Upload CelebA_bbox_and_facepoints.py

CelebA_bbox_and_facepoints.py

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+# 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