计算Matlab中每个条形码的宽度

Question

我有一个条形码，我想在matlab中对其进行处理，并以像素为单位计算一维条形码中每个条的宽度。

我已经尝试通过灰度阈值将图像转换为灰度，并将其也转换为二进制。

%read the image code3
barz=imread('barcode1.jpg');
grayBarz=rgb2gray(barz);

binImage = imbinarize(barz,graythresh(barz));

s = regionprops(binImage == 0,'Area','PixelIdxList');
imshow(barz);

我想要条形码中每个条的像素宽度。

Answer 1

假设您已经具有条形的区域属性，则可以通过以下方式轻松获得宽度

'MinFeretProperties'

或

'MinorAxisLength'

如果条形图与图像栅格并列，您也可以使用较小尺寸的'BoundingBox'

https://de.mathworks.com/help/images/ref/regionprops.html

Answer 2

有时，无需完整的图像处理工具箱就可以做事很有趣。

下面的解决方案允许您计算每个黑条的像素宽度，而无需任何其他工具箱：

%% Read the image
barz=imread('barcode.jpg');
grayBarz=rgb2gray(barz);

%% Extract an horizontal line in the middle
sz = size(grayBarz) ;
idxMidLine = round(sz(1)/2) ; % index of a line roughly in the middle
eline = grayBarz(idxMidLine,:) ;    % extract a line
eline(eline<128) = 0 ;              % sharpen transitions
eline = ~logical(eline) ;           % convert to logical (0=white / 1=black)

%% Now count the pixels
npts = numel(eline) ;   % number of points in the line

% Find every transition:
    % high to low   => -1
    % no change     =>  0
    % low to high   => +1
idd = find( diff(eline) ) ;

% this contain the start and end indices of every interval
ddd = [ 1 , idd ; ...
        idd , npts ] ;

% This contains the width of every bar (white and black),
% in order from left to right
barWidth = diff(ddd) ;

if ~eline(1)
    % The first interval is 0 (is white)
    pixBarWhite = barWidth( 1:2:end ) ;
    pixBarBlack = barWidth( 2:2:end ) ;
else
    % The first interval is 1 (is black)
    pixBarBlack = barWidth( 1:2:end ) ;
    pixBarWhite = barWidth( 2:2:end ) ;
end

nBarWhite = numel(pixBarWhite) ;
nBarBlack = numel(pixBarBlack) ;

%% Display results
fprintf('Found a total of %d black pixels along the horizontal,\n',sum(pixBarBlack))
fprintf('spread over %d black bars,\n',nBarBlack)
fprintf('Individual bar pixel thickness:\n')
for k=1:nBarBlack
    fprintf('Bar %02d : Thickness: %02d pixels\n',k,pixBarBlack(k))
end

对于您的图像，它将返回：

Found a total of 599 black pixels along the horizontal,
spread over 49 black bars,
Individual bar pixel thinchness:,
Bar 01 : Thickness: 13 pixels
Bar 02 : Thickness: 07 pixels
Bar 03 : Thickness: 20 pixels
% [edited to keep it short]
Bar 47 : Thickness: 20 pixels
Bar 48 : Thickness: 07 pixels
Bar 49 : Thickness: 13 pixels

请注意，变量pixBarWhite还包含黑条之间所有白色间隔的像素厚度。稍后可能会派上用场...

Answer 3

为了好玩，这是使用Python OpenCV的实现

• 将图像转换为灰度和大津的阈值
• 检测所有垂直线并绘制到蒙版上
• 在蒙版上找到轮廓并从左到右排序
• 遍历轮廓并找到每条线的像素宽度

---

每个条的宽度保存到列表中

[[13，7，20，27，7，19，12，13，13，7，6，13，20，7，14，7，6，12，20，7，13，27，19，7 ，6，6，13，7，7，27，7，14，19，6，19，6，13，13，7，5，6，6，26，6，6，13，6，12，20，7，13 ]

import cv2
from imutils import contours
import numpy as np

# Load in image, grayscale, and Otsu's threshold
image = cv2.imread('1.jpg')
mask = np.zeros(image.shape, dtype=np.uint8)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray,0,255,cv2.THRESH_OTSU + cv2.THRESH_BINARY_INV)[1]

# Detect vertical lines
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,80))        
remove_vertical = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, vertical_kernel)
cnts = cv2.findContours(remove_vertical, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cv2.fillPoly(mask, cnts, (255,255,255))

# Find contours on mask and sort from left to right
mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
cnts = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cnts, _ = contours.sort_contours(cnts, method="left-to-right")

# Iterate through contours and find width of each line
barcode_width = []
current = image.copy()
cv2.imshow('current', current)
cv2.waitKey(2000)
for c in cnts:
    x,y,w,h = cv2.boundingRect(c)
    current = image.copy()
    cv2.rectangle(current, (x, y), (x + w, y + h), (36,255,12), -1)
    cv2.putText(current, 'Width: {}'.format(w), (15,50), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (36,255,12), 3)
    barcode_width.append(w)
    cv2.imshow('current', current)
    cv2.waitKey(175)

print(barcode_width)
cv2.waitKey()