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<li class="toctree-l4"><a class="reference internal" href="../../../getting-started/crash-course/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li>
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<li class="toctree-l5"><a class="reference internal" href="datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li>
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<li class="toctree-l1 current"><a class="reference internal" href="../../../index.html">Python Tutorials</a><ul class="current">
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<li class="toctree-l3"><a class="reference internal" href="../../../getting-started/crash-course/index.html">Crash Course</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../getting-started/crash-course/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../getting-started/crash-course/2-nn.html">Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../getting-started/crash-course/4-train.html">Train the neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../getting-started/crash-course/6-use_gpus.html">Use GPUs</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../getting-started/logistic_regression_explained.html">Logistic regression explained</a></li>
<li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li>
<li class="toctree-l5"><a class="reference internal" href="../blocks/hybridize.html">Hybridize</a></li>
<li class="toctree-l5"><a class="reference internal" href="../blocks/init.html">Initialization</a></li>
<li class="toctree-l5"><a class="reference internal" href="../blocks/naming.html">Parameter and Block Naming</a></li>
<li class="toctree-l5"><a class="reference internal" href="../blocks/nn.html">Layers and Blocks</a></li>
<li class="toctree-l5"><a class="reference internal" href="../blocks/parameters.html">Parameter Management</a></li>
<li class="toctree-l5"><a class="reference internal" href="../blocks/save_load_params.html">Saving and Loading Gluon Models</a></li>
<li class="toctree-l5"><a class="reference internal" href="../blocks/activations/activations.html">Activation Blocks</a></li>
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<li class="toctree-l5"><a class="reference internal" href="#Color-Augmentation">Color Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="#Composed-Augmentations">Composed Augmentations</a></li>
<li class="toctree-l5"><a class="reference internal" href="datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
<li class="toctree-l5"><a class="reference internal" href="datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
<li class="toctree-l5"><a class="reference internal" href="datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
<li class="toctree-l5"><a class="reference internal" href="datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
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<li class="toctree-l5"><a class="reference internal" href="../image/mnist.html">Handwritten Digit Recognition</a></li>
<li class="toctree-l5"><a class="reference internal" href="../image/pretrained_models.html">Using pre-trained models in MXNet</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li>
<li class="toctree-l5"><a class="reference internal" href="../loss/loss.html">Loss functions</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../text/transformer.html">Machine Translation with Transformer</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../training/learning_rates/index.html">Learning Rates</a><ul>
<li class="toctree-l6"><a class="reference internal" href="../training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li>
<li class="toctree-l6"><a class="reference internal" href="../training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li>
<li class="toctree-l6"><a class="reference internal" href="../training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../training/normalization/index.html">Normalization Blocks</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../kvstore/kvstore.html">Distributed Key-Value Store</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../ndarray/02-ndarray-operations.html">NDArray Operations</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../ndarray/sparse/index.html">Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../onnx/super_resolution.html">Importing an ONNX model into MXNet</a></li>
<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../viz/index.html">Visualization</a><ul>
<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../../performance/index.html">Performance</a><ul>
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<li class="toctree-l4"><a class="reference internal" href="../../../performance/compression/int8.html">Deploy with int-8</a></li>
<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li>
<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li>
<li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li>
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<!--- Licensed to the Apache Software Foundation (ASF) under one --><!--- or more contributor license agreements. See the NOTICE file --><!--- distributed with this work for additional information --><!--- regarding copyright ownership. The ASF licenses this file --><!--- to you under the Apache License, Version 2.0 (the --><!--- "License"); you may not use this file except in compliance --><!--- with the License. You may obtain a copy of the License at --><!--- http://www.apache.org/licenses/LICENSE-2.0 --><!--- Unless required by applicable law or agreed to in writing, --><!--- software distributed under the License is distributed on an --><!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY --><!--- KIND, either express or implied. See the License for the --><!--- specific language governing permissions and limitations --><!--- under the License. --><div class="section" id="Image-Augmentation">
