【人脸检测】试用python版本Dlib人脸检测、关键点、对齐、识别

迷南。 2022-12-22 11:25 390阅读 0赞

主要参考两篇：

[https://blog.csdn.net/liuxiao214/article/details/83411820][https_blog.csdn.net_liuxiao214_article_details_83411820]

[https://www.jianshu.com/p/577af31ced74][https_www.jianshu.com_p_577af31ced74]

# 0.环境 #

windows
    python3.6
    Dlib
    numpy==1.14.5
    glob
    opencv-python==3.4.3.18

安装Dlib参考：[https://blog.csdn.net/qq\_35975447/article/details/109802787][https_blog.csdn.net_qq_35975447_article_details_109802787]

## 0.1 文件结构 ##

│  .gitignore
    │  faceAlignment.py
    │  faceDetect.py
    │  faceLandmarks.py
    │  faceRecognition.py
    │  file.txt
    │      
    ├─data
    │  │  test_1988.jpg
    │  │  
    │  ├─candidate-faces
    │  │      liushishi.jpg
    │  │      liuyifei.jpg
    │  │      tangyan.jpg
    │  │      tongliya.jpg
    │  │      yangzi.jpg
    │  │      zhaoliying.jpg
    │  │      
    │  └─faces
    │          tangyan.jpg
    │          zhaoliying.jpg
    │          
    ├─models
    │      dlib_face_recognition_resnet_model_v1.dat
    │      mmod_human_face_detector.dat
    │      shape_predictor_5_face_landmarks.dat
    │      shape_predictor_68_face_landmarks.dat
    │      
    └─results
        ├─alignment
        │      test_1988_0_Align68.jpg
        │      test_1988_1_Align68.jpg
        │      test_1988_2_Align68.jpg
        │      test_1988_3_Align68.jpg
        │      test_1988_4_Align68.jpg
        │      test_1988_5_Align68.jpg
        │      
        ├─detect
        │      test_1988_HOG.jpg
        │      test_1988_MMOD.jpg
        │      
        ├─landmarks
        │      test_1988_5Landmarks.jpg
        │      test_1988_68Landmarks.jpg
        │      
        └─recongnition
                recognition_reslut.txt

## 0.2 模型下载 ##

[https://github.com/davisking/dlib-models][https_github.com_davisking_dlib-models]

## 0.3 我的代码 ##

[https://download.csdn.net/download/qq\_35975447/13129563][https_download.csdn.net_download_qq_35975447_13129563]

# 1.人脸检测两个方法对比 #

两种方法主要包括：Dlib自带与调用深度学习模型的方法。

## 1.1 时间 ##

## 1.2 效果 ##

原图：

![20201120111841856.png][]

HOG效果：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM1OTc1NDQ3_size_16_color_FFFFFF_t_70][]

MMOD效果：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM1OTc1NDQ3_size_16_color_FFFFFF_t_70 1][]

## 1.2 代码 ##

这里代码主要参考第一个链接中的，然后根据Dlib自带与调用深度学习模型的方法，循环执行测试时间，并保存图片到results/detect/目录下：

# encoding:utf-8
    
    import dlib
    import cv2
    import os
    import time
    
    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(isHOG=False):
        image_path = "./data/"
        image_file = "test_1988.jpg"
        startTime = time.time()
        if isHOG:
            detector = dlib.get_frontal_face_detector()  # 基于HOG+SVM分类
        else:
            model_path = "./models/mmod_human_face_detector.dat"  # 基于 Maximum-Margin Object Detector 的深度学习人脸检测方案
            detector = dlib.cnn_face_detection_model_v1(model_path)
        image = cv2.imread(image_path + image_file)
        image = resize(image, width=1200)
        # image = resize(image, width=600)
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        rects = detector(gray, 1)
        print("{} method, detect spend {}s ".format(("HOG" if isHOG else "MMOD"), time.time()-startTime))
        for (i, rect) in enumerate(rects):
            if isHOG:
                (x, y, w, h) = rect_to_bb(rect)
            else:
                (x, y, w, h) = rect_to_bb(rect.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)
        savePath = "./results/detect/"
        if not os.path.exists(savePath):
            os.makedirs(savePath)
        if isHOG:
            saveName = image_file[:-4] + "_HOG.jpg"
        else:
            saveName = image_file[:-4] + "_MMOD.jpg"
        cv2.imwrite(savePath + saveName, image)
        cv2.waitKey(10)
    
