Skip to main content

Helper library for interacting with Landing AI LandingLens

Project description

ci_status PyPI version version license downloads


LandingLens Python SDK

The LandingLens Python SDK 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.

Documentation

Quick start

Install

First, install the Landing AI Python library:

pip install landingai

Acquire Your First Images

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.

Run Inference

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.

[!NOTE] If you don't have a LandingLens account, create one here. You will need to get an "endpoint ID" and "API key" from LandingLens in order to run inferences. Check our Running Inferences / Getting Started.

[!NOTE] Learn how to use LandingLens from our Support Center and Video Tutorial Library. Need help with specific use cases? Post your questions in our Community.

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)

Examples

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 Colab
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 Colab
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 Colab
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 Colab
Streaming Video This application shows how to continuously run inference on images extracted from a streaming RTSP video camera feed. Python application

Run Examples Locally

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

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

landingai-0.3.25.tar.gz (1.0 MB view details)

Uploaded Source

Built Distribution

landingai-0.3.25-py3-none-any.whl (1.0 MB view details)

Uploaded Python 3

File details

Details for the file landingai-0.3.25.tar.gz.

File metadata

  • Download URL: landingai-0.3.25.tar.gz
  • Upload date:
  • Size: 1.0 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.4.2 CPython/3.10.11 Linux/6.2.0-1018-azure

File hashes

Hashes for landingai-0.3.25.tar.gz
Algorithm Hash digest
SHA256 409f059157c72d93c906295f073faed67f8d7433dc0446a5bd714fc58f9a5ceb
MD5 d5e77586677068881a484606bc2ed0a7
BLAKE2b-256 78fe2bf6182f1a988abbbfda32288305ba85ad86e17770fda3f70fffa8d1de81

See more details on using hashes here.

File details

Details for the file landingai-0.3.25-py3-none-any.whl.

File metadata

  • Download URL: landingai-0.3.25-py3-none-any.whl
  • Upload date:
  • Size: 1.0 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.4.2 CPython/3.10.11 Linux/6.2.0-1018-azure

File hashes

Hashes for landingai-0.3.25-py3-none-any.whl
Algorithm Hash digest
SHA256 7b2895c74f3cabfa986ac85c61ff89bfe9d5017a7a85a5936e1f3d5e752b4a1b
MD5 4e1111ccac543a759f212acfb7f8f00f
BLAKE2b-256 328168ce8f7cb2b2f448ad0a0117126a4420924cc5364efb436dd4e81aa053a6

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page