答题卡检测

答题卡识别评分代码完整讲解1. 答题卡处理流程图1) 读取答题卡图像并进行灰度化、模糊处理和边缘检测2) 定位答题卡区域并进行透视变换3) 通过阈值处理和轮廓分析检测填涂的选项泡泡4) 将检测结果与标准答案对比计算得分。系统支持自定义参数调整包括泡泡最小尺寸、宽高比范围等能够处理不同形态的答题卡。5) 最终输出评分结果并在图像上标记正确/错误选项。2. Python 代码及详细讲解导入库import cv2import numpy as npimport matplotlib.pyplot as plt功能讲解cv2: OpenCV图像处理库用于图像处理numpy: 数值计算处理坐标和矩阵运算matplotlib.pyplot: 可视化用于绘制调试图像或流程图参数设置image_path rF:\project\pytorch_project\CV学习\image.pngANSWER_KEY {0:1, 1:4, 2:0, 3:3, 4:1}MIN_BUBBLE_W, MIN_BUBBLE_H 10, 10ASPECT_RATIO_MIN, ASPECT_RATIO_MAX 0.5, 1.5功能讲解image_path: 答题卡图片路径ANSWER_KEY: 正确答案索引字典MIN_BUBBLE_W/H: 泡泡最小尺寸过滤噪点ASPECT_RATIO_MIN/MAX: 宽高比范围过滤非圆形轮廓辅助函数显示图像def cv_show(name, img):cv2.imshow(name, img)cv2.waitKey(0)cv2.destroyAllWindows()功能讲解用于显示图像窗口便于调试辅助函数轮廓排序def sort_contours(cnts, methodleft-to-right):# 根据指定方向排序轮廓并返回排序后的轮廓列表...功能讲解method可选: left-to-right, top-to-bottom, right-to-left, bottom-to-top辅助函数四点排序def order_points(pts):# 将四个角点按顺序排列为 top-left, top-right, bottom-right, bottom-left...功能讲解用于透视变换前整理角点顺序辅助函数透视变换def four_point_transform(image, pts):rect order_points(pts)(tl, tr, br, bl) rectwidthA 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]], dtypefloat32)M cv2.getPerspectiveTransform(rect, dst)warped cv2.warpPerspective(image, M, (maxWidth, maxHeight))return warped功能讲解将答题卡校正为俯视视角保证泡泡排列规则主流程读取图像并预处理img cv2.imdecode(np.fromfile(image_path, dtypenp.uint8), cv2.IMREAD_COLOR)gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)blurred cv2.GaussianBlur(gray, (5,5), 0)edged cv2.Canny(blurred, 75, 150)功能讲解读取图像灰度化高斯模糊去噪Canny边缘检测主流程检测答题卡轮廓cnts, hierarchy cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)cnts sorted(cnts, keycv2.contourArea, reverseTrue)docCnt Nonefor c in cnts:peri cv2.arcLength(c, True)approx cv2.approxPolyDP(c, 0.02*peri, True)if len(approx) 4:docCnt approxbreak功能讲解找到最大四边形轮廓假设为答题卡主流程透视变换warped four_point_transform(gray, docCnt.reshape(4,2))功能讲解得到俯视图泡泡排列规则主流程二值化thresh cv2.threshold(warped, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]功能讲解泡泡为白色背景黑色主流程检测泡泡cnts, hierarchy cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)questionCnts []for c in cnts:(x, y, w, h) cv2.boundingRect(c)ar w / float(h)if w MIN_BUBBLE_W and h MIN_BUBBLE_H and ASPECT_RATIO_MIN ar ASPECT_RATIO_MAX:questionCnts.append(c)功能讲解根据尺寸和宽高比过滤噪点得到候选泡泡主流程排序泡泡questionCnts, _ sort_contours(questionCnts, methodtop-to-bottom)bubbles_per_row 5rows []for i in range(0, len(questionCnts), bubbles_per_row):row_cnts questionCnts[i:ibubbles_per_row]row_cnts, _ sort_contours(row_cnts, methodleft-to-right)rows.append(row_cnts)功能讲解先按行排序再按列排序主流程评分correct 0for q, row_cnts in enumerate(rows[:len(ANSWER_KEY)]):bubbled Nonefor j, c in enumerate(row_cnts):mask np.zeros(thresh.shape, dtypeuint8)cv2.drawContours(mask, [c], -1, 255, -1)mask cv2.bitwise_and(thresh, thresh, maskmask)total cv2.countNonZero(mask)if bubbled is None or total bubbled[0]:bubbled (total, j)k ANSWER_KEY[q]color (0,0,255)if bubbled and k bubbled[1]:color (0,255,0)correct 1cv2.drawContours(warped, [row_cnts[k]], -1, color, 3)功能讲解每行找到涂黑最多泡泡对比答案并标记正确/错误主流程输出分数score (correct / len(ANSWER_KEY)) * 100print(fScore: {score}%)cv2.putText(warped, fScore: {score:.2f}%, (10, 20),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,255,0), 2)功能讲解计算总分并在图像上显示import cv2 import numpy as np import matplotlib.pyplot as plt # 参数设置 image_path rF:\project\pytorch_project\CV学习\image.png # 正确答案字典每题的索引对应答案位置 ANSWER_KEY {0:1, 1:4, 2:0, 3:3, 4:1} # 自动适配泡泡的最小宽高和宽高比范围 MIN_BUBBLE_W, MIN_BUBBLE_H 10, 10 ASPECT_RATIO_MIN, ASPECT_RATIO_MAX 0.5, 1.5 # 支持略长或略扁的泡泡 # 辅助函数 def cv_show(name, img): 显示图像 cv2.imshow(name, img) cv2.waitKey(0) cv2.destroyAllWindows() def