- numpy
- html4vision
- shapely
- colour-science
- lensfunpy
- pytorch
- largestinteriorrectangle
Download and unzip labels.zip
to the code folder so that labels
is at same level as various scripts.
images_png.zip
and images_raw.zip
are png format images and camera raw format images respectively. images_raw
will be needed for shading computation.
In utils.py
, change paths in ALGORITHM_PATHS
to results output by each algorithm, and IMGS_PATH
to path of MAW images.
In run_cmp.py
, update names
on line 30 to algorithms you want to evaluate. Similarly in texture_score_lpips.py
, update names
on line 750 to algothims you want to evaluate. (possible names
are in utils.py starting at line 159.)
To evaluate all algorimths for albedo intensity, run:
python3 run_cmp.py --meta meta.csv --loss="si" --metric "mean" --type=metric
To evaluate all algorimths for albedo chromaticity, run:
python3 run_cmp.py --meta meta.csv --loss="per_si" --metric "deltae" --type=metric
To evaluate all algorimths for WHDR on MAW, run:
python3 run_cmp.py --meta meta.csv --type=whdr
To evaluate all algorithms for texture, run:
python3 texture_score_lpips.py maw --meta meta.csv --imgs_dir <MAW PNG IMG PATH> --use_gpu
- With
images_png
andimages_raw
in the same folder, runpython3 compute_shading.py meta.csv --imgs_dir <MAW PNG IMG PATH>
to generate shading labels. - In
run_shading_cmp.py
updatenames
on line 54 to algorithms you want to evaluate.
To evaluate all algorimths for shading, run:
python3 run_shading_cmp.py --meta meta.csv --imgs_dir <MAW PNG IMG PATH>
Unfortunately, we have to remove 14 images from scene_0 as those images reveal personal credit cards. We will consider restore those images by blackout sensitive area.
The way number of scenes are counted in the dataset release is different from the paper. In the dataset release, some scene
can contain multiple scenes as counted by the paper, as pictures in those scenes
come from physically separate areas/different rooms. scene
containing multiple scenes are listed below:
<scene_0>
: contains 2 scenes.<scene_2>
: contains 3 scenes.<scene_2>
: contains 2 scenes.<scene_31>
: contains 4 scenes.<scene_34>
: contains 2 scenes.