使用python写的opencv实时监测和解析二维码和条形码

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

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

#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

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box
return box

import numpy as np
import cv2
def 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

# import the necessary packages
import simple_barcode_detection
import cv2
import numpy as np
import zbar
from PIL import Image

# create a reader
scanner = zbar.ImageScanner()
# configure the reader
scanner.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
# video
if 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 image
box = 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.size
raw = 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.data
cv2.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 key
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# if the 'q' key is pressed, stop the loop
if key == ord("q"):
break
# cleanup the camera and close any open windows
camera.release()
cv2.destroyAllWindows()

# import the necessary packages
import simple_barcode_detection
import cv2
import numpy as np
import zbar
from PIL import Image
# create a reader
scanner = zbar.ImageScanner()
# configure the reader
scanner.parse_config('enable')
font=cv2.FONT_HERSHEY_SIMPLEX
camera=cv2.VideoCapture(0)
while True:
# grab the current frame
(grabbed, frame) = camera.read()
# check to see if we have reached the end of the
# video
if not grabbed:
break
# detect the barcode in the image
box = 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.shape
roi=cv2.cvtColor(roi,cv2.COLOR_BGR2RGB)
pil= Image.fromarray(frame).convert('L')
width, height = pil.size
raw = pil.tostring()
# wrap image data
zarimage = zbar.Image(width, height, 'Y800', raw)
# scan the image for barcodes
scanner.scan(zarimage)
# extract results
for symbol in zarimage:
  # do something useful with results
print 'decoded', symbol.type, 'symbol', '"%s"' %symbol.data
cv2.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 key
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# if the 'q' key is pressed, stop the loop
if key == ord("q"):
break
# cleanup the camera and close any open windows
camera.release()
cv2.destroyAllWindows()