importcv2importnumpy as npimportmyutilsfrom imutils importcontoursdefcv_show(str,thing):

cv2.imshow(str, thing)

cv2.waitKey(0)

cv2.destroyAllWindows()#指定信用卡類型

FIRST_NUMBER ={"3": "American Express","4": "Visa","5": "MasterCard","6": "Discover Card"}

img=cv2.imread("D:\images\ocr_a_reference.png")#灰度圖

ref =cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)#二值化

ref=cv2.threshold(ref,10,255,cv2.THRESH_BINARY_INV)[1]

cv_show("img_ref",ref)#計算輪廓#cv2.findContours()函數接受的參數為二值圖，即黑白的（不是灰度圖）,cv2.RETR_EXTERNAL只檢測外輪廓，cv2.CHAIN_APPROX_SIMPLE只保留終點坐標#返回的list中每個元素都是圖像中的一個輪廓

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)

refCnts= myutils.sort_contours(refCnts, method="left-to-right")[0]#排序，從左到右，從上到下

digits ={}for (i, c) inenumerate(refCnts):#計算外接矩形并且resize成合適大小

(x, y, w, h) =cv2.boundingRect(c)

roi= ref[y:y + h, x:x +w]

roi= cv2.resize(roi, (57, 88))#每一個數字對應每一個模板

digits[i] =roi#初始化卷積核

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

sqKernel= cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))#讀取輸入圖像，預處理

image = cv2.imread("D:\images\credit_card_01.png")

cv_show('image',image)

image= myutils.resize(image, width=300)

gray=cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

cv_show('gray',gray)#禮帽操作，突出更明亮的區域

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相當于用3*3的

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)#通過閉操作（先膨脹，再腐蝕）將數字連在一起

gradX =cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, rectKernel)

cv_show('gradX',gradX)#THRESH_OTSU會自動尋找合適的閾值，適合雙峰，需把閾值參數設置為0

thresh = cv2.threshold(gradX, 0, 255,

cv2.THRESH_BINARY| cv2.THRESH_OTSU)[1]

cv_show('thresh',thresh)#再來一個閉操作

thresh= cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, sqKernel) #再來一個閉操作

cv_show('thresh',thresh)#計算輪廓

thresh_, 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)

locs=[]#遍歷輪廓

for (i, c) inenumerate(cnts):#計算矩形

(x, y, w, h) =cv2.boundingRect(c)

ar= w /float(h)#選擇合適的區域，根據實際任務來，這里的基本都是四個數字一組

if ar > 2.5 and ar < 4.0:if (w > 40 and w < 55) and (h > 10 and h < 20):#符合的留下來

locs.append((x, y, w, h))#將符合的輪廓從左到右排序

locs = sorted(locs, key=lambdax:x[0])

output=[]#遍歷每一個輪廓中的數字

for (i, (gX, gY, gW, gH)) inenumerate(locs):#initialize the list of group digits

groupOutput =[]#根據坐標提取每一個組

group = gray[gY - 5:gY + gH + 5, gX - 5:gX + gW + 5]

cv_show('group',group)#預處理

group = cv2.threshold(group, 0, 255,

cv2.THRESH_BINARY| cv2.THRESH_OTSU)[1]

cv_show('group',group)#計算每一組的輪廓

group_,digitCnts,hierarchy =cv2.findContours(group.copy(), cv2.RETR_EXTERNAL,

cv2.CHAIN_APPROX_SIMPLE)

digitCnts=contours.sort_contours(digitCnts,

method="left-to-right")[0]#計算每一組中的每一個數值

for c indigitCnts:#找到當前數值的輪廓，resize成合適的的大小

(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)#計算匹配得分

scores =[]#在模板中計算每一個得分

for (digit, digitROI) indigits.items():#模板匹配

result =cv2.matchTemplate(roi, digitROI,

cv2.TM_CCOEFF)

(_, score, _, _)=cv2.minMaxLoc(result)

scores.append(score)#得到最合適的數字

groupOutput.append(str(np.argmax(scores)))#畫出來

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)#得到結果

output.extend(groupOutput)#打印結果

print("Credit Card Type: {}".format(FIRST_NUMBER[output[0]]))print("Credit Card #: {}".format("".join(output)))

cv2.imshow("Image", image)

cv2.waitKey(0)

總結

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