答题卡识别_答题卡外轮廓检测有什么作用

一、 前言

以下内容以及素材均来自傅老师。
案例：@ Fu Xianjun. All Rights Reserved.

二、 原图

三、 示例

1. 预处理、轮廓检测

1.1 代码

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(0)
        cv2.destroyAllWindows()  
# 读取输入
image = cv2.imread("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)
# 轮廓检测
cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,\
                        cv2.CHAIN_APPROX_SIMPLE)[0]
cv2.drawContours(contours_img,cnts,-1,(0,0,255),3) 
cv_show('contours_img',contours_img)
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1.2 结果

2. 轮廓排序，透视变换

2.1 代码

def order_points(pts):
    # 一共4个坐标点
    rect = np.zeros((4, 2), dtype = "float32")

    # 按顺序找到对应坐标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)]

    # 计算右上和左下
    diff = np.diff(pts, axis = 1)
    rect[1] = pts[np.argmin(diff)]
    rect[3] = pts[np.argmax(diff)]

    return rect

def four_point_transform(image, pts):
    # 获取输入坐标点
    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(image, M, (maxWidth, maxHeight))

    # 返回变换后结果
    return warped

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# 确保检测到了
docCnt = None
if len(cnts) > 0:
    # 根据轮廓大小进行排序
    cnts = sorted(cnts, key=cv2.contourArea, reverse=True)

    # 遍历每一个轮廓
    for c in cnts:
        # 近似
        peri = cv2.arcLength(c, True)
        approx = cv2.approxPolyDP(c, 0.02 * peri, True)

        # 准备做透视变换
        if len(approx) == 4:
            docCnt = approx
            break

# 执行透视变换

warped = four_point_transform(gray, docCnt.reshape(4, 2))
cv_show('warped',warped)
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2.2 结果

3. 寻找圆圈轮廓

3.1 代码

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
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# Otsu's 阈值处理
thresh = cv2.threshold(warped, 0, 255,
    cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1] 
cv_show('thresh',thresh)
thresh_Contours = thresh.copy()
# 找到每一个圆圈轮廓
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
    cv2.CHAIN_APPROX_SIMPLE)[0]
cv2.drawContours(thresh_Contours,cnts,-1,(0,0,255),3) 
cv_show('thresh_Contours',thresh_Contours)
questionCnts = []
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3.2 结果在这里插入图片描述

4. 输出每个轮廓，对比答案

4.1 代码

# 遍历
for c in cnts:
    # 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(warped, questionCnts, 1,(0,255,255),2)
cv_show("warp",warped)
correct = 0

# 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
    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) #-1表示填充
        cv_show('mask',mask)
        # 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(warped, [cnts[k]], -1, color, 3)


score = (correct / 5.0) * 100
print("[INFO] score: {:.2f}%".format(score))
cv2.putText(warped, "{:.2f}%".format(score), (10, 30),
    cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
cv2.imshow("Original", image)
cv2.imshow("Exam", warped)
cv2.waitKey(0)
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4.2 结果

需要的截图较多，就不一一展示了。