ValueError: too many values to unpack python 2.7 -

so trying compile following code it's showing me error on cv2.findcontours. though, using python 2.7 version. reason why error: many values unpack python 2.7 coming?

import cv2 import numpy np import time  #open camera object cap = cv2.videocapture(0)  #decrease frame size cap.set(cv2.cap_prop_frame_width, 1000) cap.set(cv2.cap_prop_frame_height, 600)  def nothing(x):     pass  # function find angle between 2 vectors def angle(v1,v2):     dot = np.dot(v1,v2)     x_modulus = np.sqrt((v1*v1).sum())     y_modulus = np.sqrt((v2*v2).sum())     cos_angle = dot / x_modulus / y_modulus     angle = np.degrees(np.arccos(cos_angle))     return angle  # function find distance between 2 points in list of lists def finddistance(a,b):      return np.sqrt(np.power((a[0][0]-b[0][0]),2) + np.power((a[0][1]-b[0][1]),2))   # creating window hsv track bars cv2.namedwindow('hsv_trackbar')  # starting 100's prevent error while masking h,s,v = 100,100,100  # creating track bar cv2.createtrackbar('h', 'hsv_trackbar',0,179,nothing) cv2.createtrackbar('s', 'hsv_trackbar',0,255,nothing) cv2.createtrackbar('v', 'hsv_trackbar',0,255,nothing)  while(1):      #measure execution time      start_time = time.time()      #capture frames camera     ret, frame = cap.read()      #blur image     blur = cv2.blur(frame,(3,3))      #convert hsv color space     hsv = cv2.cvtcolor(blur,cv2.color_bgr2hsv)      #create binary image white skin colors , rest black     mask2 = cv2.inrange(hsv,np.array([2,50,50]),np.array([15,255,255]))      #kernel matrices morphological transformation         kernel_square = np.ones((11,11),np.uint8)     kernel_ellipse= cv2.getstructuringelement(cv2.morph_ellipse,(5,5))      #perform morphological transformations filter out background noise     #dilation increase skin color area     #erosion increase skin color area     dilation = cv2.dilate(mask2,kernel_ellipse,iterations = 1)     erosion = cv2.erode(dilation,kernel_square,iterations = 1)         dilation2 = cv2.dilate(erosion,kernel_ellipse,iterations = 1)         filtered = cv2.medianblur(dilation2,5)     kernel_ellipse= cv2.getstructuringelement(cv2.morph_ellipse,(8,8))     dilation2 = cv2.dilate(filtered,kernel_ellipse,iterations = 1)     kernel_ellipse= cv2.getstructuringelement(cv2.morph_ellipse,(5,5))     dilation3 = cv2.dilate(filtered,kernel_ellipse,iterations = 1)     median = cv2.medianblur(dilation2,5)     ret,thresh = cv2.threshold(median,127,255,0)      #find contours of filtered frame     contours, hierarchy =    cv2.findcontours(thresh,cv2.retr_tree,cv2.chain_approx_simple) //error!      #draw contours     #cv2.drawcontours(frame, cnt, -1, (122,122,0), 3)     #cv2.imshow('dilation',median)      #find max contour area (assume hand in frame)     max_area=100     ci=0         in range(len(contours)):         cnt=contours[i]         area = cv2.contourarea(cnt)         if(area>max_area):             max_area=area             ci=i        #largest area contour                  cnts = contours[ci]      #find convex hull     hull = cv2.convexhull(cnts)      #find convex defects     hull2 = cv2.convexhull(cnts,returnpoints = false)     defects = cv2.convexitydefects(cnts,hull2)      #get defect points , draw them in original image     fardefect = []     in range(defects.shape[0]):         s,e,f,d = defects[i,0]         start = tuple(cnts[s][0])         end = tuple(cnts[e][0])         far = tuple(cnts[f][0])         fardefect.append(far)         cv2.line(frame,start,end,[0,255,0],1)         cv2.circle(frame,far,10,[100,255,255],3)      #find moments of largest contour     moments = cv2.moments(cnts)      #central mass of first order moments     if moments['m00']!