Datasets:
File size: 3,325 Bytes
9bb630e 681a6a9 983ff4d 681a6a9 a7d7d4c 681a6a9 a7d7d4c b3ec3ca 681a6a9 b3ec3ca 9bb630e f66c5a4 20379d6 8cc1bf2 75c0504 8cc1bf2 473e13f af11ab2 577f9b3 30ccca4 8cc1bf2 c783da5 8cc1bf2 b3ec3ca |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 |
---
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
---
[](https://colab.research.google.com/drive/1t59uyZMMyCUYVHGXdqdlNlDlb--FL_3P?usp=sharing)
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](https://github.com/LCAV/LenslessPiCam), ADMM reconstruction can be applied to the dataset with [this script](https://github.com/LCAV/LenslessPiCam/blob/main/scripts/recon/dataset.py) (handles dataset downloading from Hugging Face).
```bash
python scripts/recon/dataset.py -cn recon_celeba_digicam
```
The [simulated PSF](https://huggingface.co/datasets/bezzam/DigiCam-CelebA-10K/blob/main/psf_simulated.png) can be obtained and compared with the measured one with the following command:
```bash
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
``` |