本項目利用python以及opencv實現(xiàn)信用卡的數(shù)字識別

前期準備

模板圖像處理

信用卡圖像處理

原始數(shù)據(jù)展示

結果展示

1 前期準備

# 導入工具包# opencv讀取圖片的格式為b g r# matplotlib圖片的格式為 r g bimport numpy as npimport cv2from imutils import contoursimport 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"

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

# 定義一些功能函數(shù)# 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(diào)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展示函數(shù)def cv_show(name,img):  cv2.imshow(name,img)  cv2.waitKey(0)  cv2.destroyAllWindows()

2 對模板圖像進行預處理操作

讀取模板圖像

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