Caltech-101.py

# Copyright 2022 The HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Caltech 101 loading script"""


from __future__ import annotations

from pathlib import Path

import datasets
import numpy as np
import scipy.io
from datasets.tasks import ImageClassification

_CITATION = """\
@article{FeiFei2004LearningGV,
  title={Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories},
  author={Li Fei-Fei and Rob Fergus and Pietro Perona},
  journal={Computer Vision and Pattern Recognition Workshop},
  year={2004},
}
"""

_DESCRIPTION = """\
Pictures of objects belonging to 101 categories. 
About 40 to 800 images per category. 
Most categories have about 50 images. 
Collected in September 2003 by Fei-Fei Li, Marco Andreetto, and Marc'Aurelio Ranzato. 
The size of each image is roughly 300 x 200 pixels. 
"""

_HOMEPAGE = "https://data.caltech.edu/records/20086"

_LICENSE = "CC BY 4.0"

_DATA_URL = "caltech-101.zip"

_NAMES = [
    "accordion",
    "airplanes",
    "anchor",
    "ant",
    "background_google",
    "barrel",
    "bass",
    "beaver",
    "binocular",
    "bonsai",
    "brain",
    "brontosaurus",
    "buddha",
    "butterfly",
    "camera",
    "cannon",
    "car_side",
    "ceiling_fan",
    "cellphone",
    "chair",
    "chandelier",
    "cougar_body",
    "cougar_face",
    "crab",
    "crayfish",
    "crocodile",
    "crocodile_head",
    "cup",
    "dalmatian",
    "dollar_bill",
    "dolphin",
    "dragonfly",
    "electric_guitar",
    "elephant",
    "emu",
    "euphonium",
    "ewer",
    "faces",
    "faces_easy",
    "ferry",
    "flamingo",
    "flamingo_head",
    "garfield",
    "gerenuk",
    "gramophone",
    "grand_piano",
    "hawksbill",
    "headphone",
    "hedgehog",
    "helicopter",
    "ibis",
    "inline_skate",
    "joshua_tree",
    "kangaroo",
    "ketch",
    "lamp",
    "laptop",
    "leopards",
    "llama",
    "lobster",
    "lotus",
    "mandolin",
    "mayfly",
    "menorah",
    "metronome",
    "minaret",
    "motorbikes",
    "nautilus",
    "octopus",
    "okapi",
    "pagoda",
    "panda",
    "pigeon",
    "pizza",
    "platypus",
    "pyramid",
    "revolver",
    "rhino",
    "rooster",
    "saxophone",
    "schooner",
    "scissors",
    "scorpion",
    "sea_horse",
    "snoopy",
    "soccer_ball",
    "stapler",
    "starfish",
    "stegosaurus",
    "stop_sign",
    "strawberry",
    "sunflower",
    "tick",
    "trilobite",
    "umbrella",
    "watch",
    "water_lilly",
    "wheelchair",
    "wild_cat",
    "windsor_chair",
    "wrench",
    "yin_yang",
]
# For some reason, the category names in "101_ObjectCategories" and
# "Annotations" do not always match. This is a manual map between the
# two. Defaults to using same name, since most names are fine.
_ANNOTATION_NAMES_MAP = {
    "Faces": "Faces_2",
    "Faces_easy": "Faces_3",
    "Motorbikes": "Motorbikes_16",
    "airplanes": "Airplanes_Side_2",
}

_TRAIN_POINTS_PER_CLASS = 30


class Caltech101(datasets.GeneratorBasedBuilder):
    """Caltech 101 dataset."""

    VERSION = datasets.Version("1.0.0")

    _BUILDER_CONFIG_WITH_BACKGROUND = datasets.BuilderConfig(
        name="with_background_category",
        version=VERSION,
        description="Dataset containing the 101 categories and the additonnal background one. "
        "No annotations.",
    )
    _BUILDER_CONFIG_WITHOUT_BACKGROUND = datasets.BuilderConfig(
        name="without_background_category",
        version=VERSION,
        description="Dataset containing only the 101 categories and their annotations "
        "(object contours and box position).",
    )

