基于opencv-python--银⾏卡识别
import cv2
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
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 = size(image, dim, interpolation=inter)
return resized
import  cv2
import numpy as  np
import myutils
from imutils import contours
def cv_show(str,thing):
cv2.imshow(str, thing)
cv2.waitKey(0)
cv2.destroyAllWindows()
# 指定信⽤卡类型
FIRST_NUMBER = {
"3": "American Express",
"4": "Visa",
"5": "MasterCard",
"6": "Discover Card"
}
img=cv2.imread("D:\images\ocr_a_reference.png")
# 灰度图
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)
# 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
ref_,refCnts,hierarchy=cv2.py(), 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，从左到右，从上到下
digits = {}
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 = size(roi, (57, 88))
# 每⼀个数字对应每⼀个模板
digits[i] = roi
# 初始化卷积核
rectKernel = StructuringElement(cv2.MORPH_RECT, (9, 3))
sqKernel = StructuringElement(cv2.MORPH_RECT, (5, 5))
#读取输⼊图像，预处理
image = cv2.imread("D:\images\credit_card_01.png")
cv_show('image',image)
image = size(image, width=300)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv_show('gray',gray)
#礼帽操作，突出更明亮的区域
tophat = phologyEx(gray, cv2.MORPH_TOPHAT, rectKernel)
cv_show('tophat',tophat)
gradX = cv2.Sobel(tophat, ddepth=cv2.CV_32F, dx=1, dy=0, #ksize=-1相当于⽤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)
#通过闭操作（先膨胀，再腐蚀）将数字连在⼀起
gradX = phologyEx(gradX, cv2.MORPH_CLOSE, rectKernel)
cv_show('gradX',gradX)
#THRESH_OTSU会⾃动寻合适的阈值，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)
#再来⼀个闭操作
thresh = phologyEx(thresh, cv2.MORPH_CLOSE, sqKernel) #再来⼀个闭操作cv_show('thresh',thresh)
# 计算轮廓
thresh_, threshCnts, hierarchy = cv2.py(), cv2.RETR_EXTERNAL,    cv2.CHAIN_APPROX_SIMPLE)
cnts = threshCnts
cur_img = py()
cv2.drawContours(cur_img,cnts,-1,(0,0,255),3)
rectangle函数opencv
cv_show('img',cur_img)
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 = []
# 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
group_,digitCnts,hierarchy = cv2.py(), 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:
# 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 = size(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
(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
# 打印结果
print("Credit Card Type: {}".format(FIRST_NUMBER[output[0]])) print("Credit Card #: {}".format("".join(output)))
cv2.imshow("Image", image)
cv2.waitKey(0)
下⾯样图适⽤

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