clmtrackr.js

一、简介

clmtrackr是一个javascript库，GitHub地址，用于将面部模型与视频或图像中的面部进行拟合。正如Jason M.Saragih的论文所描述的，它目前是由正则化的地标均值漂移拟合的约束局部模型的实现。clmtrackr跟踪一个面，并将面模型的坐标位置输出为一个数组，遵循下面的模型编号：

参考-概述
该库提供了一些在MUCT数据库上训练的通用人脸模型和一些附加的自注释图像。查看用于构建自己模型的clmtools。
为了在视频中进行跟踪，建议使用支持WebGL的浏览器，尽管库应该可以在任何现代浏览器上工作。
有关受约束的本地模型的更多信息，请参阅Yang Xiaoguang的优秀教程，该教程对实现此库有很大帮助。
功能

• 图像跟踪
• 视频跟踪
• 人脸替换
• 面部遮盖
• 实时人脸变形
• 情绪检测
• 漫画脸

二、获取人脸坐标

下载GitHub源码，然后获取人脸坐标点：

在clmtrackr-dev\examples\face_mask.html页面中添加，拟合人脸的打印

var currentMask = 0;
				var animationRequest;
				var face_list = new Array()
				var face_list_index = 0
				var temp_face 
				function addFace(){
					
					var mycars = new Array()
						for(var j=0;j<71;j++){
							mycars[2* j] = temp_face[j][0];
							mycars[2 * j + 1] = temp_face[j][1];
						}
						face_list[face_list_index] = mycars
						face_list_index += 1
						console.log(face_list)
						//展示
//						alert(document.getElementById('name_input').value)
						alert("已添加" + face_list.length + "组人脸\n还差" +  (5 - face_list.length) + "组")
						data = JSON.stringify({
								'name': document.getElementById('name_input').value,
								'face': face_list
							})
						if(face_list.length == 5){
							alert("感谢你的帮助！")
							$.ajax({				
								url: 'http://127.0.0.1:80/login',
								data:data,
								type: 'post',			
								dataType: 'json',
								success:function(result){			
									console.log("succ")	
								},			  
								error:function(res){	
									console.log(res);
//									alert("res")			
								}			
							});
						}
				}
				//询问是否将这组人脸添加进去
				function thrownQustion(){
					if(window.confirm('你确定要添加这组人脸信息吗？')){
		                 addFace()
		                 return true;
		              }else{
		                 //alert("取消");
		                 return false;
		             }
				}

flask服务器端接收人脸信息

#_*_coding:utf8_*_
from face_compute.face_csv import *
from flask import Flask, request, jsonify, render_template, make_response, Response
import json
from flask_cors import CORS


app = Flask(__name__)
CORS(app, resources=r'/*')

@app.route('/test',methods = ['POST', 'GET'])
def test():
    return "sucess"


@app.route('/login', methods=['POST', 'GET'])
def login():
    face1 = []
    name1 = ""
    get_data = json.loads(request.get_data())
    print(get_data)
    result = []
    face = get_data['face']
    name = get_data['name']
    print(face)#打印出人脸信息
    print(name)

    addFacePointList(face, name)#添加人脸信息，可以注释掉
    return_dict = {'result': "sucess"}
    return Response(json.dumps(return_dict), content_type='application/json')
    # return "sucess"



if __name__ == '__main__':
    app.run(host="0.0.0.0", port=80)

三、人脸编码：

第一步：后端收到的人脸特征点坐标，排列方式( $x_0, y_1, ... , x_{70}, y_{70}$ )：

68.02969417,170.7858152,67.55409637,187.2565698,70.32396463,202.385734,75.22430006,217.1625959,82.79318217,229.8786489,93.14250218,239.6412101,105.3708386,246.6468553,119.3302982,248.5015418,132.9314337,244.8064023,144.25398,236.2561254,153.2946349,225.0977126,159.1491445,211.2710388,......,114.7377799,183.5704604,86.72838982,163.9496996,96.6436329,162.8250621,96.59075575,167.0019398,87.42136891,168.0661804,139.7337411,160.195897,129.7450104,160.5052976,130.4119868,164.6114374,139.6415895,164.3760974

