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",
    "eval_partition_file": f"{_REPO}/list_eval_partition.txt",
    "bbox_file": f"{_REPO}/list_bbox_celeba.txt",
    "landmarks_file": f"{_REPO}/list_landmarks_celeba.txt"
}

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(),
                    "bbox": datasets.Sequence(datasets.Value("int32")),
                    "facial_landmarks": datasets.Sequence(
                        {
                           "lefteye": datasets.Sequence(datasets.Value("int32")),
                           "righteye": datasets.Sequence(datasets.Value("int32")),
                           "nose": datasets.Sequence(datasets.Value("int32")),
                           "leftmouth": datasets.Sequence(datasets.Value("int32")),
                           "rightmouth": datasets.Sequence(datasets.Value("int32"))
                        }
                    )
                }
            ),
            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"],
                    "partition_type": 0,
                    "eval_partition_file": data_dir["eval_partition_file"],
                    "bbox_file": data_dir["bbox_file"],
                    "landmarks_file": data_dir["landmarks_file"]
                }
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "split": "test",
                    "data_dir": data_dir["test"],
                    "partition_type": 1,
                    "eval_partition_file": data_dir["eval_partition_file"],
                    "bbox_file": data_dir["bbox_file"],
                    "landmarks_file": data_dir["landmarks_file"]
                }
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "split": "val",
                    "data_dir": data_dir["validation"],
                    "partition_type": 2,
                    "eval_partition_file": data_dir["eval_partition_file"],
                    "bbox_file": data_dir["bbox_file"],
                    "landmarks_file": data_dir["landmarks_file"]
                }
            )
        ]

    def _generate_examples(self, split, data_dir, partition_type, eval_partition_file, bbox_file, landmarks_file):

        #Abrimos los dos ficheros
        fichero0 = open(eval_partition_file, "r", encoding="utf-8")
        fichero1 = open(bbox_file, "r", encoding="utf-8")
        fichero2 = open(landmarks_file, "r", encoding="utf-8")

        #Creamos una lista a partir del contenido de Fichero 0
        lista = fichero0.readlines()
        for i, eval_line in enumerate(lista):

            # Se obtiene el tipo de split
            eval_line = eval_line.rstrip()
            if not ".jpg" in eval_line:
                break
            strip_type_in_line = int(" ".join(eval_line.split()).split(" ")[1])
            if strip_type_in_line != partition_type:
                break

            # Se obtiene la imágen
            image_file_path = os.path.join(data_dir, eval_line[0])

            # Se lee la línea de bbox
            bbox_line = fichero1.readline(i+2)
            bbox = " ".join(bbox_line.split()).split(" ")
            bbox_total = [int(bbox[1]), int(bbox[2]), int(bbox[3]), int(bbox[4])]

            # Se lee la línea de landmarks
            landmarks_line = fichero2.readline(i+2)
            landmarks = " ".join(landmarks_line.split()).split(" ")
            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]],
            }

            yield idx, {"image": image_file_path, "facial_landmarks": facial_landmarks, "bbox": bbox_total}
            idx += 1
        else:
            fichero0.close()
            fichero1.close()
            fichero2.close()