#导入工具包
import numpy as np
import argparse
import imutils
import cv2
 
# 设置参数
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", default="./images/test_03.png",
 help="path to the input image")
args = vars(ap.parse_args())
 
# 正确答案
ANSWER_KEY = {0: 1, 1: 4, 2: 0, 3: 3, 4: 1}
 
def order_points(pts):
 # 一共4个坐标点
 rect = np.zeros((4, 2), dtype = "float32")
 
 # 按顺序找到对应坐标0123分别是 左上，右上，右下，左下
 # 计算左上，右下
 s = pts.sum(axis = 1)
 rect[0] = pts[np.argmin(s)]
 rect[2] = pts[np.argmax(s)]
 
 # 计算右上和左下
 diff = np.diff(pts, axis = 1)
 rect[1] = pts[np.argmin(diff)]
 rect[3] = pts[np.argmax(diff)]
 
 return rect
 
def four_point_transform(image, pts):
 # 获取输入坐标点
 rect = order_points(pts)
 (tl, tr, br, bl) = rect
 
 # 计算输入的w和h值
 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))
 
 # 变换后对应坐标位置
 dst = np.array([
  [0, 0],
  [maxWidth - 1, 0],
  [maxWidth - 1, maxHeight - 1],
  [0, maxHeight - 1]], dtype = "float32")
 
 # 计算变换矩阵
 M = cv2.getPerspectiveTransform(rect, dst)
 warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
 
 # 返回变换后结果
 return warped
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
def cv_show(name,img):
        cv2.imshow(name, img)
        cv2.waitKey(0)
        cv2.destroyAllWindows()  
 
# 预处理
image = cv2.imread(args["image"])
contours_img = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
cv_show('blurred',blurred)
edged = cv2.Canny(blurred, 75, 200)
cv_show('edged',edged)
 
# 轮廓检测
cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
 cv2.CHAIN_APPROX_SIMPLE)[0]
cv2.drawContours(contours_img,cnts,-1,(0,0,255),3) 
cv_show('contours_img',contours_img)
docCnt = None
 
# 确保检测到了
if len(cnts) > 0:
 # 根据轮廓大小进行排序
 cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
 
 # 遍历每一个轮廓
 for c in cnts:
  # 近似
  peri = cv2.arcLength(c, True)
  approx = cv2.approxPolyDP(c, 0.02 * peri, True)
 
  # 准备做透视变换
  if len(approx) == 4:
   docCnt = approx
   break
 
# 执行透视变换
 
warped = four_point_transform(gray, docCnt.reshape(4, 2))
cv_show('warped',warped)
# Otsu's 阈值处理
thresh = cv2.threshold(warped, 0, 255,
 cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1] 
cv_show('thresh',thresh)
thresh_Contours = thresh.copy()
# 找到每一个圆圈轮廓
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
 cv2.CHAIN_APPROX_SIMPLE)[0]
cv2.drawContours(thresh_Contours,cnts,-1,(0,0,255),3) 
cv_show('thresh_Contours',thresh_Contours)
questionCnts = []
 
# 遍历
for c in cnts:
 # 计算比例和大小
 (x, y, w, h) = cv2.boundingRect(c)
 ar = w / float(h)
 
 # 根据实际情况指定标准
 if w >= 20 and h >= 20 and ar >= 0.9 and ar = 1.1:
  questionCnts.append(c)
 
# 按照从上到下进行排序
questionCnts = sort_contours(questionCnts,
 method="top-to-bottom")[0]
correct = 0
 
# 每排有5个选项
for (q, i) in enumerate(np.arange(0, len(questionCnts), 5)):
 # 排序
 cnts = sort_contours(questionCnts[i:i + 5])[0]
 bubbled = None
 
 # 遍历每一个结果
 for (j, c) in enumerate(cnts):
  # 使用mask来判断结果
  mask = np.zeros(thresh.shape, dtype="uint8")
  cv2.drawContours(mask, [c], -1, 255, -1) #-1表示填充
  cv_show('mask',mask)
  # 通过计算非零点数量来算是否选择这个答案
  mask = cv2.bitwise_and(thresh, thresh, mask=mask)
  total = cv2.countNonZero(mask)
 
  # 通过阈值判断
  if bubbled is None or total > bubbled[0]:
   bubbled = (total, j)
 
 # 对比正确答案
 color = (0, 0, 255)
 k = ANSWER_KEY[q]
 
 # 判断正确
 if k == bubbled[1]:
  color = (0, 255, 0)
  correct += 1
 
 # 绘图
 cv2.drawContours(warped, [cnts[k]], -1, color, 3)
 
 
score = (correct / 5.0) * 100
print("[INFO] score: {:.2f}%".format(score))
cv2.putText(warped, "{:.2f}%".format(score), (10, 30),
 cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
cv2.imshow("Original", image)
cv2.imshow("Exam", warped)
cv2.waitKey(0)