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OpenCV+Python自动填涂机读卡

news 来源：原创 2024/9/20 12:39:22

接上一篇OpenCV+Python识别机读卡-CSDN博客，既然可以识别机读卡填涂答案了，将标准答案绘制到机读卡上也就简单了。

工作原理

1.答题区域为整张图片最大轮廓，先找出答题区域。

2.答题区域分为6行，每行4组，第6行只有1组，我们暂不处理第6行，只处理前面5行。

3.给定每一行第一个选项中心点坐标，该行其余选项的中心点坐标可以推算出来。

4.有了每个选项的中心点坐标，结合标准答案，就可以将答案绘制到机读卡上了。

实现步骤

1.空白机读卡长这样：

2.标准答案（内容仅供参考，以实际为准）

answers = {1: "B", 2: "B", 3: "A", 4: "B", 5: "B", 6: "A", 7: "B", 8: "B", 9: "A", 10: "B", 11: "B", 12: "A", 13: "A", 14: "B", 15: "B", 16: "A", 17: "A", 18: "B", 19: "B", 20: "A", 21: "C", 22: "C", 23: "B", 24: "D", 25: "C", 26: "D", 27: "A", 28: "D", 29: "C", 30: "B", 31: "C", 32: "D", 33: "B", 34: "A", 35: "D", 36: "C", 37: "B", 38: "B", 39: "D", 40: "D", 41: "B",42: "C", 43: "B", 44: "D", 45: "B", 46: "A", 47: "D", 48: "D", 49: "C", 50: "D", 51: "B", 52: "D", 53: "A", 54: "A", 55: "D", 56: "D", 57: "C", 58: "D", 59: "B", 60: "D", 61: "A", 62: "B", 63: "D", 64: "B", 65: "C", 66: "D", 67: "B", 68: "C", 69: "D", 70: "A", 71: "ABCD", 72: "ABCD", 73: "AC", 74: "BCD", 75: "BC", 76: "BD", 77: "ABCD", 78: "ABCD", 79: "ACD", 80: "ABD"}

