/pytorch_depth_from_videos_in_the_wild

Primary LanguagePythonMIT LicenseMIT

(PyTorch) Depth from Videos in the Wild

Introduction to the Project

This project is a Pytorch re-implementation of the following Google ICCV 2019 paper:
Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras

Environment Setup

The codes has been executed under: (Python 3.6.5 + Pytoch 1.7.1) and (Python 3.8 + Pytoch 1.5 or Pytoch 1.7.1)

A conda environment is recommended:

pip install opencv-python
# cudatoolkit=xx.x should also be included below to use GPUs for training.
conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 -c pytorch
conda install --file requirements.txt

Data Preparation

We provide gen_data.py to generate training datasets. There are two types of datasets available:

KITTI

Download Raw Data

Please visit the official website to download the entire raw KITTI dataset and unzip it to a folder named kitti_raw.
Alternatively, you can also run the follows:

./datasets/data_prep/kitti_raw_downloader.sh

Generate Training Dataset

python gen_data.py \
--dataset_name [kitti_raw_eigen or kitti_raw_stereo] \
--dataset_dir /path/to/kitti_raw \
--save_dir /path/to/save/the/generated_data \
--mask color

VIDEOs

Training datasets can also be generated from your own videos (in mp4 format) under the same folder.

[Optional] If the camera intrinsics are known, please put the 9 entries of its flattented camera intrinsics in a text file.

1344.8 0.0 640.0 0.0 1344.8 360.0 0.0 0.0 1.0

Then generate the training dataset by running:

python gen_data.py \
--dataset_name video \
--dataset_dir /path/to/your/video_folder \  # please do not use spaces in video names for now
--save_dir /path/to/save/the/generated_data \
--intrinsics /path/to/your/camera_intrinsics_file \ # If not set, default intrinsics are produced according to IPhone 
--mask color

More options for gen_data.py can be found in options/gen_data_options.py.

Train Models

python train.py --data_path /path/to/save/the/generated_data    # the folder generated by gen_data.py
                --png    # remove this if the training images are generated in jpg format
                --learn_intrinsics
                --weighted_ssim
                --boxify
                --model_name /path/to/save/model    # relative path under the $Project/models folder

Run Inference

python infer_depth.py --input_path path/to/a/folder/containing/images    # or path/to/a/video
                      --model_path path/to/a/folder/containing/checkpoints    # keep the saved folder structure since its parent folder must have opt.json

Available pretrained models:

Reference

Updates

(Feb 25th, 2022) this project was made public!