Duplicate from randall-lab/revisitop

Co-authored-by: Ian Hajra <ianhajra@users.noreply.huggingface.co>

.gitattributes

@@ -0,0 +1,59 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,107 @@
+---
+language: en
+tags:
+  - image-retrieval
+  - oxford5k
+  - paris6k
+  - revisitop1m
+---
+
+# Dataset Card for RevisitOP (Oxford5k, Paris6k, RevisitOP1M)
+
+## Dataset Description
+
+**RevisitOP** provides popular benchmark datasets for large-scale image retrieval research:
+
+- **roxford5k**: Oxford 5k buildings dataset containing ~5,000 images.
+- **rparis6k**: Paris 6k buildings dataset with ~6,000 images.
+- **revisitop1m**: RevisitOP 1M distractor dataset with ~1 million distractor images.
+- **oxfordparis**: Combination of Oxford 5k and Paris 6k datasets.
+
+These datasets are widely used for evaluating image retrieval algorithms and contain real-world building photographs and distractors.
+
+## Dataset Features
+
+Each example contains:
+
+- `image` (`Image`): An image file (JPEG or PNG).
+- `filename` (`string`): The original filename of the image.
+- `dataset` (`string`): The source dataset the image belongs to (`roxford5k`, `rparis6k`, or `revisitop1m`).
+- `query_id` (`int32`): Query ID for query images (-1 for database images).
+- `bbx` (`Sequence[float32]`): Bounding box coordinates [x1, y1, x2, y2] for query images.
+- `easy` (`Sequence[int32]`): Easy relevant images for queries.
+- `hard` (`Sequence[int32]`): Hard relevant images for queries.
+- `junk` (`Sequence[int32]`): Junk images for queries.
+
+## Dataset Splits
+
+- **qimlist**: Query images with ground truth annotations (bounding boxes and relevance labels).
+- **imlist**: Database images for retrieval.
+
+## Dataset Versions
+
+- Version 1.0.0
+
+## Example Usage
+
+Use the Hugging Face `datasets` library to load one of the configs:
+
+```python
+import datasets
+from aiohttp import ClientTimeout
+
+dataset_name = "randall-lab/revisitop"
+timeout_period = 500000  # very long timeout to prevent timeouts
+storage_options = {"client_kwargs": {"timeout": ClientTimeout(total=timeout_period)}}
+
+# These are the config names defined in the script
+dataset_configs = ["roxford5k", "rparis6k", "oxfordparis"]  # "revisitop1m" is large and may take a long time to load
+
+# Load query split for evaluation
+for i, config_name in enumerate(dataset_configs, start=1):
+    # Load query images
+    query_dataset = datasets.load_dataset(
+        path=dataset_name,
+        name=config_name,
+        split="qimlist",
+        trust_remote_code=True,
+        storage_options=storage_options,
+    )
+
+    # Load database images
+    db_dataset = datasets.load_dataset(
+        path=dataset_name,  
+        name=config_name,
+        split="imlist",
+        trust_remote_code=True,
+        storage_options=storage_options,
+    )
+
+
+    # Example query image
+    query_example = query_dataset[0]
+```
+
+## Dataset Structure
+
+- The datasets consist of images downloaded and extracted from official URLs hosted by the Oxford Visual Geometry Group and the RevisitOP project.
+- The `roxford5k` and `rparis6k` datasets come from `.tgz` archives with corresponding `.pkl` ground truth files.
+- The `revisitop1m` dataset consists of 100 `.tar.gz` archives with JPEG images as distractors.
+- The combined `oxfordparis` dataset merges the Oxford and Paris sets.
+- Ground truth files contain query lists, database lists, and annotations (bounding boxes, easy/hard/junk labels).
+
+## Dataset Citation
+
+If you use this dataset, please cite the original paper:
+
+```bibtex
+@inproceedings{Radenovic2018RevisitingOP,
+  title={Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking},
+  author={Filip Radenovic and Ahmet Iscen and Giorgos Tolias and Yannis Avrithis and Ondrej Chum},
+  year={2018}
+}
+```
+
+## Dataset Homepage
+
+[RevisitOP project page](http://cmp.felk.cvut.cz/revisitop/)