有单选题和多选题。需要实现将答案绘制到空白机读卡上。

直接贴源码，详细说明请参考代码注释。

import cv2# 1.读取图片并缩放
orginImg = cv2.imread("01.jpg")
size = ((int)(650*1.8), (int)(930*1.8))  # 尽可能将图片弄大一点，下面好处理
img = cv2.resize(orginImg, size)# 显示图像
def imshow(name, image):scale_percent = 50  # 缩放比例width = int(image.shape[1] * scale_percent / 100)height = int(image.shape[0] * scale_percent / 100)dim = (width, height)resized_image = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)cv2.imshow(name, resized_image)imshow("1.orgin", img)# 2.转灰度图
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
imshow("2.gray", gray)# 3.黑帽运算：移除干扰项
cvblackhat = cv2.morphologyEx(gray, cv2.MORPH_BLACKHAT, cv2.getStructuringElement(cv2.MORPH_RECT, (15, 15)))
imshow("3.black", cvblackhat)# 4.二值化突出轮廓，自动阈值范围 cv2.THRESH_BINARY|cv2.THRESH_OTSU
thresh = cv2.threshold(cvblackhat, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
imshow("4.thresh", thresh)# 5.提取轮廓，并在图上标记轮廓
cnts, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
mark = img.copy()
cv2.drawContours(mark, cnts, -1, (0, 0, 255), 2)
imshow("5.contours", mark)# 6.提取我们感兴趣的部分（这里我们只需要答题部分） img[y:y+h, x:x+w]
_top = 0
_left = 0
roi = None
for (i, c) in enumerate(cnts):(x, y, w, h) = cv2.boundingRect(c)ar = w/float(h)if w > 500 and h > 500 and ar > 0.9 and ar < 1.1:roi = img[y:y+h, x:x+w]_top = y_left = xbreak
imshow("5.roi", roi)# 7.查找每个选项的中心点坐标
# 思路：
# 通过分析：
# 1.答题区域分为6行，每行4组，第6行只有1组，我们暂不处理第6行，只处理前面5行。
# 2.只要给定每一行第一个选项中心坐标，该行其余选项的中心坐标可以推算出来。
# 3.通过找到每个选项中心点坐标，再加上选项宽高，就可以在答题区域绘出每个选项的范围。
# 4.通过计算每个选项范围图像里非0像素点个数，结合阈值判断该选项是否选中。
# 5.结合题目个数，遍历每个选项，构造出最终答案。
item = [34, 20]  # 每个选项宽度（跟图形缩放有关系）
x_step = 44  # x方向行距（两个选项水平方向距离）
y_step = 28  # y方向行距（两个选项垂直方向距离）
blank = 92  # 每组间距(5个一组)水平方向距离
centers = []  # 每个选项的中心点坐标，用来框选选项
# 答题区域有5行多1组，这里只处理前面5行，最后一组暂不处理
startPonits = [(25, 44), (25, 216), (26, 392), (26, 566), (28, 744)]
for (i, p) in enumerate(startPonits):temp = []  # 暂存该组选项坐标start = list(p)  # 该行起始点坐标for g in range(0, 4, 1):  # 每行有4组if len(temp) > 0:startx = temp[len(temp)-1][0] + blank  # 最后一个选项的x坐标+每组间距else:startx = start[0]start[0] = startxfor i in range(start[0], start[0]+5*x_step, x_step):  # 水平5个选项for j in range(start[1], start[1]+4*y_step, y_step):  # 垂直4个选项temp.append((i, j))for (i, c) in enumerate(temp):centers.append(c)questions = []  # 二维数组：保存每个题目ABCD4个选项对应的中心点坐标
group = []  # 将点分组，每4个1组，对应每题的4个选项
for (i, (x, y)) in enumerate(centers):group.append((x, y))if (i+1) % 4 == 0:questions.append(group)group = []
def find_key_by_value(dictionary, value):for key, val in dictionary.items():if val == value:return keyreturn None# 标准答案
answers = {1: "B", 2: "B", 3: "A", 4: "B", 5: "B", 6: "A", 7: "B", 8: "B", 9: "A", 10: "B", 11: "B", 12: "A", 13: "A", 14: "B", 15: "B", 16: "A", 17: "A", 18: "B", 19: "B", 20: "A", 21: "C", 22: "C", 23: "B", 24: "D", 25: "C", 26: "D", 27: "A", 28: "D", 29: "C", 30: "B", 31: "C", 32: "D", 33: "B", 34: "A", 35: "D", 36: "C", 37: "B", 38: "B", 39: "D", 40: "D", 41: "B",42: "C", 43: "B", 44: "D", 45: "B", 46: "A", 47: "D", 48: "D", 49: "C", 50: "D", 51: "B", 52: "D", 53: "A", 54: "A", 55: "D", 56: "D", 57: "C", 58: "D", 59: "B", 60: "D", 61: "A", 62: "B", 63: "D", 64: "B", 65: "C", 66: "D", 67: "B", 68: "C", 69: "D", 70: "A", 71: "ABCD", 72: "ABCD", 73: "AC", 74: "BCD", 75: "BC", 76: "BD", 77: "ABCD", 78: "ABCD", 79: "ACD", 80: "ABD"}# 8.将答案绘制到答题卡上
option = {0: "A", 1: "B", 2: "C", 3: "D"}
show = img.copy()
for (i, q) in enumerate(questions):if i < len(answers):an = answers.get(i+1)  # 根据题目号获取答案if an is None:continuecharacters = [char for char in an]  # 将答案拆分成字符列表，适配多选题for char in characters:index = find_key_by_value(option, char)  # 根据答案查找对应选项的索引，标记对应选项for (j, (x, y)) in enumerate(q):if j == index:left = _left + x-(int)(item[0]/2)  # 中心点坐标-1/2*选项宽度+左侧距离top = _top + y-(int)(item[1]/2)  # 中心点坐标-1/2*选项宽度+顶部距离# 绘制黑块遮挡选项cv2.rectangle(show, (left, top), (left +item[0], top + item[1]), (0, 0, 0), -1)
imshow("5.show", show)cv2.waitKey(0)
cv2.destroyAllWindows()