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<!DOCTYPE html>
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<title>motrackers.track — Multi-object trackers in Python 1.0.0 documentation</title>
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<li class="toctree-l1"><a class="reference internal" href="../../includeme/readmefile.html">Multi-object trackers in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../includeme/readmefile.html#example-tf-mobilenetssd-centroidtracker">Example: <cite>TF-MobileNetSSD + CentroidTracker</cite></a></li>
<li class="toctree-l1"><a class="reference internal" href="../../includeme/readmefile.html#example-yolov3-centroidtracker">Example: <cite>YOLOv3 + CentroidTracker</cite></a></li>
<li class="toctree-l1"><a class="reference internal" href="../../includeme/apidocuments.html">Tracker</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../includeme/apidocuments.html#sort">SORT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../includeme/apidocuments.html#iou-tracker">IOU Tracker</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../includeme/apidocuments.html#kalman-filter-based-centroid-tracker">Kalman Filter based Centroid Tracker</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../includeme/apidocuments.html#tracks">Tracks</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../includeme/apidocuments.html#object-detection">Object Detection</a></li>
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<h1>Source code for motrackers.track</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">motrackers.kalman_tracker</span> <span class="kn">import</span> <span class="n">KFTracker2D</span><span class="p">,</span> <span class="n">KFTrackerSORT</span><span class="p">,</span> <span class="n">KFTracker4D</span>
<div class="viewcode-block" id="Track"><a class="viewcode-back" href="../../includeme/apidocuments.html#motrackers.track.Track">[docs]</a><span class="k">class</span> <span class="nc">Track</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> Track containing attributes to track various objects.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> frame_id : int</span>
<span class="sd"> Camera frame id.</span>
<span class="sd"> track_id : int</span>
<span class="sd"> Track Id</span>
<span class="sd"> bbox : numpy.ndarray</span>
<span class="sd"> Bounding box pixel coordinates as (xmin, ymin, width, height) of the track.</span>
<span class="sd"> detection_confidence : float</span>
<span class="sd"> Detection confidence of the object (probability).</span>
<span class="sd"> class_id : Object</span>
<span class="sd"> Class label id.</span>
<span class="sd"> lost : int</span>
<span class="sd"> Number of times the object or track was not tracked by tracker in consecutive frames.</span>
<span class="sd"> iou_score : float</span>
<span class="sd"> Intersection over union score.</span>
<span class="sd"> data_output_format: str</span>
<span class="sd"> Output format for data in tracker. Options ``['mot_challenge', 'visdrone_challenge']``. Default is ``mot_challenge``.</span>
<span class="sd"> kwargs : dict</span>
<span class="sd"> Additional key word arguments.</span>
<span class="sd"> """</span>
<span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">metadata</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
<span class="n">data_output_formats</span><span class="o">=</span><span class="p">[</span><span class="s1">'mot_challenge'</span><span class="p">,</span> <span class="s1">'visdrone_challenge'</span><span class="p">]</span>
<span class="p">)</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">track_id</span><span class="p">,</span>
<span class="n">frame_id</span><span class="p">,</span>
<span class="n">bbox</span><span class="p">,</span>
<span class="n">detection_confidence</span><span class="p">,</span>
<span class="n">class_id</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">lost</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">iou_score</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span>
<span class="n">data_output_format</span><span class="o">=</span><span class="s1">'mot_challenge'</span><span class="p">,</span>
<span class="o">**</span><span class="n">kwargs</span>
<span class="p">):</span>
<span class="k">assert</span> <span class="n">data_output_format</span> <span class="ow">in</span> <span class="n">Track</span><span class="o">.</span><span class="n">metadata</span><span class="p">[</span><span class="s1">'data_output_formats'</span><span class="p">]</span>
<span class="n">Track</span><span class="o">.</span><span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="bp">self</span><span class="o">.</span><span class="n">id</span> <span class="o">=</span> <span class="n">track_id</span>
<span class="bp">self</span><span class="o">.</span><span class="n">detection_confidence_max</span> <span class="o">=</span> <span class="mf">0.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">lost</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">age</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">frame_id</span><span class="p">,</span> <span class="n">bbox</span><span class="p">,</span> <span class="n">detection_confidence</span><span class="p">,</span> <span class="n">class_id</span><span class="o">=</span><span class="n">class_id</span><span class="p">,</span> <span class="n">lost</span><span class="o">=</span><span class="n">lost</span><span class="p">,</span> <span class="n">iou_score</span><span class="o">=</span><span class="n">iou_score</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">if</span> <span class="n">data_output_format</span> <span class="o">==</span> <span class="s1">'mot_challenge'</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_mot_challenge_format</span>
