🏛️ roofai

everything AI about roofs. 🏛️

pip install roofai

graph LR
    dataset_ingest["roofai<br>dataset<br>ingest<br>source=AIRS|CamVid|&lt;distributed-dataset&gt;<br>&lt;dataset-object-name&gt;"]

    dataset_review["roofai<br>dataset<br>review -<br>&lt;dataset-object-name&gt;"]

    semseg_train["roofai<br>semseg<br>train -<br>&lt;dataset-object-name&gt;<br>&lt;model-object-name&gt;"]

    semseg_predict["roofai<br>semseg<br>predict -<br>&lt;model-object-name&gt;<br>&lt;dataset-object-name&gt;<br>&lt;prediction-object-name&gt;"]

    gmaps_get_static_image["@gmaps<br>get_static_image -<br>&lt;object-name&gt;<br>--lat &lt;lat&gt;<br>--lon &lt;lon&gt;"]

    gmaps_geocode["@gmaps<br>geocode - -<br>--address &lt;address&gt;"]

    dataset_ingest_gmaps["roofai<br>dataset<br>ingest<br>source=gmaps<br>&lt;object-name&gt;<br>count=&lt;count&gt;,lat=&lt;lat&gt;,lon=&lt;lon&gt;<br>roboflow,project=&lt;project-name&gt;"]

    roboflow_upload["@roboflow<br>upload<br>project=&lt;project-name&gt;<br>&lt;object-name&gt;"]

    roboflow_download["@roboflow<br>download<br>project=&lt;project-name&gt;,version=&lt;version&gt;<br>&lt;object-name&gt;<br>ingest,count=&lt;10000&gt;<br>&lt;dataset-object-name&gt;"]

    gmaps_predict["@gmaps<br>predict<br>lat=&lt;lat&gt;,lon=&lt;lon&gt; -<br>&lt;model-object-name&gt;<br>&lt;prediction-object-name&gt;"]

    address["🌐 address"]:::folder
    lat_lon["🌐 lat,lon"]:::folder
    AIRS["AIRS"]:::folder
    CamVid["CamVid"]:::folder
    dataset_object_name["📂 dataset object"]:::folder
    distributed_dataset_object_name["📂 distributed dataset object"]:::folder
    model_object_name["📂 model object"]:::folder
    prediction_object_name["📂 prediction object"]:::folder
    object_name["📂 object"]:::folder
    object_name_static_image["📂 object"]:::folder
    terminal["💻 terminal"]:::folder
    roboflow["🖼️ roboflow"]:::folder

    lat_lon --> gmaps_predict
    address --> gmaps_predict
    model_object_name --> gmaps_predict
    gmaps_predict --> prediction_object_name

    dataset_object_name --> dataset_ingest
    distributed_dataset_object_name --> dataset_ingest
    AIRS --> dataset_ingest
    CamVid --> dataset_ingest
    dataset_ingest --> dataset_object_name

    dataset_ingest_gmaps --> gmaps_get_static_image
    dataset_ingest_gmaps --> roboflow
    dataset_ingest_gmaps --> object_name

    object_name --> roboflow_upload
    roboflow_upload --> roboflow

    roboflow --> roboflow_download
    roboflow_download --> dataset_ingest
    roboflow_download --> dataset_review
    roboflow_download --> dataset_object_name

    AIRS --> dataset_review
    distributed_dataset_object_name --> dataset_review
    CamVid --> dataset_review
    dataset_object_name --> dataset_review
    dataset_review --> terminal

    dataset_object_name --> semseg_train
    semseg_train --> model_object_name

    model_object_name --> semseg_predict
    dataset_object_name --> semseg_predict
    semseg_predict --> prediction_object_name

    lat_lon --> gmaps_get_static_image
    gmaps_get_static_image --> object_name_static_image

    address --> gmaps_geocode
    gmaps_geocode --> lat_lon

    classDef folder fill:#999,stroke:#333,stroke-width:2px;

Loading

Datasets Semantic Segmentation Datasets	Semantic Segmentation (SemSeg) A Semantic Segmenter based on segmentation_models.pytorch.
Google Maps API Integrations with the Google Maps Static and Geocoding APIs.	SemSeg on Google Maps Google Maps semantic segmentation datasets and models.
	Google Earth API Integration with the Google Photorealistic 3D Tiles API.

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built by 🌀 blue_options-4.223.1, based on 🏛️ roofai-6.290.1.