参考自:Bubble sheet multiple choice scanner and test grader using OMR, Python and OpenCV
一个简易的答题卡识别与分数判断小程序
修改说明:
1.不import imutils库,直接找mutils的源码,复制需要的函数的源码过来,分析算法原理
2.在jupter notebook中测试,可以方便地分阶段测试
引入必要的库
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| import numpy as np import cv2
import matplotlib import matplotlib.pyplot as plt
%matplotlib inline
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定义需要的函数
4边形4点排序函数
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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
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4点变换函数
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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
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轮廓排序函数
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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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图像识别部分
读入图片+预处理
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| image = cv2.imread('omr_test_01.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) blurred = cv2.GaussianBlur(gray, (5, 5), 0) edged = cv2.Canny(blurred, 75, 200) fig = plt.figure(figsize=(15, 10)) plt.subplot(1, 2, 1) plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) plt.axis('off') plt.subplot(1, 2, 2) plt.imshow(edged,cmap ='gray') plt.axis('off')
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| (-0.5, 524.5, 699.5, -0.5)
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检测到图片中的答题卡
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| _,cnts,_ = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) docCnt = None
if len(cnts) > 0: 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
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透视变换来提取答题卡
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| paper = four_point_transform(image, docCnt.reshape(4, 2)) warped = four_point_transform(gray, docCnt.reshape(4, 2))
fig = plt.figure(figsize=(8, 8)) plt.imshow(warped,cmap ='gray') plt.axis('off')
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提取气泡/圆点
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| thresh = cv2.threshold(warped, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1] _,cnts,_ = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) 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)
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答案判断部分
构建答案字典
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| ANSWER_KEY = {0: 1, 1: 4, 2: 0, 3: 3, 4: 1}
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气泡排序
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| questionCnts = sort_contours(questionCnts, method="top-to-bottom")[0] correct = 0
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循环判断
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| fig = plt.figure(figsize=(15,15)) n = 1 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) mask = cv2.bitwise_and(thresh, thresh, mask=mask) total = cv2.countNonZero(mask)
plt.subplot(5, 5, n) plt.axis('off') n += 1 plt.imshow(mask,cmap ='gray') 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(paper, [cnts[k]], -1, color, 3)
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计算分数并打分
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score = (correct / 5.0) * 100 print("[INFO] score: {:.2f}%".format(score)) cv2.putText(paper, "{:.2f}%".format(score), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
fig = plt.figure(figsize=(8, 8)) plt.imshow(cv2.cvtColor(paper, cv2.COLOR_BGR2RGB)) plt.axis('off')
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