    if __name__ == "__main__":
        isHOG = True
        detect(isHOG)
        if isHOG:
            isHOG = not isHOG
            detect(isHOG)

# 2.人脸关键点检测两种对比 #

两种主要包括：5个关键点与68关键点。都需要调用模型。

## 2.1 时间 ##

<table style="width:500px;"> 
 <tbody> 
  <tr> 
   <td style="vertical-align:top;width:213.05pt;"> <p style="margin-left:0pt;">68Landmarks</p> </td> 
   <td style="vertical-align:top;width:213.05pt;"> <p style="margin-left:0pt;">5Landmarks</p> </td> 
  </tr> 
  <tr> 
   <td style="vertical-align:top;width:213.05pt;"> <p style="margin-left:0pt;">0.011994600296020508s</p> </td> 
   <td style="vertical-align:top;width:213.05pt;"> <p style="margin-left:0pt;">0.0030002593994140625s</p> </td> 
  </tr> 
 </tbody> 
</table>

## 2.2 性能效果 ##

5个landmarks

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM1OTc1NDQ3_size_16_color_FFFFFF_t_70 2][]

68个landmarks

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM1OTc1NDQ3_size_16_color_FFFFFF_t_70 3][]

## 2.3 代码 ##

这里代码主要也是参考第一个链接，分别执行两种方法，然后将结果保存至./results/landmarks/下：

# encoding:utf-8
    
    import dlib
    import numpy as np
    import cv2
    import os
    import time
    
    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, is68Landmarks=True, dtype="int"): # 将包含68个特征的的shape转换为numpy array格式
        if is68Landmarks:
            landmarkNum = 68
        else:
            landmarkNum = 5
        coords = np.zeros((landmarkNum, 2), dtype=dtype)
        for i in range(0, landmarkNum):
            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(is68Landmarks=True):
        image_path = "./data/"
        image_file = "test_1988.jpg"
        detector = dlib.get_frontal_face_detector()
        if is68Landmarks:
            predictor = dlib.shape_predictor("./models/shape_predictor_68_face_landmarks.dat")
        else:
            predictor = dlib.shape_predictor("./models/shape_predictor_5_face_landmarks.dat")
    
        image = cv2.imread(image_path + image_file)
        image = resize(image, width=1200)# 1200
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        rects = detector(gray, 1)
        shapes = []
        startTime = time.time()
        for (i, rect) in enumerate(rects):
            shape = predictor(gray, rect)
            shape = shape_to_np(shape, is68Landmarks)
            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)
        print("{} method, detect spend {}s ".format(("68Landmarks" if is68Landmarks else "5Landmarks"), time.time()-startTime))
    
        for shape in shapes:
            for (x, y) in shape:
                cv2.circle(image, (x, y), 2, (0, 0, 255), -1)
        cv2.imshow("Output", image)
        savePath = "./results/landmarks/"
        if not os.path.exists(savePath):
            os.makedirs(savePath)
        if is68Landmarks:
            saveName = image_file[:-4] + "_68Landmarks.jpg"
        else:
            saveName = image_file[:-4] + "_5Landmarks.jpg"
        cv2.imwrite(savePath + saveName, image)
        cv2.waitKey(10)
    
    if __name__ == "__main__":
        is68Landmarks = True
        feature(is68Landmarks)
        if is68Landmarks:
            is68Landmarks = not is68Landmarks
            feature(is68Landmarks)

