Fix lmd

src/dvpio/read/shapes/lmd_reader.py

@@ -2,12 +2,12 @@
 import lmd.lib as pylmd
 import numpy as np
 import shapely
-from spatialdata.models import ShapesModel
+from spatialdata.models import PointsModel, ShapesModel
 
 from .geometry import transform_shapes
 
 
-def read_lmd(path: str, calibration_points_image: gpd.GeoDataFrame, switch_orientation: bool = True) -> ShapesModel:
+def read_lmd(path: str, calibration_points_image: PointsModel, switch_orientation: bool = False) -> ShapesModel:
     """Read and parse LMD-formatted masks for the use in spatialdata
 
     Wrapper for pyLMD functions.
@@ -17,8 +17,8 @@ def read_lmd(path: str, calibration_points_image: gpd.GeoDataFrame, switch_orien
     path
         Path to LMD-formatted segmentation masks in .xml format
     calibration_points_image
-        Calibration points of the image as geopandas.DataFrame, with 3 calibration points
-        in geometry column
+        Calibration points of the image as DataFrame, with 3 calibration points. Point coordinates are
+        stored as seperate columns in `x` and `y` column.
     switch_orientation
         Per default, LMD is working in a (x, y) coordinate system while the image coordinates are in a (row=y, col=x)
         coordinate system. If True, transform the coordinate systems by mirroring the coordinate system at the
@@ -41,6 +41,10 @@ def read_lmd(path: str, calibration_points_image: gpd.GeoDataFrame, switch_orien
         geometry=[shapely.Point(point) for point in lmd_shapes.calibration_points],
     )
 
+    calibration_points_image = gpd.GeoDataFrame(
+        geometry=[shapely.Point(row["x"], row["y"]) for _, row in calibration_points_image.iterrows()]
+    )
+
     if len(calibration_points_lmd) < 3:
         raise ValueError(f"Require at least 3 calibration points, but only received {len(calibration_points_lmd)}")
     if len(calibration_points_lmd) != len(calibration_points_image):

tests/read/shapes/test_lmd_reader.py

@@ -4,7 +4,7 @@
 from numpy.typing import NDArray
 from scipy.optimize import linear_sum_assignment as lsa
 from scipy.spatial.distance import cdist
-from shapely import Point
+from spatialdata.models import PointsModel
 
 from dvpio.read.shapes import read_lmd
 
@@ -13,7 +13,7 @@ def _get_centroid_xy(geometry: gpd.GeoSeries) -> NDArray[np.float64]:
     return np.array(geometry.apply(lambda geom: [geom.centroid.x, geom.centroid.y]).tolist())
 
 
-calibration_points_image = gpd.GeoDataFrame(geometry=[Point([1015, 15]), Point([205, 15]), Point([15, 1015])])
+calibration_points_image = PointsModel.parse(np.array([[15, 1015], [15, 205], [1015, 15]]))
 
 
 @pytest.mark.parametrize(
@@ -27,7 +27,7 @@ def _get_centroid_xy(geometry: gpd.GeoSeries) -> NDArray[np.float64]:
     ],
 )
 def test_read_lmd(path: str, calibration_points: NDArray[np.float64], ground_truth_path: str) -> None:
-    lmd_shapes = read_lmd(path, calibration_points, switch_orientation=True)
+    lmd_shapes = read_lmd(path, calibration_points, switch_orientation=False)
     lmd_centroids = _get_centroid_xy(lmd_shapes["geometry"])
 
     ground_truth = gpd.read_file(ground_truth_path)