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