将whole slide切割成很多tiles并去除背景

import cv2
import numpy as np
from openslide import OpenSlide

grid_size = 224

#img = cv2.imread('/Users/xiaoying/Downloads/0066YOYqgy1fue9dkgygrj30i20pn0wk.jpg')
slide = OpenSlide('/Users/xiaoying/QQ下载/中南医院_酒精肝/2018.07.13/image1.ndpi')
img = np.array(slide.read_region((0, 0), 3, (slide.level_dimensions[3][0], slide.level_dimensions[3][1])))
histogram = np.zeros(256)
histogram.dtype = int
for i in range(img.shape[0]):
    for j in range(img.shape[1]):
        gray = img[i, j, 0]
        histogram[gray] += 1

def calc_var(histogram, threshold):
    back = histogram[:threshold]
    fore = histogram[threshold:]
    weight_b = back.sum() / histogram.sum()
    weight_f = fore.sum() / histogram.sum()
    #if back.sum() != 0 and fore.sum() != 0:
     #   sum_b = 0
      #  for i in range(back.shape[0]):
       #     sum_b += i * back[i]
        #sum_f = 0
        #for i in range(fore.shape[0]):
         #   sum_f += (i + threshold) * fore[i]
        #mean_b = sum_b / back.sum()
        #mean_f = sum_f / fore.sum()
        #var_b = 0
        #for i in range(back.shape[0]):
         #   var_b += (i - mean_b) ** 2 * back[i]
        #var_b = var_b / back.sum()
        #var_f = 0
        #for i in range(fore.shape[0]):
         #   var_f += (i + threshold - mean_f) ** 2 * fore[i]
        #var_f = var_f / fore.sum()
    #if back.sum() == 0:
     #   var_b = 0
    #if fore.sum() == 0:
     #   var_f = 0
    #var = weight_b * var_b + weight_f * var_f
    if back.sum() == 0:
        var_b = 0
    else:
        sum_b = 0
        for i in range(back.shape[0]):
            sum_b += i * back[i]
        mean_b = sum_b / back.sum()
        var_b = 0
        for i in range(back.shape[0]):
            var_b += (i - mean_b) ** 2 * back[i]
        var_b = var_b / back.sum()
    if fore.sum() == 0:
        var_f = 0
    else:
        sum_f = 0
        for i in range(fore.shape[0]):
            sum_f += (i + threshold) * fore[i]
        mean_f = sum_f / fore.sum()
        var_f = 0
        for i in range(fore.shape[0]):
            var_f += (i + threshold - mean_f) ** 2 * fore[i]
        var_f = var_f / fore.sum()
    var = weight_b * var_b + weight_f * var_f
    return var

var = []
for i in range(256):
    if i != 0:
        var.append(calc_var(histogram, i))
minvar = min(var)
thre = var.index(minvar)
print(minvar)
print(thre)
#for i in range(img.shape[0]):
   # for j in range(img.shape[0]):
     #   if img[i, j, 0] < thre:
      #      img[i, j] = (0, 0, 0, 255)
#cv2.imwrite('image1.png', img)

def slide_tiling(thre, image):
    grid = []
    width = image.shape[0]
    height = image.shape[0]
    grid_num_x = width // grid_size
    grid_num_y = height // grid_size
    for i in range(grid_num_y):
        for j in range(grid_num_x):
            flag = False
            tissue_pix = 0
            for k in range(224):
                for l in range(224):
                    if image[i * 224 + l, j * 224 + k, 0] < thre:
                        tissue_pix += 1
            if tissue_pix / (224 * 224) > 0.005:
                flag = True
            if flag == True:
                grid.append((j * 224, i * 224))
    return grid
grids = slide_tiling(thre, img)
print(grids)
for grid in grids:
    img[grid[1]:grid[1] + 224, grid[0]] = (0, 0, 0, 255)
    img[grid[1]:grid[1]+224, grid[0]+224] = (0, 0, 0, 255)
    img[grid[1], grid[0]:grid[0]+224] = (0, 0, 0, 255)
    img[grid[1]+224, grid[0]:grid[0]+224] = (0, 0, 0, 255)
cv2.imwrite('img.tiff', img)