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<!--- under the License. --><div class="section" id="multi-object-detection-using-pre-trained-ssd-model-via-java-inference-apis">
<span id="multi-object-detection-using-pre-trained-ssd-model-via-java-inference-apis"></span><h1>Multi Object Detection using pre-trained SSD Model via Java Inference APIs<a class="headerlink" href="#multi-object-detection-using-pre-trained-ssd-model-via-java-inference-apis" title="Permalink to this headline"></a></h1>
<p>This tutorial shows how to use MXNet Java Inference APIs to run inference on a pre-trained Single Shot Detector (SSD) Model.</p>
<p>The SSD model is trained on the Pascal VOC 2012 dataset. The network is a SSD model built on Resnet50 as the base network to extract image features. The model is trained to detect the following entities (classes): [‘aeroplane’, ‘bicycle’, ‘bird’, ‘boat’, ‘bottle’, ‘bus’, ‘car’, ‘cat’, ‘chair’, ‘cow’, ‘diningtable’, ‘dog’, ‘horse’, ‘motorbike’, ‘person’, ‘pottedplant’, ‘sheep’, ‘sofa’, ‘train’, ‘tvmonitor’]. For more details about the model, you can refer to the <a class="reference external" href="https://github.com/apache/incubator-mxnet/tree/master/example/ssd">MXNet SSD example</a>.</p>
<div class="section" id="prerequisites">
<span id="prerequisites"></span><h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this headline"></a></h2>
<p>To complete this tutorial, you need the following:</p>
<ul class="simple">
<li><a class="reference external" href="/versions/1.4.1/java/mxnet_java_on_intellij.html">MXNet Java Setup on IntelliJ IDEA</a> (Optional)</li>
<li><a class="reference external" href="https://www.gnu.org/software/wget/">wget</a> To download model artifacts</li>
<li>SSD Model artifacts<ul>
<li>Use the following script to get the SSD Model files :</li>
</ul>
</li>
</ul>
<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="nv">data_path</span><span class="o">=</span>/tmp/resnet50_ssd
mkdir -p <span class="s2">"</span><span class="nv">$data_path</span><span class="s2">"</span>
wget https://s3.amazonaws.com/model-server/models/resnet50_ssd/resnet50_ssd_model-symbol.json -P <span class="nv">$data_path</span>
wget https://s3.amazonaws.com/model-server/models/resnet50_ssd/resnet50_ssd_model-0000.params -P <span class="nv">$data_path</span>
wget https://s3.amazonaws.com/model-server/models/resnet50_ssd/synset.txt -P <span class="nv">$data_path</span>
</pre></div>
</div>
<ul class="simple">
<li>Test images : A few sample images to run inference on.<ul>
<li>Use the following script to download sample images :</li>
</ul>
</li>
</ul>
<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="nv">image_path</span><span class="o">=</span>/tmp/resnet50_ssd/images
mkdir -p <span class="s2">"</span><span class="nv">$image_path</span><span class="s2">"</span>
<span class="nb">cd</span> <span class="nv">$image_path</span>
wget https://cloud.githubusercontent.com/assets/3307514/20012567/cbb60336-a27d-11e6-93ff-cbc3f09f5c9e.jpg -O dog.jpg
wget https://cloud.githubusercontent.com/assets/3307514/20012563/cbb41382-a27d-11e6-92a9-18dab4fd1ad3.jpg -O person.jpg
</pre></div>
</div>
<p>Alternately, you can get the entire SSD Model artifacts + images in one single script from the MXNet Repository by running <a class="reference external" href="https://github.com/apache/incubator-mxnet/blob/1.4.1/scala-package/examples/scripts/infer/objectdetector/get_ssd_data.sh">get_ssd_data.sh script</a></p>
</div>
<div class="section" id="time-to-code">
<span id="time-to-code"></span><h2>Time to code!<a class="headerlink" href="#time-to-code" title="Permalink to this headline"></a></h2>
<p>1. Following the <a class="reference external" href="/versions/1.4.1/java/mxnet_java_on_intellij.html">MXNet Java Setup on IntelliJ IDEA</a> tutorial, in the same project <code class="docutils literal"><span class="pre">JavaMXNet</span></code>, create a new empty class called : <code class="docutils literal"><span class="pre">ObjectDetectionTutorial.java</span></code>.</p>
<p>2. In the <code class="docutils literal"><span class="pre">main</span></code> function of <code class="docutils literal"><span class="pre">ObjectDetectionTutorial.java</span></code> define the downloaded model path and the image data paths. This is the same path where we downloaded the model artifacts and images in a previous step.</p>
