facenet + fiass 实现人脸识别

from facenet_pytorch import MTCNN, InceptionResnetV1
from PIL import Image, ImageDraw
import cv2
import time
import numpy as np
import torch
import faiss


class FaissDB(object):
    def __init__(self, d=512):
        """创建相似性搜索库"""
        self.index = faiss.IndexFlatL2(d)  # build the index

    def add(self, data):
        """添加向量"""
        # index.add(1000004, xb)  # add vectors to the index
        self.index.add(data)


    def search(self, target, k=1):
        """
        查询faiss向量数据库获取欧氏距离最小的特征向量
        Args:
            target: 目标向量
            k: 返回最相近的特征向量数量

        Returns:

        """
        # 从索引中得到前K近个向量， search方法返回值D（欧氏距离值）， I（索引值）
        D, I = self.index.search(target, k)  # sanity check
        # val = filter(lambda x: x < 0.6, D)
        return I


class Video(object):
    def __init__(self, mtcnn, resnet):
        self.mtcnn = mtcnn
        self.resnet = resnet

    def detection_face(self):
        before_detection_time = 0
        # cv2.resizeWindow("cap", (480, 480))
        capture = cv2.VideoCapture(0)  # 0为电脑内置摄像头

        while (True):
            my_faiss = FaissDB()
            # 特征向量的尺寸
            d = 512  # dimension
            # 生成10万条随机特征向量
            xb = torch.randn((100000, d), dtype=torch.float)
            my_faiss.add(xb)
            fcj = torch.tensor(np.load("/Users/wb-fcj414969/project/facenet-pytorch-master/test/img/fcj.npy"))
            my_faiss.add(fcj)

            ret, frame = capture.read()  # 摄像头读取,ret为是否成功打开摄像头,true,false。 frame为视频的每一帧图像
            cur_time = time.time()
            if cur_time - before_detection_time > 0.2:
                # 格式转变，BGRtoRGB
                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
                frame = cv2.flip(frame, 1)  # 摄像头是和人对立的，将图像左右调换回来正常显示。

                # 人脸检测，显示人脸框，显示特征点
                boxes, probs, points = self.mtcnn.detect(frame, landmarks=True)
                if boxes is not None:
                    max_boxe, amx_probs, max_point = self.mtcnn.select_boxes(boxes, probs, points, frame)
                    img = Image.fromarray(frame)  # ndarry类型转Image类型
                    draw = ImageDraw.Draw(img)
                    draw.rectangle((tuple(max_boxe[0][:2].tolist()), tuple(max_boxe[0][2:].tolist())), width=1)
                    for item in max_point[0]:
                        draw.point(tuple(item), fill=(255, 0, 0))

                    # 获取人脸张量信息, 并存储
                    img_cropped = self.mtcnn(frame)
                    # Calculate embedding (unsqueeze to add batch dimension)
                    img_embedding = torch.tensor(self.resnet(img_cropped).detach().numpy())

                    # 查询向量数据库
                    data = my_faiss.search(img_embedding)

                    if data:
                        # 此处通过检测到的人脸索引值获取人脸特征查询数据库获取人员信息，并显示在屏幕中
                        draw.text(tuple(max_boxe[0][:2].tolist()), "fjc")

                # # Image类型转ndarry类型后，显示处理后的图片
                frame = np.array(img)
                # RGBtoBGR满足opencv显示格式
                frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
            cv2.imshow("video", frame)

            # 判断是否点击了退出键
            c = cv2.waitKey(50)
            if c == 27:
                break
        cv2.destroyAllWindows()


if __name__ == "__main__":
    # 116.398663,39.919528
    # 116.408149,39.929599

    # a = [[(i, j) for j in range(39919528, 39929599)] for i in range(116398663, 116408149)]
    #
    # b = torch.tensor(a)
    # b = b.view([-1, 2])
    #
    # # 测试faiss速度
    # my_faiss = FaissDB(d=2)
    # my_faiss.add(b)
    # predict = torch.tensor([[116401754, 39925781]])
    # predict = predict.repeat(1000, 1)
    # time1 = time.time()
    # result = my_faiss.search(predict)
    # time2 = time.time()
    # print(time2-time1)

    image_size = 160
    mtcnn = MTCNN(image_size=160, margin=0, keep_all=True)
    mtcnn.eval()
    # Create an inception resnet (in eval mode):
    resnet = InceptionResnetV1(pretrained='vggface2')
    resnet.eval()

    video = Video(mtcnn, resnet)
    video.detection_face()