第二步：将71个坐标点转换为2485个距离

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0.21793015066152494, 0.35399770345892656, 0.33620722392300806, 0.14439906544629072, 0.1576728585888376, 0.419936376315506, 1.130086910153311, 1.0480359894276707, 0.9373081747293222, 1.0447078009353115, 1.2411801517320968, 1.1250663388143407, 1.0183601679908425, 1.1518267500901778, 0.0897472470184302, 0.24100337078408993, 0.39568322404962086, 0.3589945408025243, 0.332136842957867, 0.3276146552875389, 0.3566219881958887, 0.38500079389341285, 0.26147024069970104, 0.18313025495690693, 0.2524393649397294, 0.2248457547622127, 0.07920300161631567, 0.24992568673756638, 0.4220513137735651, 1.1986492479804747, 1.0986764625502234, 0.9927194134063756, 1.1179274907459853, 1.1934571875438873, 1.0963623767698867, 0.9863036298424356, 1.1001483087444497, 0.15967615607395452, 0.3235151366619997, 0.3181561997186374, 0.3296336075369809, 0.37161277899489276, 0.43095699104851054, 0.4715942262564361, 0.34740610384471876, 0.23556912473261624, 0.21773917799438183, 0.1628152958240996, 0.1391092794360756, 0.3396728823204298, 0.40339656584577427, 1.2217933691807863, 1.106369169311393, 1.0053861321028033, 1.1459096648258869, 1.1267954767195596, 1.0425738901936297, 0.9308892918845943, 1.031401956747937, 0.1684014369951258, 0.21800191175353933, 0.2996362641055286, 0.41569637013147276, 0.5257751312897002, 0.5955146002388557, 0.4730723960916707, 0.31509745664339783, 0.1586894629957265, 0.07174667536060887, 0.2545468842208677, 0.48349980672914195, 0.49326909012478715, 1.3449203383285209, 1.2119798286796928, 1.1181553735393914, 1.274678131836746, 1.0954754885280114, 1.044400343202987, 0.9310832201801227, 0.9957115303813034, 0.16054535675996187, 0.30286542177791087, 0.4692900887734991, 0.6153684327542864, 0.7095166588005253, 0.5940325708196593, 0.41406476846023926, 0.1982686108598909, 0.1727250030943507, 0.38743609900843384, 0.6213927348624134, 0.6462476397335905, 1.5058250186439652, 1.3646924159421963, 1.2749132900677036, 1.4380478739713356, 1.1421423283546384, 1.1239298857927869, 1.0116656155127266, 1.0401323321620004, 0.1476843348607307, 0.32547812248369756, 0.4867839460682415, 0.5973078790998037, 0.49344258690148035, 0.3066723161995573, 0.10671650030909723, 0.16379511493663543, 0.3193793743952275, 0.5394123986230187, 0.7034697574209121, 1.539583904588504, 1.4183381570696758, 1.3202809545815941, 1.4640636669355767, 1.2870128687629128, 1.25268639978983, 1.1395137780478064, 1.1855973790773617, 0.182416946910081, 0.3525025145852245, 0.47438961560627896, 0.38531494638521907, 0.20849441295225712, 0.1409505240412359, 0.2284823625688931, 0.26656285133823354, 0.4472420622818266, 0.7330260461727387, 1.5302991024140682, 1.4290256067712013, 1.3243038315952524, 1.4483560654234213, 1.3947273126220674, 1.3411616803473825, 1.2279527327531208, 1.2946357347270745, 0.1756113995515894, 0.30772675552445, 0.24660713267558185, 0.14481877928244877, 0.27710193784992704, 0.34753239227719884, 0.2484768251063654, 0.32671127001195743, 0.7490498579298464, 1.4773958304547132, 1.4018629486196084, 1.2906095093983039, 1.3887606303325197, 1.4933835859815776, 1.414165207015517, 1.3024956397562475, 1.3961780668253208, 0.13818318526986273, 0.13685556590360268, 0.21068707100123768, 0.4171089878534387, 0.4675490005418339, 0.29192857368759, 0.22175541089516396, 0.7448240644374494, 1.3877689603197987, 1.3369920495989842, 1.2213847571421148, 1.29413935212962, 1.5497879910416201, 1.4456980695206265, 1.3373231768513658, 1.4567580049581654, 0.12429897515138186, 0.29589814134473524, 0.5141396894237237, 0.5477577369358216, 0.34248076913726166, 0.1631767590259372, 0.7158569304807725, 1.2843965609884422, 1.2507741858382384, 1.1331668094610683, 1.1881048749499576, 1.5537797820458012, 1.4307326195429393, 1.3261805134149973, 1.4650279075598245, 0.18677690841505015, 0.40262867540944147, 0.4283434199776864, 0.21901761476516993, 0.08719297581731637, 0.6169517717186834, 1.255471206246314, 1.2006469264778046, 1.0853588304098156, 1.1630899137751034, 1.4403719681261211, 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第三步：将2485个距离与一个平均脸做对比，计算偏离平均脸的程度
平均脸，从源码中得到,同样也要做人脸的2485个距离：

