今天，我实现了一个很有趣的demo，它可以在视频里找到并解析二维码，然后把解析的内容实时在屏幕上显示出来。

然后我们直入主题，首先你得确保你装了opencv,python,zbar等环境。然后这个教程对于学过opencv的人可能更好理解，但是没学过也无妨，到时候也可以直接用。

比如我的电脑上的环境是opencv2.4.x,python2.7,和最新的zbar,在Ubuntu 12.12的系统下运行的

假设你的opencv已经安装好了，那么我们就可以安装zbar

你可以先更新一下

sudo apt-get update

然后在输入

sudo apt-get install python-zbar

如果环境装好了，我们就可以接着下一步操作了。

首先让我们来实现找到在图片里面找到二维码的功能

先新建一个python文件叫做;simple_barcode_detection.py

代码如下，这定义了一个函数，实现从一副图片里面找出二维码的位置

我们要检测的二维码的图片

import numpy as np import cv2 def detect(image):# 把图像从RGB装换成灰度图gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)# 使用Scharr操作（指定使用ksize = -1）构造灰度图在水平和竖直方向上的梯度幅值表示。gradX = cv2.Sobel(gray, ddepth = cv2.cv.CV_32F, dx = 1, dy = 0, ksize = -1)gradY = cv2.Sobel(gray, ddepth = cv2.cv.CV_32F, dx = 0, dy = 1, ksize = -1)#Scharr操作后，从x的梯度减去y的梯度gradient = cv2.subtract(gradX, gradY)gradient = cv2.convertScaleAbs(gradient)#经过上面的操作后看起来是这样

# 对上述的梯度图采用用9x9的核进行平均模糊,这是有利于降噪的

#然后进行二值化处理，要么是255(白)要么是0(黑)

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

#模糊与二值化处理后，看起来是这个样子，

#上面的操作后发现有一些条形码的竖杠之间存在一些缝隙，并使我们的算法能检测到斑点区域，我们进行一些基本的形态学操作#我们首先使用cv2.getStructuringElement构造一个长方形内核。这个内核的宽度大于长度，因此我们可以消除条形码中垂直条之间的缝隙。# 这里进行形态学操作，将上一步得到的内核应用到我们的二值图中，以此来消除竖杠间的缝隙。kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (21, 7))closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)#上述操作后看起来是这个样子

#我们发现还是存在一些小斑点，这时我们可以使用opencv里面的腐蚀和膨胀来处理他们，来去除白色的斑点

closed = cv2.erode(closed, None, iterations = 4)closed = cv2.dilate(closed, None, iterations = 4)#这时候的效果看起来是这样的

# 接下来我们来找轮廓(cnts, _) = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)#如果没找到则返回为空if len(cnts) == 0:return None#找到了就通过面积来排序，并计算旋转角# 给最大的轮廓找到边框c = sorted(cnts, key = cv2.contourArea, reverse = True)[0]rect = cv2.minAreaRect(c)#box(里面是ndarry数组，包含了4个顶点的位置)box = np.int0(cv2.cv.BoxPoints(rect))# 返回boxreturn box

最终结果

好了，上面的解释里面有中文，可能python解释的时候会通不过，我下面直接给出代码

import numpy as npimport cv2def detect(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.CV_32F, dx = 1, dy = 0, ksize = -1) gradY = cv2.Sobel(gray, ddepth = cv2.cv.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 (cnts, _) = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # if no contours were found, return None if len(cnts) == 0: return None # otherwise, sort the contours by area and compute the rotated # bounding box of the largest contour c = sorted(cnts, key = cv2.contourArea, reverse = True)[0] rect = cv2.minAreaRect(c) box = np.int0(cv2.cv.BoxPoints(rect)) # return the bounding box of the barcode return box

完成了上述的工作，我们就完成了二维码和条形码的定位，接下去实现视频里面二维码的解析

你可以新建一个python文件，barcode_vid.py

解析二维码我们需要用zbar这个模块和PIL，PIL在python里面装好了

我们先导入模块

# import the necessary packagesimport simple_barcode_detectionimport cv2import numpy as npimport zbarfrom PIL import Image

