def loadFeatureTracks(self,
file,
method="estimation",
color=[0, 255, 0],
gt_color=[255, 0, 255]):
tracks = {}
colors = {}
tracks_dir = os.path.dirname(file)
color = [r for r in reversed(color)]
colors[method] = color
# load tracks
tracks[method] = getTrackData(file)
# load gt
tracks_csv = join(tracks_dir, "tracks.txt.gt.txt")
if isfile(tracks_csv):
gt = getTrackData(tracks_csv)
colors["gt"] = gt_color
# if true, crop all tracks from gt to have the same length as the predictions.
if self.params["crop_to_predictions"]:
gt = self.cropGT(gt, tracks[method])
if self.params["error_threshold"] > 0:
tracks[method], gt = self.discardOnThreshold(
tracks[method], gt, self.params["error_threshold"])
tracks["gt"] = gt
feature_ids = {
label: list(tracks_dict.keys())
for label, tracks_dict in tracks.items()
}
max_stamp = -1
min_stamp = 10**1000
for label, tracks_dict in tracks.items():
for i, track in tracks_dict.items():
min_stamp = min([min_stamp, min(track[:, 0])])
max_stamp = max([max_stamp, max(track[:, 0])])
self.min_stamp = min_stamp
self.max_stamp = max_stamp
return tracks, feature_ids, colors
def loadFeatureTracks(self,
file,
method="estimation",
color=[0, 255, 0],
gt_color=[255, 0, 255]):
tracks = {}
colors = {}
tracks_dir = os.path.dirname(file)
color = [r for r in reversed(color)]
colors[method] = color
# load tracks
data = np.genfromtxt(file, delimiter=" ")
first_len_tracks = len(data)
valid_ids, data = filter_first_tracks(data, filter_too_short=True)
track_data = {i: data[data[:, 0] == i, 1:] for i in valid_ids}
tracks[method] = track_data
if len(track_data) < first_len_tracks:
print(
"WARNING: This package only supports evaluation of tracks which have been initialized at the same"
"time. All tracks except the first have been discarded.")
# load gt
tracks_csv = join(tracks_dir, "tracks.txt.gt.txt")
if isfile(tracks_csv):
gt = getTrackData(tracks_csv)
colors["gt"] = gt_color
# if true, crop all tracks from gt to have the same length as the predictions.
if self.params["crop_to_predictions"]:
gt = self.cropGT(gt, tracks[method])
if self.params["error_threshold"] > 0:
tracks[method], gt = self.discardOnThreshold(
tracks[method], gt, self.params["error_threshold"])
tracks["gt"] = gt
feature_ids = {
label: list(tracks_dict.keys())
for label, tracks_dict in tracks.items()
}
max_stamp = -1
min_stamp = 10**1000
for label, tracks_dict in tracks.items():
for i, track in tracks_dict.items():
min_stamp = min([min_stamp, min(track[:, 0])])
max_stamp = max([max_stamp, max(track[:, 0])])
self.min_stamp = min_stamp
self.max_stamp = max_stamp
return tracks, feature_ids, colors
tracker = Tracker(tracker_config)
# get tracks
tracked_features = tracker.track(tracks_init.tracks_obj,
tracks_init.tracker_params)
# 开始跟踪
# 在 Tracker 初始化时，指定了 track 方法为：track_features_on_klt
# save tracks
out_csv = args.file + ".gt.txt"
print("Saving ground truth files to %s" % os.path.basename(out_csv))
np.savetxt(out_csv, tracked_features, fmt=["%i", "%.8f", "%.4f", "%.4f"])
# load both gt and normal tracks
print("Computing errors")
est_tracks_data = getTrackData(args.file, filter_too_short=True)
gt_tracks_data = getTrackData(out_csv)
# compute errors
error_data = compareTracks(est_tracks_data, gt_tracks_data)
errors_csv = args.file + ".errors.txt"
print("Saving error files to %s" % os.path.basename(errors_csv))
np.savetxt(errors_csv, error_data, fmt=["%i", "%.8f", "%.4f", "%.4f"])
# plotting
folder = os.path.dirname(args.file)
results_folder = os.path.join(folder, "results")
if not os.path.isdir(results_folder):
os.mkdir(results_folder)
if args.plot_3d:
# init tracker
tracker = Tracker(tracker_config)
# get tracks
tracked_features = tracker.track(tracks_init.tracks_obj,
tracks_init.tracker_params)
# save tracks
out_csv = args.file + ".gt.txt"
print("Saving ground truth files to %s" % os.path.basename(out_csv))
np.savetxt(out_csv, tracked_features, fmt=["%i", "%.8f", "%.4f", "%.4f"])
# load both gt and normal tracks
print("Computing errors")
est_tracks_data = getTrackData(args.file)
gt_tracks_data = getTrackData(out_csv)
# compute errors
error_data = compareTracks(est_tracks_data, gt_tracks_data)
errors_csv = args.file + ".errors.txt"
print("Saving error files to %s" % os.path.basename(errors_csv))
np.savetxt(errors_csv, error_data, fmt=["%i", "%.8f", "%.4f", "%.4f"])
# plotting
folder = os.path.dirname(args.file)
results_folder = os.path.join(folder, "results")
if not os.path.isdir(results_folder):
os.mkdir(results_folder)
if args.plot_3d: