The LandingLens Python API library contains the LandingLens development library and examples that show how to integrate your app with LandingLens in a variety of scenarios. The examples cover different model types, image acquisition sources, and post-procesing techniques.

• Landing AI Python Library Docs
• Landing AI Support Center
• LandingLens Walk-Through Video

First, install the Landing AI Python library:

pip install landingai

After installing the Landing AI Python library, you can start acquiring images from one of many image sources.

For example, from a single image file:

from landingai.pipeline.frameset import Frame

frame = Frame.from_image("/path/to/your/image.jpg")
frame.resize(width=512, height=512)
frame.save_image("/tmp/resized-image.png")

You can also extract frames from your webcam. For example:

from landingai.pipeline.image_source import Webcam

with Webcam(fps=0.5) as webcam:
    for frame in webcam:
        frame.resize(width=512, height=512)
        frame.save_image("/tmp/webcam-image.png")

To learn how to acquire images from more sources, go to Image Acquisition.

If you have deployed a computer vision model in LandingLens, you can use this library to send images to that model for inference.

For example, let's say we've created and deployed a model in LandingLens that detects coffee mugs. Now, we'll use the code below to extract images (frames) from a webcam and run inference on those images.

from landingai.pipeline.image_source import Webcam
from landingai.predict import Predictor

predictor = Predictor(
    endpoint_id="abcdef01-abcd-abcd-abcd-01234567890",
    api_key="land_sk_xxxxxx",
)
with Webcam(fps=0.5) as webcam:
    for frame in webcam:
        frame.resize(width=512)
        frame.run_predict(predictor=predictor)
        frame.overlay_predictions()
        if "coffee-mug" in frame.predictions:
            frame.save_image("/tmp/latest-webcam-image.png", include_predictions=True)

We've provided some examples in Jupyter Notebooks to focus on ease of use, and some examples in Python apps to provide a more robust and complete experience.

Example	Description	Type
Poker Card Suit Identification	This notebook shows how to use an object detection model from LandingLens to detect suits on playing cards. A webcam is used to take photos of playing cards.	Jupyter Notebook
Door Monitoring for Home Automation	This notebook shows how to use an object detection model from LandingLens to detect whether a door is open or closed. An RTSP camera is used to acquire images.	Jupyter Notebook
Satellite Images and Post-Processing	This notebook shows how to use a Visual Prompting model from LandingLens to identify different objects in satellite images. The notebook includes post-processing scripts that calculate the percentage of ground cover that each object takes up.	Jupyter Notebook
License Plate Detection and Recognition	This notebook shows how to extract frames from a video file and use a object detection model and OCR from LandingLens to identify and recognize different license plates.	Jupyter Notebook
Streaming Video	This application shows how to continuously run inference on images extracted from a streaming RTSP video camera feed.	Python application

All the examples in this repo can be run locally.

To give you some guidance, here's how you can run the rtsp-capture example locally in a shell environment:

1. Clone the repo to local: git clone https://github.com/landing-ai/landingai-python.git
2. Install the library: poetry install --with examples (See the poetry docs for how to install poetry)
3. Activate the virtual environment: poetry shell
4. Run: python landingai-python/examples/capture-service/run.py