Authorization notice. JEV Sees is dual-use. Only use it on software, sites and systems you own or have explicit permission to test.
Credentials. Keep the JEV API key in your environment and out of the camera script you share with others.
Who it is forA robotics developer whose camera publishes RGB and depth frames and who needs answers about objects that include metric positions.
The problemConnecting a live depth camera to a visual question model usually needs custom glue code for object positions and per-frame answers.
The outcomeA running camera loop that prints JEV answers for each observed object, with distances when camera intrinsics are provided.
What you need first
- Python 3.10 or newer
- A JEV API key from console.typesafe.ai/keys
- Internet access for the first run, unless the Florence-2 and CLIP weights are already available locally
- Git and a terminal
Steps
- Clone the project with
git clone https://github.com/CharlesFeng0314/JEV_sees.git. - Enter the folder with
cd JEV_sees. - Install it with
python -m pip install -e .. - Set your key on macOS or Linux with
export TYPESAFE_API_KEY="your-key", or in PowerShell with$env:TYPESAFE_API_KEY = "your-key". - Write a loop that reads RGB and depth frames from your camera and calls observe(rgb, depth, intrinsics=...), then passes the returned objects to ask(...) with your question. (inferred, check the README)
Prompt for your agent
In the JEV_sees repo at <repo path>, write a camera loop that calls observe(rgb, depth, intrinsics=...) on each frame from <camera source> and then ask(...) with the question What is this object? Expect printed JEV answers per object; verify that objects include 3D positions or distances when intrinsics are supplied.
Caveat. The README mentions observe and ask only briefly without full signatures, so confirm the exact call format in the source before relying on it.
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