"average" : [[121.92799191984679,184.19216240419755],[118.74113263254269,253.7017373484083],[128.07732840700828,314.0651648786312],[145.50341586402052,377.3404382903117],[175.0470179047746,428.3720278198884],[216.26268310246033,469.2344402538887],[267.42588166495466,502.128073946532],[323.6864139765614,512.5053811316307],[381.1889691089136,499.48530971536445],[429.71357990120225,463.4214900408549],[467.1292936657478,421.537754329594],[493.2308725208873,370.6466670145585],[507.3945907183312,305.3965374123],[514.1098885852615,238.51000761747102],[507.2009944162471,174.7364492942625],[465.59705810723074,136.75665747531139],[432.10874975324265,125],[384.15174446143584,125],[351.54488594535763,135.22963355336668],[162.16177451030518,144.72103952617107],[194.70376235949394,126],[241,130],[277.5198647210173,137.82992220884094],[192.5627380181407,182.35373455399292],[225.1658086004223,166.85817167285668],[262.9021389237093,184.72604899079232],[224.82421319031323,193.62679469584918],[224.9386274222809,179.73191446260716],[443.75218061508883,177.1556294105885],[407.36102478935464,162.1785032964798],[367.3426762945685,181.37362678808685],[405.2498567443763,188.75927101523848],[404.863153412407,173.65270066194788],[314, 170],[277.2539320006613,252.0592473714927],[258.790607031229,284.0832945003201],[276.64778558874696,304.54255347445314],[317.4772090972725,307.7859653833357],[364.4959193923387,299.6561959465791],[377.27275089177823,279.043842539653],[357.1140334647449,250.14961061471956],[324,222],[296.770695143374,295.6331974142146],[350.24114846328195,290.942330984987],[248.8532880314441,372.38004806995957],[272.1557077756945,356.35352520595814],[302.9902196911147,350.59821534914704],[323.11457426149127,353.0358352022737],[338.3055779254553,347.5427982113969],[366.49269601972713,353.1538257295358],[392.63652105652415,368.4911974180641],[375.0778975047938,391.4413420753004],[352.32935954043757,405.19247889714825],[320.19499419206926,411.930992226806],[288.9192573286629,407.35752671668797],[267.61253113280924,394.527019223827],[286.6817714614754,382.82667526139215],[320.16223074694074,385.86502934549657],[359.1212544588326,380.7487964985724],[361.7270998810554,365.15603335898066],[322.91210334135167,367.2901736762333],[280.7920218316411,368.2798825278876],[320.66814785515174,277.11007275979364],[206.36606604411398,171.6086547538323],[247.5375468161923,170.29657636660522],[246.36866333618227,191.67729410789994],[205.19888043799355,189.99033691329964],[429.0603263358775,166.1691180598579],[386.8504393293843,166.2774220754911],[384.7938981921405,186.5701136634426],[426.9983448269614,184.45786533091854]]					

得到读入人脸的距离与平均人脸的距离的差值

扫描二维码关注公众号，回复： 11582444 查看本文章

 [-1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 2, 1, 1, -1, 2, 3, 2, 3, 2, 2, 1, -2, -1, -1, -1, -1, -2, -1, -1, -1, -1, -1, -4, -4, -5, -4, -3, -2, -2, -5, -7, -4, -3, -4, -5, -4, -3, -2, -1, -2, -2, -3, -3, -3, -3, -3, -3, -4, -4, -3, -5, -1, -1, -2, -1, -2, -2, -1, -2, 1, 1, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 3, 4, 4, 4, 3, 3, 3, 1, 1, 1, 1, 2, -1, 1, 1, 1...

