一、预处理（一）生成掩膜

import multiresolutionimageinterface as mir
import os.path as osp
import matplotlib.pyplot as plt
from pathlib import Path
import glob
import os

os.environ["CUDA_VISIBLE_DEVICES"] = "0"

slide_path = r'F:\病理切片'
anno_path = r'F:\病理切片'
mask_path = r'F:\病理切片'
tumor_paths = glob.glob(osp.join(slide_path, '*.tif'))    # 读原图
tumor_paths.sort()
anno_tumor_paths = glob.glob(osp.join(anno_path, '*.xml'))  # 读勾画的xml文件
anno_tumor_paths.sort()

reader = mir.MultiResolutionImageReader()
i = 0
while i < len(tumor_paths):    # 从第一张开始读
    mr_image = reader.open(tumor_paths[i])

    annotation_list = mir.AnnotationList()
    xml_repository = mir.XmlRepository(annotation_list)
    xml_repository.setSource(anno_tumor_paths[i])
    xml_repository.load()

    annotation_mask = mir.AnnotationToMask()   # 由标注转换成mask

    camelyon17_type_mask = False
    label_map = {'metastases': 1, 'normal': 2} if camelyon17_type_mask else {'_0': 255, '_1': 255, '_2': 0}
    conversion_order = ['metastases', 'normal'] if camelyon17_type_mask else  ['_0', '_1', '_2']
    output_path= osp.join(mask_path, osp.basename(tumor_paths[i]).replace('.tif', '_mask.tif'))
    annotation_mask.convert(annotation_list, output_path, mr_image.getDimensions(), mr_image.getSpacing(), label_map, conversion_order)
    i=i+1