Python cv2 模块，createLBPHFaceRecognizer() 实例源码

def index():
    img_array = []
    label_array = []
    face_cascade = cv2.CascadeClassifier("https://raw.githubusercontent.com/opencv/opencv/master/data/haarcascades/haarcascade_frontalface_alt.xml")
    recognizer = cv2.createLBPHFaceRecognizer()
    for row in db(db.faces.id > 0).select():
        rtn = row
        path=os.path.join(request.folder, 'uploads', rtn.file)
#         image = response.download(open(path, 'rb'), chunk_size=4096)
        img = cv2.imread(path, 0)
        img_array.append(img)
#         faces = face_cascade.detectMultiScale(img, 1.3, 5)
#         for (x,y,w,h) in faces:
#             img_array.append(img[y: y + h, x: x + w])
        label_array.append(rtn.user_id)
    recognizer.train(img_array, np.array(label_array))
    recognizer.save(os.path.join(request.folder, 'private', "trained_recognizer.xml"))
    return response.download("trained_recognizer.xml")

def train(self): 
        """ Entraînement du jeu de données
            Méthode à surcharger
        """
        logging.info("Entraînement du trainset...")
        #self.model = cv2.createFisherFaceRecognizer() 
        #self.model = cv2.createEigenFaceRecognizer()
        self.model = cv2.createLBPHFaceRecognizer() 
        #self.model = cv2.createLBPHFaceRecognizer(radius = 1, grid_x = 6, grid_y = 6)
        self.model.train(numpy.asarray(self.trainset_images), numpy.asarray(self.trainset_index))

def model(algorithm, thresh):
    # set the choosen algorithm
    model = None
    if is_cv3():
        # OpenCV version renamed the face module
        if algorithm == 1:
            model = cv2.face.createLBPHFaceRecognizer(threshold=thresh)
        elif algorithm == 2:
            model = cv2.face.createFisherFaceRecognizer(threshold=thresh)
        elif algorithm == 3:
            model = cv2.face.createEigenFaceRecognizer(threshold=thresh)
        else:
            print("WARNING: face algorithm must be in the range 1-3")
            os._exit(1)
    else:
        if algorithm == 1:
            model = cv2.createLBPHFaceRecognizer(threshold=thresh)
        elif algorithm == 2:
            model = cv2.createFisherFaceRecognizer(threshold=thresh)
        elif algorithm == 3:
            model = cv2.createEigenFaceRecognizer(threshold=thresh)
        else:
            print("WARNING: face algorithm must be in the range 1-3")
            os._exit(1)
    return model

def train_recognizer():
    recognizer = cv2.createLBPHFaceRecognizer()
    images, labels = get_images_and_labels()
    #print images
    #print labels
    if images==False:
        return False
    cv2.destroyAllWindows()
    recognizer.train(images, np.array(labels))
    #print recognizer
    return recognizer

def train_recognizer():
    recognizer = cv2.createLBPHFaceRecognizer()
    images, labels = get_images_and_labels()
    #print images
    #print labels
    if images==False:
        return False
    cv2.destroyAllWindows()
    recognizer.train(images, np.array(labels))
    #print recognizer
    return recognizer

def initialize_recognizer():
    try:
        face_recognizer = cv2.face.createLBPHFaceRecognizer()
    except:
        face_recognizer = cv2.createLBPHFaceRecognizer()
    print "Training.........."
    Dataset = get_images("./Dataset")
    print "Recognizer trained using Dataset: "+str(Dataset[2])+" Images used"
    face_recognizer.train(Dataset[0],np.array(Dataset[1]))
    return face_recognizer

def recognizer(self):
    """
    Creates new FaceRecognizer using Local Binary Patterns (LBP)
    Returns current recognizer object
    """
    self.current_recognizer = cv2.createLBPHFaceRecognizer()
    self.current_recognizer.train(self.faces, np.array(self.index))

    return self.current_recognizer