def _load_features_nocache(self, data: DataSetBase, image):
points, features, colors, segmentation_data = data.load_features(image)
if points is None:
logger.error("Could not load features for image {}".format(image))
else:
points = np.array(points[:, :3], dtype=float)
return points, features, colors, segmentation_data
def _load_features_nocache(self, data: DataSetBase,
image: str) -> Optional[ft.FeaturesData]:
features_data = data.load_features(image)
if features_data is None:
logger.error("Could not load features for image {}".format(image))
return None
else:
features_data.points = np.array(features_data.points[:, :3],
dtype=float)
return features_data
def load_features(dataset: DataSetBase, images):
logging.info("reading features")
features = {}
colors = {}
segmentations = {}
instances = {}
for im in images:
p, f, c, s = dataset.load_features(im)
features[im] = p[:, :3]
colors[im] = c
segmentations[im] = s["segmentations"] if s else None
instances[im] = s["instances"] if s else None
return features, colors, segmentations, instances
def features_statistics(data: DataSetBase, tracks_manager, reconstructions):
stats = {}
detected = []
for im in data.images():
features_data = data.load_features(im)
if not features_data:
continue
detected.append(len(features_data.points))
if len(detected) > 0:
stats["detected_features"] = {
"min": min(detected),
"max": max(detected),
"mean": int(np.mean(detected)),
"median": int(np.median(detected)),
}
else:
stats["detected_features"] = {
"min": -1,
"max": -1,
"mean": -1,
"median": -1
}
per_shots = defaultdict(int)
for rec in reconstructions:
all_points_keys = set(rec.points.keys())
for shot_id in rec.shots:
if shot_id not in tracks_manager.get_shot_ids():
continue
for point_id in tracks_manager.get_shot_observations(shot_id):
if point_id not in all_points_keys:
continue
per_shots[shot_id] += 1
per_shots = list(per_shots.values())
stats["reconstructed_features"] = {
"min": int(min(per_shots)) if len(per_shots) > 0 else -1,
"max": int(max(per_shots)) if len(per_shots) > 0 else -1,
"mean": int(np.mean(per_shots)) if len(per_shots) > 0 else -1,
"median": int(np.median(per_shots)) if len(per_shots) > 0 else -1,
}
return stats
def load_features(dataset: DataSetBase, images):
logging.info("reading features")
features = {}
colors = {}
segmentations = {}
instances = {}
for im in images:
features_data = dataset.load_features(im)
if not features_data:
continue
features[im] = features_data.points[:, :3]
colors[im] = features_data.colors
semantic_data = features_data.semantic
if semantic_data:
segmentations[im] = semantic_data.segmentation
instances[im] = semantic_data.instances
return features, colors, segmentations, instances
