Python和OpenCV使用带网络摄像头进行人脸检测

【Python和OpenCV使用带网络摄像头进行人脸检测】OpenCV是一个库, 用于使用python等编程语言进行图像处理。该项目利用OpenCV库将网络摄像头作为主要摄像头进行实时面部检测。
以下是它的要求：

Python 2.7
OpenCV
Numpy
Haar级联正面人脸分类器

使用的方法/算法：

该项目使用LBPH(局部二进制图案直方图)算法来检测人脸。它通过对每个像素的邻域设定阈值来标记图像的像素, 并将结果视为二进制数。
LBPH使用4个参数：
(i)半径：半径用于构建圆形局部二进制图案, 并表示半径
中心像素。
(ii)邻居：构建圆形局部二进制模式的采样点数。
(iii)网格X：水平方向的像元数。
(iv)网格Y：垂直方向的像元数。
用带有标签的面部训练构建的模型, 然后为机器提供测试数据, 然后机器为其确定正确的标签。

如何使用：

在你的电脑中创建一个目录并命名(例如项目)
创建两个名为create_data.py和face_recognize.py的python文件, 分别复制其中的第一个源代码和第二个源代码。
将haarcascade_frontalface_default.xml复制到项目目录, 你可以在opencv或从
这里.
你现在准备运行以下代码。

# Creating database # It captures images and stores them in datasets # folder under the folder name of sub_data import cv2, sys, numpy, os haar_file = 'haarcascade_frontalface_default.xml'# All the faces data will be #present this folder datasets = 'datasets'# These are sub data sets of folder, # for my faces I've used my name you can # change the label here sub_data = 'http://www.srcmini.com/vivek'path = os.path.join(datasets, sub_data) if not os.path.isdir(path): os.mkdir(path)# defining the size of images (width, height) = ( 130 , 100 )#'0' is used for my webcam, # if you've any other camera #attached use '1' like this face_cascade = cv2.CascadeClassifier(haar_file) webcam = cv2.VideoCapture( 0 ) # The program loops until it has 30 images of the face. count = 1 while count < 30 : (_, im) = webcam.read() gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray, 1.3 , 4 ) for (x, y, w, h) in faces: cv2.rectangle(im, (x, y), (x + w, y + h), ( 255 , 0 , 0 ), 2 ) face = gray[y:y + h, x:x + w] face_resize = cv2.resize(face, (width, height)) cv2.imwrite( '% s/% s.png' % (path, count), face_resize) count + = 1cv2.imshow( 'OpenCV' , im) key = cv2.waitKey( 10 ) if key = = 27 : break

在对脸部模型进行训练之后, 应运行以下代码：

# It helps in identifying the faces import cv2, sys, numpy, os size = 4 haar_file = 'haarcascade_frontalface_default.xml' datasets = 'datasets'# Part 1: Create fisherRecognizer print ( 'Recognizing Face Please Be in sufficient Lights...' )# Create a list of images and a list of corresponding names (images, lables, names, id ) = ([], [], {}, 0 ) for (subdirs, dirs, files) in os.walk(datasets): for subdir in dirs: names[ id ] = subdir subjectpath = os.path.join(datasets, subdir) for filename in os.listdir(subjectpath): path = subjectpath + '/' + filename lable = id images.append(cv2.imread(path, 0 )) lables.append( int (lable)) id + = 1 (width, height) = ( 130 , 100 )# Create a Numpy array from the two lists above (images, lables) = [numpy.array(lis) for lis in [images, lables]]# OpenCV trains a model from the images # NOTE FOR OpenCV2: remove '.face' model = cv2.face.LBPHFaceRecognizer_create() model.train(images, lables)# Part 2: Use fisherRecognizer on camera stream face_cascade = cv2.CascadeClassifier(haar_file) webcam = cv2.VideoCapture( 0 ) while True : (_, im) = webcam.read() gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray, 1.3 , 5 ) for (x, y, w, h) in faces: cv2.rectangle(im, (x, y), (x + w, y + h), ( 255 , 0 , 0 ), 2 ) face = gray[y:y + h, x:x + w] face_resize = cv2.resize(face, (width, height)) # Try to recognize the face prediction = model.predict(face_resize) cv2.rectangle(im, (x, y), (x + w, y + h), ( 0 , 255 , 0 ), 3 )if prediction[ 1 ]< 500 :cv2.putText(im, '% s - %.0f' % (names[prediction[ 0 ]], prediction[ 1 ]), (x - 10 , y - 10 ), cv2.FONT_HERSHEY_PLAIN, 1 , ( 0 , 255 , 0 )) else : cv2.putText(im, 'not recognized' , (x - 10 , y - 10 ), cv2.FONT_HERSHEY_PLAIN, 1 , ( 0 , 255 , 0 ))cv2.imshow( 'OpenCV' , im)key = cv2.waitKey( 10 ) if key = = 27 : break

注意：以上程序无法在在线IDE上运行。
程序截图
可能看起来有些不同, 因为我已将上述程序集成在flask框架上
运行第二个程序产生的结果类似于下图：