del preprocessing

RAFT @ 3fa0bb0a

-Subproject commit 3fa0bb0a9c633ea0a9bb8a79c576b6785d4e6a02

preprocessing/README.md

-# Data processing
-
-This README file contains instructions to compute and process RAFT optical flows for optimizing OmniMotion.
-
-## Data format
-The input video data should be organized in the following format:
-```
-├──sequence_name/
-    ├──color/
-        ├──00000.jpg
-        ├──00001.jpg
-        .....
-    ├──mask/ (optional; only used for visualization purposes)
-        ├──00000.png
-        ├──00001.png
-        ..... 
-```
-If you want to run on [DAVIS](https://davischallenge.org/index.html) video sequences, you can run `python get_davis.py <out_dir>` 
-which will download the original dataset and organize it in our format for processing. Alternatively, you can 
-download some of our processed sequences [here](https://omnimotion.cs.cornell.edu/dataset/) to skip processing and directly start training.
-
-If you want to train on your own video sequence, we recommend you to start with
-shorter sequences (< 60 frames) and lower resolution (<= 480p) to manage computational cost. 
-You may use `ffmpeg` to extract frames from the video.
-
-
-## Preparation
-The command below moves files to the correct locations and download pretrained models (this only needs to be run once).
-```
-cd preprocessing/  
-
-mv exhaustive_raft.py filter_raft.py chain_raft.py RAFT/;
-cd RAFT; ./download_models.sh; cd ../
-
-mv extract_dino_features.py dino/
-```
-
-## Computing and processing flow
-
-Run the following command to process the input video sequence. Please use absolute path for the sequence directory.
-```
-conda activate omnimotion
-python main_processing.py --data_dir <sequence directory> --chain
-```
-The processing contains several steps:
-- computing all pairwise optical flows using `exhaustive_raft.py`
-- computing dino features for each frame using `extract_dino_features.py`
-- filtering flows using cycle consistency and appearance consistency check using`filter_raft.py`
-- (optional) chaining only cycle consistent flows to create denser correspondences using `chain_raft.py`. 
-  We found this to be helpful for handling sequences with rapid motion and large displacements. 
-  For simple motion, this may be skipped by omitting `--chain` to save processing time. 
-
-After processing the folder should look like the following:
-```
-├──sequence_name/
-    ├──color/
-    ├──mask/ (optional; only used for visualization purposes)
-    ├──count_maps/
-    ├──features/
-    ├──raft_exhaustive/
-    ├──raft_masks/
-    ├──flow_stats.json
-```
-
-## Discussion
-This processing pipeline is designed to filter and process RAFT optical flow for training our method. 
-Our method can also take as input correspondences from other methods, e.g., [TAPIR](https://deepmind-tapir.github.io/) and
-[CoTracker](https://co-tracker.github.io/). 
-If you want to use different correspondences as input supervision, note that their error patterns might be different from
-those of RAFT optical flow, and you may need to devise new filtering methods that are effective for the specific correspondences
-you are working with.

dino @ 7c446df5

-Subproject commit 7c446df5b9f45747937fb0d72314eb9f7b66930a