datasets/pr_8147/en/_app/immutable/nodes/30.cf39dfd5.js

import{s as se,n as le,o as ne}from"../chunks/scheduler.d75c11ed.js";import{S as ie,i as pe,e as i,s as l,c as o,h as oe,a as p,d as a,b as n,f as ae,g as r,j as d,k as Yt,l as re,m as s,n as c,t as f,o as m,p as h}from"../chunks/index.4ec9dfe9.js";import{C as de,H as P,E as ce}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.26f315d8.js";import{C as M}from"../chunks/CodeBlock.8762ccff.js";function fe(zt){let y,tt,K,et,w,at,T,st,U,Bt="This page shows how to create and share a dataset of medical images in NIfTI format (.nii / .nii.gz) using the <code>datasets</code> library.",lt,j,Qt="You can share a dataset with your team or with anyone in the community by creating a dataset repository on the Hugging Face Hub:",nt,J,it,b,St="There are two common ways to create a NIfTI dataset:",pt,g,Xt="<li>Create a dataset from local NIfTI files in Python and upload it with <code>Dataset.push_to_hub</code>.</li> <li>Use a folder-based convention (one file per example) and a small helper to convert it into a <code>Dataset</code>.</li>",ot,u,Vt='<p>You can control access to your dataset by requiring users to share their contact information first. Check out the <a href="https://huggingface.co/docs/hub/datasets-gated" rel="nofollow">Gated datasets</a> guide for more information.</p>',rt,$,dt,I,Et="If you already have a list of file paths to NIfTI files, the easiest workflow is to create a <code>Dataset</code> from that list and cast the column to the <code>Nifti</code> feature.",ct,Z,ft,G,Ft="The <code>Nifti</code> feature supports a <code>decode</code> parameter. When <code>decode=True</code> (the default), it loads the NIfTI file into a <code>nibabel.nifti1.Nifti1Image</code> object. You can access the image data as a numpy array with <code>img.get_fdata()</code>. When <code>decode=False</code>, it returns a dict with the file path and bytes.",mt,k,ht,_,qt="After preparing the dataset you can push it to the Hub:",yt,C,Mt,R,Ht="This will create a dataset repository containing your NIfTI dataset with a <code>data/</code> folder of parquet shards.",ut,v,wt,N,Lt="If you organize your dataset in folders you can create splits automatically (train/test/validation) by following a structure like:",Tt,W,Ut,x,At="If you have labels or other metadata, provide a <code>metadata.csv</code>, <code>metadata.jsonl</code>, or <code>metadata.parquet</code> in the folder so files can be linked to metadata rows. The metadata must contain a <code>file_name</code> (or <code>*_file_name</code>) field with the relative path to the NIfTI file next to the metadata file.",jt,Y,Dt="Example <code>metadata.csv</code>:",Jt,z,bt,B,Pt=`The <code>Nifti</code> feature works with zipped datasets too — each zip can contain NIfTI files and a metadata file. This is useful when uploading large datasets as archives.
This means your dataset structure could look like this (mixed compressed and uncompressed files):`,gt,Q,$t,S,It,X,Kt='Use the <a href="/docs/datasets/pr_8147/en/package_reference/main_classes#datasets.Dataset.set_transform">set_transform()</a> function to apply the transformation on-the-fly to batches of the dataset:',Zt,V,Gt,E,Ot="Accessing elements now (e.g. <code>ds[0]</code>) will yield torch tensors in the <code>&quot;nifti_torch&quot;</code> key.",kt,F,_t,q,te=`NifTI is a format to store the result of 3 (or even 4) dimensional brain scans. This includes 3 spatial dimensions (x,y,z)
and optionally a time dimension (t). Furthermore, the given positions here are only relative to the scanner, therefore
the dimensions (4, 5, 6) are used to lift this to real world coordinates.`,Ct,H,ee="You can visualize nifti files for instance leveraging <code>matplotlib</code> as follows:",Rt,L,vt,A,Nt,D,Wt,O,xt;return w=new de({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),T=new P({props:{title:"Create a NIfTI dataset",local:"create-a-nifti-dataset",headingTag:"h1"}}),J=new M({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwbG9hZF9kYXRhc2V0JTBBJTBBZGF0YXNldCUyMCUzRCUyMGxvYWRfZGF0YXNldCglMjIlM0N1c2VybmFtZSUzRSUyRm15X25pZnRpX2RhdGFzZXQlMjIp",highlighted:`<span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

dataset = load_dataset(<span class="hljs-string">&quot;&lt;username&gt;/my_nifti_dataset&quot;</span>)`,wrap:!1}}),$=new P({props:{title:"Local files",local:"local-files",headingTag:"h2"}}),Z=new M({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> Dataset
<span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> Nifti

