1、准备工作

1.1 安装dilb

下载安装包安装或者pip都可以。

首先Ubuntu下安装：

gpu版本我用的是python+Ubuntu+gpu，安装教程见：https://www.cdsy.xyz/computer/soft/develop/230327/cd42014.html

当然如果直接cpu版本，使用pip install dlib即可

windows下安装

windows下需要下载安装包，https://pypi.org/project/dlib/18.17.100/#files，找到你需要的安装包，然后pip install xxxx.whl即可。

2.2 下载所需要的模型

http://dlib.net/files/

任君挑选

2、人脸检测

上代码：

# encoding:utf-8

import dlib
import numpy as np
import cv2

def rect_to_bb(rect): # 获得人脸矩形的坐标信息
    x = rect.left()
    y = rect.top()
    w = rect.right() - x
    h = rect.bottom() - y
    return (x, y, w, h)

def resize(image, width=1200):  # 将待检测的image进行resize
    r = width * 1.0 / image.shape[1]
    dim = (width, int(image.shape[0] * r))
    resized = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)
    return resized

def detect():
    image_file = "test.jpg"
    detector = dlib.get_frontal_face_detector()
    image = cv2.imread(image_file)
    image = resize(image, width=1200)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    rects = detector(gray, 1)
    for (i, rect) in enumerate(rects):
        (x, y, w, h) = rect_to_bb(rect)
        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
        cv2.putText(image, "Face： {}".format(i + 1), (x - 10, y - 10), cv2.FONT_                                                                                        HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

    cv2.imshow("Output", image)
    cv2.waitKey(0)

if __name__ == "__main__":

    detect()

结果截图：

3、人脸特征点检测

代码：

# encoding:utf-8

import dlib
import numpy as np
import cv2

def rect_to_bb(rect): # 获得人脸矩形的坐标信息
    x = rect.left()
    y = rect.top()
    w = rect.right() - x
    h = rect.bottom() - y
    return (x, y, w, h)

def shape_to_np(shape, dtype="int"): # 将包含68个特征的的shape转换为numpy array格式
    coords = np.zeros((68, 2), dtype=dtype)
    for i in range(0, 68):
        coords[i] = (shape.part(i).x, shape.part(i).y)
    return coords


def resize(image, width=1200):  # 将待检测的image进行resize
    r = width * 1.0 / image.shape[1]
    dim = (width, int(image.shape[0] * r))
    resized = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)
    return resized

def feature():
    image_file = "test.jpg"
    detector = dlib.get_frontal_face_detector()
    predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
    image = cv2.imread(image_file)
    image = resize(image, width=1200)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    rects = detector(gray, 1)
    shapes = []
    for (i, rect) in enumerate(rects):
        shape = predictor(gray, rect)
        shape = shape_to_np(shape)
        shapes.append(shape)
        (x, y, w, h) = rect_to_bb(rect)
        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
        cv2.putText(image, "Face: {}".format(i + 1), (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

    for shape in shapes:
        for (x, y) in shape:
            cv2.circle(image, (x, y), 2, (0, 0, 255), -1)
    cv2.imshow("Output", image)
    cv2.waitKey(0)

if __name__ == "__main__":

    feature()

4、人脸对齐

上代码：

# encoding:utf-8

import dlib
import cv2
import matplotlib.pyplot as plt
import numpy as np
import math

def rect_to_bb(rect): # 获得人脸矩形的坐标信息
    x = rect.left()
    y = rect.top()
    w = rect.right() - x
    h = rect.bottom() - y
    return (x, y, w, h)

def face_alignment(faces):

    predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat") # 用来预测关键点
    faces_aligned = []
    for face in faces:
        rec = dlib.rectangle(0,0,face.shape[0],face.shape[1])
        shape = predictor(np.uint8(face),rec) # 注意输入的必须是uint8类型
        order = [36,45,30,48,54] # left eye, right eye, nose, left mouth, right mouth  注意关键点的顺序，这个在网上可以找
        for j in order:
            x = shape.part(j).x
            y = shape.part(j).y
            cv2.circle(face, (x, y), 2, (0, 0, 255), -1)

        eye_center =((shape.part(36).x + shape.part(45).x) * 1./2, # 计算两眼的中心坐标
                      (shape.part(36).y + shape.part(45).y) * 1./2)
        dx = (shape.part(45).x - shape.part(36).x) # note: right - right
        dy = (shape.part(45).y - shape.part(36).y)

