from imutils import contours
import numpy as np
import argparse
import imutils
import cv2
import myutils

# 启动参数
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True, help="path to imput image")
ap.add_argument("-t", "--template", required=True, help="path to template OCR-A image")
args = vars(ap.parse_args())


# 绘图提示
def cv_show(name, img):
    cv2.imshow(name, img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()


# 读取模板图像
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
refCnts, hierarchy = cv2.findContours(ref.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# 画轮廓
cv2.drawContours(img, refCnts, -1, (0, 0, 255), 3)
cv_show("img", img)
print(np.array(refCnts, dtype=object).shape)
# 排序轮廓
refCnts = myutils.sort_contours(refCnts, method="left-to-right")[0]

digits = {}
# 遍历每一个轮廓
for (i, c) in enumerate(refCnts):
    # 计算外接矩形并且resize或合适大小
    (x, y, w, h) = cv2.boundingRect(c)
    roi = ref[y:y + h, x:x + w]
    roi = cv2.resize(roi, (57, 88))

    # 每一个数字对应一个模板
    digits[i] = roi

# 初始化卷积和
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 = myutils.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, dtype=object).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)
# 二值化处理
thresh = cv2.threshold(gradX, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
cv_show("thresh", thresh)

# 再来一个闭操作
gradX = cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, sqKernel)
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):
    # 计算轮廓
    (x, y, w, h) = cv2.boundingRect(c)
    ar = w / float(h)

    # 根据实际的任务来，选择合适的区域，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))

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):
    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:
        # 找到当前数组的轮廓，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)

        # 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, "", (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("信用卡卡号 # ：", format("".join(output)))
cv_show("image", image)

from imutils import contours
import numpy as np
import argparse
import imutils
import cv2
import myutils

# 启动参数
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True, help="path to imput image")
ap.add_argument("-t", "--template", required=True, help="path to template OCR-A image")
args = vars(ap.parse_args())


# 绘图提示
def cv_show(name, img):
    cv2.imshow(name, img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()


# 读取模板图像
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
refCnts, hierarchy = cv2.findContours(ref.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# 画轮廓
cv2.drawContours(img, refCnts, -1, (0, 0, 255), 3)
cv_show("img", img)
print(np.array(refCnts, dtype=object).shape)
# 排序轮廓
refCnts = myutils.sort_contours(refCnts, method="left-to-right")[0]

digits = {}
# 遍历每一个轮廓
for (i, c) in enumerate(refCnts):
    # 计算外接矩形并且resize或合适大小
    (x, y, w, h) = cv2.boundingRect(c)
    roi = ref[y:y + h, x:x + w]
    roi = cv2.resize(roi, (57, 88))

    # 每一个数字对应一个模板
    digits[i] = roi

# 初始化卷积和
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 = myutils.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, dtype=object).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)
# 二值化处理
thresh = cv2.threshold(gradX, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
cv_show("thresh", thresh)

# 再来一个闭操作
gradX = cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, sqKernel)
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):
    # 计算轮廓
    (x, y, w, h) = cv2.boundingRect(c)
    ar = w / float(h)

    # 根据实际的任务来，选择合适的区域，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))

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):
    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:
        # 找到当前数组的轮廓，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)

        # 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, "", (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("信用卡卡号 # ：", format("".join(output)))
cv_show("image", image)

# 启动参数 # 绘图提示 # 读取模板图像 # 灰度图 # 二值图像 # 计算轮廓 # 画轮廓 # 排序轮廓 # 遍历每一个轮廓 # 计算外接矩形并且resize或合适大小 # 每一个数字对应一个模板 # 初始化卷积和 # 读取输入图像，预处理 # 礼帽操作，突出更明亮的区域 # 通过闭操作（先膨胀，再腐蚀）将数字连在一起 # 二值化处理 # 再来一个闭操作 # 计算轮廓 # 遍历轮廓 # 计算轮廓 # 根据实际的任务来，选择合适的区域，四个数字一组 # 符合的留下来 # 遍历每一个轮廓中的数字 # 根据坐标提取每一个组 # 预处理 # 计算每一组的轮廓 # 计算每一组中的每一个数值 # 找到当前数组的轮廓，resize成合适的大小 # 计算匹配得分 # 在模板中计算每一个得分 # 模板匹配 # 得到最合适的数字 # 画出来 # 得到结果 # 打印结果 # ：", format("".join(output)))