<h1>Image Augmentation<a class="headerlink" href="#Image-Augmentation" title="Permalink to this headline"></a></h1>
<p>Augmentation is the process of randomly adjusting the dataset samples used for training. As a result, a greater diversity of samples will be seen by the network and it is therefore less likely to overfit the training dataset. Some of the spurious characteristics of the dataset can be reduced using this technique. One example would be a dataset of images from the same camera having the same color tint: it’s unhelpful when you want to apply this model to images from other cameras. You can avoid
this by randomly shifting the colours of each image slightly and training your network on these augmented images.</p>
<p>Although this technique can be applied in a variety of domains, it’s very common in Computer Vision, and we will focus on image augmentations in this tutorial. Some example image augmentations include random crops and flips, and adjustments to the brightness and contrast.</p>
<div class="section" id="What-are-the-prerequisites?">
<h2>What are the prerequisites?<a class="headerlink" href="#What-are-the-prerequisites?" title="Permalink to this headline"></a></h2>
<p>You should be familiar with the concept of a transform and how to apply it to a dataset before reading this tutorial. Check out the <a href="#id1"><span class="problematic" id="id2">`Data Transforms tutorial &lt;&gt;`__</span></a> if this is new to you or you need a quick refresher.</p>
</div>
<div class="section" id="Where-can-I-find-the-augmentation-transforms?">
<h2>Where can I find the augmentation transforms?<a class="headerlink" href="#Where-can-I-find-the-augmentation-transforms?" title="Permalink to this headline"></a></h2>
<p>You can find them in the <code class="docutils literal notranslate"><span class="pre">mxnet.gluon.data.vision.transforms</span></code> module, alongside the deterministic transforms we’ve seen previously, such as <code class="docutils literal notranslate"><span class="pre">ToTensor</span></code>, <code class="docutils literal notranslate"><span class="pre">Normalize</span></code>, <code class="docutils literal notranslate"><span class="pre">CenterCrop</span></code> and <code class="docutils literal notranslate"><span class="pre">Resize</span></code>. Augmentations involve an element of randomness and all the augmentation transforms are prefixed with <code class="docutils literal notranslate"><span class="pre">Random</span></code>, such as <code class="docutils literal notranslate"><span class="pre">RandomResizedCrop</span></code> and <code class="docutils literal notranslate"><span class="pre">RandomBrightness</span></code>. We’ll start by importing MXNet and the <code class="docutils literal notranslate"><span class="pre">transforms</span></code>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">mxnet</span> <span class="k">as</span> <span class="nn">mx</span>
<span class="kn">from</span> <span class="nn">mxnet.gluon.data.vision</span> <span class="kn">import</span> <span class="n">transforms</span>
</pre></div>
</div>
</div>
<div class="section" id="Sample-Image">
<h2>Sample Image<a class="headerlink" href="#Sample-Image" title="Permalink to this headline"></a></h2>
<p>So that we can see the effects of all the vision augmentations, we’ll take a sample image of a giraffe and apply various augmentations to it. We can see what it looks like to begin with.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">image_url</span> <span class="o">=</span> <span class="s1">&#39;https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/inputs/0.jpg&#39;</span>
<span class="n">mx</span><span class="o">.</span><span class="n">test_utils</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="n">image_url</span><span class="p">,</span> <span class="s2">&quot;giraffe.jpg&quot;</span><span class="p">)</span>
<span class="n">example_image</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="s2">&quot;giraffe.jpg&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">example_image</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
</pre></div>
</div>
<img alt="png" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_5_1.png" />
<p>Since these augmentations are random, we’ll apply the same augmentation a few times and plot all of the outputs. We define a few utility functions to help with this.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">show_images</span><span class="p">(</span><span class="n">imgs</span><span class="p">,</span> <span class="n">num_rows</span><span class="p">,</span> <span class="n">num_cols</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
<span class="c1"># show augmented images in a grid layout</span>
<span class="n">aspect_ratio</span> <span class="o">=</span> <span class="n">imgs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">/</span><span class="n">imgs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="n">num_cols</span> <span class="o">*</span> <span class="n">scale</span><span class="p">,</span> <span class="n">num_rows</span> <span class="o">*</span> <span class="n">scale</span> <span class="o">*</span> <span class="n">aspect_ratio</span><span class="p">)</span>
