python OpenCV实现答题卡识别判卷

本文实例为大家分享了python OpenCV实现答题卡识别判卷的具体代码，供大家参考，具体内容如下

完整代码：

#导入工具包
import numpy as np
import argparse
import imutils
import cv2

# 设置参数
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", default="./images/test_03.png",
help="path to the input image")
args = vars(ap.parse_args())

# 正确答案
ANSWER_KEY = {0: 1, 1: 4, 2: 0, 3: 3, 4: 1}

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

# 预处理
image = cv2.imread(args["image"])
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)
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=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)
# 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 = []

# 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]
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)

test_03.png

运行效果：

来源：https://blog.csdn.net/qq_41251963/article/details/97685790