    BUILDER_CONFIGS = [
        _BUILDER_CONFIG_WITH_BACKGROUND,
        _BUILDER_CONFIG_WITHOUT_BACKGROUND,
    ]

    def _info(self):
        if self.config.name == self._BUILDER_CONFIG_WITHOUT_BACKGROUND.name:
            features = datasets.Features(
                {
                    "image": datasets.Image(),
                    "label": datasets.features.ClassLabel(names=_NAMES),
                    "annotation": {
                        "obj_contour": datasets.features.Array2D(
                            shape=(2, None), dtype="float64"
                        ),
                        "box_coord": datasets.features.Array2D(
                            shape=(1, 4), dtype="int64"
                        ),
                    },
                }
            )
        else:
            features = datasets.Features(
                {
                    "image": datasets.Image(),
                    "label": datasets.features.ClassLabel(names=_NAMES),
                }
            )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        data_root_dir = dl_manager.download_and_extract(_DATA_URL)
        img_folder_compress_path = [
            file
            for file in dl_manager.iter_files(data_root_dir)
            if Path(file).name == "101_ObjectCategories.tar.gz"
        ][0]
        annotations_folder_compress_path = [
            file
            for file in dl_manager.iter_files(data_root_dir)
            if Path(file).name == "Annotations.tar"
        ][0]
        img_dir = dl_manager.extract(img_folder_compress_path)
        annotation_dir = dl_manager.extract(annotations_folder_compress_path)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "img_dir": Path(img_dir) / "101_ObjectCategories",
                    "annotation_dir": Path(annotation_dir) / "Annotations",
                    "split": "train",
                    "config_name": self.config.name,
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "img_dir": Path(img_dir) / "101_ObjectCategories",
                    "annotation_dir": Path(annotation_dir) / "Annotations",
                    "split": "test",
                    "config_name": self.config.name,
                },
            ),
        ]

    def _generate_examples(self, img_dir, annotation_dir, split, config_name):
        # Same stratagy as the one proposed in TF datasets: 30 random examples from each class are added to the train
        # split, and the remainder are added to the test split.
        # Source: https://github.com/tensorflow/datasets/blob/1106d587f97c4fca68c5b593dc7dc48c790ffa8c/tensorflow_datasets/image_classification/caltech.py#L88-L140

        is_train_split = split == "train"

        rng = np.random.default_rng(0)

        for class_dir in img_dir.iterdir():
            class_name = class_dir.name
            index_codes = [
                image_path.name.split("_")[1][: -len(".jpg")]
                for image_path in class_dir.iterdir()
                if image_path.name.endswith(".jpg")
            ]
            # _TRAIN_POINTS_PER_CLASS datapoints are sampled for the train split,
            # the others constitute the test split.
            if _TRAIN_POINTS_PER_CLASS > len(index_codes):
                raise ValueError(
                    f"Fewer than {_TRAIN_POINTS_PER_CLASS} ({len(index_codes)}) points in class {class_dir.name}"
                )
            train_indices = rng.choice(
                index_codes, _TRAIN_POINTS_PER_CLASS, replace=False
            )

            test_indices = set(index_codes).difference(train_indices)

            indices_to_emit = train_indices if is_train_split else test_indices

            if (
                class_name == "BACKGROUND_Google"
                and config_name == self._BUILDER_CONFIG_WITHOUT_BACKGROUND.name
            ):
                print("skip BACKGROUND_Google")
                continue

            for indice in indices_to_emit:
                record = {
                    "image": str(class_dir / f"image_{indice}.jpg"),
                    "label": class_dir.name.lower(),
                }
                if config_name == self._BUILDER_CONFIG_WITHOUT_BACKGROUND.name:
                    if class_name in _ANNOTATION_NAMES_MAP:
                        annotations_class_name = _ANNOTATION_NAMES_MAP[class_name]
                    else:
                        annotations_class_name = class_name
                    data = scipy.io.loadmat(
                        str(
                            annotation_dir
                            / annotations_class_name
                            / f"annotation_{indice}.mat"
                        )
                    )
                    # raise ValueError(data["obj_contour"].dtype, data["box_coord"])
                    record["annotation"] = {
                        "obj_contour": data["obj_contour"],
                        "box_coord": data["box_coord"],
                    }
                yield f"{class_dir.name.lower()}/{f'image_{indice}.jpg'}", record