https://github.com/landing-ai/landingai-python
LandingAI Python library that enables you to use LandingLens with ease. (https://app.landing.ai/)
https://github.com/landing-ai/landingai-python
computer-vision deep-learning machine-learning
Last synced: 5 months ago
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LandingAI Python library that enables you to use LandingLens with ease. (https://app.landing.ai/)
- Host: GitHub
- URL: https://github.com/landing-ai/landingai-python
- Owner: landing-ai
- License: mit
- Created: 2023-05-03T18:58:04.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2025-08-11T02:13:03.000Z (11 months ago)
- Last Synced: 2025-08-21T06:53:30.995Z (10 months ago)
- Topics: computer-vision, deep-learning, machine-learning
- Language: Python
- Homepage: https://landing-ai.github.io/landingai-python/
- Size: 25.1 MB
- Stars: 112
- Watchers: 10
- Forks: 31
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: docs/contributing.md
- License: LICENSE.md
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# LandingLens Python Library
The LandingLens Python 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.
## Documentation
- [LandingAI Python Library Docs](https://landing-ai.github.io/landingai-python/)
- [LandingAI Support Center](https://support.landing.ai/)
- [LandingLens Walk-Through Video](https://www.youtube.com/watch?v=779kvo2dxb4)
## Quick start
### Install
First, install the LandingAI Python library:
```bash
pip install landingai
```
### Acquire Your First Images
After installing the LandingAI Python library, you can start acquiring images from one of many image sources.
For example, from a single image file:
```py
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:
```py
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](https://landing-ai.github.io/landingai-python/image-acquisition/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](https://app.landing.ai/). You will need to get an "endpoint ID" and "API key" from LandingLens in order to run inferences. Check our [Running Inferences / Getting Started](https://landing-ai.github.io/landingai-python/inferences/getting-started/).
> [!NOTE]
> Learn how to use LandingLens from our [Support Center]([https://support.landing.ai/docs/landinglens-workflow](https://support.landing.ai/landinglens/en)) and [Video Tutorial Library](https://support.landing.ai/docs/landinglens-workflow-2).
> Need help with specific use cases? Post your questions in our [Community](https://community.landing.ai/home).
```py
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](https://github.com/landing-ai/landingai-python/blob/main/examples/webcam-collab-notebook/webcam-collab-notebook.ipynb) | 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 [](https://colab.research.google.com/github/landing-ai/landingai-python/blob/main/examples/webcam-collab-notebook/webcam-collab-notebook.ipynb)|
| [Door Monitoring for Home Automation](https://github.com/landing-ai/landingai-python/blob/main/examples/rtsp-capture-notebook/rtsp-capture.ipynb) | 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 [](https://colab.research.google.com/github/landing-ai/landingai-python/blob/main/examples/rtsp-capture-notebook/rtsp-capture.ipynb) |
| [Satellite Images and Post-Processing](https://github.com/landing-ai/landingai-python/tree/main/examples/post-processings/farmland-coverage/farmland-coverage.ipynb) | 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 [](https://colab.research.google.com/github/landing-ai/landingai-python/blob/main/examples/post-processings/farmland-coverage/farmland-coverage.ipynb) |
| [License Plate Detection and Recognition](https://github.com/landing-ai/landingai-python/tree/main/examples/license-plate-ocr-notebook/license_plate_ocr.ipynb) | 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 [](https://colab.research.google.com/github/landing-ai/landingai-python/blob/main/examples/license-plate-ocr-notebook/license_plate_ocr.ipynb) |
| [Streaming Video](https://github.com/landing-ai/landingai-python/tree/main/examples/capture-service) | 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`](https://github.com/landing-ai/landingai-python/tree/main/examples/capture-service) 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](https://python-poetry.org/docs/#installation) for how to install `poetry`)
3. Activate the virtual environment: `poetry shell`
4. Run: `python landingai-python/examples/capture-service/run.py`