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INSTRUCTIONS_TO_RUN_CODE.md

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Instructions to setup dependencies and run code

  1. To Setup dependencies

    a) sudo apt-get install libhdf5-10 libhdf5-serial-dev libhdf5-dev libhdf5-cpp-11 b) sudo apt install libopenexr-dev zlib1g-dev openexr # required for openexr c) For openexr python package, install it using the following command pip install --no-binary openexr openexr d) The rest of the Python package dependencies can be found in requirements.txt

  2. To build depth2depth module a) To compile the depth2depth binary, you will first need to identify the path to libhdf5. Note the location of hdf5/serial. Run the following command in terminal:

    find /usr -iname "*hdf5.h*"

    It will look similar to: /usr/include/hdf5/serial/hdf5.h.

    b) Edit BOTH lines 28-29 of the makefile at api/depth2depth/gaps/apps/depth2depth/Makefile to add the path you just found as shown below:

    USER_LIBS=-L/usr/include/hdf5/serial/ -lhdf5_serial
USER_CFLAGS=-DRN_USE_CSPARSE "/usr/include/hdf5/serial/"

    c) Compile the binary:

    cd api/depth2depth/gaps
export CPATH="/usr/include/hdf5/serial/"  # Ensure this path is same as read from output of `find /usr -iname "*hdf5.h*"`

make

    This should create an executable, api/depth2depth/gaps/bin/x86_64/depth2depth. The config files will need the path to this executable to run the depth estimation pipeline.

    d) Check the executable, by passing in the provided sample files:

    cd api/depth2depth/gaps
bash depth2depth.sh

    This will generate gaps/sample_files/output-depth.png, which should match the expected-output-depth.png sample file. It will also generate RGB visualizations of all the intermediate files.

  3. To Run code a) Object segmentation i) All the scripts can be found inside pytorch_networks/object_segmentation directory. ii) The config for testing can be found in config_test directory in the above directory, with the name config_resnet50_psa.yaml. The appropriate model weights file and dataset paths needs to be set in the yaml file. iii) For evaluation, evaluate.py needs to be used. This script requires that the dataset has groundtruth files. iv) For saving prediction visualizations, save_prediction_vis.py needs to be used. This script requires only the input RGB images.

    b) Occlusion boundary i) All the scripts can be found inside pytorch_networks/occlusion_boundary directory. ii) The config for testing can be found in config_test directory in the above directory, with the name config_resnet50_psa.yaml. The appropriate model weights file and dataset paths needs to be set in the yaml file. iii) For evaluation, evaluate.py needs to be used. This script requires that the dataset has groundtruth files. iv) For saving prediction visualizations, save_prediction_vis.py needs to be used. This script requires only the input RGB images.

    c) Surface normal i) All the scripts can be found inside pytorch_networks/surface_normals directory. ii) The config for testing can be found in config_test directory in the above directory, with the name config_resnet50_psa.yaml. The appropriate model weights file and dataset paths needs to be set in the yaml file. iii) For evaluation, evaluate.py needs to be used. This script requires that the dataset has groundtruth files. iv) For saving prediction visualizations, save_prediction_vis.py needs to be used. This script requires only the input RGB images.

    d) Depth estimation i) All scripts can be found inside eval_depth_completion directory. ii) The config for testing can be found in config directory in the above directory, with the name config_resnet50_psa_real.yaml. The appropriate model weights file and dataset paths needs to be set in the yaml file. iii) For evaluation, eval_depth_completion.py needs to be used. This script requires that the dataset has groundtruth files. iv) For saving prediction visaulizations, run_depth_completion.py needs to be used. This script requires only the input RGB images.