项目实战——答题卡识别判卷代码个人学习笔记

一、实现逻辑

1. 导入库并设置参数和正确答案；

2. 图像预处理：灰度=>滤波去噪=>Canny边缘算子处理；

3. 找到并扶正目标区域（试卷）：检测并绘制所有轮廓=>筛选出试卷轮廓=>透视变换；

4. 在透视变换后的图中寻找试卷中的选项轮廓：二值化=>寻找轮廓=>筛选轮廓=>轮廓排序；

5. 答案匹配：在循环结构中，根据每个选项圆圈区域的像素占比判断所选的答案，并与正确

                      答案进行比对，最后格式化输出。

二、代码实现

 此项目中，环境依然使用的是信用卡实战项目中使用的pytorch环境。

所有自定义的函数都是前面信用卡实战和OCR识别中用过的，所以不再进行解释，整体代码也无过多难以理解的地方，具体见如下详细注释：

#导入工具包
import numpy as np
import argparse
import imutils
import 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 rect
 
def 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 warped
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]
    (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),
                                        key=lambda b: b[1][i], reverse=reverse))
    return cnts, boundingBoxes
def 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()
#灰度
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
#滤波
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
cv_show('blurred',blurred)
#计算边缘
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)[1]
cv2.drawContours(contours_img,cnts,-1,(0,0,255),3)  #红色线框绘出轮廓
cv_show('contours_img',contours_img)
docCnt = None
 
# 确保检测到了
if len(cnts) > 0:
	# 根据轮廓面积大小进行排序
	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-二值化
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)[1]
cv2.drawContours(thresh_Contours,cnts,-1,(0,0,255),3) #因为是单通道，所以不显示轮廓
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
#q为enumerate对象中的索引；i为对应索引的真实值
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") #用0数组建立掩膜mask
		#在mask上依次遍历每个选项的轮廓
		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
		#将thresh和mask进行图像的“与”运算，即只显示掩膜内的部分
		mask = cv2.bitwise_and(thresh, thresh, mask=mask)
		#计算像素值不为0的像素数
		total = cv2.countNonZero(mask)
 
		# 通过阈值判断每一行哪个选项被涂黑
		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]
 
	# 判断是否正确
	if k == bubbled[1]:
		color = (0, 255, 0) #这一步貌似没什么必要
		correct += 1
 
	# 绘图
	cv2.drawContours(warped, [cnts[k]], -1, color, 3)
 
 
score = (correct / 5.0) * 100
print("[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)

此项目到处结束。