├── imgs ├── method.png ├── approach.png ├── result1.png └── result2.png ├── README.md ├── barcode_detect_and_decode.py ├── detect_barcode_opencv.py └── LICENSE /imgs/method.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pyxploiter/Barcode-Detection-and-Decoding/HEAD/imgs/method.png -------------------------------------------------------------------------------- /imgs/approach.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pyxploiter/Barcode-Detection-and-Decoding/HEAD/imgs/approach.png -------------------------------------------------------------------------------- /imgs/result1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pyxploiter/Barcode-Detection-and-Decoding/HEAD/imgs/result1.png -------------------------------------------------------------------------------- /imgs/result2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pyxploiter/Barcode-Detection-and-Decoding/HEAD/imgs/result2.png -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Barcode-Detection-and-Decoding 2 | Barcode detection and decoding using openCV and Zbar. 3 | 4 | # Methodology 5 |
6 | # Approach Result 7 |

8 | # Results 9 |

10 |

11 | -------------------------------------------------------------------------------- /barcode_detect_and_decode.py: -------------------------------------------------------------------------------- 1 | import os 2 | import argparse 3 | import zbar 4 | import numpy as np 5 | import cv2 6 | 7 | def preprocess(image): 8 | # load the image 9 | image = cv2.imread(args["image"]) 10 | 11 | #resize image 12 | image = cv2.resize(image,None,fx=0.7, fy=0.7, interpolation = cv2.INTER_CUBIC) 13 | 14 | #convert to grayscale 15 | gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 16 | 17 | #calculate x & y gradient 18 | gradX = cv2.Sobel(gray, ddepth = cv2.CV_32F, dx = 1, dy = 0, ksize = -1) 19 | gradY = cv2.Sobel(gray, ddepth = cv2.CV_32F, dx = 0, dy = 1, ksize = -1) 20 | 21 | # subtract the y-gradient from the x-gradient 22 | gradient = cv2.subtract(gradX, gradY) 23 | gradient = cv2.convertScaleAbs(gradient) 24 | 25 | # blur the image 26 | blurred = cv2.blur(gradient, (3, 3)) 27 | 28 | # threshold the image 29 | (_, thresh) = cv2.threshold(blurred, 225, 255, cv2.THRESH_BINARY) 30 | thresh = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 31 | return thresh 32 | 33 | 34 | def barcode(image): 35 | # create a reader 36 | scanner = zbar.ImageScanner() 37 | 38 | # configure the reader 39 | scanner.parse_config('enable') 40 | 41 | # obtain image data 42 | width, height = image.shape 43 | raw = image.tobytes() 44 | 45 | image = zbar.Image(width, height, 'Y800', raw) 46 | 47 | # scan the image for barcodes 48 | scanner.scan(image) 49 | 50 | # extract results 51 | for symbol in image: 52 | # do something useful with results 53 | print 'format:', symbol.type, '| data:', '"%s"' % symbol.data 54 | # clean up 55 | print '-----------------------------------------------------------------------' 56 | del(image) 57 | 58 | ap = argparse.ArgumentParser() 59 | ap.add_argument("-i", "--image", required = True, help = "path to the image file") 60 | args = vars(ap.parse_args()) 61 | image = cv2.imread(args["image"],0) 62 | image = preprocess(args["image"]) 63 | barcode(image) 64 | -------------------------------------------------------------------------------- /detect_barcode_opencv.py: -------------------------------------------------------------------------------- 1 | # python detect_barcode_opencv.py --image images/barcode_01.jpg 2 | 3 | # import the necessary packages 4 | import numpy as np 5 | import argparse 6 | import cv2 7 | 8 | # construct the argument parse and parse the arguments 9 | ap = argparse.ArgumentParser() 10 | ap.add_argument("-i", "--image", required = True, help = "path to the image file") 11 | ap.add_argument("--show", help = "option to show inner images", type=int) 12 | 13 | args = vars(ap.parse_args()) 14 | show = args["show"] 15 | 16 | # load the image and convert it to grayscale 17 | image = cv2.imread(args["image"]) 18 | 19 | #resize image 20 | image = cv2.resize(image,None,fx=0.7, fy=0.7, interpolation = cv2.INTER_CUBIC) 21 | 22 | #convert to grayscale 23 | gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 24 | 25 | #calculate x & y gradient 26 | gradX = cv2.Sobel(gray, ddepth = cv2.CV_32F, dx = 1, dy = 0, ksize = -1) 27 | gradY = cv2.Sobel(gray, ddepth = cv2.CV_32F, dx = 0, dy = 1, ksize = -1) 28 | 29 | # subtract the y-gradient from the x-gradient 30 | gradient = cv2.subtract(gradX, gradY) 31 | gradient = cv2.convertScaleAbs(gradient) 32 | if show == 1: 33 | cv2.imshow("gradient-sub",cv2.resize(gradient,None, fx=0.5, fy=0.5, interpolation = cv2.INTER_CUBIC)) 34 | 35 | # blur the image 36 | blurred = cv2.blur(gradient, (3, 3)) 37 | 38 | # threshold the image 39 | (_, thresh) = cv2.threshold(blurred, 225, 255, cv2.THRESH_BINARY) 40 | 41 | if show == 1: 42 | cv2.imshow("threshed",cv2.resize(thresh,None, fx=0.5, fy=0.5, interpolation = cv2.INTER_CUBIC)) 43 | 44 | # construct a closing kernel and apply it to the thresholded image 45 | kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (21, 7)) 46 | closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel) 47 | 48 | if show == 1: 49 | cv2.imshow("morphology",cv2.resize(closed,None, fx=0.5, fy=0.5, interpolation = cv2.INTER_CUBIC)) 50 | 51 | # perform a series of erosions and dilations 52 | closed = cv2.erode(closed, None, iterations = 4) 53 | closed = cv2.dilate(closed, None, iterations = 4) 54 | 55 | if show == 1: 56 | cv2.imshow("erode/dilate",cv2.resize(closed,None, fx=0.5, fy=0.5, interpolation = cv2.INTER_CUBIC)) 57 | 58 | # find the contours in the thresholded image, then sort the contours 59 | # by their area, keeping only the largest one 60 | cnts,hierarchy = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)[-2:] 61 | 62 | c = sorted(cnts, key = cv2.contourArea, reverse = True)[0] 63 | c1 = sorted(cnts, key = cv2.contourArea, reverse = True)[1] 64 | 65 | # compute the rotated bounding box of the largest contour 66 | rect = cv2.minAreaRect(c) 67 | box = np.int0(cv2.boxPoints(rect)) 68 | rect1 = cv2.minAreaRect(c1) 69 | box1 = np.int0(cv2.boxPoints(rect1)) 70 | 71 | # draw a bounding box arounded the detected barcode and display the 72 | # image 73 | cv2.drawContours(image, [box], -1, (0, 255, 0), 3) 74 | cv2.drawContours(image, [box1], -1, (0, 255, 0), 3) 75 | 76 | image = cv2.resize(image, None, fx=0.5, fy=0.5, interpolation = cv2.INTER_CUBIC) 77 | 78 | cv2.imshow("Image", image) 79 | cv2.waitKey(0) 80 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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