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AMMICO
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<p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../readme_link.html">AMMICO - AI Media and Misinformation Content Analysis Tool</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Example%20faces.html">Facial Expression recognition with DeepFace</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Example%20text.html">Notebook for text extraction on image</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Example%20summary.html">Image summary and visual question answering</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Example%20multimodal.html">Image Multimodal Search</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Example%20colors.html">Color analysis of pictures</a></li>
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<li class="toctree-l1 current"><a class="current reference internal" href="#">Objects recognition</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="#Detect-objects-and-directly-write-to-csv">Detect objects and directly write to csv</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#Manually-inspect-what-was-detected">Manually inspect what was detected</a></li>
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</ul>
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</li>
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<li class="toctree-l1"><a class="reference internal" href="Example%20cropposts.html">Crop posts from social media posts images</a></li>
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<li class="breadcrumb-item active">Objects recognition</li>
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<a href="../_sources/notebooks/Example objects.ipynb.txt" rel="nofollow"> View page source</a>
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<section id="Objects-recognition">
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<h1>Objects recognition<a class="headerlink" href="#Objects-recognition" title="Permalink to this heading"></a></h1>
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<p>This notebooks shows how to detect objects quickly using <a class="reference external" href="https://github.com/arunponnusamy/cvlib">cvlib</a> and the <a class="reference external" href="https://github.com/AlexeyAB/darknet">YOLOv4</a> model. This library detects faces, people, and several inanimate objects; we currently have restricted the output to person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, cell phone.</p>
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<p>The first cell is only run on google colab and installs the <a class="reference external" href="https://github.com/ssciwr/AMMICO">ammico</a> package.</p>
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<p>After that, we can import <code class="docutils literal notranslate"><span class="pre">ammico</span></code> and read in the files given a folder path.</p>
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<div class="nbinput nblast docutils container">
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<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
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</pre></div>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># if running on google colab</span>
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<span class="c1"># flake8-noqa-cell</span>
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<span class="kn">import</span> <span class="nn">os</span>
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<span class="k">if</span> <span class="s2">"google.colab"</span> <span class="ow">in</span> <span class="nb">str</span><span class="p">(</span><span class="n">get_ipython</span><span class="p">()):</span>
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<span class="c1"># update python version</span>
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<span class="c1"># install setuptools</span>
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<span class="c1"># %pip install setuptools==61 -qqq</span>
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<span class="c1"># install ammico</span>
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<span class="o">%</span><span class="k">pip</span> install git+https://github.com/ssciwr/ammico.git -qqq
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<span class="c1"># mount google drive for data and API key</span>
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<span class="kn">from</span> <span class="nn">google.colab</span> <span class="kn">import</span> <span class="n">drive</span>
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<span class="n">drive</span><span class="o">.</span><span class="n">mount</span><span class="p">(</span><span class="s2">"/content/drive"</span><span class="p">)</span>
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</pre></div>
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</pre></div>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">ammico</span>
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<span class="kn">from</span> <span class="nn">ammico</span> <span class="kn">import</span> <span class="n">utils</span> <span class="k">as</span> <span class="n">mutils</span>
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<span class="kn">from</span> <span class="nn">ammico</span> <span class="kn">import</span> <span class="n">display</span> <span class="k">as</span> <span class="n">mdisplay</span>
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<span class="kn">import</span> <span class="nn">ammico.objects</span> <span class="k">as</span> <span class="nn">ob</span>
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</pre></div>
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</div>
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</div>
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<p>Set an image path as input file path.</p>
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</pre></div>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Here you need to provide the path to your google drive folder</span>
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<span class="c1"># or local folder containing the images</span>
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<span class="n">images</span> <span class="o">=</span> <span class="n">mutils</span><span class="o">.</span><span class="n">find_files</span><span class="p">(</span>
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<span class="n">path</span><span class="o">=</span><span class="s2">"data/"</span><span class="p">,</span>
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<span class="n">limit</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
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<span class="p">)</span>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mydict</span> <span class="o">=</span> <span class="n">mutils</span><span class="o">.</span><span class="n">initialize_dict</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
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</pre></div>
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</div>
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<section id="Detect-objects-and-directly-write-to-csv">
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<h2>Detect objects and directly write to csv<a class="headerlink" href="#Detect-objects-and-directly-write-to-csv" title="Permalink to this heading"></a></h2>
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<p>You can directly carry out the analysis and export the result into a csv. This may take a while depending on how many images you have loaded.</p>
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</pre></div>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">mydict</span><span class="p">:</span>
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<span class="n">mydict</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">ob</span><span class="o">.</span><span class="n">ObjectDetector</span><span class="p">(</span><span class="n">mydict</span><span class="p">[</span><span class="n">key</span><span class="p">])</span><span class="o">.</span><span class="n">analyse_image</span><span class="p">()</span>
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0% | |
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Downloading yolov4.cfg from https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg
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Downloading yolov4.weights from https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights
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</pre></div></div>
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99% |####################################################################### |
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</pre></div></div>
