🥚 Overeasy
Create powerful zero-shot vision models!
Overeasy allows you to chain zero-shot vision models to create custom end-to-end pipelines for tasks like:
- 📦 Bounding Box Detection
- 🏷️ Classification
- 🖌️ Segmentation (Coming Soon!)
All of this can be achieved without needing to collect and annotate large training datasets.
Overeasy makes it simple to combine pre-trained zero-shot models to build powerful custom computer vision solutions.
Installation
It's as easy as
pip install overeasy
For installing extras refer to our Docs.
Key Features
🤖 Agents: Specialized tools that perform specific image processing tasks.🧩 Workflows: Define a sequence of Agents to process images in a structured manner.🔗 Execution Graphs: Manage and visualize the image processing pipeline.🔎 Detections: Represent bounding boxes, segmentation, and classifications.
Documentation
For more details on types, library structure, and available models please refer to our Docs.
Example Usage
Note: If you don't have a local GPU, you can run our examples by making a copy of this Colab notebook.
Download example image
!wget https://github.com/overeasy-sh/overeasy/blob/73adbaeba51f532a7023243266da826ed1ced6ec/examples/construction.jpg?raw=true -O construction.jpg
Example workflow to identify if a person is wearing a PPE on a work site:
from overeasy import *
from overeasy.models import OwlV2
from PIL import Image
workflow = Workflow([
# Detect each head in the input image
BoundingBoxSelectAgent(classes=["person's head"], model=OwlV2()),
# Applies Non-Maximum Suppression to remove overlapping bounding boxes
NMSAgent(iou_threshold=0.5, score_threshold=0),
# Splits the input image into images of each detected head
SplitAgent(),
# Classifies the split images using CLIP
ClassificationAgent(classes=["hard hat", "no hard hat"]),
# Maps the returned class names
ClassMapAgent({"hard hat": "has ppe", "no hard hat": "no ppe"}),
# Combines results back into a BoundingBox Detection
JoinAgent()
])
image = Image.open("./construction.jpg")
result, graph = workflow.execute(image)
workflow.visualize(graph)
Diagram
Here's a diagram of this workflow. Each layer in the graph represents a step in the workflow:
The image and data attributes in each node are used together to visualize the current state of the workflow. Calling the visualize function on the workflow will spawn a Gradio instance that looks like this.
Support
If you have any questions or need assistance, please open an issue or reach out to us at help@overeasy.sh.
Let's build amazing vision models together 🍳!
1 maintainer
Metadata
1 maintainer
Release files for overeasy 0.2.16
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| overeasy-0.2.16.tar.gz | 64.3 kB | Details |
Release files / overeasy-0.2.16.tar.gz
| Download URL | overeasy-0.2.16.tar.gz |
|---|---|
| Size | 64.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
67d6a478afb62c2ed995cd01cc0a47246998fd894a5a7d4d0c0f119f1f90d1a7
|
|
BLAKE2b-256 checksum How to use checksums |
bb39c572f7803fd0d302ef8c8364a1fc125ccee7fa6047800fbfba1f7d9fd01f
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/5.1.0 CPython/3.12.5
|