<span class="hljs-comment"># simple example: create a dataset from file paths</span>
files = [<span class="hljs-string">&quot;/path/to/scan_001.nii.gz&quot;</span>, <span class="hljs-string">&quot;/path/to/scan_002.nii.gz&quot;</span>]
ds = Dataset.from_dict({<span class="hljs-string">&quot;nifti&quot;</span>: files}).cast_column(<span class="hljs-string">&quot;nifti&quot;</span>, Nifti())

<span class="hljs-comment"># access a decoded nibabel image (if decode=True)</span>
<span class="hljs-comment"># ds[0][&quot;nifti&quot;] will be a nibabel.Nifti1Image object when decode=True</span>
<span class="hljs-comment"># or a dict {&#x27;bytes&#x27;: None, &#x27;path&#x27;: &#x27;...&#x27;} when decode=False</span>`,wrap:!1}}),k=new M({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwRGF0YXNldCUyQyUyME5pZnRpJTBBJTBBZHMlMjAlM0QlMjBEYXRhc2V0LmZyb21fZGljdCglN0IlMjJuaWZ0aSUyMiUzQSUyMCU1QiUyMiUyRnBhdGglMkZ0byUyRnNjYW4ubmlpLmd6JTIyJTVEJTdEKS5jYXN0X2NvbHVtbiglMjJuaWZ0aSUyMiUyQyUyME5pZnRpKGRlY29kZSUzRFRydWUpKSUwQWltZyUyMCUzRCUyMGRzJTVCMCU1RCU1QiUyMm5pZnRpJTIyJTVEJTIwJTIwJTIzJTIwaW5zdGFuY2UlMjBvZiUzQSUyMG5pYmFiZWwubmlmdGkxLk5pZnRpMUltYWdlJTBBYXJyJTIwJTNEJTIwaW1nLmdldF9mZGF0YSgp",highlighted:`<span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> Dataset, Nifti

