add print method
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6f3379e394
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@ -26,8 +26,7 @@ class KittiEval:
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dic_cnt = defaultdict(int)
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errors = defaultdict(lambda: defaultdict(list))
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def __init__(self, show=False, thresh_iou_our=0.3, thresh_iou_m3d=0.5, thresh_conf_m3d=0.5, thresh_conf_our=0.3):
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self.show = show
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def __init__(self, thresh_iou_our=0.3, thresh_iou_m3d=0.5, thresh_conf_m3d=0.5, thresh_conf_our=0.3):
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self.dir_gt = os.path.join('data', 'kitti', 'gt')
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self.dir_m3d = os.path.join('data', 'kitti', 'm3d')
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self.dir_3dop = os.path.join('data', 'kitti', '3dop')
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@ -70,7 +69,7 @@ class KittiEval:
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# Extract annotations for the same file
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if len(boxes_gt) > 0:
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boxes_m3d, dds_m3d = self._parse_txts(path_m3d, method='m3d')
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boxes_m3d, dds_m3d = self._parse_txts(path_m3d, method='m3d')
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boxes_3dop, dds_3dop = self._parse_txts(path_3dop, method='3dop')
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boxes_md, dds_md = self._parse_txts(path_md, method='md')
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boxes_our, dds_our, stds_ale, stds_epi, kk_list, dds_geom, xyzs, xy_kps = \
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@ -120,8 +119,8 @@ class KittiEval:
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print("\n Number of matched annotations: {:.1f} %".format(self.errors[key]['matched']))
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print("-"*100)
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# Print images
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print_results(self.dic_stats, self.show)
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def print(self, show):
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print_results(self.dic_stats, show)
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def _parse_txts(self, path, method):
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boxes = []
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@ -134,7 +133,7 @@ class KittiEval:
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xy_kps = []
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# Iterate over each line of the txt file
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if method == '3dop' or method == 'm3d':
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if method in ['3dop', 'm3d']:
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try:
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with open(path, "r") as ff:
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for line in ff:
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@ -268,7 +267,7 @@ class KittiEval:
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occs_gt.pop(idx_max)
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def _compare_error(self, boxes_m3d, dds_m3d, boxes_3dop, dds_3dop, boxes_md, dds_md, boxes_our, dds_our,
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boxes_gt, dds_gt, truncs_gt, occs_gt, dds_geom):
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boxes_gt, dds_gt, truncs_gt, occs_gt, dds_geom):
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boxes_gt = copy.deepcopy(boxes_gt)
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dds_gt = copy.deepcopy(dds_gt)
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@ -401,3 +400,4 @@ def find_cluster(dd, clusters):
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return clusters[-1]
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@ -143,8 +143,9 @@ def main():
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run_kitti.run()
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if args.dataset == 'kitti':
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kitti_eval = KittiEval(show=args.show)
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kitti_eval = KittiEval()
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kitti_eval.run()
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kitti_eval.print(show=args.show)
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if 'nuscenes' in args.dataset:
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training = Trainer(joints=args.joints)
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