# 3.人脸对齐 #

本来这里也要测试两种方法的，但是5个关键点的好像不太好改，就放弃了，不过参考代码还是放在里面了，可以自行删去。

## 3.1 时间 ##

<table style="width:500px;"> 
 <tbody> 
  <tr> 
   <td style="vertical-align:top;width:213.05pt;"> <p style="margin-left:0pt;">Alignment</p> </td> 
  </tr> 
  <tr> 
   <td style="vertical-align:top;width:213.05pt;"> <p style="margin-left:0pt;">0.04295229911804199s</p> </td> 
  </tr> 
 </tbody> 
</table>

## 3.2 效果 ##

由于图太模糊了，挑出几张来：

![2020112011551833.png][]![20201120115444592.png][]![20201120115453440.png][]![202011201155051.png][]

## 3.3 代码 ##

这里的关键点是根据：

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTM4NDExOTY_size_16_color_FFFFFF_t_70]

结果会保存至./results/alignment/：

# encoding:utf-8
    
    import dlib
    import cv2
    import numpy as np
    import math
    import os
    import time
    
    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 face_alignment_68(faces):
        # 使用68点关键点模型，根据关键点信息求解变换矩阵，然后把变换矩阵应用到整个图像上。
        predictor = dlib.shape_predictor("./models/shape_predictor_68_face_landmarks.dat") # 用来预测关键点
        faces_aligned = []
        global startTime
        startTime = time.time()
        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])) # 进行仿射变换，即旋转
            faces_aligned.append(RotImg)
        return faces_aligned
    
    
    def face_alignment_5(rgb_img, faces):
        startTime = time.time()
        faces_aligned = []
        for face in faces:
            # RotImg = dlib.get_face_chip(rgb_img, face)
            RotImg = dlib.get_face_chip(np.uint8(rgb_img), np.uint8(face))
            # RotImg = dlib.get_face_chip(rgb_img, face, size=224, padding=0.25)
            faces_aligned.append(RotImg)
        return faces_aligned
    
    def demo(isAlignment_5=True):
        image_path = "./data/"
        image_file = "test_1988.jpg"
        im_raw = cv2.imread(image_path + image_file).astype('uint8')
    
        # detector = dlib.get_frontal_face_detector()
        model_path = "./models/mmod_human_face_detector.dat"  # 基于 Maximum-Margin Object Detector 的深度学习人脸检测方案
        detector = dlib.cnn_face_detection_model_v1(model_path)
        im_raw = resize(im_raw, width=1200)
        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.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)
        if isAlignment_5:
            faces_aligned = face_alignment_5(im_raw, src_faces)
        else:
            faces_aligned = face_alignment_68(src_faces)
        print("{} method, detect spend {}s ".format(("Alignment_5" if isAlignment_5 else "Alignment_68"), time.time()-startTime))
    
        cv2.imshow("src", im_raw)
        savePath = "./results/alignment/"
        if not os.path.exists(savePath):
            os.makedirs(savePath)
        if isAlignment_5:
            saveName = "_Align5.jpg"
        else:
            saveName = "_Align68.jpg"
        i = 0
        for face in faces_aligned:
            cv2.imshow("det_{}".format(i), face)
            cv2.imwrite(savePath + image_file[:-4] + "_{}".format(i) + saveName, face)
            i = i + 1
        cv2.waitKey(10)
    
    if __name__ == "__main__":
        isAlignment_5 = False
        demo(isAlignment_5)
        if isAlignment_5:
            isAlignment_5 = not isAlignment_5
            demo(isAlignment_5)

# 4.人脸识别 #

这个地方的时间花的比较多，主要出现识别不好时，没有沉下心来去分析代码，这里根据参考一中的代码进行了修改，无需人为去设置candidate列表，只需要我们自己将candidate-faces文件夹中的候选人库以可区分的该人命名就好。

