add lables to detections

Author: adam-dziedzic

demo.py

@@ -18,7 +18,8 @@ def main(args):
     detections = load_mot(args.detection_path)
 
     start = time()
-    tracks = track_iou(detections, args.sigma_l, args.sigma_h, args.sigma_iou, args.t_min)
+    tracks = track_iou(detections, args.sigma_l, args.sigma_h, args.sigma_iou,
+                       args.t_min)
     end = time()
 
     num_frames = len(detections)
@@ -28,12 +29,12 @@ def main(args):
 
 
 if __name__ == '__main__':
-
     parser = argparse.ArgumentParser(description="IOU Tracker demo script")
     parser.add_argument('-d', '--detection_path', type=str, required=True,
                         help="full path to CSV file containing the detections")
     parser.add_argument('-o', '--output_path', type=str, required=True,
-                        help="output path to store the tracking results (MOT challenge devkit compatible format)")
+                        help="output path to store the tracking results "
+                             "(MOT challenge devkit compatible format)")
     parser.add_argument('-sl', '--sigma_l', type=float, default=0,
                         help="low detection threshold")
     parser.add_argument('-sh', '--sigma_h', type=float, default=0.5,

iou_tracker.py

@@ -13,11 +13,13 @@
 def track_iou(detections, sigma_l, sigma_h, sigma_iou, t_min):
     """
     Simple IOU based tracker.
-    See "High-Speed Tracking-by-Detection Without Using Image Information by E. Bochinski, V. Eiselein, T. Sikora" for
+    See "High-Speed Tracking-by-Detection Without Using Image Information
+    by E. Bochinski, V. Eiselein, T. Sikora" for
     more information.
 
     Args:
-         detections (list): list of detections per frame, usually generated by util.load_mot
+         detections (list): list of detections per frame, usually generated by
+         util.load_mot
          sigma_l (float): low detection threshold.
          sigma_h (float): high detection threshold.
          sigma_iou (float): IOU threshold.
@@ -38,10 +40,12 @@ def track_iou(detections, sigma_l, sigma_h, sigma_iou, t_min):
         for track in tracks_active:
             if len(dets) > 0:
                 # get det with highest iou
-                best_match = max(dets, key=lambda x: iou(track['bboxes'][-1], x['bbox']))
+                best_match = max(dets, key=lambda x: iou(track['bboxes'][-1],
+                                                         x['bbox']))
                 if iou(track['bboxes'][-1], best_match['bbox']) >= sigma_iou:
                     track['bboxes'].append(best_match['bbox'])
-                    track['max_score'] = max(track['max_score'], best_match['score'])
+                    track['max_score'] = max(track['max_score'],
+                                             best_match['score'])
 
                     updated_tracks.append(track)
 
@@ -51,16 +55,19 @@ def track_iou(detections, sigma_l, sigma_h, sigma_iou, t_min):
             # if track was not updated
             if len(updated_tracks) == 0 or track is not updated_tracks[-1]:
                 # finish track when the conditions are met
-                if track['max_score'] >= sigma_h and len(track['bboxes']) >= t_min:
+                if track['max_score'] >= sigma_h and len(
+                        track['bboxes']) >= t_min:
                     tracks_finished.append(track)
 
         # create new tracks
-        new_tracks = [{'bboxes': [det['bbox']], 'max_score': det['score'], 'start_frame': frame_num} for det in dets]
+        new_tracks = [{'bboxes': [det['bbox']], 'max_score': det['score'],
+                       'start_frame': frame_num} for det in dets]
         tracks_active = updated_tracks + new_tracks
 
