OpenCV + RaspberyPI像一个房间监视器

Question

我是新的使用opencv和python，我的项目是关于一个智能家居。

我设法在raspberrypi上安装opencv并使用网络摄像头。

我的程序将在以下三种情况下工作。 1.一个人进入房间，探测到脸和人，发送信息“爸爸在房间1”。 2.一个人进入房间，检测面部而不是人，发送消息“不明的人在房间1” 3.没有人在房间里，发送消息“没有人在房间1”

方案1和方案2我有想法如何解决它们，但我卡在哪里是esceanrio 3.我试图将检测到的人的名字保存在一个变量中，如果这是空的，应该发送消息，但它没有为我工作。

我使用的代码如下，我有问题的代码的末尾：

import cv2, sys, numpy, os 
size = 1 
fn_haar = 'haarcascade_frontalface_default.xml' 
fn_dir = 'att_faces' 

# Part 1: Create fisherRecognizer 
print('Training...') 

# Create a list of images and a list of corresponding names 
(images, lables, names, id) = ([], [], {}, 0) 

# Get the folders containing the training data 
for (subdirs, dirs, files) in os.walk(fn_dir): 

    # Loop through each folder named after the subject in the photos 
    for subdir in dirs: 
     names[id] = subdir 
     subjectpath = os.path.join(fn_dir, subdir) 

     # Loop through each photo in the folder 
     for filename in os.listdir(subjectpath): 

      # Skip non-image formates 
      f_name, f_extension = os.path.splitext(filename) 
      if(f_extension.lower() not in 
        ['.png','.jpg','.jpeg','.gif','.pgm']): 
       print("Skipping "+filename+", wrong file type") 
       continue 
      path = subjectpath + '/' + filename 
      lable = id 

      # Add to training data 
      images.append(cv2.imread(path, 0)) 
      lables.append(int(lable)) 
     id += 1 
(im_width, im_height) = (112, 92) 

# 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 
model = cv2.face.createFisherFaceRecognizer() 
model.train(images, lables) 




# Part 2: Use fisherRecognizer on camera stream 
haar_cascade = cv2.CascadeClassifier(fn_haar) 
webcam = cv2.VideoCapture(0) 
while True: 

    # Loop until the camera is working 
    rval = False 
    while(not rval): 
     # Put the image from the webcam into 'frame' 
     (rval, frame) = webcam.read() 
     if(not rval): 
      print("Failed to open webcam. Trying again...") 

    # Flip the image (optional) 
    frame=cv2.flip(frame,1,0) 

    # Convert to grayscalel 
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) 

    # Resize to speed up detection (optinal, change size above) 
    mini = cv2.resize(gray, (int(gray.shape[1]/size), int(gray.shape[0]/size))) 

    # Detect faces and loop through each one 
    faces = haar_cascade.detectMultiScale(mini) 
    for i in range(len(faces)): 
     face_i = faces[i] 

     # Coordinates of face after scaling back by `size` 
     (x, y, w, h) = [v * size for v in face_i] 
     face = gray[y:y + h, x:x + w] 
     face_resize = cv2.resize(face, (im_width, im_height)) 

     # Try to recognize the face 
     prediction = model.predict(face_resize) 
     cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3) 

     # [1] 
     # Write the name of recognized face 
     cv2.putText(frame, 
      '%s - %.0f' % (names[prediction[0]],prediction[1]), 
      (x-10, y-10), cv2.FONT_HERSHEY_PLAIN,1,(0, 255, 0)) 
      face = '%S' % (names[prediction[0]]) #Guardar nombre en variable 
      #Start to validate the name 
      if face != "" : #If a name is detected 
       print(face + "Is in the room..") #Print the name in terminal 
      elif face == "" : #If a name is not detected 
       print("The room is empty...") #Print the text in terminal 
       #This last part is where I have problem, when a face is not detected, the text is not printed in the terminal 
    # Show the image and check for ESC being pressed 
    cv2.imshow('OpenCV', frame) 
    key = cv2.waitKey(10) 
    if key == 27: 
     break 

我使用的代码是基于以下教程：Face Detection

任何帮助表示感谢，谢谢。问候

Answer 1

如果在房间内没有检测到面部，您的代码不会进入for i in range(len(faces))循环，顺便说一句可以简化为for i in faces。

因此，else你for循环结束解决您的问题：

for face in faces: 
    do_stuff(face) 
else: 
    print("room is empty")