宜配屋

在OpenCV里实现条码区域识别的方法示例

yipeiwu_com5年前 (2020-03-06)Python基础

在我们识别条码的过程里，首先要找到条码所在的区域，那么怎么样来找到这个条码的区域呢？如果仔细地观察条码，会发现条码有一个特性，就是水平的梯度和垂值的梯度会不一样，如果进行相减，会发现差值比较大。如果其它位置的图像一般不会这样。利用这个特性，就可以把条码所在区域求出来。

演示的代码如下：

#python 3.7.4,opencv4.1
#蔡军生 https://blog.csdn.net/caimouse/article/details/51749579
#9073204@qq.com
#
import numpy as np
import cv2
from matplotlib import pyplot as plt
 
#读取图片
img = cv2.imread('barcode6.jpg')
#
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow('gray', gray)
 
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)
 
gradient = cv2.subtract(gradX, gradY)
gradient = cv2.convertScaleAbs(gradient)
cv2.imshow('gradient', gradient)
 
blurred = cv2.blur(gradient, (9, 9))
(_, thresh) = cv2.threshold(blurred, 225, 255, cv2.THRESH_BINARY)
cv2.imshow('thresh', thresh)
 
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (21, 7))
closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
 
closed = cv2.erode(closed, None, iterations = 4)
closed = cv2.dilate(closed, None, iterations = 4)
cv2.imshow('closed', closed)
 
cnts,hierarchy = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL,
	cv2.CHAIN_APPROX_SIMPLE)
c = sorted(cnts, key = cv2.contourArea, reverse = True)[0]
 
#找最大的边框
rect = cv2.minAreaRect(c)
box = cv2.boxPoints(rect)
box = np.int0(box)
 
# 画一个找到的方框
cv2.drawContours(img, [box], -1, (0, 255, 0), 3)
  
cv2.imshow('img', img)
 
#
cv2.waitKey(0)
cv2.destroyAllWindows()

结果输出如下：