使用 OpenCV-Python 识别答题卡判卷功能

import cv2 as cv
import numpy as np
 
# 正确答案
right_key = {0: 1, 1: 4, 2: 0, 3: 3, 4: 1}
 
# 输入图像
img = cv.imread('./images/test_01.jpg')
img_copy = img.copy()
img_gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
cvshow('img-gray', img_gray)

# 图像预处理
# 高斯降噪
img_gaussian = cv.GaussianBlur(img_gray, (5, 5), 1)
cvshow('gaussianblur', img_gaussian)
# canny边缘检测
img_canny = cv.Canny(img_gaussian, 80, 150)
cvshow('canny', img_canny)

# 轮廓识别——答题卡边缘识别
cnts, hierarchy = cv.findContours(img_canny, cv.RETR_EXTERNAL, cv.CHaiN_APPROX_SIMPLE)
cv.drawContours(img_copy, cnts, -1, (0, 0, 255), 3)
cvshow('contours-show', img_copy)

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=cv.contourArea, reverse=True)
 
    # 遍历每一个轮廓
    for c in cnts:
        # 近似
        peri = cv.arcLength(c, True)
        # arclength 计算一段曲线的长度或者闭合曲线的周长；
        # 第一个参数输入一个二维向量，第二个参数表示计算曲线是否闭合
 
        approx = cv.approxPolyDP(c, 0.02 * peri, True)
        # 用一条顶点较少的曲线/多边形来近似曲线/多边形，以使它们之间的距离<=指定的精度；
        # c是需要近似的曲线，0.02*peri是精度的最大值，True表示曲线是闭合的
 
        # 准备做透视变换
        if len(approx) == 4:
            docCnt = approx
            break

# 透视变换——提取答题卡主体
docCnt = docCnt.reshape(4, 2)
warped = four_point_transfORM(img_gray, docCnt)
cvshow('warped', warped)

def four_point_transform(img, four_points):
    rect = order_points(four_points)
    (tl, tr, br, bl) = rect
 
    # 计算输入的w和h的值
    widthA = np.sqrt((tr[0] - tl[0]) ** 2 + (tr[1] - tl[1]) ** 2)
    widthB = np.sqrt((br[0] - bl[0]) ** 2 + (br[1] - bl[1]) ** 2)
    maxWidth = max(int(widthA), int(widthB))
 
    heightA = np.sqrt((tl[0] - bl[0]) ** 2 + (tl[1] - bl[1]) ** 2)
    heightB = np.sqrt((tr[0] - br[0]) ** 2 + (tr[1] - br[1]) ** 2)
    maxHeight = max(int(heightA), int(heightB))
 
    # 变换后对应的坐标位置
    dst = np.array([
        [0, 0],
        [maxWidth - 1, 0],
        [maxWidth - 1, maxHeight - 1],
        [0, maxHeight - 1]], dtype='float32')
 
    # 最主要的函数就是 cv2.getPerspectiveTransform(rect, dst) 和 cv2.warpPerspective(image, M, (maxWidth, maxHeight))
    M = cv.getPerspectiveTransform(rect, dst)
    warped = cv.warpPerspective(img, M, (maxWidth, maxHeight))
    return warped
 
 
def order_points(points):
    res = np.zeros((4, 2), dtype='float32')
    # 按照从前往后0，1，2，3分别表示左上、右上、右下、左下的顺序将points中的数填入res中
 
    # 将四个坐标x与y相加，和最大的那个是右下角的坐标，最小的那个是左上角的坐标
    sum_hang = points.sum(axis=1)
    res[0] = points[np.argmin(sum_hang)]
    res[2] = points[np.argmax(sum_hang)]
 
    # 计算坐标x与y的离散插值np.diff()
    diff = np.diff(points, axis=1)
    res[1] = points[np.argmin(diff)]
    res[3] = points[np.argmax(diff)]
 
    # 返回result
    return res

# 轮廓识别——识别出选项
thresh = cv.threshold(warped, 0, 255, cv.THRESH_BINARY_INV | cv.THRESH_OTSU)[1]
cvshow('thresh', thresh)
thresh_cnts, _ = cv.findContours(thresh, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
w_copy = warped.copy()
cv.drawContours(w_copy, thresh_cnts, -1, (0, 0, 255), 2)
cvshow('warped_contours', w_copy)
 
questionCnts = []
# 遍历，挑出选项的cnts
for c in thresh_cnts:
    (x, y, w, h) = cv.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)
 