第四步：将4个人，每个人提供5张脸数据，放入PCA中进行降维处理，最后保留100维数据例如：

[  0   1   2   9  10  13  30  14  15   7   8  31  32  26  27  23  24  36
  34  35  11  12  41  42  53  54  39  40  69  70  67  68  65  66  59  60
 102 103  98  99  61  62 119 120 104 105  89  90 123 124  79  80 139 140
 129 130 174  75  76 162 163 121 122 179 180 185 186  47  48 115 116 228
 229 222 223 172 173 238 239 113 114 262 263 226 227 177 178 170 171 127
 128 236 237 266 267 297 298 303 285 286]

第五步：将4个人，100维数据放入KNN中

# coding:utf-8

from numpy import *
import operator


##给出训练数据以及对应的类别
def createDataSet():

    group = array([[-1, 1, 1, 2, 1, -1, -1, -1, 2, 2, 2, -1, 1, -1, -2, -1, -1, -4, -5, -5, 1, 1, -7, -5, -2, -3, -2, -4, -2, 1, -1, -1, -1, -2, -5, -4, -1, -2, 2, 2, -5, -1, 1, 1, -3, -2, 5, 5, -3, -2, 2, 2, -1, 1, -3, -2, 1, 2, 2, 5, 4, -1, -1, 1, -3, 1, 1, -3, -2, -3, -2, 5, 5, 6, 6, 1, 2, 5, 3, -3, -4, -1, -1, 4, 5, 2, -1, 3, 2, -3, -3, 6, 5, 4, 4, 3, 5, 7, 8, 9]
,[-1, 1, 1, 3, 2, 2, 1, 1, 3, 1, 2, 1, 1, 1, -1, 1, -1, -4, -4, -5, 3, 3, -7, -5, -3, -3, -2, -4, -1, 1, -1, 1, -1, -1, -4, -4, 1, -2, 2, 2, -5, -1, 1, -1, -2, -2, 5, 4, -3, -3, 3, 3, -1, 1, -2, -2, 1, 1, 2, 5, 5, -2, -2, 1, -3, 1, 1, -3, -2, -3, -2, 5, 5, 6, 6, 1, 2, 5, 4, -3, -4, -1, -1, 4, 5, 2, -1, 3, 3, -3, -3, 6, 5, 5, 5, 4, 5, 7, 8, 8]
,[-1, 1, -1, 2, 2, 2, -2, -1, 1, -1, 1, -2, -2, -2, -3, -2, -2, -5, -6, -6, 2, 2, -8, -6, -4, -4, -4, -6, -3, 1, -3, -2, -2, -3, -6, -5, -3, -3, -1, -1, -7, -2, -2, -3, -4, -4, 2, 2, -4, -4, 3, 3, -1, 1, -4, -5, -3, -1, -1, 3, 3, -3, -3, -3, -4, -2, -1, -5, -4, -5, -4, 3, 3, 4, 4, -2, -2, 3, 1, -4, -5, -3, -3, 2, 3, -2, -4, 1, -1, -5, -5, 4, 3, 3, 3, 2, 3, 5, 6, 6]
,[-1, -1, -1, 2, 1, 1, -2, -2, 1, 2, 2, -2, -2, -2, -3, -2, -2, -3, -3, -4, 2, 1, -6, -4, -1, -1, -2, -5, -2, 1, -3, -2, -2, -3, -3, -1, -3, -2, -1, -1, -5, -3, 1, 1, -3, -2, -1, -1, -1, -1, 2, 2, 1, 2, -1, -4, -2, 3, 2, 1, -1, 1, 1, -2, -1, -1, -1, -2, -1, -3, -1, 1, 1, 2, 2, -2, -2, 1, -1, -1, -3, -3, -2, -1, -1, -3, -4, -2, -1, -2, -2, 2, 1, -1, -1, 1, 1, 3, 4, 5]
