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<h1 id="making-predictions-using-image-classifier-tensorflow">Making Predictions using Image Classifier (TensorFlow)</h1>
<p><em>This was tested on TF 2.x and works as of 2019-12-10</em></p>
<p>If you want to understand how to make your own custom image classifier, please refer to my previous post.</p>
<p>If you followed my last post, then you created a model which took an image of dimensions 50x50 as an input.</p>
<p>First we import the following if we have not imported these before</p>
<div class="codehilite">
<pre><span></span><code><span class="kn">import</span> <span class="nn">cv2</span>
<span class="kn">import</span> <span class="nn">os</span>
</code></pre>
</div>
<p>Then we read the file using OpenCV.</p>
<div class="codehilite">
<pre><span></span><code><span class="n">image</span><span class="o">=</span><span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">imagePath</span><span class="p">)</span>
</code></pre>
</div>
<p>The cv2. imread() function returns a NumPy array representing the image. Therefore, we need to convert it before we can use it.</p>
<div class="codehilite">
<pre><span></span><code><span class="n">image_from_array</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="s1">'RGB'</span><span class="p">)</span>
</code></pre>
</div>
<p>Then we resize the image</p>
<div class="codehilite">
<pre><span></span><code><span class="n">size_image</span> <span class="o">=</span> <span class="n">image_from_array</span><span class="o">.</span><span class="n">resize</span><span class="p">((</span><span class="mi">50</span><span class="p">,</span><span class="mi">50</span><span class="p">))</span>
</code></pre>
</div>
<p>After this we create a batch consisting of only one image</p>
<div class="codehilite">
<pre><span></span><code><span class="n">p</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">size_image</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
</code></pre>
</div>
<p>We then convert this uint8 datatype to a float32 datatype</p>
<div class="codehilite">
<pre><span></span><code><span class="n">img</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
</code></pre>
</div>
<p>Finally we make the prediction</p>
<div class="codehilite">
<pre><span></span><code><span class="nb">print</span><span class="p">([</span><span class="s1">'Infected'</span><span class="p">,</span><span class="s1">'Uninfected'</span><span class="p">][</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">img</span><span class="p">))])</span>
</code></pre>
</div>
<p><code>Infected</code></p>
</div>
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