OpenCV pythonモジュールのエラー

Question

このコードは、haarカスケードアルゴリズムの助けを借りて顔の検出と認識を行うためのものです。 OpenCV 3.2.0とPython 2.7で動作します。 Module Error

私を助けることができる人がいますか？それは私にとって大きな助けになるでしょう。ありがとうございました。

# facerec.py 
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 
# NOTE FOR OpenCV2: remove '.face' 
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)) 

    # Show the image and check for ESC being pressed 
    cv2.imshow('OpenCV', frame) 
    key = cv2.waitKey(10) 
    if key == 27: 
     break 

Answer 1

あなた自身のコードでコメントに耳を傾けていないようです。エラーメッセージは、cv2モジュールがfaceを定義していないことを示しており、コメントは同じことについて警告するようです。

# NOTE FOR OpenCV2: remove '.face' 
model = cv2.face.createFisherFaceRecognizer() 

あなたが代わりにコメントし、コード

model = cv2.createFisherFaceRecognizer() 

に耳を傾けた場合はどうなりますか？

Answer 2

githubからopencv_Contrib zipをインストールするだけでエラーを解決できます。