<div class="highlight-java"><div class="highlight"><pre><span></span><span class="n">String</span> <span class="n">modelPathPrefix</span> <span class="o">=</span> <span class="s">"/tmp/resnet50_ssd/resnet50_ssd_model"</span><span class="o">;</span>
<span class="n">String</span> <span class="n">inputImagePath</span> <span class="o">=</span> <span class="s">"/tmp/resnet50_ssd/images/dog.jpg"</span><span class="o">;</span>
</pre></div>
</div>
<p>3. We can run the inference code in this example on either CPU or GPU (if you have a GPU backed machine) by choosing the appropriate context.</p>
<div class="highlight-java"><div class="highlight"><pre><span></span>
<span class="n">List</span><span class="o"><</span><span class="n">Context</span><span class="o">></span> <span class="n">context</span> <span class="o">=</span> <span class="n">getContext</span><span class="o">();</span>
<span class="o">...</span>
<span class="kd">private</span> <span class="kd">static</span> <span class="n">List</span><span class="o"><</span><span class="n">Context</span><span class="o">></span> <span class="nf">getContext</span><span class="o">()</span> <span class="o">{</span>
<span class="n">List</span><span class="o"><</span><span class="n">Context</span><span class="o">></span> <span class="n">ctx</span> <span class="o">=</span> <span class="k">new</span> <span class="n">ArrayList</span><span class="o"><>();</span>
<span class="n">ctx</span><span class="o">.</span><span class="na">add</span><span class="o">(</span><span class="n">Context</span><span class="o">.</span><span class="na">cpu</span><span class="o">());</span> <span class="c1">// Choosing CPU Context here</span>
<span class="k">return</span> <span class="n">ctx</span><span class="o">;</span>
<span class="o">}</span>
</pre></div>
</div>
<p>4. To provide an input to the model, define the input shape to the model and the Input Data Descriptor (DataDesc) as shown below :</p>
<div class="highlight-java"><div class="highlight"><pre><span></span><span class="n">Shape</span> <span class="n">inputShape</span> <span class="o">=</span> <span class="k">new</span> <span class="n">Shape</span><span class="o">(</span><span class="k">new</span> <span class="kt">int</span><span class="o">[]</span> <span class="o">{</span><span class="mi">1</span><span class="o">,</span> <span class="mi">3</span><span class="o">,</span> <span class="mi">512</span><span class="o">,</span> <span class="mi">512</span><span class="o">});</span>
<span class="n">List</span><span class="o"><</span><span class="n">DataDesc</span><span class="o">></span> <span class="n">inputDescriptors</span> <span class="o">=</span> <span class="k">new</span> <span class="n">ArrayList</span><span class="o"><</span><span class="n">DataDesc</span><span class="o">>();</span>
<span class="n">inputDescriptors</span><span class="o">.</span><span class="na">add</span><span class="o">(</span><span class="k">new</span> <span class="n">DataDesc</span><span class="o">(</span><span class="s">"data"</span><span class="o">,</span> <span class="n">inputShape</span><span class="o">,</span> <span class="n">DType</span><span class="o">.</span><span class="na">Float32</span><span class="o">(),</span> <span class="s">"NCHW"</span><span class="o">));</span>
</pre></div>
</div>
<p>The input shape can be interpreted as follows : The input has a batch size of 1, with 3 RGB channels in the image, and the height and width of the image is 512 each.</p>
<p>5. To run an actual inference on the given image, add the following lines to the <code class="docutils literal"><span class="pre">ObjectDetectionTutorial.java</span></code> class :</p>
<div class="highlight-java"><div class="highlight"><pre><span></span><span class="n">BufferedImage</span> <span class="n">img</span> <span class="o">=</span> <span class="n">ObjectDetector</span><span class="o">.</span><span class="na">loadImageFromFile</span><span class="o">(</span><span class="n">inputImagePath</span><span class="o">);</span>
<span class="n">ObjectDetector</span> <span class="n">objDet</span> <span class="o">=</span> <span class="k">new</span> <span class="n">ObjectDetector</span><span class="o">(</span><span class="n">modelPathPrefix</span><span class="o">,</span> <span class="n">inputDescriptors</span><span class="o">,</span> <span class="n">context</span><span class="o">,</span> <span class="mi">0</span><span class="o">);</span>
