Incremental commit on Python ICP

dev-environment.yml

@@ -47,8 +47,5 @@ dependencies:
     # Optional dependency
     - -e ./
 
-    # "Headless" needed for opencv to install without requiring system dependencies
-    - opencv-contrib-python-headless
-
     # To run CI against latest GeoUtils
 #    - git+https://github.com/rhugonnet/geoutils.git

doc/source/coregistration.md

@@ -311,8 +311,6 @@ plt.tight_layout()
 Iterative Closest Point (ICP) coregistration is an iterative point cloud registration method from [Besl and McKay (1992)](https://doi.org/10.1117/12.57955). It aims at iteratively minimizing the distance between closest neighbours by applying sequential rigid transformations. If DEMs are used as inputs, they are converted to point clouds.
 As for Nuth and Kääb (2011), the iteration stops if it reaches the maximum number of iteration limit or if the iterative transformation amplitude falls below a specified tolerance.
 
-ICP is currently based on [OpenCV's implementation](https://docs.opencv.org/4.x/dc/d9b/classcv_1_1ppf__match__3d_1_1ICP.html) (an optional dependency), which includes outlier removal arguments. This may improve results significantly on outlier-prone data, but care should still be taken, as the risk of landing in [local minima](https://en.wikipedia.org/wiki/Maxima_and_minima) increases.
-
 ```{code-cell} ipython3
 :tags: [hide-cell]
 :mystnb:

pyproject.toml

@@ -21,3 +21,6 @@ testpaths = [
 	"tests",
 	"xdem"
 ]
+log_cli = "True"
+log_cli_level = "INFO"
+

setup.cfg

@@ -49,8 +49,6 @@ include =
 
 [options.extras_require]
 opt =
-    opencv
-    openh264
     pytransform3d
     noisyopt
 test =

tests/test_coreg/test_affine.py

@@ -2,6 +2,7 @@
 from __future__ import annotations
 
 import warnings
+import logging
 
 import geopandas as gpd
 import geoutils
@@ -18,7 +19,6 @@
 from xdem import coreg, examples
 from xdem.coreg.affine import AffineCoreg, _reproject_horizontal_shift_samecrs
 
-
 def load_examples(crop: bool = True) -> tuple[RasterType, RasterType, Vector]:
     """Load example files to try coregistration methods with."""
 
@@ -199,8 +199,8 @@ def test_raise_all_nans(self) -> None:
         pytest.raises(ValueError, icp.fit, dem1, dem2, transform=affine)
 
     @pytest.mark.parametrize("fit_args", all_fit_args)  # type: ignore
-    @pytest.mark.parametrize("shifts", [(20, 5, 2), (-50, 100, 2)])  # type: ignore
-    @pytest.mark.parametrize("coreg_method", [coreg.NuthKaab, coreg.DhMinimize, coreg.ICP])  # type: ignore
+    @pytest.mark.parametrize("shifts", [(-1000, 2000, 200)])  # type: ignore
+    @pytest.mark.parametrize("coreg_method", [coreg.ICP])  # type: ignore
     def test_coreg_translations__synthetic(self, fit_args, shifts, coreg_method) -> None:
         """
         Test the horizontal/vertical shift coregistrations with synthetic shifted data. These tests include NuthKaab,