Upload dataset_script.py

dataset_script.py

@@ -0,0 +1,136 @@
+
+import os
+import pandas as pd
+from pathlib import Path
+import datasets
+
+_CITATION = """
+@inproceedings{your_neurips_submission,
+  title={Multimodal Street-level Place Recognition Dataset},
+  author={Ou, Yiwei},
+  year={2025},
+  booktitle={NeurIPS Datasets and Benchmarks Track}
+}
+"""
+
+_DESCRIPTION = """
+Multimodal Street-level Place Recognition Dataset (Resized version).
+This version loads images, videos, and associated annotations for place recognition tasks,
+including GPS, camera metadata, and temporal information.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/Yiwei-Ou/Multimodal_Street-level_Place_Recognition_Dataset"
+_LICENSE = "cc-by-4.0"
+
+class MultimodalPlaceRecognition(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                "image_path": datasets.Value("string"),
+                "video_path": datasets.Value("string"),
+                "location_code": datasets.Value("string"),
+                "spatial_type": datasets.Value("string"),
+                "index": datasets.Value("int32"),
+                "shop_names": datasets.Value("string"),
+                "sign_text": datasets.Value("string"),
+                "image_metadata": datasets.Value("string"),
+                "video_metadata": datasets.Value("string"),
+            }),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        archive_path = dl_manager.download_and_extract(
+            "https://huggingface.co/datasets/Yiwei-Ou/Multimodal_Street-level_Place_Recognition_Dataset/resolve/main/Annotated_Resized.tar.gz"
+        )
+        base_dir = os.path.join(archive_path, "03 Annotated_Resized", "Dataset_Full")
+        image_dir = os.path.join(base_dir, "Images")
+        video_dir = os.path.join(base_dir, "Videos")
+        text_dir = os.path.join(base_dir, "Texts")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "image_dir": image_dir,
+                    "video_dir": video_dir,
+                    "annotations_path": os.path.join(text_dir, "Annotations.xlsx"),
+                    "image_meta_path": os.path.join(text_dir, "Media_Metadata-Images.xlsx"),
+                    "video_meta_path": os.path.join(text_dir, "Media_Metadata-Videos.xlsx"),
+                },
+            )
+        ]
+
+    def _generate_examples(self, image_dir, video_dir, annotations_path, image_meta_path, video_meta_path):
+        id_ = 0
+
+        annotations_df = pd.read_excel(annotations_path, engine="openpyxl")
+        annotations_dict = {
+            str(row["Code"]).strip(): {
+                "spatial_type": str(row["Type"]).strip(),
+                "index": int(row["Index"]),
+                "shop_names": str(row["List of Store Names and Signs"]) if not pd.isna(row["List of Store Names and Signs"]) else "",
+                "sign_text": "",  # Not explicitly available
+            }
+            for _, row in annotations_df.iterrows()
+        }
+
+        image_meta_df = pd.read_excel(image_meta_path, engine="openpyxl")
+        image_meta_dict = {
+            str(row["Filename"]).strip(): row.drop("Filename").dropna().to_dict()
+            for _, row in image_meta_df.iterrows()
+        }
+
+        video_meta_df = pd.read_excel(video_meta_path, engine="openpyxl")
+        video_meta_dict = {
+            str(row["Filename"]).strip(): row.drop("Filename").dropna().to_dict()
+            for _, row in video_meta_df.iterrows()
+        }
+
+        for spatial_type in os.listdir(image_dir):
+            spatial_path = os.path.join(image_dir, spatial_type)
+            if not os.path.isdir(spatial_path):
+                continue
+            for location_code in os.listdir(spatial_path):
+                loc_img_path = os.path.join(spatial_path, location_code)
+                if not os.path.isdir(loc_img_path):
+                    continue
+
+                loc_vid_path = os.path.join(video_dir, spatial_type, location_code) if os.path.exists(os.path.join(video_dir, spatial_type, location_code)) else None
+                vid_files = set(os.listdir(loc_vid_path)) if loc_vid_path else set()
+
+                for file_name in os.listdir(loc_img_path):
+                    if file_name.lower().endswith((".jpg", ".jpeg", ".png")):
+                        base_name = os.path.splitext(file_name)[0]
+                        video_match = [v for v in vid_files if v.startswith(base_name) and v.endswith(".mp4")]
+                        video_file = video_match[0] if video_match else ""
+                        video_path = os.path.join(loc_vid_path, video_file) if video_file else ""
+
+                        meta = annotations_dict.get(location_code, {
+                            "spatial_type": spatial_type,
+                            "index": -1,
+                            "shop_names": "",
+                            "sign_text": "",
+                        })
+
+                        img_meta = image_meta_dict.get(file_name, {})
+                        vid_meta = video_meta_dict.get(video_file, {}) if video_file else {}
+
+                        yield id_, {
+                            "image_path": os.path.join(loc_img_path, file_name),
+                            "video_path": video_path,
+                            "location_code": location_code,
+                            "spatial_type": meta["spatial_type"],
+                            "index": meta["index"],
+                            "shop_names": meta["shop_names"],
+                            "sign_text": meta["sign_text"],
+                            "image_metadata": str(img_meta),
+                            "video_metadata": str(vid_meta),
+                        }
+                        id_ += 1