nii convert to 2D image【python】

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可以自己精简，我的label是二分类

import SimpleITK as sitk
import cv2
from PIL import Image
import numpy as np
import nibabel as nib  # nii格式一般都会用到这个包
import imageio  # 转换成图像
import osimport numpy as np
from scipy.ndimage import rotate
from scipy.ndimage import median_filter
import matplotlib.pyplot as pltxy = 128
vol1 = int(xy/2)
vol2 = int(xy/4)
vol3 = int(vol2+16)def preprocess(image):result = median_filter(image, size=3)"""# 添加高斯噪声noise = np.random.normal(0, 25, size=image.shape)noise_img = image + noise.astype('uint8')# 双边滤波result = cv2.bilateralFilter(noise_img, 9, 75, 75)# 显示图像cv2.imshow('src', image)cv2.imshow('noise', noise_img)cv2.imshow('result', result)cv2.waitKey()cv2.destroyAllWindows()"""return resultdef create_dirs(out_path, num):for i in range(1, num):  # 这里需要注意，i取不到6，因为range()是前闭后开的，即i的取值范围为1-5。dir = os.path.join(r'crop/test4/ct/')# 前者为路径，后者为待创建文件夹的名称。注意，批量创建文件夹时不能有重复名称的，因此可以对文件夹加上序号信息。isExists = os.path.exists(dir)if not isExists:os.mkdir(dir)def mask2d(input_path, output_folder, idx):# 加载NIfTI文件img = nib.load(input_path)data = img.get_fdata()# 获取数据的形状信息num_slices = data.shape[2]  # 切片数量print(data.shape, num_slices)# 遍历每个切片并保存为PNG图像for i in range(num_slices):slice_data = data[:, :, i]  # 提取当前切片数据# Image.fromarray(255*img_array_list[foo].astype('int')).convert('L')image = Image.fromarray(255 * slice_data.astype('int')).convert('L')image = image.rotate(270)# image = Image.fromarray(slice_data)  # 创建PIL图像对象output_name = f"{output_folder}slice_{idx}_{i}.png"  # 设置输出文件名image.save(output_name)  # 保存为PNG图像def nii2d(img_addr, target_folder, idx):img_addr_n = nib.load(img_addr)# Convert them to numpy format,data = img_addr_n.get_fdata()# clip the images within [-125, 275],data_clipped = np.clip(data, -125, 275)# normalize each 3D image to [0, 1], anddata_normalised = (data_clipped - (-125)) / (275 - (-125))split_root = img_addr.split('\\')  # 通过\\来进行截断print(split_root) # ['crop/test4/ct/volume-0.nii']# extract 2D slices from 3D volume for training cases while# e.g. slice 000for i in range(data.shape[2]):formattedi = "{:03d}".format(i)slice000 = data_normalised[:, :, i] * 255image = Image.fromarray(slice000)image = image.convert("L")image = image.rotate(270)image = image.transpose(Image.Transpose.FLIP_LEFT_RIGHT)image.save(target_folder +str(idx)+"-"+str(i)+ ".png")for i in range(20):k = i + 1image_path = "ct/volume-{}.nii".format(str(i))label_path = "label/segmentation-{}.nii.gz".format(str(i))label = sitk.ReadImage(label_path, sitk.sitkInt16)label_array = sitk.GetArrayFromImage(label)image = sitk.ReadImage(image_path, sitk.sitkInt32)image_array = sitk.GetArrayFromImage(image)  # 分别读图像和标签数据print("\nimage_array=",image_array.shape, " label_array=",label_array.shape)center_x = (image_array.shape[1]) // 2center_y = (image_array.shape[2]) // 2center_z = (image_array.shape[0]) / 2  # 分别计算出xyz方向上的中心print("center_x=", center_x, "center_y=", center_y, "center_z=",center_z)center_x = center_x - vol2image_array = image_array[:, center_x - vol3:center_x + vol3, center_y - xy:center_y + xy]label_array = label_array[:, center_x - vol3:center_x + vol3, center_y - xy:center_y + xy]  # 在XY裁剪出一个256 * 256的区域#####只需要保存有标签的序列就行了z = np.any(label_array, axis=(1, 2))start_slice, end_slice = np.where(z)[0][[0, -1]]# 截取保留区域image_array = image_array[start_slice:end_slice + 1, :, :]label_array = label_array[start_slice:end_slice + 1, :, :]# print("Preprocessed shape:",ct_array.shape,seg_array.shape)new_image = sitk.GetImageFromArray(image_array)new_image.SetDirection(image.GetDirection())new_image.SetOrigin(image.GetOrigin())new_image.SetSpacing(image.GetSpacing())new_seg = sitk.GetImageFromArray(label_array)new_seg.SetDirection(label.GetDirection())new_seg.SetOrigin(label.GetOrigin())new_seg.SetSpacing(label.GetSpacing())sitk.WriteImage(new_image, "crop/test4/ct/volume-{}.nii".format(str(i)))sitk.WriteImage(new_seg, "crop/test4/label/segmentation-{}.nii.gz".format(str(i)))nii2d("crop/test4/ct/volume-" + str(i) + ".nii", "crop/png_ct/", i)mask2d("crop/test4/label/segmentation-" + str(i) + ".nii.gz", "crop/png_label/", i)"""对于label来说是ok的，但是对于那个来说不行"""