feat(opendataset): add dataloader for VOC2012Segmentation dataset

tensorbay/opendataset/VOC2012Segmentation/__init__.py

@@ -0,0 +1,11 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021 Graviti. Licensed under MIT License.
+#
+# pylint: disable=invalid-name
+
+"""Dataloader of VOC2012Segmentation."""
+
+from .loader import VOC2012Segmentation
+
+__all__ = ["VOC2012Segmentation"]

tensorbay/opendataset/VOC2012Segmentation/catalog.json

@@ -0,0 +1,95 @@
+{
+    "SEMANTIC_MASK": {
+        "categories": [
+            {
+                "name": "background",
+                "categoryId": 0
+            },
+            {
+                "name": "aeroplane",
+                "categoryId": 1
+            },
+            {
+                "name": "bicycle",
+                "categoryId": 2
+            },
+            {
+                "name": "bird",
+                "categoryId": 3
+            },
+            {
+                "name": "boat",
+                "categoryId": 4
+            },
+            {
+                "name": "bottle",
+                "categoryId": 5
+            },
+            {
+                "name": "bus",
+                "categoryId": 6
+            },
+            {
+                "name": "car",
+                "categoryId": 7
+            },
+            {
+                "name": "cat",
+                "categoryId": 8
+            },
+            {
+                "name": "chair",
+                "categoryId": 9
+            },
+            {
+                "name": "cow",
+                "categoryId": 10
+            },
+            {
+                "name": "diningtable",
+                "categoryId": 11
+            },
+            {
+                "name": "dog",
+                "categoryId": 12
+            },
+            {
+                "name": "horse",
+                "categoryId": 13
+            },
+            {
+                "name": "motorbike",
+                "categoryId": 14
+            },
+            {
+                "name": "person",
+                "categoryId": 15
+            },
+            {
+                "name": "pottedplant",
+                "categoryId": 16
+            },
+            {
+                "name": "sheep",
+                "categoryId": 17
+            },
+            {
+                "name": "sofa",
+                "categoryId": 18
+            },
+            {
+                "name": "train",
+                "categoryId": 19
+            },
+            {
+                "name": "tvmonitor",
+                "categoryId": 20
+            },
+            {
+                "name": "void",
+                "categoryId": 255
+            }
+        ]
+    },
+    "INSTANCE_MASK": {}
+}

tensorbay/opendataset/VOC2012Segmentation/loader.py

@@ -0,0 +1,70 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021 Graviti. Licensed under MIT License.
+#
+# pylint: disable=invalid-name, missing-module-docstring
+
+import os
+
+from ...dataset import Data, Dataset
+from ...label import InstanceMask, SemanticMask
+
+_SEGMENT_NAMES = ("train", "val")
+DATASET_NAME = "VOC2012Segmentation"
+
+
+def VOC2012Segmentation(path: str) -> Dataset:
+    """Dataloader of the 'VOC2012Segmentation'_ dataset.
+
+    .. _VOC2012Segmentation: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/
+
+    The file structure should be like::
+
+        <path>/
+            JPEGImages/
+                <image_name>.jpg
+                ...
+            SegmentationClass/
+                <mask_name>.png
+                ...
+            SegmentationObject/
+                <mask_name>.png
+                ...
+            ImageSets/
+                Segmentation/
+                    train.txt
+                    val.txt
+                    ...
+                ...
+            ...
+
+    Arguments:
+        path: The root directory of the dataset.
+
+    Returns:
+        Loaded :class: `~tensorbay.dataset.dataset.Dataset` instance.
+
+    """
+    root_path = os.path.abspath(os.path.expanduser(path))
+
+    image_path = os.path.join(root_path, "JPEGImages")
+    semantic_mask_path = os.path.join(root_path, "SegmentationClass")
+    instance_mask_path = os.path.join(root_path, "SegmentationObject")
+    image_set_path = os.path.join(root_path, "ImageSets", "Segmentation")
+
+    dataset = Dataset(DATASET_NAME)
+    dataset.load_catalog(os.path.join(os.path.dirname(__file__), "catalog.json"))
+
+    for segment_name in _SEGMENT_NAMES:
+        segment = dataset.create_segment(segment_name)
+        with open(os.path.join(image_set_path, f"{segment_name}.txt")) as fp:
+            for stem in fp:
+                stem = stem.strip()
+                data = Data(os.path.join(image_path, f"{stem}.jpg"))
+                label = data.label
+                label.semantic_mask = SemanticMask(os.path.join(semantic_mask_path, f"{stem}.png"))
+                label.instance_mask = InstanceMask(os.path.join(instance_mask_path, f"{stem}.png"))
+
+                segment.append(data)
+
+    return dataset

tensorbay/opendataset/__init__.py

@@ -48,6 +48,7 @@
 from .UAVDT import UAVDT
 from .VOC2012ActionClassification import VOC2012ActionClassification
 from .VOC2012Detection import VOC2012Detection
+from .VOC2012Segmentation import VOC2012Segmentation
 from .WIDER_FACE import WIDER_FACE
 
 __all__ = [
@@ -100,4 +101,5 @@
     "COVID_CT",
     "VOC2012Detection",
     "VOC2012ActionClassification",
+    "VOC2012Segmentation",
 ]