[doc] use new typing hints from samgis_core

docs/external_links_md.md

@@ -1,2 +1,4 @@
 - My [SamGIS demo website](https://ml-trinca.tornidor.com)
+- My [HuggingFace SamGIS demo](https://huggingface.co/spaces/aletrn/samgis)
+- My [HuggingFace SamGIS - LISA on CUDA demo](https://huggingface.co/spaces/aletrn/samgis-lisa-on-cuda)
 - My [personal website](https://trinca.tornidor.com)

samgis/io/coordinates_pixel_conversion.py

@@ -1,5 +1,5 @@
 """functions useful to convert to/from latitude-longitude coordinates to pixel image coordinates"""
-from samgis_core.utilities.type_hints import tuple_float, tuple_float_any
+from samgis_core.utilities.type_hints import TupleFloat, TupleFloatAny
 
 from samgis import app_logger
 from samgis.utilities.constants import TILE_SIZE, EARTH_EQUATORIAL_RADIUS
@@ -82,7 +82,7 @@ def get_latlng_to_pixel_coordinates(
     return point
 
 
-def _from4326_to3857(lat: float, lon: float) -> tuple_float or tuple_float_any:
+def _from4326_to3857(lat: float, lon: float) -> TupleFloat or TupleFloatAny:
     from math import radians, log, tan
 
     x_tile: float = radians(lon) * EARTH_EQUATORIAL_RADIUS

samgis/io/geo_helpers.py

@@ -2,11 +2,11 @@
 from affine import Affine
 from numpy import ndarray as np_ndarray
 
-from samgis_core.utilities.type_hints import list_float, tuple_float, dict_str_int
+from samgis_core.utilities.type_hints import ListFloat, DictStrInt, TupleFloat
 from samgis import app_logger
 
 
-def load_affine_transformation_from_matrix(matrix_source_coefficients: list_float) -> Affine:
+def load_affine_transformation_from_matrix(matrix_source_coefficients: ListFloat) -> Affine:
     """
     Wrapper for rasterio.Affine.from_gdal() method
 
@@ -32,7 +32,7 @@ def load_affine_transformation_from_matrix(matrix_source_coefficients: list_floa
         raise e
 
 
-def get_affine_transform_from_gdal(matrix_source_coefficients: list_float or tuple_float) -> Affine:
+def get_affine_transform_from_gdal(matrix_source_coefficients: ListFloat or TupleFloat) -> Affine:
     """wrapper for rasterio Affine from_gdal method
 
     Args:
@@ -44,7 +44,7 @@ def get_affine_transform_from_gdal(matrix_source_coefficients: list_float or tup
     return Affine.from_gdal(*matrix_source_coefficients)
 
 
-def get_vectorized_raster_as_geojson(mask: np_ndarray, transform: tuple_float) -> dict_str_int:
+def get_vectorized_raster_as_geojson(mask: np_ndarray, transform: TupleFloat) -> DictStrInt:
     """
         Get shapes and values of connected regions in a dataset or array
 

samgis/io/tms2geotiff.py

@@ -1,7 +1,7 @@
 import os
 
 from numpy import ndarray
-from samgis_core.utilities.type_hints import tuple_float
+from samgis_core.utilities.type_hints import TupleFloat
 from xyzservices import TileProvider
 
 from samgis import app_logger
@@ -70,7 +70,7 @@ def download_extent(w: float, s: float, e: float, n: float, zoom: int or str = z
         raise e_download_extent
 
 
-def crop_raster(w: float, s: float, e: float, n: float, raster: ndarray, raster_bbox: tuple_float,
+def crop_raster(w: float, s: float, e: float, n: float, raster: ndarray, raster_bbox: TupleFloat,
                 crs: str = OUTPUT_CRS_STRING, driver: str = DRIVER_RASTERIO_GTIFF) -> tuple_ndarray_transform:
     """
     Crop a raster using given bounding box (w, s, e, n) values
@@ -134,7 +134,7 @@ def crop_raster(w: float, s: float, e: float, n: float, raster: ndarray, raster_
         raise e_crop_raster
 
 
-def get_transform_raster(raster: ndarray, raster_bbox: tuple_float) -> tuple_ndarray_transform:
+def get_transform_raster(raster: ndarray, raster_bbox: TupleFloat) -> tuple_ndarray_transform:
     """
     Convert the input raster image to RGB and extract the Affine 
 

samgis/prediction_api/predictors.py

@@ -8,7 +8,7 @@ from samgis.utilities.constants import DEFAULT_URL_TILES, SLOPE_CELLSIZE
 from samgis_core.prediction_api.sam_onnx import SegmentAnythingONNX
 from samgis_core.prediction_api.sam_onnx import get_raster_inference, get_raster_inference_with_embedding_from_dict
 from samgis_core.utilities.constants import MODEL_ENCODER_NAME, MODEL_DECODER_NAME, DEFAULT_INPUT_SHAPE
-from samgis_core.utilities.type_hints import llist_float, dict_str_int, list_dict
+from samgis_core.utilities.type_hints import LlistFloat, DictStrInt, ListDict
 
 
 models_dict = {"fastsam": {"instance": None}}
@@ -16,13 +16,13 @@ embedding_dict = {}
 
 
 def samexporter_predict(
-        bbox: llist_float,
-        prompt: list_dict,
+        bbox: LlistFloat,
+        prompt: ListDict,
         zoom: float,
         model_name: str = "fastsam",
         source: str = DEFAULT_URL_TILES,
         source_name: str = None
-) -> dict_str_int:
+) -> DictStrInt:
     """
     Return predictions as a geojson from a geo-referenced image using the given input prompt.