## 4.1 准备过程 ##

（1）候选人库candidate-faces，就是我们去这个库查询，是已知的、且有正确身份的。

![20201120113653385.png][]![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM1OTc1NDQ3_size_16_color_FFFFFF_t_70 4][]![20201120113805459.png][]![2020112011381482.png][]![20201120113851200.png][]![2020112011390471.png][]

（2）候选人库candidate-faces图片命名：

![20201120114012293.png][]

（3）待查询的faces：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM1OTc1NDQ3_size_16_color_FFFFFF_t_70 5][]**tangyan.jpg**

![zhaoliying.jpg][] zhaoliying.jpg

（4）**结果 **

Processing file: ./data/candidate-faces\liushishi.jpg
    Number of faces detected: 1
    Processing file: ./data/candidate-faces\liuyifei.jpg
    Number of faces detected: 1
    Processing file: ./data/candidate-faces\tangyan.jpg
    Number of faces detected: 1
    Processing file: ./data/candidate-faces\tongliya.jpg
    Number of faces detected: 1
    Processing file: ./data/candidate-faces\yangzi.jpg
    Number of faces detected: 1
    Processing file: ./data/candidate-faces\zhaoliying.jpg
    Number of faces detected: 1
    c_d :[('tangyan', 0.45614611065543303), ('liushishi', 0.4777414300544273), ('yangzi', 0.520176500668875), ('tongliya', 0.547071465533885), ('zhaoliying', 0.64414064895386), ('liuyifei', 0.669962308077882)]
    The person_test--./data/faces/tangyan.jpg is:  tangyan
    c_d :[('zhaoliying', 0.4041512584817519), ('liushishi', 0.4681194204229278), ('tangyan', 0.4728928349513442), ('yangzi', 0.47474913579746303), ('tongliya', 0.5446001882500634), ('liuyifei', 0.6104574640831666)]
    The person_test--./data/faces/zhaoliying.jpg is:  zhaoliying

## 4.2 代码 ##

其中结果会存到./results/recongnition/recognition\_reslut.txt文件中。

# encoding:utf-8
    
    import dlib
    import cv2
    import numpy as np
    import os, glob
    
    
    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 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 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 = "./data/candidate-faces/"
        # 候选人脸描述子list
        descriptors = []
        candidates = []
        for f in glob.glob(os.path.join(faces_folder_path, "*.jpg")):
            print("Processing file: {}".format(f))
            img = cv2.imread(f)
            # img = resize(img, width=300)
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
            # 1.人脸检测
            dets = detector(img, 1)
            print("Number of faces detected: {}".format(len(dets)))
            candidate = f.split('\\')[-1][:-4]
            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)
                candidates.append(candidate)
        return descriptors, candidates
    
    
    def predict(descriptors, path):
        # 对需识别人脸进行同样处理
        # 提取描述子
        img = cv2.imread(path)
        # img = io.imread(path)
        # img = resize(img, width=300)
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        dets = detector(gray, 1)
        dist = []
        if len(dets) == 0:
            pass
        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_)
                # print(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("./models/shape_predictor_68_face_landmarks.dat")
    
        # 3. 加载人脸识别模型
        facerec = dlib.face_recognition_model_v1("./models/dlib_face_recognition_resnet_model_v1.dat")
    
        # 提取候选人特征与候选人名单
        descriptors, candidates = create_face_space()
        savePath = "./results/recongnition/"
        if not os.path.exists(savePath):
            os.makedirs(savePath)
        fp = open(savePath + 'recognition_reslut.txt', 'a')
        predict_path = "./data/faces/*.jpg"
        for f in glob.glob(predict_path):
            f = f.replace("\\", '/')
            # print("f :{}".format(f))
            dist = predict(descriptors, f)
            # 候选人和距离组成一个dict
            c_d = dict(zip(candidates, dist))
            if not c_d:
                print(str(c_d) + " is None")
                continue
            cd_sorted = sorted(c_d.items(), key=lambda d:d[1])
            print("c_d :{}".format(cd_sorted))
    
            print("The person_test--{} is: ".format(f), cd_sorted[0][0])
            fp.write("\nThe person_test--{} is: with similar : {}".format(f, cd_sorted[0][0]))
        fp.close()
    
    if __name__ == "__main__":
    
        demo()