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Downloading yolov3_classes.txt from https://github.com/arunponnusamy/object-detection-opencv/raw/master/yolov3.txt
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</pre></div></div>
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100% | |
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<p>Convert the dictionary of dictionarys into a dictionary with lists:</p>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">outdict</span> <span class="o">=</span> <span class="n">mutils</span><span class="o">.</span><span class="n">append_data_to_dict</span><span class="p">(</span><span class="n">mydict</span><span class="p">)</span>
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<span class="n">df</span> <span class="o">=</span> <span class="n">mutils</span><span class="o">.</span><span class="n">dump_df</span><span class="p">(</span><span class="n">outdict</span><span class="p">)</span>
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</pre></div>
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</div>
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</div>
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<p>Check the dataframe:</p>
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</pre></div>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
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</pre></div>
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</pre></div>
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<style scoped>
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.dataframe tbody tr th:only-of-type {
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}
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.dataframe tbody tr th {
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.dataframe thead th {
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}
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<table border="1" class="dataframe">
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<thead>
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<tr style="text-align: right;">
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<th></th>
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<th>filename</th>
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<th>person</th>
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<th>bicycle</th>
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<th>car</th>
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<th>motorcycle</th>
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<th>airplane</th>
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<th>bus</th>
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<th>train</th>
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<th>truck</th>
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<th>boat</th>
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<th>traffic light</th>
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<th>cell phone</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<th>0</th>
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<td>data/106349S_por.png</td>
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<td>yes</td>
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<td>no</td>
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<td>no</td>
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<td>no</td>
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<td>no</td>
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<td>no</td>
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<td>no</td>
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<td>no</td>
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<td>no</td>
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<td>yes</td>
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</tr>
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<tr>
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<th>1</th>
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<td>data/102141_2_eng.png</td>
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<td>yes</td>
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<td>no</td>
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<td>no</td>
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<td>no</td>
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<td>no</td>
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<td>no</td>
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<td>no</td>
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<td>no</td>
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<td>no</td>
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<td>no</td>
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<td>no</td>
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</tr>
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<tr>
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<th>2</th>
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<td>data/102730_eng.png</td>
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<td>yes</td>
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<td>no</td>
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<td>no</td>
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<td>no</td>
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<td>no</td>
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<td>no</td>
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<p>Write the csv file:</p>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s2">"data_out.csv"</span><span class="p">)</span>
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</pre></div>
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</div>
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</div>
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</section>
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<section id="Manually-inspect-what-was-detected">
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<h2>Manually inspect what was detected<a class="headerlink" href="#Manually-inspect-what-was-detected" title="Permalink to this heading"></a></h2>
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<p>To check the analysis, you can inspect the analyzed elements here. Loading the results takes a moment, so please be patient. If you are sure of what you are doing, you can directly export a csv file in the step above. Here, we display the object detection results provided by the above library. Click on the tabs to see the results in the right sidebar. You may need to increment the <code class="docutils literal notranslate"><span class="pre">port</span></code> number if you are already running several notebook instances on the same server.</p>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">analysis_explorer</span> <span class="o">=</span> <span class="n">mdisplay</span><span class="o">.</span><span class="n">AnalysisExplorer</span><span class="p">(</span><span class="n">mydict</span><span class="p">,</span> <span class="n">identify</span><span class="o">=</span><span class="s2">"objects"</span><span class="p">)</span>
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<span class="n">analysis_explorer</span><span class="o">.</span><span class="n">run_server</span><span class="p">(</span><span class="n">port</span><span class="o">=</span><span class="mi">8056</span><span class="p">)</span>
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<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
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<span class="ansi-red-fg">TypeError</span> Traceback (most recent call last)
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Cell <span class="ansi-green-fg">In[9], line 1</span>
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<span class="ansi-green-fg">----> 1</span> analysis_explorer <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">mdisplay</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">AnalysisExplorer</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">mydict</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">identify</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">"</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">objects</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">"</span><span class="ansi-yellow-bg">)</span>
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<span class="ansi-green-intense-fg ansi-bold"> 2</span> analysis_explorer<span style="color: rgb(98,98,98)">.</span>run_server(port<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(98,98,98)">8056</span>)
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<span class="ansi-red-fg">TypeError</span>: __init__() got an unexpected keyword argument 'identify'
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</pre></div></div>
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