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So I am trying to compile the following code but it's showing me the error on cv2.findContours. Though, I am using Python 2.7 version. Any reason as to why the error: too many values to unpack python 2.7 is coming?

import cv2
import numpy as 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 to find angle between two 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 to find distance between two points in a 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 a window for HSV track bars
cv2.namedWindow('HSV_TrackBar')

# Starting with 100's to 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 from the camera
    ret, frame = cap.read()

    #Blur the image
    blur = cv2.blur(frame,(3,3))

    #Convert to HSV color space
    hsv = cv2.cvtColor(blur,cv2.COLOR_BGR2HSV)

    #Create a binary image with where white will be skin colors and rest is black
    mask2 = cv2.inRange(hsv,np.array([2,50,50]),np.array([15,255,255]))

    #Kernel matrices for morphological transformation    
    kernel_square = np.ones((11,11),np.uint8)
    kernel_ellipse= cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))

    #Perform morphological transformations to filter out the 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 the 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 that hand is in the frame)
    max_area=100
    ci=0    
    for i 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 and draw them in the original image
    FarDefect = []
    for i 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 the 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 from each finger defect(finger webbing) to the center mass
    distanceBetweenDefectsToCenter = []
    for i 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 an average of three shortest distances from finger webbing to center mass
    sortedDefectsDistances = sorted(distanceBetweenDefectsToCenter)
    AverageDefectDistance = np.mean(sortedDefectsDistances[0:2])

    #Get fingertip points from contour hull
    #If points are in proximity of 80 pixels, consider as a single point in the group
    finger = []
    for i 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 are 5 hull points with largest y coordinates  
    finger =  sorted(finger,key=lambda x: x[1])   
    fingers = finger[0:5]

    #Calculate distance of each finger tip to the center mass
    fingerDistance = []
    for i 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 is pointed/raised if the distance of between fingertip to the center mass is larger
    #than the distance of average finger webbing to center mass by 130 pixels
    result = 0
    for i 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 the output video by pressing 'ESC'
    k = cv2.waitKey(5) & 0xFF
    if k == 27:
        break

cap.release()
cv2.destroyAllWindows()
Rick Smith
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zorospain
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  • fix your indentation please – jramirez Jun 18 '15 at 20:00
  • Please include the *full traceback*, don't leave us guessing as to *where* in this code the error occurs. – Martijn Pieters Jun 18 '15 at 20:00
  • I made a best-guess effort at fixing your indentation, can you verify I got it right? Please in the future use the `{}` button on the toolbar (select your code, click the button) to indent your code correctly for inclusion in a post. – Martijn Pieters Jun 18 '15 at 20:02
  • Please include the method definition for findContours in your code. Probably you are passing more arguments than what method allows. – melis Jun 18 '15 at 20:01
  • Possible duplicate of [OpenCV python: ValueError: too many values to unpack](http://stackoverflow.com/questions/25504964/opencv-python-valueerror-too-many-values-to-unpack) – DarkCygnus May 05 '17 at 18:01

1 Answers1

7

The issue is in the line -

contours, hierarchy =    cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

cv2.findContours() returns 3 values , not just 2 , hence the too many values to unpack error , do -

image, contours, hierarchy =    cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

cv2.findContours() returns the image , contours and hierarchy , in that order.

Anand S Kumar
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  • According to the documentation http://docs.opencv.org/modules/imgproc/doc/structural_analysis_and_shape_descriptors.html#findcontours only two - contours and hierarchy – Delimitry Jun 18 '15 at 20:05
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    According to http://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_imgproc/py_contours/py_contours_begin/py_contours_begin.html it returns 3 – Anand S Kumar Jun 18 '15 at 20:07
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    Anyway the too many values to unpack error always comes when the left side of the assignment operators has less variables than in the right side, try something like - `a, b = [1, 2, 3]` , you will understand. – Anand S Kumar Jun 18 '15 at 20:08
  • I tried with 3 values on the left as you said but now I'm encountering with the following error: Traceback (most recent call last): File "HandDetection.py", line 93, in cnts = contours[ci] IndexError: list index out of range – zorospain Jun 18 '15 at 20:15
  • Print out the list of contours , maybe there are none – Anand S Kumar Jun 18 '15 at 20:17
  • It's not printing anything :( could you fix the code please? – zorospain Jun 18 '15 at 20:38
  • That must be because there are no contours to find in the data you provided, could you please check your data , if you can see any issues? – Anand S Kumar Jun 18 '15 at 20:41
  • It's basically a hand detection code taken from github. It's opening a camera first then taking the data.. Could you try compiling it at your end? Thanks – zorospain Jun 18 '15 at 20:46