python opencv实现信用卡的数字识别

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) <matplotlib.image.AxesImage at 0x2b2e04ad128>

模板图像转灰度图像

# 转灰度图 ref = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) cv_show("ref",ref) plt.imshow(ref) <matplotlib.image.AxesImage at 0x2b2e25d9e48>

转为二值图像

ref = cv2.threshold(ref,10,255,cv2.THRESH_BINARY_INV)[1] cv_show("ref",ref) plt.imshow(ref) <matplotlib.image.AxesImage at 0x2b2e2832a90>

计算轮廓

#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 #返回的list中每个元素都是图像中的一个轮廓 # 在二值化后的图像中计算轮廓 refCnts,hierarchy = cv2.findContours(ref.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) # 在原图上画出轮廓 cv2.drawContours(img,refCnts,-1,(0,0,255),3) cv_show("img",img) plt.imshow(img) <matplotlib.image.AxesImage at 0x2b2e256f908>

print(np.array(refCnts).shape) # 排序，从左到右，从上到下 refCnts = sort_contours(refCnts,method="left-to-right")[0] digits = {} # 遍历每一个轮廓 for (i, c) in enumerate(refCnts): # 计算外接矩形并且resize成合适大小 (x, y, w, h) = cv2.boundingRect(c) roi = ref[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] = roi

(10,)

3 对信用卡进行处理

初始化卷积核

rectKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 3)) sqKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))

读取信用卡

image = cv2.imread(predict_card) cv_show("image",image) plt.imshow(image) <matplotlib.image.AxesImage at 0x2b2e294c9b0>

对图像进行预处理操作

# 先对图像进行resize操作 image = resize(image,width=300) # 灰度化处理 gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY) cv_show("gray",gray) plt.imshow(gray) <matplotlib.image.AxesImage at 0x2b2e255d828>

对图像礼帽操作

礼帽 = 原始输入-开运算结果 开运算:先腐蚀，再膨胀 突出更明亮的区域 tophat = cv2.morphologyEx(gray,cv2.MORPH_TOPHAT,rectKernel) cv_show("tophat",tophat) plt.imshow(tophat) <matplotlib.image.AxesImage at 0x2b2eb008e48>

用Sobel算子边缘检测

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) plt.imshow(gradX) (189, 300) <matplotlib.image.AxesImage at 0x2b2e0797400>

对图像闭操作

闭操作:先膨胀，再腐蚀 可以将数字连在一起 gradX = cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, rectKernel) cv_show("gradX",gradX) plt.imshow(gradX) <matplotlib.image.AxesImage at 0x2b2e097cc88>

#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 thresh = cv2.threshold(gradX, 0, 255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1] cv_show("thresh",thresh) plt.imshow(thresh) <matplotlib.image.AxesImage at 0x2b2e24a0dd8>

# 再进行一次闭操作 thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, sqKernel) #再来一个闭操作 cv_show("thresh",thresh) plt.imshow(thresh) <matplotlib.image.AxesImage at 0x2b2e25fe748>

计算轮廓

threshCnts, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) cnts = threshCnts cur_img = image.copy() cv2.drawContours(cur_img,cnts,-1,(0,0,255),3) cv_show("img",cur_img) plt.imshow(cur_img) <matplotlib.image.AxesImage at 0x2b2eb17c780>

locs = [] # 遍历轮廓 for (i, c) in enumerate(cnts): # 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 locs.append((x, y, w, h)) # 将符合的轮廓从左到右排序 locs = sorted(locs, key=lambda x:x[0]) output = []

模板匹配

# 遍历每一个轮廓中的数字 for (i, (gX, gY, gW, gH)) in enumerate(locs): # initialize the list of group digits groupOutput = [] # 根据坐标提取每一个组 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_BINARY | cv2.THRESH_OTSU)[1] cv_show("group",group) # 计算每一组的轮廓 digitCnts,hierarchy = cv2.findContours(group.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) digitCnts = contours.sort_contours(digitCnts,method="left-to-right")[0] # 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)) cv_show("roi",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) # 打印结果 print("Credit Card Type: {}".format(FIRST_NUMBER[output[0]])) print("Credit Card #: {}".format("".join(output))) cv_show("Image",image) plt.imshow(image) Credit Card Type: Visa Credit Card #: 4000123456789010 <matplotlib.image.AxesImage at 0x2b2eb040748>

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信用 信用卡 opencv Python