OPENCV中的批改答题卡

@Fu Xianjun. All Rights Reserved.

前言

批改答题卡对于许多来说简直就是折磨，眼睛都看花了还要继续批改，还要保持正确率，实在是太费老师了，所以我们使用OPENCV来解决这个问题，自动批改答题卡

提示：以下是本篇文章正文内容，下面案例可供参考

一、为什么使用OPENCV？

不要问，问就是因为我只学了这个，好，下面开始了

二、使用步骤

1.引入库

代码如下（示例）：

import cv2
import numpy as np

2.写入基本函数，这样打包好就可以套用了，多少方便不知道

代码如下（示例）：

import cv2
import numpy as np

def cv_show(name,img):
    cv2.imshow(name,img)
    cv2.waitKey()
    cv2.destroyAllWindows()

def order_points(pts):
    rect = np.zeros((4,2),dtype= "float32")
    s =pts.sum(axis = 1)
    rect[0] = pts[np.argmin(s)]
    rect[2] = pts[np.argmax(s)]
    d = np.diff(pts,axis= 1)
    rect[1] = pts[np.argmin(d)]
    rect[3] = pts[np.argmax(d)]
    return rect
def four_point_transform(img,pts):
    rect = order_points(pts)
    (tl,tr,br,bl) = rect
    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((br[0]-tr[0])**2+(br[1]-tr[1])**2)
    heightB = np.sqrt((bl[0]-tl[0])**2+(bl[1]-tl[1])**2)
    maxHeight = max(int(heightA),int(heightB))
    
    dst = np.array([[0,0],[maxWidth-1,0],[maxWidth-1,maxHeight-1],\
                  [0,maxHeight-1]],dtype= "float32")
    M = cv2.getPerspectiveTransform(rect,dst)
    warp = cv2.warpPerspective(img,M,(maxWidth,maxHeight))
    return warp

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

3.导入图片利用上面的函数来进行处理，没错就是这么简单

img = cv2.imread("test_01.png")
contours_img= img.copy()
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
gs = cv2.GaussianBlur(gray,(5,5),0)
edge = cv2.Canny(gs,75,200)
cnts = cv2.findContours(edge,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)[0]
cv2.drawContours(contours_img,cnts,-1,(255,255,0),2)
docCnt = None
if len(cnts)>0:
    cnts = sorted(cnts,key = cv2.contourArea,reverse = True)
    for c in cnts:
        peri = cv2.arcLength(c,True)
        approx = cv2.approxPolyDP(c,0.02*peri,True)
        if len(approx)==4:
            docCnt = approx
            break
warp = four_point_transform(gray,docCnt.reshape(4,2))

thresh = cv2.threshold(warp,0,255,cv2.THRESH_OTSU|cv2.THRESH_BINARY_INV)[1]
thresh_contours = thresh.copy()
cnts =cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)[0]
cv2.drawContours(thresh_contours,cnts,-1,(255,255,0),2)

question_Cnts=[]
ANSWER_KEY = {0:1,1:4,2:0,3:3,4:1}
for c in cnts:
    (x,h,w,h) = cv2.boundingRect(c)
    ar = w/float(h)
    if w >= 20 and h >= 20 and ar>=0.9 and ar<=1.1:
        question_Cnts.append(c)

question_Cnts = sort_contours(question_Cnts,method="top-to-bottom")[0]
correct =0
for (q,i) in enumerate(np.arange(0,len(question_Cnts),5)):
    cnts =sort_contours(question_Cnts[i:i+5])[0]
    bubbled = None
    for (j,c) in enumerate(cnts):
        mask = np.zeros(thresh.shape,dtype ="uint8")
        cv2.drawContours(mask,[c],-1,255,-1)
        mask = cv2.bitwise_and(thresh,thresh,mask=mask)
        total = cv2.countNonZero(mask)
        if bubbled is None or total>bubbled[0]:
            bubbled = (total,j)
        color = (0,0,255)
        k=ANSWER_KEY[q]
        if k==bubbled[1]:
            color = (0,255,0)
            correct +=1
        cv2.drawContours(warp,[cnts[k]],-1,color,3)
score = (correct/5.0)*100

cv_show("warp",warp)

下面是原图和效果图对比，简单明了

总结

非常的实用有没有，虽然有一点点的复杂，但这个是值得的