| import os |
| import datasets |
|
|
| import numpy as np |
|
|
| _DESCRIPTION = """\ |
| GTEA is composed of 50 recorded videos of 25 participants making two different mixed salads. The videos are captured by a camera with a top-down view onto the work-surface. The participants are provided with recipe steps which are randomly sampled from a statistical recipe model. |
| """ |
|
|
| _CITATION = """\ |
| @inproceedings{stein2013combining, |
| title={Combining embedded accelerometers with computer vision for recognizing food preparation activities}, |
| author={Stein, Sebastian and McKenna, Stephen J}, |
| booktitle={Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing}, |
| pages={729--738}, |
| year={2013} |
| } |
| """ |
|
|
| _HOMEPAGE = "" |
|
|
| _LICENSE = "xxx" |
|
|
| _URLS = {"full": "https://huggingface.co/datasets/dinggd/breakfast/resolve/main/breakfast.zip"} |
|
|
|
|
| class Breakfast(datasets.GeneratorBasedBuilder): |
|
|
| VERSION = datasets.Version("1.0.0") |
|
|
| BUILDER_CONFIGS = [ |
| datasets.BuilderConfig( |
| name="split1", version=VERSION, description="Cross Validation Split1" |
| ), |
| datasets.BuilderConfig( |
| name="split2", version=VERSION, description="Cross Validation Split2" |
| ), |
| datasets.BuilderConfig( |
| name="split3", version=VERSION, description="Cross Validation Split3" |
| ), |
| datasets.BuilderConfig( |
| name="split4", version=VERSION, description="Cross Validation Split4" |
| ), |
| ] |
|
|
| DEFAULT_CONFIG_NAME = "1" |
|
|
| def _info(self): |
| features = datasets.Features( |
| { |
| "video_id": datasets.Value("string"), |
| "video_feature": datasets.Array2D(shape=(None, 2048), dtype="float32"), |
| "video_label": datasets.Sequence(datasets.Value(dtype="int32")), |
| } |
| ) |
|
|
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=features, |
| homepage=_HOMEPAGE, |
| license=_LICENSE, |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| urls_to_download = _URLS |
| data_dir = dl_manager.download_and_extract(urls_to_download)["full"] |
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "filepath": os.path.join( |
| data_dir, f"breakfast/splits/train.{self.config.name}.bundle" |
| ), |
| "featurefolder": os.path.join(data_dir, "breakfast/features"), |
| "gtfolder": os.path.join(data_dir, "breakfast/groundTruth"), |
| "mappingpath": os.path.join(data_dir, "breakfast/mapping.txt"), |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, |
| gen_kwargs={ |
| "filepath": os.path.join( |
| data_dir, f"breakfast/splits/test.{self.config.name}.bundle" |
| ), |
| "featurefolder": os.path.join(data_dir, "breakfast/features"), |
| "gtfolder": os.path.join(data_dir, "breakfast/groundTruth"), |
| "mappingpath": os.path.join(data_dir, "breakfast/mapping.txt"), |
| }, |
| ), |
| ] |
|
|
| def _generate_examples(self, filepath, featurefolder, gtfolder, mappingpath): |
| with open(mappingpath, "r") as f: |
| actions = f.read().splitlines() |
| actions_dict = {} |
| for a in actions: |
| actions_dict[a.split()[1]] = int(a.split()[0]) |
|
|
| with open(filepath, "r") as f: |
| lines = f.read().splitlines() |
| for key, line in enumerate(lines): |
| vid = line[:-4] |
| featurepath = os.path.join(featurefolder, f"{vid}.npy") |
| gtpath = os.path.join(gtfolder, line) |
| feature = np.load(featurepath).T |
| with open(gtpath, "r") as f: |
| content = f.read().splitlines() |
|
|
| label = np.zeros(min(np.shape(feature)[1], len(content))) |
| for i in range(len(label)): |
| label[i] = actions_dict[content[i]] |
| yield key, { |
| "video_id": vid, |
| "video_feature": feature, |
| "video_label": label, |
| } |
|
|
