Python-OpenCv-答题卡识别

用OpenCv进行答题卡的扫描获取信息，其中用到平滑处理，边缘检测，透视变换，坐标点处理

import cv2
import numpy as np
 
def cv_show(name,img):
    cv2.imshow(name,img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
 
ANSWER_KEY = {0:1,1:4,2:0,3:3,4:1}
img = cv2.imread("test_01.png")
contours_Img = img.copy()
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)#灰度图
blur = cv2.GaussianBlur(gray,(5,5),0)#高斯（平滑处理）
edge = cv2.Canny(blur,75,200)#边缘检测
#轮廓检测
cnts,h = cv2.findContours(edge,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)#外边缘
#cnts = cv2.findContours(edge,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)【0】功能一样
cv2.drawContours(img,cnts,-1,(0,255,0),2)#绘制轮廓
cv_show("img",img)

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))
    #y2-y1的平方+x2-x1的平方再开根号
    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))
    
    
    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#返回变换后的结果
dotCnt = 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:
            dotCnt=approx
 
warp = four_point_transform(gray,dotCnt.reshape(4,2))
 
cv_show("warp",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
 
thresh = cv2.threshold(warp,0,255,cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
thresh_contours = thresh.copy()
cnts,h = cv2.findContours(thresh.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
 
cv2.drawContours(thresh_contours,cnts,-1,(0,255,0),2)
cv_show("thresh_contours",thresh_contours)

questionCnts = []
 
for c in cnts:
    (x,y,w,h) = cv2.boundingRect(c)
    ar = w / float(h)#宽高比
    if w > 20 and h > 20 and ar > 0.9 and ar < 1.1:#宽大于20个像素.....
        questionCnts.append(c)
   
questionCnts = sort_contours(questionCnts, method = "top-to-bottom")
cv2.drawContours(warp,questionCnts,1,(0,255,255),2)
correct = 0
 
for (q,i) in enumerate(np.arange(0,len(questionCnts),5)):   
    cnts = sort_contours(questionCnts[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)
        cv_show("mask",mask)
       
        mask = cv2.bitwise_and(thresh,thresh,mask = mask)
        total = cv2.countNonZero(mask)#看mask里面那个是空的
       
        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
cv2.putText(warp,"Total:{:.2f}".format(score),(10,30),cv2.FONT_HERSHEY_SIMPLEX,
            0.9,(0,0,0),2)
cv_show("warp",warp)