#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Mar 12 16:13:56 2024 @author: tominhanh """ # 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. # Test 6 import pandas as pd from PIL import Image as PilImage # Import PIL Image with an alias import datasets from datasets import DatasetBuilder, GeneratorBasedBuilder, DownloadManager, DatasetInfo, Features, Image, ClassLabel, Value, Sequence, load_dataset, SplitGenerator import os import io from typing import Tuple, Dict, List import numpy as np import zipfile import requests import random from io import BytesIO import csv _CITATION = """\ https://arxiv.org/abs/2102.09099 """ _DESCRIPTION = """\ The comprehensive dataset contains over 220,000 single-rater and multi-rater labeled nuclei from breast cancer images obtained from TCGA, making it one of the largest datasets for nucleus detection, classification, and segmentation in hematoxylin and eosin-stained digital slides of breast cancer. This version of the dataset is a revised single-rater dataset, featuring over 125,000 nucleus csvs. These nuclei were annotated through a collaborative effort involving pathologists, pathology residents, and medical students, using the Digital Slide Archive. """ _HOMEPAGE = "https://sites.google.com/view/nucls/home?authuser=0" _LICENSE = "CC0 1.0 license" _URL = "https://www.dropbox.com/scl/fi/srq574rdgvp7f5gwr60xw/NuCLS_dataset.zip?rlkey=qjc9q8shgvnqpfy4bktbqybd1&dl=1" class NuCLSDataset(GeneratorBasedBuilder): """The NuCLS dataset.""" VERSION = datasets.Version("1.1.0") def _info(self): """Returns the dataset info.""" # Define the classes for the classifications raw_classification = ClassLabel(names=[ 'apoptotic_body', 'ductal_epithelium', 'eosinophil','fibroblast', 'lymphocyte', 'macrophage', 'mitotic_figure', 'myoepithelium', 'neutrophil', 'plasma_cell','tumor', 'unlabeled', 'vascular_endothelium' ]) main_classification = ClassLabel(names=[ 'AMBIGUOUS', 'lymphocyte', 'macrophage', 'nonTILnonMQ_stromal', 'plasma_cell', 'tumor_mitotic', 'tumor_nonMitotic', ]) super_classification = ClassLabel(names=[ 'AMBIGUOUS','nonTIL_stromal','sTIL', 'tumor_any', ]) type = ClassLabel(names=['rectangle', 'polyline']) # Assuming a maximum length for polygon coordinates. max_polygon_length = 20 # Define features features = Features({ # Images will be loaded as arrays; you'll dynamically handle the varying sizes in the generator function 'rgb_image': Image(decode=False), 'mask_image': Image(decode=False), 'visualization_image': Image(decode=False), # Annotation coordinates 'annotation_coordinates': Features({ 'raw_classification': raw_classification, 'main_classification': main_classification, 'super_classification': super_classification, 'type': type, 'xmin': Value('int64'), 'ymin': Value('int64'), 'xmax': Value('int64'), 'ymax': Value('int64'), 'coords_x': Sequence(Value('float32')), 'coords_y': Sequence(Value('float32')), }) }) return DatasetInfo( description=_DESCRIPTION, features=features, supervised_keys=None, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager: DownloadManager): # Download source data data_dir = dl_manager.download_and_extract(_URL) # Directory paths base_dir = os.path.join(data_dir, "NuCLS_dataset") rgb_dir = os.path.join(base_dir, "rgb") visualization_dir = os.path.join(base_dir, "visualization") mask_dir = os.path.join(base_dir, "mask") csv_dir = os.path.join(base_dir, "csv") # Generate a list of unique filenames (without extensions) unique_filenames = [os.path.splitext(f)[0] for f in os.listdir(rgb_dir)] # Split filenames into training and testing sets random.shuffle(unique_filenames) split_idx = int(0.8 * len(unique_filenames)) train_filenames = unique_filenames[:split_idx] test_filenames = unique_filenames[split_idx:] # Map filenames to file paths for each split train_filepaths = self._map_filenames_to_paths(train_filenames, rgb_dir, visualization_dir, mask_dir, csv_dir) test_filepaths = self._map_filenames_to_paths(test_filenames, rgb_dir, visualization_dir, mask_dir, csv_dir) # Create the split generators return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_filepaths} ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"filepaths": test_filepaths} ), ] def _map_filenames_to_paths(self, filenames, rgb_dir, visualization_dir, mask_dir, csv_dir): """Maps filenames to file paths for each split.""" filepaths = {} for filename in filenames: filepaths[filename] = { 'fov': os.path.join(rgb_dir, filename + '.png'), 'visualization': os.path.join(visualization_dir, filename + '.png'), 'mask': os.path.join(mask_dir, filename + '.png'), 'csv': os.path.join(csv_dir, filename + '.csv'), } return filepaths def _generate_examples(self, filepaths): """Yield examples as (key, example) tuples.""" for key, paths in filepaths.items(): # Initialize an example dictionary example = { 'rgb_image': self._read_image_file(paths['fov']), 'mask_image': self._read_image_file(paths['mask']), 'visualization_image': self._read_image_file(paths['visualization']), 'annotation_coordinates': self._read_csv_file(paths['csv']), } yield key, example def _read_image_file(self, file_path: str) -> PilImage: """Reads an image file and returns it as a PIL Image object.""" try: with open(file_path, 'rb') as f: image = PilImage.open(f) return np.array(image) except Exception as e: print(f"Error reading image file {file_path}: {e}") return None def _read_csv_file(self, file_path: str): """Reads a CSV file and returns the contents in the expected format.""" try: csv_df = pd.read_csv(file_path) if csv_df.empty: print(f"Warning: CSV file {file_path} is empty.") return None else: # Convert the DataFrame into the structure that matches your features' annotation_coordinates return self._process_csv_data(csv_df) except Exception as e: print(f"Error reading CSV file {file_path}: {e}") return None # Implement this method to process and convert CSV data into the format expected by your dataset's features def _process_csv_data(self, csv_df): # Process the DataFrame and return the data in the correct format pass