# 检查是否挑出了选项
w_copy2 = warped.copy()
cv.drawContours(w_copy2, questionCnts, -1, (0, 0, 255), 2)
cvshow('questionCnts', w_copy2)

# 检测每一行选择的是哪一项，并将结果储存在元组bubble中，记录正确的个数correct
# 按照从上到下t2b对轮廓进行排序
questionCnts = sort_contours(questionCnts, method="t2b")[0]
correct = 0
# 每行有5个选项
for (i, q) in enumerate(np.arange(0, len(questionCnts), 5)):
    # 排序
    cnts = sort_contours(questionCnts[q:q+5])[0]
 
    bubble = None
    # 得到每一个选项的mask并填充，与正确答案进行按位与操作获得重合点数
    for (j, c) in enumerate(cnts):
        mask = np.zeros(thresh.shape, dtype='uint8')
        cv.drawContours(mask, [c], -1, 255, -1)
        # cvshow('mask', mask)
 
        # 通过按位与操作得到thresh与mask重合部分的像素数量
        bitand = cv.bitwise_and(thresh, thresh, mask=mask)
        totalPixel = cv.countNonZero(bitand)
 
        if bubble is None or bubble[0] < totalPixel:
            bubble = (totalPixel, j)
 
    k = bubble[1]
    color = (0, 0, 255)
    if k == right_key[i]:
        correct += 1
        color = (0, 255, 0)
 
    # 绘图
    cv.drawContours(warped, [cnts[right_key[i]]], -1, color, 3)
    cvshow('final', warped)

def sort_contours(contours, method="l2r"):
    # 用于给轮廓排序，l2r, r2l, t2b, b2t
    reverse = False
    i = 0
    if method == "r2l" or method == "b2t":
        reverse = True
    if method == "t2b" or method == "b2t":
        i = 1
 
    boundingBoxes = [cv.boundingRect(c) for c in contours]
    (contours, boundingBoxes) = zip(*sorted(zip(contours, boundingBoxes), key=lambda a: a[1][i], reverse=reverse))
    return contours, boundingBoxes

# 计算最终得分并在图中标注
score = (correct / 5.0) * 100
print(f"Score: {score}%")
cv.putText(warped, f"Score: {score}%", (10, 30), cv.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
cv.imshow("Original", img)
cv.imshow("Exam", warped)
cv.waiTKEy(0)

import cv2 as cv
import numpy as np
 
 
def cvshow(name, img):
    cv.imshow(name, img)
    cv.waitKey(0)
    cv.destroyAllwindows()
 
def four_point_transform(img, four_points):
    rect = order_points(four_points)
    (tl, tr, br, bl) = rect
 
    # 计算输入的w和h的值
    widthA = np.sqrt((tr[0] - tl[0]) ** 2 + (tr[1] - tl[1]) ** 2)
    widthB = np.sqrt((br[0] - bl[0]) ** 2 + (br[1] - bl[1]) ** 2)
    maxWidth = max(int(widthA), int(widthB))
 
    heightA = np.sqrt((tl[0] - bl[0]) ** 2 + (tl[1] - bl[1]) ** 2)
    heightB = np.sqrt((tr[0] - br[0]) ** 2 + (tr[1] - br[1]) ** 2)
    maxHeight = max(int(heightA), int(heightB))
 
    # 变换后对应的坐标位置
    dst = np.array([
        [0, 0],
        [maxWidth - 1, 0],
        [maxWidth - 1, maxHeight - 1],
        [0, maxHeight - 1]], dtype='float32')
 
    # 最主要的函数就是 cv2.getPerspectiveTransform(rect, dst) 和 cv2.warpPerspective(image, M, (maxWidth, maxHeight))
    M = cv.getPerspectiveTransform(rect, dst)
    warped = cv.warpPerspective(img, M, (maxWidth, maxHeight))
    return warped
 
 
def order_points(points):
    res = np.zeros((4, 2), dtype='float32')
    # 按照从前往后0，1，2，3分别表示左上、右上、右下、左下的顺序将points中的数填入res中
 
    # 将四个坐标x与y相加，和最大的那个是右下角的坐标，最小的那个是左上角的坐标
    sum_hang = points.sum(axis=1)
    res[0] = points[np.argmin(sum_hang)]
    res[2] = points[np.argmax(sum_hang)]
 
    # 计算坐标x与y的离散插值np.diff()
    diff = np.diff(points, axis=1)
    res[1] = points[np.argmin(diff)]
    res[3] = points[np.argmax(diff)]
 
