【深度学习】| 基于keras以及cv2实现人脸识别

人脸识别

了解常见的机器学习库Scikit-learn后,也想入门下深度学习库keras

以下就以Python来实现简单的人脸识别技术！

一、人脸识别认证

1.对照人脸获取

#-----获取人脸样本-----

#1.导入库
import cv2
 
#2.打开摄像头(调用笔记本内置摄像头,参数0保持摄像头不关闭,如果有其他的摄像头可以调整参数为1,2)
cap = cv2.VideoCapture(0)

#3.加载人脸模型(费舍尔人脸建模,调用人脸分类器,要根据实际路径调整)
face_detector = cv2.CascadeClassifier(r'D:\SoftWare\Anaconda\Anaconda\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml')   

#为即将录入的脸标记一个id
face_id = input('\n 用户数据输入face_id，看摄像头等待 ...')

#sampleNum用来计数样本数目
count = 0

#3获取摄像头的实时画面
while True:    
    #从摄像头读取图片
    success,img = cap.read()    
    #4.图像的灰度处理(opencv不适用RGB通道，用GBR，所以需转化)
    #转为灰度图片，减少程序符合，提高识别度
    if success is True: 
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 
    else:   
        break
        
    #5.检查人脸(120x120的识别框)
    #检测人脸，将每一帧摄像头记录的数据带入OpenCv中，让Classifier判断人脸
    faces = face_detector.detectMultiScale(gray, 1.3, 5) #其中gray为要检测的灰度图像，1.3为每次图像尺寸减小的比例，5为minNeighbors
 
    #6.标记人脸(10是线的宽度 ，元组三元素是GBR,2是线宽度)
    #框选人脸，for循环保证一个能检测的实时动态视频流
    for (x, y, w, h) in faces:
        cv2.rectangle(img, (x, y), (x+w, y+w), (255, 0, 0)) #xy为左上角的坐标,w为宽，h为高，用rectangle为人脸标记画框
        #成功框选则样本数增加
        count += 1  
        #保存图像，把灰度图片看成二维数组来检测人脸区域
        #(这里是建立了data的文件夹，当然也可以设置为其他路径或者调用数据库)
        cv2.imwrite("data/User."+str(face_id)+'.'+str(count)+'.jpg',gray[y:y+h,x:x+w]) 
        #显示图片
        cv2.imshow('image',img)       
        #保持画面的连续。waitkey方法可以绑定按键保证画面的收放，通过q键退出摄像
        
    k = cv2.waitKey(1)        
    if k == '27':
        break        
        #或者得到100个样本后退出摄像，这里可以根据实际情况修改数据量，实际测试后800张的效果是比较理想的
    elif count >= 100:
        break
        
        
#关闭摄像头，释放资源
cap.realease()
cv2.destroyAllWindows()

2.通过算法建立对照模型

#-----建立模型、创建数据集-----#-----建立模型、创建数据集-----
 
import os
import cv2
import numpy as np
from PIL import Image
#导入pillow库，用于处理图像
#设置之前收集好的数据文件路径
path = 'data'
 
#初始化识别的方法
recog = cv2.face.LBPHFaceRecognizer_create()
 
#调用熟悉的人脸分类器
detector = cv2.CascadeClassifier('D:\SoftWare\Anaconda\Anaconda\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml')
 
#创建一个函数，用于从数据集文件夹中获取训练图片,并获取id
#注意图片的命名格式为User.id.sampleNum
def get_images_and_labels(path):
    image_paths = [os.path.join(path,f) for f in os.listdir(path)]
    #新建连个list用于存放
    face_samples = []
    ids = []
 
    #遍历图片路径，导入图片和id添加到list中
    for image_path in image_paths:
 
        #通过图片路径将其转换为灰度图片
        img = Image.open(image_path).convert('L')
 
        #将图片转化为数组
        img_np = np.array(img,'uint8')
 
        if os.path.split(image_path)[-1].split(".")[-1] != 'jpg':
            continue
 
        #为了获取id，将图片和路径分裂并获取
        id = int(os.path.split(image_path)[-1].split(".")[1])
        faces = detector.detectMultiScale(img_np)
 
        #将获取的图片和id添加到list中
        for(x,y,w,h) in faces:
            face_samples.append(img_np[y:y+h,x:x+w])
            ids.append(id)
    return face_samples,ids
 
#调用函数并将数据喂给识别器训练
print('Training...')
faces,ids = get_images_and_labels(path)
#训练模型
recog.train(faces,np.array(ids))
#保存模型
recog.save('./trainner.yml') 

3.认证识别

#-----检测、校验并输出结果-----
import cv2
 
#准备好识别方法
recognizer = cv2.face.LBPHFaceRecognizer_create()
 
#使用之前训练好的模型
recognizer.read('./trainner.yml')
 
#再次调用人脸分类器
cascade_path = "D:\SoftWare\Anaconda\Anaconda\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml" 
face_cascade = cv2.CascadeClassifier(cascade_path)
 
#加载一个字体，用于识别后，在图片上标注出对象的名字
font = cv2.FONT_HERSHEY_SIMPLEX
 
idnum = 0
#设置好与ID号码对应的用户名，如下，如0对应的就是初始
 
names = ['kyle','patton','nicole','anthony','venus']

#调用摄像头
cam = cv2.VideoCapture(0)
minW = 0.1*cam.get(3)
minH = 0.1*cam.get(4)
 
while True:
    ret,img = cam.read()
    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    #识别人脸
    faces = face_cascade.detectMultiScale(
            gray,
            scaleFactor = 1.2,
            minNeighbors = 5,
            minSize = (int(minW),int(minH))
            )
    #进行校验
    for(x,y,w,h) in faces:
        cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
        idnum,confidence = recognizer.predict(gray[y:y+h,x:x+w])
        
        print(confidence)
        print(idnum)
        
