Tile your Dataset to Cope with High-Resolution Images

In this notebook example, we’ll take a look at Datumaro TileTransform which is a useful dataset transformation for enhancing small object detection tasks. We are going to look at the example codes for object detection task with Six-sided Dice Dataset.

Prerequisite

Download Six-sided Dice dataset

This is a download link for Six-sided Dice dataset in Kaggle. Please download using this link and extract to your workspace directory. Then, you will have a d6-dice directory with annotations and images in YOLO format as follows.

d6-dice
├── Annotations
│   ├── classes.txt
│   ├── IMG_20191208_111228.txt
│   ├── IMG_20191208_111246.txt
│   ├── ...
└── Images
    ├── IMG_20191208_111228.jpg
    ├── IMG_20191208_111246.jpg
    ├── ...

However, for import compatibility, obj.names file must be added to d6-dice/obj.names filepath for import compatibility. This obj.names file includes the label names of the dataset, e.g., [dice1, ..., dice6]. Therefore, you can write it with the following simple code. Please see Yolo Loose format for more details.

# Copyright (C) 2023 Intel Corporation
#
# SPDX-License-Identifier: MIT

import os

root_dir = "d6-dice"

names = """
dice1
dice2
dice3
dice4
dice5
dice6
"""

fpath = os.path.join(root_dir, "obj.names")
with open(fpath, "w") as fp:
    fp.write(names)

Import dataset

Firstly, we import this dataset using Datumaro Python API.

from datumaro import Dataset


def get_ids(dataset: Dataset, subset: str):
    ids = []
    for item in dataset:
        if item.subset == subset:
            ids += [item.id]

    return ids


dataset = Dataset.import_from("./d6-dice", format="yolo")
subset = "default"
ids = get_ids(dataset, subset)

dataset

Dataset
        size=250
        source_path=./d6-dice
        media_type=<class 'datumaro.components.media.Image'>
        annotated_items_count=250
        annotations_count=1795
subsets
        default: # of items=250, # of annotated items=250, # of annotations=1795, annotation types=['bbox']
infos
        categories
        label: ['dice1', 'dice2', 'dice3', 'dice4', 'dice5', 'dice6']

Let’s take a closer look at one of the samples using Visualizer. This sample contains a high-resolution image and several small objects.

from datumaro.components.visualizer import Visualizer

target_id = ids[10]
vizualizer = Visualizer(dataset, alpha=0.7)
fig = vizualizer.vis_one_sample(target_id, subset)
fig.show()

After a tiling transformation in the dataset, the previous samples are split into 2x2 slices of tiled samples.

from datumaro.plugins.tiling import Tile

tiled_dataset = dataset.transform(
    Tile, grid_size=(2, 2), overlap=(0.1, 0.1), threshold_drop_ann=0.5
)
target_ids = [tiled_id for tiled_id in get_ids(tiled_dataset, subset) if target_id in tiled_id]
print(target_ids)

['IMG_20191209_100155_tile_0', 'IMG_20191209_100155_tile_1', 'IMG_20191209_100155_tile_2', 'IMG_20191209_100155_tile_3']

vizualizer = Visualizer(tiled_dataset, alpha=0.7)
fig = vizualizer.vis_gallery(target_ids, subset)
fig.tight_layout(pad=3.0)
fig.show()

Export tile transformed dataset

Next, we will export the transformed dataset for the next machine learning workflow. The following code will export the dataset in d6-dice-coco path with COCO format.

You should set ``save_media=True`` to save the tiled image also.

tiled_dataset.export("d6-dice-tiled-coco", "coco_instances", save_media=True)

Then, we will check whether the dataset export is successful by importing d6-dice-coco dataset again.

coco_dataset = Dataset.import_from("d6-dice-tiled-coco", "coco_instances")

vizualizer = Visualizer(coco_dataset, alpha=0.7)
fig = vizualizer.vis_gallery(target_ids, subset)
fig.tight_layout(pad=3.0)
fig.show()