<span class="n">_</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">num_rows</span><span class="p">,</span> <span class="n">num_cols</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_rows</span><span class="p">):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_cols</span><span class="p">):</span>
<span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">imgs</span><span class="p">[</span><span class="n">i</span> <span class="o">*</span> <span class="n">num_cols</span> <span class="o">+</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
<span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">axes</span><span class="o">.</span><span class="n">get_xaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">axes</span><span class="o">.</span><span class="n">get_yaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">wspace</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="k">return</span> <span class="n">axes</span>
<span class="k">def</span> <span class="nf">apply</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">aug</span><span class="p">,</span> <span class="n">num_rows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">num_cols</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">3</span><span class="p">):</span>
<span class="c1"># apply augmentation multiple times to obtain different samples</span>
<span class="n">Y</span> <span class="o">=</span> <span class="p">[</span><span class="n">aug</span><span class="p">(</span><span class="n">img</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_rows</span> <span class="o">*</span> <span class="n">num_cols</span><span class="p">)]</span>
<span class="n">show_images</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">num_rows</span><span class="p">,</span> <span class="n">num_cols</span><span class="p">,</span> <span class="n">scale</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="Spatial-Augmentation">
<h1>Spatial Augmentation<a class="headerlink" href="#Spatial-Augmentation" title="Permalink to this headline"></a></h1>
<p>One form of augmentation affects the spatial position of pixel values. Using combinations of slicing, scaling, translating, rotating and flipping the values of the original image can be shifted to create new images. Some operations (like scaling and rotation) require interpolation as pixels in the new image are combinations of pixels in the original image.</p>
<p>Many Computer Visions tasks, such as image classification and object detection, should be robust to changes in the scale and position of objects in the image. You can incorporate this into the network using pooling layers, but an alternative method is to crop random regions of the original image.</p>
<p>As an example, we randomly (using a uniform distribution) crop a region of the image with:</p>
<ul class="simple">
<li><p>an area of 10% to 100% of the original area</p></li>
<li><p>a ratio of width to height between 0.5 and 2</p></li>
</ul>
<p>And then we resize this cropped region to 200 by 200 pixels.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">shape_aug</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">RandomResizedCrop</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">200</span><span class="p">,</span> <span class="mi">200</span><span class="p">),</span>
<span class="n">scale</span><span class="o">=</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
<span class="n">ratio</span><span class="o">=</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
<span class="n">apply</span><span class="p">(</span><span class="n">example_image</span><span class="p">,</span> <span class="n">shape_aug</span><span class="p">)</span>
</pre></div>
</div>
<img alt="png" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_12_0.png" />
<p>A simple augmentation technique is flipping. Usually flipping horizontally doesn’t change the category of object and results in an image that’s still plausible in the real world. Using <code class="docutils literal notranslate"><span class="pre">RandomFlipLeftRight</span></code>, we randomly flip the image horizontally 50% of the time.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">apply</span><span class="p">(</span><span class="n">example_image</span><span class="p">,</span> <span class="n">transforms</span><span class="o">.</span><span class="n">RandomFlipLeftRight</span><span class="p">())</span>
</pre></div>
</div>
<img alt="png" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_15_0.png" />
<p>Although it’s not as common as flipping left and right, you can flip the image vertically 50% of the time with <code class="docutils literal notranslate"><span class="pre">RandomFlipTopBottom</span></code>. With our giraffe example, we end up with less plausible samples that horizontal flipping, with the ground above the sky in some cases.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">apply</span><span class="p">(</span><span class="n">example_image</span><span class="p">,</span> <span class="n">transforms</span><span class="o">.</span><span class="n">RandomFlipTopBottom</span><span class="p">())</span>
</pre></div>
</div>
<img alt="png" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_18_0.png" />
</div>
<div class="section" id="Color-Augmentation">
