Choose a correlation resize factor rounded to an int

src/openflexure_microscope_server/stitching.py

@@ -23,7 +23,10 @@ from openflexure_microscope_server.utilities import is_path_safe
 IS_WINDOWS = os.name == "nt"
 
 STITCHING_CMD = "openflexure-stitch"
-STITCHING_RESOLUTION = (820, 616)
+
+# The target width and height to correlate images. Used to choose
+# a suitable correlation_resize factor in scan_workflows
+TARGET_STITCHING_DIMENSION = 700
 STITCH_TILE_SIZE = 8192
 
 DEFAULT_OVERLAP = 0.1

src/openflexure_microscope_server/things/scan_workflows.py

@@ -24,7 +24,7 @@ from openflexure_microscope_server.scan_planners import (
     SmartSpiral,
 )
 from openflexure_microscope_server.stitching import (
-    STITCHING_RESOLUTION,
+    TARGET_STITCHING_DIMENSION,
     StitchingSettings,
 )
 from openflexure_microscope_server.things.autofocus import (
@@ -261,9 +261,19 @@ class RectGridWorkflow(
 
     def _get_stitching_settings_model(self) -> StitchingSettings:
         """Return a stitching settings model based on current settings."""
+        width, height = self.save_resolution
+
+        # Target area in pixels
+        target_area = TARGET_STITCHING_DIMENSION**2
+
+        # Find N so that (width/N) * (height/N) ~ target_area
+        # N^2 ~ (width * height) / target_area
+        downsample_factor = max(1, round((width * height / target_area) ** 0.5))
+
+        correlation_resize = 1 / downsample_factor
         return StitchingSettings(
             overlap=self.overlap,
-            correlation_resize=STITCHING_RESOLUTION[0] / self.save_resolution[0],
+            correlation_resize=correlation_resize,
         )
 
     def _build_scan_settings(self, base_kwargs: dict) -> RectGridSettingModelType:

tests/unit_tests/test_scan_workflows.py

@@ -431,3 +431,33 @@ def test_histo_workflow_settings_ui(histo_workflow, mocker):
         "equal_distances",
     ]
     assert names == expected_names
+
+
+@pytest.mark.parametrize(
+    ("save_res,expected_resize",)[
+        ((1400, 1400), 1 / 2),  # width=1400 -> N=2
+        ((1640, 1232), 1 / 2),  # width=1640 -> round(1640/750)=2
+        ((760, 750), 1 / 1),  # width=760 -> N=1
+        ((1499, 1000), 1 / 2),  # width ~1500 -> N=2
+        ((2250, 1800), 1 / 3),  # width=2250 -> N=3
+        ((700, 700), 1 / 1),  # width < 750 -> N=1
+    ]
+)
+def test_correlation_resize(histo_workflow, save_res, expected_resize):
+    """Test that scan_workflows chooses a suitable correlation_resize factor.
+
+    correlation_resize is always a unit fraction (1/N, N integer) so that the
+    downsampled image has an area roughly equal to TARGET_STITCHING_DIMENSION**2.
+    This ensures stitching correlations are fast, robust, and avoid artefacts.
+
+    The value is found by taking the image area (width * height), dividing by the
+    target area, taking the square root, and rounding to the nearest integer N. Then
+    correlation_resize = 1 / N.
+    """
+    histo_workflow.save_resolution = save_res
+    histo_workflow.overlap = 0.1
+    settings = histo_workflow._get_stitching_settings_model()
+
+    assert isinstance(settings, StitchingSettings)
+    assert settings.correlation_resize == expected_resize
+    assert 0 < settings.correlation_resize <= 1