292 lines
12 KiB
Python
292 lines
12 KiB
Python
# flake8: noqa
|
|
|
|
import os
|
|
|
|
import numpy as np
|
|
from scipy.optimize import linear_sum_assignment
|
|
|
|
from .. import _timing
|
|
from ._base_metric import _BaseMetric
|
|
|
|
|
|
class HOTA(_BaseMetric):
|
|
"""Class which implements the HOTA metrics.
|
|
See: https://link.springer.com/article/10.1007/s11263-020-01375-2
|
|
"""
|
|
|
|
def __init__(self, config=None):
|
|
super().__init__()
|
|
self.plottable = True
|
|
self.array_labels = np.arange(0.05, 0.99, 0.05)
|
|
self.integer_array_fields = ["HOTA_TP", "HOTA_FN", "HOTA_FP"]
|
|
self.float_array_fields = [
|
|
"HOTA",
|
|
"DetA",
|
|
"AssA",
|
|
"DetRe",
|
|
"DetPr",
|
|
"AssRe",
|
|
"AssPr",
|
|
"LocA",
|
|
"OWTA",
|
|
]
|
|
self.float_fields = ["HOTA(0)", "LocA(0)", "HOTALocA(0)"]
|
|
self.fields = (
|
|
self.float_array_fields + self.integer_array_fields + self.float_fields
|
|
)
|
|
self.summary_fields = self.float_array_fields + self.float_fields
|
|
|
|
@_timing.time
|
|
def eval_sequence(self, data):
|
|
"""Calculates the HOTA metrics for one sequence"""
|
|
|
|
# Initialise results
|
|
res = {}
|
|
for field in self.float_array_fields + self.integer_array_fields:
|
|
res[field] = np.zeros((len(self.array_labels)), dtype=float)
|
|
for field in self.float_fields:
|
|
res[field] = 0
|
|
|
|
# Return result quickly if tracker or gt sequence is empty
|
|
if data["num_tracker_dets"] == 0:
|
|
res["HOTA_FN"] = data["num_gt_dets"] * np.ones(
|
|
(len(self.array_labels)), dtype=float
|
|
)
|
|
res["LocA"] = np.ones((len(self.array_labels)), dtype=float)
|
|
res["LocA(0)"] = 1.0
|
|
return res
|
|
if data["num_gt_dets"] == 0:
|
|
res["HOTA_FP"] = data["num_tracker_dets"] * np.ones(
|
|
(len(self.array_labels)), dtype=float
|
|
)
|
|
res["LocA"] = np.ones((len(self.array_labels)), dtype=float)
|
|
res["LocA(0)"] = 1.0
|
|
return res
|
|
|
|
# Variables counting global association
|
|
potential_matches_count = np.zeros(
|
|
(data["num_gt_ids"], data["num_tracker_ids"])
|
|
)
|
|
gt_id_count = np.zeros((data["num_gt_ids"], 1))
|
|
tracker_id_count = np.zeros((1, data["num_tracker_ids"]))
|
|
|
|
# First loop through each timestep and accumulate global track information.
|
|
for t, (gt_ids_t, tracker_ids_t) in enumerate(
|
|
zip(data["gt_ids"], data["tracker_ids"])
|
|
):
|
|
# Count the potential matches between ids in each timestep
|
|
# These are normalised, weighted by the match similarity.
|
|
similarity = data["similarity_scores"][t]
|
|
sim_iou_denom = (
|
|
similarity.sum(0)[np.newaxis, :]
|
|
+ similarity.sum(1)[:, np.newaxis]
|
|
- similarity
|
|
)
|
|
sim_iou = np.zeros_like(similarity)
|
|
sim_iou_mask = sim_iou_denom > 0 + np.finfo("float").eps
|
|
sim_iou[sim_iou_mask] = (
|
|
similarity[sim_iou_mask] / sim_iou_denom[sim_iou_mask]
|
|
)
|
|
potential_matches_count[
|
|
gt_ids_t[:, np.newaxis], tracker_ids_t[np.newaxis, :]
|
|
] += sim_iou
|
|
|
|
# Calculate the total number of dets for each gt_id and tracker_id.
|
|
gt_id_count[gt_ids_t] += 1
|
|
tracker_id_count[0, tracker_ids_t] += 1
|
|
|
|
# Calculate overall jaccard alignment score (before unique matching) between IDs
|
|
global_alignment_score = potential_matches_count / (
|
|
gt_id_count + tracker_id_count - potential_matches_count
|
|
)
|
|
matches_counts = [
|
|
np.zeros_like(potential_matches_count) for _ in self.array_labels
|
|
]
|
|
|
|
# Calculate scores for each timestep
|
|
for t, (gt_ids_t, tracker_ids_t) in enumerate(
|
|
zip(data["gt_ids"], data["tracker_ids"])
|
|
):
