本文实例为大家分享了opencv实现答题卡识别的具体代码，供大家参考，具体内容如下
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"""识别答题卡""" import cv2import numpy as np def showImg(img_name, img):cv2.imshow(img_name, img)cv2.waitKey()cv2.destroyAllWindows() def get_max_rect(sorted_cnts):for cnt in sorted_cnts:# 轮廓近似possible_cnts = []epsilon = 0.1 * cv2.arcLength(cnt, True)approx = cv2.approxPolyDP(cnt, epsilon, True)if len(approx) == 4:possible_cnts.append(cnt)possible_cnts = sorted(possible_cnts, key=lambda x: cv2.arcLength(x, True))return possible_cnts def get_max_bounding_rect(possible_cnts):# for cnt in possible_cnts:# x, y, w, h = cv2.boundingRect(cnt) sorted_cnts = sorted(possible_cnts, key=lambda cnt: cv2.boundingRect(cnt)[2]*cv2.boundingRect(cnt)[3], reverse=True)print(sorted_cnts[0]) def show_countour(img, cnt):img_copy = img.copy()cv2.drawContours(img_copy, cnt, -1, (0,255, 0), 3)showImg("img_copy", img_copy) # 读取答题卡图片,并显示answer_sheet_img = cv2.imread("t1.jpg")print(answer_sheet_img.shape)showImg("answer_sheet_img", answer_sheet_img) # 高斯滤波，去除噪音blur = cv2.GaussianBlur(answer_sheet_img,(5,5),0)showImg("blur", blur) # 图像转灰度值sheet_gray = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)showImg("sheet_gray", sheet_gray) # 二值化retval, sheet_threshold = cv2.threshold(sheet_gray,177, 255, cv2.THRESH_BINARY)# print(type(sheet_threshold), sheet_threshold)showImg("sheet_threshold", sheet_threshold) # 边界检测edges = cv2.Canny(sheet_threshold, 100, 200)showImg("edges", edges)# print(type(edges)) # 寻找轮廓copy_edges = edges.copy()img_copy = answer_sheet_img.copy()img, cnts, hierarchy = cv2.findContours(copy_edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)cv2.drawContours(img_copy, cnts, -1, (0,0,255), 1)showImg("img_copy", img_copy) # 对所有轮廓加一个外接矩形，找最大的外接矩形max_area_index = Nonearea = 0for index, cnt in enumerate(cnts):x, y, w, h = cv2.boundingRect(cnt)if w*h > area:max_area_index = indexshow_countour(answer_sheet_img, cnts[max_area_index]) # 仿射，拿到答题卡主要部位x, y, w, h = cv2.boundingRect(cnts[max_area_index]) # 最大的边界cv2.rectangle(answer_sheet_img, (x, y),(x+w, y+h), (0,0,255), 2)showImg("answer_sheet_img", answer_sheet_img)pts1 = np.float32([[x,y], [x+w, y], [x+w, y+h]])pts2 = np.float32([[0,0], [w, 0], [w, h]]) M = cv2.getAffineTransform(pts1, pts2)sheet_threshold_copy = sheet_threshold.copy()dst = cv2.warpAffine(sheet_threshold_copy, M, (w, h))showImg("dst", dst)print(answer_sheet_img.shape)part_sheet_img = answer_sheet_img[y:y+h, x:x+w]showImg("part_sheet_img", part_sheet_img) # 对答案区域灰度，二值，找轮廓part_answer_gray = cv2.cvtColor(part_sheet_img, cv2.COLOR_BGR2GRAY) # 灰度ret, threshold_answer = cv2.threshold(part_answer_gray, 175, 255, cv2.THRESH_BINARY)showImg("threshold_answer", threshold_answer) img, answer_cnts, x = cv2.findContours(threshold_answer, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)part_sheet_img_copy = part_sheet_img.copy()cv2.drawContours(part_sheet_img_copy, answer_cnts, -1, (0, 