        #计算出一个检验结果
        if confidence < 100:
            idum = names[idnum]
            confidence = "{0}%",format(round(100-confidence))
        else:
            idum = "unknown"
            confidence = "{0}%",format(round(100-confidence))
 
        #输出检验结果以及用户名
        cv2.putText(img,str(idum),(x+5,y-5),font
                    ,1,(0,0,255),1)
        cv2.putText(img,str(confidence),(x+5,y+h-5),font
                    ,1,(0,0,0),1)
 
        #展示结果
        cv2.imshow('camera',img)
        k = cv2.waitKey(20)
        if k == 27:
            break
            
#释放资源
cam.release()
cv2.destroyAllWindows()

二、人脸检测微表情识别

识别7种微表情–愤怒、厌恶、恐惧、快乐、悲伤、惊喜、自然

性别识别

from statistics import mode
import cv2
import matplotlib.pyplot as plt
from keras.models import load_model
import numpy as np
import pandas as pd
from keras.preprocessing import image
from random import shuffle
import os
from scipy.io import loadmat


def get_labels(dataset_name):
    if dataset_name == 'fer2013':
        return {0: 'angry', 1: 'disgust', 2: 'fear', 3: 'happy',
                4: 'sad', 5: 'surprise', 6: 'neutral'}
    elif dataset_name == 'imdb':
        return {0: 'woman', 1: 'man'}
    elif dataset_name == 'KDEF':
        return {0: 'AN', 1: 'DI', 2: 'AF', 3: 'HA', 4: 'SA', 5: 'SU', 6: 'NE'}
    else:
        raise Exception('Invalid dataset name')
        
def detect_faces(detection_model, gray_image_array):
    return detection_model.detectMultiScale(gray_image_array, 1.3, 5)

def draw_text(coordinates, image_array, text, color, x_offset=0, y_offset=0,
                                                font_scale=2, thickness=2):
    x, y = coordinates[:2]
    cv2.putText(image_array, text, (x + x_offset, y + y_offset),
                cv2.FONT_HERSHEY_SIMPLEX,
                font_scale, color, thickness, cv2.LINE_AA)
    
def draw_bounding_box(face_coordinates, image_array, color):
    x, y, w, h = face_coordinates
    cv2.rectangle(image_array, (x, y), (x + w, y + h), color, 2)
    

def apply_offsets(face_coordinates, offsets):
    x, y, width, height = face_coordinates
    x_off, y_off = offsets
    return (x - x_off, x + width + x_off, y - y_off, y + height + y_off)


def load_detection_model(model_path):
    detection_model = cv2.CascadeClassifier(model_path)
    return detection_model

def preprocess_input(x, v2=True):
    x = x.astype('float32')
    x = x / 255.0
    if v2:
        x = x - 0.5
        x = x * 2.0
    return x


# 加载数据和图像的参数
detection_model_path = 'D:/work/jupyter notebook/2022/face_classification-master/face_classification-master/trained_models/detection_models/haarcascade_frontalface_default.xml'
emotion_model_path   = 'D:/work/jupyter notebook/2022/face_classification-master/face_classification-master/trained_models/emotion_models/fer2013_mini_XCEPTION.102-0.66.hdf5'
emotion_labels = get_labels('fer2013')

# 边界框形状的超参数
frame_window = 10
emotion_offsets = (20, 40)

#加载模型
face_detection = load_detection_model(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)

# 获取输入模型形状以进行推理
emotion_target_size = emotion_classifier.input_shape[1:3]

# 计算模式的起始列表
emotion_window = []

# 开始视频流
cv2.namedWindow('window_frame')
video_capture = cv2.VideoCapture(0)
while True:
    bgr_image = video_capture.read()[1]
    gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)
    rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
    faces = detect_faces(face_detection, gray_image)

    for face_coordinates in faces:

        x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
        gray_face = gray_image[y1:y2, x1:x2]
        try:
            gray_face = cv2.resize(gray_face, (emotion_target_size))
        except:
            continue

        gray_face = preprocess_input(gray_face, True)
        gray_face = np.expand_dims(gray_face, 0)
        gray_face = np.expand_dims(gray_face, -1)
        emotion_prediction = emotion_classifier.predict(gray_face)
        emotion_probability = np.max(emotion_prediction)
        emotion_label_arg = np.argmax(emotion_prediction)
        emotion_text = emotion_labels[emotion_label_arg]
        emotion_window.append(emotion_text)

        if len(emotion_window) > frame_window:
            emotion_window.pop(0)
        try:
            emotion_mode = mode(emotion_window)
        except:
            continue

        if emotion_text == 'angry':
            color = emotion_probability * np.asarray((255, 0, 0))
        elif emotion_text == 'sad':
            color = emotion_probability * np.asarray((0, 0, 255))
        elif emotion_text == 'happy':
            color = emotion_probability * np.asarray((255, 255, 0))
        elif emotion_text == 'surprise':
            color = emotion_probability * np.asarray((0, 255, 255))
        else:
            color = emotion_probability * np.asarray((0, 255, 0))

        color = color.astype(int)
        color = color.tolist()

        draw_bounding_box(face_coordinates, rgb_image, color)
        draw_text(face_coordinates, rgb_image, emotion_mode,
                  color, 0, -45, 1, 1)

    bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
    cv2.imshow('window_frame', bgr_image)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
        
video_capture.release()
cv2.destroyAllWindows()