本项目利用python以及opencv实现信用卡的数字识别

前期准备

导入工具包 定义功能函数

模板图像处理

读取模板图像 cv2.imread(img) 灰度化处理 cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) 二值化 cv2.threshold() 轮廓 - 轮廓

信用卡图像处理

读取信用卡图像 cv2.imread(img) 灰度化处理 cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) 礼帽处理 cv2.morphologyEx(gray,cv2.MORPH_TOPHAT,rectKernel) Sobel边缘检测 cv2.Sobel(tophat, ddepth=cv2.CV_32F, dx=1, dy=0, ksize=-1) 闭操作 cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, rectKernel) 计算轮廓 cv2.findContours 模板检测 cv2.matchTemplate(roi, digitROI,cv2.TM_CCOEFF)

原始数据展示

结果展示

1 前期准备

# 导入工具包
# opencv读取图片的格式为b g r
# matplotlib图片的格式为 r g b
import numpy as np
import cv2
from imutils import contours
import matplotlib.pyplot as plt
%matplotlib inline

# 信用卡的位置
predict_card = "images/credit_card_01.png"
# 模板的位置
template = "images/ocr_a_reference.png"

# 指定信用卡类型
FIRST_NUMBER = {
  "3": "American Express",
  "4": "Visa",
  "5": "MasterCard",
  "6": "Discover Card"
}

# 定义一些功能函数

# 对框进行排序
def sort_contours(cnts, method="left-to-right"):
  reverse = False
  i = 0

  if method == "right-to-left" or method == "bottom-to-top":
    reverse = True

  if method == "top-to-bottom" or method == "bottom-to-top":
    i = 1
  boundingBoxes = [cv2.boundingRect(c) for c in cnts] #用一个最小的矩形，把找到的形状包起来x,y,h,w
  (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),
                    key=lambda b: b[1][i], reverse=reverse))

  return cnts, boundingBoxes

# 调整图片尺寸大小
def resize(image, width=None, height=None, inter=cv2.INTER_AREA):
  dim = None
  (h, w) = image.shape[:2]
  if width is None and height is None:
    return image
  if width is None:
    r = height / float(h)
    dim = (int(w * r), height)
  else:
    r = width / float(w)
    dim = (width, int(h * r))
  resized = cv2.resize(image, dim, interpolation=inter)
  return resized

# 定义cv2展示函数
def cv_show(name,img):
  cv2.imshow(name,img)
  cv2.waitKey(0)
  cv2.destroyAllWindows()

2 对模板图像进行预处理操作

读取模板图像

# 读取模板图像
img = cv2.imread(template)
cv_show("img",img)
plt.imshow(img)