globox 2.9.0

Globox is a package and command line interface to read and convert object detection databases (COCO, YOLO, PascalVOC, LabelMe, CVAT, OpenImage, ...) and evaluate them with COCO and PascalVOC.

Globox — Object Detection Toolbox

This framework can:

• parse all kinds of object detection datasets (ImageNet, COCO, YOLO, PascalVOC, OpenImage, CVAT, LabelMe, etc.) and show statistics,
• convert them to other formats (ImageNet, COCO, YOLO, PascalVOC, OpenImage, CVAT, LabelMe, etc.),
• and evaluate predictions using standard object detection metrics such as $AP_{[.5:.05:.95]}$, $AP_{50}$, $mAP$, $AR_{1}$, $AR_{10}$, $AR_{100}$.

This framework can be used both as a library in your own code and as a command line tool. This tool is designed to be simple to use, fast and correct.

Install

You can install the package using pip:

pip install globox

Use as a Library

Parse Annotations

The library has three main components:

• BoundingBox: represents a bounding box with a label and an optional confidence score
• Annotation: represent the bounding boxes annotations for one image
• AnnotationSet: represents annotations for a set of images (a database)

The AnnotationSet class contains static methods to read different dataset formats:

import globox as gx


# COCO
coco = gx.AnnotationSet.from_coco(file_path="path/to/file.json")

# YOLOv5
yolo = gx.AnnotationSet.from_yolo_v5(
    folder="path/to/files/",
    image_folder="path/to/images/",
)

# Pascal VOC
pascal = gx.AnnotationSet.from_pascal_voc(folder="path/to/files/")

Annotation offers file-level granularity for compatible datasets:

annotation = gx.Annotation.from_labelme(file_path="path/to/file.xml")

For more specific implementations the BoundingBox class contains lots of utilities to parse bounding boxes in different formats, like the create() method.

AnnotationsSets are set-like objects. They can be combined and annotations can be added:

gts = coco | yolo
gts.add(annotation)

Inspect Datasets

Iterators and efficient lookup by image_id's are easy to use:

if annotation in gts:
    print("This annotation is present.")

if "image_123.jpg" in gts.image_ids:
    print("Annotation of image 'image_123.jpg' is present.")

for box in gts.all_boxes:
    print(box.label, box.area, box.is_ground_truth)

for annotation in gts:
    nb_boxes = len(annotation.boxes)
    print(f"{annotation.image_id}: {nb_boxes} boxes")

Datasets stats can printed to the console:

coco_gts.show_stats()

         Database Stats         
┏━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ Label       ┃ Images ┃ Boxes ┃
┡━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ aeroplane   │     10 │    15 │
│ bicycle     │      7 │    14 │
│ bird        │      4 │     6 │
│ boat        │      7 │    11 │
│ bottle      │      9 │    13 │
│ bus         │      5 │     6 │
│ car         │      6 │    14 │
│ cat         │      4 │     5 │
│ chair       │      9 │    15 │
│ cow         │      6 │    14 │
│ diningtable │      7 │     7 │
│ dog         │      6 │     8 │
│ horse       │      7 │     7 │
│ motorbike   │      3 │     5 │
│ person      │     41 │    91 │
│ pottedplant │      6 │     7 │
│ sheep       │      4 │    10 │
│ sofa        │     10 │    10 │
│ train       │      5 │     6 │
│ tvmonitor   │      8 │     9 │
├─────────────┼────────┼───────┤
│ Total       │    100 │   273 │
└─────────────┴────────┴───────┘

Edit and Filter Datasets

AnnotationSets can be filtered and mapped:

# Keep annotations that are not empty.
annotations = annotations.filter(lambda a: len(a.boxes) > 0)

# Only keep training annotations.
annotations = annotations.filter(lambda a: a.image_id in train_ids)

# Replace "/" with "_" in all image ids.
annotations = annotations.map(lambda a: a.with_image_id(a.image_id.replace("/", "_")))

# Treat all animals as one class using a convenience method to map labels (in-place update).
annotations.map_labels({"cat": "animal", "dog": "animal"})

Convert and Save to Many Formats

Datasets can be converted to and saved in other formats:

