def load_gt_and_det_data(gt_loc, det_json, data_type):
"""
Function for loading ground-truth and detection instances for a single folder/sequence
:param gt_loc: location of folder/file where ground-truth information can be extracted
:param det_json: detection json file for the given folder/sequence
:param data_type: string defining if we are analysing 'coco' or 'rvc1' data as this will effect
what, and how, data is loaded given the specific filtering process of rvc1.
:return: gt_instances, det_instances, and class list
"""
if data_type == 'coco':
# output is a generator of lists of GroundTruthInstance objects and a map of gt_class_ids
gt_instances, gt_class_ids_map = read_files.read_COCO_gt(gt_loc, ret_classes=True)
# output is a generator of lists of DetectionInstance objects (BBox or PBox depending)
det_instances = read_files.read_pbox_json(det_json, gt_class_ids_map, override_cov=args.set_cov)
class_idxs = [gt_class_ids_map[key] for key in sorted(gt_class_ids_map.keys())]
class_names = list(sorted(gt_class_ids_map.keys()))
class_list = [class_names[idx] for idx in np.argsort(class_idxs)]
elif data_type == 'rvc1':
gt_instances = rvc1_gt_loader.SequenceGTLoader(gt_loc)
det_instances = rvc1_submission_loader.DetSequenceLoader(det_json)
class_list = rvc1_class_list.CLASSES
else:
sys.exit("ERROR! Invalid data type provided")
# check gt_instances and det_instances are the same size
if len(gt_instances) != len(det_instances):
sys.exit("ERROR! gt and det not same length. gt: {0}, det: {1}".format(len(gt_instances), len(det_instances)))
return gt_instances, det_instances, class_list
def gen_param_sequence():
"""
Function for generating the parameter sequence to be used in evaluation procedure.
Parameter sequence holds all GroundTruthInstances, DetectionInstances, and ground-truth filter flags
across all sequences.
:return: param_sequences: ParamSequenceHolder containing all GroundTruthInstances, DetectionInstances,
and ground-truth filter flags across all sequences being evaluated.
len_sequences: list of sequence lengths for all sequences being evaluated.
"""
# Load GTs and Detections as appropriate for different data sets (multiple sequences or one folder)
if args.test_set == 'coco':
# output is a generator of lists of GTInstance objects and a map of gt_class_ids
gt_instances, gt_class_ids = read_files.read_COCO_gt(
coco_gt_file, ret_classes=True, bbox_gt=args.bbox_gt)
det_filename = args.det_loc
# output is a generator of lists of DetectionInstance objects (BBox or PBox depending on detection)
det_instances = read_files.read_pbox_json(det_filename,
gt_class_ids,
override_cov=args.set_cov,
prob_seg=args.prob_seg)
all_gt_instances = [gt_instances]
all_det_instances = [det_instances]
elif args.test_set == 'rvc1':
# output is a list of generator of generators of GTInstance objects
all_gt_instances = rvc1_gt_loader.read_ground_truth(
rvc1_gt_folder, bbox_gt=args.bbox_gt)
# Currently set this version to assume there is a .json file for all sequences in ground-truth
all_det_instances = rvc1_submission_loader.read_submission(
args.det_loc,
["{0:06d}".format(idx) for idx in range(len(all_gt_instances))],
override_cov=args.set_cov)
else:
sys.exit(
"ERROR! Invalid test_set parameter (must be 'coco' or 'rvc1')")
param_sequence = ParamSequenceHolder(all_gt_instances, all_det_instances)
len_sequences = [
len(all_gt_instances[idx]) for idx in range(len(all_gt_instances))
]
return param_sequence, len_sequences
def gen_param_sequence():
"""
Function for generating the parameter sequence to be used in evaluation procedure.
Parameter sequence holds all GroundTruthInstances, DetectionInstances, and ground-truth filter flags
across all sequences.
:return: param_sequences: ParamSequenceHolder containing all GroundTruthInstances, DetectionInstances,
and ground-truth filter flags across all sequences being evaluated.
len_sequences: list of sequence lengths for all sequences being evaluated.
"""
# Load GTs and Detections as appropriate for different data sets (multiple sequences or one folder)
#if args.test_set == 'coco':
# output is a generator of lists of GTInstance objects and a map of gt_class_ids
coco_gt_file = 'gt.json'
det_loc = 'pred.json'
gt_instances, gt_class_ids = read_files.read_COCO_gt(coco_gt_file,
ret_classes=True,
bbox_gt=True)
det_filename = det_loc
# output is a generator of lists of DetectionInstance objects (BBox or PBox depending on detection)
det_instances = read_files.read_pbox_json(det_filename,
gt_class_ids,
prob_seg=False)
all_gt_instances = [gt_instances]
all_det_instances = [det_instances]
#print("all_gt = ", all_gt_instances[0], all_det_instances)
param_sequence = ParamSequenceHolder(all_gt_instances, all_det_instances)
len_sequences = [
len(all_gt_instances[idx]) for idx in range(len(all_gt_instances))
]
return param_sequence, len_sequences