raft

RAFT: Recurrent All Pairs Field Transforms for Optical Flow

input

（1 frame before）

（1 frame after）

(from https://pixabay.com/videos/car-racing-motor-sports-action-74/)

output

（estimated optical flow）

Color is an estimation of the direction in which the object moved between frames.

（video's output）

usage

Automatically downloads the onnx and prototxt files on the first run. It is necessary to be connected to the Internet while downloading.

For the sample image,

$ python raft.py
(ex on CPU)  $ python3 raft.py -e 0
(ex on BLAS) $ python3 raft.py -e 1
(ex on GPU)  $ python3 raft.py -e 2

If you want to specify the input images, put the two images path after the --inputs option. Specify the frame images in the video in the order of front and back. You can use --savepath option to change the name of the output file to save.

$ python3 raft.py --inputs IMAGE_PATH_BEFORE_FRAME IMAGE_PATH_AFTER_FRAME --savepath SAVE_IMAGE_PATH
$ python3 raft.py -i IMAGE_PATH_BEFORE_FRAME IMAGE_PATH_AFTER_FRAME -s SAVE_IMAGE_PATH
(ex) $ python3 raft.py --inputs input_before.png input_after.png --savepath output.png

By adding the --video option, you can input the video.

$ python3 raft.py --video VIDEO_PATH --savepath SAVE_VIDEO_PATH
$ python3 raft.py -v VIDEO_PATH -s SAVE_VIDEO_PATH
(ex) $ python3 raft.py --video input.mp4 --savepath output.mp4

By the way, if the input data has a high resolution then the accuracy tends to be high, and if the input data has a low resolution then the processing speed tends to be high.

Reference

[RAFT: Recurrent All Pairs Field Transforms for Optical Flow (https://github.com/princeton-vl/RAFT)

Framework

Pytorch

Model Format

ONNX opset = 11

Netron

raft-things_fnet.onnx.prototxt
raft-things_cnet.onnx.prototxt
raft-things_update_block.onnx.prototxt
raft-small_fnet.onnx.prototxt
raft-small_cnet.onnx.prototxt
raft-small_update_block.onnx.prototxt