revisitop.py

@@ -0,0 +1,247 @@
+import os
+import tarfile
+import urllib.request
+import pickle
+import datasets
+
+_VERSION = datasets.Version("1.0.0")
+
+_URLS = {
+    "roxford5k": {
+        "images": [
+            "https://www.robots.ox.ac.uk/~vgg/data/oxbuildings/oxbuild_images-v1.tgz"
+        ],
+        "ground_truth": [
+            "http://cmp.felk.cvut.cz/revisitop/data/datasets/roxford5k/gnd_roxford5k.pkl"
+        ],
+    },
+    "rparis6k": {
+        "images": [
+            "https://www.robots.ox.ac.uk/~vgg/data/parisbuildings/paris_1-v1.tgz",
+            "https://www.robots.ox.ac.uk/~vgg/data/parisbuildings/paris_2-v1.tgz",
+        ],
+        "ground_truth": [
+            "http://cmp.felk.cvut.cz/revisitop/data/datasets/rparis6k/gnd_rparis6k.pkl"
+        ],
+    },
+    "revisitop1m": {
+        "images": [
+            f"http://ptak.felk.cvut.cz/revisitop/revisitop1m/jpg/revisitop1m.{i+1}.tar.gz"
+            for i in range(100)
+        ]
+    },
+}
+
+_DESCRIPTION = (
+    "Oxford5k, Paris6k, and RevisitOP1M benchmark datasets for image retrieval."
+)
+
+_CITATION = """\
+@inproceedings{Radenovic2018RevisitingOP,
+  title={Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking},
+  author={Filip Radenovic and Ahmet Iscen and Giorgos Tolias and Yannis Avrithis and Ondrej Chum},
+  year={2018}
+}
+"""
+
+BUILDER_CONFIGS = [
+    datasets.BuilderConfig(
+        name="roxford5k",
+        version=_VERSION,
+        description="Oxford 5k image retrieval dataset.",
+    ),
+    datasets.BuilderConfig(
+        name="rparis6k",
+        version=_VERSION,
+        description="Paris 6k image retrieval dataset.",
+    ),
+    datasets.BuilderConfig(
+        name="revisitop1m",
+        version=_VERSION,
+        description="RevisitOP 1M distractor images.",
+    ),
+    datasets.BuilderConfig(
+        name="oxfordparis",
+        version=_VERSION,
+        description="Oxford + Paris combined dataset.",
+    ),
+]
+
+
+class RevisitOP(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = BUILDER_CONFIGS
+    DEFAULT_CONFIG_NAME = "roxford5k"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "filename": datasets.Value("string"),
+                    "dataset": datasets.Value("string"),
+                    "query_id": datasets.Value("int32"),
+                    "bbx": datasets.Sequence(
+                        datasets.Value("float32")
+                    ),  # bounding box [x1, y1, x2, y2]
+                    "easy": datasets.Sequence(
+                        datasets.Value("int32")
+                    ),  # easy relevant images
+                    "hard": datasets.Sequence(
+                        datasets.Value("int32")
+                    ),  # hard relevant images
+                    "junk": datasets.Sequence(datasets.Value("int32")),  # junk images
+                }
+            ),
+            supervised_keys=None,
+            homepage="http://cmp.felk.cvut.cz/revisitop/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        cfg_name = self.config.name
+
+        if cfg_name == "revisitop1m":
+            urls = _URLS[cfg_name]["images"]
+            archive_paths = dl_manager.download(urls)
+            extracted_paths = dl_manager.extract(archive_paths)
+
+            return [
+                datasets.SplitGenerator(
+                    name="imlist",
+                    gen_kwargs={
+                        "image_dirs": (
+                            extracted_paths
+                            if isinstance(extracted_paths, list)
+                            else [extracted_paths]
+                        ),
+                        "ground_truth_file": None,
+                        "split_type": "imlist",
+                        "dataset_name": cfg_name,
+                    },
+                )
+            ]
+
+        if cfg_name == "oxfordparis":
+            # Handle combined dataset
+            image_urls = _URLS["roxford5k"]["images"] + _URLS["rparis6k"]["images"]
+            gt_urls = (
+                _URLS["roxford5k"]["ground_truth"] + _URLS["rparis6k"]["ground_truth"]
+            )
+        else:
+            image_urls = _URLS[cfg_name]["images"]
+            gt_urls = _URLS[cfg_name]["ground_truth"]
+
+        # Download and extract image archives
+        archive_paths = dl_manager.download(image_urls)
