text: # 导入工具包
  from imutils import contours
  import numpy as np
  import argparse
  import cv2
  import myutils
  
  # 设置参数
  ap = argparse.ArgumentParser()
  ap.add_argument("-i", "--image", required=True,
  	help="path to input image")
  ap.add_argument("-t", "--template", required=True,
  	help="path to template OCR-A image")
  args = vars(ap.parse_args())
  
  # 指定信用卡类型
  FIRST_NUMBER = {
  	"3": "American Express",
  	"4": "Visa",
  	"5": "MasterCard",
  	"6": "Discover Card"
  }
  # 绘图展示
  def cv_show(name,img):
  	cv2.imshow(name, img)
  	cv2.waitKey(0)
  	cv2.destroyAllWindows()
  # 读取一个模板图像
  img = cv2.imread(args["template"])
  cv_show('img',img)
  # 灰度图
  # 颜色通道BGR to GRAY
  ref = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
  cv_show('ref',ref)
  # 二值图像(因为模板的边缘都是白色的)
  ref = cv2.threshold(ref, 10, 255, cv2.THRESH_BINARY_INV)[1]
  cv_show('ref',ref)
  
  # 计算轮廓
  # cv2.findContours()函数接受的参数为二值图，即黑白的（不是灰度图）,
  # cv2.RETR_EXTERNAL只检测外轮廓（内没用，我需要得到他的外接矩形），
  # cv2.CHAIN_APPROX_SIMPLE只保留终点坐标
  # 返回的list中每个元素都是图像中的一个轮廓（其他的不要）
  
  ref_, refCnts, hierarchy = cv2.findContours(ref.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
  
  cv2.drawContours(img,refCnts,-1,(0,0,255),3)
  cv_show('img',img)
  print (np.array(refCnts).shape)
  refCnts = myutils.sort_contours(refCnts, method="left-to-right")[0] #排序，从左到右，从上到下
  digits = {}
  # 打印轮廓为 (10,)
  
  
  # 遍历每一个轮廓
  for (i, c) in enumerate(refCnts):
  	# 计算外接矩形并且resize成合适大小
  	(x, y, w, h) = cv2.boundingRect(c)
  	roi = ref[y:y + h, x:x + w]
      # resize一下合适的大小
  	roi = cv2.resize(roi, (57, 88))
  
  	# 每一个数字对应每一个模板
  	digits[i] = roi
  
  # 初始化卷积核
  rectKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 3))
  sqKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
  
  #读取输入图像，预处理
  image = cv2.imread(args["image"])
  cv_show('image',image)
  image = myutils.resize(image, width=300)
  gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
  cv_show('gray',gray)
  
  #礼帽操作，突出更明亮的区域
  tophat = cv2.morphologyEx(gray, cv2.MORPH_TOPHAT, rectKernel)
  cv_show('tophat',tophat)
  # 根据字体的大小进行过滤
  gradX = cv2.Sobel(tophat, ddepth=cv2.CV_32F, dx=1, dy=0, #ksize=-1相当于用3*3的
  	ksize=-1)
  
  
  gradX = np.absolute(gradX)
  (minVal, maxVal) = (np.min(gradX), np.max(gradX))
  gradX = (255 * ((gradX - minVal) / (maxVal - minVal)))
  gradX = gradX.astype("uint8")
  
  print (np.array(gradX).shape)
  cv_show('gradX',gradX)
  
  #通过闭操作（先膨胀，再腐蚀）将数字连在一起
  gradX = cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, rectKernel)
  cv_show('gradX',gradX)
  #THRESH_OTSU会【自动】寻找合适的阈值，适合双峰，需把阈值参数设置为0
  thresh = cv2.threshold(gradX, 0, 255,
  	cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
  cv_show('thresh',thresh)
  
  #再来一个闭操作
  
  thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, sqKernel) #再来一个闭操作
  cv_show('thresh',thresh)
  
  # 计算轮廓
  
  thresh_, threshCnts, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
  	cv2.CHAIN_APPROX_SIMPLE)
  
  cnts = threshCnts
  cur_img = image.copy()
  cv2.drawContours(cur_img,cnts,-1,(0,0,255),3)
  cv_show('img',cur_img)
  locs = []
  
  # 遍历轮廓
  for (i, c) in enumerate(cnts):
  	# 计算矩形
  	(x, y, w, h) = cv2.boundingRect(c)
  	ar = w / float(h)
  
  	# 选择合适的区域，根据实际任务来，这里的基本都是四个数字一组
  	if ar > 2.5 and ar < 4.0:
  
  		if (w > 40 and w < 55) and (h > 10 and h < 20):
  			#符合的留下来
  			locs.append((x, y, w, h))
  
  # 将符合的轮廓从左到右排序
  locs = sorted(locs, key=lambda x:x[0])
  output = []
  
  # 遍历每一个轮廓中的数字
  for (i, (gX, gY, gW, gH)) in enumerate(locs):
  	# initialize the list of group digits
  	groupOutput = []
  
  	# 根据坐标提取每一个组
  	group = gray[gY - 5:gY + gH + 5, gX - 5:gX + gW + 5]
  	cv_show('group',group)
  	# 预处理
  	group = cv2.threshold(group, 0, 255,
  		cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
  	cv_show('group',group)
  	# 计算每一组的轮廓
  	group_,digitCnts,hierarchy = cv2.findContours(group.copy(), cv2.RETR_EXTERNAL,
  		cv2.CHAIN_APPROX_SIMPLE)
  	digitCnts = contours.sort_contours(digitCnts,
  		method="left-to-right")[0]
  
  	# 计算每一组中的每一个数值
  	for c in digitCnts:
  		# 找到当前数值的轮廓，resize成合适的的大小
  		(x, y, w, h) = cv2.boundingRect(c)
  		roi = group[y:y + h, x:x + w]
  		roi = cv2.resize(roi, (57, 88))
  		cv_show('roi',roi)
  
  		# 计算匹配得分
  		scores = []
  
  		# 在模板中计算每一个得分
  		for (digit, digitROI) in digits.items():
  			# 模板匹配
  			result = cv2.matchTemplate(roi, digitROI,
  				cv2.TM_CCOEFF)
  			(_, score, _, _) = cv2.minMaxLoc(result)
  			scores.append(score)
  
  		# 得到最合适的数字
  		groupOutput.append(str(np.argmax(scores)))
  
  	# 画出来
  	cv2.rectangle(image, (gX - 5, gY - 5),
  		(gX + gW + 5, gY + gH + 5), (0, 0, 255), 1)
  	cv2.putText(image, "".join(groupOutput), (gX, gY - 15),
  		cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 0, 255), 2)
  
  	# 得到结果
  	output.extend(groupOutput)
  
  # 打印结果
  print("Credit Card Type: {}".format(FIRST_NUMBER[output[0]]))
  print("Credit Card #: {}".format("".join(output)))
  cv2.imshow("Image", image)
  cv2.waitKey(0)