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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""
lesion dataset - ISIC 2018 Task 2
"""
import numpy as np
import os
from PIL import Image
import datasets
from datasets import Sequence, Value
from urllib.parse import urlparse
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This dataset has been modified for project use case.
"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""
# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
# _URL = "https://storage.googleapis.com/lesion-dataset/"
_URLS = {
"full": "https://storage.googleapis.com/lesion-dataset/dataset-images.zip",
"globules": "https://storage.googleapis.com/lesion-dataset/dataset-globules.zip",
"milia_like_cyst": "https://storage.googleapis.com/lesion-dataset/dataset-milia_like_cyst.zip",
"negative_network": "https://storage.googleapis.com/lesion-dataset/dataset-negative_network.zip",
"pigment_network": "https://storage.googleapis.com/lesion-dataset/dataset-pigment_network.zip",
"streaks": "https://storage.googleapis.com/lesion-dataset/dataset-streaks.zip",
"task1": "https://storage.googleapis.com/lesion-dataset/dataset-task1.zip",
}
# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class LesionDataset(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name='full', version=VERSION, description="This will return the full dataset with all classes"),
datasets.BuilderConfig(name="globules", version=VERSION, description="This will return the dataset with only globules class"),
datasets.BuilderConfig(name="milia_like_cyst", version=VERSION, description="This will return the dataset with only milia_like_cyst class"),
datasets.BuilderConfig(name="negative_network", version=VERSION, description="This will return the dataset with only negative_network class"),
datasets.BuilderConfig(name="pigment_network", version=VERSION, description="This will return the dataset with only pigment_network class"),
datasets.BuilderConfig(name="streaks", version=VERSION, description="This will return the dataset with only streaks class"),
datasets.BuilderConfig(name="task1", version=VERSION, description="This will return the dataset for task1"),
]
DEFAULT_CONFIG_NAME = "task1" # It's not mandatory to have a default configuration. Just use one if it make sense.
def _info(self):
# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
if self.config.name == "full": # This is the name of the configuration selected in BUILDER_CONFIGS above
features=datasets.Features(
{
"image": datasets.Image(),
"label0": datasets.Image(),
"label1": datasets.Image(),
"label2": datasets.Image(),
"label3": datasets.Image(),
"label4": datasets.Image(),
}
)
elif self.config.name in ['globules', 'milia_like_cyst', 'negative_network', 'pigment_network', 'streaks', 'task1']:
features = datasets.Features(
{
"image": datasets.Image(),
"label": datasets.Image(),
}
)
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=features, # 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
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
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
url = _URLS[self.config.name]
data_dir = dl_manager.download_and_extract(url)
# Parse the URL
parsed_url = urlparse(url)
# Get the base name
base_name_with_extension = os.path.basename(parsed_url.path)
# Remove the extension
base_name = os.path.splitext(base_name_with_extension)[0]
# Label to ID mapping
self.label2id = {
'globules': 0,
'milia_like_cyst': 1,
'negative_network': 2,
'pigment_network': 3,
'streaks': 4
}
# Task 2
if self.config.name in ['full', 'globules', 'milia_like_cyst', 'negative_network', 'pigment_network', 'streaks']:
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, f"{base_name}/ISIC2018_Task1-2_Training_Input"),
"labelpath": os.path.join(data_dir, f"{base_name}/ISIC2018_Task2_Training_GroundTruth_v3"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, f"{base_name}/ISIC2018_Task1-2_Validation_Input"),
"labelpath": os.path.join(data_dir, f"{base_name}/ISIC2018_Task2_Validation_GroundTruth"),
"split": "validation",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, f"{base_name}/ISIC2018_Task1-2_Test_Input"),
"labelpath": os.path.join(data_dir, f"{base_name}/ISIC2018_Task2_Test_GroundTruth"),
"split": "test"
},
),
]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, f"{base_name}/ISIC2018_Task1-2_Training_Input"),
"labelpath": os.path.join(data_dir, f"{base_name}/ISIC2018_Task1_Training_GroundTruth"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, f"{base_name}/ISIC2018_Task1-2_Validation_Input"),
"labelpath": os.path.join(data_dir, f"{base_name}/ISIC2018_Task1_Validation_GroundTruth"),
"split": "validation",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, f"{base_name}/ISIC2018_Task1-2_Test_Input"),
"labelpath": os.path.join(data_dir, f"{base_name}/ISIC2018_Task1_Test_GroundTruth"),
"split": "test"
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath, labelpath, split):
# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
# if the configuration is full, return all the labels
if self.config.name == "full":
# Loop through every file in the filepath directory
for filename in os.listdir(filepath):
# Check if the file is an image
if filename.endswith('.jpg') or filename.endswith('.jpeg'):
# Get the base name of the image file (without the extension) (e.g. ISIC_0000000)
base_name = os.path.splitext(filename)[0]
yield_result = {"image": os.path.join(filepath, filename)}
for k, v in self.label2id.items():
label_filename = f'{base_name}_attribute_{k}.png'
label_file_path = os.path.join(labelpath, label_filename)
# if attribute label does not exist, create a black mask for it
if not os.path.exists(label_file_path):
# Load the corresponding image to get its size
img = Image.open(yield_result['image'])
width, height = img.size
# Create a black image of the same size
black_img = Image.fromarray(np.zeros((height, width), dtype=np.uint8))
# Save the black image
black_img.save(label_file_path)
yield_result[f"label{v}"] = label_file_path
yield base_name, yield_result
elif self.config.name in ['globules', 'milia_like_cyst', 'negative_network', 'pigment_network', 'streaks']:
for filename in os.listdir(filepath):
if filename.endswith('.jpg') or filename.endswith('.jpeg'):
base_name = os.path.splitext(filename)[0]
yield_result = {"image": os.path.join(filepath, filename)}
label_filename = f'{base_name}_attribute_{self.config.name}.png'
label_file_path = os.path.join(labelpath, label_filename)
# if attribute label does not exist, create a black mask for it
if not os.path.exists(label_file_path):
# Load the corresponding image to get its size
img = Image.open(yield_result['image'])
width, height = img.size
# Create a black image of the same size
black_img = Image.fromarray(np.zeros((height, width), dtype=np.uint8))
# Save the black image
black_img.save(label_file_path)
yield_result["label"] = label_file_path
yield base_name, yield_result
elif self.config.name == "task1":
for filename in os.listdir(filepath):
if filename.endswith('.jpg') or filename.endswith('.jpeg'):
base_name = os.path.splitext(filename)[0]
yield_result = {"image": os.path.join(filepath, filename)}
label_filename = f'{base_name}_segmentation.png'
label_file_path = os.path.join(labelpath, label_filename)
# if attribute label does not exist, create a black mask for it
if not os.path.exists(label_file_path):
# Load the corresponding image to get its size
img = Image.open(yield_result['image'])
width, height = img.size
# Create a black image of the same size
black_img = Image.fromarray(np.zeros((height, width), dtype=np.uint8))
# Save the black image
black_img.save(label_file_path)
yield_result["label"] = label_file_path
yield base_name, yield_result
# datasets-cli test /Users/jon/code/school/t8/DeepLearning/proj/lesion-dataset.py --save_info --all_configs |