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"""functions using machine learning instance model(s)""" |
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from samgis_lisa_on_cuda import app_logger, MODEL_FOLDER |
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from samgis_lisa_on_cuda.io.geo_helpers import get_vectorized_raster_as_geojson |
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from samgis_lisa_on_cuda.io.raster_helpers import get_raster_terrain_rgb_like, get_rgb_prediction_image |
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from samgis_lisa_on_cuda.io.tms2geotiff import download_extent |
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from samgis_lisa_on_cuda.io.wrappers_helpers import check_source_type_is_terrain |
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from samgis_lisa_on_cuda.prediction_api.global_models import models_dict, embedding_dict |
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from samgis_lisa_on_cuda.utilities.constants import DEFAULT_URL_TILES, SLOPE_CELLSIZE |
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from samgis_core.prediction_api.sam_onnx import SegmentAnythingONNX, get_raster_inference_with_embedding_from_dict |
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from samgis_core.utilities.constants import MODEL_ENCODER_NAME, MODEL_DECODER_NAME, DEFAULT_INPUT_SHAPE |
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from samgis_core.utilities.type_hints import LlistFloat, DictStrInt, ListDict |
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def samexporter_predict( |
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bbox: LlistFloat, |
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prompt: ListDict, |
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zoom: float, |
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model_name_key: str = "fastsam", |
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source: str = DEFAULT_URL_TILES, |
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source_name: str = None |
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) -> DictStrInt: |
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""" |
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Return predictions as a geojson from a geo-referenced image using the given input prompt. |
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1. if necessary instantiate a segment anything machine learning instance model |
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2. download a geo-referenced raster image delimited by the coordinates bounding box (bbox) |
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3. get a prediction image from the segment anything instance model using the input prompt |
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4. get a geo-referenced geojson from the prediction image |
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Args: |
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bbox: coordinates bounding box |
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prompt: machine learning input prompt |
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zoom: Level of detail |
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model_name_key: machine learning model name |
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source: xyz |
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source_name: name of tile provider |
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Returns: |
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Affine transform |
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""" |
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if models_dict[model_name_key]["instance"] is None: |
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app_logger.info(f"missing instance model {model_name_key}, instantiating it now!") |
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model_instance = SegmentAnythingONNX( |
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encoder_model_path=MODEL_FOLDER / MODEL_ENCODER_NAME, |
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decoder_model_path=MODEL_FOLDER / MODEL_DECODER_NAME |
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) |
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models_dict[model_name_key]["instance"] = model_instance |
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app_logger.debug(f"using a {model_name_key} instance model...") |
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models_instance = models_dict[model_name_key]["instance"] |
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pt0, pt1 = bbox |
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app_logger.info(f"tile_source: {source}: downloading geo-referenced raster with bbox {bbox}, zoom {zoom}.") |
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img, transform = download_extent(w=pt1[1], s=pt1[0], e=pt0[1], n=pt0[0], zoom=zoom, source=source) |
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if check_source_type_is_terrain(source): |
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app_logger.info("terrain-rgb like raster: transforms it into a DEM") |
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dem = get_raster_terrain_rgb_like(img, source.name) |
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slope_cellsize = int(img.shape[1] * SLOPE_CELLSIZE / DEFAULT_INPUT_SHAPE[1]) |
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app_logger.info(f"terrain-rgb like raster: compute slope, curvature using {slope_cellsize} as cell size.") |
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img = get_rgb_prediction_image(dem, slope_cellsize) |
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app_logger.info( |
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f"img type {type(img)} with shape/size:{img.size}, transform type: {type(transform)}, transform:{transform}.") |
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app_logger.info(f"source_name:{source_name}, source_name type:{type(source_name)}.") |
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embedding_key = f"{source_name}_z{zoom}_w{pt1[1]},s{pt1[0]},e{pt0[1]},n{pt0[0]}" |
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mask, n_predictions = get_raster_inference_with_embedding_from_dict( |
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img, prompt, models_instance, model_name_key, embedding_key, embedding_dict) |
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app_logger.info(f"created {n_predictions} masks, preparing conversion to geojson...") |
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return { |
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"n_predictions": n_predictions, |
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**get_vectorized_raster_as_geojson(mask, transform) |
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} |
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