<h1>Color Augmentation<a class="headerlink" href="#Color-Augmentation" title="Permalink to this headline"></a></h1>
<p>Usually, exact coloring doesn’t play a significant role in the classification or detection of objects, so augmenting the colors of images is a good technique to make the network invariant to color shifts. Color properties that can be changed include brightness, contrast, saturation and hue.</p>
<p>Use <code class="docutils literal notranslate"><span class="pre">RandomBrightness</span></code> to add a random brightness jitter to images. Use the <code class="docutils literal notranslate"><span class="pre">brightness</span></code> parameter to control the amount of jitter in brightness, with value from 0 (no change) to 1 (potentially large change). <code class="docutils literal notranslate"><span class="pre">brightness</span></code> doesn’t specify whether the brightness of the augmented image will be lighter or darker, just the potential strength of the effect. Specifically the augmentation is given by:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">alpha</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">+</span> <span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="n">brightness</span><span class="p">,</span> <span class="n">brightness</span><span class="p">)</span>
<span class="n">image</span> <span class="o">*=</span> <span class="n">alpha</span>
</pre></div>
</div>
<p>So by setting this to 0.5 we randomly change the brightness of the image to a value between 50% (<span class="math notranslate nohighlight">\(1-0.5\)</span>) and 150% (<span class="math notranslate nohighlight">\(1+0.5\)</span>) of the original image.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">apply</span><span class="p">(</span><span class="n">example_image</span><span class="p">,</span> <span class="n">transforms</span><span class="o">.</span><span class="n">RandomBrightness</span><span class="p">(</span><span class="mf">0.5</span><span class="p">))</span>
</pre></div>
</div>
<img alt="png" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_23_0.png" />
<p>Use <code class="docutils literal notranslate"><span class="pre">RandomContrast</span></code> to add a random contrast jitter to an image. Contrast can be thought of as the degree to which light and dark colors in the image differ. Use the <code class="docutils literal notranslate"><span class="pre">contrast</span></code> parameter to control the amount of jitter in contrast, with value from 0 (no change) to 1 (potentially large change). <code class="docutils literal notranslate"><span class="pre">contrast</span></code> doesn’t specify whether the contrast of the augmented image will be higher or lower, just the potential strength of the effect. Specifically, the augmentation is given by:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">coef</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[[</span><span class="mf">0.299</span><span class="p">,</span> <span class="mf">0.587</span><span class="p">,</span> <span class="mf">0.114</span><span class="p">]]])</span>
<span class="n">alpha</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">+</span> <span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="n">contrast</span><span class="p">,</span> <span class="n">contrast</span><span class="p">)</span>
<span class="n">gray</span> <span class="o">=</span> <span class="n">image</span> <span class="o">*</span> <span class="n">coef</span>
<span class="n">gray</span> <span class="o">=</span> <span class="p">(</span><span class="mf">3.0</span> <span class="o">*</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">alpha</span><span class="p">)</span> <span class="o">/</span> <span class="n">gray</span><span class="o">.</span><span class="n">size</span><span class="p">)</span> <span class="o">*</span> <span class="n">nd</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">gray</span><span class="p">)</span>
<span class="n">image</span> <span class="o">*=</span> <span class="n">alpha</span>
<span class="n">image</span> <span class="o">+=</span> <span class="n">gray</span>
</pre></div>
</div>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">apply</span><span class="p">(</span><span class="n">example_image</span><span class="p">,</span> <span class="n">transforms</span><span class="o">.</span><span class="n">RandomContrast</span><span class="p">(</span><span class="mf">0.5</span><span class="p">))</span>
</pre></div>
</div>
<img alt="png" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_26_0.png" />
<p>Use <code class="docutils literal notranslate"><span class="pre">RandomSaturation</span></code> to add a random saturation jitter to an image. Saturation can be thought of as the ‘amount’ of color in an image. Use the <code class="docutils literal notranslate"><span class="pre">saturation</span></code> parameter to control the amount of jitter in saturation, with value from 0 (no change) to 1 (potentially large change). <code class="docutils literal notranslate"><span class="pre">saturation</span></code> doesn’t specify whether the saturation of the augmented image will be higher or lower, just the potential strength of the effect. Specifically the augmentation is using the method detailed
<a class="reference external" href="https://beesbuzz.biz/code/16-hsv-color-transforms">here</a>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">apply</span><span class="p">(</span><span class="n">example_image</span><span class="p">,</span> <span class="n">transforms</span><span class="o">.</span><span class="n">RandomSaturation</span><span class="p">(</span><span class="mf">0.5</span><span class="p">))</span>