<span class="n">List</span><span class="o"><</span><span class="n">List</span><span class="o"><</span><span class="n">ObjectDetectorOutput</span><span class="o">>></span> <span class="n">output</span> <span class="o">=</span> <span class="n">objDet</span><span class="o">.</span><span class="na">imageObjectDetect</span><span class="o">(</span><span class="n">img</span><span class="o">,</span> <span class="mi">3</span><span class="o">);</span> <span class="c1">// Top 3 objects detected will be returned</span>
</pre></div>
</div>
<p>6. Let’s piece all of the above steps together by showing the final contents of the <code class="docutils literal"><span class="pre">ObjectDetectionTutorial.java</span></code>.</p>
<div class="highlight-java"><div class="highlight"><pre><span></span><span class="kn">package</span> <span class="nn">mxnet</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.mxnet.infer.javaapi.ObjectDetector</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.mxnet.infer.javaapi.ObjectDetectorOutput</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.mxnet.javaapi.Context</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.mxnet.javaapi.DType</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.mxnet.javaapi.DataDesc</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.mxnet.javaapi.Shape</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">java.awt.image.BufferedImage</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">java.util.ArrayList</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">java.util.Arrays</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">java.util.List</span><span class="o">;</span>
<span class="kd">public</span> <span class="kd">class</span> <span class="nc">ObjectDetectionTutorial</span> <span class="o">{</span>
<span class="kd">public</span> <span class="kd">static</span> <span class="kt">void</span> <span class="nf">main</span><span class="o">(</span><span class="n">String</span><span class="o">[]</span> <span class="n">args</span><span class="o">)</span> <span class="o">{</span>
<span class="n">String</span> <span class="n">modelPathPrefix</span> <span class="o">=</span> <span class="s">"/tmp/resnet50_ssd/resnet50_ssd_model"</span><span class="o">;</span>
<span class="n">String</span> <span class="n">inputImagePath</span> <span class="o">=</span> <span class="s">"/tmp/resnet50_ssd/images/dog.jpg"</span><span class="o">;</span>
<span class="n">List</span><span class="o"><</span><span class="n">Context</span><span class="o">></span> <span class="n">context</span> <span class="o">=</span> <span class="n">getContext</span><span class="o">();</span>
<span class="n">Shape</span> <span class="n">inputShape</span> <span class="o">=</span> <span class="k">new</span> <span class="n">Shape</span><span class="o">(</span><span class="k">new</span> <span class="kt">int</span><span class="o">[]</span> <span class="o">{</span><span class="mi">1</span><span class="o">,</span> <span class="mi">3</span><span class="o">,</span> <span class="mi">512</span><span class="o">,</span> <span class="mi">512</span><span class="o">});</span>
<span class="n">List</span><span class="o"><</span><span class="n">DataDesc</span><span class="o">></span> <span class="n">inputDescriptors</span> <span class="o">=</span> <span class="k">new</span> <span class="n">ArrayList</span><span class="o"><</span><span class="n">DataDesc</span><span class="o">>();</span>
<span class="n">inputDescriptors</span><span class="o">.</span><span class="na">add</span><span class="o">(</span><span class="k">new</span> <span class="n">DataDesc</span><span class="o">(</span><span class="s">"data"</span><span class="o">,</span> <span class="n">inputShape</span><span class="o">,</span> <span class="n">DType</span><span class="o">.</span><span class="na">Float32</span><span class="o">(),</span> <span class="s">"NCHW"</span><span class="o">));</span>
<span class="n">BufferedImage</span> <span class="n">img</span> <span class="o">=</span> <span class="n">ObjectDetector</span><span class="o">.</span><span class="na">loadImageFromFile</span><span class="o">(</span><span class="n">inputImagePath</span><span class="o">);</span>
