# 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