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# Copyright 2024 Big Vision Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# pylint: disable=line-too-long
r"""Import widgetcap into TFDS format.
Widget Captioning all requires images from the RICO dataset:
mkdir -p /tmp/data/rico_images ; cd /tmp/data/rico_images
wget
https://storage.googleapis.com/crowdstf-rico-uiuc-4540/rico_dataset_v0.1/unique_uis.tar.gz
tar xvfz unique_uis.tar.gz
rm unique_uis.tar.gz
Widget Captioning:
mkdir - /tmp/data/widget_captioning ; cd /tmp/data/widget_captioning
git clone https://github.com/google-research-datasets/widget-caption.git
cp widget-caption/widget_captions.csv ./
cp widget-caption/split/*.txt ./
rm -rf widget-caption
Then, run conversion locally (make sure to install tensorflow-datasets for the
`tfds` util):
cd big_vision/datasets
env TFDS_DATA_DIR=/tmp/tfds tfds build --datasets=widgetcap
Example to load:
import tensorflow_datasets as tfds
dataset_augmented = tfds.load('widgetcap', split='train',
data_dir='/tmp/tfds')
"""
import csv
import json
import os
import numpy as np
from PIL import Image
import tensorflow_datasets as tfds
_DATASET_DIR = '/tmp/data/widget_captioning'
# Dataset property indicating the y-dim of the canvas
_RICO_CANVAS_Y = 2560
_IMAGE_DIR = '/tmp/data/rico_images/combined'
_CITATION = (
'@inproceedings{Li2020WidgetCG,title={Widget Captioning: Generating Natural'
' Language Description for MobileUser Interface Elements},author={Y. Li and'
' Gang Li and Luheng He and Jingjie Zheng and Hong Li and Zhiwei'
' Guan},booktitle={Conference on Empirical Methods in Natural Language'
' Processing},year={2020},}'
)
class Widgetcap(tfds.core.GeneratorBasedBuilder):
"""DatasetBuilder for widgetcap dataset."""
VERSION = tfds.core.Version('1.0.0')
RELEASE_NOTES = {'1.0.0': 'Format as needed for PaliGemma'}
def _info(self) -> tfds.core.DatasetInfo:
"""Returns the metadata."""
return tfds.core.DatasetInfo(
builder=self,
description='The widgetcap dataset.',
features=tfds.features.FeaturesDict({
'image/id': tfds.features.Text(),
'image/filename': tfds.features.Text(),
'image': tfds.features.Image(encoding_format='jpeg'),
'texts': tfds.features.Sequence(tfds.features.Text()),
'bbox': tfds.features.BBoxFeature(),
'screen_id': tfds.features.Text(),
'node_id': tfds.features.Text(),
'height': np.int32,
'width': np.int32,
}),
homepage='https://github.com/google-research-datasets/widget-caption',
citation=_CITATION,
)
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
return {
'train': self._generate_examples('train'),
'dev': self._generate_examples('dev'),
'test': self._generate_examples('test'),
}
def _generate_examples(self, split):
"""Yields (key, example) tuples from the dataset."""
split_screen_ids = set()
with open(os.path.join(_DATASET_DIR, split + '.txt')) as f:
for line in f:
split_screen_ids.add(line.strip())
with open(os.path.join(_DATASET_DIR, 'widget_captions.csv')) as f:
for row in csv.DictReader(f):
if row['screenId'] in split_screen_ids:
id_, example = self._get_example(
row['screenId'], row['nodeId'], row['captions']
)
yield id_, example
def _get_node_box(self, screen_id, node_id, height):
index_list = [int(i) for i in node_id.split('.')[1:]]
with open(os.path.join(_IMAGE_DIR, screen_id + '.json')) as f:
view = json.load(f)
curr_node = view['activity']['root']
for index in index_list:
curr_node = curr_node['children'][index]
normalized_bounds = map(
lambda x: x * height / _RICO_CANVAS_Y, curr_node['bounds']
)
return normalized_bounds
def _get_example(self, screen_id, node_id, captions):
image = Image.open(os.path.join(_IMAGE_DIR, screen_id + '.jpg'))
width, height = image.size
# get bounding box coordinates
xmin, ymin, xmax, ymax = self._get_node_box(screen_id, node_id, height)
image_id = f'{screen_id}_{node_id}'
example = {
'image/id': image_id,
'image/filename': screen_id + '.jpg',
'image': os.path.join(_IMAGE_DIR, screen_id + '.jpg'),
'texts': captions.split('|'),
'bbox': tfds.features.BBox(
ymin=ymin / height,
xmin=xmin / width,
ymax=ymax / height,
xmax=xmax / width,
),
'screen_id': screen_id,
'node_id': node_id,
'height': height,
'width': width,
}
return image_id, example
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