dat"
prototxt_path = 'deploy.proto.txt'
model_path = 'res10_300x300_ssd_iter_140000_fp16.caffemodel'

net = cv2.dnn.readNetFromCaffe(prototxt_path, model_path)
sp = dlib.shape_predictor(predictor_path) #关键点检测
facerec = dlib.face_recognition_model_v1(face_rec_model_path)# 编码

image_path='train_images/11.jpg'
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
(h, w) = image.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0,
                             (300, 300), (104.0, 177.0, 123.0))
net.setInput(blob)
detections = net.forward()
startX, startY, endX, endY = 0, 0, 0, 0
for i in range(0, detections.shape[2]):
    # extract the confidence (i.e., probability) associated with the
    # prediction
    confidence = detections[0, 0, i, 2]
    # filter out weak detections by ensuring the `confidence` is
    # greater than the minimum confidence
    if confidence > 0.5:
        # compute the (x, y)-coordinates of the bounding box for the
        # object
        box = detections[0, 0, i, 3:7] * numpy.array([w, h, w, h])
        (startX, startY, endX, endY) = box.astype("int")
        break
rect = dlib.rectangle(startX, startY, endX, endY)

shape = sp(image, rect)
print(shape)
# 提取特征
face_descriptor = facerec.compute_face_descriptor(image, shape)#获取到128位的编码
v = numpy.array(face_descriptor)
print(v)

pip install insightface
pip install onnxruntime-gpu==1.9.0 

import insightface
import cv2

model = insightface.app.FaceAnalysis()
model.prepare(ctx_id=0, det_thresh=0.45)
face_img = cv2.imread('train_images/11.jpg')
res = model.get(face_img)
print('embedding: ', res[0].embedding)