<span class="n">ObjectDetector</span> <span class="n">objDet</span> <span class="o">=</span> <span class="k">new</span> <span class="n">ObjectDetector</span><span class="o">(</span><span class="n">modelPathPrefix</span><span class="o">,</span> <span class="n">inputDescriptors</span><span class="o">,</span> <span class="n">context</span><span class="o">,</span> <span class="mi">0</span><span class="o">);</span>
<span class="n">List</span><span class="o"><</span><span class="n">List</span><span class="o"><</span><span class="n">ObjectDetectorOutput</span><span class="o">>></span> <span class="n">output</span> <span class="o">=</span> <span class="n">objDet</span><span class="o">.</span><span class="na">imageObjectDetect</span><span class="o">(</span><span class="n">img</span><span class="o">,</span> <span class="mi">3</span><span class="o">);</span>
<span class="n">printOutput</span><span class="o">(</span><span class="n">output</span><span class="o">,</span> <span class="n">inputShape</span><span class="o">);</span>
<span class="o">}</span>
<span class="kd">private</span> <span class="kd">static</span> <span class="n">List</span><span class="o"><</span><span class="n">Context</span><span class="o">></span> <span class="nf">getContext</span><span class="o">()</span> <span class="o">{</span>
<span class="n">List</span><span class="o"><</span><span class="n">Context</span><span class="o">></span> <span class="n">ctx</span> <span class="o">=</span> <span class="k">new</span> <span class="n">ArrayList</span><span class="o"><>();</span>
<span class="n">ctx</span><span class="o">.</span><span class="na">add</span><span class="o">(</span><span class="n">Context</span><span class="o">.</span><span class="na">cpu</span><span class="o">());</span>
<span class="k">return</span> <span class="n">ctx</span><span class="o">;</span>
<span class="o">}</span>
<span class="kd">private</span> <span class="kd">static</span> <span class="kt">void</span> <span class="nf">printOutput</span><span class="o">(</span><span class="n">List</span><span class="o"><</span><span class="n">List</span><span class="o"><</span><span class="n">ObjectDetectorOutput</span><span class="o">>></span> <span class="n">output</span><span class="o">,</span> <span class="n">Shape</span> <span class="n">inputShape</span><span class="o">)</span> <span class="o">{</span>
<span class="n">StringBuilder</span> <span class="n">outputStr</span> <span class="o">=</span> <span class="k">new</span> <span class="n">StringBuilder</span><span class="o">();</span>
<span class="kt">int</span> <span class="n">width</span> <span class="o">=</span> <span class="n">inputShape</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="mi">3</span><span class="o">);</span>
<span class="kt">int</span> <span class="n">height</span> <span class="o">=</span> <span class="n">inputShape</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="mi">2</span><span class="o">);</span>
<span class="k">for</span> <span class="o">(</span><span class="n">List</span><span class="o"><</span><span class="n">ObjectDetectorOutput</span><span class="o">></span> <span class="n">ele</span> <span class="o">:</span> <span class="n">output</span><span class="o">)</span> <span class="o">{</span>
<span class="k">for</span> <span class="o">(</span><span class="n">ObjectDetectorOutput</span> <span class="n">i</span> <span class="o">:</span> <span class="n">ele</span><span class="o">)</span> <span class="o">{</span>
<span class="n">outputStr</span><span class="o">.</span><span class="na">append</span><span class="o">(</span><span class="s">"Class: "</span> <span class="o">+</span> <span class="n">i</span><span class="o">.</span><span class="na">getClassName</span><span class="o">()</span> <span class="o">+</span> <span class="s">"\n"</span><span class="o">);</span>
<span class="n">outputStr</span><span class="o">.</span><span class="na">append</span><span class="o">(</span><span class="s">"Probabilties: "</span> <span class="o">+</span> <span class="n">i</span><span class="o">.</span><span class="na">getProbability</span><span class="o">()</span> <span class="o">+</span> <span class="s">"\n"</span><span class="o">);</span>
<span class="n">List</span><span class="o"><</span><span class="n">Float</span><span class="o">></span> <span class="n">coord</span> <span class="o">=</span> <span class="n">Arrays</span><span class="o">.</span><span class="na">asList</span><span class="o">(</span><span class="n">i</span><span class="o">.</span><span class="na">getXMin</span><span class="o">()</span> <span class="o">*</span> <span class="n">width</span><span class="o">,</span>
