def loadPredictions(
self,
pred_folder # type: str
):
# type: (...) -> None
"""Loads all predictions from the given folder.
Args:
pred_folder (str): Prediction folder
"""
logger.info("Loading predictions...")
predictions = getFiles(pred_folder)
predictions.sort()
logger.info("Found {} prediction files.".format(len(predictions)))
for p in predictions:
preds_for_image = []
# extract CITY_RECORDID_IMAGE from filepath
base = os.path.basename(p)
base = base[:base.rfind("_")]
# check for valid json file
try:
with open(p) as f:
data = json.load(f)
except json.decoder.JSONDecodeError:
logger.error("Invalid prediction json file: {}".format(base))
raise
# check for 'objects'
if "objects" not in data.keys():
logger.error("'objects' missing in prediction json file: {}".format(base))
raise
for d in data["objects"]:
if (
"label" in d.keys() and
d["label"] in self.eval_params.labels_to_evaluate
):
try:
box_data = CsBbox3d()
box_data.fromJsonText(d)
except Exception:
logger.critical("Found incorrect annotation in {}.".format(p))
continue
preds_for_image.append(box_data)
self.preds[base] = {
"objects": preds_for_image
}
def loadGT(
self,
gt_folder # type: str
):
# type: (...) -> None
"""Loads ground truth from the given folder.
Args:
gt_folder (str): Ground truth folder
"""
logger.info("Loading GT...")
gts = getFiles(gt_folder)
logger.info("Found {} GT files.".format(len(gts)))
self._stats["GT_stats"] = OrderedDict(
(x, 0) for x in self.eval_params.labels_to_evaluate)
for p in gts:
gts_for_image = []
ignores_for_image = []
# extract CITY_RECORDID_IMAGE from filepath
base = os.path.basename(p)
base = base[:base.rfind("_")]
with open(p) as f:
data = json.load(f)
# load 3D boxes
for d in data["objects"]:
if d["label"] in self.eval_params.labels_to_evaluate:
self._stats["GT_stats"][d["label"]] += 1
box_data = CsBbox3d()
box_data.fromJsonText(d)
gts_for_image.append(box_data)
# load ignore regions
for d in data["ignore"]:
box_data = CsIgnore2d()
box_data.fromJsonText(d)
ignores_for_image.append(box_data)
self.gts[base] = {
"objects": gts_for_image,
"ignores": ignores_for_image
}
def loadGT(self, gt_folder: str) -> None:
"""Loads ground truth from the given folder.
Args:
gt_folder (str): Ground truth folder
"""
logger.info("Loading GT...")
gts = getFiles(gt_folder)
logger.info("Found %d GT files." % len(gts))
self._stats["GT_stats"] = {
x: 0
for x in self.eval_params.labels_to_evaluate
}
for p in gts:
gts_for_image = []
ignores_for_image = []
# extract CITY_RECORDID_IMAGE from filepath
base = os.path.basename(p)
base = base[:base.rfind("_")]
with open(p) as f:
data = json.load(f)
# load 3D boxes
for d in data["annotation"]:
if d["class_name"] in self.eval_params.labels_to_evaluate:
self._stats["GT_stats"][d["class_name"]] += 1
box_data = Box3DObject(d)
gts_for_image.append(box_data)
# load ignore regions
for d in data["ignore"]:
box_data = IgnoreObject(d)
ignores_for_image.append(box_data)
self.gts[base] = {
"objects": gts_for_image,
"ignores": ignores_for_image
}
def loadPredictions(self, pred_folder: str) -> None:
"""Loads all predictions from the given folder.
Args:
pred_folder (str): Prediction folder
"""
logger.info("Loading predictions...")
predictions = getFiles(pred_folder)
predictions.sort()
logger.info("Found %d prediction files." % len(predictions))
for p in predictions:
preds_for_image = []
# extract CITY_RECORDID_IMAGE from filepath
base = os.path.basename(p)
base = base[:base.rfind("_")]
with open(p) as f:
data = json.load(f)
for d in data["annotation"]:
if ("class_name" in d.keys() and d["class_name"]
in self.eval_params.labels_to_evaluate):
try:
box_data = Box3DObject(d)
except:
logger.critical("Found incorrect annotation in %s." %
p)
continue
preds_for_image.append(box_data)
self.preds[base] = {"objects": preds_for_image}
def loadGT(
self,
gt_folder # type: str
):
# type: (...) -> None
"""Loads ground truth from the given folder.
Args:
gt_folder (str): Ground truth folder
"""
logger.info("Loading GT...")
gts = getFiles(gt_folder)
logger.info("Found {} GT files.".format(len(gts)))
self._stats["GT_stats"] = OrderedDict(
(x, 0) for x in self.eval_params.labels_to_evaluate)
for p in gts:
gts_for_image = []
ignores_for_image = []
# extract CITY_RECORDID_IMAGE from filepath
base = os.path.basename(p)
base = base[:base.rfind("_")]
# check for valid json file
try:
with open(p) as f:
data = json.load(f)
except json.decoder.JSONDecodeError:
logger.error("Invalid GT json file: {}".format(base))
raise
# check for 'objects' and 'sensor'
if "objects" not in data.keys():
msg = "'objects' missing in GT json file: {}".format(base)
logger.error(msg)
raise KeyError(msg)
if "sensor" not in data.keys():
msg = "'sensor' missing in GT json file: {}".format(base)
logger.error(msg)
raise KeyError(msg)
# load Camera object
camera = Camera(data["sensor"]["fx"], data["sensor"]["fy"],
data["sensor"]["u0"], data["sensor"]["v0"],
data["sensor"]["sensor_T_ISO_8855"])
# load 3D boxes
for d in data["objects"]:
if d["label"] in self.eval_params.labels_to_evaluate:
self._stats["GT_stats"][d["label"]] += 1
box_data = CsBbox3d()
box_data.fromJsonText(d)
gts_for_image.append(box_data)
# load ignore regions
for d in data["ignore"]:
box_data = CsIgnore2d()
box_data.fromJsonText(d)
ignores_for_image.append(box_data)
self.gts[base] = {
"objects": gts_for_image,
"ignores": ignores_for_image
}
self.cameras[base] = camera