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superverse

https://github.com/khulnasoft-lab/superverse

Science Score: 44.0%

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Created about 1 year ago · Last pushed 10 months ago
Metadata Files
Readme Contributing License Code of conduct Citation

README.md

MetaVision

notebooks | inference | autodistill | maestro


version downloads snyk license python-version colab gradio discord built-with-material-for-mkdocs

👋 hello

We write your reusable computer vision tools. Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us! 🤝

💻 install

Pip install the superverse package in a Python>=3.8 environment.

bash pip install superverse

Read more about conda, mamba, and installing from source in our guide.

🔥 quickstart

models

Superverse was designed to be model agnostic. Just plug in any classification, detection, or segmentation model. For your convenience, we have created connectors for the most popular libraries like Ultralytics, Transformers, or MMDetection.

```python import cv2 import superverse as sv from ultralytics import YOLO

image = cv2.imread(...) model = YOLO("yolov8s.pt") result = model(image)[0] detections = sv.Detections.from_ultralytics(result)

len(detections)

5

```

👉 more model connectors - inference ```python import cv2 import superverse as sv from inference import get_model image = cv2.imread(...) model = get_model(model_id="yolov8s-640", api_key=) result = model.infer(image)[0] detections = sv.Detections.from_inference(result) len(detections) # 5 ```

annotators

```python import cv2 import superverse as sv

image = cv2.imread(...) detections = sv.Detections(...)

boxannotator = sv.BoxAnnotator() annotatedframe = box_annotator.annotate( scene=image.copy(), detections=detections) ```

datasets

```python import superverse as sv from khulnasoft import Khulnasoft

project = Khulnasoft().workspace().project() dataset = project.version().download("coco")

ds = sv.DetectionDataset.fromcoco( imagesdirectorypath=f"{dataset.location}/train", annotationspath=f"{dataset.location}/train/_annotations.coco.json", )

path, image, annotation = ds[0] # loads image on demand

for path, image, annotation in ds: # loads image on demand ```

👉 more dataset utils - load ```python dataset = sv.DetectionDataset.from_yolo( images_directory_path=..., annotations_directory_path=..., data_yaml_path=... ) dataset = sv.DetectionDataset.from_pascal_voc( images_directory_path=..., annotations_directory_path=... ) dataset = sv.DetectionDataset.from_coco( images_directory_path=..., annotations_path=... ) ``` - split ```python train_dataset, test_dataset = dataset.split(split_ratio=0.7) test_dataset, valid_dataset = test_dataset.split(split_ratio=0.5) len(train_dataset), len(test_dataset), len(valid_dataset) # (700, 150, 150) ``` - merge ```python ds_1 = sv.DetectionDataset(...) len(ds_1) # 100 ds_1.classes # ['dog', 'person'] ds_2 = sv.DetectionDataset(...) len(ds_2) # 200 ds_2.classes # ['cat'] ds_merged = sv.DetectionDataset.merge([ds_1, ds_2]) len(ds_merged) # 300 ds_merged.classes # ['cat', 'dog', 'person'] ``` - save ```python dataset.as_yolo( images_directory_path=..., annotations_directory_path=..., data_yaml_path=... ) dataset.as_pascal_voc( images_directory_path=..., annotations_directory_path=... ) dataset.as_coco( images_directory_path=..., annotations_path=... ) ``` - convert ```python sv.DetectionDataset.from_yolo( images_directory_path=..., annotations_directory_path=..., data_yaml_path=... ).as_pascal_voc( images_directory_path=..., annotations_directory_path=... ) ```


📚 documentation

Visit our documentation page to learn how superverse can help you build computer vision applications faster and more reliably.

🏆 contribution

We love your input! Please see our contributing guide to get started. Thank you 🙏 to all our contributors!

Owner

  • Name: KhulnaSoft Ltd.
  • Login: khulnasoft-lab
  • Kind: organization
  • Email: info@khulnasoft.com
  • Location: Bangladesh

KhulnaSoft ❤️ Open Source

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: Superverse
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Khulnasoft
    email: support@khulnasoft.com
repository-code: "https://github.com/khulnasoft/superverse"
url: "https://khulnasoft.github.io/superverse/"
abstract: >-
  superverse features a range of utilities for use in
  computer vision projects, from detections processing and
  filtering to confusion matrix calculation.
keywords:
  - computer vision
  - image processing
  - video processing
license: MIT

GitHub Events

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Last Year
  • Delete event: 1
  • Issue comment event: 34
  • Push event: 47
  • Pull request review event: 6
  • Pull request review comment event: 56
  • Pull request event: 16
  • Create event: 7

Dependencies

.github/workflows/clear-cache.yml actions
.github/workflows/combine-dependabot-prs.yml actions
.github/workflows/notebook-bot.yml actions
.github/workflows/poetry-test.yml actions
.github/workflows/publish-dev-docs.yml actions
.github/workflows/publish-docs.yml actions
.github/workflows/publish-test.yml actions
.github/workflows/publish.yml actions
.github/workflows/test-doc.yml actions
.github/workflows/test-min.yml actions
.github/workflows/test.yml actions
examples/count_people_in_zone/requirements.txt pypi
examples/heatmap_and_track/requirements.txt pypi
examples/speed_estimation/requirements.txt pypi
examples/time_in_zone/requirements.txt pypi
examples/tracking/requirements.txt pypi
examples/traffic_analysis/requirements.txt pypi
poetry.lock pypi
pyproject.toml pypi
uv.lock pypi