#  参考 #

[1.【Dlib】人脸检测、特征点检测、人脸对齐、人脸识别][https_blog.csdn.net_liuxiao214_article_details_83411820]

[2.【Tool】Dlib 接口学习和常见功能介绍][https_www.jianshu.com_p_577af31ced74]

[3.dlib-models][https_github.com_davisking_dlib-models]

[4.关键点检测——68点图例][4._68]

[5.Dlib提取人脸特征点（68点，opencv画图）][5.Dlib_68_opencv]

[https_blog.csdn.net_liuxiao214_article_details_83411820]: https://blog.csdn.net/liuxiao214/article/details/83411820
[https_www.jianshu.com_p_577af31ced74]: https://www.jianshu.com/p/577af31ced74
[https_blog.csdn.net_qq_35975447_article_details_109802787]: https://blog.csdn.net/qq_35975447/article/details/109802787
[https_github.com_davisking_dlib-models]: https://github.com/davisking/dlib-models
[https_download.csdn.net_download_qq_35975447_13129563]: https://download.csdn.net/download/qq_35975447/13129563
[20201120111841856.png]: /images/20221120/7e4a11f96e5c44d9b0f3d596ccfc5f71.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM1OTc1NDQ3_size_16_color_FFFFFF_t_70]: /images/20221120/72251100e58e44b596c6621070455f19.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM1OTc1NDQ3_size_16_color_FFFFFF_t_70 1]: /images/20221120/f48d0a6d9f904d7d9285f2e64a7698bd.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM1OTc1NDQ3_size_16_color_FFFFFF_t_70 2]: /images/20221120/eebf0f6b0e974fb78e77afc20ee24eb2.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM1OTc1NDQ3_size_16_color_FFFFFF_t_70 3]: /images/20221120/467861d18f7f4ca4bc208d8e9863c0c7.png
[2020112011551833.png]: https://img-blog.csdnimg.cn/2020112011551833.png
[20201120115444592.png]: https://img-blog.csdnimg.cn/20201120115444592.png
[20201120115453440.png]: https://img-blog.csdnimg.cn/20201120115453440.png
[202011201155051.png]: /images/20221120/0df7f74617b241c0b22a140e8fe49b0d.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTM4NDExOTY_size_16_color_FFFFFF_t_70]: /images/20221120/bb7f2a5c4ef04d46a497e3539a287fac.png
[20201120113653385.png]: https://img-blog.csdnimg.cn/20201120113653385.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM1OTc1NDQ3_size_16_color_FFFFFF_t_70 4]: https://img-blog.csdnimg.cn/20201120113701763.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM1OTc1NDQ3,size_16,color_FFFFFF,t_70
[20201120113805459.png]: https://img-blog.csdnimg.cn/20201120113805459.png
[2020112011381482.png]: https://img-blog.csdnimg.cn/2020112011381482.png
[20201120113851200.png]: https://img-blog.csdnimg.cn/20201120113851200.png
[2020112011390471.png]: /images/20221120/3248575ee7c34176abd9642ef586cf65.png
[20201120114012293.png]: /images/20221120/fc28a7b86d534e04b730362f4187e2da.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM1OTc1NDQ3_size_16_color_FFFFFF_t_70 5]: /images/20221120/cd5bafdbce06447d92c23346f7e325e5.png
[zhaoliying.jpg]: /images/20221120/d4e790e3b38847b391b4b9a2e2dd6d6d.png
[4._68]: https://blog.csdn.net/u013841196/article/details/85720897
[5.Dlib_68_opencv]: https://blog.csdn.net/zmdsjtu/article/details/53454071