基于OpenCV+python的答题卡识别源码+使用文档+全部资料（高分项目）.zip

# -*- coding:utf-8 -*- from imutils.perspective import four_point_transform from imutils import contours import numpy as np import cv2 as cv import sys ANSWER_KEY_SCORE = {0: 1, 1: 4, 2: 0, 3: 3, 4: 1} ANSWER_KEY = {0: "A", 1: "B", 2: "C", 3: "D", 4: "E"} def main(argv): # 加载一个图片到opencv中 if len(argv) == 2: img = cv.imread(argv[1]) else: img = cv.imread('./t1.png') cv.imshow("orgin", img) # 转化成灰度图片 gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY) cv.imshow("gray", gray) gaussian_bulr = cv.GaussianBlur(gray, (5, 5), 0) # 高斯模糊 cv.imshow("gaussian", gaussian_bulr) edged = cv.Canny(gaussian_bulr, 75, 200) # 边缘检测,灰度值小于2参这个值的会被丢弃，大于3参这个值会被当成边缘，在中间的部分，自动检测 cv.imshow("edged", edged) # 寻找轮廓 # image, cts, hierarchy = cv.findContours(edged.copy(), cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE) cts, hierarchy = cv.findContours(edged.copy(), cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE) # 给轮廓加标记，便于我们在原图里面观察，注意必须是原图才能画出红色，灰度图是没有颜色的 # cv.drawContours(img, cts, -1, (0,0,255), 3) # 按面积大小对所有的轮廓排序 list = sorted(cts, key=cv.contourArea, reverse=True) print("寻找轮廓的个数：", len(cts)) cv.imshow("draw_contours", img) # 正确题的个数 correct_count = 0 for c in list: # 周长，第1个参数是轮廓，第二个参数代表是否是闭环的图形 peri = 0.01 * cv.arcLength(c, True) # 获取多边形的所有定点，如果是四个定点，就代表是矩形 approx = cv.approxPolyDP(c, peri, True) # 打印定点个数 print("顶点个数：", len(approx)) if len(approx) == 4: # 矩形 # 透视变换提取原图内容部分 ox_sheet = four_point_transform(img, approx.reshape(4, 2)) # 透视变换提取灰度图内容部分 tx_sheet = four_point_transform(gray, approx.reshape(4, 2)) cv.imshow("ox", ox_sheet) cv.imshow("tx", tx_sheet) # 使用ostu二值化算法对灰度图做一个二值化处理 ret, thresh2 = cv.threshold(tx_sheet, 0, 255, cv.THRESH_BINARY_INV | cv.THRESH_OTSU) cv.imshow("ostu", thresh2) # 继续寻找轮廓 r_cnt, r_hierarchy = cv.findContours(thresh2.copy(), cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE) print("找到轮廓个数：", len(r_cnt)) # 使用红色标记所有的轮廓 # cv.drawContours(ox_sheet,r_cnt,-1,(0,0,255),2) # 把所有找到的轮廓，给标记出来 questionCnts = [] for cxx in r_cnt: # 通过矩形，标记每一个指定的轮廓 x, y, w, h = cv.boundingRect(cxx) ar = w / float(h) if w >= 20 and h >= 20 and ar >= 0.9 and ar <= 1.1: # 使用红色标记，满足指定条件的图形 # cv.rectangle(ox_sheet, (x, y), (x + w, y + h), (0, 0, 255), 2) # 把每个选项，保存下来 questionCnts.append(cxx) cv.imshow("ox_1", ox_sheet) # 按坐标从上到下排序 questionCnts = contours.sort_contours(questionCnts, method="top-to-bottom")[0] # 使用np函数，按5个元素，生成一个集合 for (q, i) in enumerate(np.arange(0, len(questionCnts), 5)): # 获取按从左到右的排序后的5个元素 cnts = contours.sort_contours(questionCnts[i:i + 5])[0] bubble_rows = [] # 遍历每一个选项 for (j, c) in enumerate(cnts): # 生成一个大小与透视图一样的全黑背景图布 mask = np.zeros(tx_sheet.shape, dtype="uint8") # 将指定的轮廓+白色的填充写到画板上,255代表亮度值，亮度=255的时候，颜色是白色，等于0的时候是黑色 cv.drawContours(mask, [c], -1, 255, -1) # 做两个图片做位运算，把每个选项独自显示到画布上，为了统计非0像素值使用，这部分像素最大的其实就是答案 mask = cv.bitwise_and(thresh2, thresh2, mask=mask) # cv.imshow("c" + str(i), mask) # 获取每个答案的像素值 total = cv.countNonZero(mask) # 存到一个数组里面，tuple里面的参数分别是，像素大小和答案的序号值 # print(total,j) bubble_rows.append((total, j)) bubble_rows = sorted(bubble_rows, key=lambda x: x[0], reverse=True) # 选择的答案序号 choice_num = bubble_rows[0][1] print("答案：{} 数据: {}".format(ANSWER_KEY.get(choice_num), bubble_rows)) fill_color = None # 如果做对就加1 if ANSWER_KEY_SCORE.get(q) == choice_num: fill_color = (0, 255, 0) # 正确 绿色 correct_count = correct_count + 1 else: fill_color = (0, 0, 255) # 错误 红色 cv.drawContours(ox_sheet, cnts[choice_num], -1, fill_color, 2) cv.imshow("answer_flagged", ox_sheet) text1 = "total: " + str(len(ANSWER_KEY)) + "" text2 = "right: " + str(correct_count) text3 = "score: " + str(correct_count * 1.0 / len(ANSWER_KEY) * 100) + "" font = cv.FONT_HERSHEY_SIMPLEX cv.putText(ox_sheet, text1 + " " + text2 + " " + text3, (10, 30), font, 0.5, (0, 0, 255), 2) cv.imshow("score", ox_sheet) break cv.waitKey(0) if __name__ == "__main__": main(sys.argv)