insightface 人脸检测与识别

参考：https://huaweicloud.csdn.net/638088d7dacf622b8df89c0c.html

insightface模型下载可能需要连接外网，模型自动下载保存再models\buffalo_l下，人脸注册自动保存再face_db目录下

1、具体人脸录入

python face_label.py --picture 刘亦菲.jpg --register Ture
python face_label.py --picture 杨幂.jpg --register Ture
（运行后会把照片保存一份到face_db目录下）

2、具体检测识别人脸

python face_label.py --picture 11.jpg --recognition Ture （11.jpg是一种杨幂照片）

运行后结果：

如果检测的是face_db目录不存在的图片，这里找的一张黄晓明照片：
python face_label.py
–picture 22.jpg --recognition Ture

运行结果，结果未知默认显示unknown：

3、 一对多图像库检索加速

embedding是实时检测的单个人头像embedding，可以

## 单个人头像embedding
embedding = np.array(face.embedding).reshape((1, -1))
embedding = preprocessing.normalize(embedding)

## 图库里所以图片faces_embeddings 
for file in files:
    input_image = cv2.imdecode(np.fromfile(os.path.join(root, file), dtype=np.uint8), 1)
    user_name = file.split(".")[0]
    face = self.model.get(input_image)[0]
    embedding = np.array(face.embedding).reshape((1, -1))
    embedding = preprocessing.normalize(embedding)
    faces_embedding.append({
        "user_name": user_name,
        "feature": embedding
    })
faces_embeddings = []
for com_face in faces_embedding:
    faces_embeddings.append(com_face["feature"])

计算相似度:
print(“cosine”, (faces_embeddings @ embedding.T))

代码

face_label.py 代码

import os

import cv2
import insightface
import numpy as np
from sklearn import preprocessing
import argparse



class FaceRecognition:
    def __init__(self, gpu_id=0, face_db='face_db', threshold=1.24, det_thresh=0.50, det_size=(640, 640)):
        """
        人脸识别工具类
        :param gpu_id: 正数为GPU的ID，负数为使用CPU
        :param face_db: 人脸库文件夹
        :param threshold: 人脸识别阈值
        :param det_thresh: 检测阈值
        :param det_size: 检测模型图片大小
        """
        self.gpu_id = gpu_id
        self.face_db = face_db
        self.threshold = threshold
        self.det_thresh = det_thresh
        self.det_size = det_size

        # 加载人脸识别模型，当allowed_modules=['detection', 'recognition']时，只单纯检测和识别
        self.model = insightface.app.FaceAnalysis(root='./',
                                                #   allowed_modules=None,
                                                  allowed_modules=['detection', 'recognition'],
                                                  providers=['CUDAExecutionProvider'])
        self.model.prepare(ctx_id=self.gpu_id, det_thresh=self.det_thresh, det_size=self.det_size)
        # 人脸库的人脸特征
        self.faces_embedding = list()
        # 加载人脸库中的人脸
        self.load_faces(self.face_db)

    # 加载人脸库中的人脸
    def load_faces(self, face_db_path):
        if not os.path.exists(face_db_path):
            os.makedirs(face_db_path)
        for root, dirs, files in os.walk(face_db_path):
            for file in files:
                input_image = cv2.imdecode(np.fromfile(os.path.join(root, file), dtype=np.uint8), 1)
                user_name = file.split(".")[0]
                face = self.model.get(input_image)[0]
                embedding = np.array(face.embedding).reshape((1, -1))
                embedding = preprocessing.normalize(embedding)
                self.faces_embedding.append({
                    "user_name": user_name,
                    "feature": embedding
                })

    # 人脸识别
    def recognition(self, image):
        faces = self.model.get(image)
        results = list()
        for face in faces:
            # 开始人脸识别
            embedding = np.array(face.embedding).reshape((1, -1))
            embedding = preprocessing.normalize(embedding)
            user_name = "unknown"
            for com_face in self.faces_embedding:
                r = self.feature_compare(embedding, com_face["feature"], self.threshold)
                if r:
                    user_name = com_face["user_name"]
            results.append(user_name)
        return results

    @staticmethod
    def feature_compare(feature1, feature2, threshold):
        diff = np.subtract(feature1, feature2)
        dist = np.sum(np.square(diff), 1)
        if dist < threshold:
            return True
        else:
            return False

    def register(self, image, user_name):
        faces = self.model.get(image)
        if len(faces) != 1:
            return '图片检测不到人脸'
        # 判断人脸是否存在
        embedding = np.array(faces[0].embedding).reshape((1, -1))
        embedding = preprocessing.normalize(embedding)
        is_exits = False
        for com_face in self.faces_embedding:
            r = self.feature_compare(embedding, com_face["feature"], self.threshold)
            if r:
                is_exits = True
        if is_exits:
            return '该用户已存在'
        # 符合注册条件保存图片，同时把特征添加到人脸特征库中
        cv2.imencode('.png', image)[1].tofile(os.path.join(self.face_db, '%s.png' % user_name))
        self.faces_embedding.append({
            "user_name": user_name,
            "feature": embedding
        })
        return "success"

    # 检测人脸
    def detect(self, image):
        faces = self.model.get(image)
        results = list()
        for face in faces:
            result = dict()
            # 获取人脸属性
            result["bbox"] = np.array(face.bbox).astype(np.int32).tolist()
            # result["kps"] = np.array(face.kps).astype(np.int32).tolist()
            # print(np.array(face.landmark_3d_68))
            # result["landmark_3d_68"] = np.array(face.landmark_3d_68).astype(np.int32).tolist()
            # result["landmark_2d_106"] = np.array(face.landmark_2d_106).astype(np.int32).tolist()
            # result["pose"] = np.array(face.pose).astype(np.int32).tolist()
            # result["age"] = face.age
            # gender = '男'
            # if face.gender == 0:
            #     gender = '女'
            # result["gender"] = gender
            # 开始人脸识别
            embedding = np.array(face.embedding).reshape((1, -1))
            embedding = preprocessing.normalize(embedding)
            result["embedding"] = embedding
            results.append(result)
        return results


if __name__ == '__main__':

    parser = argparse.ArgumentParser()
    parser.add_argument('--picture', type=str,help="picture adress")
    # parser.add_argument('--name', type=str,help="user name")
    parser.add_argument('--register', default=False,help="people register")
    parser.add_argument('--recognition', default=False,help="people recognition")
    args = parser.parse_args()
    # print(args.picture,args.picture.split(".")[0])
    if args.register:
        img = cv2.imdecode(np.fromfile(args.picture, dtype=np.uint8), -1)
        face_recognitio = FaceRecognition()
        # 人脸注册 录入保存
        result = face_recognitio.register(img, user_name=args.picture.split(".")[0])
        print(result) 

    if args.recognition:
        img = cv2.imdecode(np.fromfile(args.picture, dtype=np.uint8), -1)
        face_recognitio = FaceRecognition()
        # 人脸识别
        results = face_recognitio.recognition(img)
        for result in results:
            print("识别结果：{}".format(result))

        results = face_recognitio.detect(img)
        for result in results:
            print('人脸框坐标：{}'.format(result["bbox"]))
            # print('人脸五个关键点：{}'.format(result["kps"]))
            # print('人脸3D关键点:{}'.format(result["landmark_3d_68"]))
            # print('人脸2D关键点:{}'.format(result["landmark_2d_106"]))
            # print('人脸姿态：{}'.format(result["pose"]))
            # print('年龄：{}'.format(result["age"]))
            # print('性别：{}'.format(result["gender"]))