| import csv | |
| import json | |
| import os | |
| import pdb | |
| import datasets | |
| import pandas as pd | |
| # TODO: Add description of the dataset here | |
| # You can copy an official description | |
| _DESCRIPTION = """\ | |
| Dataset for commy test eye diabetic | |
| """ | |
| # TODO: Add a link to an official homepage for the dataset here | |
| _HOMEPAGE = "NawinCom/CommyTesting" | |
| # TODO: Add the licence for the dataset here if you can find it | |
| _LICENSE = "" | |
| _URL = "https://huggingface.co/datasets/NawinCom/CommyTesting/resolve/main/images.zip" | |
| # classes = [0,0] | |
| # create class | |
| train = pd.read_csv('./Train.csv') | |
| lis1 = train['id_code'] | |
| lis2 = train['diagnosis'] | |
| dic = dict(zip(lis1, lis2)) | |
| # classes = [0] * 4209 | |
| # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case | |
| class ImagesDemo(datasets.GeneratorBasedBuilder): | |
| """TODO: Short description of my dataset.""" | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| # This is the description that will appear on the datasets page. | |
| description=_DESCRIPTION, | |
| # This defines the different columns of the dataset and their types | |
| features=datasets.Features( | |
| { | |
| "image": datasets.Image(), | |
| "label": datasets.Value("string"), | |
| } | |
| ), # Here we define them above because they are different between the two configurations | |
| # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and | |
| # specify them. They'll be used if as_supervised=True in builder.as_dataset. | |
| # supervised_keys=("sentence", "label"), | |
| # Homepage of the dataset for documentation | |
| supervised_keys=None, | |
| homepage=_HOMEPAGE, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration | |
| # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name | |
| # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS | |
| # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. | |
| # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive | |
| data_dir = dl_manager.download(_URL) | |
| image_iters = dl_manager.iter_archive(data_dir) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "images": image_iters | |
| }, | |
| ), | |
| ] | |
| # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
| def _generate_examples(self, images): | |
| idx = 0 | |
| for filepath, image in images: | |
| print(filepath) | |
| check = filepath.split('/')[-1].replace('.jpg', '') | |
| yield idx, { | |
| "image" : {"path": filepath, "bytes": image.read()}, | |
| "label" : dic[check] | |
| } | |
| idx+=1 |