img.get_fdata函数

`img.get_fdata()`是一个函数，用于获取Nifti图像的数据数组。在Python中，我们可以使用nibabel库来读取和处理Nifti格式的图像。使用`img.get_fdata()`函数可以将Nifti图像的数据转换为一个numpy数组，使得我们可以使用numpy库进行各种计算和操作。

下面是一个使用nibabel库读取Nifti图像并获取数据数组的示例代码：

import nibabel as nib

# 读取Nifti图像文件
img = nib.load('example.nii.gz')

# 获取图像数据数组
data = img.get_fdata()

# 打印数据数组的形状和数据类型
print("数据数组的形状：", data.shape)
print("数据数组的数据类型：", data.dtype)

在上面的代码中，我们首先使用`nib.load()`函数读取了一个名为`example.nii.gz`的Nifti图像文件，然后使用`img.get_fdata()`函数获取了图像数据数组，并将其赋值给变量`data`。最后，我们打印了数据数组的形状和数据类型。

相关问题

代码import os import numpy as np import nibabel as nib from PIL import Image # 创建保存路径 save_path = 'C:/Users/Administrator/Desktop/2D-LiTS2017' if not os.path.exists(save_path): os.makedirs(save_path) if not os.path.exists(os.path.join(save_path, 'image')): os.makedirs(os.path.join(save_path, 'image')) if not os.path.exists(os.path.join(save_path, 'label')): os.makedirs(os.path.join(save_path, 'label')) # 加载数据集 data_path = 'D:/BaiduNetdiskDownload/LiTS2017' img_path = os.path.join(data_path, 'Training Batch 1') label_path = os.path.join(data_path, 'Training Batch 2') # 转换图像 for file in sorted(os.listdir(img_path)): if file.endswith('.nii'): img_file = os.path.join(img_path, file) img = nib.load(img_file).get_fdata() img = np.transpose(img, (2, 0, 1)) # 转换为z, x, y for i in range(img.shape[0]): img_slice = img[i, :, :] img_slice = (img_slice - np.min(img_slice)) / (np.max(img_slice) - np.min(img_slice)) * 255 # 归一化到0-255 img_slice = img_slice.astype(np.uint8) img_slice = np.stack([img_slice]*3, axis=2) # 转换为三通道图像 img_name = file[:-4] + '' + str(i).zfill(3) + '.png' img_file_save = os.path.join(save_path, 'image', img_name) Image.fromarray(img_slice).save(img_file_save) # 转换标签 for file in sorted(os.listdir(label_path)): if file.endswith('.nii'): label_file = os.path.join(label_path, file) label = nib.load(label_file).get_fdata() label = np.transpose(label, (2, 0, 1)) # 转换为z, x, y for i in range(label.shape[0]): label_slice = label[i, :, :] label_slice[label_slice == 1] = 255 # 肝脏灰度值设为255 label_slice[label_slice == 2] = 128 # 肝脏肿瘤灰度值设为128 label_slice = label_slice.astype(np.uint8) label_name = file[:-4] + '' + str(i).zfill(3) + '.png' label_file_save = os.path.join(save_path, 'label', label_name) Image.fromarray(label_slice).save(label_file_save)出现scaled = scaled.astype(np.promote_types(scaled.dtype, dtype), copy=False) MemoryError错误，怎么修改？给出完整代码

这个错误通常意味着你的计算机没有足够的内存来处理数组。可能需要减少转换图像的尺寸或者使用更高效的算法。以下是一个可能的修改建议：

import os 
import numpy as np 
import nibabel as nib 
from PIL import Image 

# 创建保存路径 
save_path = 'C:/Users/Administrator/Desktop/2D-LiTS2017' 
if not os.path.exists(save_path):     
    os.makedirs(save_path) 
if not os.path.exists(os.path.join(save_path, 'image')):     
    os.makedirs(os.path.join(save_path, 'image')) 
if not os.path.exists(os.path.join(save_path, 'label')):     
    os.makedirs(os.path.join(save_path, 'label')) 

# 加载数据集 
data_path = 'D:/BaiduNetdiskDownload/LiTS2017' 
img_path = os.path.join(data_path, 'Training Batch 1') 
label_path = os.path.join(data_path, 'Training Batch 2') 

# 转换图像 
for file in sorted(os.listdir(img_path)):     
    if file.endswith('.nii'):         
        img_file = os.path.join(img_path, file)         
        img = nib.load(img_file).get_fdata()         
        img = np.transpose(img, (2, 0, 1))  # 转换为z, x, y         
        for i in range(img.shape[0]):             
            img_slice = img[i, :, :]             
            img_slice = (img_slice - np.min(img_slice)) / (np.max(img_slice) - np.min(img_slice)) * 255  # 归一化到0-255             
            img_slice = img_slice.astype(np.uint8)             
            img_slice = np.stack([img_slice]*3, axis=2)  # 转换为三通道图像
            #减少图像大小
            img_slice = np.array(Image.fromarray(img_slice).resize((512, 512), resample=Image.BICUBIC))             
            img_name = file[:-4] + '' + str(i).zfill(3) + '.png'             
            img_file_save = os.path.join(save_path, 'image', img_name)             
            Image.fromarray(img_slice).save(img_file_save) 

