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from datetime import datetime |
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from lisa_on_cuda.utils import app_helpers |
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from samgis_core.utilities.type_hints import LlistFloat, DictStrInt |
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from samgis_lisa_on_cuda import app_logger |
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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 write_raster_png, write_raster_tiff |
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from samgis_lisa_on_cuda.io.tms2geotiff import download_extent |
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from samgis_lisa_on_cuda.prediction_api.global_models import models_dict |
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from samgis_lisa_on_cuda.utilities.constants import DEFAULT_URL_TILES |
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msg_write_tmp_on_disk = "found option to write images and geojson output..." |
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def lisa_predict( |
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bbox: LlistFloat, |
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prompt: str, |
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zoom: float, |
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inference_function_name_key: str = "lisa", |
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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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inference_function_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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from os import getenv |
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app_logger.info("start lisa inference...") |
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if models_dict[inference_function_name_key]["inference"] is None: |
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app_logger.info(f"missing inference function {inference_function_name_key}, instantiating it now!") |
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parsed_args = app_helpers.parse_args([]) |
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inference_fn = app_helpers.get_inference_model_by_args(parsed_args) |
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models_dict[inference_function_name_key]["inference"] = inference_fn |
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app_logger.debug(f"using a {inference_function_name_key} instance model...") |
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inference_fn = models_dict[inference_function_name_key]["inference"] |
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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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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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folder_write_tmp_on_disk = getenv("WRITE_TMP_ON_DISK", "") |
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prefix = f"w{pt1[1]},s{pt1[0]},e{pt0[1]},n{pt0[0]}_" |
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if bool(folder_write_tmp_on_disk): |
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now = datetime.now().strftime('%Y%m%d_%H%M%S') |
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app_logger.info(msg_write_tmp_on_disk + f"with coords {prefix}, shape:{img.shape}, {len(img.shape)}.") |
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if img.shape and len(img.shape) == 2: |
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write_raster_tiff(img, transform, f"{prefix}_{now}_", f"raw_tiff", folder_write_tmp_on_disk) |
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if img.shape and len(img.shape) == 3 and img.shape[2] == 3: |
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write_raster_png(img, transform, f"{prefix}_{now}_", f"raw_img", folder_write_tmp_on_disk) |
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else: |
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app_logger.info("keep all temp data in memory...") |
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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}_{prefix}" |
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_, mask, output_string = inference_fn(prompt, img, app_logger, embedding_key) |
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return { |
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"output_string": output_string, |
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**get_vectorized_raster_as_geojson(mask, transform) |
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} |
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