#!/usr/bin/env python3
#
# Copyright 2021 Graviti. Licensed under MIT License.
#
# pylint: disable=invalid-name, missing-module-docstring
import os
from tensorbay.dataset import Data, Dataset
from tensorbay.label import LabeledBox2D
try:
import xmltodict
except ModuleNotFoundError:
from tensorbay.opendataset._utility.mocker import xmltodict # pylint:disable=ungrouped-imports
_SEGMENT_NAMES = ("train", "val")
_BOOLEAN_ATTRIBUTES = {"occluded", "difficult", "truncated"}
DATASET_NAME = "VOC2012Detection"
[docs]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
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))
annotation_path = os.path.join(root_path, "Annotations")
image_path = os.path.join(root_path, "JPEGImages")
main_path = os.path.join(root_path, "ImageSets", "Main")
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(main_path, f"{segment_name}.txt"), encoding="utf-8") as fp:
for filename in fp:
segment.append(_get_data(filename.rstrip(), image_path, annotation_path))
return dataset
def _get_data(filename: str, image_path: str, annotation_path: str) -> Data:
"""Get all information of the datum corresponding to filename.
Arguments:
filename: The filename of the data.
image_path: The path of the image directory.
annotation_path: The path of the annotation directory.
Returns:
Data: class: `~tensorbay.dataset.data.Data` instance.
"""
data = Data(os.path.join(image_path, f"{filename}.jpg"))
box2d = []
with open(os.path.join(annotation_path, f"{filename}.xml"), "r", encoding="utf-8") as fp:
objects = xmltodict.parse(fp.read())["annotation"]["object"]
if not isinstance(objects, list):
objects = [objects]
for obj in objects:
attributes = {attribute: bool(int(obj[attribute])) for attribute in _BOOLEAN_ATTRIBUTES}
attributes["pose"] = obj["pose"]
bndbox = obj["bndbox"]
box2d.append(
LabeledBox2D(
float(bndbox["xmin"]),
float(bndbox["ymin"]),
float(bndbox["xmax"]),
float(bndbox["ymax"]),
category=obj["name"],
attributes=attributes,
)
)
data.label.box2d = box2d
return data