opencv-actual-credit card digital recognition

Credit card-digital recognition

Function Description

Brief steps

• First get the outline of the circumscribed rectangle

• Then perform contour detection on the template and the image to obtain the outer contour

• For example, first perform external contour detection on 4 and then one by one match with the one in the template

• deal with

  • First read the image into a grayscale image
  • resizeSame size for two images
  • Perform filtering operations on other data (through the ratio of data length to width)
  • Some image processing
  • Single processing for each small contour
  • Last template match

[External chain image transfer failed, the source site may have an anti-theft chain mechanism, it is recommended to save the image and upload it directly (img-iNIzzQsy-1585898446628) (05.assets / image-20200317153722048-1585531100630.png)]

Process

Code analysis

# 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)