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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>
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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.tracker</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</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">scipy.spatial</span> <span class="kn">import</span> <span class="n">distance</span>
<span class="kn">from</span> <span class="nn">motrackers.utils.misc</span> <span class="kn">import</span> <span class="n">get_centroid</span>
<span class="kn">from</span> <span class="nn">motrackers.track</span> <span class="kn">import</span> <span class="n">Track</span>
<div class="viewcode-block" id="Tracker"><a class="viewcode-back" href="../../includeme/apidocuments.html#motrackers.tracker.Tracker">[docs]</a><span class="k">class</span> <span class="nc">Tracker</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> Greedy Tracker with tracking based on ``centroid`` location of the bounding box of the object.</span>
<span class="sd"> This tracker is also referred as ``CentroidTracker`` in this repository.</span>
<span class="sd"> Args:</span>
<span class="sd"> max_lost (int): Maximum number of consecutive frames object was not detected.</span>
<span class="sd"> tracker_output_format (str): Output format of the tracker.</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">max_lost</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">tracker_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">next_track_id</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tracks</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">max_lost</span> <span class="o">=</span> <span class="n">max_lost</span>
<span class="bp">self</span><span class="o">.</span><span class="n">frame_count</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tracker_output_format</span> <span class="o">=</span> <span class="n">tracker_output_format</span>
<span class="k">def</span> <span class="nf">_add_track</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="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Add a newly detected object to the queue.</span>
<span class="sd"> Args:</span>
<span class="sd"> frame_id (int): Camera frame id.</span>
<span class="sd"> bbox (numpy.ndarray): Bounding box pixel coordinates as (xmin, ymin, xmax, ymax) of the track.</span>
<span class="sd"> detection_confidence (float): Detection confidence of the object (probability).</span>
<span class="sd"> class_id (str or int): Class label id.</span>
<span class="sd"> kwargs (dict): Additional key word arguments.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tracks</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">next_track_id</span><span class="p">]</span> <span class="o">=</span> <span class="n">Track</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">next_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">data_output_format</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tracker_output_format</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">next_track_id</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">def</span> <span class="nf">_remove_track</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="sd">"""</span>
<span class="sd"> Remove tracker data after object is lost.</span>
<span class="sd"> Args:</span>
<span class="sd"> track_id (int): track_id of the track lost while tracking.</span>
<span class="sd"> """</span>
<span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">tracks</span><span class="p">[</span><span class="n">track_id</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">_update_track</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="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 track state.</span>
<span class="sd"> Args:</span>
<span class="sd"> track_id (int): ID of the track.</span>
<span class="sd"> frame_id (int): Frame count.</span>
<span class="sd"> bbox (numpy.ndarray or list): Bounding box coordinates as `(xmin, ymin, width, height)`.</span>
<span class="sd"> detection_confidence (float): Detection confidence (a.k.a. detection probability).</span>
<span class="sd"> class_id (int): ID of the class (aka label) of the object being tracked.</span>
<span class="sd"> lost (int): Number of frames the object was lost while tracking.</span>
<span class="sd"> iou_score (float): Intersection over union.</span>
<span class="sd"> kwargs (dict): Additional keyword arguments.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tracks</span><span class="p">[</span><span class="n">track_id</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="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">_get_tracks</span><span class="p">(</span><span class="n">tracks</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Output the information of tracks.</span>
<span class="sd"> Args:</span>
<span class="sd"> tracks (OrderedDict): Tracks dictionary with (key, value) as (track_id, corresponding `Track` objects).</span>
<span class="sd"> Returns:</span>
<span class="sd"> list: List of tracks being currently tracked by the tracker.</span>
<span class="sd"> """</span>
<span class="n">outputs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">trackid</span><span class="p">,</span> <span class="n">track</span> <span class="ow">in</span> <span class="n">tracks</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">track</span><span class="o">.</span><span class="n">lost</span><span class="p">:</span>
<span class="n">outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">track</span><span class="o">.</span><span class="n">output</span><span class="p">())</span>
<span class="k">return</span> <span class="n">outputs</span>
<div class="viewcode-block" id="Tracker.preprocess_input"><a class="viewcode-back" href="../../includeme/apidocuments.html#motrackers.tracker.Tracker.preprocess_input">[docs]</a> <span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">preprocess_input</span><span class="p">(</span><span class="n">bboxes</span><span class="p">,</span> <span class="n">class_ids</span><span class="p">,</span> <span class="n">detection_scores</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Preprocess the input data.</span>
<span class="sd"> Args:</span>
<span class="sd"> bboxes (list or numpy.ndarray): Array of bounding boxes with each bbox as a tuple containing `(xmin, ymin, width, height)`.</span>
<span class="sd"> class_ids (list or numpy.ndarray): Array of Class ID or label ID.</span>
<span class="sd"> detection_scores (list or numpy.ndarray): Array of detection scores (a.k.a. detection probabilities).</span>
<span class="sd"> Returns:</span>
<span class="sd"> detections (list[Tuple]): Data for detections as list of tuples containing `(bbox, class_id, detection_score)`.</span>
<span class="sd"> """</span>
<span class="n">new_bboxes</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">bboxes</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'int'</span><span class="p">)</span>
<span class="n">new_class_ids</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">class_ids</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'int'</span><span class="p">)</span>
<span class="n">new_detection_scores</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">detection_scores</span><span class="p">)</span>
<span class="n">new_detections</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">new_bboxes</span><span class="p">,</span> <span class="n">new_class_ids</span><span class="p">,</span> <span class="n">new_detection_scores</span><span class="p">))</span>
<span class="k">return</span> <span class="n">new_detections</span></div>
