项目概述：
基于opencv实现信用卡数字识别，如下图所示：
项目流程如下：
1.处理模板，进行轮廓检测(检测外轮廓)
2.得到当前轮廓的外接矩形，并将模板中的外接矩形切割出来，得到0-9对应的模板图片，并resize
3.使用形态学操作对信用卡图片进行处理，得到轮廓
4.根据矩形轮廓的长宽比挑选出信用卡的数字矩形框，并resize
5.使用for循环依次检测
代码如下：
ocr_template_match.py
1 # 导入工具包 2 from imutils import contours 3 import numpy as np 4 import argparse 5 import cv2 6 import myutils 7 8 # 设置参数 9 ap = argparse.ArgumentParser() 10 ap.add_argument("-i", "--image", default=\'images/credit_card_01.png\', 11 help="path to input image") 12 ap.add_argument("-t", "--template", default=\'ocr_a_reference.png\', 13 help="path to template OCR-A image") 14 args = vars(ap.parse_args()) 15 16 # 指定信用卡类型 17 FIRST_NUMBER = { 18 "3": "American Express", 19 "4": "Visa", 20 "5": "MasterCard", 21 "6": "Discover Card" 22 } 23 # 绘图展示 24 def cv_show(name,img): 25 cv2.imshow(name,img) 26 cv2.waitKey(0) 27 cv2.destroyAllWindows() 28 # 读取一个模板图像 29 img = cv2.imread(args["template"]) 30 cv_show(\'img\',img) 31 # 灰度图 32 ref = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 33 cv_show(\'ref\',ref) 34 # 二值图像 35 ref = cv2.threshold(ref, 10, 255, cv2.THRESH_BINARY_INV)[1] 36 cv_show(\'ref\',ref) 37 38 # 计算轮廓 39 #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，cv2.CHAIN_APPROX_SIMPLE只保留终点坐标 40 #返回的list中每个元素都是图像中的一个轮廓 41 42 ref_, refCnts, hierarchy = cv2.findContours(ref.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) 43 44 cv2.drawContours(img,refCnts,-1,(0,0,255),3) 45 cv_show(\'img\',img) 46 print (np.array(refCnts).shape) 47 refCnts = myutils.sort_contours(refCnts, method="left-to-right")[0] #排序，从左到右，从上到下 48 digits = {} 49 50 # 遍历每一个轮廓 51 for (i, c) in enumerate(refCnts): 52 # 计算外接矩形并且resize成合适大小 53 (x, y, w, h) = cv2.boundingRect(c) 54 roi = ref[y:y + h, x:x + w] 55 roi = cv2.resize(roi, (57, 88)) 56 57 # 每一个数字对应每一个模板 58 digits[i] = roi 59 60 # 初始化卷积核 61 rectKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 3)) 62 sqKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) 63 64 #读取输入图像，预处理 65 image = cv2.imread(args["image"]) 66 cv_show(\'image\',image) 67 image = myutils.resize(image, width=300) 68 gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 69 cv_show(\'gray\',gray) 70 71 #礼帽操作，突出更明亮的区域 72 tophat = cv2.morphologyEx(gray, cv2.MORPH_TOPHAT, rectKernel) 73 cv_show(\'tophat\',tophat) 74 # 75 gradX = cv2.Sobel(tophat, ddepth=cv2.CV_32F, dx=1, dy=0, #ksize=-1相当于用3*3的 76 ksize=-1) 77 78 79 gradX = np.absolute(gradX) 80 (minVal, maxVal) = (np.min(gradX), np.max(gradX)) 81 gradX = (255 * ((gradX - minVal) / (maxVal - minVal))) 82 gradX = gradX.astype("uint8") 83 84 print (np.array(gradX).shape) 85 cv_show(\'gradX\',gradX) 86 87 #通过闭操作（先膨胀，再腐蚀）将数字连在一起 88 gradX = cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, rectKernel) 89 cv_show(\'gradX\',gradX) 90 #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 91 thresh = cv2.threshold(gradX, 0, 255, 92 cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1] 93 cv_show(\'thresh\',thresh) 94 95 #再来一个闭操作 96 97 thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, sqKernel) #再来一个闭操作 98 cv_show(\'thresh\',thresh) 99 100 # 计算轮廓 101 102 thresh_, threshCnts, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, 103 cv2.CHAIN_APPROX_SIMPLE) 104 105 cnts = threshCnts 106 cur_img = image.copy() 107 cv2.drawContours(cur_img,cnts,-1,(0,0,255),3) 108 cv_show(\'img\',cur_img) 109 locs = [] 110 111 # 遍历轮廓 112 for (i, c) in enumerate(cnts): 113 # 计算矩形 114 (x, y, w, h) = cv2.boundingRect(c) 115 ar = w / float(h) 116 117 # 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 118 if ar > 2.5 and ar < 4.0: 119 120 if (w > 40 and w < 55) and (h > 10 and h < 20): 121 #符合的留下来 122 locs.append((x, y, w, h)) 123 124 # 将符合的轮廓从左到右排序 125 locs = sorted(locs, key=lambda x:x[0]) 126 output = [] 127 128 # 遍历每一个轮廓中的数字 129 for (i, (gX, gY, gW, gH)) in enumerate(locs): 130 # initialize the list of group digits 131 groupOutput = [] 132 133 # 根据坐标提取每一个组 134 group = gray[gY - 5:gY + gH + 5, gX - 5:gX + gW + 5] 135 cv_show(\'group\',group) 136 # 预处理 137 group = cv2.threshold(group, 0, 255, 138 cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1] 139 cv_show(\'group\',group) 140 # 计算每一组的轮廓 141 group_,digitCnts,hierarchy = cv2.findContours(group.copy(), cv2.RETR_EXTERNAL, 142 cv2.CHAIN_APPROX_SIMPLE) 143 digitCnts = contours.sort_contours(digitCnts, 144 method="left-to-right")[0] 145 146 # 计算每一组中的每一个数值 147 for c in digitCnts: 148 # 找到当前数值的轮廓，resize成合适的的大小 149 (x, y, w, h) = cv2.boundingRect(c) 150 roi = group[y:y + h, x:x + w] 151 roi = cv2.resize(roi, (57, 88)) 152 cv_show(\'roi\',roi) 153 154 # 计算匹配得分 155 scores = [] 156 157 # 在模板中计算每一个得分 158 for (digit, digitROI) in digits.items(): 159 # 模板匹配 160 result = cv2.matchTemplate(roi, digitROI, 161 cv2.TM_CCOEFF) 162 (_, score, _, _) = cv2.minMaxLoc(result) 163 scores.append(score) 164 165 # 得到最合适的数字 166 groupOutput.append(str(np.argmax(scores))) 167 168 # 画出来 169 cv2.rectangle(image, (gX - 5, gY - 5), 170 (gX + gW + 5, gY + gH + 5), (0, 0, 255), 1) 171 cv2.putText(image, "".join(groupOutput), (gX, gY - 15), 172 cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 0, 255), 2) 173 174 # 得到结果 175 output.extend(groupOutput) 176 177 # 打印结果 178 print("Credit Card Type: {}".format(FIRST_NUMBER[output[0]])) 179 print("Credit Card #: {}".format("".join(output))) 180 cv2.imshow("Image", image) 181 cv2.waitKey(0)
myutils.py
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] #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 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
ocr_a_reference.png
credit_card_01.png
credit_card_02.png
识别结果：