概述

今天带大家使用我们之前学会的知识来实现银行卡卡号读取. 代码分为四个部分: 主函程序, 预处理, 计算轮廓, 其他程序.

预处理

通过灰度转换, 二值化, 膨胀, 腐蚀, 边缘检测等方法, 去除图片噪声, 突出我们想要得到的结果.

代码

import numpy as np
import cv2
from matplotlib import pyplot as plt
from my_functions import resizedef read_template(image_path, visualize=False):"""读取模板:param image_path: 图片路径:param visualize: 可视化, 默认为False:return: 返回模板, 二值化后的模板"""# 读取模板template = cv2.imread(image_path)# 转换成灰度图template_gray = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)# 二值化ret, template_thresh = cv2.threshold(template_gray, 10, 255, cv2.THRESH_BINARY_INV)# 如果展示为真if visualize:"""图片展示"""# 绘制子图f, ax = plt.subplots(3, 1, figsize=(10, 8))ax[0].imshow(template)ax[1].imshow(template_gray, "gray")ax[2].imshow(template_thresh, "gray")# 标题ax[0].set_title("template")ax[1].set_title("template gray")ax[2].set_title("template binary inverse")plt.show()# 返回return template, template_threshdef read_image(image_path, visualize=False):"""读取银行卡图片:param image_path: 图片路径:param visualize: 可视化, 默认为False:return: 返回裁剪后的图片, 灰度图, 处理后的图"""# 初始化卷积核rectKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 3))sqKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))# 读取图片image = cv2.imread(image_path)# 更改尺寸image_resize = resize(image, width=300)# 转换成灰度图image_gray = cv2.cvtColor(image_resize, cv2.COLOR_BGR2GRAY)# 礼帽操作, 突出明亮区域tophat = cv2.morphologyEx(image_gray, cv2.MORPH_TOPHAT, rectKernel)# Sobel边缘检测edge = cv2.Sobel(tophat, ddepth=cv2.CV_32F, dx=1, dy=0, ksize=-1)edge = np.absolute(edge)# 标准化edge = 255 * cv2.normalize(edge, None, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)edge = edge.astype("uint8")# 通过闭操作（先膨胀，再腐蚀）将数字连在一起edge_close = cv2.morphologyEx(edge, cv2.MORPH_CLOSE, rectKernel)# 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0ret, thresh = cv2.threshold(edge_close, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)# 再来一个闭操作thresh_close = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, sqKernel)  # 再来一个闭操作# 如果展示为真if visualize:"""图片展示"""# 绘制子图f, ax = plt.subplots(2, 2, figsize=(12, 8))ax[0, 0].imshow(cv2.cvtColor(image_resize, cv2.COLOR_BGR2RGB))ax[0, 1].imshow(image_gray, "gray")ax[1, 0].imshow(tophat, "gray")ax[1, 1].imshow(edge, "gray")# 标题ax[0, 0].set_title("image resize")ax[0, 1].set_title("image gray")ax[1, 0].set_title("image tophat")ax[1, 1].set_title("image edge")plt.show()# 绘制子图f, ax = plt.subplots(2, 2, figsize=(12, 8))ax[0, 0].imshow(edge, "gray")ax[0, 1].imshow(edge_close, "gray")ax[1, 0].imshow(thresh, "gray")ax[1, 1].imshow(thresh_close, "gray")# 标题ax[0, 0].set_title("image edge")ax[0, 1].set_title("image close")ax[1, 0].set_title("image binary")ax[1, 1].set_title("image binary close")plt.show()# 返回return image_resize, image_gray, thresh