#接下去是创建一个扫描器,他可以解析二维码的内容

# create a readerscanner = zbar.ImageScanner()# configure the readerscanner.parse_config('enable')#设置屏幕显示字体font=cv2.FONT_HERSHEY_SIMPLEX#启用摄像头camera=cv2.VideoCapture(0)#接下去是一个大的while循环while True:#得到当前的帧# grab the current frame(grabbed, frame) = camera.read()#检测视频是否到底了，如果检测视频文件里面的二维码或条形码用这个，如果开启摄像头就无所谓了# check to see if we have reached the end of the# videoif not grabbed:break调用刚才我们建的那个函数来查找二维码返回二维码的位置# detect the barcode in the imagebox = simple_barcode_detection.detect(frame)if box != None:#这下面的3步得到扫描区域，扫描区域要比检测出来的位置要大min=np.min(box,axis=0)max=np.max(box,axis=0)roi=frame[min[1]-10:max[1]+10,min[0]-10:max[0]+10]#把区域里的二维码传换成RGB，并把它转换成pil里面的图像，因为zbar得调用pil里面的图像，而不能用opencv的图像roi=cv2.cvtColor(roi,cv2.COLOR_BGR2RGB)pil= Image.fromarray(frame).convert('L')width, height = pil.sizeraw = pil.tostring()#把图像装换成数据zarimage = zbar.Image(width, height, 'Y800', raw)#扫描器进行扫描scanner.scan(zarimage)#得到结果for symbol in zarimage: # 对结果进行一些有用的处理print 'decoded', symbol.type, 'symbol', '"%s"' %symbol.datacv2.drawContours(frame, [box], -1, (0, 255, 0), 2)#把解析的内容放到视频上cv2.putText(frame,symbol.data,(20,100),font,1,(0,255,0),4)# show the frame and record if the user presses a keycv2.imshow("Frame", frame)key = cv2.waitKey(1) & 0xFF# if the 'q' key is pressed, stop the loopif key == ord("q"):break# cleanup the camera and close any open windowscamera.release()cv2.destroyAllWindows()

CSDN不能上传视频，我下面传一下图片

下面还是上源码

# import the necessary packagesimport simple_barcode_detectionimport cv2import numpy as npimport zbarfrom PIL import Image# create a readerscanner = zbar.ImageScanner()# configure the readerscanner.parse_config('enable')font=cv2.FONT_HERSHEY_SIMPLEXcamera=cv2.VideoCapture(0)while True:# grab the current frame(grabbed, frame) = camera.read()# check to see if we have reached the end of the# videoif not grabbed:break# detect the barcode in the imagebox = simple_barcode_detection.detect(frame)if box != None:min=np.min(box,axis=0)max=np.max(box,axis=0)roi=frame[min[1]-10:max[1]+10,min[0]-10:max[0]+10]print roi.shaperoi=cv2.cvtColor(roi,cv2.COLOR_BGR2RGB)pil= Image.fromarray(frame).convert('L')width, height = pil.sizeraw = pil.tostring()# wrap image datazarimage = zbar.Image(width, height, 'Y800', raw)# scan the image for barcodesscanner.scan(zarimage)# extract resultsfor symbol in zarimage: # do something useful with resultsprint 'decoded', symbol.type, 'symbol', '"%s"' %symbol.datacv2.drawContours(frame, [box], -1, (0, 255, 0), 2)cv2.putText(frame,symbol.data,(20,100),font,1,(0,255,0),4)# if a barcode was found, draw a bounding box on the frame# show the frame and record if the user presses a keycv2.imshow("Frame", frame)key = cv2.waitKey(1) & 0xFF# if the 'q' key is pressed, stop the loopif key == ord("q"):break# cleanup the camera and close any open windowscamera.release()cv2.destroyAllWindows()

总结

以上所述是小编给大家介绍的使用python写的opencv实时监测和解析二维码和条形码,希望对大家有所帮助，如果大家有任何疑问请给我留言，小编会及时回复大家的。在此也非常感谢大家对网站的支持！
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