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| <section id="quickstart"> | ||
| <span id="quick-start"></span><h1>Quickstart<a class="headerlink" href="#quickstart" title="Permalink to this heading"></a></h1> | ||
| <section id="installation"> | ||
| <h2>Installation<a class="headerlink" href="#installation" title="Permalink to this heading"></a></h2> | ||
| <p>Create an Anaconda environment, and install <a class="reference external" href="https://pytorch.org/get-started/locally/">torch</a> (with GPU | ||
| if you need training besides prediction). Check the following environment are installed as well | ||
| (use Python 3.11 if you work with SPIMquant environment):</p> | ||
| <p>Then <code class="code docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">cvpl_tools</span></code> to finish the installation.</p> | ||
| </section> | ||
| <section id="ome-zarr"> | ||
| <h2>OME ZARR<a class="headerlink" href="#ome-zarr" title="Permalink to this heading"></a></h2> | ||
| <p>Create an example OME ZARR, write it to disk and read back:</p> | ||
| <p>Read and write can be done on network location. An example of read and display:</p> | ||
| </section> | ||
| <section id="nn-unet"> | ||
| <h2>nn-UNet<a class="headerlink" href="#nn-unet" title="Permalink to this heading"></a></h2> | ||
| <p>Download the training (o22)/testing (o23) annotations from Google Drive as follows:</p> | ||
| <ol class="arabic simple"> | ||
| <li><p><a class="reference external" href="https://drive.google.com/file/d/1S8xNdWD1pznAMaKsrjncBySkHVO5b3HR/view?usp=drive_link">o22</a></p></li> | ||
| <li><p><a class="reference external" href="https://drive.google.com/file/d/1xhxLA0RnxoL3c1ojSGTCAIB5yp-Wfrgh/view?usp=drive_link">o23</a></p></li> | ||
| </ol> | ||
| <p>Put the canvas_o22.tiff file in the same folder as your script, then pair o22 annotation tiff file with the | ||
| corresponding training input image volume and start training:</p> | ||
| <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">cvpl_tools.examples.mousebrain_processing</span> <span class="kn">import</span> <span class="n">main</span><span class="p">,</span> <span class="n">get_subject</span> | ||
| <span class="kn">import</span> <span class="nn">cvpl_tools.nnunet.triplanar</span> <span class="k">as</span> <span class="nn">triplanar</span> | ||
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| <span class="n">SUBJECT_ID</span> <span class="o">=</span> <span class="s1">'o22'</span> | ||
| <span class="n">SUBJECTS_DIR</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">'subjects'</span> | ||
| <span class="n">NNUNET_CACHE_DIR</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">'nnunet_250epoch'</span> | ||
| <span class="n">subject</span> <span class="o">=</span> <span class="n">get_subject</span><span class="p">(</span><span class="n">SUBJECT_ID</span><span class="p">,</span> <span class="n">SUBJECTS_DIR</span><span class="p">,</span> <span class="n">NNUNET_CACHE_DIR</span><span class="p">)</span> | ||
| <span class="n">main</span><span class="p">(</span><span class="n">subject</span><span class="o">=</span><span class="n">subject</span><span class="p">,</span> <span class="n">run_nnunet</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">run_coiled_process</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> | ||
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| <span class="n">train_args</span> <span class="o">=</span> <span class="p">{</span> | ||
| <span class="s2">"cache_url"</span><span class="p">:</span> <span class="n">NNUNET_CACHE_DIR</span><span class="p">,</span> | ||
| <span class="s2">"train_im"</span><span class="p">:</span> <span class="n">subject</span><span class="o">.</span><span class="n">SECOND_DOWNSAMPLE_CORR_PATH</span><span class="p">,</span> <span class="c1"># image</span> | ||
| <span class="s2">"train_seg"</span><span class="p">:</span> <span class="s1">'canvas_o22.tiff'</span><span class="p">,</span> <span class="c1"># label</span> | ||
| <span class="s2">"nepoch"</span><span class="p">:</span> <span class="mi">250</span><span class="p">,</span> | ||
| <span class="s2">"stack_channels"</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> | ||
| <span class="s2">"triplanar"</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span> | ||
| <span class="s2">"dataset_id"</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> | ||
| <span class="s2">"fold"</span><span class="p">:</span> <span class="s1">'0'</span><span class="p">,</span> | ||
| <span class="s2">"max_threshold"</span><span class="p">:</span> <span class="mf">7500.</span><span class="p">,</span> | ||
| <span class="p">}</span> | ||
| <span class="n">triplanar</span><span class="o">.</span><span class="n">train_triplanar</span><span class="p">(</span><span class="n">train_args</span><span class="p">)</span> | ||
| </pre></div> | ||
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