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import os
import pandas as pd
import datasets
from glob import glob
import zipfile
class dummy(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(features=datasets.Features({'image':datasets.Image(),'text':datasets.Value('string')}))
def extract_all(self, dir):
zip_files = glob(dir+'/**/**.zip', recursive=True)
for file in zip_files:
with zipfile.ZipFile(file) as item:
item.extractall('/'.join(file.split('/')[:-1]))
def get_all_files(self, dir):
files = []
valid_file_ext = ['txt', 'csv', 'tsv', 'xlsx', 'xls', 'xml', 'json', 'jsonl', 'html', 'wav', 'mp3', 'jpg', 'png']
for ext in valid_file_ext:
files += glob(f"{dir}/**/**.{ext}", recursive = True)
return files
def _split_generators(self, dl_manager):
url = [os.path.abspath(os.path.expanduser(dl_manager.manual_dir))]
downloaded_files = dl_manager.download_and_extract(url)
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={'filepaths':{'inputs':sorted(glob(downloaded_files[0]+'/data/**.png')),'targets1':sorted(glob(downloaded_files[0]+'/data/**.txt')),} })]
def read_image(self, filepath):
if filepath.endswith('.jpg') or filepath.endswith('.png'):
raw_data = {'bytes':[filepath]}
else:
raw_data = {'text':[open(filepath).read()]}
return pd.DataFrame(raw_data)
def _generate_examples(self, filepaths):
_id = 0
for i,filepath in enumerate(filepaths['inputs']):
df = self.read_image(filepath)
dfs = [df]
dfs.append(self.read_image(filepaths['targets1'][i]))
df = pd.concat(dfs, axis = 1)
if len(df.columns) != 2:
continue
df.columns = ['image', 'text']
for _, record in df.iterrows():
yield str(_id), {'image':record['image'],'text':record['text']}
_id += 1
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