<div class="viewcode-block" id="Tracker.update"><a class="viewcode-back" href="../../includeme/apidocuments.html#motrackers.tracker.Tracker.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">bboxes</span><span class="p">,</span> <span class="n">detection_scores</span><span class="p">,</span> <span class="n">class_ids</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Update the tracker based on the new bounding boxes.</span>
<span class="sd"> Args:</span>
<span class="sd"> bboxes (numpy.ndarray or list): List of bounding boxes detected in the current frame. Each element of the list represent</span>
<span class="sd"> coordinates of bounding box as tuple `(top-left-x, top-left-y, width, height)`.</span>
<span class="sd"> detection_scores(numpy.ndarray or list): List of detection scores (probability) of each detected object.</span>
<span class="sd"> class_ids (numpy.ndarray or list): List of class_ids (int) corresponding to labels of the detected object. Default is `None`.</span>
<span class="sd"> Returns:</span>
<span class="sd"> list: List of tracks being currently tracked by the tracker. Each track is represented by the tuple with elements `(frame_id, track_id, bb_left, bb_top, bb_width, bb_height, conf, x, y, z)`.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">frame_count</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">bboxes</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">lost_ids</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tracks</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="k">for</span> <span class="n">track_id</span> <span class="ow">in</span> <span class="n">lost_ids</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tracks</span><span class="p">[</span><span class="n">track_id</span><span class="p">]</span><span class="o">.</span><span class="n">lost</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">tracks</span><span class="p">[</span><span class="n">track_id</span><span class="p">]</span><span class="o">.</span><span class="n">lost</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lost</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_remove_track</span><span class="p">(</span><span class="n">track_id</span><span class="p">)</span>
<span class="n">outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_tracks</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tracks</span><span class="p">)</span>
<span class="k">return</span> <span class="n">outputs</span>
<span class="n">detections</span> <span class="o">=</span> <span class="n">Tracker</span><span class="o">.</span><span class="n">preprocess_input</span><span class="p">(</span><span class="n">bboxes</span><span class="p">,</span> <span class="n">class_ids</span><span class="p">,</span> <span class="n">detection_scores</span><span class="p">)</span>
<span class="n">track_ids</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tracks</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="n">updated_tracks</span><span class="p">,</span> <span class="n">updated_detections</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">track_ids</span><span class="p">):</span>
<span class="n">track_centroids</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="bp">self</span><span class="o">.</span><span class="n">tracks</span><span class="p">[</span><span class="n">tid</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span> <span class="k">for</span> <span class="n">tid</span> <span class="ow">in</span> <span class="n">track_ids</span><span class="p">])</span>
<span class="n">detection_centroids</span> <span class="o">=</span> <span class="n">get_centroid</span><span class="p">(</span><span class="n">bboxes</span><span class="p">)</span>
<span class="n">centroid_distances</span> <span class="o">=</span> <span class="n">distance</span><span class="o">.</span><span class="n">cdist</span><span class="p">(</span><span class="n">track_centroids</span><span class="p">,</span> <span class="n">detection_centroids</span><span class="p">)</span>
<span class="n">track_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">amin</span><span class="p">(</span><span class="n">centroid_distances</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">argsort</span><span class="p">()</span>
<span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">track_indices</span><span class="p">:</span>
<span class="n">track_id</span> <span class="o">=</span> <span class="n">track_ids</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
<span class="n">remaining_detections</span> <span class="o">=</span> <span class="p">[</span>
<span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">centroid_distances</span><span class="p">[</span><span class="n">idx</span><span class="p">,</span> <span class="p">:])</span> <span class="k">if</span> <span class="n">i</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">updated_detections</span><span class="p">]</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">remaining_detections</span><span class="p">):</span>
<span class="n">detection_idx</span><span class="p">,</span> <span class="n">detection_distance</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">remaining_detections</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</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="n">bbox</span><span class="p">,</span> <span class="n">class_id</span><span class="p">,</span> <span class="n">confidence</span> <span class="o">=</span> <span class="n">detections</span><span class="p">[</span><span class="n">detection_idx</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_update_track</span><span class="p">(</span><span class="n">track_id</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">frame_count</span><span class="p">,</span> <span class="n">bbox</span><span class="p">,</span> <span class="n">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">updated_detections</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">detection_idx</span><span class="p">)</span>
<span class="n">updated_tracks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">track_id</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">updated_tracks</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">track_id</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">updated_tracks</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tracks</span><span class="p">[</span><span class="n">track_id</span><span class="p">]</span><span class="o">.</span><span class="n">lost</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">tracks</span><span class="p">[</span><span class="n">track_id</span><span class="p">]</span><span class="o">.</span><span class="n">lost</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lost</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_remove_track</span><span class="p">(</span><span class="n">track_id</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">bbox</span><span class="p">,</span> <span class="n">class_id</span><span class="p">,</span> <span class="n">confidence</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">detections</span><span class="p">):</span>
<span class="k">if</span> <span class="n">i</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">updated_detections</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_add_track</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">frame_count</span><span class="p">,</span> <span class="n">bbox</span><span class="p">,</span> <span class="n">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">outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_tracks</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tracks</span><span class="p">)</span>
<span class="k">return</span> <span class="n">outputs</span></div></div>
</pre></div>
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