,[-2, -2, -3, -2, -2, -2, 1, 1, 3, -4, -3, 1, 1, 1, -1, 1, -1, -2, -3, -3, -2, -2, -5, -3, -4, -4, -1, -3, -1, 1, -1, 1, 1, -1, -4, -3, 1, -1, 1, 1, -3, -1, -1, -2, -2, -1, 3, 2, -3, -3, -2, -2, -1, -2, -1, -1, -1, -3, -3, 1, 1, -2, -2, 1, -2, 1, 1, -3, -2, -2, -1, 1, -1, 2, 2, 1, 2, 2, 2, -2, -2, -1, 1, -2, -1, 1, -1, 2, 2, -3, -2, 2, 2, -2, -1, -2, -1, 2, 4, 5]
,[-2, -1, 1, -6, -7, -5, -1, -3, -2, -2, -5, -2, -2, -1, -2, -3, -1, 7, 8, 7, -7, -6, 4, 5, 6, 7, 4, -1, -1, 2, -3, -1, -1, -3, 7, 9, -1, 4, -5, -4, 5, -3, 2, 3, 4, 4, -8, -8, 7, 5, -8, -8, 1, 2, 8, 1, -5, 1, -2, -10, -9, 5, 6, -5, 4, -2, -3, 6, 5, 4, 4, -9, -10, -4, -6, -3, -2, -9, -8, 8, 5, -7, -5, -9, -9, -10, -6, -7, -4, 3, 5, -9, -12, -9, -9, -9, -9, -14, -5, -5]
,[-2, -1, 2, -6, -7, -6, -1, -3, -2, 1, -3, -2, -1, -1, -2, -3, -1, 8, 9, 8, -8, -7, 5, 6, 8, 8, 5, 1, -1, 2, -3, -1, -1, -2, 8, 10, 1, 5, -5, -4, 7, -3, 3, 5, 5, 5, -8, -8, 8, 6, -8, -8, 2, 2, 9, 3, -5, 3, 1, -9, -9, 6, 8, -4, 6, -1, -3, 8, 6, 5, 6, -9, -9, -4, -6, -2, -2, -9, -7, 9, 6, -6, -4, -8, -9, -9, -5, -7, -3, 3, 6, -9, -11, -8, -9, -9, -9, -13, -5, -6]
,[-2, -1, 2, -2, -4, -4, -1, -3, -2, 3, 1, -2, -3, -1, -3, -3, -2, 7, 8, 7, -4, -4, 4, 4, 9, 10, 4, -1, -2, 2, -3, -1, 1, -3, 8, 11, -1, 5, -5, -4, 5, -3, 5, 7, 5, 4, -9, -9, 10, 7, -4, -5, 2, 3, 10, 1, -5, 5, 3, -11, -10, 8, 9, -5, 6, -1, -1, 7, 7, 5, 6, -10, -11, -4, -6, -2, -2, -9, -8, 9, 6, -6, -4, -10, -10, -10, -6, -7, -3, 4, 7, -10, -12, -10, -10, -9, -9, -13, -5, -5]
,[-2, 1, -1, 2, 2, 2, 3, 2, 4, 1, 2, 3, 3, 3, 2, 2, 2, 6, 6, 6, 2, 2, 4, 5, 4, 4, 5, 2, 3, 2, 2, 3, 2, 2, 6, 7, 4, 5, 2, 2, 5, 1, 4, 4, 5, 6, 1, 1, 5, 4, 3, 3, -1, 1, 8, 4, 2, 3, 2, -2, -1, 5, 5, 2, 4, 4, 3, 6, 5, 5, 6, -2, -3, 2, 1, 3, 4, -1, 1, 7, 6, 1, 2, -3, -2, -2, 1, 1, 3, 4, 6, -1, -3, -3, -2, -3, -2, -5, 3, 3]
