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代码见

 

答题卡识别判卷

识别出考生选择的答案并能自动判分

• Python3.7
• OpenCV 4.2.0

 

#导入工具包import numpy as npimport argparseimport imutilsimport cv2# 设置参数ap = argparse.ArgumentParser()ap.add_argument("-i", "--image", required=True,	help="path to the input image")args = vars(ap.parse_args())# 正确答案ANSWER_KEY = {0: 1, 1: 4, 2: 0, 3: 3, 4: 1}def order_points(pts):	# 一共4个坐标点	rect = np.zeros((4, 2), dtype = "float32")	# 按顺序找到对应坐标0123分别是 左上，右上，右下，左下	# 计算左上，右下	s = pts.sum(axis = 1)	rect[0] = pts[np.argmin(s)]	rect[2] = pts[np.argmax(s)]	# 计算右上和左下	diff = np.diff(pts, axis = 1)	rect[1] = pts[np.argmin(diff)]	rect[3] = pts[np.argmax(diff)]	return rectdef four_point_transform(image, pts):	# 获取输入坐标点	rect = order_points(pts)	(tl, tr, br, bl) = rect	# 计算输入的w和h值	widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))	widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))	maxWidth = max(int(widthA), int(widthB))	heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))	heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))	maxHeight = max(int(heightA), int(heightB))	# 变换后对应坐标位置	dst = np.array([		[0, 0],		[maxWidth - 1, 0],		[maxWidth - 1, maxHeight - 1],		[0, maxHeight - 1]], dtype = "float32")	# 计算变换矩阵	M = cv2.getPerspectiveTransform(rect, dst)	warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))	# 返回变换后结果	return warpeddef 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]    (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),                                        key=lambda b: b[1][i], reverse=reverse))    return cnts, boundingBoxesdef cv_show(name,img):        cv2.imshow(name, img)        cv2.waitKey(0)        cv2.destroyAllWindows()  # 预处理image = cv2.imread(args["image"])contours_img = image.copy()# print(contours_img)gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)blurred = cv2.GaussianBlur(gray, (5, 5), 0)cv_show('blurred',blurred)edged = cv2.Canny(blurred, 75, 200)cv_show('edged',edged)# 轮廓检测cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,	cv2.CHAIN_APPROX_SIMPLE)[0]# print(contours_img)cv2.drawContours(contours_img,cnts,-1,(0,0,255),3) # cv2.drawContours(contours_img,cnts.reshape(-1,1,2),-1,(0,0,255),3) cv_show('contours_img',contours_img)docCnt = None# 确保检测到了if len(cnts) > 0:	# 根据轮廓大小进行排序	cnts = sorted(cnts, key=cv2.contourArea, reverse=True)	# 遍历每一个轮廓	for c in cnts:		# 近似		peri = cv2.arcLength(c, True)		approx = cv2.approxPolyDP(c, 0.02 * peri, True)		# 准备做透视变换		if len(approx) == 4:			docCnt = approx			break# 执行透视变换warped = four_point_transform(gray, docCnt.reshape(4, 2))cv_show('warped',warped)# Otsu's 阈值处理thresh = cv2.threshold(warped, 0, 255,	cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1] cv_show('thresh',thresh)thresh_Contours = thresh.copy()# 找到每一个圆圈轮廓cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,	cv2.CHAIN_APPROX_SIMPLE)[0]cv2.drawContours(thresh_Contours,cnts,-1,(0,0,255),3) cv_show('thresh_Contours',thresh_Contours)questionCnts = []# 遍历for c in cnts:	# 计算比例和大小	(x, y, w, h) = cv2.boundingRect(c)	ar = w / float(h)	# 根据实际情况指定标准	if w >= 20 and h >= 20 and ar >= 0.9 and ar <= 1.1:		questionCnts.append(c)# 按照从上到下进行排序questionCnts = sort_contours(questionCnts,	method="top-to-bottom")[0]correct = 0# 每排有5个选项for (q, i) in enumerate(np.arange(0, len(questionCnts), 5)):	# 排序	cnts = sort_contours(questionCnts[i:i + 5])[0]	bubbled = None	# 遍历每一个结果	for (j, c) in enumerate(cnts):		# 使用mask来判断结果		mask = np.zeros(thresh.shape, dtype="uint8")		cv2.drawContours(mask, [c], -1, 255, -1) #-1表示填充		cv_show('mask',mask)		# 通过计算非零点数量来算是否选择这个答案		mask = cv2.bitwise_and(thresh, thresh, mask=mask)		total = cv2.countNonZero(mask)		# 通过阈值判断		if bubbled is None or total > bubbled[0]:			bubbled = (total, j)	# 对比正确答案	color = (0, 0, 255)	k = ANSWER_KEY[q]	# 判断正确	if k == bubbled[1]:		color = (0, 255, 0)		correct += 1	# 绘图	cv2.drawContours(warped, [cnts[k]], -1, color, 3)score = (correct / 5.0) * 100print("[INFO] score: {:.2f}%".format(score))cv2.putText(warped, "{:.2f}%".format(score), (10, 30),	cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)cv2.imshow("Original", image)cv2.imshow("Exam", warped)cv2.waitKey(0)

运行方式

python get_answer.py -i images/test_01.png

 

图像滤波

边缘检测

 

特征变换

二值处理

轮廓检测

...

 

 

参考

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