本專案利用python以及opencv實現信用卡的數字識別

前期準備

• 匯入工具包
• 定義功能函式

模板影象處理

• 讀取模板影象 cv2.imread(img)
• 灰度化處理 cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
• 二值化 cv2.threshold()
• 輪廓 - 輪廓

信用卡影象處理

• 讀取信用卡影象 cv2.imread(img)
• 灰度化處理 cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
• 禮帽處理 cv2.morphologyEx(gray,cv2.MORPH_TOPHAT,rectKernel)
• Sobel邊緣檢測 cv2.Sobel(tophat,ddepth=cv2.CV_32F,dx=1,dy=0,ksize=-1)
• 閉操作 cv2.morphologyEx(gradX,cv2.MORPH_CLOSE,rectKernel)
• 計算輪廓 cv2.findContours
• 模板檢測 cv2.matchTemplate(roi,digitROI,cv2.TM_CCOEFF)
  

原始資料展示

結果展示

1 前期準備

# 匯入工具包
# opencv讀取圖片的格式為b g r
# matplotlib圖片的格式為 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"

# 指定信用卡型別
FIRST_NUMBER = {
  "3": "American Express","4": "Visa","5": "MasterCard","6": "Discover Card"
}

# 定義一些功能函式

# 對框進行排序
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] #用一個最小的矩形，把找到的形狀包起來x,y,h,w
  (cnts,boundingBoxes) = zip(*sorted(zip(cnts,boundingBoxes),key=lambda b: b[1][i],reverse=reverse))

  return cnts,boundingBoxes

# 調整圖片尺寸大小
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

# 定義cv2展示函式
def cv_show(name,img):
  cv2.imshow(name,img)
  cv2.waitKey(0)
  cv2.destroyAllWindows()

2 對模板影象進行預處理操作

讀取模板影象

# 讀取模板影象
img = cv2.imread(template)
cv_show("img",img)
plt.imshow(img)

<matplotlib.image.AxesImage at 0x2b2e04ad128>

模板影象轉灰度影象

# 轉灰度圖
ref = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
cv_show("ref",ref)
plt.imshow(ref)

<matplotlib.image.AxesImage at 0x2b2e25d9e48>

轉為二值影象

ref = cv2.threshold(ref,10,255,cv2.THRESH_BINARY_INV)[1]
cv_show("ref",ref)
plt.imshow(ref)

<matplotlib.image.AxesImage at 0x2b2e2832a90>

計算輪廓

#cv2.findContours()函式接受的引數為二值圖，即黑白的（不是灰度圖）,cv2.RETR_EXTERNAL只檢測外輪廓，cv2.CHAIN_APPROX_SIMPLE只保留終點座標
#返回的list中每個元素都是影象中的一個輪廓
# 在二值化後的影象中計算輪廓
refCnts,hierarchy = cv2.findContours(ref.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
# 在原圖上畫出輪廓
cv2.drawContours(img,refCnts,-1,(0,255),3)
cv_show("img",img)
plt.imshow(img)

<matplotlib.image.AxesImage at 0x2b2e256f908>

print(np.array(refCnts).shape)
# 排序，從左到右，從上到下
refCnts = sort_contours(refCnts,method="left-to-right")[0] 
digits = {}

# 遍歷每一個輪廓
for (i,c) in enumerate(refCnts):
  # 計算外接矩形並且resize成合適大小
  (x,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

(10,)

3 對信用卡進行處理

初始化卷積核

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

讀取信用卡

image = cv2.imread(predict_card)
cv_show("image",image)
plt.imshow(image)

<matplotlib.image.AxesImage at 0x2b2e294c9b0>

對影象進行預處理操作

# 先對影象進行resize操作
image = resize(image,width=300)
# 灰度化處理
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
cv_show("gray",gray)
plt.imshow(gray)

<matplotlib.image.AxesImage at 0x2b2e255d828>

對影象禮帽操作

• 禮帽 = 原始輸入-開運算結果
• 開運算:先腐蝕，再膨脹
• 突出更明亮的區域

tophat = cv2.morphologyEx(gray,rectKernel)
cv_show("tophat",tophat)
plt.imshow(tophat)

<matplotlib.image.AxesImage at 0x2b2eb008e48>

用Sobel運算元邊緣檢測

gradX = cv2.Sobel(tophat,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>

對影象閉操作

• 閉操作:先膨脹，再腐蝕
• 可以將數字連在一起

gradX = cv2.morphologyEx(gradX,rectKernel) 
cv_show("gradX",gradX)
plt.imshow(gradX)

<matplotlib.image.AxesImage at 0x2b2e097cc88>

#THRESH_OTSU會自動尋找合適的閾值，適合雙峰，需把閾值引數設定為0
thresh = cv2.threshold(gradX,cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1] 
cv_show("thresh",thresh)
plt.imshow(thresh)

<matplotlib.image.AxesImage at 0x2b2e24a0dd8>

# 再進行一次閉操作
thresh = cv2.morphologyEx(thresh,sqKernel) #再來一個閉操作
cv_show("thresh",thresh)
plt.imshow(thresh)

<matplotlib.image.AxesImage at 0x2b2e25fe748>

計算輪廓

threshCnts,hierarchy = cv2.findContours(thresh.copy(),cv2.CHAIN_APPROX_SIMPLE)
cnts = threshCnts
cur_img = image.copy()
cv2.drawContours(cur_img,cnts,3) 
cv_show("img",cur_img)
plt.imshow(cur_img)

<matplotlib.image.AxesImage at 0x2b2eb17c780>

locs = []

# 遍歷輪廓
for (i,c) in enumerate(cnts):
  # 計算矩形
  (x,h) = cv2.boundingRect(c)
  ar = w / float(h)

  # 選擇合適的區域，根據實際任務來，這裡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,h))

# 將符合的輪廓從左到右排序
locs = sorted(locs,key=lambda x:x[0])
output = []

模板匹配

# 遍歷每一個輪廓中的數字
for (i,(gX,gY,gW,gH)) in enumerate(locs):
  # initialize the list of group digits
  groupOutput = []

  # 根據座標提取每一個組
  group = gray[gY - 5:gY + gH + 5,gX - 5:gX + gW + 5]
  cv_show("group",group)

  # 預處理
  group = cv2.threshold(group,cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
  cv_show("group",group)

  # 計算每一組的輪廓
  digitCnts,hierarchy = cv2.findContours(group.copy(),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 in digitCnts:
    # 找到當前數值的輪廓，resize成合適的的大小
    (x,h) = cv2.boundingRect(c)
    roi = group[y:y + h,x:x + w]
    roi = cv2.resize(roi,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) in digits.items():
      # 模板匹配
      result = cv2.matchTemplate(roi,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),1)
  cv2.putText(image,"".join(groupOutput),gY - 15),cv2.FONT_HERSHEY_SIMPLEX,0.65,2)

  # 得到結果
  output.extend(groupOutput)

# 列印結果
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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