ds = Dataset.from_dict({<span class="hljs-string">&quot;nifti&quot;</span>: [<span class="hljs-string">&quot;/path/to/scan.nii.gz&quot;</span>]}).cast_column(<span class="hljs-string">&quot;nifti&quot;</span>, Nifti(decode=<span class="hljs-literal">True</span>))
img = ds[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;nifti&quot;</span>]  <span class="hljs-comment"># instance of: nibabel.nifti1.Nifti1Image</span>
arr = img.get_fdata()`,wrap:!1}}),C=new M({props:{code:"ZHMucHVzaF90b19odWIoJTIyJTNDdXNlcm5hbWUlM0UlMkZteV9uaWZ0aV9kYXRhc2V0JTIyKQ==",highlighted:'ds.push_to_hub(<span class="hljs-string">&quot;&lt;username&gt;/my_nifti_dataset&quot;</span>)',wrap:!1}}),v=new P({props:{title:"Folder conventions and metadata",local:"folder-conventions-and-metadata",headingTag:"h2"}}),W=new M({props:{code:"ZGF0YXNldCUyRnRyYWluJTJGc2Nhbl8wMDAxLm5paSUwQWRhdGFzZXQlMkZ0cmFpbiUyRnNjYW5fMDAwMi5uaWklMEFkYXRhc2V0JTJGdmFsaWRhdGlvbiUyRnNjYW5fMTAwMS5uaWklMEFkYXRhc2V0JTJGdGVzdCUyRnNjYW5fMjAwMS5uaWk=",highlighted:`dataset<span class="hljs-regexp">/train/</span>scan_0001.nii
dataset<span class="hljs-regexp">/train/</span>scan_0002.nii
dataset<span class="hljs-regexp">/validation/</span>scan_1001.nii
dataset<span class="hljs-regexp">/test/</span>scan_2001.nii`,wrap:!1}}),z=new M({props:{code:"ZmlsZV9uYW1lJTJDcGF0aWVudF9pZCUyQ2FnZSUyQ2RpYWdub3NpcyUwQXNjYW5fMDAwMS5uaWkuZ3olMkNQMDAxJTJDNDUlMkNoZWFsdGh5JTBBc2Nhbl8wMDAyLm5paS5neiUyQ1AwMDIlMkM1OSUyQ2Rpc2Vhc2VfeA==",highlighted:`file_name,patient_id,age,diagnosis
scan_0001<span class="hljs-selector-class">.nii</span><span class="hljs-selector-class">.gz</span>,P001,<span class="hljs-number">45</span>,healthy
scan_0002<span class="hljs-selector-class">.nii</span><span class="hljs-selector-class">.gz</span>,P002,<span class="hljs-number">59</span>,disease_x`,wrap:!1}}),Q=new M({props:{code:"ZGF0YXNldCUyRnRyYWluJTJGc2Nhbl8wMDAxLm5paS5neiUwQWRhdGFzZXQlMkZ0cmFpbiUyRnNjYW5fMDAwMi5uaWklMEFkYXRhc2V0JTJGdmFsaWRhdGlvbiUyRnNjYW5fMTAwMS5uaWkuZ3olMEFkYXRhc2V0JTJGdGVzdCUyRnNjYW5fMjAwMS5uaWk=",highlighted:`dataset<span class="hljs-regexp">/train/</span>scan_0001.nii.gz
dataset<span class="hljs-regexp">/train/</span>scan_0002.nii
dataset<span class="hljs-regexp">/validation/</span>scan_1001.nii.gz
dataset<span class="hljs-regexp">/test/</span>scan_2001.nii`,wrap:!1}}),S=new P({props:{title:"Converting to PyTorch tensors",local:"converting-to-pytorch-tensors",headingTag:"h2"}}),V=new M({props:{code:"aW1wb3J0JTIwdG9yY2glMjAlMEFpbXBvcnQlMjBuaWJhYmVsJTBBaW1wb3J0JTIwbnVtcHklMjBhcyUyMG5wJTBBJTBBZGVmJTIwdHJhbnNmb3JtX3RvX3B5dG9yY2goZXhhbXBsZSklM0ElMEElMjAlMjAlMjAlMjBleGFtcGxlJTVCJTIybmlmdGlfdG9yY2glMjIlNUQlMjAlM0QlMjAlNUJ0b3JjaC50ZW5zb3IoZXguZ2V0X2ZkYXRhKCkpJTIwZm9yJTIwZXglMjBpbiUyMGV4YW1wbGUlNUIlMjJuaWZ0aSUyMiU1RCU1RCUwQSUyMCUyMCUyMCUyMHJldHVybiUyMGV4YW1wbGUlMEElMEFkcy5zZXRfdHJhbnNmb3JtKHRyYW5zZm9ybV90b19weXRvcmNoKSUwQQ==",highlighted:`<span class="hljs-keyword">import</span> torch 
<span class="hljs-keyword">import</span> nibabel
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np

<span class="hljs-keyword">def</span> <span class="hljs-title function_">transform_to_pytorch</span>(<span class="hljs-params">example</span>):
    example[<span class="hljs-string">&quot;nifti_torch&quot;</span>] = [torch.tensor(ex.get_fdata()) <span class="hljs-keyword">for</span> ex <span class="hljs-keyword">in</span> example[<span class="hljs-string">&quot;nifti&quot;</span>]]
    <span class="hljs-keyword">return</span> example

ds.set_transform(transform_to_pytorch)
`,wrap:!1}}),F=new P({props:{title:"Usage of NifTI1Image",local:"usage-of-nifti1image",headingTag:"h2"}}),L=new M({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> matplotlib.pyplot <span class="hljs-keyword">as</span> plt
<span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-keyword">def</span> <span class="hljs-title function_">show_slices</span>(<span class="hljs-params">slices</span>):
   <span class="hljs-string">&quot;&quot;&quot; Function to display row of image slices &quot;&quot;&quot;</span>
   fig, axes = plt.subplots(<span class="hljs-number">1</span>, <span class="hljs-built_in">len</span>(slices))
   <span class="hljs-keyword">for</span> i, <span class="hljs-built_in">slice</span> <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(slices):
       axes[i].imshow(<span class="hljs-built_in">slice</span>.T, cmap=<span class="hljs-string">&quot;gray&quot;</span>, origin=<span class="hljs-string">&quot;lower&quot;</span>)

nifti_ds = load_dataset(<span class="hljs-string">&quot;&lt;username&gt;/my_nifti_dataset&quot;</span>)
<span class="hljs-keyword">for</span> epi_img <span class="hljs-keyword">in</span> nifti_ds:
    nifti_img = epi_img[<span class="hljs-string">&quot;nifti&quot;</span>].get_fdata()
    show_slices([nifti_img[:, :, <span class="hljs-number">16</span>], nifti_img[<span class="hljs-number">26</span>, :, :], nifti_img[:, <span class="hljs-number">30</span>, :]])
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