This file provides instructions on how to perform detections upon BOP19 Dataset
-
Set up
mmdetection
baseline according to here. -
Download all testsets in
detection/bop19
from here(or you could download somewhere else and set up a soft link).
Takelm
as an exampleexport SRC=http://ptak.felk.cvut.cz/6DB/public/bop_datasets wget $SRC/lm_base.zip # Base archive with dataset info, camera parameters, etc. wget $SRC/lm_models.zip # 3D object models. wget $SRC/lm_test_all.zip # All test images ("_bop19" for a subset used in the BOP Challenge 2019). wget $SRC/lm_train.zip # Training images. unzip lm_base.zip # Contains folder "lm". unzip lm_models.zip -d lm # Unpacks to "lm". unzip lm_test_all.zip -d lm # Unpacks to "lm". unzip lm_train.zip -d lm # Unpacks to "lm".
Note: All datasets should be in
detection/bop19
whose file system should be as following:├── lmo │ ├── test │ │ ├── <scene_id> │ │ │ ├── rgb │ │ │ │ ├── <img_id>.png │ │ │ │ └── ... │ │ │ ├── mask_visib │ │ │ ├── depth │ │ │ └── ... ├── ycbv │ ├── test │ │ └── ... └── ...
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Find the root directory of your
mmdetction
path# Please specify the path below export MMDETECTION_PATH=<your mmdetection path> export DETECTION_PATH=<detection> # Set up soft link here to self-define the bop19-dataset cd ${MMDETECTION_PATH}/mmdet/ ln -s ${DETECTION_PATH}/datasets # return to your work place cd ${DETECTION_PATH}
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Download models from here (Password: 7yjb) and store them in
${DETECTION_PATH}/models
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Generate the detection results by
pip install tqdm -y python inference.py <dataset name>
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Then you have your detection results in
${DETECTION_PATH}/out
, and the file system is as below:├── hb │ ├── hb_<scene_id>_detection_result.json │ └── ... └── ...
You can go on test on the Pose Part using these files.