</pre></div>
</div>
<img alt="png" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_29_0.png" />
<p>Use <code class="docutils literal notranslate"><span class="pre">RandomHue</span></code> to add a random hue jitter to images. Hue can be thought of as the ‘shade’ of the colors in an image. Use the <code class="docutils literal notranslate"><span class="pre">hue</span></code> parameter to control the amount of jitter in hue, with value from 0 (no change) to 1 (potentially large change). <code class="docutils literal notranslate"><span class="pre">hue</span></code> doesn’t specify whether the hue of the augmented image will be shifted one way or the other, just the potential strength of the effect. Specifically the augmentation is using the method detailed
<a class="reference external" href="https://beesbuzz.biz/code/16-hsv-color-transforms">here</a>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">apply</span><span class="p">(</span><span class="n">example_image</span><span class="p">,</span> <span class="n">transforms</span><span class="o">.</span><span class="n">RandomHue</span><span class="p">(</span><span class="mf">0.5</span><span class="p">))</span>
</pre></div>
</div>
<img alt="png" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_32_0.png" />
<p><code class="docutils literal notranslate"><span class="pre">RandomColorJitter</span></code> is a convenience transform that can be used to perform multiple color augmentations at once. You can set the <code class="docutils literal notranslate"><span class="pre">brightness</span></code>, <code class="docutils literal notranslate"><span class="pre">contrast</span></code>, <code class="docutils literal notranslate"><span class="pre">saturation</span></code> and <code class="docutils literal notranslate"><span class="pre">hue</span></code> jitters, that function the same as above for their individual transforms.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">color_aug</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">RandomColorJitter</span><span class="p">(</span><span class="n">brightness</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
<span class="n">contrast</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
<span class="n">saturation</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
<span class="n">hue</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">apply</span><span class="p">(</span><span class="n">example_image</span><span class="p">,</span> <span class="n">color_aug</span><span class="p">)</span>
</pre></div>
</div>
<img alt="png" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_35_0.png" />
<p>Use <code class="docutils literal notranslate"><span class="pre">RandomLighting</span></code> for an AlexNet-style PCA-based noise augmentation.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">apply</span><span class="p">(</span><span class="n">example_image</span><span class="p">,</span> <span class="n">transforms</span><span class="o">.</span><span class="n">RandomLighting</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
</pre></div>
</div>
<img alt="png" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_38_0.png" />
</div>
<div class="section" id="Composed-Augmentations">
<h1>Composed Augmentations<a class="headerlink" href="#Composed-Augmentations" title="Permalink to this headline"></a></h1>
<p>In practice, we apply multiple augmentation techniques to an image to increase the variety of images in the dataset. Using the <code class="docutils literal notranslate"><span class="pre">Compose</span></code> transform that was introduced in the <a href="#id1"><span class="problematic" id="id3">`Data Transforms tutorial &lt;&gt;`__</span></a>, we can apply 3 of the transforms we previously used above.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">augs</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span>
<span class="n">transforms</span><span class="o">.</span><span class="n">RandomFlipLeftRight</span><span class="p">(),</span> <span class="n">color_aug</span><span class="p">,</span> <span class="n">shape_aug</span><span class="p">])</span>
<span class="n">apply</span><span class="p">(</span><span class="n">example_image</span><span class="p">,</span> <span class="n">augs</span><span class="p">)</span>
</pre></div>
</div>
<img alt="png" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/transforms/output_41_0.png" />
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<span class="caption-text">Table Of Contents</span>
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<ul>
<li><a class="reference internal" href="#">Image Augmentation</a><ul>
<li><a class="reference internal" href="#What-are-the-prerequisites?">What are the prerequisites?</a></li>
<li><a class="reference internal" href="#Where-can-I-find-the-augmentation-transforms?">Where can I find the augmentation transforms?</a></li>
<li><a class="reference internal" href="#Sample-Image">Sample Image</a></li>
</ul>
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<li><a class="reference internal" href="#Spatial-Augmentation">Spatial Augmentation</a></li>
<li><a class="reference internal" href="#Color-Augmentation">Color Augmentation</a></li>
<li><a class="reference internal" href="#Composed-Augmentations">Composed Augmentations</a></li>
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