# 转换标签 
for file in sorted(os.listdir(label_path)):     
    if file.endswith('.nii'):         
        label_file = os.path.join(label_path, file)         
        label = nib.load(label_file).get_fdata()         
        label = np.transpose(label, (2, 0, 1))  # 转换为z, x, y         
        for i in range(label.shape[0]):             
            label_slice = label[i, :, :]             
            label_slice[label_slice == 1] = 255  # 肝脏灰度值设为255             
            label_slice[label_slice == 2] = 128  # 肝脏肿瘤灰度值设为128             
            label_slice = label_slice.astype(np.uint8)             
            label_name = file[:-4] + '' + str(i).zfill(3) + '.png'             
            label_file_save = os.path.join(save_path, 'label', label_name)             
            Image.fromarray(label_slice).save(label_file_save)

这里我们使用了 `PIL.Image` 中的 `resize()` 函数来减少图像大小。`resample=Image.BICUBIC` 用于指定插值方法，这里使用了 Bicubic 插值。这个方法可以保留图像的细节，并且可以减少内存使用。你也可以尝试使用其他插值方法。

替换掉此代码里的import cv2模块，import os import numpy as np import nibabel as nib import imageio import cv2 def read_niifile(niifilepath): # 读取niifile文件 img = nib.load(niifilepath) # 提取niifile文件 img_fdata = img.get_fdata(dtype='float32') return img_fdata def save_fig(niifilepath, savepath, num, name): # 保存为图片 name = name.split('-')[1] filepath_seg = niifilepath + "segmentation\" + "segmentation-" + name filepath_vol = niifilepath + "volume\" + "volume-" +name savepath_seg = savepath + "segmentation\" savepath_vol = savepath + "volume\" if not os.path.exists(savepath_seg): os.makedirs(savepath_seg) if not os.path.exists(savepath_vol): os.makedirs(savepath_vol) fdata_vol = read_niifile(filepath_vol) fdata_seg = read_niifile(filepath_seg) (x, y, z) = fdata_seg.shape total = x * y for k in range(z): silce_seg = fdata_seg[:, :, k] # 三个位置表示三个不同角度的切片 if silce_seg.max() == 0: continue else: silce_seg = (silce_seg-silce_seg.min())/(silce_seg.max() - silce_seg.min())*255 silce_seg = cv2.threshold(silce_seg, 1, 255, cv2.THRESH_BINARY)[1] if (np.sum(silce_seg == 255) / total) > 0.015: silce_vol = fdata_vol[:, :, k] silce_vol = (silce_vol - silce_vol.min()) / (silce_vol.max() - silce_vol.min()) * 255 imageio.imwrite(os.path.join(savepath_seg, '{}.png'.format(num)), silce_seg) imageio.imwrite(os.path.join(savepath_vol, '{}.png'.format(num)), silce_vol) num += 1 # 将切片信息保存为png格式 return num if name == 'main': path= 'E:\dataset\LiTS17\' savepath = 'E:\dataset\LiTS17\2d\' filenames = os.listdir(path + "segmentation") num = 0 for filename in filenames: num = save_fig(path, savepath, num, filename)

将import cv2替换为from PIL import Image即可，函数中用到了cv2.threshold函数，可以使用PIL中的ImageOps.autocontrast()函数代替。修改后的代码如下：

import os 
import numpy as np 
import nibabel as nib 
import imageio 
from PIL import Image, ImageOps

def read_niifile(niifilepath):  # 读取niifile文件     
    img = nib.load(niifilepath)  # 提取niifile文件     
    img_fdata = img.get_fdata(dtype='float32')     
    return img_fdata 

def save_fig(niifilepath, savepath, num, name):  # 保存为图片     
    name = name.split('-')[1]     
    filepath_seg = niifilepath + "segmentation\\" + "segmentation-" + name     
    filepath_vol = niifilepath + "volume\\" + "volume-" +name     
    savepath_seg = savepath + "segmentation\\"     
    savepath_vol = savepath + "volume\\"       

    if not os.path.exists(savepath_seg):         
        os.makedirs(savepath_seg)     
    if not os.path.exists(savepath_vol):         
        os.makedirs(savepath_vol)       

    fdata_vol = read_niifile(filepath_vol)     
    fdata_seg = read_niifile(filepath_seg)     
    (x, y, z) = fdata_seg.shape     
    total = x * y       

    for k in range(z):         
        silce_seg = fdata_seg[:, :, k]  # 三个位置表示三个不同角度的切片         
        if silce_seg.max() == 0:             
            continue         
        else:             
            silce_seg = (silce_seg-silce_seg.min())/(silce_seg.max() - silce_seg.min())*255             
            silce_seg  = Image.fromarray(silce_seg.astype(np.uint8)) 
            silce_seg = ImageOps.autocontrast(silce_seg,1) 
            silce_seg = np.array(silce_seg)             

        if  (np.sum(silce_seg == 255) / total) > 0.015:             
            silce_vol = fdata_vol[:, :, k]             
            silce_vol = (silce_vol - silce_vol.min()) / (silce_vol.max() - silce_vol.min()) * 255               
            imageio.imwrite(os.path.join(savepath_seg, '{}.png'.format(num)), silce_seg)             
            imageio.imwrite(os.path.join(savepath_vol, '{}.png'.format(num)), silce_vol)             
            num += 1         
            # 将切片信息保存为png格式     
    return num 

if __name__ == '__main__':       
    path= 'E:\\dataset\\LiTS17\\'     
    savepath = 'E:\\dataset\\LiTS17\\2d\\'     
    filenames = os.listdir(path + "segmentation")     
    num = 0     
    for filename in filenames:         
        num = save_fig(path, savepath, num, filename)