,[-3, -1, 1, -4, -4, -2, 2, -2, -1, -2, -3, 1, 1, 2, -1, -1, 2, 11, 12, 11, -4, -3, 8, 8, 11, 11, 7, 3, 2, 2, -2, 2, 3, -1, 11, 14, 3, 8, -4, -3, 10, -1, 6, 8, 8, 8, -11, -10, 12, 9, -5, -5, 2, 2, 13, 5, -4, 2, -1, -12, -12, 9, 11, -3, 8, 1, -1, 10, 9, 8, 8, -12, -13, -8, -10, 1, 1, -10, -8, 12, 9, -5, -3, -12, -13, -10, -4, -7, -2, 6, 9, -12, -14, -13, -13, -11, -12, -17, -8, -9]
,[-1, 1, 1, 2, 2, 2, -2, -1, 1, -1, 1, -2, -3, -2, -2, -3, -2, -1, -1, -1, 2, 2, -4, -3, 1, 2, -2, -5, -2, 2, -3, -2, -2, -3, 1, 3, -3, -1, -2, -2, -4, -3, 1, -1, -1, -2, 1, -1, 2, -1, 3, 3, 1, 1, 3, -4, -4, 1, 1, -3, -2, 1, 2, -5, 1, -2, -1, -1, 1, -1, -1, -2, -2, 5, 4, -3, -3, -4, -4, 2, -1, -5, -4, -2, -1, -6, -5, -4, -3, -2, 1, -1, -5, -2, -2, -2, -2, -4, 4, 5]
,[-1, 2, 3, 2, 2, 2, 2, 2, 3, 4, 3, 1, 1, 1, 1, -1, 1, 5, 5, 5, 2, 2, 2, 2, 7, 7, 3, -1, 1, 2, -1, 1, 1, -1, 6, 8, 1, 4, 1, 1, 2, -2, 4, 5, 4, 4, -1, -1, 7, 5, 2, 2, 2, 3, 8, 1, -2, 5, 4, -2, -2, 6, 7, -1, 5, 2, 2, 5, 5, 4, 5, -1, -2, 4, 3, 1, 1, -3, -2, 7, 4, -3, -1, -2, -1, -4, -3, -2, 1, 3, 5, 1, -4, -1, -1, -1, -1, -4, 5, 5]
,[-2, 1, 1, 1, 1, 1, -1, -1, 1, -1, 1, -2, -2, -2, -2, -3, -2, 1, 2, 1, 2, 2, -3, -2, 2, 2, -1, -4, -2, 2, -3, -1, -2, -2, 2, 4, -2, 1, -2, -2, -3, -3, 1, 1, 1, -1, -1, -2, 3, 1, 2, 2, 1, 2, 4, -3, -4, 1, 1, -3, -2, 2, 2, -4, 2, -1, -1, 1, 2, 1, 1, -2, -3, 4, 3, -2, -2, -4, -4, 3, 1, -4, -3, -3, -2, -5, -5, -4, -2, -1, 1, -2, -5, -2, -2, -3, -2, -4, 4, 4]
,[-1, 2, 3, 2, 1, 2, 5, 5, 7, 3, 2, 4, 6, 4, 4, 4, 4, 7, 7, 7, 1, 2, 5, 6, 6, 6, 7, 4, 4, 2, 4, 5, 4, 4, 7, 8, 6, 7, 4, 5, 7, 3, 6, 6, 7, 8, 5, 5, 7, 6, 2, 2, 2, 3, 9, 7, 4, 4, 4, 2, 3, 7, 7, 5, 6, 5, 5, 7, 7, 7, 7, 3, 2, 6, 6, 6, 6, 3, 4, 8, 7, 2, 4, 1, 3, 3, 4, 4, 5, 6, 7, 5, 2, 2, 3, 2, 3, 1, 8, 8]
,[-1, 1, 1, 3, 3, 2, -2, -1, 1, 1, 2, -2, -2, -2, -2, -3, -2, -2, -1, -1, 3, 3, -4, -4, 1, 1, -2, -5, -2, 2, -3, -2, -2, -3, -1, 2, -2, -1, -2, -2, -4, -3, -1, -1, -1, -2, 1, -1, 2, -1, 3, 3, 1, 1, 3, -4, -4, 2, 2, -1, -1, -1, 1, -4, 1, -2, -1, -1, -1, -2, -1, 1, -1, 5, 4, -2, -3, -2, -3, 2, -1, -4, -3, -1, 1, -4, -5, -3, -2, -2, -1, 1, -3, 1, 1, -1, 1, -2, 5, 5]
