opencv笔记（8）—— OCR模板匹配[实战]

清园暖歌

已于 2024-05-17 17:36:06 修改

阅读量255

点赞数 1

分类专栏： OpenCV 文章标签： opencv 计算机视觉笔记

于 2023-06-21 21:09:26 首次发布

本文链接：https://blog.youkuaiyun.com/qq_47941078/article/details/131333666

版权

OpenCV 专栏收录该内容

12 篇文章

订阅专栏

一、前期设置

# 导入工具包
from imutils import contours
import numpy as np
import argparse
import cv2
import myutils


# 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())
print(args)
print(args["image"])

# 形参配置，运行-编辑配置
# --image
# ./images/credit_card_04.png
# --template
# ./images/ocr_a_reference.png

# 另一种设置参数的方式
# args={"image": "E:/Jupyter opencv-python/9：template-matching-ocr/images/credit_card_03.png",
# 	  "template": "E:/Jupyter opencv-python/9：template-matching-ocr/images/ocr_a_reference.png"}

# 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()

二、去除模板的每个数字

# 读模板
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，轮廓检测一般都是二值图像
ref = cv2.threshold(ref, 10, 255, cv2.THRESH_BINARY_INV)[1] # 返回ret，det两个参数，ret是阈值，det是输出图
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)   # -1就是画所有的轮廓
cv_show('img',img)
# print(np.array(refCnts).shape)

refCnts = myutils.sort_contours(refCnts, method="left-to-right")[0] # 给轮廓排序，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]
	# cv_show('ref2', ref[y:y + h, x:x + w])
	# cv_show('ref2', roi)
	roi = cv2.resize(roi, (57, 88))

	# 每一个数字对应每一个模板
	digits[i] = roi

三、图像处理

# 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)
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相当于用3*3的
	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)

四、圈出需要的数字

# 计算轮廓

# thresh_, threshCnts, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
# 	cv2.CHAIN_APPROX_SIMPLE)
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))
print(locs)
print(list(locs[0])[0])
abc2 = list(locs[0])
print(abc2)
cv2.rectangle(cur_img, (abc2[0], abc2[1]), (abc2[0]+abc2[2],abc2[1]+abc2[3]), (0, 255, 0), 2)
cv_show('locs[0]', cur_img)
# 将符合的轮廓从左到右排序
locs = sorted(locs, key=lambda x:x[0])
output = []

五、模板匹配计算得分

# 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
	# group_,digitCnts,hierarchy = cv2.findContours(group.copy(), cv2.RETR_EXTERNAL,
	# 	cv2.CHAIN_APPROX_SIMPLE)
	digitCnts, hierarchy = cv2.findContours(group.copy(), cv2.RETR_EXTERNAL,
													cv2.CHAIN_APPROX_SIMPLE)
	digitCnts = contours.sort_contours(digitCnts,
		method="left-to-right")[0]  # 返回值：轮廓， 外接矩形

	# 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)
            # 根据模板匹配的方式，得分是最大或最小
			(_, 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)
    # 写上数字
	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)

六、打印结果

# 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)

七、其他函数

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]
    print(image.shape)
    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