def main():
args = parse_args()
print('Loading ground truth and results information')
gt_load_func = getattr(bt.datasets, 'load_' + args.gt_type)
res_load_func = getattr(bt.datasets, 'load_' + args.res_type)
gt_infos, gt_cls = gt_load_func(img_dir=args.img_dir,
ann_dir=args.gt_ann_dir,
classes=args.classes,
nproc=args.nproc)
res_infos, res_cls = res_load_func(img_dir=args.img_dir,
ann_dir=args.res_ann_dir,
classes=args.classes,
nproc=args.nproc)
bt.change_cls_order(res_infos, res_cls, gt_cls)
print('Parsing ground truth and results information')
id_mapper = {info['id']: i for i, info in enumerate(res_infos)}
gts, res = [], []
for gt_info in gt_infos:
img_id = gt_info['id']
res_info = res_infos[id_mapper[img_id]]
assert 'scores' in res_info['ann'], \
"f{args.res_type} don't have scores information"
res_bboxes = res_info['ann']['bboxes']
res_labels = res_info['ann']['labels']
res_scores = res_info['ann']['scores']
res_dets = np.concatenate([res_bboxes, res_scores[..., None]], axis=1)
res_dets = [res_dets[res_labels == i] for i in range(len(gt_cls))]
res.append(res_dets)
gt_bboxes = gt_info['ann']['bboxes']
gt_labels = gt_info['ann']['labels']
diffs = gt_info['ann'].get('diffs',
np.zeros(gt_bboxes.shape[0], dtype=np.int))
gt_ann = {}
if args.ign_diff > 0:
gt_ann['bboxes_ignore'] = gt_bboxes[diffs == 1]
gt_ann['labels_ignore'] = gt_labels[diffs == 1]
gt_bboxes = gt_bboxes[diffs == 0]
gt_labels = gt_labels[diffs == 0]
gt_ann['bboxes'] = gt_bboxes
gt_ann['labels'] = gt_labels
gts.append(gt_ann)
print('Starting calculating mAP')
bt.eval_map(res,
gts,
iou_thr=args.iou_thr,
use_07_metric=args.voc_metric == '07',
nproc=args.nproc,
dataset=gt_cls)
def get_subset_by_classes(self):
bt.change_cls_order(self.data_infos, self.ori_CLASSES, self.CLASSES)
return self.data_infos