Datasets:
metadata
license: mit
dataset_info:
features:
- name: lensless
dtype: image
- name: lensed
dtype: image
- name: 5_o_Clock_Shadow
dtype: bool
- name: Arched_Eyebrows
dtype: bool
- name: Attractive
dtype: bool
- name: Bags_Under_Eyes
dtype: bool
- name: Bald
dtype: bool
- name: Bangs
dtype: bool
- name: Big_Lips
dtype: bool
- name: Big_Nose
dtype: bool
- name: Black_Hair
dtype: bool
- name: Blond_Hair
dtype: bool
- name: Blurry
dtype: bool
- name: Brown_Hair
dtype: bool
- name: Bushy_Eyebrows
dtype: bool
- name: Chubby
dtype: bool
- name: Double_Chin
dtype: bool
- name: Eyeglasses
dtype: bool
- name: Goatee
dtype: bool
- name: Gray_Hair
dtype: bool
- name: Heavy_Makeup
dtype: bool
- name: High_Cheekbones
dtype: bool
- name: Male
dtype: bool
- name: Mouth_Slightly_Open
dtype: bool
- name: Mustache
dtype: bool
- name: Narrow_Eyes
dtype: bool
- name: No_Beard
dtype: bool
- name: Oval_Face
dtype: bool
- name: Pale_Skin
dtype: bool
- name: Pointy_Nose
dtype: bool
- name: Receding_Hairline
dtype: bool
- name: Rosy_Cheeks
dtype: bool
- name: Sideburns
dtype: bool
- name: Smiling
dtype: bool
- name: Straight_Hair
dtype: bool
- name: Wavy_Hair
dtype: bool
- name: Wearing_Earrings
dtype: bool
- name: Wearing_Hat
dtype: bool
- name: Wearing_Lipstick
dtype: bool
- name: Wearing_Necklace
dtype: bool
- name: Wearing_Necktie
dtype: bool
- name: Young
dtype: bool
splits:
- name: train
num_bytes: 11236670416.5
num_examples: 8500
- name: test
num_bytes: 1981621309.5
num_examples: 1500
download_size: 13157231113
dataset_size: 13218291726
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
task_categories:
- image-to-image
- image-classification
tags:
- lensless
- computational-imaging
size_categories:
- 1K<n<10K
Dataset for the paper: https://opg.optica.org/abstract.cfm?uri=pcAOP-2023-JTu4A.45
Data is measured with a computer monitor at 30 cm as shown below (except for the in-the-wild mug measurement which is measured at 12 cm).
After cloning and installing LenslessPiCam, ADMM reconstruction can be applied to the dataset with this script (handles dataset downloading from Hugging Face).
python scripts/recon/dataset.py -cn recon_celeba_digicam
The simulated PSF can be obtained and compared with the measured one with the following command:
python scripts/sim/digicam_psf.py \
digicam.pattern=mask_pattern.npy \
digicam.psf=psf_measured.png \
digicam.ap_center=[59,76] \
digicam.ap_shape=[19,26] \
digicam.rotate=-0.8 \
digicam.vertical_shift=-20 \
digicam.horizontal_shift=-100 \
sim.waveprop=False