sort_contours(cnts, methodleft-to-right): 对轮廓进行排序 method: left-to-right, top-to-bottom, right-to-left, bottom-to-top if len(cnts) 0: return [], [] reverse False i 0 # 0: x, 1: y if method in [right-to-left, bottom-to-top]: reverse True if method in [top-to-bottom, bottom-to-top]: i 1 # 获取轮廓的边界矩形 boundingBoxes [cv2.boundingRect(c) for c in cnts] # 按指定方向排序 (cnts, boundingBoxes) zip(*sorted(zip(cnts, boundingBoxes), keylambda b: b[1][i], reversereverse)) return list(cnts), list(boundingBoxes) def order_points(pts): 将四个点按顺序排列 [top-left, top-right, bottom-right, bottom-left] rect np.zeros((4,2), dtypefloat32) s pts.sum(axis1) rect[0] pts[np.argmin(s)] # top-left rect[2] pts[np.argmax(s)] # bottom-right diff np.diff(pts, axis1) rect[1] pts[np.argmin(diff)] # top-right rect[3] pts[np.argmax(diff)] # bottom-left return rect def four_point_transform(image, pts): 对答题卡进行透视变换得到俯视图 rect order_points(pts) (tl, tr, br, bl) rect # 计算宽度 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)) # 计算高度 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)) # 目标透视坐标 dst np.array([[0,0], [maxWidth-1,0], [maxWidth-1,maxHeight-1], [0,maxHeight-1]], dtypefloat32) # 计算透视变换矩阵 M cv2.getPerspectiveTransform(rect, dst) warped cv2.warpPerspective(image, M, (maxWidth, maxHeight)) return warped # 主流程 # 1. 读取原图 img cv2.imdecode(np.fromfile(image_path, dtypenp.uint8), cv2.IMREAD_COLOR) if img is None: raise FileNotFoundError(f无法读取输入图像: {image_path}) cv_show(Original, img) # 2. 灰度化 高斯模糊 边缘检测 gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) blurred cv2.GaussianBlur(gray, (5,5), 0) # 去噪 cv_show(Blurred, blurred) edged cv2.Canny(blurred, 75, 150) # 边缘检测 cv_show(Edged, edged) # 3. 找最大四边形轮廓作为答题卡做透视变换 cnts, hierarchy cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnts sorted(cnts, keycv2.contourArea, reverseTrue) docCnt None for c in cnts: peri cv2.arcLength(c, True) approx cv2.approxPolyDP(c, 0.02*peri, True) if len(approx) 4: docCnt approx break if docCnt is None: raise ValueError(未找到答题卡四边形轮廓) # 透视变换得到俯视图 warped four_point_transform(gray, docCnt.reshape(4,2)) cv_show(Warped, warped) # 4. 二值化反转OTSU thresh cv2.threshold(warped, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1] cv_show(Threshold, thresh) # 5. 找轮廓 cnts, hierarchy cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # 6. 筛选可能的泡泡轮廓 questionCnts [] for c in cnts: (x, y, w, h) cv2.boundingRect(c) ar w / float(h) if w MIN_BUBBLE_W and h MIN_BUBBLE_H and ASPECT_RATIO_MIN ar ASPECT_RATIO_MAX: questionCnts.append(c) # 7. 全局排序先 top-to-bottom questionCnts, _ sort_contours(questionCnts, methodtop-to-bottom) if len(questionCnts) 0: raise ValueError(未检测到有效 bubbles请检查图像质量或阈值) # 每行泡泡数量可修改 bubbles_per_row 5 rows [] for i in range(0, len(questionCnts), bubbles_per_row): row_cnts questionCnts[i:ibubbles_per_row] row_cnts, _ sort_contours(row_cnts, methodleft-to-right) rows.append(row_cnts) # 8. 评分 correct 0 for q, row_cnts in enumerate(rows[:len(ANSWER_KEY)]): bubbled None for j, c in enumerate(row_cnts): # 生成泡泡掩膜 mask np.zeros(thresh.shape, dtypeuint8) cv2.drawContours(mask, [c], -1, 255, -1) # 与二值化图像结合 mask cv2.bitwise_and(thresh, thresh, maskmask) total cv2.countNonZero(mask) # 统计白色像素数量 if bubbled is None or total bubbled[0]: bubbled (total, j) # 对比答案 k ANSWER_KEY[q] color (0,0,255) # 红色默认错误 if bubbled and k bubbled[1]: color (0,255,0) # 绿色表示正确 correct 1 # 绘制标记 cv2.drawContours(warped, [row_cnts[k]], -1, color, 3) # 9. 计算总分 score (correct / len(ANSWER_KEY)) * 100 print(fScore: {score}%) cv2.putText(warped, fScore: {score:.2f}%, (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,255,0), 2) cv_show(Graded, warped)