【CV】Python+OpenCV模板匹配实现机器视觉

# 导入工具包from imutils import contoursimport numpy as npimport argparseimport cv2import 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

# 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())

# 利用信用卡第一的数字来指定信用卡类型FIRST_NUMBER = {  "3": "American Express",  "4": "Visa",  "5": "MasterCard",  "6": "Discover Card"}

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

# 读取一个模板图像img = cv2.imread(args["template"])# 展示模板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
# ref_, refCnts, hierarchy = cv2.findContours(ref.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)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)

# 打印有多少个轮廓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 = {}
# 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 = myutils.resize(image, width=300)
#灰度图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)

#sobel算子边缘检测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))
#归一化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0thresh = 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 = threshCntscur_img = image.copy()cv2.drawContours(cur_img,cnts,-1,(0,0,255),3) cv_show('img',cur_img)

#最终想要的区域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，+-5稍微外扩一点儿  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)