###############头文件
import matplotlib.pyplot as plt
import os
import cv2
import numpy as np
from PIL import Image
#from skimage import io
import random
from PIL import Image

img = cv2.imread("1_3img.png")############图片读取
#cv2.imshow('picture', img) 
#cv2.waitKey(0)
pictue_size=img.shape
picture_height=pictue_size[0]
picture_width=pictue_size[1]

################灰度化，以及二值化
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, binary = cv2.threshold(gray,1,255,cv2.THRESH_BINARY) 
############################

######################边界提取，contours包含边界值的坐标
contours, hierarchy = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE) 

img2=[0 for i in range(len(contours))]
print(len(contours))
for i in range(len(contours)):
  img2[i]=cv2.imread("./blackboard/test.jpg")############黑色底板图片读取
  print(img2[i].shape)
#  cv2.drawContours(img2[i],contours[i],-1,(0,0,255),3) #########画边界 
  ###############全图片遍历找到相应的在轮廓之内的点
  for a in range(picture_height):
    for b in range(picture_width):
      #############################################辨别是否在轮廓内是定义为1，不是定义为-1
      result = cv2.pointPolygonTest(contours[i], (a,b), False)
      if result>0:
        img2[i][b,a]=100
  ##############下面填写保存代码
  scipy.misc.imsave('picture'+str(i)+'.jpg',img2[i])