windows+python+dlib+人脸识别-优快云博客

本文链接：https://blog.youkuaiyun.com/qq114480/article/details/116754409

1.安装基础环境

安装python
python-3.6.0-amd64.exe
安装Anaconda3
Anaconda3-2020.11-Windows-x86_64.exe
安装pycharm
pycharm-community-2020.3.5.exe
安装CMake，安装完后将CMake的路径配置到环境变量Path路径中
cmake-3.17.3-win64-x64.exe
安装cuda 10.2
cuda_10.2.89_441.22_win10
cudnn-10.2-windows10-x64-v7.6.5.32

2.dlib下载

下载地址：http://dlib.net/files/
dlib-19.17.zip
dlib_face_recognition_resnet_model_v1.dat.bz2
shape_predictor_68_face_landmarks.dat.bz2

3.安装依赖包

在这里插入图片描述

conda create -n dlib python=3.6
conda activate dlib

pip install python-skimage
pip install opencv-python
pip install python-skimage
pip install scikit-image

编译dlib库
cd到D:\Projects\dlib\dlib-19.17
在这里插入图片描述
执行python setup.py install

备注：
如果报出floor相关的错误，解决如下：
在这里插入图片描述

将所有的floor替换为floorf
在这里插入图片描述

4.执行代码

Pycharm打开dlib代码
在这里插入图片描述

模型数据配置：

关键点检测：

import cv2
import dlib
from skimage import io

# 使用特征提取器get_frontal_face_detector
detector = dlib.get_frontal_face_detector()
# dlib的68点模型，使用作者训练好的特征预测器
predictor = dlib.shape_predictor("D:\\Projects\\dlib\\dlib-19.17\\data\\shape_predictor_68_face_landmarks.dat")
# 图片所在路径
img = io.imread("D:\\Projects\\dlib\\dlib-19.17\\data\\test.jpg")
cv2.imwrite("D:\\1.png", img)
# 生成dlib的图像窗口
win = dlib.image_window()
win.clear_overlay()
win.set_image(img)

# 特征提取器的实例化
dets = detector(img, 1)
print("人脸数：", len(dets))

for k, d in enumerate(dets):
    print("第", k + 1, "个人脸d的坐标：",
          "left:", d.left(),
          "right:", d.right(),
          "top:", d.top(),
          "bottom:", d.bottom())

    width = d.right() - d.left()
    heigth = d.bottom() - d.top()

    print('人脸面积为：', (width * heigth))

    # 利用预测器预测
    shape = predictor(img, d)
    # 标出68个点的位置
    for i in range(68):
        cv2.circle(img, (shape.part(i).x, shape.part(i).y), 4, (0, 255, 0), -1, 8)
        cv2.putText(img, str(i), (shape.part(i).x, shape.part(i).y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255))
    # 显示一下处理的图片，然后销毁窗口
    cv2.imshow('face', img)
    cv2.imwrite("D:\\1.png", img)
    cv2.waitKey(0)

在这里插入图片描述
人脸识别：

# -*- coding: UTF-8 -*-
import sys, os, dlib, glob, numpy
from skimage import io

# if len(sys.argv) != 5:
#     print('请检查参数是否正确')
#     exit()
# 1.人脸关键点检测器
predictor_path = "D:\\Projects\\dlib\\dlib-19.17\\data\\shape_predictor_68_face_landmarks.dat"
# 2.人脸识别模型
face_rec_model_path = "D:\\Projects\\dlib\\dlib-19.17\\data\\dlib_face_recognition_resnet_model_v1.dat"
# 3.候选人脸文件夹
faces_folder_path = "D:\\Projects\\dlib\\dlib-19.17\\data\\face"
# 4.需识别的人脸
img_path = "D:\\Projects\\dlib\\dlib-19.17\\data\\1.jpg"

# 1.加载正脸检测器
detector = dlib.get_frontal_face_detector()
# 2.加载人脸关键点检测器
sp = dlib.shape_predictor(predictor_path)
# 3. 加载人脸识别模型
facerec = dlib.face_recognition_model_v1(face_rec_model_path)

# win = dlib.image_window()
# 候选人脸描述子list
descriptors = []
# 对文件夹下的每一个人脸进行:
# 1.人脸检测
# 2.关键点检测
# 3.描述子提取

for f in glob.glob(os.path.join(faces_folder_path, "*.jpg")):
    print("Processing file: {}".format(f))
    img = io.imread(f)
    # win.clear_overlay()
    # win.set_image(img)

    # 1.人脸检测
    dets = detector(img, 1)
    print("Number of faces detected: {}".format(len(dets)))
    for k, d in enumerate(dets):
        # 2.关键点检测
        shape = sp(img, d)
        # 画出人脸区域和和关键点
        # win.clear_overlay()
        # win.add_overlay(d)
        # win.add_overlay(shape)
        # 3.描述子提取，128D向量
        face_descriptor = facerec.compute_face_descriptor(img, shape)
        # 转换为numpy array
        v = numpy.array(face_descriptor)
        descriptors.append(v)

# 对需识别人脸进行同样处理
# 提取描述子，不再注释
img = io.imread(img_path)
dets = detector(img, 1)
dist = []
for k, d in enumerate(dets):
    shape = sp(img, d)
    face_descriptor = facerec.compute_face_descriptor(img, shape)
    d_test = numpy.array(face_descriptor)

    # 计算欧式距离
    for i in descriptors:
        dist_ = numpy.linalg.norm(i - d_test)
        dist.append(dist_)

# 候选人名单
candidate = ['shishi', 'bingbing']
# 候选人和距离组成一个dict
c_d = dict(zip(candidate, dist))
cd_sorted = sorted(c_d.items(), key=lambda d: d[1])
print('\n The person is: %s' % (cd_sorted[0][0]))
dlib.hit_enter_to_continue()

在这里插入图片描述