Question

我有以下图片，我试图读取MICR E-13B font中的数字
我正在使用OpenCV的模板匹配来查找与图像对应的数字。所以，问题分为3个步骤：
步骤1：使用findcontours（）检测MICR E-13B字体，并检测矩形以实现以下输出。

步骤2：在步骤2中，我们从上方获取每个轮廓（包含MICR E-13B字体），以便它们可以分开以进一步单独打破数字。
第3步： This is where the error occurs.获取上面的输出，现在的任务是将数字组分解为单个数字，以便可以使用OpenCV中的模板匹配来匹配它们。但是当我应用findcontour方法并继续检测每个轮廓的boundRect时，我没有得到任何边界矩形以下是各个步骤的代码：
第1步：

# apply a tophat (whitehat) morphological operator to find light
# regions against a dark background
tophat = cv2.morphologyEx(gray, cv2.MORPH_TOPHAT, rectKernel)

# compute the Scharr gradient of the tophat image, then scale
# the rest back into the range [0, 255]
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")

# apply a closing operation using the rectangular kernel to help
# cloes gaps in between credit card number digits, then apply
# Otsu's thresholding method to binarize the image
gradX = cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, rectKernel)
thresh = cv2.threshold(gradX, 0, 255,
    cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]

# apply a second closing operation to the binary image, again
# to help close gaps between credit card number regions
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, sqKernel)

thresh = cv2.dilate(thresh, None, iterations = 3)
clone = np.dstack([thresh.copy()] * 3
# find contours in the thresholded image, then initialize the
# list of digit locations
cnts = cv2.findContours(thresh.copy(), cv2.RETR_TREE,
    cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
locs = []

# loop over the contours
for (i, c) in enumerate(cnts):
    (x, y, w, h) = cv2.boundingRect(c)
    if i<=3:   #First 4 images are the MICR font
        cv2.rectangle(clone, (x,y), (x+w, y+h), (255,0,0), 1) #color is set to blue but it shows white (error?)
        locs.append((x, y, w, h))

第2步：

locs = sorted(locs, key=lambda x:x[0])
output = []
print (locs)

for (i, (gX, gY, gW, gH)) in enumerate(locs):
    if i==0:
        group = gray[gY:gY+gH, gX:gX+gW]
        cv2.rectangle(gray, (gX, gY), (gX+gW, gY+gH), (255,0,0), 1)
        group = cv2.threshold(group, 0, 255,
            cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]

         # re-initialize the clone image so we can draw on it again
         clone = np.dstack([group.copy()]*3)
         cv2.rectangle(clone, (gX, gY), (gX+gW, gY+gH), (255,0,0), 1)
         cv2.imshow('Image5', clone)
         cv2.waitKey()

错误输出：绿色遮罩未正确绑定矩形。

第3步：

# detect the contours of each individual digit in the group,
# then sort the digit contours from left to right
digitCnts = cv2.findContours(group.copy(), cv2.RETR_TREE,
        cv2.CHAIN_APPROX_SIMPLE)
digitCnts = digitCnts[0] if imutils.is_cv2() else digitCnts[1]
cv2.drawContours(clone, digitCnts, -1, (0,255,0), 1)
cv2.imshow('Image6', clone)
cv2.waitKey()
for c in digitCnts:
    # compute the bounding box of the individual digit, extract
    # the digit, and resize it to have the same fixed size as
    # the reference MICR images
    (x, y, w, h) = cv2.boundingRect(c)
    print (x, y, x+w, y+h)
    cv2.rectangle(clone, (x,y), (x+w, y+h), (255,0,0), 3)
    cv2.imshow('Image7', clone) 
    cv2.waitKey()

错误输出：

这是错误产生的地方，因为它应该将步骤2的输出分成单个数字，如“9”，“5”等等，可以传递给模板匹配。但是轮廓的长度变为11，并且在前一个输出的最后一步没有绘制边界框以指示单独的数字。

OpenCV findcontours（）无法检测轮廓

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