Detector perf

motmetrics/metrics.py

@@ -500,6 +500,44 @@ def num_fragmentations(df, obj_frequencies):
 simple_add_func.append(num_fragmentations)
 
 
+def average_overlap(df):
+    mdf = df.full[(df.full.Type == 'MATCH') | (df.full.Type == 'MISS')]
+    overlaps = mdf.reset_index().set_index('FrameId')['D'].fillna(0).groupby('FrameId')
+    return overlaps.agg(np.mean)
+
+
+simple_add_func.append(average_overlap)
+
+
+def num_overlaps(df):
+    mdf = df.full[(df.full.Type == 'MATCH') | (df.full.Type == 'MISS')]
+    overlaps = mdf.reset_index().set_index('FrameId')['D'].fillna(0).groupby('FrameId')
+    return overlaps.count().sum()
+
+
+simple_add_func.append(num_overlaps)
+
+
+def modp(df, average_overlap):
+    del df
+    return average_overlap.mean()
+
+
+def modp_m(partials, average_overlap):
+    del partials
+    return average_overlap.mean()
+
+
+def moda(df, num_overlaps, num_misses, num_false_positives):
+    del df
+    return math_util.quiet_divide(num_misses+num_false_positives, num_overlaps)
+
+
+def moda_m(partials, num_overlaps, num_misses, num_false_positives):
+    del partials
+    return math_util.quiet_divide(num_misses+num_false_positives, num_overlaps)
+
+
 def motp(df, num_detections):
     """Multiple object tracker precision."""
     return math_util.quiet_divide(df.noraw['D'].sum(), num_detections)
@@ -742,6 +780,10 @@ def create():
     m.register(partially_tracked, formatter='{:d}'.format)
     m.register(mostly_lost, formatter='{:d}'.format)
     m.register(num_fragmentations)
+    m.register(average_overlap, formatter='{:d}'.format)
+    m.register(num_overlaps, formatter='{:d}'.format)
+    m.register(modp, formatter='{:.3f}'.format)
+    m.register(moda, formatter='{:.3f}'.format)
     m.register(motp, formatter='{:.3f}'.format)
     m.register(mota, formatter='{:.1%}'.format)
     m.register(precision, formatter='{:.1%}'.format)
@@ -772,6 +814,8 @@ def create():
     'num_misses',
     'num_switches',
     'num_fragmentations',
+    'modp',
+    'moda',
     'mota',
     'motp',
     'num_transfer',

motmetrics/tests/test_metrics.py

@@ -369,10 +369,10 @@ def compute_motchallenge(dname):
     print()
     print(mm.io.render_summary(summary, namemap=mm.io.motchallenge_metric_names, formatters=mh.formatters))
     # assert ((summary['num_transfer'] - summary['num_migrate']) == (summary['num_switches'] - summary['num_ascend'])).all() # False assertion
-    summary = summary[mm.metrics.motchallenge_metrics[:15]]
+    summary = summary[mm.metrics.motchallenge_metrics[:17]]
     expected = pd.DataFrame([
-        [0.557659, 0.729730, 0.451253, 0.582173, 0.941441, 8.0, 1, 6, 1, 13, 150, 7, 7, 0.526462, 0.277201],
-        [0.644619, 0.819760, 0.531142, 0.608997, 0.939920, 10.0, 5, 4, 1, 45, 452, 7, 6, 0.564014, 0.345904],
-        [0.624296, 0.799176, 0.512211, 0.602640, 0.940268, 18.0, 6, 10, 2, 58, 602, 14, 13, 0.555116, 0.330177],
+        [0.557659, 0.729730, 0.451253, 0.582173, 0.941441, 8.0, 1, 6, 1, 13, 150, 7, 7, 0.160351, 0.463068, 0.526462, 0.277201],
+        [0.644619, 0.819760, 0.531142, 0.608997, 0.939920, 10.0, 5, 4, 1, 45, 452, 7, 6, 0.210594, 0.432550, 0.564014, 0.345904],
+        [0.624296, 0.799176, 0.512211, 0.602640, 0.940268, 18.0, 6, 10, 2, 58, 602, 14, 13, 0.351243, 0.439707, 0.555116, 0.330177],
     ])
     np.testing.assert_allclose(summary, expected, atol=1e-3)