Spaces:
Running
Running
| from datetime import datetime | |
| from lisa_on_cuda.utils import app_helpers | |
| from samgis_core.utilities.type_hints import LlistFloat, DictStrInt | |
| from samgis_lisa_on_cuda import app_logger | |
| from samgis_lisa_on_cuda.io.geo_helpers import get_vectorized_raster_as_geojson | |
| from samgis_lisa_on_cuda.io.raster_helpers import write_raster_png, write_raster_tiff | |
| from samgis_lisa_on_cuda.io.tms2geotiff import download_extent | |
| from samgis_lisa_on_cuda.prediction_api.global_models import models_dict | |
| from samgis_lisa_on_cuda.utilities.constants import DEFAULT_URL_TILES | |
| msg_write_tmp_on_disk = "found option to write images and geojson output..." | |
| def lisa_predict( | |
| bbox: LlistFloat, | |
| prompt: str, | |
| zoom: float, | |
| inference_function_name_key: str = "lisa", | |
| source: str = DEFAULT_URL_TILES, | |
| source_name: str = None | |
| ) -> DictStrInt: | |
| """ | |
| Return predictions as a geojson from a geo-referenced image using the given input prompt. | |
| 1. if necessary instantiate a segment anything machine learning instance model | |
| 2. download a geo-referenced raster image delimited by the coordinates bounding box (bbox) | |
| 3. get a prediction image from the segment anything instance model using the input prompt | |
| 4. get a geo-referenced geojson from the prediction image | |
| Args: | |
| bbox: coordinates bounding box | |
| prompt: machine learning input prompt | |
| zoom: Level of detail | |
| inference_function_name_key: machine learning model name | |
| source: xyz | |
| source_name: name of tile provider | |
| Returns: | |
| Affine transform | |
| """ | |
| from os import getenv | |
| app_logger.info("start lisa inference...") | |
| if models_dict[inference_function_name_key]["inference"] is None: | |
| app_logger.info(f"missing inference function {inference_function_name_key}, instantiating it now!") | |
| parsed_args = app_helpers.parse_args([]) | |
| inference_fn = app_helpers.get_inference_model_by_args(parsed_args) | |
| models_dict[inference_function_name_key]["inference"] = inference_fn | |
| app_logger.debug(f"using a {inference_function_name_key} instance model...") | |
| inference_fn = models_dict[inference_function_name_key]["inference"] | |
| pt0, pt1 = bbox | |
| app_logger.info(f"tile_source: {source}: downloading geo-referenced raster with bbox {bbox}, zoom {zoom}.") | |
| img, transform = download_extent(w=pt1[1], s=pt1[0], e=pt0[1], n=pt0[0], zoom=zoom, source=source) | |
| app_logger.info( | |
| f"img type {type(img)} with shape/size:{img.size}, transform type: {type(transform)}, transform:{transform}.") | |
| folder_write_tmp_on_disk = getenv("WRITE_TMP_ON_DISK", "") | |
| prefix = f"w{pt1[1]},s{pt1[0]},e{pt0[1]},n{pt0[0]}_" | |
| if bool(folder_write_tmp_on_disk): | |
| now = datetime.now().strftime('%Y%m%d_%H%M%S') | |
| app_logger.info(msg_write_tmp_on_disk + f"with coords {prefix}, shape:{img.shape}, {len(img.shape)}.") | |
| if img.shape and len(img.shape) == 2: | |
| write_raster_tiff(img, transform, f"{prefix}_{now}_", f"raw_tiff", folder_write_tmp_on_disk) | |
| if img.shape and len(img.shape) == 3 and img.shape[2] == 3: | |
| write_raster_png(img, transform, f"{prefix}_{now}_", f"raw_img", folder_write_tmp_on_disk) | |
| else: | |
| app_logger.info("keep all temp data in memory...") | |
| app_logger.info(f"source_name:{source_name}, source_name type:{type(source_name)}.") | |
| embedding_key = f"{source_name}_z{zoom}_{prefix}" | |
| _, mask, output_string = inference_fn(prompt, img, app_logger, embedding_key) | |
| # app_logger.info(f"created {n_predictions} masks, preparing conversion to geojson...") | |
| return { | |
| "output_string": output_string, | |
| **get_vectorized_raster_as_geojson(mask, transform) | |
| } | |