# ImageNet
gts.save_imagenet(save_dir="pascalVOC_db/")

# YOLO Darknet
gts.save_yolo_darknet(
    save_dir="yolo_train/", 
    label_to_id={"cat": 0, "dog": 1, "racoon": 2},
)

# YOLOv5
gts.save_yolo_v5(
    save_dir="yolo_train/", 
    label_to_id={"cat": 0, "dog": 1, "racoon": 2},
)

# CVAT
gts.save_cvat(path="train.xml")

COCO Evaluation

COCO Evaluation is also supported:

evaluator = gx.COCOEvaluator(
    ground_truths=gts, 
    predictions=dets,
)

ap = evaluator.ap()
ar_100 = evaluator.ar_100()
ap_75 = evaluator.ap_75()
ap_small = evaluator.ap_small()
...

All COCO standard metrics can be displayed in a pretty printed table with:

evaluator.show_summary()

which outputs:

                              COCO Evaluation
┏━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳...┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┓
┃ Label     ┃ AP 50:95 ┃  AP 50 ┃   ┃   AR S ┃   AR M ┃   AR L ┃
┡━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇...╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━┩
│ airplane  │    22.7% │  25.2% │   │   nan% │  90.0% │   0.0% │
│ apple     │    46.4% │  57.4% │   │  48.5% │   nan% │   nan% │
│ backpack  │    54.8% │  85.1% │   │ 100.0% │  72.0% │   0.0% │
│ banana    │    73.6% │  96.4% │   │   nan% │ 100.0% │  70.0% │
.           .          .        .   .        .        .        .
.           .          .        .   .        .        .        .
.           .          .        .   .        .        .        .
├───────────┼──────────┼────────┼...┼────────┼────────┼────────┤
│ Total     │    50.3% │  69.7% │   │  65.4% │  60.3% │  55.3% │
└───────────┴──────────┴────────┴...┴────────┴────────┴────────┘

The array of results can be saved in CSV format:

evaluator.save_csv("where/to/save/results.csv")

Custom evaluations can be achieved with:

evaluation = evaluator.evaluate(
    iou_threshold=0.33,
    max_detections=1_000,
    size_range=(0.0, 10_000),
)

ap = evaluation.ap()
cat_ar = evaluation["cat"].ar

Evaluations are cached by (iou_threshold, max_detections, size_range) keys. This means that repetead queries to the evaluator are fast!

Use in Command Line

If you only need to use Globox from the command line like an application, you can install the package through pipx:

pipx install globox

Globox will then be in your shell path and usable from anywhere.

Usage

Get a summary of annotations for one dataset:

globox summary /yolo/folder/ --format yolo

Convert annotations from one format to another one:

globox convert input/yolo/folder/ output_coco_file_path.json --format yolo --save_fmt coco

Evaluate a set of detections with COCO metrics, display them and save them in a CSV file:

globox evaluate groundtruths/ predictions.json --format yolo --format_dets coco -s results.csv

Show the help message for an exhaustive list of options:

globox summary -h
globox convert -h
globox evaluate -h

Run Tests

Clone the repo with its test data:

git clone https://github.com/laclouis5/globox --recurse-submodules=tests/globox_test_data
cd globox

Install dependencies with uv:

uv sync --dev

Run the tests:

uv run pytest tests

Speed Banchmarks

Speed benchmark can be executed with:

uv run python tests/benchmark.py -n 5

The following speed test is performed using Python 3.11 and timeit with 5 iterations on a 2021 MacBook Pro 14" (M1 Pro 8 Cores and 16 GB of RAM). The dataset is COCO 2017 Validation which comprises 5k images and 36 781 bounding boxes.

Task	COCO	CVAT	OpenImage	LabelMe	PascalVOC	YOLO	TXT
Parsing	0.22s	0.12s	0.44s	0.60s	0.97s	1.45s	1.12s
Saving	0.32s	0.17s	0.14s	1.06s	1.08s	0.91s	0.85s

• AnnotationSet.show_stats(): 0.02 s
• Evalaution: 0.30 s

Acknowledgement

This repo is based on the work of Rafael Padilla.

Contribution

Feel free to contribute, any help you can offer with this project is most welcome.