    # 返回result
    return res
 
 
def sort_contours(contours, method="l2r"):
    # 用于给轮廓排序，l2r, r2l, t2b, b2t
    reverse = False
    i = 0
    if method == "r2l" or method == "b2t":
        reverse = True
    if method == "t2b" or method == "b2t":
        i = 1
 
    boundingBoxes = [cv.boundingRect(c) for c in contours]
    (contours, boundingBoxes) = zip(*sorted(zip(contours, boundingBoxes), key=lambda a: a[1][i], reverse=reverse))
    return contours, boundingBoxes
 
# 正确答案
right_key = {0: 1, 1: 4, 2: 0, 3: 3, 4: 1}
 
# 输入图像
img = cv.imread('./images/test_01.jpg')
img_copy = img.copy()
img_gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
cvshow('img-gray', img_gray)
 
# 图像预处理
# 高斯降噪
img_gaussian = cv.GaussianBlur(img_gray, (5, 5), 1)
cvshow('gaussianblur', img_gaussian)
# canny边缘检测
img_canny = cv.Canny(img_gaussian, 80, 150)
cvshow('canny', img_canny)
 
# 轮廓识别——答题卡边缘识别
cnts, hierarchy = cv.findContours(img_canny, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
cv.drawContours(img_copy, cnts, -1, (0, 0, 255), 3)
cvshow('contours-show', img_copy)
 
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=cv.contourArea, reverse=True)
 
    # 遍历每一个轮廓
    for c in cnts:
        # 近似
        peri = cv.arcLength(c, True)  # arclength 计算一段曲线的长度或者闭合曲线的周长；
        # 第一个参数输入一个二维向量，第二个参数表示计算曲线是否闭合
 
        approx = cv.approxPolyDP(c, 0.02 * peri, True)
        # 用一条顶点较少的曲线/多边形来近似曲线/多边形，以使它们之间的距离<=指定的精度；
        # c是需要近似的曲线，0.02*peri是精度的最大值，True表示曲线是闭合的
 
        # 准备做透视变换
        if len(approx) == 4:
            docCnt = approx
            break
 
 
# 透视变换——提取答题卡主体
docCnt = docCnt.reshape(4, 2)
warped = four_point_transform(img_gray, docCnt)
cvshow('warped', warped)
 
 
# 轮廓识别——识别出选项
thresh = cv.threshold(warped, 0, 255, cv.THRESH_BINARY_INV | cv.THRESH_OTSU)[1]
cvshow('thresh', thresh)
thresh_cnts, _ = cv.findContours(thresh, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
w_copy = warped.copy()
cv.drawContours(w_copy, thresh_cnts, -1, (0, 0, 255), 2)
cvshow('warped_contours', w_copy)
 
questionCnts = []
# 遍历，挑出选项的cnts
for c in thresh_cnts:
    (x, y, w, h) = cv.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)
 
# 检查是否挑出了选项
w_copy2 = warped.copy()
cv.drawContours(w_copy2, questionCnts, -1, (0, 0, 255), 2)
cvshow('questionCnts', w_copy2)
 
 
# 检测每一行选择的是哪一项，并将结果储存在元组bubble中，记录正确的个数correct
# 按照从上到下t2b对轮廓进行排序
questionCnts = sort_contours(questionCnts, method="t2b")[0]
correct = 0
# 每行有5个选项
for (i, q) in enumerate(np.arange(0, len(questionCnts), 5)):
    # 排序
    cnts = sort_contours(questionCnts[q:q+5])[0]
 
    bubble = None
    # 得到每一个选项的mask并填充，与正确答案进行按位与操作获得重合点数
    for (j, c) in enumerate(cnts):
        mask = np.zeros(thresh.shape, dtype='uint8')
        cv.drawContours(mask, [c], -1, 255, -1)
        cvshow('mask', mask)
 
        # 通过按位与操作得到thresh与mask重合部分的像素数量
        bitand = cv.bitwise_and(thresh, thresh, mask=mask)
        totalPixel = cv.countNonZero(bitand)
 
        if bubble is None or bubble[0] < totalPixel:
            bubble = (totalPixel, j)
 
    k = bubble[1]
    color = (0, 0, 255)
    if k == right_key[i]:
        correct += 1
        color = (0, 255, 0)
 
    # 绘图
    cv.drawContours(warped, [cnts[right_key[i]]], -1, color, 3)
    cvshow('final', warped)
 
 
# 计算最终得分并在图中标注
score = (correct / 5.0) * 100
print(f"Score: {score}%")
cv.putText(warped, f"Score: {score}%", (10, 30), cv.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
cv.imshow("Original", img)
cv.imshow("Exam", warped)
cv.waitKey(0)