|
|
# Deal with the case that there are no gt_det/tracker_det in a timestep.
|
|
if len(gt_ids_t) == 0:
|
|
for a, alpha in enumerate(self.array_labels):
|
|
res["HOTA_FP"][a] += len(tracker_ids_t)
|
|
continue
|
|
if len(tracker_ids_t) == 0:
|
|
for a, alpha in enumerate(self.array_labels):
|
|
res["HOTA_FN"][a] += len(gt_ids_t)
|
|
continue
|
|
|
|
# Get matching scores between pairs of dets for optimizing HOTA
|
|
similarity = data["similarity_scores"][t]
|
|
score_mat = (
|
|
global_alignment_score[
|
|
gt_ids_t[:, np.newaxis], tracker_ids_t[np.newaxis, :]
|
|
]
|
|
* similarity
|
|
)
|
|
|
|
# Hungarian algorithm to find best matches
|
|
match_rows, match_cols = linear_sum_assignment(-score_mat)
|
|
|
|
# Calculate and accumulate basic statistics
|
|
for a, alpha in enumerate(self.array_labels):
|
|
actually_matched_mask = (
|
|
similarity[match_rows, match_cols] >= alpha - np.finfo("float").eps
|
|
)
|
|
alpha_match_rows = match_rows[actually_matched_mask]
|
|
alpha_match_cols = match_cols[actually_matched_mask]
|
|
num_matches = len(alpha_match_rows)
|
|
res["HOTA_TP"][a] += num_matches
|
|
res["HOTA_FN"][a] += len(gt_ids_t) - num_matches
|
|
res["HOTA_FP"][a] += len(tracker_ids_t) - num_matches
|
|
if num_matches > 0:
|
|
res["LocA"][a] += sum(
|
|
similarity[alpha_match_rows, alpha_match_cols]
|
|
)
|
|
matches_counts[a][
|
|
gt_ids_t[alpha_match_rows], tracker_ids_t[alpha_match_cols]
|
|
] += 1
|
|
|
|
# Calculate association scores (AssA, AssRe, AssPr) for the alpha value.
|
|
# First calculate scores per gt_id/tracker_id combo and then average over the number of detections.
|
|
for a, alpha in enumerate(self.array_labels):
|
|
matches_count = matches_counts[a]
|
|
ass_a = matches_count / np.maximum(
|
|
1, gt_id_count + tracker_id_count - matches_count
|
|
)
|
|
res["AssA"][a] = np.sum(matches_count * ass_a) / np.maximum(
|
|
1, res["HOTA_TP"][a]
|
|
)
|
|
ass_re = matches_count / np.maximum(1, gt_id_count)
|
|
res["AssRe"][a] = np.sum(matches_count * ass_re) / np.maximum(
|
|
1, res["HOTA_TP"][a]
|
|
)
|
|
ass_pr = matches_count / np.maximum(1, tracker_id_count)
|
|
res["AssPr"][a] = np.sum(matches_count * ass_pr) / np.maximum(
|
|
1, res["HOTA_TP"][a]
|
|
)
|
|
|
|
# Calculate final scores
|
|
res["LocA"] = np.maximum(1e-10, res["LocA"]) / np.maximum(1e-10, res["HOTA_TP"])
|
|
res = self._compute_final_fields(res)
|
|
return res
|
|
|
|
def combine_sequences(self, all_res):
|
|
"""Combines metrics across all sequences"""
|
|
res = {}
|
|
for field in self.integer_array_fields:
|
|
res[field] = self._combine_sum(all_res, field)
|
|
for field in ["AssRe", "AssPr", "AssA"]:
|
|
res[field] = self._combine_weighted_av(
|
|
all_res, field, res, weight_field="HOTA_TP"
|
|
)
|
|
loca_weighted_sum = sum(
|
|
[all_res[k]["LocA"] * all_res[k]["HOTA_TP"] for k in all_res.keys()]
|
|
)
|
|
res["LocA"] = np.maximum(1e-10, loca_weighted_sum) / np.maximum(
|
|
1e-10, res["HOTA_TP"]
|
|
)
|
|
res = self._compute_final_fields(res)
|
|
return res
|
|
|
|
def combine_classes_class_averaged(self, all_res, ignore_empty_classes=False):
|
|
"""Combines metrics across all classes by averaging over the class values.
|
|
If 'ignore_empty_classes' is True, then it only sums over classes with at least one gt or predicted detection.
