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Human3.6M preprocessing scripts

These scripts help preprocess Human3.6M dataset so that it can be used with class Human36MMultiViewDataset.

Here is how we do it (brace yourselves):

  1. Make sure you have a lot (around 200 GiB?) of free disk space. Otherwise, be prepared to always carefully delete intermediate files (e.g. after you extract movies, delete the archives).

  2. Allocate a folder for the dataset. Make it accessible as $THIS_REPOSITORY/data/human36m/, i.e. either

    • store your data $SOMEWHERE_ELSE and make a soft symbolic link:
    mkdir $THIS_REPOSITORY/data
ln -s $SOMEWHERE_ELSE $THIS_REPOSITORY/data/human36m
    • or just store the dataset along with the code at $THIS_REPOSITORY/data/human36m/.

  3. Clone this toolbox. Follow their manual to download, extract and unpack Human3.6M into image files. Move the result to $THIS_REPOSITORY/data/human36m.

    After that, you should have images unpacked as e.g. $THIS_REPOSITORY/data/human36m/processed/S1/Phoning-1/imageSequence/54138969/img_000001.jpg.

  4. Additionally, if you want to use ground truth bounding boxes for training, download them as well (the website calls them "Segments BBoxes MAT") and unpack them like so: "$THIS_REPOSITORY/data/human36m/processed/S1/MySegmentsMat/ground_truth_bb/Phoning 1.58860488.mat".

  5. Convert those bounding boxes into sane format. This will create $THIS_REPOSITORY/data/human36m/extra/bboxes-Human36M-GT.npy:

    cd $THIS_REPOSITORY/mvn/datasets/human36m_preprocessing
# in our environment, this took around 6 minutes with 40 processes
python3 collect-bboxes.py $THIS_REPOSITORY/data/human36m <number-of-parallel-processes>

  6. Existing 3D keypoint positions and camera intrinsics are difficult to decipher, so initially we used the converted ones from Julieta Martinez:

    mkdir -p $THIS_REPOSITORY/data/human36m/extra/una-dinosauria-data
cd $THIS_REPOSITORY/data/human36m/extra/una-dinosauria-data

    Download h36m.zip from Google Drive and uzip it to current directory:

    unzip h36m.zip
cd -

  7. Wrap the 3D keypoint positions, bounding boxes and camera intrinsics together. This will create $THIS_REPOSITORY/data/human36m/extra/human36m-multiview-labels-GTbboxes.npy:

    python3 generate-labels-npy-multiview.py $THIS_REPOSITORY/data/human36m $THIS_REPOSITORY/data/human36m/extra/una-dinosauria-data/h36m $THIS_REPOSITORY/data/human36m/extra/bboxes-Human36M-GT.npy

    You should see only one warning saying camera 54138969 isn't present in S11/Directions-2. That's fine.

    Now you can train and evaluate models by setting these config values (already set by default in the example configs):

    dataset:
  {train,val}:
    h36m_root: "data/human36m/processed/"
    labels_path: "data/human36m/extra/human36m-multiview-labels-GTbboxes.npy"

  8. To use undistort_images: true, undistort the images beforehand. This will put undistorted images to e.g. $THIS_REPOSITORY/data/human36m/processed/S1/Phoning-1/imageSequence-undistorted/54138969/img_000001.jpg:

    # in our environment, this took around 90 minutes with 50 processes
python3 undistort-h36m.py $THIS_REPOSITORY/data/human36m $THIS_REPOSITORY/data/human36m/extra/human36m-multiview-labels-GTbboxes.npy <number-of-parallel-processes>`

    TODO: move undistortion to the dataloader. We can do it on the fly during training.

  9. Optionally, you can test if everything went well by viewing frames with skeletons and bounding boxes on a GUI machine:

    python3 view-dataset.py $THIS_REPOSITORY/data/human36m/processed $THIS_REPOSITORY/data/human36m/extra/human36m-multiview-labels-GTbboxes.npy <start-sample-number> <samples-per-step>

    You can test different settings by changing dataset constructor parameters in view-dataset.py.