Add conversion script

script.py

@@ -0,0 +1,195 @@
+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')