     # finish all remaining active tracks
     tracks_finished += [track for track in tracks_active
-                        if track['max_score'] >= sigma_h and len(track['bboxes']) >= t_min]
+                        if track['max_score'] >= sigma_h and len(
+            track['bboxes']) >= t_min]
 
     return tracks_finished
 
@@ -91,7 +98,8 @@ def track_iou_matlab_wrapper(detections, sigma_l, sigma_h, sigma_iou, t_min):
     out = []
     for track in tracks:
         for i, bbox in enumerate(track['bboxes']):
-            out += [float(bbox[0]), float(bbox[1]), float(bbox[2] - bbox[0]), float(bbox[3] - bbox[1]),
+            out += [float(bbox[0]), float(bbox[1]), float(bbox[2] - bbox[0]),
+                    float(bbox[3] - bbox[1]),
                     float(track['start_frame'] + i), float(id_)]
         id_ += 1
 

mot16.py

@@ -28,24 +28,29 @@ def main(args):
             detections = load_mot(det_path)
 
             start = time()
-            tracks = track_iou(detections, args.sigma_l, args.sigma_h, args.sigma_iou, args.t_min)
+            tracks = track_iou(detections, args.sigma_l, args.sigma_h,
+                               args.sigma_iou, args.t_min)
             end = time()
 
             num_frames = len(detections)
-            print("finished " + seq + " at " + str(int(num_frames / (end - start))) + " fps!")
+            print("finished " + seq + " at " + str(
+                int(num_frames / (end - start))) + " fps!")
 
             save_to_csv(out_path, tracks)
 
 
 if __name__ == '__main__':
-
-    parser = argparse.ArgumentParser(description="IOU Tracker MOT demo script. Default parameters are set to reproduce "
-                                                 "the results using the SDP detections.")
-    parser.add_argument('-m', '--seqmap', type=str, required=True,
+    parser = argparse.ArgumentParser(
+        description="IOU Tracker MOT demo script. Default parameters are set to "
+                    "reproduce the results using the SDP detections.")
+    parser.add_argument('-m', '--seqmap', type=str,
+                        default="../motchallenge/seqmaps/c5-train.txt",
                         help="full path to the seqmap file to evaluate")
-    parser.add_argument('-o', '--res_dir', type=str, required=True,
+    parser.add_argument('-o', '--res_dir', type=str,
+                        default="../motchallenge/res/MOT16/iou-tracker",
                         help="path to the results directory")
-    parser.add_argument('-b', '--benchmark_dir', type=str, required=True,
+    parser.add_argument('-b', '--benchmark_dir', type=str,
+                        default="../motchallenge/MOT16/train",
                         help="path to the sequence directory")
     parser.add_argument('-sl', '--sigma_l', type=float, default=0.3,
                         help="low detection threshold")

mot17.py

@@ -38,10 +38,13 @@ def main(args):
                 sigma_iou = 0.2
                 t_min = 2
             else:
-                print("No detector name found, this could happen with the wrong seqmap seqmap file. "
-                      "Please use c10-train.txt, c10-test.txt or c10-all.txt")
+                print(
+                    "No detector name found, this could happen with the "
+                    "wrong seqmap seqmap file. "
+                    "Please use c10-train.txt, c10-test.txt or c10-all.txt")
                 exit()
 
+            # Take the pre-computed detections.
             det_path = args.benchmark_dir + "/" + seq + "/det/det.txt"
             out_path = args.res_dir + "/" + seq + ".txt"
 
@@ -52,19 +55,24 @@ def main(args):
             end = time()
 
             num_frames = len(detections)
-            print("finished " + seq + " at " + str(int(num_frames / (end - start))) + " fps!")
+            print("finished " + seq + " at " + str(
+                int(num_frames / (end - start))) + " fps!")
 
             save_to_csv(out_path, tracks)
 
-if __name__ == '__main__':
 