=0:         cx = int(moments['m10']/moments['m00']) # cx = m10/m00         cy = int(moments['m01']/moments['m00']) # cy = m01/m00     centermass=(cx,cy)          #draw center mass     cv2.circle(frame,centermass,7,[100,0,255],2)     font = cv2.font_hershey_simplex     cv2.puttext(frame,'center',tuple(centermass),font,2,(255,255,255),2)           #distance each finger defect(finger webbing) center mass     distancebetweendefectstocenter = []     in range(0,len(fardefect)):         x =  np.array(fardefect[i])         centermass = np.array(centermass)         distance = np.sqrt(np.power(x[0]-centermass[0],2)+np.power(x[1]-centermass[1],2))         distancebetweendefectstocenter.append(distance)      #get average of 3 shortest distances finger webbing center mass     sorteddefectsdistances = sorted(distancebetweendefectstocenter)     averagedefectdistance = np.mean(sorteddefectsdistances[0:2])      #get fingertip points contour hull     #if points in proximity of 80 pixels, consider single point in group     finger = []     in range(0,len(hull)-1):         if (np.absolute(hull[i][0][0] - hull[i+1][0][0]) > 80) or ( np.absolute(hull[i][0][1] - hull[i+1][0][1]) > 80):             if hull[i][0][1] <500 :                 finger.append(hull[i][0])      #the fingertip points 5 hull points largest y coordinates       finger =  sorted(finger,key=lambda x: x[1])        fingers = finger[0:5]      #calculate distance of each finger tip center mass     fingerdistance = []     in range(0,len(fingers)):         distance = np.sqrt(np.power(fingers[i][0]-centermass[0],2)+np.power(fingers[i][1]-centermass[0],2))         fingerdistance.append(distance)      #finger pointed/raised if distance of between fingertip center mass larger     #than distance of average finger webbing center mass 130 pixels     result = 0     in range(0,len(fingers)):         if fingerdistance[i] > averagedefectdistance+130:             result = result +1      #print number of pointed fingers     cv2.puttext(frame,str(result),(100,100),font,2,(255,255,255),2)      #show height raised fingers     #cv2.puttext(frame,'finger1',tuple(finger[0]),font,2,(255,255,255),2)     #cv2.puttext(frame,'finger2',tuple(finger[1]),font,2,(255,255,255),2)     #cv2.puttext(frame,'finger3',tuple(finger[2]),font,2,(255,255,255),2)     #cv2.puttext(frame,'finger4',tuple(finger[3]),font,2,(255,255,255),2)     #cv2.puttext(frame,'finger5',tuple(finger[4]),font,2,(255,255,255),2)     #cv2.puttext(frame,'finger6',tuple(finger[5]),font,2,(255,255,255),2)     #cv2.puttext(frame,'finger7',tuple(finger[6]),font,2,(255,255,255),2)     #cv2.puttext(frame,'finger8',tuple(finger[7]),font,2,(255,255,255),2)      #print bounding rectangle     x,y,w,h = cv2.boundingrect(cnts)     img = cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),2)      cv2.drawcontours(frame,[hull],-1,(255,255,255),2)      ##### show final image ########     cv2.imshow('dilation',frame)     ###############################      #print execution time     #print time.time()-start_time      #close output video pressing 'esc'     k = cv2.waitkey(5) & 0xff     if k == 27:         break  cap.release() cv2.destroyallwindows() 

the issue in line -

contours, hierarchy =    cv2.findcontours(thresh,cv2.retr_tree,cv2.chain_approx_simple) 

cv2.findcontours() returns 3 values , not 2 , hence too many values unpack error , -

image, contours, hierarchy =    cv2.findcontours(thresh,cv2.retr_tree,cv2.chain_approx_simple) 

cv2.findcontours() returns image , contours , hierarchy , in order.