Fix HOTA overall aggregation

motmetrics/io.py

@@ -17,7 +17,6 @@
 import numpy as np
 import pandas as pd
 import scipy.io
-import xmltodict
 
 
 class Format(Enum):
@@ -267,6 +266,7 @@ def load_detrac_xml(fname):
             'X', 'Y', 'Width', 'Height', 'Confidence', 'ClassId', 'Visibility'
         The dataframe is indexed by ('FrameId', 'Id')
     """
+    import xmltodict
 
     with io.open(fname) as fd:
         doc = xmltodict.parse(fd.read())

motmetrics/metrics.py

@@ -619,11 +619,9 @@ def deta_alpha(df, num_detections, num_objects, num_false_positives):
     return math_util.quiet_divide(num_detections, max(1, num_objects + num_false_positives))
 
 
-def deta_alpha_m(partials):
-    res = 0
-    for v in partials:
-        res += v["deta_alpha"]
-    return math_util.quiet_divide(res, len(partials))
+def deta_alpha_m(partials, num_detections, num_objects, num_false_positives):
+    del partials  # unused
+    return math_util.quiet_divide(num_detections, max(1, num_objects + num_false_positives))
 
 
 def assa_alpha(df, num_detections, num_gt_ids, num_dt_ids):
@@ -654,11 +652,11 @@ def assa_alpha(df, num_detections, num_gt_ids, num_dt_ids):
     return math_util.quiet_divide((ass_a * match_count_array).sum(), max(1, num_detections))
 
 
-def assa_alpha_m(partials):
-    res = 0
+def assa_alpha_m(partials, num_detections):
+    weighted_sum = 0
     for v in partials:
-        res += v["assa_alpha"]
-    return math_util.quiet_divide(res, len(partials))
+        weighted_sum += v["assa_alpha"] * v["num_detections"]
+    return math_util.quiet_divide(weighted_sum, max(1, num_detections))
 
 
 def hota_alpha(df, deta_alpha, assa_alpha):
@@ -667,11 +665,9 @@ def hota_alpha(df, deta_alpha, assa_alpha):
     return (deta_alpha * assa_alpha) ** 0.5
 
 
-def hota_alpha_m(partials):
-    res = 0
-    for v in partials:
-        res += v["hota_alpha"]
-    return math_util.quiet_divide(res, len(partials))
+def hota_alpha_m(partials, deta_alpha, assa_alpha):
+    del partials  # unused
+    return (deta_alpha * assa_alpha) ** 0.5
 
 
 class DataFrameMap:  # pylint: disable=too-few-public-methods

motmetrics/tests/test_metrics.py

@@ -544,6 +544,11 @@ def test_hota():
         "TUD-Campus": {"hota": 0.3913974378451139, "deta": 0.418047030142763, "assa": 0.36912068120832836},
         "TUD-Stadtmitte": {"hota": 0.3978490169927877, "deta": 0.3922675723693166, "assa": 0.4088407518112996}
     }
+    TUD_combined_golden_ans = {  # TrackEval combine_sequences over both TUD sequences.
+        "hota": 0.3999570912884786,
+        "deta": 0.3976832912424188,
+        "assa": 0.4124495298453543,
+    }
 
     DATA_DIR = "motmetrics/data"
 
@@ -583,3 +588,25 @@ def compute_motchallenge(dname):
         assert deta == approx(TUD_golden_ans[dname]["deta"])
         assert assa == approx(TUD_golden_ans[dname]["assa"])
         assert hota == approx(TUD_golden_ans[dname]["hota"])
+
+    deta = []
+    assa = []
+    hota = []
+    for alpha_idx in range(len(accs[0])):
+        summary = mh.compute_many(
+            [seq_accs[alpha_idx] for seq_accs in accs],
+            metrics=[
+                "deta_alpha",
+                "assa_alpha",
+                "hota_alpha",
+            ],
+            names=list(TUD_golden_ans),
+            generate_overall=True,
+        )
+        deta.append(float(summary.loc["OVERALL", "deta_alpha"]))
+        assa.append(float(summary.loc["OVERALL", "assa_alpha"]))
+        hota.append(float(summary.loc["OVERALL", "hota_alpha"]))
+
+    assert sum(deta) / len(deta) == approx(TUD_combined_golden_ans["deta"])
+    assert sum(assa) / len(assa) == approx(TUD_combined_golden_ans["assa"])
+    assert sum(hota) / len(hota) == approx(TUD_combined_golden_ans["hota"])