-    parser = argparse.ArgumentParser(description="IOU Tracker MOT17 demo script. The best parameters for each detector "
-                                     "are hardcoded.")
-    parser.add_argument('-m', '--seqmap', type=str, required=True,
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(
+        description="IOU Tracker MOT17 demo script. The best parameters for "
+                    "each detector are hardcoded.")
+    parser.add_argument('-m', '--seqmap', type=str,
+                        default="../motchallenge/seqmaps/c10-train-sample.txt",
                         help="full path to the seqmap file to evaluate")
-    parser.add_argument('-o', '--res_dir', type=str, required=True,
+    parser.add_argument('-o', '--res_dir', type=str,
+                        default="../motchallenge/res/MOT17/iou-tracker",
                         help="path to the results directory")
-    parser.add_argument('-b', '--benchmark_dir', type=str, required=True,
+    parser.add_argument('-b', '--benchmark_dir', type=str,
+                        default="../motchallenge/",
                         help="path to the sequence directory")
 
     args = parser.parse_args()

seqmaps/mot16-dpm-all.txt

@@ -0,0 +1,15 @@
+name
+MOT16-01
+MOT16-02
+MOT16-03
+MOT16-04
+MOT16-05
+MOT16-06
+MOT16-07
+MOT16-08
+MOT16-09
+MOT16-10
+MOT16-11
+MOT16-12
+MOT16-13
+MOT16-14

seqmaps/mot16-dpm-test.txt

@@ -0,0 +1,8 @@
+name
+MOT16-01
+MOT16-03
+MOT16-06
+MOT16-07
+MOT16-08
+MOT16-12
+MOT16-14

seqmaps/mot16-dpm-train.txt

@@ -0,0 +1,8 @@
+name
+MOT16-02
+MOT16-04
+MOT16-05
+MOT16-09
+MOT16-10
+MOT16-11
+MOT16-13

util.py

@@ -11,8 +11,9 @@
 
 def load_mot(detections):
     """
-    Loads detections stored in a mot-challenge like formatted CSV or numpy array (fieldNames = ['frame', 'id', 'x', 'y',
-    'w', 'h', 'score']).
+    Loads detections stored in a mot-challenge like formatted CSV or numpy array
+    (fieldNames = ['frame', 'id', 'x', 'y', 'w', 'h', 'score', 'class',
+    'visibility_ratio']).
 
     Args:
         detections
@@ -26,18 +27,21 @@ def load_mot(detections):
         raw = np.genfromtxt(detections, delimiter=',', dtype=np.float32)
     else:
         # assume it is an array
-        assert isinstance(detections, np.ndarray), "only numpy arrays or *.csv paths are supported as detections."
+        assert isinstance(detections,
+                          np.ndarray), "only numpy arrays or *.csv paths are supported as detections."
         raw = detections.astype(np.float32)
 
     end_frame = int(np.max(raw[:, 0]))
-    for i in range(1, end_frame+1):
+    for i in range(1, end_frame + 1):
         idx = raw[:, 0] == i
         bbox = raw[idx, 2:6]
         bbox[:, 2:4] += bbox[:, 0:2]  # x1, y1, w, h -> x1, y1, x2, y2
         scores = raw[idx, 6]
+        labels = raw[idx, 7]  # extract column 7 for the indexes (idx) from raw
         dets = []
-        for bb, s in zip(bbox, scores):
-            dets.append({'bbox': (bb[0], bb[1], bb[2], bb[3]), 'score': s})
+        for bb, s, label in zip(bbox, scores, labels):
+            dets.append({'bbox': (bb[0], bb[1], bb[2], bb[3]), 'score': s,
+                         'label': label})
         data.append(dets)
 
     return data
@@ -52,8 +56,9 @@ def save_to_csv(out_path, tracks):
         tracks (list): list of tracks to store.
     """
 
-    with open(out_path, "w") as ofile:
-        field_names = ['frame', 'id', 'x', 'y', 'w', 'h', 'score', 'wx', 'wy', 'wz']
+    with open(out_path, "w", newline="") as ofile:
+        field_names = ['frame', 'id', 'x', 'y', 'w', 'h', 'score', 'wx', 'wy',
+                       'wz']
 
         odict = csv.DictWriter(ofile, field_names)
         id_ = 1