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EventStream-SR

This repository is for the ICCV 2021 paper: Event Stream Super-Resolution via Spatiotemporal Constraint Learning.

This is our raw, unintegrated code.

Requirements

  1. Python 3 with the following packages installed:

    • torch==1.3.1
    • torchvision==0.4.2
    • tqdm==4.61.1
    • numpy==1.19.2
    • imageio==2.9.0
    • Pillow==8.2.0
    • tensorboardX==2.2
    • pyyaml==5.4.1

  2. slayerPytorch

  3. cuda

    • A CUDA enabled GPU is required for training any model. We test our code with CUDA 10.0 V10.0.130 and cudnn 7.6.5.

Data preparing

  1. Our datasets are available at https://github.com/lisiqi19971013/event-based-datasets.
  2. Download the datasets and unzip them to the folder (./dataset/)
  3. Change the corresponding data path in each .py file

Train and test

Training

This is the raw and unintegrated code. If you want to train the model on a dataset, such as N-MNIST, run this code

>>> cd nMnist
>>> python trainNmnist.py --bs 64 --savepath './ckpt/' --epoch 30 --showFreq 50  --lr 0.1 --cuda '1' --j 4
or just change the file train.sh and run
>>> sh train.sh

Testing

After training, run the following code to generating results. NOTICE: the default output path is (./dataset/N-MNIST/ResConv/HRPre).

>>> python testNmnist.py

Calculate metrics

After generating results, run calRMSE.py to calculate the metrics. NOTICE: the output path should be changed.

>>> python calRMSE.py

Downstream application

  1. Classification

    The classification experiment is done with the N-MNIST, Cifar10-DVS and ASL-DVS datasets. After generating the super-resolution results, the classification can be done by the following code (for the N-MNIST dataset for example).

    >>> cd nMnist
>>> python trainNmnistClassification.py

    In the classification experiment, the training sets are the ground truth event streams from the training sets of the super-resolution task. The test sets are the test set of the super-resolution task, containing the LR event streams, HR ground truth event streams, output event streams and others. The test mode and other parameters is selected in the trainNmnistClassification.py file.

  2. Image reconstruction

    The image reconstruction task is done following the E2VID, the code and the pretrain model is proposed in https://github.com/uzh-rpg/rpg_e2vid.