信用卡识别

  • ​​轮廓模板​​
  • ​​显示模板图像​​
  • ​​模板转灰度图转阈值​​
  • ​​计算轮廓​​
  • ​​轮廓排序并且保留​​
  • ​​显示图像​​
  • ​​转灰度图​​
  • ​​进行礼帽操作​​
  • ​​Sobel边缘算子​​
  • ​​闭操作 补洞​​
  • ​​轮廓排序​​
  • ​​结果依次识别​​

轮廓模板

深度学习从入门到精通——Opencv模板匹配完成信用卡识别_pytorch


识别出来的数字需要比对,需要找个模板对应一下。

# 导入工具包
import argparse

import cv2
import numpy as np
from imutils import contours
import myutils
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
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
def mainargs():
# 设置参数
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", default="./images/credit_card_01.png",
help="input image")
ap.add_argument("-t", "--template", default="ocr_a_reference.png",
help="template image")
args = vars(ap.parse_args())
return args
# 绘图展示


def cv_show(name, img):
cv2.imshow(name, img)
cv2.waitKey(0)
cv2.destroyAllWindows()

读取模板图像

# 读取一个模板图像
img = cv2.imread(args["template"])

显示模板图像

cv_show('img', img)

深度学习从入门到精通——Opencv模板匹配完成信用卡识别_pytorch_02

模板转灰度图转阈值

# 灰度图
ref = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ref = cv2.threshold(ref, 10, 255, cv2.THRESH_BINARY_INV)[1]
cv_show('ref', ref)

深度学习从入门到精通——Opencv模板匹配完成信用卡识别_灰度图_03

计算轮廓

refCnts, hierarchy = cv2.findContours(ref.copy(),         cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# 数据、全部、颜色、线厚
cv2.drawContours(img, refCnts, -1, (0, 0, 255), 3)
cv_show('img', img)

深度学习从入门到精通——Opencv模板匹配完成信用卡识别_git_04


10个轮廓

深度学习从入门到精通——Opencv模板匹配完成信用卡识别_深度学习_05

轮廓排序并且保留

refCnts = myutils.sort_contours(refCnts, method="left-to-right")[0]  # 排序,从左到右,从上到下
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
  • 接下来处理我们需要的比对的图像

显示图像

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

# 读取输入图像,预处理
image = cv2.imread(args["image"])
cv_show('image', image)

深度学习从入门到精通——Opencv模板匹配完成信用卡识别_深度学习_06

转灰度图

image = myutils.resize(image, width=300)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv_show('gray', gray)

深度学习从入门到精通——Opencv模板匹配完成信用卡识别_pytorch_07

进行礼帽操作

原始 - 开操作

# 礼帽操作,突出更明亮的区域
tophat = cv2.morphologyEx(gray, cv2.MORPH_TOPHAT, rectKernel)
cv_show('tophat', tophat)

这里面背景色是主题部分,利用礼帽操作特性,可以获取到白色噪点。

深度学习从入门到精通——Opencv模板匹配完成信用卡识别_git_08

Sobel边缘算子

gradX = cv2.Sobel(tophat, ddepth=cv2.CV_32F, dx=1, dy=0, ksize=-1)  # ksize=-1相当于用3*3的
gradX = np.absolute(gradX)
(minVal, maxVal) = (np.min(gradX), np.max(gradX))
# 归一化
gradX = (255 * ((gradX - minVal) / (maxVal - minVal)))
gradX = gradX.astype("uint8")

print(np.array(gradX).shape)
cv_show('gradX', gradX)

深度学习从入门到精通——Opencv模板匹配完成信用卡识别_灰度图_09

闭操作 补洞

通过闭操作(先膨胀,再腐蚀)将数字连在一起

gradX = cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, rectKernel)
cv_show('gradX', gradX)
# 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0
thresh = cv2.threshold(gradX, 0, 255,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
cv_show('thresh', thresh)
```
![在这里插入图片描述](https://img-blog.csdnimg.cn/cb6da869493544c3a7f00b14b299fc7d.png?x-oss-process=image/watermark,type_ZHJvaWRzYW5zZmFsbGJhY2s,shadow_50,text_Q1NETiBA5bCP6ZmIcGhk,size_11,color_FFFFFF,t_30,g_se,x_16)
再次闭操作
![在这里插入图片描述](https://img-blog.csdnimg.cn/62dba347791a497bbaecc420138d1753.png?x-oss-process=image/watermark,type_ZHJvaWRzYW5zZmFsbGJhY2s,shadow_20,text_Q1NETiBA5bCP6ZmIcGhk,size_11,color_FFFFFF,t_70,g_se,x_16)

### 计算轮廓
```python
# 计算轮廓

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)

深度学习从入门到精通——Opencv模板匹配完成信用卡识别_深度学习_10

轮廓排序

这里知道我们需要的银行卡数字在中心,大小长度宽度,我们可以进行筛选。

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])

结果依次识别

# 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)))
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)

# 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)

深度学习从入门到精通——Opencv模板匹配完成信用卡识别_pytorch_11


完整识别代码

# 导入工具包
import argparse

import cv2
import numpy as np
from imutils import contours

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
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
def mainargs():
# 设置参数
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", default="./images/credit_card_01.png",
help="input image")
ap.add_argument("-t", "--template", default="ocr_a_reference.png",
help="template image")
args = vars(ap.parse_args())
return args
# 绘图展示


def cv_show(name, img):
cv2.imshow(name, img)
cv2.waitKey(0)
cv2.destroyAllWindows()


def main(args):
# 指定信用卡类型
FIRST_NUMBER = {
"3": "American Express",
"4": "Visa",
"5": "MasterCard",
"6": "Discover Card"
}
# 读取一个模板图像
img = cv2.imread(args["template"])

# cv_show('img', img)
# 灰度图
ref = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# cv_show('ref', ref)
# 二值图像
ref = cv2.threshold(ref, 10, 255, cv2.THRESH_BINARY_INV)[1]
# cv_show('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

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, dtype=object).shape)

refCnts = sort_contours(refCnts, method="left-to-right")[0] # 排序,从左到右,从上到下
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

# 初始化卷积核
rectKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 3))
sqKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))

# 读取输入图像,预处理
image = cv2.imread(args["image"])
# cv_show('image', image)

image = resize(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 = 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)

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 = cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, rectKernel)
# cv_show('gradX', gradX)
# 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 = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, sqKernel) # 再来一个闭操作
# cv_show('thresh', thresh)

# 计算轮廓

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
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)

# 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)


if __name__ == '__main__':
args = mainargs()
main(args)

深度学习从入门到精通——Opencv模板匹配完成信用卡识别_人工智能_12