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Extended preparation of data sets 

Signed-off-by: Jim Martens <github@2martens.de>
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Jim Martens 2019-08-05 16:44:25 +02:00
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@ -656,8 +656,24 @@ Wordnet ID and searching for a fitting COCO class.
The ground truth for SceneNet RGB-D is stored in protobuf files
and had to be converted into Python format to use it in the
codebase. Only ground truth instances that had a matching
COCO class were saved, the rest discarded.
codebase. The trajectories are not sorted inside the protobuf,
therefore, the first action was to sort them. For each trajectory,
all instances are stored independently of the views in the
trajectory. Therefore, the trajectories and their respective
instances were looped through and all
background instances and those without corresponding COCO class were
skipped. The rest was stored in a dictionary per trajectory.
Subsequently, all views of the trajectory were traversed and
for every view all stored instances were looped through.
For every instance, the segmentation map was modified by
setting all pixels not having the instance ID as value to zero
and the rest to one. If no objects were found then that instance
was skipped. In the other case a copy of its data from the
aforementioned dictionary plus the bounding box information was
stored in a list of instances for that view. The list of instances
per view was added to a list of such lists for the trajectory.
Ultimately this list of lists was added to a global list across
all trajectories: a list of lists of lists.
\section{Replication of Miller et al.}