import numpy as np
 import cv2
 def detect(image):
# 把圖像從RGB裝換成灰度圖
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# 使用Scharr操作（指定使用ksize = -1）構造灰度圖在水平和豎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，從x的梯度減去y的梯度
gradient = cv2.subtract(gradX, gradY)
gradient = cv2.convertScaleAbs(gradient)
#經過上面的操作后看起來是這樣

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

#上面的操作后發(fā)現有一些條形碼的豎杠之間存在一些縫隙，并使我們的算法能檢測到斑點區(qū)域，我們進行一些基本的形態(tài)學操作
#我們首先使用cv2.getStructuringElement構造一個長方形內核。這個內核的寬度大于長度，因此我們可以消除條形碼中垂直條之間的縫隙。
# 這里進行形態(tài)學操作，將上一步得到的內核應用到我們的二值圖中，以此來消除豎杠間的縫隙。
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (21, 7))
closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
#上述操作后看起來是這個樣子

closed = cv2.erode(closed, None, iterations = 4)
closed = cv2.dilate(closed, None, iterations = 4)
#這時候的效果看起來是這樣的

# 接下來我們來找輪廓
(cnts, _) = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
#如果沒找到則返回為空
if len(cnts) == 0:
return None
#找到了就通過面積來排序，并計算旋轉角
# 給最大的輪廓找到邊框
c = sorted(cnts, key = cv2.contourArea, reverse = True)[0]
rect = cv2.minAreaRect(c)
#box(里面是ndarry數組，包含了4個頂點的位置)
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')
#設置屏幕顯示字體
font=cv2.FONT_HERSHEY_SIMPLEX
#啟用攝像頭
camera=cv2.VideoCapture(0)
#接下去是一個大的while循環(huán)
while True:
#得到當前的幀
# grab the current frame
(grabbed, frame) = camera.read()
#檢測視頻是否到底了，如果檢測視頻文件里面的二維碼或條形碼用這個，如果開啟攝像頭就無所謂了
# check to see if we have reached the end of the
# video
if not grabbed:
break
調用剛才我們建的那個函數來查找二維碼返回二維碼的位置
# detect the barcode in the image
box = simple_barcode_detection.detect(frame)
if box != None:
#這下面的3步得到掃描區(qū)域，掃描區(qū)域要比檢測出來的位置要大
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]
#把區(qū)域里的二維碼傳換成RGB，并把它轉換成pil里面的圖像，因為zbar得調用pil里面的圖像，而不能用opencv的圖像
roi=cv2.cvtColor(roi,cv2.COLOR_BGR2RGB)
pil= Image.fromarray(frame).convert('L')
width, height = pil.size
raw = pil.tostring()
#把圖像裝換成數據
zarimage = zbar.Image(width, height, 'Y800', raw)
#掃描器進行掃描
scanner.scan(zarimage)
#得到結果
for symbol in zarimage:
  # 對結果進行一些有用的處理
print 'decoded', symbol.type, 'symbol', '"%s"' %symbol.data
cv2.drawContours(frame, [box], -1, (0, 255, 0), 2)
#把解析的內容放到視頻上
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()