Release

doc/release_notes/release_1.01.md

@@ -1,5 +1,13 @@
 # Version 1.1 #
 
+## Sigima Version 1.1.4 ##
+
+### 🛠️ Bug Fixes since version 1.1.3 ###
+
+* **Ellipse/circle contour detection**: Fixed regression in axis orientation after the v1.1.3 coordinate swap fix. This closes [Issue #27](https://github.com/DataLab-Platform/Sigima/issues/27).
+  * The sign correction applied in v1.1.3 for the `(row, col)` → `(x, y)` conversion introduced a regression in ellipse axis orientation, causing the semi-major and semi-minor axes to be transposed
+  * Added ground-truth unit tests for `fit_circle_model` and `fit_ellipse_model` verifying all output parameters (center coordinates, radius/semi-axes, and rotation angle)
+
 ## Sigima Version 1.1.3 ##
 
 ### 🛠️ Bug Fixes since version 1.1.2 ###

sigima/__init__.py

@@ -144,7 +144,7 @@
 # Set validation mode to ENABLED by default (issue warnings for invalid inputs)
 set_validation_mode(ValidationMode.ENABLED)
 
-__version__ = "1.1.3"
+__version__ = "1.1.4"
 __docurl__ = "https://sigima.readthedocs.io/"
 __homeurl__ = "https://github.com/DataLab-Platform/Sigima"
 __supporturl__ = "https://github.com/DataLab-Platform/sigima/issues/new/choose"

sigima/tests/image/preprocessing_tools_unit_test.py

@@ -11,6 +11,7 @@
 
 from sigima.enums import BinningOperation
 from sigima.tools.image.preprocessing import (
+    _USE_NEW_SHAPE_API,
     binning,
     distance_matrix,
     fit_circle_model,
@@ -84,6 +85,53 @@ def test_fit_ellipse_model_failure() -> None:
     assert result is None or isinstance(result, tuple)
 
 
+def test_fit_circle_model_ground_truth() -> None:
+    """Verify that ``fit_circle_model`` recovers **all** circle parameters
+    (xc, yc, radius) from a noise-free contour.
+
+    Note: the functions swap x/y because scikit-image models interpret the
+    contour columns as (row, col), so the returned ``xc`` corresponds to the
+    contour's second column and ``yc`` to the first.
+    """
+    if not _USE_NEW_SHAPE_API:
+        pytest.skip()
+    xc_in, yc_in, r_in = 7.0, -5.0, 12.0
+    contour = _circle_contour(xc_in, yc_in, r_in, n=256)
+    result = fit_circle_model(contour)
+    assert result is not None
+    # xc/yc are swapped by the row/col → x/y conversion
+    yc, xc, r = result
+    assert xc == pytest.approx(xc_in, abs=1e-6)
+    assert yc == pytest.approx(yc_in, abs=1e-6)
+    assert r == pytest.approx(r_in, abs=1e-6)
+
+
+def test_fit_ellipse_model_ground_truth() -> None:
+    """Verify that ``fit_ellipse_model`` recovers **all** ellipse parameters
+    (xc, yc, a, b, theta) from a noise-free contour.
+
+    Note: the functions swap x/y and a/b because scikit-image models interpret
+    the contour columns as (row, col), so centre and semi-axes are transposed.
+    """
+    if not _USE_NEW_SHAPE_API:
+        pytest.skip()
+    xc_in, yc_in = -3.0, 5.0
+    a_in, b_in = 4.0, 8.0
+    theta_in = np.pi / 6
+    contour = _ellipse_contour(xc_in, yc_in, a_in, b_in, theta0=theta_in, n=256)
+    result = fit_ellipse_model(contour)
+    assert result is not None
+    # xc/yc are swapped by the row/col → x/y conversion
+    yc, xc, a, b, theta = result
+    assert xc == pytest.approx(xc_in, abs=1e-4)
+    assert yc == pytest.approx(yc_in, abs=1e-4)
+    assert a == pytest.approx(a_in, abs=1e-4)
+    assert b == pytest.approx(b_in, abs=1e-4)
+    # The fitted angle is expected to differ by π/2,
+    # theta along y axis instead of x axis
+    assert theta == pytest.approx(theta_in + np.pi / 2, abs=1e-4)
+
+
 # ===========================================================================
 # get_absolute_level
 # ===========================================================================

sigima/tools/coordinates.py

@@ -98,7 +98,7 @@ def ellipse_to_diameters(
         Ellipse X/Y diameters (major/minor axes) coordinates
     """
     dxa, dya = a * np.cos(theta), a * np.sin(theta)
-    dxb, dyb = -b * np.sin(theta), b * np.cos(theta)
+    dxb, dyb = b * np.sin(theta), b * np.cos(theta)
     x0, y0, x1, y1 = xc - dxa, yc - dya, xc + dxa, yc + dya
     x2, y2, x3, y3 = xc - dxb, yc - dyb, xc + dxb, yc + dyb
     return x0, y0, x1, y1, x2, y2, x3, y3
@@ -117,7 +117,7 @@ def array_ellipse_to_diameters(data: np.ndarray) -> np.ndarray:
     """
     xc, yc, a, b, theta = data[:, 0], data[:, 1], data[:, 2], data[:, 3], data[:, 4]
     dxa, dya = a * np.cos(theta), a * np.sin(theta)
-    dxb, dyb = -b * np.sin(theta), b * np.cos(theta)
+    dxb, dyb = b * np.sin(theta), b * np.cos(theta)
     x0, y0, x1, y1 = xc - dxa, yc - dya, xc + dxa, yc + dya
     x2, y2, x3, y3 = xc - dxb, yc - dyb, xc + dxb, yc + dyb
     result = np.column_stack((x0, y0, x1, y1, x2, y2, x3, y3)).astype(float)