1、预处理、轮廓检测

import cv2
import numpy as np
# 正确答案
ANSWER_KEY = {0:1,1:4,2:0,3:3,4:1}
def cv_show(name,img):
    cv2.imshow(name,img)
    cv2.waitKey()
    cv2.destroyAllWindows()
# 读图
img = cv2.imread("test_01.png")
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray,(5,5),0)
edge = cv2.Canny(blur,75,200)
# 轮廓检测
cnt = cv2.findContours(edge,cv2.RETR_EXTERNAL,
                       cv2.CHAIN_APPROX_SIMPLE)[0]
cv2.drawContours(contour_img,cnt,-1,(0,255,0),2)
cv_show("contour_img",contour_img)

2、轮廓排序，透视变化

def order_points(pts):
# 一共四个坐标点
    rect = np.zeros((4,2),dtype="float32")
#     按照顺序找到对应坐标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，左下
    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):
#     获取输入坐标
    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(img,M,(maxWidth,maxHeight))
#     反回变换后的结果
    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

3、寻找圆圈轮廓

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

dotCnt =None
if len(cnt)>0:
    cnt = sorted(cnt,key = cv2.contourArea,reverse=True)
    for c in cnt:
        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))
# otsu's 阈值处理
thresh = cv2.threshold(warp,0,255,cv2.THRESH_BINARY_INV|
                       cv2.THRESH_OTSU)[1]
thresh_contours = thresh.copy()
# 找到每一个圆圈轮廓
cnt = cv2.findContours(thresh_contours,cv2.RETR_EXTERNAL,
                       cv2.CHAIN_APPROX_SIMPLE)[0]
cv2.drawContours(thresh_contours,cnt,-1,(0,255,0),2)

questionCnts = []

4、输出每个轮廓，对比答案

# 遍历
for c in cnt:
#     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]
# cv2.drawContours(warp,questionCnts,1,(0,255,255),2)  
# 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
    correct=0
    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) 
#         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(warp,cnts[k],-1,color,2)
cv2.imshow("warp",warp)
cv2.waitKey()
cv2.destroyAllWindows()

标签：img,cv2,答题卡,OpenCV,cnts,np,识别,pts,method
来源： https://blog.csdn.net/j_lyn/article/details/118046006