Python基于模板怎么实现匹配信用卡数字识别功能

本篇内容主要讲解"Python基于模板怎么实现匹配信用卡数字识别功能"，感兴趣的朋友不妨来看看。本文介绍的方法操作简单快捷，实用性强。下面就让小编来带大家学习"Python基于模板怎么实现匹配信用卡数字识别功能"吧!

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。

完整代码

# ！/usr/bin/env python# -*- coding: utf-8 -*-# @Time: 2020/1/11 14:57# @Author: Martin# @File: utils.py# @Software:PyCharmimport cv2def 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, boundingboxesdef 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 cv2import utilsimport 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 = threshCntscur_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: VisaCredit Card #: 4020340002345678

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