Traceback <most recent call last>:
File "ASL.py", line 24, in <module>
img1=img[100:500, 900:1300]
TypeError: 'NoneType' object has no attribute '__getitem__'

import cv2
import numpy as np
import util as ut
import svm_train as st
import re
model=st.trainSVM(17)
#create and train SVM model each time coz bug in opencv 3.1.0 svm.load() https://github.com/Itseez/opencv/issues/4969
cam=int(raw_input("Enter Camera number: "))
cap=cv2.VideoCapture(cam)
font = cv2.FONT_HERSHEY_SIMPLEX

def nothing(x) :
    pass

text= " "

temp=0
previouslabel=None
previousText=" "
label = [None]
while(cap.isOpened()):
    _,img=cap.read()
    cv2.rectangle(img,(900,100),(1300,500),(255,0,0),3)
    img1=img[100:500, 900:1300]
    img_ycrcb = cv2.cvtColor(img1, cv2.COLOR_BGR2YCR_CB)
    blur = cv2.GaussianBlur(img_ycrcb,(11,11),0)
    skin_ycrcb_min = np.array((0, 138, 67))
    skin_ycrcb_max = np.array((255, 183, 133))
    mask = cv2.inRange(blur, skin_ycrcb_min, skin_ycrcb_max)  # detecting the hand in the bounding box using skin detection
    contours,hierarchy = cv2.findContours(mask.copy(),cv2.RETR_EXTERNAL, 2)
    cnt=ut.getMaxContour(contours,4000)                       # using contours to capture the skin filtered image of the hand
    if cnt!=None:
        gesture,label=ut.getGestureImg(cnt,img1,mask,model)   # passing the trained model for prediction and fetching the result
        if(label!=None):
            if(temp==0):
                previouslabel=label
            if previouslabel==label :
                previouslabel=label
                temp+=1
            else :
                temp=0
            if(temp==40):
                if(label=='P'):

                    label=" "
                text= text + label
                if(label=='Q'):
                    words = re.split(" +",text)
                    words.pop()
                    text = " ".join(words)
                    #text=previousText
                print text

        cv2.imshow('PredictedGesture',gesture)                # showing the best match or prediction
        cv2.putText(img,label,(50,150), font,8,(0,125,155),2)  # displaying the predicted letter on the main screen
        cv2.putText(img,text,(50,450), font,3,(0,0,255),2)
    cv2.imshow('Frame',img)
    cv2.imshow('Mask',mask)
    k = 0xFF & cv2.waitKey(10)
    if k == 27:
        break


cap.release()
cv2.destroyAllWindows()