睿智的目标检测14——face_recognition人脸识别库的安装与简要使用（Windows）

亦凉 2023-06-23 07:55 105阅读 0赞

### 睿智的目标检测14——face\_recognition人脸识别库的安装与简要使用 ###

*  学习前言
 *  什么是face\_recognition
 *  安装步骤（Windows）
 *   *  1、下载dlib的whl
     *  2、安装face\_recognition
 *  简要使用方法

# 学习前言 #

我死了我死了我死了！  
![在这里插入图片描述][20190723165901974.jpg_pic_center]

# 什么是face\_recognition #

face\_recognition是一个基于python的开源的人脸识别库，据说识别准确率达到了99.38%，效果比较好！代码网址为：[https://github.com/ageitgey/face\_recognition][https_github.com_ageitgey_face_recognition]

# 安装步骤（Windows） #

## 1、下载dlib的whl ##

点击链接：[https://pypi.org/simple/dlib/][https_pypi.org_simple_dlib]。  
下载“dlib-19.7.0-cp36-cp36m-win\_amd64.whl“安装包。  
face\_recognition在Windows上使用貌似需要dlib-19.6以上的版本，因此建议大家安装python3.6。  
下载完后呢，进入目标文件夹，进行whl的安装。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center]

## 2、安装face\_recognition ##

利用如下代码进行face\_recognition的安装。

pip install face_recognition

之后就能完成安装了！  
![在这里插入图片描述][2019121809502010.png_pic_center]

# 简要使用方法 #

摆放格式如下，face\_dataset用于放人脸：  
![在这里插入图片描述][20191218103547505.png_pic_center]  
face\_recognition的使用demo

import face_recognition
    import numpy as np
    import cv2
    import os
    
    class face_rec():
        def __init__(self):
            #-----------------------------------------------#
            #   对数据库中的人脸进行编码
            #   known_face_encodings中存储的是编码后的人脸
            #   known_face_names为人脸的名字
            #-----------------------------------------------#
            face_list = os.listdir("face_dataset")
            self.known_face_encodings=[]
            self.known_face_names=[]
            for face in face_list:
                name = face.split(".")[0]
                image = face_recognition.load_image_file("./face_dataset/"+face)
                face_encoding = face_recognition.face_encodings(image)[0]
                self.known_face_encodings.append(face_encoding)
                self.known_face_names.append(name)
    
        def recognize(self,draw):
            #-----------------------------------------------#
            #   人脸识别
            #   先定位，再进行数据库匹配
            #-----------------------------------------------#
            height,width,_ = np.shape(draw)
            draw_rgb = cv2.cvtColor(draw,cv2.COLOR_BGR2RGB)
    
            # 根据上述参数进行人脸检测
            # face_locations的格式为(top, right, bottom, left)
            face_locations = face_recognition.face_locations(draw_rgb, number_of_times_to_upsample=1, model="hog")
            if(len(face_locations) == 0):
                return draw
    
            # 批量编码
            face_encodings = face_recognition.face_encodings(draw_rgb, face_locations)
    
            face_names = []
            for face_encoding in face_encodings:
                # 取出一张脸并与数据库中所有的人脸进行对比，计算得分
                matches = face_recognition.compare_faces(self.known_face_encodings, face_encoding, tolerance = 0.4)
                name = "Unknown"
                # 找出距离最近的人脸
                face_distances = face_recognition.face_distance(self.known_face_encodings, face_encoding)
                # 取出这个最近人脸的评分
                best_match_index = np.argmin(face_distances)
                if matches[best_match_index]:
                    name = self.known_face_names[best_match_index]
                face_names.append(name)
    
            #-----------------------------------------------#
            #   画框~!~
            #-----------------------------------------------#
            for (top, right, bottom, left), name in zip(face_locations, face_names):
                print(top, right, bottom, left)
                if top < 0:
                    top = 0
                if left < 0:
                    left = 0
                if right > width:
                    right = width
                if bottom > height:
                    bottom = height
                cv2.rectangle(draw, (left, top), (right, bottom), (0, 0, 255), 2)
                font = cv2.FONT_HERSHEY_SIMPLEX
                cv2.putText(draw, name, (left , bottom - 15), font, 0.75, (255, 255, 255), 2) 
            return draw
    
    if __name__ == "__main__":
    
        dududu = face_rec()
        video_capture = cv2.VideoCapture(0)
    
        while True:
            ret, draw = video_capture.read()
            dududu.recognize(draw) 
            cv2.imshow('Video', draw)
            if cv2.waitKey(20) & 0xFF == ord('q'):
                break
    
        video_capture.release()
        cv2.destroyAllWindows()

[20190723165901974.jpg_pic_center]: https://img-blog.csdnimg.cn/20190723165901974.jpg#pic_center
[https_github.com_ageitgey_face_recognition]: https://github.com/ageitgey/face_recognition
[https_pypi.org_simple_dlib]: https://pypi.org/simple/dlib/
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA_size_16_color_FFFFFF_t_70_pic_center]: https://img-blog.csdnimg.cn/20191218094847864.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NDc5MTk2NA==,size_16,color_FFFFFF,t_70#pic_center
[2019121809502010.png_pic_center]: https://img-blog.csdnimg.cn/2019121809502010.png#pic_center
[20191218103547505.png_pic_center]: https://img-blog.csdnimg.cn/20191218103547505.png#pic_center