Optical-Flow-GPU
Calculate dense optical flow using TV-L1 algorithm with NVIDIA GPU acceleration. The CPU version is also included. Dockerhub link
Docker image environment: OpenCV 2.4, CUDA 8, cuDNN 5.
The dense flow C++ source code for building is from yjxiong/dense_flow. The docker image is based on willprice/opencv2-cuda8. If you want to build opencv with cuda support yourself, you can refer to this dockerfile.
Requirements: docker, nvidia-docker. If you have libseccomp2 version conflict problems when installing docker on ubuntu, you can refer to this solution.
Usage
1. Directly use the prebuilt binaries (no multi gpu support)
extract_cpu [OPTION] ... # using cpu
extract_gpu [OPTION] ... # using gpu
extract_warp_gpu [OPTION] ... # using gpu to extract warp flowAvaliable options:
-f: video path.-x: filename of flow x component.-y: filename of flow y component.-i: filename of extracted RGB image.-b: boundary to clip the flow value. For example,-b 20means clip the flow value beyond [-20, 20] and maps the [-20, 20] interval to [0, 255] in grayscale image space.-t: flow calculation method. 0: Brox, 1: TVL1.-s: step for frame sampling. 1 means no skipping frame in calculation.-d: gpu id.-w,-h: resize the image before flow calculation. w = resized_width, h = resized_height. 0 means no resize. Note: If you want to resize the image, w and h must be both specified to take effect.-o: output format. 'dir' means saving the flow image data in directory format. 'zip' means saving the flow image data in zipped file format.
Example:
# first mount the folder containing videos to /data
nvidia-docker run -it -v path_to_mount:/data wizyoung/optical-flow-gpu bash
mkdir /data/result
# RGB image data is abandoned here
extract_gpu -f /data/video.mp4 -x /data/result/flow_x -y /data/result/flow_y -i /dev/null -b 20 -t 1 -s 1 -d 0 -w 100 -h 100 -o zipResult:
# path: /data/
.
├── result
│ ├── flow_x.zip
│ └── flow_y.zip
└── video.mp42. Batch processing using python script with multi gpu support
I included the python wrapper script with multi gpu support in the /src path:
root@eca86f630747:/src# python multi_gpu_extract.py -h
usage: multi_gpu_extract.py [-h] [--flow_type {tvl1,warp_tvl1}]
[--out_fmt {dir,zip}] [--num_gpu NUM_GPU]
[--step STEP] [--keep_frames KEEP_FRAMES]
[--width WIDTH] [--height HEIGHT] [--log LOG]
vid_txt_path out_dir
Extract optical flows with multi-gpu support.
positional arguments:
vid_txt_path Input txt files containing video paths.
out_dir Destination directory to store flow results.
optional arguments:
-h, --help show this help message and exit
--flow_type {tvl1,warp_tvl1}
Optical flow type. Default: tvl1
--out_fmt {dir,zip} Output file format. Default: zip
--num_gpu NUM_GPU Number of GPUs. Default: 4
--step STEP Specify the step for frame sampling. Default: 1
--keep_frames KEEP_FRAMES
Whether to save RGB frame data. Default: False
--width WIDTH Resize image width. Default: 0 (no resize)
--height HEIGHT Resize image height. Default: 0 (no resize)
--log LOG Output log file path. Default: ./out.logNOTE:
(1) vid_txt_path should be a txt file each line containing a video path.
Example:
# NOTE: /data/ is the mounting point rather than the actual path
# vid_txt_path: /data/video_list.txt
/data/videos/1.mp4
/data/videos/2.mp4
/data/videos/3.mp4
...(2) num_gpu means using gpus from 0 to num_gpu - 1.
(3) The log file records the processing progress and error (likely corrupted videos) during processing.
Example:
python multi_gpu_extract.py /data/video_list.txt /data/results --flow_type tvl1 --out_fmt zip --num_gpu 4 --step 1 --keep_frames True --width 100 --height 100 --log /data/log.log