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ssynth_data.py

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+# Copyright 2022 for msynth dataset
+#
+# 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.
+'''
+Custom dataset-builder for ssynth dataset
+'''
+
+import os
+import datasets
+import glob
+import re
+
+
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = """\
+@article{kim2024ssynth,
+  title={Knowledge-based in silico models and dataset for the comparative evaluation of mammography AI for a range of breast characteristics, lesion conspicuities and doses},
+  author={Kim, Andrea and Saharkhiz, Niloufar and Sizikova, Elena and Lago, Miguel, and Sahiner, Berkman and Delfino, Jana G., and Badano, Aldo},
+  journal={International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)},
+  volume={},
+  pages={},
+  year={2024}
+}
+"""
+
+
+_DESCRIPTION = """\
+S-SYNTH is an open-source, flexible skin simulation framework to rapidly generate synthetic skin models and images using digital rendering of an anatomically inspired multi-layer, multi-component skin and growing lesion model. It allows for generation of highly-detailed 3D skin models and digitally rendered synthetic images of diverse human skin tones, with full control of underlying parameters and the image formation process.
+Curated by: Andrea Kim, Niloufar Saharkhiz, Elena Sizikova, Miguel Lago, Berkman Sahiner, Jana Delfino, Aldo Badano
+License: Creative Commons 1.0 Universal License (CC0)
+"""
+
+
+_HOMEPAGE = "https://github.com/DIDSR/ssynth-release?tab=readme-ov-file"
+
+_REPO = "https://huggingface.co/datasets/didsr/ssynth_data/resolve/main"
+
+# Initialize an empty list to store the file paths
+_CROPPED = True
+
+_URLS = {
+    "synthetic_data": f"{_REPO}/data/synthetic_dataset/output_10k.zip",
+    "read_me": f"{_REPO}/README.md"
+}
+
+DATA_DIR = {"all_data": "output_10k"}
+
+class ssynth_dataConfig(datasets.BuilderConfig):
+    """ssynth dataset"""
+    def __init__(self, name, **kwargs):
+        super(ssynth_dataConfig, self).__init__(
+        version=datasets.Version("1.0.0"),
+        name=name,
+        description="ssynth_data",
+        **kwargs,
+        )
+
+class ssynth_data(datasets.GeneratorBasedBuilder):
+    """ssynth dataset."""
+    
+    DEFAULT_WRITER_BATCH_SIZE = 256
+    BUILDER_CONFIGS = [
+        ssynth_dataConfig("output_10k"),
+    ]
+    
+    def _info(self):
+        if self.config.name == "output_10k":
+            # Define dataset features and keys
+            features = datasets.Features(
+                {       
+                    "Cropped": datasets.Features({
+                        "image": datasets.Value("string"),
+                        "mask": datasets.Value("string")
+                    }),
+                    "Uncropped": datasets.Features({
+                        "image": datasets.Value("string"),
+                        "mask": datasets.Value("string")
+                    })
+                }
+            )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+    
+    def _split_generators(
+        self, dl_manager: datasets.utils.download_manager.DownloadManager):
+        
+        if self.config.name == "output_10k":
+            data_dir = dl_manager.download_and_extract(_URLS['synthetic_data'])
+            return [
+                datasets.SplitGenerator(
+                    name="output_10k",
+                    gen_kwargs={
+                        "files": data_dir,
+                        "name": "all_data",
+                    },
+                ),
+            ]
+        
+    def get_all_file_paths(self, root_directory):
+        file_paths = []  # List to store file paths
+
+        # Walk through the directory and its subdirectories using os.walk
+        for folder, _, files in os.walk(root_directory):
+            for file in files:
+                if file == "cropped_image.png":
+                # Get the full path of the file
+                    file_path = os.path.join(folder, file)
+                    file_paths.append(file_path)
+        return file_paths
+    
+    def get_other_images(self, cropped_image_path, file_name):
+        other_image_paths = []
+
+        # Get the directory containing the cropped_image.png
+        directory = os.path.dirname(cropped_image_path)
+
+        # Walk through the directory to find other image files
+        for file in os.listdir(directory):
+            if file == file_name:
+                # Get the full path of the other image file
+                file_path = os.path.join(directory, file)
+                #other_image_paths.append(file_path)
+                return file_path
+        return None
+   
+    
+    def _generate_examples(self, files, name):
+        if self.config.name == "output_10k":
+            key = 0
+            data_paths = self.get_all_file_paths(os.path.join(files, DATA_DIR[name]))
+            
+            cropped_images = []
+            uncropped_images = []
+            for path in data_paths:
+                res_dic = {}
+                cropped_image = path
+                cropped_mask = self.get_other_images(path,"cropped_mask.png")
+                image = self.get_other_images(path,"image.png")
+                mask = self.get_other_images(path,"mask.png")
+                cropped_data = {
+                    "image": cropped_image,
+                    "mask": cropped_mask
+                }
+                uncropped_data = {
+                    "image": image,
+                    "mask": mask
+                }
+                res_dic["Cropped"] = cropped_data
+                res_dic["Uncropped"] = uncropped_data
+                
+                yield key, res_dic
+                key += 1
+                
+                
+