script.py

import json

import datasets
import pandas as pd

id_to_original = {
    "1": "5-5-10-H-A1000C 100h-30k-3-crop",
    "2": "5-5-A1000C 100h-30k-9 crop",
    "3": "5-5-A1000C 100h-30k-9 crop2",
    "4": "5-5-A1000C 100h-30k-9-crop",
    "5": "5k-Cr-10-10-20Fe-H-Ageing1200C 4h-6-crop",
    "6": "Cr-5-5-10Fe-A1200C 4h-6 crop1",
    "7": "Cr-5-5-10Fe-A1200C 4h-6 crop2",
    "8": "Cr-5-5-10Fe-H1400-20h-A800-240h-80k-9crop1",
    "9": "Cr-5-5-10Fe-H1400-20h-A800-240h-80k-9crop2",
    "10": "Cr-5-5-10Fe-H1400-20h-A800-240h-80k-10 crop",
    "11": "Cr-5-5-10Fe-H1400-20h-A800-240h-80k-10 crop2",
    "12": "Cr-5-5-10Fe-H1400-20h-A1000-20h-50k-10 crop",
    "13": "Cr-5-5-10Fe-H1400-20h-A1000-240h-30k-8 crop2",
    "14": "Cr-5-5-A1200C 4h-20k-5-crop1",
    "15": "Cr-5-5-A1200C 4h-20k-5-crop2",
    "16": "Cr-10-10-20Fe-H20h-A1200C 20h-7-crop1",
    "17": "J955-H2-7-crop1",
    "18": "J955-H2-7-crop2",
    "19": "Cr-10-10-20Fe-A100h-1-crop1",
    "20": "Cr-10-10-20Fe-A100h-4-crop1",
    "21": "Cr-10Ni-10Al-20Fe-8 crop1",
    "22": "Cr-10Ni-10Al-20Fe-8 crop2",
    "23": "Cr-10Ni-10Al-20Fe-H1400C20h-9 crop1",
    "24": "Cr-10Ni-10Al-20Fe-H1400C20h-9 crop2",
}
ids_split = {
    datasets.Split.TEST: [
        "1",
        "5",
        "9",
        "14",
        "20",
    ],
    datasets.Split.VALIDATION: [
        "2",
        "7",
        "18",
        "22",
    ],
    datasets.Split.TRAIN: [
        "3",
        "4",
        "6",
        "8",
        "10",
        "11",
        "12",
        "13",
        "15",
        "16",
        "17",
        "19",
        "21",
        "23",
        "24",
    ]
}

_CITATION = """\
@article{xia2023Accurate,
  author = {Zeyu Xia and Kan Ma and Sibo Cheng and Thomas Blackburn and Ziling Peng and Kewei Zhu and Weihang Zhang and Dunhui Xiao and Alexander J Knowles and Rossella Arcucci},
  copyright = {CC BY-NC 3.0},
  doi = {10.1039/d3cp00402c},
  issn = {1463-9076},
  journal = {Physical Chemistry Chemical Physics},
  keywords = {},
  language = {English},
  month = {6},
  number = {23},
  pages = {15970--15987},
  pmid = {37265373},
  publisher = {Royal Society of Chemistry (RSC)},
  title = {Accurate Identification and Measurement of the Precipitate Area by Two-Stage Deep Neural Networks in Novel Chromium-Based Alloy},
  url = {https://doi.org/10.1039/d3cp00402c},
  volume = {25},
  year = {2023}
}
"""

_DESCRIPTION = 'A comprehensive, two-tiered deep learning approach designed for precise object detection and segmentation in electron microscopy (EM) images.'

_CATEGORIES = ["precipitate"]

_HOMEPAGE = 'https://github.com/xiazeyu/DT_SegNet'

_LICENSE = 'CC BY-NC 3.0'


def convert_image(image_path):
    with open(image_path, "rb") as image_file:
        return image_file.read()
    # return Image.open(image_path)


def convert_json(json_path):
    with open(json_path, "r") as json_file:
        json_str = json.dumps(json.load(json_file))
        return json_str  # .encode('utf-8')


def convert_txt(txt_path):
    yolo_data = {"bbox": [], "category": []}

    # Open and read the text file
    with open(txt_path, "r") as file:
        for line in file:
            # Split each line into components
            parts = line.strip().split()

            # The first part is the category, which is added directly to the 'category' list
            yolo_data["category"].append(int(parts[0]))

            # The rest of the parts are the bounding box coordinates, which need to be converted to floats
            # and added as a sublist to the 'bbox' list
            bbox = [float(coord) for coord in parts[1:]]
            yolo_data["bbox"].append(bbox)

    return yolo_data


def get_ds(pfx):
    image_array = []
    seg_annotation_array = []
    raw_seg_annotation_array = []
    det_annotation_array = []

    for img_idx in ids_split[pfx]:
        ydt = convert_txt(f"{pfx}/{img_idx}_label.txt")
        det_annotation_array.append({
            "bbox": ydt["bbox"],
            "category": ydt["category"],
        })
        image_array.append(convert_image(f"{pfx}/{img_idx}.png"))
        seg_annotation_array.append(convert_image(f"{pfx}/{img_idx}_label.png"))
        raw_seg_annotation_array.append(convert_json(f"{pfx}/{img_idx}.json"))

    data = {
        "id": ids_split[pfx],
        "original_name": [id_to_original[file] for file in ids_split[pfx]],
        "image": image_array,
        "det_annotation": det_annotation_array,
        "seg_annotation": seg_annotation_array,
        "raw_seg_annotation": raw_seg_annotation_array,
    }

    df = pd.DataFrame(data)

    features = datasets.Features({
        'id': datasets.Value('int8'),
        'original_name': datasets.Value('string'),
        'image': datasets.Image(),
        "det_annotation": datasets.Sequence(
            {
                "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
                "category": datasets.ClassLabel(num_classes=1, names=_CATEGORIES),
            }
        ),
        'seg_annotation': datasets.Image(),
        'raw_seg_annotation': datasets.Value(dtype='string'),
    })

    data_info = datasets.DatasetInfo(
        description=_DESCRIPTION,
        features=features,
        homepage=_HOMEPAGE,
        license=_LICENSE,
        citation=_CITATION,
    )

    ds = datasets.Dataset.from_pandas(df,
                                      features=features,
                                      info=data_info,
                                      split=pfx)

    ds.VERSION = datasets.Version("1.0.0")

    return ds


ddd = datasets.DatasetDict(
    {
        str(datasets.Split.TRAIN): get_ds(datasets.Split.TRAIN),
        str(datasets.Split.VALIDATION): get_ds(datasets.Split.VALIDATION),
        str(datasets.Split.TEST): get_ds(datasets.Split.TEST),
    }
)

# ddd.save_to_disk('data/')
# ddd.push_to_hub('xiazeyu/DT_SegNet')