Upload CelebA_bbox_and_facepoints.py

CelebA_bbox_and_facepoints.py

@@ -29,7 +29,10 @@ _REPO = "https://huggingface.co/datasets/hfaus/CelebA_bbox_and_facepoints/resolv
 _URLS = {
     "train": f"{_REPO}/celebA_train.zip",
     "validation": f"{_REPO}/celebA_val.zip",
-    "test": f"{_REPO}/celebA_test.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):
@@ -50,15 +53,15 @@ class CelebA(datasets.GeneratorBasedBuilder):
                            "righteye": datasets.Sequence(datasets.Value("int32")),
                            "nose": datasets.Sequence(datasets.Value("int32")),
                            "leftmouth": datasets.Sequence(datasets.Value("int32")),
-                           "rightmouth": datasets.Sequence(datasets.Value("int32")),
+                           "rightmouth": datasets.Sequence(datasets.Value("int32"))
                         }
-                    ),
+                    )
                 }
             ),
             supervised_keys=None,
             homepage=_HOMEPAGE,
             license=_LICENSE,
-            citation=_CITATION,
+            citation=_CITATION
         )
 
     def _split_generators(self, dl_manager):
@@ -68,49 +71,67 @@ class CelebA(datasets.GeneratorBasedBuilder):
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
                     "split": "train",
-                    "data_dir": data_dir["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"]
-                },
+                    "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"]
-                },
-            ),
+                    "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):
-        bbox_fname = os.path.join(data_dir, "list_bbox_celeba.txt")
-        landmarks_fname = os.path.join(data_dir, "list_landmarks_celeba.txt")
+    def _generate_examples(self, split, data_dir, partition_type, eval_partition_file, bbox_file, landmarks_file):
 
         #Abrimos los dos ficheros
-        fichero1 = open(bbox_fname, "r", encoding="utf-8")
-        fichero2 = open(landmarks_fname, "r", encoding="utf-8")
+        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 Fichero2
-        lista = fichero1.readlines()
-        for i, bbox_line in enumerate(lista):
+        #Creamos una lista a partir del contenido de Fichero 0
+        lista = fichero0.readlines()
+        for i, eval_line in enumerate(lista):
 
-            # Se leen las líneas de ambos ficheros
-            bbox_line = bbox_line.rstrip()
-            if not ".jpg" in bbox_line:
+            # Se obtiene el tipo de split
+            eval_line = eval_line.rstrip()
+            if not ".jpg" in eval_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(data_dir, bbox[0])
+            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])
 
-            # Read number of bounding boxes
+            # 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]],
@@ -122,5 +143,6 @@ class CelebA(datasets.GeneratorBasedBuilder):
             yield idx, {"image": image_file_path, "facial_landmarks": facial_landmarks, "bbox": bbox_total}
             idx += 1
         else:
+            fichero0.close()
             fichero1.close()
             fichero2.close()