<span class="k">elif</span> <span class="n">data_output_format</span> <span class="o">==</span> <span class="s1">'visdrone_challenge'</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_vis_drone_format</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span>
<div class="viewcode-block" id="Track.update"><a class="viewcode-back" href="../../includeme/apidocuments.html#motrackers.track.Track.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">frame_id</span><span class="p">,</span> <span class="n">bbox</span><span class="p">,</span> <span class="n">detection_confidence</span><span class="p">,</span> <span class="n">class_id</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">lost</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">iou_score</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Update the track.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> frame_id : int</span>
<span class="sd"> Camera frame id.</span>
<span class="sd"> bbox : numpy.ndarray</span>
<span class="sd"> Bounding box pixel coordinates as (xmin, ymin, width, height) of the track.</span>
<span class="sd"> detection_confidence : float</span>
<span class="sd"> Detection confidence of the object (probability).</span>
<span class="sd"> class_id : Object</span>
<span class="sd"> Class label id.</span>
<span class="sd"> lost : int</span>
<span class="sd"> Number of times the object or track was not tracked by tracker in consecutive frames.</span>
<span class="sd"> iou_score : float</span>
<span class="sd"> Intersection over union score.</span>
<span class="sd"> kwargs : dict</span>
<span class="sd"> Additional key word arguments.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">class_id</span> <span class="o">=</span> <span class="n">class_id</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bbox</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">bbox</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">detection_confidence</span> <span class="o">=</span> <span class="n">detection_confidence</span>
<span class="bp">self</span><span class="o">.</span><span class="n">frame_id</span> <span class="o">=</span> <span class="n">frame_id</span>
<span class="bp">self</span><span class="o">.</span><span class="n">lost</span> <span class="o">=</span> <span class="n">lost</span>
<span class="bp">self</span><span class="o">.</span><span class="n">iou_score</span> <span class="o">=</span> <span class="n">iou_score</span>
<span class="k">if</span> <span class="n">lost</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">lost</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">lost</span> <span class="o">+=</span> <span class="n">lost</span>
<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="nb">setattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">detection_confidence_max</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">detection_confidence_max</span><span class="p">,</span> <span class="n">detection_confidence</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">age</span> <span class="o">+=</span> <span class="mi">1</span></div>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">centroid</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Return the centroid of the bounding box.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> numpy.ndarray: Centroid (x, y) of bounding box.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">bbox</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="mf">0.5</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">bbox</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">bbox</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="mf">0.5</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">bbox</span><span class="p">[</span><span class="mi">3</span><span class="p">]))</span>
<div class="viewcode-block" id="Track.get_mot_challenge_format"><a class="viewcode-back" href="../../includeme/apidocuments.html#motrackers.track.Track.get_mot_challenge_format">[docs]</a> <span class="k">def</span> <span class="nf">get_mot_challenge_format</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Get the tracker data in MOT challenge format as a tuple of elements containing</span>
<span class="sd"> `(frame, id, bb_left, bb_top, bb_width, bb_height, conf, x, y, z)`</span>
<span class="sd"> References</span>
<span class="sd"> ----------</span>
<span class="sd"> - Website : https://motchallenge.net/</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> mot_tuple : tuple</span>
<span class="sd"> Tuple of 10 elements representing `(frame, id, bb_left, bb_top, bb_width, bb_height, conf, x, y, z)`.</span>
<span class="sd"> """</span>
<span class="n">mot_tuple</span> <span class="o">=</span> <span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">frame_id</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">id</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bbox</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">bbox</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">bbox</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">bbox</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">detection_confidence</span><span class="p">,</span>
<span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">mot_tuple</span></div>
<div class="viewcode-block" id="Track.get_vis_drone_format"><a class="viewcode-back" href="../../includeme/apidocuments.html#motrackers.track.Track.get_vis_drone_format">[docs]</a> <span class="k">def</span> <span class="nf">get_vis_drone_format</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Track data output in VISDRONE Challenge format with tuple as</span>
<span class="sd"> `(frame_index, target_id, bbox_left, bbox_top, bbox_width, bbox_height, score, object_category,</span>
<span class="sd"> truncation, occlusion)`.</span>
