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ꟻLIP: A Tool for Visualizing and Communicating Errors in Rendered Images (v1.2)

By Pontus Andersson, Jim Nilsson, and Tomas Akenine-Möller, with Magnus Oskarsson, Kalle Åström, Mark D. Fairchild, and Peter Shirley.

This repository holds implementations of the LDR-ꟻLIP and HDR-ꟻLIP image error metrics in Python. It also holds code for the ꟻLIP tool, presented in Ray Tracing Gems II.

License

Copyright © 2020-2022, NVIDIA Corporation & Affiliates. All rights reserved.

This work is made available under a BSD 3-Clause License.

The repository distributes code for tinyexr, which is subject to a BSD 3-Clause License,
and stb_image, which is subject to an MIT License.

For individual contributions to the project, please confer the Individual Contributor License Agreement.

For business inquiries, please visit our website and submit the form: NVIDIA Research Licensing.

Python (API and Tool)

  • Setup (with Anaconda3):

    conda create -n flip python numpy matplotlib
conda activate flip
conda install -c conda-forge opencv
conda install -c conda-forge openexr-python

  • Remember to activate the flip environment through conda activate flip before using the tool.

  • Usage: python flip.py --reference reference.{exr|png} --test test.{exr|png} [--options], where the list of options can be seen by python flip.py -h.

  • Tested with Conda 4.10.0, Python 3.8.3, NumPy 1.19.0, OpenCV 4.0.1, and OpenEXR b1.3.2.

  • The ꟻLIP tool is provided in flip.py, which also contains several tool-specific utility functions. The API is provided in flip-api.py and image loading/saving/manipulation functions in data.py. ../tests/test.py contains simple tests used to test whether code updates alter results.

  • The naming convention used for the ꟻLIP tool's output is as follows (where ppd is the assumed number of pixels per degree, tm is the tone mapper assumed by HDR-ꟻLIP, cstart and cstop are the shortest and longest exposures, respectively, assumed by HDR-ꟻLIP, with p indicating a positive value and m indicating a negative value, N is the number of exposures used in the HDR-ꟻLIP calculation, nnn is a counter used to sort the intermediate results, and exp is the exposure used for the intermediate LDR image / ꟻLIP map):

    Default:

    Low dynamic range images:

    LDR-ꟻLIP: flip.<reference>.<test>.<ppd>ppd.ldr.png
    Weighted histogram: weighted_histogram.reference>.<test>.<ppd>ppd.ldr.pdf
    Overlapping weighted histogram: overlapping_weighted_histogram.<reference>.<test1>.<test2>.<ppd>ppd.ldr.pdf
    Text file: pooled_values.<reference>.<test>.<ppd>ppd.ldr.txt

    High dynamic range images:

    HDR-ꟻLIP: flip.<reference>.<test>.<ppd>ppd.hdr.<tm>.<cstart>_to_<cstop>.<N>.png
    Exposure map: exposure_map.<reference>.<test>.<ppd>ppd.hdr.<tm>.<cstart>_to_<cstop>.<N>.png
    Intermediate LDR-ꟻLIP maps: flip.<reference>.<test>.<ppd>ppd.ldr.<tm>.<nnn>.<exp>.png
    Intermediate LDR images: <reference|test>.<tm>.<nnn>.<exp>.png
    Weighted histogram: weighted_histogram.<reference>.<test>.<ppd>ppd.hdr.<tm>.<cstart>_to_<cstop>.<N>.pdf
    Overlapping weighted histogram: overlapping_weighted_histogram.<reference>.<test1>.<test2>.<ppd>ppd.hdr.<tm>.<cstart>_to_<cstop>.<N>.pdf
    Text file: pooled_values.<reference>.<test>.<ppd>ppd.hdr.<tm>.<cstart>_to_<cstop>.<N>.txt

    With --basename <name> (note: not applicable if more than one test image is evaluated):

    Low dynamic range images:

    LDR-ꟻLIP: <name>.png
    Weighted histogram: <name>.pdf
    Text file: <name>.txt

    High dynamic range images:

    HDR-ꟻLIP: <name>.png
    Exposure map: <name>.exposure_map.png
    Intermediate LDR-ꟻLIP maps: <name>.<nnn>.png
    Intermediate LDR images: <name>.reference|test.<nnn>.png
    Weighted histogram: <name>.pdf
    Overlapping weighted histogram: N/A
    Text file: <name>.txt

Example usage: First navigate to the directory containing the flip.py script and the rest of the Python files. Then start an Ananconda prompt and try:

conda activate flip
python flip.py -r ../images/reference.exr -t ../images/test.exr

The result should be:

Invoking HDR-FLIP
      Pixels per degree: 67
      Assumed tone mapper: ACES
      Start exposure: -12.5423
      Stop exposure: 0.9427
      Number of exposures: 14

FLIP between reference image <reference.exr> and test image <test.exr>:
      Mean: 0.283547
      Weighted median: 0.339469
      1st weighted quartile: 0.251148
      3rd weighted quartile: 0.434763
      Min: 0.003120
      Max: 0.962022
      Evaluation time: <t> seconds

where <t> is the time it took to evaluate HDR-ꟻLIP. In addition, you will now find the files flip.reference.test.67ppd.hdr.aces.m12.5423_to_p0.9427.14.png and exposure_map.reference.test.67ppd.hdr.aces.m12.5423_to_p0.9427.14.png in the directory containing the flip.py script, and we urge you to inspect those, which will reveal where the errors in the test image are located.