环境介绍

python 3.6 + OpenCV 3.4.1.15

原理介绍

首先，提取出模板中每一个数字的轮廓，再对信用卡图像进行处理，提取其中的数字部分，将该部分数字与模板进行匹配，即可得到结果。

模板展示

完整代码 # ！/usr/bin/env python

# —*— coding: utf-8 —*—

# @Time: 2020/1/11 14:57

# @Author: Martin

# @File: utils.py

# @Software:PyCharm

import cv2

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]

(cnts, boundingboxes) = zip(*sorted(zip(cnts, boundingboxes), key=lambda b: b[1][i], reverse=reverse))

return cnts, boundingboxes

def resize(image, width=None, height=None, inter=cv2.INTER_AREA):

(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

# ！/usr/bin/env python

# —*— coding: utf-8 —*—

# @Time: 2020/1/11 14:57

# @Author: Martin

# @File: template_match.py

# @Software:PyCharm

"""

基于模板匹配的信用卡数字识别

"""

import cv2

import utils

import numpy as np

# 指定信用卡类型

FIRST_NUMBER = {

'3' : 'American Express',

'4' : 'Visa',

'5' : 'MasterCard',

'6' : 'Discover Card'

}

# 绘图显示

def cv_show(name, image):

cv2.imshow(name, image)

cv2.waitKey(0)

cv2.destroyAllWindows()

# 读取模板图像

img = cv2.imread('./images/ocr_a_reference.png')

cv_show('img', img)

# 转化成灰度图

ref = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

cv_show('ref', ref)

# 转化成二值图像

ref = cv2.threshold(ref, 10, 255, cv2.THRESH_BINARY_INV)[1]

cv_show('ref', ref)

# 计算轮廓

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)

# 排序，从左到右，从上到下

refCnts = utils.sort_contours(refCnts, method='left-to-right')[0]

digits = {}

# 遍历每一个轮廓

for (i, c) in enumerate(refCnts):

(x, y , w, h) = cv2.boundingRect(c)

roi = ref[y:y+h, x:x+w]

roi = cv2.resize(roi, (57, 88))

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

img_path = input("Input the path and image name: ")

image_input = cv2.imread(img_path)

cv_show('image', image_input)

image_input = utils.resize(image_input, width=300)

gray = cv2.cvtColor(image_input, 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)

gradX = cv2.Sobel(tophat, ddepth=cv2.CV_32F, dx=1, dy=0, 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)

# 闭操作

gradX = cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, rectKernel)

cv_show('gradX', gradX)

thresh = cv2.threshold(gradX, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]

cv_show('thresh', thresh)

thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, sqKernel)

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_input.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):

(x, y, w, h) = cv2.boundingRect(c)

ar = w / float(h)

if 2.5 < ar < 4.0 and (40 < w < 55) and (10 < h < 20):

locs.append((x, y, w, h))

# 将符合的轮廓从左到右排序

locs = sorted(locs, key=lambda ix: ix[0])

output = []

# 遍历每一个轮廓中的数字

for (i, (gX, gY, gW, gH)) in enumerate(locs):

groupOutput = []

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_OTSU)[1]

cv_show('group', group)

# 计算每一组轮廓

group_, digitCnts, hierarchy = cv2.findContours(group.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

digitCnts = utils.sort_contours(digitCnts, method='left-to-right')[0]

# 计算每一组的每个数值

for c in digitCnts:

(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)

scores = []

for (digit, digitROI) in digits.items():

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)))

cv2.rectangle(image_input, (gX - 5, gY - 5), (gX + gW + 5, gY + gH + 5), (0, 0, 255), 1)

cv2.putText(image_input, "".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_input)

cv2.waitKey(0)

cv2.destroyAllWindows()

结果展示

Credit Card Type: Visa

Credit Card #: 4020340002345678

总结

以上所述是小编给大家介绍的Python开发之基于模板匹配的信用卡数字识别功能，希望对大家有所帮助，如果大家有任何疑问请给我留言，小编会及时回复大家的。在此也非常感谢大家对聚米学院网站的支持！

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