模板预处理

银行卡预处理

计算轮廓

代码

import cv2
from matplotlib import pyplot as plt, gridspec
from my_functions import sort_contoursdef template_calculate_contours(template, template_binary, visualize=False):"""计算模板轮廓:param template: 模板:param template_binary: 二值化的模板:return: 轮廓"""# 获取轮廓contours, hierarchy = cv2.findContours(template_binary.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)# 轮廓排序, 从左到右, 从上到下contours = sort_contours(contours)digits = {}# 遍历每一个轮廓for (i, c) in enumerate(contours):# 计算矩阵(x, y, w, h) = cv2.boundingRect(c)# 获取roiroi = template_binary[y:y + h, x:x + w]roi = cv2.resize(roi, (57, 88))# 每一个数字对应每一个模板digits[i] = roiif visualize:"""图片展示"""# 绘制子图plt.figure(figsize=(12, 6))gs = gridspec.GridSpec(2, 10)# 轴1plt.subplot(gs[0, :10])plt.imshow(template, "gray")plt.xticks([])plt.yticks([])plt.title("original")# 轴2for (number, image) in digits.items():plt.subplot(gs[1, number])plt.xticks([])plt.yticks([])plt.imshow(image, "gray")plt.title("number: {}".format(number))plt.show()print(digits)return digitsdef image_calculate_contours(image, thresh, visualize=False):"""计算轮廓:param image: 图片:param thresh: 处理后的图片:param visualize: 可视化, 默认为False:return: 轮廓"""# 获取轮廓contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)# 绘制轮廓cur_img = image.copy()image_with_contour = cv2.drawContours(cur_img, contours, -1, (0, 0, 255), 3)# 位置locations = []# 遍历轮廓for (i, c) in enumerate(contours):# 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locations.append((x, y, w, h))# 将符合的轮廓从左到右排序locs = sorted(locations, key=lambda x: x[0])if visualize:"""图片展示"""# 绘制子图f, ax = plt.subplots(2, 1, figsize=(12, 8))ax[0].imshow(cv2.cvtColor(image_with_contour, cv2.COLOR_BGR2RGB))for r in locs:(x, y, w, h) = rrectangle = cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)ax[1].imshow(cv2.cvtColor(rectangle, cv2.COLOR_BGR2RGB))# 标题ax[0].set_title("original")ax[1].set_title("detect")plt.show()return locs

模板轮廓

银行卡轮廓

其他程序

import cv2def img_show(name, img):"""图片展示"""cv2.imshow(name, img)cv2.waitKey(0)cv2.destroyAllWindows()def sort_contours(contours):"""轮廓排序 (从左到右):param contours: 轮廓:return: 返回排序好的轮廓"""boundingBoxes = [cv2.boundingRect(c) for c in contours]  # 用一个最小的矩形，把找到的形状包起来x,y,h,w(contours, boundingBoxes) = zip(*sorted(zip(contours, boundingBoxes), key=lambda b: b[1][0]))return contoursdef resize(image, width=None, height=None, inter=cv2.INTER_AREA):"""修改图片大小:param image: 原图:param width: 宽:param height: 高:param inter: 模式:return: 修改好的图片"""dim = None(h, w) = image.shape[:2]if width is None and height is None:return imageif 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

主函数

代码

import numpy as np
import argparse
import cv2
from matplotlib import pyplot as plt
from my_functions import sort_contours
from pre_process import read_template
from pre_process import read_image
from calculate_contours import template_calculate_contours
from calculate_contours import image_calculate_contoursdef extract_number(image_gray, locations, digits, visualize=False):"""提取数字:param image_gray: 灰度图:param locations: 图片轮廓:param digits: 模板轮廓:param visualize: 可视化, 默认为False:return: 读取完数字的图片"""# 输出output = []# 图片total_img = []# 遍历每一个轮廓中的数字for (i, (gX, gY, gW, gH)) in enumerate(locations):# 组输出groupOutput = []group_img = []# 根据坐标提取每一个组group = image_gray[gY - 5:gY + gH + 5, gX - 5:gX + gW + 5]# 预处理group_binary = cv2.threshold(group, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]# 计算每一组的轮廓digitCnts, hierarchy = cv2.findContours(group_binary.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)digitCnts = sort_contours(digitCnts)# 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))group_img.append(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)# 添加图片total_img.append(group_img)if visualize:"""图片展示"""# 绘制子图f, ax = plt.subplots(4, 4, figsize=(8, 8))for i, group_im in enumerate(total_img):for j, im in enumerate(group_im):ax[i, j].imshow(im, "gray")ax[i, j].set_xticks([])ax[i, j].set_yticks([])ax[i, j].set_title("group: {}".format(i + 1))plt.show()# 展示最终图片plt.figure(figsize=(10, 8))plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))plt.title("Final Result")plt.show()return imagedef parse_opt():"""设置参数"""parser = argparse.ArgumentParser()parser.add_argument("--image_path", type=str, default="images/credit_card_01.png", help="输入图片路径")parser.add_argument("--template_path", type=str, default="template/template.png", help="模板图片路径")args = parser.parse_args()return argsif __name__ == "__main__":args = parse_opt()# 读取模板template, template_binary = read_template(args.template_path, True)# 计算模板轮廓digits = template_calculate_contours(template=template, template_binary=template_binary, visualize=True)# 读取图片image, image_gray, thresh = read_image(args.image_path, visualize=True)# 计算图片轮廓locations = image_calculate_contours(image, thresh, visualize=True)# 提取数字result = extract_number(image_gray=image_gray, locations=locations, digits=digits, visualize=True)# 保存最终结果cv2.imwrite("Final_result.png", result)

数字分割

最终结果