import cv2
import numpy as np
from PIL import Image
 
from joblib import Parallel
from joblib import delayed
# Parallel 和 delayed是为了使用多线程处理
# 使用前需要安装joblib：pip install joblib
 
# img_stack的shape为：num, h, w
# 是三维的图像，可以理解为是num张二维的图像组成
# mask是用来保存最后的结果的
mask = np.ones_like(img_stack)
for i in range(num):
  # 阈值化
  _, binaryzation = cv2.threshold(img_stack[i], 5, 255, cv2.THRESH_BINARY_INV)
  # 找到所有的轮廓
  contours, _ = cv2.findContours(binaryzation, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
  area = []
  # 找到最大的轮廓
  for k in range(len(contours)):
    area.append(cv2.contourArea(contours[k]))
  max_idx = np.argmax(np.array(area))
  # cv2.fillContexPoly(mask[i], contours[max_idx], 0)
  # 填充最大的轮廓
  cv2.drawContours(mask[i], contours, max_idx, 0, cv2.FILLED)
  del area 
 
 
# 保存
def _write_mask(mask, i):
  Image.fromarray(mask.astype(np.uint8)*255).save(os.path.join(path, 'm%d.png' % i))
 
# 使用多线程进行保存
num_cores = 10
parallel = Parallel(n_jobs=num_cores, backend='threading')
parallel(delayed(_write_mask)(mask[i, :, :], i) for i in range(0, num))

import cv2
import numpy as np
 
# 以灰度方式读取图像
img = cv2.imread('img.png', cv2.IMREAD_GRAYSCALE)
mask = img.copy()
 
# 二值化，100为阈值，小于100的变为255，大于100的变为0
# 也可以根据自己的要求，改变参数：
# cv2.THRESH_BINARY
# cv2.THRESH_BINARY_INV
# cv2.THRESH_TRUNC
# cv2.THRESH_TOZERO_INV
# cv2.THRESH_TOZERO
_, binaryzation = cv2.threshold(img, 100, 255, cv2.THRESH_BINARY_INV)
 
# 找到所有的轮廓
contours, _ = cv2.findContours(binaryzation, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
 
area = []
 
# 找到最大的轮廓
for k in range(len(contours)):
	area.append(cv2.contourArea(contours[k]))
max_idx = np.argmax(np.array(area))
 
# 填充最大的轮廓
mask = cv2.drawContours(mask, contours, max_idx, 0, cv2.FILLED)
 
# 保存填充后的图像
cv2.imwrite('masked.png', mask)