Delete NuCLS_dataset.py

NuCLS_dataset.py

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-#!/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.
-
-import csv
-import json
-import os
-import logging
-import pandas as pd
-import numpy as np
-from PIL import Image
-import datasets
-from datasets import DatasetBuilder, GeneratorBasedBuilder, DownloadManager, DatasetInfo, Features, Image, ClassLabel, Value, Sequence, load_dataset, SplitGenerator
-import io
-from typing import List, Tuple, Dict
-import random
-import zipfile
-
-_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 annotations. 
-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"
-
-_GITHUB_REPO = "https://github.com/PathologyDataScience/NuCLS"
-
-_LICENSE = "CC0 1.0 license"
-
-_URLS_GD = {
-  'visualization': 'https://drive.google.com/uc?export=download&id=1LxngBja8yga2PDE4nLh-sDt7zylbU6LX',
-  'fov': 'https://drive.google.com/uc?export=download&id=1WRlgzL2y3A4Vw2qrgZMeSrG2QLWsSbbt',
-  'annotation': 'https://drive.google.com/uc?export=download&id=1ac6lwgjDgukYbabSJXYGFUp1XC6hrTq0',
-  'mask': 'https://drive.google.com/uc?export=download&id=1PdLKC_d2W_-58AUYEECknjzf9UUnmXeL',
-  'train_test_splits': 'https://drive.google.com/uc?export=download&id=1Hidb8WsKn2-35-EzyvZdOzJtM7OujVE5',
-}
-
-_URLS_DB = {
-  'visualization': 'https://www.dropbox.com/scl/fi/xvh0oe2tf7uw2hr4qooju/visualization-20240214T034323Z-001.zip?rlkey=sabkedpkaxviecfbozjffqeqq&dl=1',
-  'fov': 'https://www.dropbox.com/scl/fi/7ob7mub0kgjcvicrvmlm2/rgb-20240213T171654Z-001.zip?rlkey=jupumuk8emtdls1m4jui2230l&dl=1',
-  'annotation': 'https://www.dropbox.com/scl/fi/co71y09x1sukm41dvjbrb/csv-20240213T170834Z-001.zip?rlkey=sycxq8udvaqbljz0lfoeu9mdi&dl=1',
-  'mask': 'https://www.dropbox.com/scl/fi/14n7c604ontck1tjp4mi9/mask-20240213T171332Z-001.zip?rlkey=12m9967vsxvudk1gcv6t5u7ia&dl=1',
-}
-
-_URLS = "https://www.dropbox.com/scl/fi/srq574rdgvp7f5gwr60xw/NuCLS_dataset.zip?rlkey=qjc9q8shgvnqpfy4bktbqybd1&dl=1"
-
-class NuCLSDataset(GeneratorBasedBuilder):
-    """NuCLS dataset."""
-        # TODO: Add description of the dataset here
-    # You can copy an official description
-    _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 annotations.
-    These nuclei were annotated through a collaborative effort involving pathologists, pathology residents, and medical students, using the Digital Slide Archive.
-    """
-
-    _CITATION = """\
-    https://arxiv.org/abs/2102.09099
-    """
-
-    # TODO: Add a link to an official homepage for the dataset here
-    _HOMEPAGE = "https://sites.google.com/view/nucls/home?authuser=0"
-
-    _GITHUB_REPO = "https://github.com/PathologyDataScience/NuCLS"
-
-    # TODO: Add the licence for the dataset here if you can find it
-    _LICENSE = "CC0 1.0 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)
-    _URLS = {
-      'visualization': 'https://drive.google.com/uc?export=download&id=1LxngBja8yga2PDE4nLh-sDt7zylbU6LX',
-      'fov': 'https://drive.google.com/uc?export=download&id=1WRlgzL2y3A4Vw2qrgZMeSrG2QLWsSbbt',
-      'annotation': 'https://drive.google.com/uc?export=download&id=1ac6lwgjDgukYbabSJXYGFUp1XC6hrTq0',
-      'mask': 'https://drive.google.com/uc?export=download&id=1PdLKC_d2W_-58AUYEECknjzf9UUnmXeL',
-      'train_test_splits': 'https://drive.google.com/uc?export=download&id=1Hidb8WsKn2-35-EzyvZdOzJtM7OujVE5',
-    }
-
-    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
-                'fov_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=NuCLSDataset._DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=NuCLSDataset._HOMEPAGE,
-            license=NuCLSDataset._LICENSE,
-            citation=NuCLSDataset._CITATION,
-          )
-
-    def _split_generators(self, dl_manager: datasets.DownloadManager):
-        """Returns the split generators."""
