CelebA_bbox_and_facepoints.py

# coding=utf-8
"""CelebA FACE dataset."""

import os
import datasets

_HOMEPAGE = "https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html"

_LICENSE = "Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)"

_CITATION = """\
@inproceedings{liu2015faceattributes,
  title = {Deep Learning Face Attributes in the Wild},
  author = {Liu, Ziwei and Luo, Ping and Wang, Xiaogang and Tang, Xiaoou},
  booktitle = {Proceedings of International Conference on Computer Vision (ICCV)},
  month = {December},
  year = {2015} 
}
"""

_DESCRIPTION = """\
CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset with more than 200K celebrity images,
each with 40 attribute annotations. The images in this dataset cover large pose variations and background clutter.
CelebA has large diversities, large quantities, and rich annotations, including 10,177 number of identities, 202,599 number of face images,
and 5 landmark locations, 40 binary attributes annotations per image.
"""

_REPO = "https://huggingface.co/datasets/hfaus/CelebA_bbox_and_facepoints/resolve/main/data"
_URLS = {
    "train": f"{_REPO}/celebA_train.zip",
    "validation": f"{_REPO}/celebA_val.zip",
    "test": f"{_REPO}/celebA_test.zip"
}

class CelebA(datasets.GeneratorBasedBuilder):
    """CelebA dataset."""

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "image": datasets.Image(),
                    "faces": datasets.Sequence(
                        {
                            "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
                            "points": datasets.Sequence(datasets.Value("int"), length=10)
                        }
                    ),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        data_dir = dl_manager.download_and_extract(_URLS)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "split": "train",
                    "data_dir": data_dir["train"]
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "split": "test",
                    "data_dir": data_dir["test"]
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "split": "val",
                    "data_dir": data_dir["validation"]
                },
            ),
        ]

    def _generate_examples(self, split, data_dir):
        image_dir = os.path.join(data_dir)
        bbox_fname = "list_bbox_celeba.txt"
        landmarks_fname = "list_landmarks_celeba.txt"

        #Abrimos los dos ficheros
        fichero1 = open(bbox_fname, "r", encoding="utf-8")
        fichero2 = open(landmarks_fname, "r", encoding="utf-8")

        #Creamos una lista a partir del contenido de Fichero2
        lista = fichero1.readlines()
        for i, bbox_line in enumerate(lista):

            # Se leen las líneas de ambos ficheros
            bbox_line = bbox_line.rstrip()
            if not ".jpg" in bbox_line:
                break
            landmarks_line = fichero1.readline(i)
            bbox = " ".join(bbox_line.split()).split(" ")
            landmarks = " ".join(landmarks_line.split()).split(" ")
            image_name = bbox[0];
            image_file_path = os.path.join(image_dir, bbox[0])

            # Read number of bounding boxes
            bbox_total = [int(bbox[1]), int(bbox[2]), int(bbox[3]), int(bbox[4])]
            facial_landmarks =  {
               'lefteye': (landmarks[1], landmarks[2]),
               'righteye': (landmarks[3], landmarks[4]),
               'nose': (landmarks[5], landmarks[6]),
               'leftmouth': (landmarks[7], landmarks[8]),
               'rightmouth': (landmarks[9], landmarks[10]),
            }

            # Cases with 0 bounding boxes, still have one line with all zeros.
            # So we have to read it and discard it.
            if nbboxes == 0:
                f.readline()
            else:
                for _ in range(nbboxes):
                    line = f.readline()
                    line = line.rstrip()
                    line_split = line.split()
                    assert len(line_split) == 10, f"Cannot parse line: {line_split}"
                    line_parsed = [int(n) for n in line_split]
                    (
                        xmin,
                        ymin,
                        wbox,
                        hbox,
                        blur,
                        expression,
                        illumination,
                        invalid,
                        occlusion,
                        pose,
                    ) = line_parsed
                    faces.append(
                        {
                            "bbox": [xmin, ymin, wbox, hbox],
                            "blur": blur,
                            "expression": expression,
                            "illumination": illumination,
                            "occlusion": occlusion,
                            "pose": pose,
                            "invalid": invalid,
                        }
                    )
            yield idx, {"image": image_file_path, "facial_landmarks": facial_landmarks, "bbox": bbox_total}
            idx += 1