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附一张效果图

效果图

闲言少叙，言归正传。

S.1 调用摄像头
cap = cv2.VideoCapture(0)

S.2 用Sobel算子进行边缘检测，滤波后二值化

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gradX = cv2.Sobel(gray, ddepth = cv2.CV_16S, dx = 1, dy = 0, ksize = -1)
gradY = cv2.Sobel(gray, ddepth = cv2.CV_16S, dx = 0, dy = 1, ksize = -1)
gradient = cv2.subtract(gradX, gradY)
gradient = cv2.convertScaleAbs(gradient)
blurred = cv2.blur(gradient, (9, 9))
(_, thresh) = cv2.threshold(blurred, 225, 255, cv2.THRESH_BINARY)

S.3 通过闭运算排除小型黑洞，并处理掉条形码中的黑块

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 = 8)    #这里原来是4,因为通过查看二值图发现小白块太多，所以改成8,效果大大优化

S.4 边缘检测，并绘制矩形框，框选条形码区域

im, contours, hierarchy = cv2.findContours(closed,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) 
rois = []
x = y = w = h = 0
for c in contours:
    x, y, w, h = cv2.boundingRect(c)
    if w > 10 and h > 20:
        rois.append((x,y,w,h))
for r in rois:
    x, y, w, h = r
    cv2.rectangle(frame, (x,y), (x+w,y+h), (153,153,0), 2)

完整代码如下

#!/usr/bin/python3
# -*- coding: UTF-8 -*- 
import cv2
import numpy as np

cap = cv2.VideoCapture(0)
while(1):
    ret, frame = cap.read()
    if ret == True:
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

        gradX = cv2.Sobel(gray, ddepth = cv2.CV_16S, dx = 1, dy = 0, ksize = -1)
        gradY = cv2.Sobel(gray, ddepth = cv2.CV_16S, dx = 0, dy = 1, ksize = -1)

        gradient = cv2.subtract(gradX, gradY)
        gradient = cv2.convertScaleAbs(gradient)
        blurred = cv2.blur(gradient, (9, 9))
        (_, thresh) = cv2.threshold(blurred, 225, 255, cv2.THRESH_BINARY)

        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 = 8)
        
        im, contours, hierarchy = cv2.findContours(closed,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) 
        rois = []
        x = y = w = h = 0
        for c in contours:
            x, y, w, h = cv2.boundingRect(c)
            if w > 10 and h > 20:
                rois.append((x,y,w,h))
        for r in rois:
            x, y, w, h = r
            cv2.rectangle(frame, (x,y), (x+w,y+h), (153,153,0), 2)
        cv2.imshow("123",frame)
        k = cv2.waitKey(20)
        if k & 0xFF == ord('q'):
            break

这样的检测方式当条形码旋转时效果不是很好，等日后熟悉了hough变换再来改进。

参考原文：http://blog.jobbole.com/80448/
感谢前辈们的经验与博客。