-
-        # Download and extract files
-        downloaded_files = dl_manager.download_and_extract(self._URLS)
-
-        # Check if files are ZIP and extract them
-        for key, file_path in downloaded_files.items():
-            if zipfile.is_zipfile(file_path):  # Check if it's a ZIP file
-                with zipfile.ZipFile(file_path, 'r') as zip_ref:
-                    # Define a directory to extract to
-                    extract_dir = os.path.join(os.path.dirname(file_path), key)
-                    os.makedirs(extract_dir, exist_ok=True)
-                    zip_ref.extractall(extract_dir)
-                    # Update downloaded_files to point to the extracted directory
-                    downloaded_files[key] = extract_dir
-            else:
-                # If not a ZIP file, assume it's a directory
-                downloaded_files[key] = file_path
-
-        # Extract unique base filenames (without extensions)
-        unique_filenames = self._extract_unique_base_filenames(downloaded_files)
-
-        # Shuffle and split filenames
-        random.shuffle(unique_filenames)
-        split_index = int(0.8 * len(unique_filenames))
-        train_filenames = unique_filenames[:split_index]
-        test_filenames = unique_filenames[split_index:]
-
-        # Map filenames to file paths for each split
-        train_filepaths = self._map_filenames_to_paths(train_filenames, downloaded_files)
-        test_filepaths = self._map_filenames_to_paths(test_filenames, downloaded_files)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"filepaths": train_filepaths},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"filepaths": test_filepaths},
-            ),
-        ]
-
-    def _generate_examples(self, filepaths):
-        """Yield examples as (key, example) tuples."""
-
-        for key, paths in filepaths.items():
-            # Initialize an example dictionary
-            example = {}
-
-            # Read the Field of View image file
-            example['fov_image'] = self._read_image_file(paths['fov'])
-
-            # Read the Mask image file
-            example['mask_image'] = self._read_image_file(paths['mask'])
-
-            # Read the Visualization image file
-            example['visualization_image'] = self._read_image_file(paths['visualization'])
-
-            # Read the Annotation CSV file and populate the corresponding fields
-            example['annotation_coordinates'] = self._read_annotation_file(paths['annotation'])
-
-            yield key, example
-
-    def _map_filenames_to_paths(self, filenames, downloaded_files):
-        """Map base filenames to their respective file paths for different types."""
-        filepaths = {}
-        for filename in filenames:
-            # Construct paths for each file type
-            filepaths[filename] = {
-                'fov': os.path.join(downloaded_files['fov'], filename + '.png'),
-                'mask': os.path.join(downloaded_files['mask'], filename + '.png'),  
-                'visualization': os.path.join(downloaded_files['visualization'], filename + '.png'),
-                'annotation': os.path.join(downloaded_files['annotation'], filename + '.csv')
-            }
-        return filepaths
-
-    def _list_files_in_directory(self, directory):
-        """List all files in a given directory."""
-        return [os.path.join(directory, file) for file in os.listdir(directory) if os.path.isfile(os.path.join(directory, file))]
-
-    def _extract_unique_base_filenames(self, downloaded_files):
-        """Extract unique base filenames from downloaded files."""
-        
-        base_filenames = set()
-        for filename in downloaded_files['fov']:
-            filename = os.path.splitext(filename)[0]
-            base_filenames.add(filename)
-        return list(base_filenames)
-
-    def _read_image_file(self, file_path: str) -> Image:
-        """ Reads an image file and returns it as an Image object."""
-        # Read the image file
-        try:
-            image_bytes = self._read_image_as_bytes(file_path)
-            # Convert the image bytes to a PIL Image object
-            image = Image.open(io.BytesIO(image_bytes))
-            return image
-
-        except Exception as e:
-            print(f"Error reading image file {file_path}: {e}")
-            return None
-
-    def _read_image_as_bytes(self, file_path: str) -> bytes:
-        """ Reads an image file and returns it as a bytes object."""
-        with open(file_path, 'rb') as f:
-            return f.read()
-
-    def _read_annotation_file(self, file_path: str):
-        """ Reads an annotation file and returns it as an AnnotationCoordinates object."""
-        # Read the annotation file
-        try:
-            annotation_df = pd.read_csv(file_path)
-            if annotation_df.empty:
-                print(f"Warning: Annotation file {file_path} is empty.")
-                return None
-            else:
-                print("Successfully read the file. Here are the first few rows:")
-                print(annotation_df.head())
-                return annotation_df
-        except Exception as e:
-            print(f"Error reading annotation file {file_path}: {e}")
-            return None
-