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---
dataset_info:
features:
- name: image
dtype: image
- name: tl
sequence: int64
- name: tr
sequence: int64
- name: br
sequence: int64
- name: bl
sequence: int64
- name: is_clean
dtype: bool
- name: split
dtype: string
- name: text
dtype: string
- name: image_name
dtype: string
splits:
- name: validation
num_bytes: 64018244
num_examples: 101
- name: test
num_bytes: 125460818
num_examples: 199
download_size: 189448472
dataset_size: 189479062
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
- split: test
path: data/test-*
license: mit
task_categories:
- image-to-text
- object-detection
size_categories:
- n<1K
---
# DM codes dataset
The dataset contains photos of Data Matrix (DM) codes and their annotations. The photos were taken on an iPhone and annotated manually by a human.
The annotations contain **text**, which is encoded in the DM code and the pixel coordinates of the DM code vertices.
The vertices are: **tl** = top left, **tr** = top right, **br** = bottom right, **bl** = bottom left.
Attribute **is_clean** specifies whether the DM code on the image is expected to be easily readable. For every DM code, there is exactly one image
with `is_clean=true` and several images with `is_clean=false`.
If you want to crop the DM codes from the images, use the following code:
```python
import numpy as np
import datasets
from PIL import Image
from skimage import transform
def crop_dm_code(example: dict, square_side: int = 200, square_padding: int = 25) -> dict:
vertices = np.asarray((example["tl"], example["tr"], example["br"], example["bl"]))
unit_square = np.asarray([
[square_padding, square_padding],
[square_side + square_padding, square_padding],
[square_side + square_padding, square_side + square_padding],
[square_padding, square_side + square_padding]
])
transf = transform.ProjectiveTransform()
if not transf.estimate(unit_square, vertices): raise Exception("estimate failed")
cropped_np_image = transform.warp(
np.array(example["image"]),
transf,
output_shape=(square_side + square_padding * 2, square_side + square_padding * 2)
)
cropped_image = Image.fromarray((cropped_np_image * 255).astype(np.uint8))
return {"cropped_image": cropped_image}
dataset = datasets.load_dataset("shortery/dm-codes")
dataset = dataset.map(crop_dm_code)
```
## DataMatrix Image Reconstruction to Enhance Decodability
This dataset is a part of the Diploma thesis <https://is.muni.cz/th/ppu25/dp-dmcodes-thesis.pdf>.
This thesis compares various encoder-decoder CNNs to enhance the DM code image quality before decoding it with a code reader.
The code is available on GitHub <https://github.com/shortery/dp-dm-codes>.
## Citing
```
@thesis{dmcodes-thesis,
author = {Petra Krátká},
title = {DataMatrix Image Reconstruction to Enhance Decodability},
address = {Brno},
year = {2024},
school = {Masaryk University, Faculty of Informatics},
type = {Diploma thesis},
url = {https://is.muni.cz/th/ppu25/dp-dmcodes-thesis.pdf},
}
``` |