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ope_benchmark.py
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import numpy as np
from colorama import Style, Fore
from ..utils.statistics import success_overlap, success_error
class OPEBenchmark:
"""
Args:
result_path: result path of your tracker
should the same format like VOT
"""
def __init__(self, dataset):
self.dataset = dataset
def convert_bb_to_center(self, bboxes):
return np.array([(bboxes[:, 0] + (bboxes[:, 2] - 1) / 2),
(bboxes[:, 1] + (bboxes[:, 3] - 1) / 2)]).T
def convert_bb_to_norm_center(self, bboxes, gt_wh):
return self.convert_bb_to_center(bboxes) / (gt_wh+1e-16)
def eval_success(self, eval_trackers=None):
"""
Args:
eval_trackers: list of tracker name or single tracker name
Return:
res: dict of results
"""
if eval_trackers is None:
eval_trackers = self.dataset.tracker_names
if isinstance(eval_trackers, str):
eval_trackers = [eval_trackers]
success_ret = {}
for tracker_name in eval_trackers:
success_ret_ = {}
for video in self.dataset:
gt_traj = np.array(video.gt_traj)
if tracker_name not in video.pred_trajs:
tracker_traj = video.load_tracker(self.dataset.tracker_path,
tracker_name, False)
tracker_traj = np.array(tracker_traj)
else:
tracker_traj = np.array(video.pred_trajs[tracker_name])
n_frame = len(gt_traj)
if hasattr(video, 'absent'):
gt_traj = gt_traj[video.absent == 1]
tracker_traj = tracker_traj[video.absent == 1]
success_ret_[video.name] = success_overlap(gt_traj, tracker_traj, n_frame)
success_ret[tracker_name] = success_ret_
return success_ret
def eval_precision(self, eval_trackers=None):
"""
Args:
eval_trackers: list of tracker name or single tracker name
Return:
res: dict of results
"""
if eval_trackers is None:
eval_trackers = self.dataset.tracker_names
if isinstance(eval_trackers, str):
eval_trackers = [eval_trackers]
precision_ret = {}
for tracker_name in eval_trackers:
precision_ret_ = {}
for video in self.dataset:
gt_traj = np.array(video.gt_traj)
if tracker_name not in video.pred_trajs:
tracker_traj = video.load_tracker(self.dataset.tracker_path,
tracker_name, False)
tracker_traj = np.array(tracker_traj)
else:
tracker_traj = np.array(video.pred_trajs[tracker_name])
n_frame = len(gt_traj)
if hasattr(video, 'absent'):
gt_traj = gt_traj[video.absent == 1]
tracker_traj = tracker_traj[video.absent == 1]
gt_center = self.convert_bb_to_center(gt_traj)
tracker_center = self.convert_bb_to_center(tracker_traj)
thresholds = np.arange(0, 51, 1)
precision_ret_[video.name] = success_error(gt_center, tracker_center,
thresholds, n_frame)
precision_ret[tracker_name] = precision_ret_
return precision_ret
def eval_norm_precision(self, eval_trackers=None):
"""
Args:
eval_trackers: list of tracker name or single tracker name
Return:
res: dict of results
"""
if eval_trackers is None:
eval_trackers = self.dataset.tracker_names
if isinstance(eval_trackers, str):
eval_trackers = [eval_trackers]
norm_precision_ret = {}
for tracker_name in eval_trackers:
norm_precision_ret_ = {}
for video in self.dataset:
gt_traj = np.array(video.gt_traj)
if tracker_name not in video.pred_trajs:
tracker_traj = video.load_tracker(self.dataset.tracker_path,
tracker_name, False)
tracker_traj = np.array(tracker_traj)
else:
tracker_traj = np.array(video.pred_trajs[tracker_name])
n_frame = len(gt_traj)
if hasattr(video, 'absent'):
gt_traj = gt_traj[video.absent == 1]
tracker_traj = tracker_traj[video.absent == 1]
gt_center_norm = self.convert_bb_to_norm_center(gt_traj, gt_traj[:, 2:4])
tracker_center_norm = self.convert_bb_to_norm_center(tracker_traj, gt_traj[:, 2:4])
thresholds = np.arange(0, 51, 1) / 100
norm_precision_ret_[video.name] = success_error(gt_center_norm,
tracker_center_norm, thresholds, n_frame)
norm_precision_ret[tracker_name] = norm_precision_ret_
return norm_precision_ret
def show_result(self, success_ret, precision_ret=None,
norm_precision_ret=None, show_video_level=False, helight_threshold=0.6,sortmethod='name'):
"""pretty print result
Args:
result: returned dict from function eval
"""
# sort tracker
tracker_auc = {}
for tracker_name in success_ret.keys():
auc = np.mean(list(success_ret[tracker_name].values()))
tracker_auc[tracker_name] = auc
if sortmethod=='name':
tracker_auc_ = sorted(tracker_auc.items(),
key=lambda x:x[0],
reverse=True)[:] #[:20] sort as success
else:
tracker_auc_ = sorted(tracker_auc.items(),
key=lambda x:x[1],
reverse=True)[:] #sort as name
tracker_names = [x[0] for x in tracker_auc_]
tracker_name_len = max((max([len(x) for x in success_ret.keys()])+2), 12)
header = ("|{:^"+str(tracker_name_len)+"}|{:^9}|{:^16}|{:^11}|").format(
"Tracker name", "Success", "Norm Precision", "Precision")
formatter = "|{:^"+str(tracker_name_len)+"}|{:^9.3f}|{:^16.3f}|{:^11.3f}|"
print('-'*len(header))
print(header)
print('-'*len(header))
for tracker_name in tracker_names:
# success = np.mean(list(success_ret[tracker_name].values()))
success = tracker_auc[tracker_name]
if precision_ret is not None:
precision = np.mean(list(precision_ret[tracker_name].values()), axis=0)[20]
else:
precision = 0
if norm_precision_ret is not None:
norm_precision = np.mean(list(norm_precision_ret[tracker_name].values()),
axis=0)[20]
else:
norm_precision = 0
print(formatter.format(tracker_name, success, norm_precision, precision))
print('-'*len(header))
if show_video_level and len(success_ret) < 10 \
and precision_ret is not None \
and len(precision_ret) < 10:
print("\n\n")
header1 = "|{:^21}|".format("Tracker name")
header2 = "|{:^21}|".format("Video name")
for tracker_name in success_ret.keys():
# col_len = max(20, len(tracker_name))
header1 += ("{:^21}|").format(tracker_name)
header2 += "{:^9}|{:^11}|".format("success", "precision")
print('-'*len(header1))
print(header1)
print('-'*len(header1))
print(header2)
print('-'*len(header1))
videos = list(success_ret[tracker_name].keys())
for video in videos:
row = "|{:^21}|".format(video)
for tracker_name in success_ret.keys():
success = np.mean(success_ret[tracker_name][video])
precision = np.mean(precision_ret[tracker_name][video])
success_str = "{:^9.3f}".format(success)
if success < helight_threshold:
row += Fore.RED+success_str+Style.RESET_ALL+'|'
else:
row += success_str+'|'
precision_str = "{:^11.3f}".format(precision)
if precision < helight_threshold:
row += Fore.RED+precision_str+Style.RESET_ALL+'|'
else:
row += precision_str+'|'
print(row)
print('-'*len(header1))