|
|
"""
|
|
res = {}
|
|
for field in self.integer_array_fields:
|
|
if ignore_empty_classes:
|
|
res[field] = self._combine_sum(
|
|
{
|
|
k: v
|
|
for k, v in all_res.items()
|
|
if (
|
|
v["HOTA_TP"] + v["HOTA_FN"] + v["HOTA_FP"]
|
|
> 0 + np.finfo("float").eps
|
|
).any()
|
|
},
|
|
field,
|
|
)
|
|
else:
|
|
res[field] = self._combine_sum(
|
|
{k: v for k, v in all_res.items()}, field
|
|
)
|
|
|
|
for field in self.float_fields + self.float_array_fields:
|
|
if ignore_empty_classes:
|
|
res[field] = np.mean(
|
|
[
|
|
v[field]
|
|
for v in all_res.values()
|
|
if (
|
|
v["HOTA_TP"] + v["HOTA_FN"] + v["HOTA_FP"]
|
|
> 0 + np.finfo("float").eps
|
|
).any()
|
|
],
|
|
axis=0,
|
|
)
|
|
else:
|
|
res[field] = np.mean([v[field] for v in all_res.values()], axis=0)
|
|
return res
|
|
|
|
def combine_classes_det_averaged(self, all_res):
|
|
"""Combines metrics across all classes by averaging over the detection values"""
|
|
res = {}
|
|
for field in self.integer_array_fields:
|
|
res[field] = self._combine_sum(all_res, field)
|
|
for field in ["AssRe", "AssPr", "AssA"]:
|
|
res[field] = self._combine_weighted_av(
|
|
all_res, field, res, weight_field="HOTA_TP"
|
|
)
|
|
loca_weighted_sum = sum(
|
|
[all_res[k]["LocA"] * all_res[k]["HOTA_TP"] for k in all_res.keys()]
|
|
)
|
|
res["LocA"] = np.maximum(1e-10, loca_weighted_sum) / np.maximum(
|
|
1e-10, res["HOTA_TP"]
|
|
)
|
|
res = self._compute_final_fields(res)
|
|
return res
|
|
|
|
@staticmethod
|
|
def _compute_final_fields(res):
|
|
"""Calculate sub-metric ('field') values which only depend on other sub-metric values.
|
|
This function is used both for both per-sequence calculation, and in combining values across sequences.
|
|
"""
|
|
res["DetRe"] = res["HOTA_TP"] / np.maximum(1, res["HOTA_TP"] + res["HOTA_FN"])
|
|
res["DetPr"] = res["HOTA_TP"] / np.maximum(1, res["HOTA_TP"] + res["HOTA_FP"])
|
|
res["DetA"] = res["HOTA_TP"] / np.maximum(
|
|
1, res["HOTA_TP"] + res["HOTA_FN"] + res["HOTA_FP"]
|
|
)
|
|
res["HOTA"] = np.sqrt(res["DetA"] * res["AssA"])
|
|
res["OWTA"] = np.sqrt(res["DetRe"] * res["AssA"])
|
|
|
|
res["HOTA(0)"] = res["HOTA"][0]
|
|
res["LocA(0)"] = res["LocA"][0]
|
|
res["HOTALocA(0)"] = res["HOTA(0)"] * res["LocA(0)"]
|
|
return res
|
|
|
|
def plot_single_tracker_results(self, table_res, tracker, cls, output_folder):
|
|
"""Create plot of results"""
|
|
|
|
# Only loaded when run to reduce minimum requirements
|
|
from matplotlib import pyplot as plt
|
|
|
|
res = table_res["COMBINED_SEQ"]
|
|
styles_to_plot = ["r", "b", "g", "b--", "b:", "g--", "g:", "m"]
|
|
for name, style in zip(self.float_array_fields, styles_to_plot):
|
|
plt.plot(self.array_labels, res[name], style)
|
|
plt.xlabel("alpha")
|
|
plt.ylabel("score")
|
|
plt.title(tracker + " - " + cls)
|
|
plt.axis([0, 1, 0, 1])
|
|
legend = []
|
|
for name in self.float_array_fields:
|
|
legend += [name + " (" + str(np.round(np.mean(res[name]), 2)) + ")"]
|
|
plt.legend(legend, loc="lower left")
|
|
out_file = os.path.join(output_folder, cls + "_plot.pdf")
|
|
os.makedirs(os.path.dirname(out_file), exist_ok=True)
|
|
plt.savefig(out_file)
|
|
plt.savefig(out_file.replace(".pdf", ".png"))
|
|
plt.clf()
|