barcode_scanner.py

import numpy as np
import cv2

class BarcodeScanner:

    def __init__(self):
        print "BARCODE SCANNER STARTED"

    def  detect_barcode(self,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
        im2, cc, hierarchy = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL,
                                     cv2.CHAIN_APPROX_SIMPLE)

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

        # otherwise, sort the contours by area and compute the rotated
        # bounding box of the largest contour
        if len(cc) >= 0:
            c = sorted(cc, 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