Source code for paper "Occlusions are Fleeting: Texture is Forever" by Ham et al.
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
src
.gitignore
LICENSE
README.md
requirements.txt

README.md

Boundaries are Fleeting: Texture is Forever

Reference

If you use or reference this work, please cite the following paper:

  • Boundaries are Fleeting: Texture is Forever - Moving Past Brightness Constancy; C. Ham, S. Singh, S. Lucey; In WACV 2017

Citations

For the paper we used Piotr Dollár's Structured Forests Edge Detector. Source can be found at https://github.com/pdollar/edges. Reference:

  • Structured Forests for Fast Edge Detection; P. Dollár and C. Zitnick; In ICCV 2013

Foreword

In the spirit of open source and enabling reproducibility of academic work, this code is almost the exact code used in the paper. It has had some minor refactoring and cleaning up of comments. And so, it is quite raw, and rough around the edges.

I will release a version 2 in the coming year as I rewrite parts of it to be integrated with my final thesis.

Requirements

This project has been developed for Python 3. You may be able to tweak it to work with Python 2.7, but there are no guarantees that it will work.

Install the basic Python modules required with:

pip install -r requirements.txt

This project also relies on Numba to accelerate the logic and enable multithreaded processing in Python. Refer to the Numba website for installation instructions.

For your own dataset, if you wish to use the Structured Forest Edge Detector, source can be found at https://github.com/pdollar/edges. Alternatively, you could use any edge detector you like and place the images in a subdirectory of the sequence called edges (see Usage instructions below). To work, the functions edgesTrain and edgesDetect must be added to the path in Matlab.

Usage

To process a sequence in a directory, use Pipeline.py

python Pipeline.py <path/to/sequence>

Sequence datasets used in the paper can be downloaded in the Release section of the GitHub project.

At minimum a sequence directory should contain the following files:

sequence_name
  |- cam.yaml
  |- config.yaml
  |- rects.yaml
  |- {edges/, seq/, video.mp4}

edges should be a directory of images with the edge response map of the original sequence. If Pipeline cannot find this directory, it will attempt to generate the edge maps using the Structured Forest Edge Detector in Matlab using images in the seq directory. If seq doesn't exist, it will generate the images from video.mp4 using avconv.

Camera calibration

cam.yaml should contain the intrinsic camera calibration of the sequence. An example:

fx: 1100.0
fy: 1100.0
cx: 640.0
cy: 360.0

Configuration

See the paper datasets for example configuration files. They set the values of the Config object passed to different stages of the pipeline. Important configurations are:

  • frame_support: The temporal window radius when looking for neighbor edge segments.
  • min_inlier: The minimum fraction of inliers in the RANSAC fitting for an edge to be considered good quality.
  • min_neighbors: The minimum number of neighbors to reciprocate a correspondence for an edge to be considered good quality

Bounding Rectangles

I will soon update the code to make this optional. This currently a nice way to constrain the bounds of the voxel grid used to speed up look-up times of edge rays. It has no affect on the results, it simply helps increase the computational efficiency. An example:

rects:
	- frame: 500
	  bounds: [300, 150, 350, 200]
	- frame: 950
	  bounds: [200, 400, 250, 450]

Where bounds is a simple bounding box defined by the upper left and lower right vertices: [x1, y1, x2, y2].