凑数的文章

import numpy as np
import argparse
import cv2
import myutils

"""
银行卡识别项目
"""
# 设置参数
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
                help="path to input image")
ap.add_argument("-t", "--template", required=True,
                help="path to template OCR-A image")
args = vars(ap.parse_args())  # vars() 是Python中的一个内置函数，用于返回对象的属性和值的字典

# 指定信用卡类型
FIRST_NUMBER = {"3": "American Express",
                "4": "Visa",
                "5": "Master Card",
                "6": "Discover Card"}


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


"""------模板图像中数字的定位处理------"""
img = cv2.imread(args["template"])
cv_show('img', img)

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

# 计算轮廓：
#
_, 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)

refCnts = myutils.sort_contours(refCnts, method="left-to-right")[0]  # 排序 ，从左到右，从上到下
digits = {}  # 保存模板中每个数字对应的像素值
for (i, c) in enumerate(refCnts):  # 遍历每一个轮廓
    (x, y, w, h) = cv2.boundingRect(c)  # 计算外接矩形并且resize成合适大小
    roi = ref[y:y + h, x:x + w]
    roi = cv2.resize(roi, (57, 88))  # 缩放到指定的大小
    digits[i] = roi  # 每一个数字对应每一个模板

""" 信用卡的图像处理 """
# 读取输入图片，预处理
image = cv2.imread(args["image"])
cv_show('image', image)
image = myutils.resize(image, width=300)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv_show('gray', gray)

rectKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 3))
sqKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
tophat = cv2.morphologyEx(gray, cv2.MORPH_TOPHAT, rectKernel)
cv_show('tophat', tophat)

# 找到数字边框
closeX = cv2.morphologyEx(tophat, cv2.MORPH_CLOSE, rectKernel)
cv_show('gradX', closeX)

thresh = cv2.threshold(closeX, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
cv_show('thresh', thresh)
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, sqKernel)
cv_show('thresh', thresh)

# 计算轮廓
t_, threshCnts, h = 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)

# 遍历轮廓，找到数字部分像素区域
locs = []
for (i, c) in enumerate(cnts):
    (x, y, w, h) = cv2.boundingRect(c)
    ar = w / float(h)
    # 选择合适的区域，根据实际任务设置
    if ar > 2.5 and ar < 4.0:
        if (w > 40 and w < 55) and (h > 10 and h < 20):
            locs.append((x, y, w, h))

locs = sorted(locs, key=lambda x: x[0])
output = []
for (i, (gX, gY, gW, gH)) in enumerate(locs):
    groupOutput = []
    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.findContours(group.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    digitCnts = myutils.sort_contours(digitCnts, method="left-to-right")[0]
    # 计算每一组中的每一个数据
    for c in digitCnts:
        # 找到当前数值的轮廓， resize成合适的大小
        (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)

        ''' 使用模板匹配，计算匹配得分 '''
        scores = []
        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)

    # 写出在原图上写出银行卡的卡号
    cv2.putText(image, "".join(groupOutput), (gX, gY - 15), cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 0, 255), 2)

    output.extend(groupOutput)

# 打印结果
print("Credit Card Type:{}".format(FIRST_NUMBER[output[0]]))
print("Credit Card #:{}".format("".join(output)))
cv_show("Image", image)
cv2.waitKey(0)