        angle = math.atan2(dy,dx) * 180. / math.pi # 计算角度
        RotateMatrix = cv2.getRotationMatrix2D(eye_center, angle, scale=1) # 计算仿射矩阵
        RotImg = cv2.warpAffine(face, RotateMatrix, (face.shape[0], face.shape[1])) # 进行放射变换，即旋转
        faces_aligned.append(RotImg)
    return faces_aligned

def demo():

    im_raw = cv2.imread('test2.jpeg').astype('uint8')

    detector = dlib.get_frontal_face_detector()
    gray = cv2.cvtColor(im_raw, cv2.COLOR_BGR2GRAY)
    rects = detector(gray, 1)

    src_faces = []
    for (i, rect) in enumerate(rects):
        (x, y, w, h) = rect_to_bb(rect)
        detect_face = im_raw[y:y+h,x:x+w]
        src_faces.append(detect_face)
        cv2.rectangle(im_raw, (x, y), (x + w, y + h), (0, 255, 0), 2)
        cv2.putText(im_raw, "Face: {}".format(i + 1), (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

    faces_aligned = face_alignment(src_faces)

    cv2.imshow("src", im_raw)
    i = 0
    for face in faces_aligned:
        cv2.imshow("det_{}".format(i), face)
        i = i + 1
    cv2.waitKey(0)

if __name__ == "__main__":

    demo()

示例一：

示例二：

示例三：

5、人脸识别

这里我们不需要训练模型，因为已经有训练好的现成的模型了。只需要做一个验证。

准备测试数据，即图像对，两张图像为一个图像对，可以是同一个人，或者不同人，进行结果验证。

准备候选人数据，即我们数据库中有的人，此处为“赵丽颖”、“刘亦菲”、“刘诗诗”、“唐嫣”

准备测试人数据，可以是数据库中有的人，也可以是没有的人，此处有6张测试数据，“赵丽颖”、“刘亦菲”、“刘诗诗”、“唐嫣”、“佟丽娅”、“杨紫”

上代码：

主要流程：

1. 先对候选人进行人脸检测、关键点提取、描述子生成后，把候选人描述子保存起来。
2. 然后对测试人脸进行人脸检测、关键点提取、描述子生成。
3. 最后求测试图像人脸描述子和候选人脸描述子之间的欧氏距离，距离最小者判定为同一个人。

# encoding:utf-8

import dlib
import cv2
import matplotlib.pyplot as plt
import numpy as np
import math
import os, glob
from skimage import io

def create_face_space():

    # 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

    # 候选人脸文件夹
    faces_folder_path = "candidate-faces/"
    # 候选人脸描述子list
    descriptors = []
    for f in glob.glob(os.path.join(faces_folder_path, "*.jpeg")):
        print("Processing file: {}".format(f))
        img = io.imread(f)

        # 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)

            # 3.描述子提取，128D向量
            face_descriptor = facerec.compute_face_descriptor(img, shape)

            # 转换为numpy array
            v = np.array(face_descriptor)
            descriptors.append(v)

    return descriptors


def predict(descriptors,path):
    # 对需识别人脸进行同样处理
    # 提取描述子

    img = io.imread(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 = np.array(face_descriptor)

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

def demo():
    global detector, sp, facerec
    # 加载正脸检测器
    detector = dlib.get_frontal_face_detector()

    # 加载人脸关键点检测器
    sp = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

    # 3. 加载人脸识别模型
    facerec = dlib.face_recognition_model_v1("dlib_face_recognition_resnet_model_v1.dat")

    descriptors = create_face_space()
    # 候选人名单
    candidate = ['zhaoliying', 'liuyifei',
                 'liushishi', 'tangyan',
                 'tongliya', 'yangzi']

    predict_path = "test--faces/"
    for f in glob.glob(os.path.join(predict_path, "*.jpeg")):
        dist = predict(descriptors, f)
        # 候选人和距离组成一个dict
        c_d = dict(zip(candidate, dist))

        cd_sorted = sorted(c_d.iteritems(), key=lambda d:d[1])
        print "The person_{} is: ".format(f),cd_sorted[0][0]

if __name__ == "__main__":

    demo()

结果：

其中，只用赵丽颖被正确识别出来。

在已有数据库的刘亦菲被识别为刘诗诗，刘诗诗被识别为唐嫣，唐嫣被识别为刘亦菲

未在数据库中的佟丽娅被识别为刘亦菲，杨紫被识别为唐嫣