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rekall: compositional video event specification

Build Status Documentation Status

Rekall is a library for compositional video event specification. We use Rekall to detect new events in video -- such as interviews and commercials in TV news broadcasts, or action sequences in Hollywood films -- by composing the outputs of pre-trained computer vision models.

Check out our tech report for more details and demo videos.

Rekall has a main Python API for all the core interval processing operations. Rekall also has a Javascript API which we use for the vgrid video metadata visualization widget.

Getting Started

Sample Usage

Rekall provides utilities for processing spatiotemporal intervals (like bounding boxes in a video). This code sample shows how bounding boxes for a few videos can be loaded into Rekall:

from rekall import Interval, IntervalSet, IntervalSetMapping, Bounds3D
import urllib3, requests, os

urllib3.disable_warnings()
VIDEO_COLLECTION_BASEURL = "http://olimar.stanford.edu/hdd/rekall_tutorials/cydet/" 
VIDEO_METADATA_FILENAME = "metadata.json"
req = requests.get(os.path.join(VIDEO_COLLECTION_BASEURL, VIDEO_METADATA_FILENAME), verify=False)
video_collection = sorted(req.json(), key=lambda vm: vm['filename'])

video_metadata = [
    VideoMetadata(v["filename"], v["id"], v["fps"], int(v["num_frames"]), v["width"], v["height"])
    for v in video_collection
]

maskrcnn_bbox_files = [ 'maskrcnn_bboxes_0001.pkl', 'maskrcnn_bboxes_0004.pkl' ]

maskrcnn_bboxes = []
for bbox_file in maskrcnn_bbox_files:
    req = requests.get(os.path.join(VIDEO_COLLECTION_BASEURL, bbox_file), verify=False)
    maskrcnn_bboxes.append(pickle.loads(req.content))

# Load Mask R-CNN data into Rekall
maskrcnn_bboxes_ism = IntervalSetMapping({
    vm.id: IntervalSet([
        Interval(
            Bounds3D(
                t1 = frame_num / vm.fps,
                t2 = (frame_num + 1) / vm.fps,
                x1 = bbox[0] / vm.width,
                x2 = bbox[2] / vm.width,
                y1 = bbox[1] / vm.height,
                y2 = bbox[3] / vm.height
            ),
            payload = {
                'class': bbox[4],
                'score': bbox[5]
            }
        )
        for frame_num, bboxes_in_frame in enumerate(maskrcnn_frame_list)
        for bbox in bboxes_in_frame
    ])
    for vm, maskrcnn_frame_list in zip(video_metadata, maskrcnn_bboxes)
})

Check out the tutorials for more on how Rekall can be used to operate on this spatiotemporal data.

Installation

Python API

Rekall requires Python 3.5 or greater.

pip3 install rekallpy

JavaScript API

The Rekall JavaScript API must be installed in the context of a JavaScript application using the npm package structure. You must have npm installed.

npm install --save @wcrichto/rekall

Now that you've installed Rekall, check out the tutorials!

Developer Guidelines

If you are interested in contributing to Rekall (and we welcome contribution via pull requests!), you should install Rekall from source:

[1] Clone the rekall repo

git clone https://github.com/scanner-research/rekall

[2] Install Python API from source

cd rekall/rekallpy
pip3 install -e .

And run tests:

python3 -m unittest discover test

[3] Install JavaScript API from source

cd rekall/rekalljs
npm install
npm run prepublishOnly
npm link

Citation

If you used Rekall or found it useful for you research, please cite our arXiv paper:

@article{fu2019rekall,
  author = {Daniel Y. Fu and Will Crichton and James Hong and Xinwei Yao and Haotian Zhang and Anh Truong and Avanika Narayan and Maneesh Agrawala and Christopher R\'e and Kayvon Fatahalian},
  title = {Rekall: Specifying Video Events using Compositions of Spatiotemporal Labels},
  year = {2019},
  journal={arXiv preprint arXiv:1910.02993},
}