osam 0.1.0

Get up and running vision foundational models locally.

Osam

Get up and running vision foundational models locally.

Osam (/oʊˈsɑm/) is a tool to run open source vision foundational models locally, built inspired by Ollama.

Osam gives you:

• Visual foundational models - Segment-Anything Model, Efficient-SAM, etc;
• Local APIs - CLI & Python & HTTP interface;
• Customization - Host custom vision models.

Installation

pip install osam

Usage

CLI

# Run a model with an image
osam run efficient-sam:25m --image examples/_images/dogs.jpg > output.jpg

# Get a JSON output
osam run efficient-sam:25m --image examples/_images/dogs.jpg --json
# {"model": "efficient-sam:25m", "mask": "..."}

# Give a prompt
osam run efficient-sam:25m --image examples/_images/dogs.jpg \
  --prompt '{"points": [[1439, 504], [1439, 1289]], "point_labels": [1, 1]}' > output.jpg

Input and output images ('dogs.jpg', 'output.jpg').

Python

import osam.apis
import osam.types

request = osam.types.GenerateRequest(
    model="efficient-sam:25m",
    image=np.asarray(PIL.Image.open("examples/_images/dogs.jpg")),
    prompt=osam.types.Prompt(points=[[1439, 504], [1439, 1289]], point_labels=[1, 1]),
)
response = osam.apis.generate(request=request)
PIL.Image.fromarray(response.mask).save("mask.jpg")

Input and output images ('dogs.jpg', 'mask.jpg').

HTTP

# Get up the server
osam serve

# POST request
curl 127.0.0.1:11368/api/generate -X POST \
  -H "Content-Type: application/json" \
  -d "{\"model\": \"efficient-sam:25m\", \"image\": \"$(cat examples/_images/dogs.jpg | base64)\"}" \
  | jq -r .mask | base64 --decode > mask.jpg

License

MIT