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| author | Navan Chauhan <navanchauhan@gmail.com> | 2021-05-26 23:59:17 +0530 |
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| committer | Navan Chauhan <navanchauhan@gmail.com> | 2021-05-26 23:59:17 +0530 |
| commit | dfc509b95ff03d0c9027ee74d31d7b171f867bf1 (patch) | |
| tree | 4a7bfc10cc0e8c1cf41a0d50d9ec30d5f97d95e5 /docs/posts/2019-12-10-TensorFlow-Model-Prediction.html | |
| parent | d8b9e9c58b1ee676b4eaf605d94b38325775d12c (diff) | |
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diff --git a/docs/posts/2019-12-10-TensorFlow-Model-Prediction.html b/docs/posts/2019-12-10-TensorFlow-Model-Prediction.html new file mode 100644 index 0000000..46eb777 --- /dev/null +++ b/docs/posts/2019-12-10-TensorFlow-Model-Prediction.html @@ -0,0 +1,76 @@ +<!DOCTYPE html> +<html lang="en"> +<head> + + <link rel="stylesheet" href="/assets/main.css" /> + <link rel="stylesheet" href="/assets/sakura.css" /> + <meta charset="utf-8"> + <meta name="viewport" content="width=device-width, initial-scale=1.0"> + <title>Hey - Post</title> + +</head> +<body> + <nav style="display: block;"> +| +<a href="/">home</a> | +<a href="/about/">about/links</a> | +<a href="/posts/">posts</a> | +<a href="/publications/">publications</a> | +<a href="/repo/">iOS repo</a> | +<a href="/feed.rss">RSS Feed</a> | +</nav> + +<main> + <h1>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> + +</main> + + +<script src="assets/manup.min.js"></script> +<script src="/pwabuilder-sw-register.js"></script> +</body> +</html>
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