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# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file.
"""
Contour finding test
"""
# pylint: disable=invalid-name # Allows short reference names like x, y, ...
# pylint: disable=duplicate-code
import sys
import time
import numpy as np
import pytest
import sigima.objects
import sigima.params
import sigima.proc.image
from sigima.enums import ContourShape
from sigima.tests import guiutils
from sigima.tests.data import get_peak2d_data
from sigima.tests.env import execenv
from sigima.tests.helpers import (
check_array_result,
check_scalar_result,
)
from sigima.tools.image import get_2d_peaks_coords, get_contour_shapes
@pytest.mark.gui
def test_contour_interactive():
"""2D peak detection test"""
data, _coords = get_peak2d_data()
with guiutils.lazy_qt_app_context(force=True):
# pylint: disable=import-outside-toplevel
from sigima import viz
items = [viz.create_image(data, colormap="hsv")]
t0 = time.time()
peak_coords = get_2d_peaks_coords(data)
dt = time.time() - t0
for x, y in peak_coords:
items.append(viz.create_marker(x, y))
execenv.print(f"Calculation time: {int(dt * 1e3):d} ms\n", file=sys.stderr)
execenv.print(f"Peak coordinates: {peak_coords}")
# Add contour shapes for all shape types
for shape in ContourShape:
coords = get_contour_shapes(data, shape=shape)
items.extend(viz.create_contour_shapes(coords, shape))
viz.view_image_items(items)
@pytest.mark.validation
def test_contour_shape() -> None:
"""Test contour shape computation function"""
# Create test data with known shapes
data, _expected_coords = get_peak2d_data()
# Test each contour shape type with ROI creation
for shape in ContourShape:
execenv.print(f"Testing contour shape: {shape}")
# Get contour shapes from the function
detected_shapes = get_contour_shapes(data, shape=shape)
execenv.print(f"Detected {len(detected_shapes)} {shape}(s)")
image = sigima.objects.create_image("Contour Test Image", data=data)
param = sigima.params.ContourShapeParam.create(shape=shape)
results = sigima.proc.image.contour_shape(image, param)
sigima.proc.image.apply_detection_rois(image, results)
check_array_result(f"Contour shapes ({shape})", detected_shapes, results.coords)
# Basic validation checks
assert isinstance(detected_shapes, np.ndarray), (
f"get_contour_shapes should return numpy array for {shape}"
)
if len(detected_shapes) > 0:
# Check that we detected at least some shapes
execenv.print(f"Successfully detected contours for {shape}")
# Validate shape-specific properties
if shape == ContourShape.CIRCLE:
# For circles: [xc, yc, r]
assert detected_shapes.shape[1] == 3, (
"Circle contours should have 3 parameters (xc, yc, r)"
)
# Check that radius values are positive
radii = detected_shapes[:, 2]
assert np.all(radii > 0), "All circle radii should be positive"
check_scalar_result(
"Circle radius range",
np.mean(radii),
np.mean(radii), # Just check it's finite
rtol=1.0,
)
elif shape == ContourShape.ELLIPSE:
# For ellipses: [xc, yc, a, b, theta]
assert detected_shapes.shape[1] == 5, (
"Ellipse contours should have 5 parameters (xc, yc, a, b, theta)"
)
# Check that semi-axes are positive
a_values = detected_shapes[:, 2]
b_values = detected_shapes[:, 3]
assert np.all(a_values > 0), (
"All ellipse semi-axes 'a' should be positive"
)
assert np.all(b_values > 0), (
"All ellipse semi-axes 'b' should be positive"
)
check_scalar_result(
"Ellipse semi-axis 'a' range",
np.mean(a_values),
np.mean(a_values), # Just check it's finite
rtol=1.0,
)
elif shape == ContourShape.POLYGON:
# For polygons: flattened x,y coordinates
# Shape should be (n_contours, max_points) where max_points is even
assert detected_shapes.shape[1] % 2 == 0, (
"Polygon contours should have even number of coordinates "
"(x,y pairs)"
)
# Check that we have valid coordinates (not all NaN)
valid_coords = ~np.isnan(detected_shapes)
assert np.any(valid_coords), (
"Polygon should have some valid coordinates"
)
# Check that the function handles different threshold levels
for level in [0.3, 0.5, 0.7]:
shapes_at_level = get_contour_shapes(data, shape=shape, level=level)
assert isinstance(shapes_at_level, np.ndarray), (
f"get_contour_shapes should return numpy array for {shape} "
f"at level {level}"
)
execenv.print(f" At level {level}: detected {len(shapes_at_level)} shapes")
execenv.print("All contour shape tests passed!")
if __name__ == "__main__":
test_contour_interactive()
test_contour_shape()
|
