2020-11-15

标题树莓派：人脸识别和口罩检测

依赖

# coding=utf-8
# import the necessary packages

import time
import cv2 as cv
import numpy as np

打开摄像头及加载模型

#打开摄像头
cap = cv.VideoCapture(0)
# 创建检测器
face_recognizer = cv.face.LBPHFaceRecognizer_create()
#低版本的opencv使用下面的语句
#face_recognizer = cv.face.createLBPHFaceRecognizer()
# 加载 人脸识别model
face_recognizer.read('fm_model.xml')
#低版本的opencv使用下面的语句
#face_recognizer.laod('fm_model.xml')
# 加载 人脸检测model
face_cascade=cv.CascadeClassifier('model/haarcascade_frontalface_default.xml')
# 加载 人脸的鼻子检测model
nose_cascade=cv.CascadeClassifier('model/haarcascade_mcs_nose.xml')

如果能opencv的版本>=3.3,可以使用dnn，我是在安装openVINO时装的opencv，可能这样才能使用dnn去加载Intel的模型，dnn模型会比检测器更快，而且可以使用Intel的神经计算棒，极大提高检测速度。

# Load the model 
net = cv.dnn.readNet('face-detection-adas-0001.xml', 'face-detection-adas-0001.bin') 
# Specify target device 
net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)

交互界面

# create switch for ON/OFF functionality
def nothing(x):
    pass
cv.namedWindow('Frame')
switch1 = "face_Recognize"
switch2 = "face_mask"
#创建滑条作为开关
cv.createTrackbar(switch1, 'Frame',0,1,nothing)
cv.createTrackbar(switch2, 'Frame',0,1,nothing)
# 画框
def draw_rectangle(img, rect):
    for x,y,w,h in rect:
        cv.rectangle(img, (x, y), (x+w, y+h), (128, 128, 0), 2)
# 写文字
def draw_text(img, text, rect):
    for x,y,w,h in rect:
        cv.putText(img, text, (x, y), cv.FONT_HERSHEY_COMPLEX, 1, (0, 0, 255), 2)

模型信息

# 用户列表
def user_list():
    label = 0人脸检测
    while True:
        name = face_recognizer.getLabelInfo(label)
        if len(name) != 0:
            print('label:', label, 'name:', name)
        else:
            name =  input("inoutID:")
            face_recognizer.setLabelInfo(label, name)
            return label
        label += 1

截取人脸图片

def cut_face(image):
	#运行人脸检测模型
    results=face_cascade.detectMultiScale(gray,1.1,5)
    if results != ():
        for x,y,w,h in results:
            return results,gray[y:y+h, x:x+w]
    else:
        return [[0, 0, 0, 0]], None

或使用dnn

def detect_face(image):
	blob = cv.dnn.blobFromImage(image, size=(672,384), ddepth=cv.CV_8U)
	net.setInput(blob) 
	out = net.forward()
    # Draw detected faces on the frame 
	for detection in out.reshape(-1, 7): 
		confidence = float(detection[2]) 
		xmin = int(detection[3] * image.shape[1]) 
		ymin = int(detection[4] * image.shape[0]) 
		xmax = int(detection[5] * image.shape[1]) 
		ymax = int(detection[6] * image.shape[0])
		if confidence > 0.5:
			return [[xmin,ymin,xmax-xmin,ymax-ymin]],image[ymin:ymax, xmin:xmax]
		else:
			return [[0, 0, 0, 0]], None

口罩检测

# 口罩判别
def Mask_detection(image,rect):
	#运行模型
    nose = nose_cascade.detectMultiScale(gray,1.3,5)
    #判断是否带口罩
    if nose!=() and rect[0][0]+rect[0][2] != 0:
        return "NO"
    elif rect[0][0]+rect[0][2] != 0: 
        return "YES"
    return '**'

人脸识别

 人脸识别 预测
def predict(image):
    if image is not None:
        #预测人脸
        results = face_recognizer.predict(image)
        print(results[1])
        #置信度阈值
        if results[1] < 60:
            label_text = face_recognizer.getLabelInfo(results[0])
        else:
            label_text = 'stranger'
        return label_text
    else:
    #print eye
        return 'not whole face'

主函数

train_faces = []
train_labels = []
num = -1
#训练每个人的图片数
train_face_num = 15
label_mask = ""

while( cap.isOpened() ):
    # USB摄像头工作时,读取一帧图像
    start = time.time()
    #读取图片
    ret, image = cap.read()
    #二值化
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    #识别人脸并截取人脸图片
    rect,cut_img = cut_face(gray)
    #画人脸框
    draw_rectangle(image, rect)
    #获取滑条开关的值
    f = cv.getTrackbarPos(switch1,'Frame')
    m = cv.getTrackbarPos(switch2,'Frame'）
    if f == 1:
    	 # 预测 返回人脸标签
    	label_text = predict(cut_img)
    	draw_text(image, label_text, rect)
    if m == 1:
     	# 检测口罩
    	label_mask = Mask_detection(gray,rect)
    	draw_text(image, "mask:"+label_mask, [[10,40,10,10]])
    	
    key = cv.waitKey(1) & 0xFF
    end = time.time()
    #显示fps
    draw_text(image, "fps:"+str(1//(end-start)), [[10,60,10,10]])
    #显示视频
    cv.imshow("Frame", image)
   #当num=0时训练
    if num > -1 and num < train_face_num :
        rect,cut_img=cut_face(gray)
        if cut_img is not None:
            train_faces.append(cut_img)
            train_labels.append(train_label)
            num=num+1
        draw_text(image, "condition:"+str(train_face_num-num), [[10,100,10,10]])
    elif num >= train_face_num:
        face_recognizer.update(train_faces, np.array(train_labels))
        face_recognizer.save('fm_model3.0.xml')
        train_faces = []
        train_labels = []
        num = -1
     # if the `q` key was pressed, break from the loop
    if key == ord("q"):
        break
     # 按‘s’键训练
    if key == ord("s"):
        if num == -1:
            train_label = user_list()
            num=0
            
# 释放资源和关闭窗口
cap.release()
cv.destroyAllWindows()

要提前创建好模型

# coding=utf-8
import time
import cv2 as cv
face_recognizer = cv.face.createLBPHFaceRecognizer()
face_recognizer.save('fm_model.xml')

OpenVINO
OpenVINO安装

源码、模型及资料
链接: https://pan.baidu.com/s/1EFxwwNkLfDKrAhcwm2DxKA
提取码: tb28