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<h6 id="path-to-notebook-httpswwwgithubcomdata-privacy-stackpresidioblobmaindocssamplespythonplot_custom_bboxesipynb">Path to notebook: <a href="https://www.github.com/data-privacy-stack/presidio/blob/main/docs/samples/python/plot_custom_bboxes.ipynb">https://www.github.com/data-privacy-stack/presidio/blob/main/docs/samples/python/plot_custom_bboxes.ipynb</a></h6>
<h1 id="plot-custom-bounding-boxes">Plot custom bounding boxes</h1>
<p>This notebook covers how to use the image verification engines to plot custom bounding boxes. This can be helpful when you want to verify a provided set of bounding boxes against an image.</p>
<h2 id="prerequisites">Prerequisites</h2>
<p>Before getting started, make sure presidio and the latest version of Tesseract OCR are installed. For detailed documentation, see the <a href="https://data-privacy-stack.github.io/presidio/installation">installation docs</a>.</p>
<div class="highlight"><pre><span></span><code><span class="err">!</span><span class="n">pip</span> <span class="n">install</span> <span class="n">presidio_analyzer</span> <span class="n">presidio_anonymizer</span> <span class="n">presidio_image_redactor</span>
<span class="err">!</span><span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">spacy</span> <span class="n">download</span> <span class="n">en_core_web_lg</span>
</code></pre></div>
<h2 id="0-imports-and-initializations">0. Imports and initializations</h2>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">PIL</span><span class="w"> </span><span class="kn">import</span> <span class="n">Image</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">pydicom</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">presidio_image_redactor</span><span class="w"> </span><span class="kn">import</span> <span class="n">ImageAnalyzerEngine</span><span class="p">,</span> <span class="n">ImagePiiVerifyEngine</span><span class="p">,</span> <span class="n">DicomImagePiiVerifyEngine</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
</code></pre></div>
<p>Initialize engines used for image redaction</p>
<div class="highlight"><pre><span></span><code><span class="c1"># Image analyzer engine</span>
<span class="n">image_analyzer_engine</span> <span class="o">=</span> <span class="n">ImageAnalyzerEngine</span><span class="p">()</span>
<span class="c1"># Verification engines</span>
<span class="n">verify_engine</span> <span class="o">=</span> <span class="n">ImagePiiVerifyEngine</span><span class="p">()</span> <span class="c1"># standard images</span>
<span class="n">dicom_verify_engine</span> <span class="o">=</span> <span class="n">DicomImagePiiVerifyEngine</span><span class="p">()</span> <span class="c1"># DICOM images</span>
<span class="n">padding_width</span> <span class="o">=</span> <span class="mi">3</span>
</code></pre></div>
<h2 id="1-example-images">1. Example images</h2>
<p>In this notebook, we will use the following examples images.</p>
<h3 id="11-standard-example-image">1.1 Standard example image</h3>
<div class="highlight"><pre><span></span><code><span class="n">image</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s2">&quot;../../image-redactor/ocr_text.png&quot;</span><span class="p">)</span>
<span class="n">display</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
</code></pre></div>
<p><img alt="png" src="../plot_custom_bboxes_files/plot_custom_bboxes_9_0.png" /></p>
<p>And this is what the image looks like with the standard, default behavior of the verification engine.</p>
<div class="highlight"><pre><span></span><code><span class="n">verify_image</span> <span class="o">=</span> <span class="n">verify_engine</span><span class="o">.</span><span class="n">verify</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">display_image</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</code></pre></div>
<p><img alt="png" src="../plot_custom_bboxes_files/plot_custom_bboxes_11_0.png" /></p>
<h3 id="12-dicom-medical-image">1.2 DICOM medical image</h3>
<p>For more information on DICOM image redaction, please see <a href=".././example_dicom_image_redactor/">example_dicom_image_redactor.ipynb</a> and the <a href="../../../../image-redactor/">Image redactor module documentation</a>.</p>
<div class="highlight"><pre><span></span><code><span class="n">instance</span> <span class="o">=</span> <span class="n">pydicom</span><span class="o">.</span><span class="n">dcmread</span><span class="p">(</span><span class="s2">&quot;./sample_data/0_ORIGINAL.dcm&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">instance</span><span class="o">.</span><span class="n">pixel_array</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;gray&quot;</span><span class="p">)</span>
</code></pre></div>
<div class="highlight"><pre><span></span><code>&lt;matplotlib.image.AxesImage at 0x1f2a91512b0&gt;
</code></pre></div>
<p><img alt="png" src="../plot_custom_bboxes_files/plot_custom_bboxes_13_1.png" /></p>
<p>And this is what the image looks like with the standard, default behavior verification.</p>
<div class="highlight"><pre><span></span><code><span class="n">dicom_image</span><span class="p">,</span> <span class="n">dicom_ocr_bboxes</span><span class="p">,</span> <span class="n">dicom_analyzer_bboxes</span> <span class="o">=</span> <span class="n">dicom_verify_engine</span><span class="o">.</span><span class="n">verify_dicom_instance</span><span class="p">(</span>
