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
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# 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 bimport numpy as npimport cv2from imutils import contoursimport matplotlib.pyplot as plt%matplotlib inline |
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# 信用卡的位置predict_card = "images/credit_card_01.png"# 模板的位置template = "images/ocr_a_reference.png" |
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# 指定信用卡类型FIRST_NUMBER = { "3": "American Express", "4": "Visa", "5": "MasterCard", "6": "Discover Card"} |
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# 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
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# 读取模板图像img = cv2.imread(template)cv_show("img",img)plt.imshow(img) |
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<matplotlib.image.AxesImage at 0x2b2e04ad128> |
模板图像转灰度图像
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# 转灰度图ref = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)cv_show("ref",ref)plt.imshow(ref) |
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<matplotlib.image.AxesImage at 0x2b2e25d9e48> |
转为二值图像
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ref = cv2.threshold(ref,10,255,cv2.THRESH_BINARY_INV)[1]cv_show("ref",ref)plt.imshow(ref) |
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<matplotlib.image.AxesImage at 0x2b2e2832a90> |
计算轮廓
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#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) |
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<matplotlib.image.AxesImage at 0x2b2e256f908> |
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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
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rectKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 3))sqKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) |
读取信用卡
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image = cv2.imread(predict_card)cv_show("image",image)plt.imshow(image) |
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<matplotlib.image.AxesImage at 0x2b2e294c9b0> |
对图像进行预处理操作
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# 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) |
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<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
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tophat = cv2.morphologyEx(gray,cv2.MORPH_TOPHAT,rectKernel)cv_show("tophat",tophat)plt.imshow(tophat) |
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<matplotlib.image.AxesImage at 0x2b2eb008e48> |
用Sobel算子边缘检测
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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) |
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(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
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gradX = cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, rectKernel) cv_show("gradX",gradX)plt.imshow(gradX) |
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<matplotlib.image.AxesImage at 0x2b2e097cc88> |
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#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0thresh = cv2.threshold(gradX, 0, 255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1] cv_show("thresh",thresh)plt.imshow(thresh) |
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<matplotlib.image.AxesImage at 0x2b2e24a0dd8> |
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# 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) |
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<matplotlib.image.AxesImage at 0x2b2e25fe748> |
计算轮廓
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threshCnts, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)cnts = threshCntscur_img = image.copy()cv2.drawContours(cur_img,cnts,-1,(0,0,255),3) cv_show("img",cur_img)plt.imshow(cur_img) |
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<matplotlib.image.AxesImage at 0x2b2eb17c780> |
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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
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# 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) |
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# 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) |
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Credit Card Type: VisaCredit Card #: 4000123456789010<matplotlib.image.AxesImage at 0x2b2eb040748> |
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/Mind_programmonkey/article/details/99650303
