A tool for creating, reading and visualizing Pascal VOC annotations
Project description
Express Pascal Voc Tools
A tool for creating, reading and visualizing Pascal VOC annotations. Report Bugs here
Getting Started
Install
pip install express-pascal-voc-tools
Single file Parsing
from voc_tools import reader as voc_reader
# `from_xml()` parse XML
for anno in voc_reader.from_xml(r"sixray_data\train\Annotations\P00002.xml"):
print(anno.xmin, anno.xmax)
# `from_image()` Parse XML by providing image path(it will automatically choose the correct XML)
for anno in voc_reader.from_image(r"sixray_data\train\JPEGImages\P00002.jpeg"):
print(anno.xmin, anno.xmax)
# `from_file()` Get the parsed metadata as a tuple
for anno in voc_reader.from_file(r"sixray_data\train\JPEGImages\P00002.xml"):
print(anno.raw())
for anno in voc_reader.from_file(r"sixray_data\train\JPEGImages\P00002.jpeg"):
print(anno.raw())
# `from_dir()` Get the parsed metadata as a tuple for entire directory
for anno in voc_reader.from_dir("sixray_data\train")):
print(anno.raw())
Dataset level parsing
Using VOCDataset
class we can address a Pascal VOC dataset. In general Pascal VOC
Datasets are organised as below:
my_dataset
|
+- train
| |
| +- Annotations
| | |
| | +- ITEM001.xml
| | +- ITEM002.xml
| +- JPEGImages
| |
| +- ITEM001.jpeg
| +- ITEM002.jpeg
+- test
|
+- Annotations
| |
| +- ITEM0010.xml
| +- ITEM0020.xml
+- JPEGImages
|
+- ITEM0010.jpeg
+- ITEM0020.jpeg
from voc_tools.utils import VOCDataset
dataset_path = "/my_dataset"
# initialize a dataset
my_dataset = VOCDataset(dataset_path)
# fetch annotation bulk
for annotations, jpeg in my_dataset.train.fetch():
print(annotations[0].filename, jpeg.image.shape)
# fetch annotation
for anno, jpeg in my_dataset.train.fetch(bulk=False):
print(anno, jpeg.image.shape)
# parse the annotations into memory for train dataset
my_dataset.train.load()
my_dataset.test.load()
# returns a list of class names in train dataset
my_dataset.train.class_names()
my_dataset.test.class_names()
# save parsed information into csv
my_dataset.train.load().to_csv("./train_metadata.csv")
my_dataset.test.load().to_csv("./train_metadata.csv")
# purge the parsed metadata to free memory
my_dataset.train.unload()
my_dataset.test.unload()
Caption Support
This is an optional feature introduced to facilitate the new trends in prompt engineering and text based Generative AI.
In this case the dataset must contain a text
directory as below:
my_dataset
|
+- train
| |
| +- Annotations
| | |
| | +- ITEM001.xml
| | +- ITEM002.xml
| +- JPEGImages
| |
| +- ITEM001.jpeg
| +- ITEM002.jpeg
| +- text
| |
| +- ITEM001.text
| +- ITEM002.text
+- test
|
+- Annotations
| |
| +- ITEM0010.xml
| +- ITEM0020.xml
+- JPEGImages
|
+- ITEM0010.jpeg
+- ITEM0020.jpeg
+- text
|
+- ITEM0010.text
+- ITEM0020.text
from voc_tools.utils import VOCDataset
dataset_path = "/my_dataset"
voc_caption_data = VOCDataset(dataset_path, caption_support=True) # init dataset with caption
# read caption bulk
for captions in voc_caption_data.train.caption.fetch():
print(captions[0].raw())
# read caption one by one
for caption in voc_caption_data.train.caption.fetch(bulk=False):
print(caption.raw())
# save captions to a CSV
voc_caption_data.train.caption.to_csv("train_captions.csv")
Visualize
from voc_tools.visulizer import from_jpeg, see_jpeg
jpeg = from_jpeg(r"sixray_data\train\JPEGImages\P00002.jpg")
jpeg.see()
# OR
see_jpeg(r"sixray_data\train\JPEGImages\P00002.jpg")
Collaborate
GitHub: https://github.com/Redcof/pascal_voc_tools.git
Build and Publish
python setup.py sdist bdist_wheel
python -m twine upload dist/*
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