feat(opendataset): add dataloader for VOC2012Detection dataset

tensorbay/opendataset/VOC2012Detection/__init__.py

@@ -0,0 +1,11 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021 Graviti. Licensed under MIT License.
+#
+# pylint: disable=invalid-name
+
+"""Dataloader of VOC2012Detection."""
+
+from .loader import VOC2012Detection
+
+__all__ = ["VOC2012Detection"]

tensorbay/opendataset/VOC2012Detection/catalog.json

@@ -0,0 +1,84 @@
+{
+    "BOX2D": {
+        "categories": [
+            {
+                "name": "aeroplane"
+            },
+            {
+                "name": "bicycle"
+            },
+            {
+                "name": "bird"
+            },
+            {
+                "name": "boat"
+            },
+            {
+                "name": "bottle"
+            },
+            {
+                "name": "bus"
+            },
+            {
+                "name": "car"
+            },
+            {
+                "name": "cat"
+            },
+            {
+                "name": "chair"
+            },
+            {
+                "name": "cow"
+            },
+            {
+                "name": "diningtable"
+            },
+            {
+                "name": "dog"
+            },
+            {
+                "name": "horse"
+            },
+            {
+                "name": "motorbike"
+            },
+            {
+                "name": "person"
+            },
+            {
+                "name": "pottedplant"
+            },
+            {
+                "name": "sheep"
+            },
+            {
+                "name": "sofa"
+            },
+            {
+                "name": "train"
+            },
+            {
+                "name": "tvmonitor"
+            }
+        ],
+        "attributes": [
+            {
+                "name": "difficult",
+                "type": "boolean"
+            },
+            {
+                "name": "occluded",
+                "type": "boolean"
+            },
+            {
+                "name": "pose",
+                "enum": ["Frontal", "Left", "Rear", "Right", "Unspecified"]
+            },
+            {
+                "name": "truncated",
+                "type": "boolean"
+            }
+        ]
+    }
+}

tensorbay/opendataset/VOC2012Detection/loader.py

@@ -0,0 +1,91 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021 Graviti. Licensed under MIT License.
+#
+# pylint: disable=invalid-name, missing-module-docstring
+
+import os
+from xml.etree import ElementTree
+
+from ...dataset import Data, Dataset
+from ...label import LabeledBox2D
+
+SEGMENTS_NAME = (
+    "train",
+    "trainval",
+    "val",
+)
+DATASET_NAME = "VOC2012Detection"
+
+
+def VOC2012Detection(path: str) -> Dataset:
+    """Dataloader of the 'VOC2012Detection'_ dataset.
+
+    .. _VOC2012Detection: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/
+
+    The file structure should be like::
+
+        <path>
+            Annotations/
+                <image_name>.xml
+                ...
+            JPEGImages/
+                <image_name>.jpg
+                ...
+            ImageSets/
+                Main/
+                    train.txt
+                    trainval.txt
+                    val.txt
+                    ...
+                ...
+            ...
+
+    Arguments:
+        path: The root directory of the dataset.
+
+    Returns:
+        Loaded :class: `~tensorbay.dataset.dataset.Dataset` instance.
+
+    """
+    paths = {
+        "Annotations": os.path.join(path, "Annotations"),
+        "Image": os.path.join(path, "JPEGImages"),
+        "Main": os.path.join(os.path.join(path, "ImageSets"), "Main"),
+    }
+
+    dataset = Dataset(DATASET_NAME)
+    dataset.load_catalog("catalog.json")
+
+    for segment_name in SEGMENTS_NAME:
+        segment = dataset.create_segment(segment_name)
+        with open(os.path.join(paths["Main"], f"{segment_name}.txt")) as fp:
+            for filename in fp:
+                filename = filename[:-1]
+                data = Data(os.path.join(paths["Image"], f"{filename}.jpg"))
+                data.label.box2d = []
+                tree = ElementTree.parse(os.path.join(paths["Annotations"], f"{filename}.xml"))
+                for obj in tree.findall("object"):
+                    attributes = {}
+                    for child in obj:
+                        if child.tag == "name":
+                            category = child.text
+                        elif child.tag == "bndbox":
+                            box = [
+                                float(child.find("xmin").text),  # type:ignore[arg-type, union-attr]
+                                float(child.find("ymin").text),  # type:ignore[arg-type, union-attr]
+                                float(child.find("xmax").text),  # type:ignore[arg-type, union-attr]
+                                float(child.find("ymax").text),  # type:ignore[arg-type, union-attr]
+                            ]
+                        elif child.tag == "pose":
+                            attributes[child.tag] = child.text
+                        elif child.text[0] != "\n":  # type: ignore[index]
+                            attributes[child.tag] = bool(
+                                int(child.text)  # type:ignore[assignment, arg-type]
+                            )
+                    data.label.box2d.append(
+                        LabeledBox2D(*box, category=category, attributes=attributes)
+                    )
+                segment.append(data)
+        dataset.add_segment(segment)
+    return dataset

tensorbay/opendataset/__init__.py

@@ -44,6 +44,7 @@
 from .THUCNews import THUCNews
 from .TLR import TLR
 from .UAVDT import UAVDT
+from .VOC2012Detection import VOC2012Detection
 from .WIDER_FACE import WIDER_FACE
 
 __all__ = [
@@ -91,4 +92,5 @@
     "UAVDT",
     "WIDER_FACE",
     "COVID_CT",
+    "VOC2012Detection",
 ]