利用百度API实现人脸特征检测

前言

在进行人脸识别之前，要对人脸的特征进行检测，获取关键部位信息。百度的api提供这一接口。对一张照片的人脸进行关键特征部位的检测，本文提供以下代码供参考。

五官位置会标记具体坐标；72个关键点坐标也包含具体坐标，但不包含对应位置的详细位置描述。

# -*- coding: utf-8 -*-# from aip import AipFaceimport cv2import base64import mathimport matplotlib.pyplot as pltfrom graphics import*#import tkinter as tkfrom tkinter import filedialogfrom time import sleep APP_ID = '16290061'API_KEY = '4uKwGeC2Ci7aHbn6dHuYIrGc'SECRET_KEY = 'KRdPZdtYwLsdEthIGu03jb7GEeRtTUMF'aipFace = AipFace(APP_ID, API_KEY, SECRET_KEY) filepath = "C:/Users/ZXY13/Desktop/train_face/1.jpg"win = GraphWin('人脸关键点检测', 500, 90)win.setBackground("yellow")Text(Point(win.getWidth()/2, 40), "请在文件夹中选择一张人脸图片进行关键点检测").draw(win)sleep(1)filepath = filedialog.askopenfilename() from PIL import Imageim = Image.open(filepath) with open(filepath, "rb") as fp:    base64_data = base64.b64encode(fp.read())image = str(base64_data, 'utf-8')imageType = "BASE64" options = {}options["face_field"] = "age,beauty,expression,faceshape,gender,glasses,landmark,race,quality,facetype"options["max_face_num"] = 10options["face_type"] = "LIVE" result = aipFace.detect(image, imageType, options)img = cv2.imread(filepath) face_num = result['result']['face_num']face_list = result['result']['face_list'] xvalue = []yvalue = []xvaluef = []yvaluef = [] plt.figure(figsize=(9,6.5))for num in range(0,int(face_num)):           location = result['result']['face_list'][num-1]['location']    landmark72 = result['result']['face_list'][num-1]['landmark72']     xvaluef1 = []    yvaluef1 = []    xvaluef2 = []    yvaluef2 = []    xvaluef3 = []    yvaluef3 = []    xvaluef4 = []    yvaluef4 = []    xvaluef5 = []    yvaluef5 = []    xvaluef6 = []    yvaluef6 = []    xvaluef7 = []    yvaluef7 = []    xvaluef8 = []    yvaluef8 = []     for i in range(0,72):        xvalue.append(landmark72[i]['x'])        yvalue.append(landmark72[i]['y'])        xvaluef.append(im.size[0]-landmark72[i]['x'])        yvaluef.append(im.size[1]-landmark72[i]['y'])      for i in range(0,13):        xvaluef1.append(landmark72[i]['x'])        yvaluef1.append(im.size[1]-landmark72[i]['y'])     for i in range(13,21):        xvaluef2.append(landmark72[i]['x'])        yvaluef2.append(im.size[1]-landmark72[i]['y'])    xvaluef2.append(landmark72[13]['x'])    yvaluef2.append(im.size[1]-landmark72[13]['y'])     for i in range(22,30):        xvaluef3.append(landmark72[i]['x'])        yvaluef3.append(im.size[1]-landmark72[i]['y'])    xvaluef3.append(landmark72[22]['x'])    yvaluef3.append(im.size[1]-landmark72[22]['y'])     for i in range(30,38):        xvaluef4.append(landmark72[i]['x'])        yvaluef4.append(im.size[1]-landmark72[i]['y'])    xvaluef4.append(landmark72[30]['x'])    yvaluef4.append(im.size[1]-landmark72[30]['y'])     for i in range(39,47):        xvaluef5.append(landmark72[i]['x'])        yvaluef5.append(im.size[1]-landmark72[i]['y'])    xvaluef5.append(landmark72[39]['x'])    yvaluef5.append(im.size[1]-landmark72[39]['y'])     for i in range(47,57):        xvaluef6.append(landmark72[i]['x'])        yvaluef6.append(im.size[1]-landmark72[i]['y'])    xvaluef6.append(landmark72[47]['x'])    yvaluef6.append(im.size[1]-landmark72[47]['y'])     for i in range(58,66):        xvaluef7.append(landmark72[i]['x'])        yvaluef7.append(im.size[1]-landmark72[i]['y'])    xvaluef7.append(landmark72[58]['x'])    yvaluef7.append(im.size[1]-landmark72[58]['y'])     for i in range(66,71):        xvaluef8.append(landmark72[i]['x'])        yvaluef8.append(im.size[1]-landmark72[i]['y'])        xvaluef8.append(landmark72[66]['x'])    yvaluef8.append(im.size[1]-landmark72[66]['y'])     plt.subplot(2,2,num+2)    plt.plot(xvaluef1,yvaluef1)#'ro',markersize=1    plt.plot(xvaluef2,yvaluef2)    plt.plot(xvaluef3,yvaluef3)    plt.plot(xvaluef4,yvaluef4)    plt.plot(xvaluef5,yvaluef5)    plt.plot(xvaluef6,yvaluef6)    plt.plot(xvaluef7,yvaluef7)    plt.plot(xvaluef8,yvaluef8)    plt.axis('off')     Theta = location['rotation'] / 60     A = (int(location['left']),int(location['top']))    B = (int(location['left'])+int(location['width']*math.cos(Theta)),int(location['top'])+int(location['width']*math.sin(Theta)))    AC_Len = math.sqrt(location['width']**2 + location['height']**2)    AC_Theta = math.atan(location['height']/location['width'])+location['rotation']/60     C = (int(location['left']) + int(AC_Len*math.cos(AC_Theta)), int(location['top'])+int(AC_Len*math.sin(AC_Theta)))    D = (int(location['left'])-int(location['height']*math.sin(Theta)), int(location['top']) + int(location['height']*math.cos(Theta)))    cv2.line(img, A, B, (0, 0, 255), 2)    cv2.line(img, B, C, (0, 0, 255), 2)    cv2.line(img, C, D, (0, 0, 255), 2)    cv2.line(img, D, A, (0, 0, 255), 2) plt.axis('off')plt.subplot(2,2,1)img = img[:,:,[2,1,0]]plt.imshow(img,cmap='gray')plt.plot(xvalue,yvalue,'bo',markersize=1)plt.axis('off')plt.show() plt.figure(figsize=(9,6.5))plt.imshow(img,'gray')plt.plot(xvalue,yvalue,'bo',markersize=1)plt.axis('off')plt.show()  

部分函数解释

BASE64:图片的base64值，base64编码后的图片数据，需urlencode，编码后的图片大小不超过2M； options[“face_field”]：在”face_field”我们可以选择我们需要百度云服务器回传的信息，如年龄（age），美貌评分（beauty）,表情（expression），等等。若想选择这些参数，只需将其所表示的英文名称赋值于options[“face_field”]就可以了，比如我想检测出我上传的图片的人是啥人种，年龄大致多少，就可以这么写。 aipFace.detect(image, imageType, options)：即为面部检测函数，内部可填入三个参数，image,imageType,options。这三个参数你也已经在上面的代码中赋值好了

人脸检测返回数据参数详情

获取结果

这些就是我们在进行人脸识别的依据的特征信息。

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