import cv2
import numpy as np

class Convert(object):
 """docstring for Convert"""
 def __init__(self):
 super(Convert, self).__init__()
 
 def _get_dynamic_binary_image(self,img):
 '''
 自适应阀值二值化
 '''
 img = cv2.imdecode(np.frombuffer(img, np.uint8), cv2.IMREAD_COLOR)
 img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
 th1 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 21, 1)
 return th1

 def clear_border(self,img):
 '''去除边框
 '''
 h, w = img.shape[:2]
 for y in range(0, w):
 for x in range(0, h):
 # if y ==0 or y == w -1 or y == w - 2:
 if y < 4 or y > w -4:
 img[x, y] = 255
 # if x == 0 or x == h - 1 or x == h - 2:
 if x < 4 or x > h - 4:
 img[x, y] = 255
 return img

 def interference_line(self,img):
 '''
 干扰线降噪
 '''
 h, w = img.shape[:2]
 # !!!opencv矩阵点是反的
 # img[1,2] 1:图片的高度,2:图片的宽度
 for y in range(1, w - 1):
 for x in range(1, h - 1):
 count = 0
 if img[x, y - 1] > 245:
 count = count + 1
 if img[x, y + 1] > 245:
 count = count + 1
 if img[x - 1, y] > 245:
 count = count + 1
 if img[x + 1, y] > 245:
 count = count + 1
 if count > 2:
 img[x, y] = 255
 return img

 def interference_point(self,img, x = 0, y = 0):
 """点降噪
 9邻域框,以当前点为中心的田字框,黑点个数
 :param x:
 :param y:
 :return:
 """
 # todo 判断图片的长宽度下限
 cur_pixel = img[x,y]# 当前像素点的值
 height,width = img.shape[:2]

 for y in range(0, width - 1):
 for x in range(0, height - 1):
 if y == 0: # 第一行
 if x == 0: # 左上顶点,4邻域
 # 中心点旁边3个点
 sum = int(cur_pixel) \
 + int(img[x, y + 1]) \
 + int(img[x + 1, y]) \
 + int(img[x + 1, y + 1])
 if sum <= 2 * 245:
 img[x, y] = 0
 elif x == height - 1: # 右上顶点
 sum = int(cur_pixel) \
 + int(img[x, y + 1]) \
 + int(img[x - 1, y]) \
 + int(img[x - 1, y + 1])
 if sum <= 2 * 245:
 img[x, y] = 0
 else: # 最上非顶点,6邻域
 sum = int(img[x - 1, y]) \
 + int(img[x - 1, y + 1]) \
 + int(cur_pixel) \
 + int(img[x, y + 1]) \
 + int(img[x + 1, y]) \
 + int(img[x + 1, y + 1])
 if sum <= 3 * 245:
 img[x, y] = 0
 elif y == width - 1: # 最下面一行
 if x == 0: # 左下顶点
 # 中心点旁边3个点
 sum = int(cur_pixel) \
 + int(img[x + 1, y]) \
 + int(img[x + 1, y - 1]) \
 + int(img[x, y - 1])
 if sum <= 2 * 245:
 img[x, y] = 0
 elif x == height - 1: # 右下顶点
 sum = int(cur_pixel) \
 + int(img[x, y - 1]) \
 + int(img[x - 1, y]) \
 + int(img[x - 1, y - 1])

 if sum <= 2 * 245:
 img[x, y] = 0
 else: # 最下非顶点,6邻域
 sum = int(cur_pixel) \
 + int(img[x - 1, y]) \
 + int(img[x + 1, y]) \
 + int(img[x, y - 1]) \
 + int(img[x - 1, y - 1]) \
 + int(img[x + 1, y - 1])
 if sum <= 3 * 245:
 img[x, y] = 0
 else: # y不在边界
 if x == 0: # 左边非顶点
 sum = int(img[x, y - 1]) \
 + int(cur_pixel) \
 + int(img[x, y + 1]) \
 + int(img[x + 1, y - 1]) \
 + int(img[x + 1, y]) \
 + int(img[x + 1, y + 1])

 if sum <= 3 * 245:
 img[x, y] = 0
 elif x == height - 1: # 右边非顶点
 sum = int(img[x, y - 1]) \
 + int(cur_pixel) \
 + int(img[x, y + 1]) \
 + int(img[x - 1, y - 1]) \
 + int(img[x - 1, y]) \
 + int(img[x - 1, y + 1])

 if sum <= 3 * 245:
 img[x, y] = 0
 else: # 具备9领域条件的
 sum = int(img[x - 1, y - 1]) \
 + int(img[x - 1, y]) \
 + int(img[x - 1, y + 1]) \
 + int(img[x, y - 1]) \
 + int(cur_pixel) \
 + int(img[x, y + 1]) \
 + int(img[x + 1, y - 1]) \
 + int(img[x + 1, y]) \
 + int(img[x + 1, y + 1])
 if sum <= 4 * 245:
 img[x, y] = 0
 return img 

 def run(self,img):
 # 自适应阈值二值化
 img = self._get_dynamic_binary_image(img)
 # 去除边框
 img = self.clear_border(img)
 # 对图片进行干扰线降噪
 img = self.interference_line(img)
 # 对图片进行点降噪
 img = self.interference_point(img)
 return img