<span class="sd"> References</span>
<span class="sd"> ----------</span>
<span class="sd"> - Website : http://aiskyeye.com/</span>
<span class="sd"> - Paper : https://arxiv.org/abs/2001.06303</span>
<span class="sd"> - GitHub : https://github.com/VisDrone/VisDrone2018-MOT-toolkit</span>
<span class="sd"> - GitHub : https://github.com/VisDrone/</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> mot_tuple : tuple</span>
<span class="sd"> Tuple containing the elements as `(frame_index, target_id, bbox_left, bbox_top, bbox_width, bbox_height,</span>
<span class="sd"> score, object_category, truncation, occlusion)`.</span>
<span class="sd"> """</span>
<span class="n">mot_tuple</span> <span class="o">=</span> <span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">frame_id</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">id</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bbox</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">bbox</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">bbox</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">bbox</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>
<span class="bp">self</span><span class="o">.</span><span class="n">detection_confidence</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">class_id</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">mot_tuple</span></div>
<div class="viewcode-block" id="Track.predict"><a class="viewcode-back" href="../../includeme/apidocuments.html#motrackers.track.Track.predict">[docs]</a> <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Implement to prediction the next estimate of track.</span>
<span class="sd"> """</span>
<span class="k">raise</span> <span class="bp">NotImplemented</span></div>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">print_all_track_output_formats</span><span class="p">():</span>
<span class="nb">print</span><span class="p">(</span><span class="n">Track</span><span class="o">.</span><span class="n">metadata</span><span class="p">[</span><span class="s1">'data_output_formats'</span><span class="p">])</span></div>
<div class="viewcode-block" id="KFTrackSORT"><a class="viewcode-back" href="../../includeme/apidocuments.html#motrackers.track.KFTrackSORT">[docs]</a><span class="k">class</span> <span class="nc">KFTrackSORT</span><span class="p">(</span><span class="n">Track</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Track based on Kalman filter tracker used for SORT MOT-Algorithm.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> track_id : int</span>
<span class="sd"> Track Id</span>
<span class="sd"> frame_id : int</span>
<span class="sd"> Camera frame id.</span>
<span class="sd"> bbox : numpy.ndarray</span>
<span class="sd"> Bounding box pixel coordinates as (xmin, ymin, width, height) of the track.</span>
<span class="sd"> detection_confidence : float</span>
<span class="sd"> Detection confidence of the object (probability).</span>
<span class="sd"> class_id : Object</span>
<span class="sd"> Class label id.</span>
<span class="sd"> lost : int</span>
<span class="sd"> Number of times the object or track was not tracked by tracker in consecutive frames.</span>
<span class="sd"> iou_score : float</span>
<span class="sd"> Intersection over union score.</span>
<span class="sd"> data_output_format: str</span>
<span class="sd"> Output format for data in tracker. Options ``['mot_challenge', 'visdrone_challenge']``. Default is ``mot_challenge``.</span>
<span class="sd"> process_noise_scale: float</span>
<span class="sd"> Process noise covariance scale or covariance magnitude as scalar value.</span>
<span class="sd"> measurement_noise_scale: float</span>
<span class="sd"> Measurement noise covariance scale or covariance magnitude as scalar value.</span>
<span class="sd"> kwargs : dict</span>
<span class="sd"> Additional key word arguments.</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">track_id</span><span class="p">,</span> <span class="n">frame_id</span><span class="p">,</span> <span class="n">bbox</span><span class="p">,</span> <span class="n">detection_confidence</span><span class="p">,</span> <span class="n">class_id</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">lost</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">iou_score</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span>
<span class="n">data_output_format</span><span class="o">=</span><span class="s1">'mot_challenge'</span><span class="p">,</span> <span class="n">process_noise_scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">measurement_noise_scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="n">bbz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">bbox</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="mf">0.5</span><span class="o">*</span><span class="n">bbox</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">bbox</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="mf">0.5</span><span class="o">*</span><span class="n">bbox</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">bbox</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">*</span><span class="n">bbox</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">bbox</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">/</span><span class="nb">float</span><span class="p">(</span><span class="n">bbox</span><span class="p">[</span><span class="mi">3</span><span class="p">])])</span>
<span class="bp">self</span><span class="o">.</span><span class="n">kf</span> <span class="o">=</span> <span class="n">KFTrackerSORT</span><span class="p">(</span>