+        extracted_paths = dl_manager.extract(archive_paths)
+
+        # Download ground truth files
+        gt_paths = dl_manager.download(gt_urls)
+
+        # Normalize lists if single items
+        if not isinstance(extracted_paths, list):
+            extracted_paths = [extracted_paths]
+        if not isinstance(gt_paths, list):
+            gt_paths = [gt_paths]
+
+        return [
+            datasets.SplitGenerator(
+                name="qimlist",
+                gen_kwargs={
+                    "image_dirs": extracted_paths,
+                    "ground_truth_files": gt_paths,
+                    "split_type": "qimlist",
+                    "dataset_name": cfg_name,
+                },
+            ),
+            datasets.SplitGenerator(
+                name="imlist",
+                gen_kwargs={
+                    "image_dirs": extracted_paths,
+                    "ground_truth_files": gt_paths,
+                    "split_type": "imlist",
+                    "dataset_name": cfg_name,
+                },
+            ),
+        ]
+
+    def _generate_examples(
+        self, image_dirs, ground_truth_files, split_type, dataset_name
+    ):
+        # Build image path mapping
+        image_path_mapping = {}
+        for image_dir in image_dirs:
+            for root, _, files in os.walk(image_dir):
+                for fname in files:
+                    if fname.lower().endswith((".jpg", ".jpeg", ".png")):
+                        fpath = os.path.join(root, fname)
+                        # Remove extension for mapping
+                        fname_no_ext = os.path.splitext(fname)[0]
+                        image_path_mapping[fname_no_ext] = fpath
+
+        # Handle revisitop1m case (no ground truth)
+        if ground_truth_files is None:
+            key = 0
+            for fname_no_ext, fpath in image_path_mapping.items():
+                yield key, {
+                    "image": fpath,
+                    "filename": fname_no_ext + ".jpg",
+                    "dataset": dataset_name,
+                    "query_id": -1,
+                    "bbx": [],
+                    "easy": [],
+                    "hard": [],
+                    "junk": [],
+                }
+                key += 1
+            return
+
+        # Load ground truth files
+        ground_truth_data = []
+        for gt_file in ground_truth_files:
+            with open(gt_file, "rb") as f:
+                gt_data = pickle.load(f)
+                ground_truth_data.append(gt_data)
+
+        key = 0
+
+        for gt_data in ground_truth_data:
+            imlist = gt_data["imlist"]
+            qimlist = gt_data["qimlist"]
+            gnd = gt_data["gnd"]
+
+            if split_type == "qimlist":
+                # Generate query examples
+                for i, query_name in enumerate(qimlist):
+                    query_name_no_ext = os.path.splitext(query_name)[0]
+                    if query_name_no_ext in image_path_mapping:
+                        query_gnd = gnd[i]
+                        yield key, {
+                            "image": image_path_mapping[query_name_no_ext],
+                            "filename": query_name,
+                            "dataset": dataset_name,
+                            "query_id": i,
+                            "bbx": query_gnd.get("bbx", []),
+                            "easy": query_gnd.get("easy", []),
+                            "hard": query_gnd.get("hard", []),
+                            "junk": query_gnd.get("junk", []),
+                        }
+                        key += 1
+
+            elif split_type == "imlist":
+                # Generate image pool examples
+                for i, image_name in enumerate(imlist):
+                    image_name_no_ext = os.path.splitext(image_name)[0]
+                    if image_name_no_ext in image_path_mapping:
+                        yield key, {
+                            "image": image_path_mapping[image_name_no_ext],
+                            "filename": image_name,
+                            "dataset": dataset_name,
+                            "query_id": -1,  # Not a query image
+                            "bbx": [],
+                            "easy": [],
+                            "hard": [],
+                            "junk": [],
+                        }
+                        key += 1