,[-2, -1, -1, -4, -6, -4, 3, -1, 3, 1, -2, 2, 3, 2, 1, 2, 2, 7, 5, 5, -5, -5, 2, 5, 6, 5, 5, 2, 2, 1, 2, 3, 2, 1, 6, 7, 3, 4, 2, 2, 6, 1, 5, 5, 4, 6, 2, 2, 5, 5, -5, -5, 1, 1, 7, 5, 2, 3, 1, -1, -1, 6, 6, 3, 4, 4, 3, 7, 6, 5, 7, -2, -2, 2, 2, 2, 3, -3, -2, 6, 5, -1, 1, -2, -2, -2, -1, -1, 1, 4, 6, 1, -4, -2, -2, -2, -2, -5, 3, 4]
,[-2, -2, -2, -6, -7, -5, -3, -5, -4, -4, -5, -3, -5, -3, -5, -5, -4, -1, 1, -1, -6, -6, -4, -3, 2, 2, -3, -6, -4, 1, -5, -3, -4, -5, 1, 3, -6, -3, -5, -5, -3, -5, -2, -1, -3, -3, -4, -5, 2, -1, -7, -7, -1, -1, 2, -6, -7, -3, -4, -6, -6, -1, 1, -7, -1, -4, -4, 1, 1, -2, -1, -6, -6, -1, -2, -7, -6, -9, -9, 1, -2, -8, -7, -5, -6, -11, -10, -9, -7, -3, -1, -6, -11, -5, -6, -6, -5, -10, -2, -1]
,[-2, -2, -2, -5, -6, -5, 2, -1, 3, -2, -4, 2, 3, 2, -1, 1, 1, 5, 3, 3, -6, -5, 1, 4, 3, 3, 4, 2, 1, 1, 1, 2, 1, 1, 4, 5, 2, 3, 2, 2, 4, 1, 3, 3, 3, 5, 3, 3, 3, 3, -6, -6, -1, -1, 5, 4, 1, 1, -2, -1, -1, 3, 4, 2, 3, 3, 2, 5, 4, 4, 5, -2, -2, 3, 2, 1, 3, -2, -1, 4, 3, -1, 1, -2, -2, -1, -1, -1, 1, 3, 4, 1, -3, -2, -2, -2, -2, -4, 3, 4]
,[-2, -1, -1, -6, -6, -4, -1, -2, 1, -4, -5, -2, -2, -2, -3, -2, -2, 1, 1, 1, -5, -5, -4, -2, 2, 2, -1, -4, -2, 1, -3, -1, -2, -2, 2, 3, -3, -2, -2, -2, -2, -3, -1, -1, -2, -1, 1, -1, 1, -1, -7, -6, -1, -1, 3, -3, -5, -3, -4, -3, -2, 1, 1, -4, -1, -2, -3, 2, 2, -1, 1, -2, -2, 3, 2, -4, -3, -5, -6, 2, -1, -6, -5, -2, -2, -6, -7, -5, -4, -2, -1, -2, -6, -2, -3, -3, -3, -6, 2, 4]
,[-2, -2, -2, -7, -8, -5, -3, -5, -3, -6, -7, -3, -4, -3, -5, -4, -4, -3, -2, -3, -7, -6, -6, -4, -2, -1, -3, -7, -4, 1, -5, -3, -4, -4, -2, 1, -6, -4, -5, -4, -5, -5, -3, -4, -5, -4, -2, -3, -2, -3, -8, -8, -1, -2, -1, -7, -8, -5, -6, -4, -4, -3, -2, -7, -3, -5, -5, -1, -2, -4, -3, -5, -4, 1, -1, -7, -7, -8, -8, -2, -4, -9, -8, -4, -4, -9, -10, -8, -7, -5, -3, -5, -9, -4, -5, -5, -5, -8, -2, 1]
])
    labels = ['A','A','A','A','A', 'B', 'B','B','B','B','C','C','C','C','C', 'D', 'D', 'D', 'D', 'D']
    return group, labels