<span class="n">bbz</span><span class="p">,</span> <span class="n">process_noise_scale</span><span class="o">=</span><span class="n">process_noise_scale</span><span class="p">,</span> <span class="n">measurement_noise_scale</span><span class="o">=</span><span class="n">measurement_noise_scale</span><span class="p">)</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">track_id</span><span class="p">,</span> <span class="n">frame_id</span><span class="p">,</span> <span class="n">bbox</span><span class="p">,</span> <span class="n">detection_confidence</span><span class="p">,</span> <span class="n">class_id</span><span class="o">=</span><span class="n">class_id</span><span class="p">,</span> <span class="n">lost</span><span class="o">=</span><span class="n">lost</span><span class="p">,</span>
<span class="n">iou_score</span><span class="o">=</span><span class="n">iou_score</span><span class="p">,</span> <span class="n">data_output_format</span><span class="o">=</span><span class="n">data_output_format</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<div class="viewcode-block" id="KFTrackSORT.predict"><a class="viewcode-back" href="../../includeme/apidocuments.html#motrackers.track.KFTrackSORT.predict">[docs]</a> <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Predicts the next estimate of the bounding box of the track.</span>
<span class="sd"> Returns:</span>
<span class="sd"> numpy.ndarray: Bounding box pixel coordinates as (xmin, ymin, width, height) of the track.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">kf</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">6</span><span class="p">]</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">kf</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">kf</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">6</span><span class="p">]</span> <span class="o">*=</span> <span class="mf">0.0</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kf</span><span class="o">.</span><span class="n">predict</span><span class="p">()</span>
<span class="k">if</span> <span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o"><</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">])</span>
<span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
<span class="n">h</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
<span class="n">bb</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="mf">0.5</span><span class="o">*</span><span class="n">w</span><span class="p">,</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="mf">0.5</span><span class="o">*</span><span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">h</span><span class="p">])</span>
<span class="k">return</span> <span class="n">bb</span></div>
<div class="viewcode-block" id="KFTrackSORT.update"><a class="viewcode-back" href="../../includeme/apidocuments.html#motrackers.track.KFTrackSORT.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">frame_id</span><span class="p">,</span> <span class="n">bbox</span><span class="p">,</span> <span class="n">detection_confidence</span><span class="p">,</span> <span class="n">class_id</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">lost</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">iou_score</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">update</span><span class="p">(</span>
<span class="n">frame_id</span><span class="p">,</span> <span class="n">bbox</span><span class="p">,</span> <span class="n">detection_confidence</span><span class="p">,</span> <span class="n">class_id</span><span class="o">=</span><span class="n">class_id</span><span class="p">,</span> <span class="n">lost</span><span class="o">=</span><span class="n">lost</span><span class="p">,</span> <span class="n">iou_score</span><span class="o">=</span><span class="n">iou_score</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">bbox</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="mf">0.5</span><span class="o">*</span><span class="n">bbox</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">bbox</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="mf">0.5</span><span class="o">*</span><span class="n">bbox</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">bbox</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">*</span><span class="n">bbox</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">bbox</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">/</span><span class="nb">float</span><span class="p">(</span><span class="n">bbox</span><span class="p">[</span><span class="mi">3</span><span class="p">])])</span>
<span class="bp">self</span><span class="o">.</span><span class="n">kf</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">z</span><span class="p">)</span></div></div>
<div class="viewcode-block" id="KFTrack4DSORT"><a class="viewcode-back" href="../../includeme/apidocuments.html#motrackers.track.KFTrack4DSORT">[docs]</a><span class="k">class</span> <span class="nc">KFTrack4DSORT</span><span class="p">(</span><span class="n">Track</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Track based on Kalman filter tracker used for SORT MOT-Algorithm.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> track_id : int</span>
<span class="sd"> Track Id</span>
<span class="sd"> frame_id : int</span>
<span class="sd"> Camera frame id.</span>
<span class="sd"> bbox : numpy.ndarray</span>
<span class="sd"> Bounding box pixel coordinates as (xmin, ymin, width, height) of the track.</span>
<span class="sd"> detection_confidence : float</span>
<span class="sd"> Detection confidence of the object (probability).</span>
<span class="sd"> class_id : Object</span>
<span class="sd"> Class label id.</span>
<span class="sd"> lost : int</span>
<span class="sd"> Number of times the object or track was not tracked by tracker in consecutive frames.</span>
<span class="sd"> iou_score : float</span>
<span class="sd"> Intersection over union score.</span>
<span class="sd"> data_output_format: str</span>