0, 255), 1)showImg("dst_copy", part_sheet_img_copy) # 对所有轮廓找外接矩形，想过滤掉不合适的矩形print("画矩形")answer_filter_cnts = []answer_circles = []img_ = part_sheet_img.copy()for cnt in answer_cnts:x, y, w, h = cv2.boundingRect(cnt)if 30<w<40 and 30<h<40:print(x, y, w, h)circle_x = int(x + w/2)circle_y = int(y+h/2)r = int((w+h)/4)answer_circles.append((circle_x, circle_y, r))answer_filter_cnts.append(cnt) answer_filter_cnts = np.array(answer_filter_cnts)cv2.drawContours(img_, answer_filter_cnts, -1, (0, 0, 255), 1)# cv2.rectangle(img, (x, y), (x+w, y+h), (0,255,0), 2)showImg("img_", img_)print("geshu", len(answer_circles)) # 从answer_circles中取25个mask_dict = {1:[],2:[], 3:[], 4:[],5:[]} # 一共不一定是25个圆，将圆按照题目行分类，sorted_y_answer_circles = sorted(answer_circles, key=lambda circle: circle[1])print("sorted_y_answer_circles", sorted_y_answer_circles)set_num = 1for index, circle in enumerate(sorted_y_answer_circles):if index == 0:mask_dict[1].append(circle)else:if circle[1] - sorted_y_answer_circles[index-1][1] > 30:set_num += 1mask_dict[set_num].append(circle)else:mask_dict[set_num].append(circle) print("mask_dict", mask_dict) for k, mask_circle_list in mask_dict.items(): # 对每一个题目，保留五个答案，多余的舍去if len(mask_circle_list) == 5:sorted_x_mask_circle_list = sorted(mask_circle_list, key=lambda x:x[0])mask_dict[k]=sorted_x_mask_circle_listelse:sorted_x_mask_circle_list = sorted(mask_circle_list, key=lambda x: x[0])sorted_x_mask_circle_list_5 = []for i, c in enumerate(sorted_x_mask_circle_list):if i == 0:sorted_x_mask_circle_list_5.append(c)else:if abs(c[0] - sorted_x_mask_circle_list[i-1][0]) < 10:passelse:sorted_x_mask_circle_list_5.append(c)mask_dict[k] = sorted_x_mask_circle_list_5 print("mask_dict", mask_dict) # mask_dict 分好组的按照顺序的圈圈 # 做掩码mask_img = np.zeros_like(part_sheet_img, dtype='uint8') # 全黑图showImg("threshold_answer", threshold_answer)threshold_answer = np.array(threshold_answer)# mask_dict = sorted(mask_dict, key=lambda x: mask_dict.keys())all_scores = [] # 所有答案处的评分for exercise_num, circle_mask_list in mask_dict.items():# 对于每一题score_list = [] # 每一题的每个选项的评分，涂黑的为选择的，值越接近0， 评分较低for circle_mask in circle_mask_list:mask_img_copy = cv2.cvtColor(mask_img, cv2.COLOR_BGR2GRAY)# 做一个当前圆的掩码：cv2.circle(mask_img_copy, (circle_mask[0], circle_mask[1]), circle_mask[2], (255, 255, 255), -1)print(threshold_answer.shape, mask_img_copy.shape)mask_img_ = cv2.bitwise_and(threshold_answer, threshold_answer, mask=mask_img_copy)score = mask_img_.sum()score_list.append(score)# showImg("mask_img_", mask_img_)all_scores.append(score_list) all_score_np = np.array(all_scores)s = np.argmin(all_score_np, axis=1) # 找评分最低处即为选择项 answer_dict = {0: "A",1: "B",2: "C",3: "D",4: "E"} for index, v in enumerate(s):print("第%s题的答案是%s" %(index+1, answer_dict[v])) |
效果图：
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原文链接：https://blog.csdn.net/qq_36008031/article/details/104464227
