{"id":42952658,"url":"https://github.com/landing-ai/landingai-python","last_synced_at":"2026-01-30T21:33:38.783Z","repository":{"id":164224410,"uuid":"635927667","full_name":"landing-ai/landingai-python","owner":"landing-ai","description":"LandingAI Python library that enables you to use LandingLens with ease. 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The examples cover different model types, image acquisition sources, and post-procesing techniques.\n\n## Documentation\n\n-  [LandingAI Python Library Docs](https://landing-ai.github.io/landingai-python/)\n-  [LandingAI Support Center](https://support.landing.ai/)\n-  [LandingLens Walk-Through Video](https://www.youtube.com/watch?v=779kvo2dxb4)\n\n\n## Quick start\n\n### Install\nFirst, install the LandingAI Python library:\n\n```bash\npip install landingai\n```\n\n\n### Acquire Your First Images\n\nAfter installing the LandingAI Python library, you can start acquiring images from one of many image sources.\n\nFor example, from a single image file:\n\n```py\nfrom landingai.pipeline.frameset import Frame\n\nframe = Frame.from_image(\"/path/to/your/image.jpg\")\nframe.resize(width=512, height=512)\nframe.save_image(\"/tmp/resized-image.png\")\n```\n\nYou can also extract frames from your webcam. For example:\n\n```py\nfrom landingai.pipeline.image_source import Webcam\n\nwith Webcam(fps=0.5) as webcam:\n    for frame in webcam:\n        frame.resize(width=512, height=512)\n        frame.save_image(\"/tmp/webcam-image.png\")\n```\n\n\nTo learn how to acquire images from more sources, go to [Image Acquisition](https://landing-ai.github.io/landingai-python/image-acquisition/image-acquisition/).\n\n\n### Run Inference\n\nIf you have deployed a computer vision model in LandingLens, you can use this library to send images to that model for inference.\n\nFor 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.\n\n\u003e [!NOTE]\n\u003e 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/).\n\n\u003e [!NOTE]\n\u003e 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).\n\u003e Need help with specific use cases? Post your questions in our [Community](https://community.landing.ai/home).\n\n\n```py\nfrom landingai.pipeline.image_source import Webcam\nfrom landingai.predict import Predictor\n\npredictor = Predictor(\n    endpoint_id=\"abcdef01-abcd-abcd-abcd-01234567890\",\n    api_key=\"land_sk_xxxxxx\",\n)\nwith Webcam(fps=0.5) as webcam:\n    for frame in webcam:\n        frame.resize(width=512)\n        frame.run_predict(predictor=predictor)\n        frame.overlay_predictions()\n        if \"coffee-mug\" in frame.predictions:\n            frame.save_image(\"/tmp/latest-webcam-image.png\", include_predictions=True)\n```\n\n\n## Examples\n\nWe'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.\n\n\u003c!-- Generated using https://www.tablesgenerator.com/markdown_tables --\u003e\n\n| Example | Description | Type |\n|---|---|---|\n| [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 [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/landing-ai/landingai-python/blob/main/examples/webcam-collab-notebook/webcam-collab-notebook.ipynb)|\n| [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 [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/landing-ai/landingai-python/blob/main/examples/rtsp-capture-notebook/rtsp-capture.ipynb) |\n| [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 [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/landing-ai/landingai-python/blob/main/examples/post-processings/farmland-coverage/farmland-coverage.ipynb) |\n| [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 [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/landing-ai/landingai-python/blob/main/examples/license-plate-ocr-notebook/license_plate_ocr.ipynb) |\n| [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 |\n\n\n## Run Examples Locally\n\nAll the examples in this repo can be run locally.\n\nTo 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:\n\n1. Clone the repo to local: `git clone https://github.com/landing-ai/landingai-python.git`\n2. Install the library: `poetry install --with examples` (See the [poetry docs](https://python-poetry.org/docs/#installation) for how to install `poetry`)\n3. Activate the virtual environment: `poetry shell`\n4. Run: `python landingai-python/examples/capture-service/run.py`\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flanding-ai%2Flandingai-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flanding-ai%2Flandingai-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flanding-ai%2Flandingai-python/lists"}