TypeError: 'int' object is not iterable error

Question

I am trying to run a python code but I get an error, I am not familiar with python so I don't know how to debug the code. please help and thank you. I just found this code on this website : http://www.paulvangent.com/2016/04/01/emotion-recognition-with-python-opencv-and-a-face-dataset/ here is the code :

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!")

here is the output of this code :

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

Answer 1

int is not iterable

a, b = c means that it will attempt to unpack c as an iterable object and assign to a, b. So, a, b = [1, 2] will set a to 1 and b to 2. a, b = [1] will error because there aren't enough values to unpack. a, b = [1, 2, 3] will error because there are too many values to unpack.

In your case, a, b = 1 would error because 1 can't be unpacked, because you cannot iterate through it. Iterating through an object means going through all of its elements (like a list, tuple, set, dict, etc). An integer is just a value; it isn't iterable.

This means that fishface.predict returns a number. I'm not sure what pred, conf are meant to be (I'm guessing prediction and confidence), but check the docs for FisherFaceRecognizer#predict to see what it returns.