opencv 答题卡检测项目实现

news2024/10/12 4:31:24

在教育和考试领域，答题卡自动评分系统能够大大提高效率和准确性。本文将介绍如何使用OpenCV库实现一个简单的答题卡检测项目。这个项目包括读取图像、检测答题卡轮廓、透视变换、二值化处理、检测选择题答案，并最终计算得分。

导入库和定义常量

import cv2  
import numpy as np  
  
ANSWER_KEY = {0: 1, 1: 4, 2: 0, 3: 3, 4: 1}  # 假设的答题卡答案密钥

辅助函数

cv_show

这个函数用于显示图像，便于调试。

def cv_show(name, img):  
    cv2.imshow(name, img)  
    cv2.waitKey(0)

order_points

这个函数用于对四个点按照左上、右上、右下、左下的顺序进行排序。

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)]  
    diff = np.diff(pts, axis=1)  
    rect[1] = pts[np.argmin(diff)]  
    rect[3] = pts[np.argmax(diff)]  
    return rect

sort_contours

这个函数用于根据指定方法对轮廓进行排序。

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

four_point_transform

这个函数用于将透视扭曲的矩形变换成一个规则的矩形。

def four_point_transform(image, 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(((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)  
    warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))  
    return warped

主流程

读取图像和预处理

image = cv2.imread(r'./images/test_01.png')  
contours_img = image.copy()  
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)  
blurred = cv2.GaussianBlur(gray, (5, 5), 0)  
cv_show("blurred", blurred)  
edged = cv2.Canny(blurred, 75, 200)  
cv_show('edged', edged)

检测轮廓

cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[1]  
cv2.drawContours(contours_img, cnts, -1, (0, 0, 255), 3)  
cv_show('contours_img', contours_img)  
docCnt = None  
  
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

透视变换

warped_t = four_point_transform(image, docCnt.reshape(4, 2))  
warped_new = warped_t.copy()  
cv_show('warped', warped_t)

检测选择题答案

thresh_Contours = thresh.copy()  
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[1]  
warped_Contours = cv2.drawContours(warped_t, cnts, -1, (0, 255, 0), 1)  
cv_show('warped_Contours', warped_Contours)  
  
questionCnts = []  
for c in cnts:  
    (x, y, w, h) = cv2.boundingRect(c)  
    ar = w / float(h)  
    if w >= 20 and h >= 20 and ar >= 0.9 and ar <= 1.1:  
        questionCnts.append(c)  
  
questionCnts = sort_contours(questionCnts, method="top-to-bottom")[0]  
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)  
        thresh_mask_and = cv2.bitwise_and(thresh, thresh, mask=mask)  
        total = cv2.countNonZero(thresh_mask_and)  
        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(warped_new, [cnts[k]], -1, color, 3)  
  
score = (correct / 5.0) * 100  
print("[INFO] score: {:.2f}%".format(score))  
cv2.putText(warped_new, "{:.2f}%".format(score), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)  
cv2.imshow("Original", image)  
cv2.imshow("Exam", warped_new)  
cv2.waitKey(0)