<span class="n">i</span><span class="o">.</span><span class="na">getXMax</span><span class="o">()</span> <span class="o">*</span> <span class="n">height</span><span class="o">,</span> <span class="n">i</span><span class="o">.</span><span class="na">getYMin</span><span class="o">()</span> <span class="o">*</span> <span class="n">width</span><span class="o">,</span> <span class="n">i</span><span class="o">.</span><span class="na">getYMax</span><span class="o">()</span> <span class="o">*</span> <span class="n">height</span><span class="o">);</span>
<span class="n">StringBuilder</span> <span class="n">sb</span> <span class="o">=</span> <span class="k">new</span> <span class="n">StringBuilder</span><span class="o">();</span>
<span class="k">for</span> <span class="o">(</span><span class="kt">float</span> <span class="n">c</span><span class="o">:</span> <span class="n">coord</span><span class="o">)</span> <span class="o">{</span>
<span class="n">sb</span><span class="o">.</span><span class="na">append</span><span class="o">(</span><span class="s">", "</span><span class="o">).</span><span class="na">append</span><span class="o">(</span><span class="n">c</span><span class="o">);</span>
<span class="o">}</span>
<span class="n">outputStr</span><span class="o">.</span><span class="na">append</span><span class="o">(</span><span class="s">"Coord:"</span> <span class="o">+</span> <span class="n">sb</span><span class="o">.</span><span class="na">substring</span><span class="o">(</span><span class="mi">2</span><span class="o">)+</span> <span class="s">"\n"</span><span class="o">);</span>
<span class="o">}</span>
<span class="o">}</span>
<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="n">outputStr</span><span class="o">);</span>
<span class="o">}</span>
<span class="o">}</span>
</pre></div>
</div>
<p>7. To compile and run this code, change directories to this project’s root folder, then run the following:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>mvn clean install dependency:copy-dependencies
</pre></div>
</div>
<p>The build generates a new jar file in the <code class="docutils literal"><span class="pre">target</span></code> folder called <code class="docutils literal"><span class="pre">javaMXNet-1.0-SNAPSHOT.jar</span></code>.</p>
<p>To run the ObjectDetectionTutorial.java use the following command from the project’s root folder.</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>java -cp <span class="s2">"target/javaMXNet-1.0-SNAPSHOT.jar:target/dependency/*"</span> mxnet.ObjectDetectionTutorial
</pre></div>
</div>
<p>You should see a similar output being generated for the dog image that we used:</p>
<div class="highlight-bash"><div class="highlight"><pre><span></span>Class: car
Probabilties: <span class="m">0</span>.99847263
Coord:312.21335, <span class="m">72</span>.02908, <span class="m">456</span>.01443, <span class="m">150</span>.66176
Class: bicycle
Probabilties: <span class="m">0</span>.9047381
Coord:155.9581, <span class="m">149</span>.96365, <span class="m">383</span>.83694, <span class="m">418</span>.94516
Class: dog
Probabilties: <span class="m">0</span>.82268167
Coord:83.82356, <span class="m">179</span>.14001, <span class="m">206</span>.63783, <span class="m">476</span>.78754
</pre></div>
</div>
<p><img alt="dog_1" src="https://cloud.githubusercontent.com/assets/3307514/20012567/cbb60336-a27d-11e6-93ff-cbc3f09f5c9e.jpg"/></p>
<p>The results returned by the inference call translate into the regions in the image where the model detected objects.</p>
<p><img alt="dog_2" src="https://cloud.githubusercontent.com/assets/3307514/19171063/91ec2792-8be0-11e6-983c-773bd6868fa8.png"/></p>
</div>
<div class="section" id="next-steps">
<span id="next-steps"></span><h2>Next Steps<a class="headerlink" href="#next-steps" title="Permalink to this headline"></a></h2>
<p>For more information about MXNet Java resources, see the following:</p>
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<li class="toctree-l1"><a class="reference external" href="/versions/1.4.1/tutorials/index.html">MXNet Tutorials Index</a></li>
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<li><a class="reference internal" href="#">Multi Object Detection using pre-trained SSD Model via Java Inference APIs</a><ul>
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