###通过KNN进行分类
def classify(input, dataSet, label, k):
    dataSize = dataSet.shape[0]
    ####计算欧式距离
    diff = tile(input, (dataSize, 1)) - dataSet
    sqdiff = diff ** 2
    squareDist = sum(sqdiff, axis=1)  ###行向量分别相加，从而得到新的一个行向量
    dist = squareDist ** 0.5

    ##对距离进行排序
    sortedDistIndex = argsort(dist)  ##argsort()根据元素的值从大到小对元素进行排序，返回下标

    classCount = {}
    for i in range(k):
        voteLabel = label[sortedDistIndex[i]]
        print(voteLabel, end=" ")
        ###对选取的K个样本所属的类别个数进行统计
        classCount[voteLabel] = classCount.get(voteLabel, 0) + 1

    ###选取出现的类别次数最多的类别发
    maxCount = 0
    for key, value in classCount.items():
        if value > maxCount:
            maxCount = value
            classes = key

    return classes

第六步：将一个人脸输入到KNN模型中，分类：

#-*-coding:utf-8 -*-
import sys
sys.path.append("...文件路径...")
import one
from numpy import *
dataSet,labels = one.createDataSet()
input = array([
[-1, 1, 1, 2, 2, 1, -1, -1, 2, 1, 2, -1, -1, -1, -2, -1, -1, -4, -4, -5, 2, 1, -7, -4, -3, -3, -2, -5, -2, 1, -2, -1, -1, -2, -4, -3, -1, -2, 1, 1, -5, -1, 1, -1, -3, -2, 3, 3, -3, -3, 2, 2, -1, 1, -2, -2, -1, 1, 1, 3, 3, -2, -2, -1, -3, 1, 1, -3, -2, -3, -2, 3, 3, 4, 4, -1, 1, 3, 2, -3, -3, -2, -1, 2, 3, 1, -2, 2, 1, -3, -3, 4, 3, 2, 3, 2, 3, 5, 6, 6]
,[-2, -1, 1, -4, -5, -4, 1, -2, -1, -1, -2, -1, -1, 1, -2, -2, -1, 7, 8, 8, -5, -4, 4, 5, 8, 8, 5, 1, -1, 2, -2, 1, 1, -2, 8, 10, 1, 5, -5, -3, 6, -2, 4, 5, 5, 5, -8, -8, 8, 6, -5, -6, 2, 2, 9, 3, -4, 3, 1, -10, -9, 6, 8, -4, 5, -1, -2, 7, 6, 5, 6, -9, -10, -4, -6, -1, -1, -9, -8, 9, 6, -6, -4, -9, -9, -10, -5, -7, -3, 4, 6, -9, -12, -9, -9, -9, -9, -13, -5, -5]
,[-1, 1, 2, 2, 2, 2, -1, 1, 2, 1, 2, -1, -1, -1, -2, -2, -1, 2, 2, 2, 2, 2, -2, -1, 3, 3, 1, -4, -1, 2, -2, -1, -1, -2, 3, 5, -1, 1, -1, -1, -2, -3, 2, 2, 1, 1, 1, -1, 4, 2, 2, 3, 1, 2, 5, -2, -3, 2, 2, -2, -1, 3, 4, -3, 3, -1, -1, 2, 3, 1, 2, -1, -2, 4, 4, -2, -2, -3, -3, 4, 2, -4, -3, -2, -1, -4, -4, -3, -2, 1, 2, 1, -4, -1, -1, -1, -1, -3, 5, 5]
,[-2, -1, -2, -6, -6, -5, -1, -3, -1, -3, -5, -1, -1, -1, -3, -2, -2, 2, 2, 1, -6, -5, -3, -1, 2, 2, 1, -3, -2, 1, -2, -1, -2, -2, 2, 4, -3, -1, -2, -2, -1, -3, 1, 1, -1, 1, -1, -1, 2, 1, -7, -6, -1, -1, 3, -2, -4, -2, -3, -3, -2, 1, 2, -3, 1, -1, -2, 3, 2, 1, 2, -3, -3, 2, 1, -3, -3, -5, -5, 2, 1, -5, -4, -3, -3, -6, -6, -5, -4, -1, 1, -2, -6, -3, -3, -4, -3, -6, 1, 3]

])
K = 5
output = one.classify(input[3],dataSet,labels,K)
print("分类结果为：",output)