# coding=utf-8 import cPickle import cv2 from imutils import contours from e import ContourCountError, ContourPerimeterSizeError, PolyNodeCountError from settings import ANS_IMG_THRESHOLD, CNT_PERIMETER_THRESHOLD, CHOICE_IMG_THRESHOLD, ANS_IMG_DILATE_ITERATIONS, \ ANS_IMG_ERODE_ITERATIONS, CHOICE_IMG_DILATE_ITERATIONS, CHOICE_IMG_ERODE_ITERATIONS, CHOICE_MAX_AREA, \ CHOICE_CNT_COUNT, ANS_IMG_KERNEL, CHOICE_IMG_KERNEL, CHOICE_MIN_AREA from utils import detect_cnt_again, get_init_process_img, get_max_area_cnt, get_ans, \ get_left_right, get_top_bottom, sort_by_row, sort_by_col, insert_null_2_rows, get_vertical_projective, \ get_h_projective def get_answer_from_sheet(base_img): # cv2.imshow('temp', base_img) # cv2.waitKey(0) # 灰度化然后进行边缘检测、二值化等等一系列处理 img = cv2.cvtColor(base_img, cv2.COLOR_BGR2GRAY) img = get_init_process_img(img) # cv2.imshow('temp', img) # cv2.waitKey(0) # 获取最大面积轮廓并和图片大小作比较，看轮廓周长大小判断是否是答题卡的轮廓 cnt = get_max_area_cnt(img) cnt_perimeter = cv2.arcLength(cnt, True) base_img_perimeter = (base_img.shape[0] + base_img.shape[1]) * 2 if not cnt_perimeter > CNT_PERIMETER_THRESHOLD * base_img_perimeter: raise ContourPerimeterSizeError # cv2.drawContours(base_img, [cnt], 0, (0, 255, 0), 1) # cv2.imshow('temp', base_img) # cv2.waitKey(0) # 计算多边形的顶点，并看是否是四个顶点 poly_node_list = cv2.approxPolyDP(cnt, cv2.arcLength(cnt, True) * 0.1, True) if not poly_node_list.shape[0] == 4: raise PolyNodeCountError # 根据计算的多边形顶点继续处理图片，主要是是纠偏 processed_img = detect_cnt_again(poly_node_list, base_img) # cv2.imshow('temp', processed_img) # cv2.waitKey(0) # 调整图片的亮度 processed_img = cv2.cvtColor(processed_img, cv2.COLOR_BGR2GRAY) processed_img = cv2.GaussianBlur( processed_img, (9, 9), 0) # cv2.imshow('temp', processed_img) # cv2.waitKey(0) # 通过二值化和膨胀腐蚀获得填涂区域 # ret, ans_img = cv2.threshold(processed_img, ANS_IMG_THRESHOLD[0], ANS_IMG_THRESHOLD[1], cv2.THRESH_BINARY_INV) ans_img = cv2.adaptiveThreshold(processed_img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 41, 35) ans_img = cv2.dilate(ans_img, ANS_IMG_KERNEL, iterations=ANS_IMG_DILATE_ITERATIONS) ans_img = cv2.erode(ans_img, ANS_IMG_KERNEL, iterations=ANS_IMG_ERODE_ITERATIONS) ret, ans_img = cv2.threshold(ans_img, ANS_IMG_THRESHOLD[0], ANS_IMG_THRESHOLD[1], cv2.THRESH_BINARY_INV) # cv2.imshow('temp', ans_img) # cv2.waitKey(0) # 通过二值化和膨胀腐蚀获得选项框区域 choice_img = cv2.adaptiveThreshold(processed_img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 51, 10) # ret, choice_img = cv2.threshold(processed_img, CHOICE_IMG_THRESHOLD[0], CHOICE_IMG_THRESHOLD[1], # cv2.THRESH_BINARY_INV) # cv2.imshow('temp', choice_img) # cv2.waitKey(0) # get_vertical_projective(choice_img) # get_h_projective(choice_img) for i in range(1): choice_img = cv2.dilate(choice_img, CHOICE_IMG_KERNEL, iterations=CHOICE_IMG_DILATE_ITERATIONS) choice_img = cv2.erode(choice_img, CHOICE_IMG_KERNEL, iterations=CHOICE_IMG_ERODE_ITERATIONS) # get_vertical_projective(choice_img) # get_h_projective(choice_img) # cv2.imshow('temp', choice_img) # cv2.waitKey(0) # 查找选项框以及前面题号的轮廓 cnts, h = cv2.findContours(choice_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) question_cnts = [] temp1_ans_img = ans_img.copy() for i, c in enumerate(cnts): # 如果面积小于某值，则认为这个轮廓是选项框或题号 if CHOICE_MIN_AREA < cv2.contourArea(c) < CHOICE_MAX_AREA: cv2.drawContours(temp1_ans_img, cnts, i, (0, 0, 0), 1) question_cnts.append(c) # cv2.imshow('temp', temp1_ans_img) # cv2.waitKey(0) question_cnts = get_left_right(question_cnts) question_cnts = get_top_bottom(question_cnts) temp2_ans_img = ans_img.copy() for i in range(len(question_cnts)): cv2.drawContours(temp2_ans_img, question_cnts, i, (0, 0, 0), 1) # cv2.imshow('temp', temp2_ans_img) # cv2.waitKey(0) # 如果轮廓小于特定值，重新扫描 # TODO 运用统计分析排除垃圾轮廓 # if len(question_cnts) != CHOICE_CNT_COUNT: # raise ContourCountError # cv2.imshow('temp', temp_ans_img) # cv2.waitKey(0) # 对轮廓之上而下的排序 question_cnts, cnts_pos = contours.sort_contours(question_cnts, method="top-to-bottom") rows = sort_by_row(list(cnts_pos)) cols = sort_by_col(list(cnts_pos)) # cv2.imshow('temp', temp2_ans_img) # cv2.waitKey(0) insert_null_2_rows(cols, rows) # 获得答案 rows, res = get_ans(ans_img, rows) if not res[0]: print res[1] cv2.imshow('temp1', temp2_ans_img) cv2.waitKey(0) print 'end' else: print res