我正在尝试运行 python 代码，但出现错误，我不熟悉 python，所以我不知道如何调试代码。 请帮忙，谢谢。 我刚刚在这个网站上找到了这段代码: http://www.paulvangent.com/2016/04/01/emotion-recognition-with-python-opencv-and-a-face-dataset/ 这是代码:

import cv2 
import glob 
import random 
import numpy as np 
 
emotions = ["neutral", "anger", "contempt", "disgust", "fear", "happy", "sadness", "surprise"] #Emotion list 
fishface = cv2.face.createFisherFaceRecognizer() #Initialize fisher face classifier 
 
data = {} 
 
def get_files(emotion): #Define function to get file list, randomly shuffle it and split 80/20 
    files = glob.glob("dataset\\%s\\*" %emotion) 
    random.shuffle(files) 
    training = files[:int(len(files)*0.8)] #get first 80% of file list 
    prediction = files[-int(len(files)*0.2):] #get last 20% of file list 
    return training, prediction 
 
def make_sets(): 
    training_data = [] 
    training_labels = [] 
    prediction_data = [] 
    prediction_labels = [] 
    for emotion in emotions: 
        training, prediction = get_files(emotion) 
        #Append data to training and prediction list, and generate labels 0-7 
        for item in training: 
            image = cv2.imread(item) #open image 
            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) #convert to grayscale 
            training_data.append(gray) #append image array to training data list 
            training_labels.append(emotions.index(emotion)) 
 
        for item in prediction: #repeat above process for prediction set 
            image = cv2.imread(item) 
            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 
            prediction_data.append(gray) 
            prediction_labels.append(emotions.index(emotion)) 
 
    return training_data, training_labels, prediction_data, prediction_labels 
 
def run_recognizer(): 
    training_data, training_labels, prediction_data, prediction_labels = make_sets() 
 
    print ("training fisher face classifier") 
    print ("size of training set is:", len(training_labels), "images") 
    fishface.train(training_data, np.asarray(training_labels)) 
    print ("predicting classification set") 
    cnt = 0 
    correct = 0 
    incorrect = 0 
    for image in prediction_data: 
        pred, conf = fishface.predict(image) 
        if pred == prediction_labels[cnt]: 
            correct += 1 
            cnt += 1 
        else: 
            incorrect += 1 
            cnt += 1 
    return ((100*correct)/(correct + incorrect)) 
#Now run it 
metascore = [] 
for i in range(0,10): 
    correct = run_recognizer() 
    print ("got", correct, "percent correct!") 
    metascore.append(correct) 
 
print ("\n\nend score:", np.mean(metascore), "percent correct!") 

这是这段代码的输出:

training fisher face classifier 
size of training set is: 351 images 
predicting classification set 
Traceback (most recent call last): 
  File "splitData.py", line 62, in <module> 
    correct = run_recognizer() 
  File "splitData.py", line 51, in run_recognizer 
    pred, conf = fishface.predict(image) 
TypeError: 'int' object is not iterable 

请您参考如下方法:

int 不可迭代

a, b = c 表示它将尝试将 c 解包为可迭代对象并分配给 a, b。因此，a, b = [1, 2] 会将 a 设置为 1 并将 b 设置为 2。 a, b = [1] 会出错，因为没有足够的值来解包。 a, b = [1, 2, 3] 会出错，因为要解包的值太多。

在您的情况下，a, b = 1 会出错，因为 1 无法解包，因为您无法遍历它。遍历一个对象意味着遍历它的所有元素(如列表、元组、集合、字典等)。整数只是一个值；它不可迭代。

这意味着 fishface.predict 返回一个数字。我不确定 pred, conf 是什么意思(我猜是预测和置信度)，但请查看 FisherFaceRecognizer#predict 的文档以查看它返回的内容.