CODEWITHSUNDEEP
pythonimageimage-processingcomputer-visioncontour
user3377126
user3377126

Reputation: 2171

Python Computer Vision Contours: Too many values to unpack?

I was taking source code from pyimagesearch.com to make a mobile document scanner and tried to test out the code. The edge detection part works but whenever I arrive at the part where it tries to find contours of an image, the program outputs an error saying that there are too many values to unpack, despite the programming working on the author's side.

What's the problem and how do I fix it?

Blog Post about the source code: http://www.pyimagesearch.com/2014/09/01/build-kick-ass-mobile-document-scanner-just-5-minutes/?__vid=c35c22a06af30132982122000b2a88d7

Youtube video about the program: https://www.youtube.com/watch?v=yRer1GC2298

Terminal Command in Ubuntu

python scan.py --image images/page.jpg

Result:

STEP 1: Edge Detection
Traceback (most recent call last):
  File "scan.py", line 40, in <module>
    (cnts, _) = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
ValueError: too many values to unpack

Code:

# USAGE
# python scan.py --image images/page.jpg 

# import the necessary packages
from pyimagesearch.transform import four_point_transform
from pyimagesearch import imutils
from skimage.filter import threshold_adaptive
import numpy as np
import argparse
import cv2

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required = True,
        help = "Path to the image to be scanned")
args = vars(ap.parse_args())

# load the image and compute the ratio of the old height
# to the new height, clone it, and resize it
image = cv2.imread(args["image"])
ratio = image.shape[0] / 500.0
orig = image.copy()
image = imutils.resize(image, height = 500)

# convert the image to grayscale, blur it, and find edges
# in the image
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(gray, 75, 200)

# show the original image and the edge detected image
print "STEP 1: Edge Detection"
cv2.imshow("Image", image)
cv2.imshow("Edged", edged)
cv2.waitKey(0)
cv2.destroyAllWindows()

# find the contours in the edged image, kee
(cnts, _) = cv2.findContours(edged.copy(), cv2.RETR_LIST, ping only the
# largest ones, and initialize the screen contourcv2.CHAIN_APPROX_SIMPLE)
cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:5]

# loop over the contours
for c in cnts:
        # approximate the contour
        peri = cv2.arcLength(c, True)
        approx = cv2.approxPolyDP(c, 0.02 * peri, True)

        # if our approximated contour has four points, then we
        # can assume that we have found our screen
        if len(approx) == 4:
                screenCnt = approx
                break

# show the contour (outline) of the piece of paper
print "STEP 2: Find contours of paper"
cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)
cv2.imshow("Outline", image)
cv2.waitKey(0)
cv2.destroyAllWindows()

# apply the four point transform to obtain a top-down
# view of the original image
warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)

# convert the warped image to grayscale, then threshold it
# to give it that 'black and white' paper effect
warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
warped = threshold_adaptive(warped, 250, offset = 10)
warped = warped.astype("uint8") * 255

# show the original and scanned images
print "STEP 3: Apply perspective transform"
cv2.imshow("Original", imutils.resize(orig, height = 650))
cv2.imshow("Scanned", imutils.resize(warped, height = 650))
cv2.waitKey(0)

Upvotes: 1

Views: 9238

Answers (3)

Isru
Isru

Reputation: 61

(_,cnts,hierarchy) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
        cv2.CHAIN_APPROX_SIMPLE)

Upvotes: 1

epinal
epinal

Reputation: 1465

This is the answer at least works for me. The function return 3 values so:

_,contours,hierarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)

Upvotes: 6

user4691553
user4691553

Reputation: 11

In OpenCV 3.0.0 (beta) they have added a return value. This works:

derp,contours,hierarchy = cv2.findContours(dilation.copy(),cv2.RETR_LIST ,cv2.CHAIN_APPROX_SIMPLE)

I have no idea what derp is and it can be ignored.

Upvotes: 0

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