This is the version of "simple_barcode_detection.py" from Adrian Rosebrock, updated for use with OpenCV 3.0 (note lines 14, 15, 34, 44)

simple_barcode_detection.py

# Original files and article is here: 
# http://www.pyimagesearch.com/2014/12/15/real-time-barcode-detection-video-python-opencv
# import the necessary packages
import numpy as np
import cv2


def detect(image):
    # convert the image to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # compute the Scharr gradient magnitude representation of the images
    # in both the x and y direction
    gradX = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=1, dy=0, ksize=-1)
    gradY = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=0, dy=1, ksize=-1)

    # subtract the y-gradient from the x-gradient
    gradient = cv2.subtract(gradX, gradY)
    gradient = cv2.convertScaleAbs(gradient)

    # blur and threshold the image
    blurred = cv2.blur(gradient, (9, 9))
    (_, thresh) = cv2.threshold(blurred, 225, 255, cv2.THRESH_BINARY)

    # construct a closing kernel and apply it to the thresholded image
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (21, 7))
    closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)

    # perform a series of erosions and dilations
    closed = cv2.erode(closed, None, iterations=4)
    closed = cv2.dilate(closed, None, iterations=4)

    # find the contours in the thresholded image
    (_, cnts, _) = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # if no contours were found, return None
    if len(cnts) == 0:
        return None

    # otherwise, sort the contours by area and compute the rotated
    # bounding box of the largest contour
    c = sorted(cnts, key=cv2.contourArea, reverse=True)[0]
    rect = cv2.minAreaRect(c)
    box = np.int0(cv2.boxPoints(rect))

    # return the bounding box of the barcode
    return box