0a683f79d74ac0c3aa2f1551d0b7123f06c49735,docs/examples/run_keras_model.py,,deploy_keras_model,#Any#Any#Any#,22

Before Change

    metadata_client = dl.Metadata()
    // NOTE substitute your own trained model here.
    model = tf.keras.applications.resnet50.ResNet50()
    scene = metadata_client.search(
        src_product_id, geom=raster_client.dltile(dltile), limit=1
    )["features"][0]["id"]
    tile, meta = raster_client.ndarray(
        scene,
        bands=["red", "green", "blue"],

After Change

    // create a new raster of the tile area with one channel of the prediction from the model.
    image_ndarray = np.full(tile.shape[:-1], class_, dtype=np.uint16)
    // upload a tile of this "prediction" to catalog
    image_name = "-".join([src_product_id.replace(":", "_"), dltile.replace(":", "_")])
    image = Image(name=image_name, product_id=dest_product_id, acquired=scene.acquired)
    image.upload_ndarray(image_ndarray, raster_meta=meta)


if __name__ == "__main__":

Instances