<span class="sd"> Output format for data in tracker. Options ``['mot_challenge', 'visdrone_challenge']``. Default is ``mot_challenge``.</span>
<span class="sd"> process_noise_scale: float</span>
<span class="sd"> Process noise covariance scale or covariance magnitude as scalar value.</span>
<span class="sd"> measurement_noise_scale: float</span>
<span class="sd"> Measurement noise covariance scale or covariance magnitude as scalar value.</span>
<span class="sd"> kwargs : dict</span>
<span class="sd"> Additional key word arguments.</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">track_id</span><span class="p">,</span> <span class="n">frame_id</span><span class="p">,</span> <span class="n">bbox</span><span class="p">,</span> <span class="n">detection_confidence</span><span class="p">,</span> <span class="n">class_id</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">lost</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">iou_score</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span>
<span class="n">data_output_format</span><span class="o">=</span><span class="s1">'mot_challenge'</span><span class="p">,</span> <span class="n">process_noise_scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">measurement_noise_scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
<span class="n">kf_time_step</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">kf</span> <span class="o">=</span> <span class="n">KFTracker4D</span><span class="p">(</span>
<span class="n">bbox</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">process_noise_scale</span><span class="o">=</span><span class="n">process_noise_scale</span><span class="p">,</span> <span class="n">measurement_noise_scale</span><span class="o">=</span><span class="n">measurement_noise_scale</span><span class="p">,</span>
<span class="n">time_step</span><span class="o">=</span><span class="n">kf_time_step</span><span class="p">)</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">track_id</span><span class="p">,</span> <span class="n">frame_id</span><span class="p">,</span> <span class="n">bbox</span><span class="p">,</span> <span class="n">detection_confidence</span><span class="p">,</span> <span class="n">class_id</span><span class="o">=</span><span class="n">class_id</span><span class="p">,</span> <span class="n">lost</span><span class="o">=</span><span class="n">lost</span><span class="p">,</span>
<span class="n">iou_score</span><span class="o">=</span><span class="n">iou_score</span><span class="p">,</span> <span class="n">data_output_format</span><span class="o">=</span><span class="n">data_output_format</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<div class="viewcode-block" id="KFTrack4DSORT.predict"><a class="viewcode-back" href="../../includeme/apidocuments.html#motrackers.track.KFTrack4DSORT.predict">[docs]</a> <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kf</span><span class="o">.</span><span class="n">predict</span><span class="p">()</span>
<span class="n">bb</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="mi">6</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="mi">9</span><span class="p">]])</span>
<span class="k">return</span> <span class="n">bb</span></div>
<div class="viewcode-block" id="KFTrack4DSORT.update"><a class="viewcode-back" href="../../includeme/apidocuments.html#motrackers.track.KFTrack4DSORT.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">frame_id</span><span class="p">,</span> <span class="n">bbox</span><span class="p">,</span> <span class="n">detection_confidence</span><span class="p">,</span> <span class="n">class_id</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">lost</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">iou_score</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">update</span><span class="p">(</span>
<span class="n">frame_id</span><span class="p">,</span> <span class="n">bbox</span><span class="p">,</span> <span class="n">detection_confidence</span><span class="p">,</span> <span class="n">class_id</span><span class="o">=</span><span class="n">class_id</span><span class="p">,</span> <span class="n">lost</span><span class="o">=</span><span class="n">lost</span><span class="p">,</span> <span class="n">iou_score</span><span class="o">=</span><span class="n">iou_score</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">kf</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">bbox</span><span class="o">.</span><span class="n">copy</span><span class="p">())</span></div></div>
<div class="viewcode-block" id="KFTrackCentroid"><a class="viewcode-back" href="../../includeme/apidocuments.html#motrackers.track.KFTrackCentroid">[docs]</a><span class="k">class</span> <span class="nc">KFTrackCentroid</span><span class="p">(</span><span class="n">Track</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Track based on Kalman filter used for Centroid Tracking of bounding box in MOT.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> track_id : int</span>
<span class="sd"> Track Id</span>
<span class="sd"> frame_id : int</span>
<span class="sd"> Camera frame id.</span>
<span class="sd"> bbox : numpy.ndarray</span>
<span class="sd"> Bounding box pixel coordinates as (xmin, ymin, width, height) of the track.</span>
<span class="sd"> detection_confidence : float</span>
<span class="sd"> Detection confidence of the object (probability).</span>
<span class="sd"> class_id : Object</span>
<span class="sd"> Class label id.</span>
<span class="sd"> lost : int</span>
<span class="sd"> Number of times the object or track was not tracked by tracker in consecutive frames.</span>
<span class="sd"> iou_score : float</span>
<span class="sd"> Intersection over union score.</span>
<span class="sd"> data_output_format: str</span>
<span class="sd"> Output format for data in tracker. Options ``['mot_challenge', 'visdrone_challenge']``. Default is ``mot_challenge``.</span>