<span class="n">instance</span><span class="p">,</span>
<span class="n">padding_width</span><span class="o">=</span><span class="n">padding_width</span><span class="p">,</span>
<span class="n">display_image</span><span class="o">=</span><span class="kc">True</span>
<span class="p">)</span>
</code></pre></div>
<p><img alt="png" src="../plot_custom_bboxes_files/plot_custom_bboxes_15_0.png" /></p>
<div class="highlight"><pre><span></span><code><span class="nb">len</span><span class="p">(</span><span class="n">dicom_ocr_bboxes</span><span class="p">)</span>
</code></pre></div>
<div class="highlight"><pre><span></span><code>9
</code></pre></div>
<div class="highlight"><pre><span></span><code><span class="nb">len</span><span class="p">(</span><span class="n">dicom_analyzer_bboxes</span><span class="p">)</span>
</code></pre></div>
<div class="highlight"><pre><span></span><code>4
</code></pre></div>
<h2 id="2-plot-custom-bounding-boxes">2. Plot custom bounding boxes</h2>
<p>There may be situations where you want to visually validate whether a set of given bounding boxes match your expectations on an image. In these cases, we can call <code>ImageAnalyzerEngine.add_custom_bbox()</code> instead of using the verify methods which include OCR and text analysis.</p>
<p>Let's look at the format of the analyzer results bounding boxes returned by the DICOM verification engine. This the general format expected of custom bounding boxes passed into <code>add_custom_bbox()</code>.</p>
<div class="highlight"><pre><span></span><code><span class="nb">type</span><span class="p">(</span><span class="n">dicom_analyzer_bboxes</span><span class="p">)</span>
</code></pre></div>
<div class="highlight"><pre><span></span><code>list
</code></pre></div>
<div class="highlight"><pre><span></span><code><span class="nb">type</span><span class="p">(</span><span class="n">dicom_analyzer_bboxes</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
</code></pre></div>
<div class="highlight"><pre><span></span><code>dict
</code></pre></div>
<div class="highlight"><pre><span></span><code><span class="n">dicom_analyzer_bboxes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
</code></pre></div>
<div class="highlight"><pre><span></span><code>{&#39;entity_type&#39;: &#39;PERSON&#39;,
&#39;score&#39;: 0.85,
&#39;left&#39;: 3,
&#39;top&#39;: 3,
&#39;width&#39;: 241,
&#39;height&#39;: 37,
&#39;is_PII&#39;: True}
</code></pre></div>
<p>For our custom bounding boxes, the "entity_type" and "is_PII" fields are optional and "score" is not used. However, the "is_PII" field is helpful in visually identifying which bounding boxes from your given bounding box list are considered PII.</p>
<div class="highlight"><pre><span></span><code><span class="c1"># Example provided bounding box list</span>
<span class="n">given_bboxes</span> <span class="o">=</span> <span class="p">[</span>
<span class="p">{</span>
<span class="s1">&#39;entity_type&#39;</span><span class="p">:</span> <span class="s1">&#39;PERSON&#39;</span><span class="p">,</span>
<span class="s1">&#39;left&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
<span class="s1">&#39;top&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
<span class="s1">&#39;width&#39;</span><span class="p">:</span> <span class="mi">241</span><span class="p">,</span>
<span class="s1">&#39;height&#39;</span><span class="p">:</span> <span class="mi">37</span><span class="p">,</span>
<span class="s1">&#39;is_PII&#39;</span><span class="p">:</span> <span class="kc">True</span>
<span class="p">},</span>
<span class="p">{</span>
<span class="s1">&#39;entity_type&#39;</span><span class="p">:</span> <span class="s1">&#39;PERSON&#39;</span><span class="p">,</span>
<span class="s1">&#39;left&#39;</span><span class="p">:</span> <span class="mi">179</span><span class="p">,</span>
<span class="s1">&#39;top&#39;</span><span class="p">:</span> <span class="mi">150</span><span class="p">,</span>
<span class="s1">&#39;width&#39;</span><span class="p">:</span> <span class="mi">300</span><span class="p">,</span>
<span class="s1">&#39;height&#39;</span><span class="p">:</span> <span class="mi">74</span><span class="p">,</span>
<span class="s1">&#39;is_PII&#39;</span><span class="p">:</span> <span class="kc">False</span>
<span class="p">}</span>
<span class="p">]</span>
</code></pre></div>
<p>Let's plot the given bounding boxes.</p>
<div class="highlight"><pre><span></span><code><span class="c1"># Let&#39;s plot with the given bounding boxes</span>
<span class="n">test_image</span> <span class="o">=</span> <span class="n">image_analyzer_engine</span><span class="o">.</span><span class="n">add_custom_bboxes</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">given_bboxes</span><span class="p">,</span> <span class="n">show_text_annotation</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</code></pre></div>
<p><img alt="png" src="../plot_custom_bboxes_files/plot_custom_bboxes_26_0.png" /></p>
<p>While the placement of our example bounding boxes here is not ideal, this shows how you can easily visualize if a provided set of bounding boxes match your expectations.</p>
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