<span class="sd"> process_noise_scale: float</span>
<span class="sd"> Process noise covariance scale or covariance magnitude as scalar value.</span>
<span class="sd"> measurement_noise_scale: float</span>
<span class="sd"> Measurement noise covariance scale or covariance magnitude as scalar value.</span>
<span class="sd"> kwargs : dict</span>
<span class="sd"> Additional key word arguments.</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">track_id</span><span class="p">,</span> <span class="n">frame_id</span><span class="p">,</span> <span class="n">bbox</span><span class="p">,</span> <span class="n">detection_confidence</span><span class="p">,</span> <span class="n">class_id</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">lost</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">iou_score</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span>
<span class="n">data_output_format</span><span class="o">=</span><span class="s1">'mot_challenge'</span><span class="p">,</span> <span class="n">process_noise_scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">measurement_noise_scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="n">bbox</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="mf">0.5</span><span class="o">*</span><span class="n">bbox</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">bbox</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="mf">0.5</span><span class="o">*</span><span class="n">bbox</span><span class="p">[</span><span class="mi">3</span><span class="p">]))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">kf</span> <span class="o">=</span> <span class="n">KFTracker2D</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="n">process_noise_scale</span><span class="o">=</span><span class="n">process_noise_scale</span><span class="p">,</span> <span class="n">measurement_noise_scale</span><span class="o">=</span><span class="n">measurement_noise_scale</span><span class="p">)</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">track_id</span><span class="p">,</span> <span class="n">frame_id</span><span class="p">,</span> <span class="n">bbox</span><span class="p">,</span> <span class="n">detection_confidence</span><span class="p">,</span> <span class="n">class_id</span><span class="o">=</span><span class="n">class_id</span><span class="p">,</span> <span class="n">lost</span><span class="o">=</span><span class="n">lost</span><span class="p">,</span>
<span class="n">iou_score</span><span class="o">=</span><span class="n">iou_score</span><span class="p">,</span> <span class="n">data_output_format</span><span class="o">=</span><span class="n">data_output_format</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<div class="viewcode-block" id="KFTrackCentroid.predict"><a class="viewcode-back" href="../../includeme/apidocuments.html#motrackers.track.KFTrackCentroid.predict">[docs]</a> <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Predicts the next estimate of the bounding box of the track.</span>
<span class="sd"> Returns:</span>
<span class="sd"> numpy.ndarray: Bounding box pixel coordinates as (xmin, ymin, width, height) of the track.</span>
<span class="sd"> """</span>
<span class="n">s</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kf</span><span class="o">.</span><span class="n">predict</span><span class="p">()</span>
<span class="n">xmid</span><span class="p">,</span> <span class="n">ymid</span> <span class="o">=</span> <span class="n">s</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">s</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
<span class="n">w</span><span class="p">,</span> <span class="n">h</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bbox</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">bbox</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
<span class="n">xmin</span> <span class="o">=</span> <span class="n">xmid</span> <span class="o">-</span> <span class="mf">0.5</span><span class="o">*</span><span class="n">w</span>
<span class="n">ymin</span> <span class="o">=</span> <span class="n">ymid</span> <span class="o">-</span> <span class="mf">0.5</span><span class="o">*</span><span class="n">h</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">xmin</span><span class="p">,</span> <span class="n">ymin</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">h</span><span class="p">])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span></div>
<div class="viewcode-block" id="KFTrackCentroid.update"><a class="viewcode-back" href="../../includeme/apidocuments.html#motrackers.track.KFTrackCentroid.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">frame_id</span><span class="p">,</span> <span class="n">bbox</span><span class="p">,</span> <span class="n">detection_confidence</span><span class="p">,</span> <span class="n">class_id</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">lost</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">iou_score</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">update</span><span class="p">(</span>
<span class="n">frame_id</span><span class="p">,</span> <span class="n">bbox</span><span class="p">,</span> <span class="n">detection_confidence</span><span class="p">,</span> <span class="n">class_id</span><span class="o">=</span><span class="n">class_id</span><span class="p">,</span> <span class="n">lost</span><span class="o">=</span><span class="n">lost</span><span class="p">,</span> <span class="n">iou_score</span><span class="o">=</span><span class="n">iou_score</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">kf</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">centroid</span><span class="p">)</span></div></div>
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