1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
|
# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file.
"""
Blob detection tests
"""
# pylint: disable=invalid-name # Allows short reference names like x, y, ...
# pylint: disable=duplicate-code
from __future__ import annotations
import importlib.util
import numpy as np
import pytest
import sigima.enums
import sigima.objects
import sigima.params
import sigima.proc.image
from sigima.tests import guiutils
from sigima.tests.env import execenv
from sigima.tests.helpers import validate_detection_rois
CV2_AVAILABLE = importlib.util.find_spec("cv2") is not None
def create_simple_blob_test_image() -> np.ndarray:
"""Create a simple test image with one obvious blob for debugging.
Returns:
Simple test image with a clear blob
"""
# Create a simple 100x100 image with a clear circular blob
size = 100
data = np.zeros((size, size), dtype=np.float64)
# Add a clear circular blob in the center
y, x = np.ogrid[:size, :size]
center_x, center_y = size // 2, size // 2
radius = 10
# Create a circular blob (step function, not Gaussian)
mask = (x - center_x) ** 2 + (y - center_y) ** 2 < radius**2
data[mask] = 1.0
# Keep as float64 for blob detection
return data
def create_blob_test_image(
size: int = 200,
n_blobs: int = 5,
blob_radius: float = 10.0,
blob_intensity: float = 1000.0,
noise_level: float = 50.0,
seed: int | None = None,
) -> tuple[np.ndarray, np.ndarray]:
"""Create a test image with synthetic blobs for blob detection testing.
Args:
size: Image size (square image)
n_blobs: Number of blobs to create
blob_radius: Radius of the blobs in pixels
blob_intensity: Intensity of the blobs
noise_level: Standard deviation of Gaussian noise
seed: Random seed for reproducible results
Returns:
Tuple of (image_data, expected_coords) where expected_coords is an array
of shape (n_blobs, 3) with columns [x, y, radius]
"""
rng = np.random.default_rng(seed)
# Create base image with noise
data = rng.normal(0, noise_level, size=(size, size)).astype(np.float64)
# Generate blob centers avoiding edges
margin = int(blob_radius * 2)
valid_range = size - 2 * margin
blob_centers = []
for _ in range(n_blobs):
x = margin + rng.random() * valid_range
y = margin + rng.random() * valid_range
blob_centers.append((x, y))
# Add circular blobs to the image
expected_coords = []
y_grid, x_grid = np.ogrid[:size, :size]
for x_center, y_center in blob_centers:
# Create a circular blob using step function
mask = (x_grid - x_center) ** 2 + (y_grid - y_center) ** 2 < blob_radius**2
data[mask] += blob_intensity
# Store expected coordinates with radius
expected_coords.append([x_center, y_center, blob_radius])
# Ensure positive values and convert to float64 for blob detection
data = np.maximum(data, 0)
data = data.astype(np.float64)
# Normalize to [0, 1] range for blob detection algorithms
if data.max() > 0:
data = data / data.max()
return data, np.array(expected_coords)
@pytest.mark.validation
def test_image_blob_dog():
"""Blob detection using Difference of Gaussian (DoG) method validation test"""
execenv.print("Testing blob_dog detection...")
# Test 1: Simple single blob
data = create_simple_blob_test_image()
obj = sigima.objects.create_image("blob_dog_simple", data=data)
param = sigima.params.BlobDOGParam.create(
min_sigma=1.0,
max_sigma=20.0,
threshold_rel=0.01,
overlap=0.5,
exclude_border=False,
)
result = sigima.proc.image.blob_dog(obj, param)
assert result is not None, "Simple blob detection should return results"
assert len(result.coords) > 0, "Should detect at least one blob in simple case"
execenv.print(f"✓ DoG simple: detected {len(result.coords)} blobs")
# Test 2: Multiple blobs with ROI creation
data, expected_coords = create_blob_test_image(
size=150, n_blobs=2, blob_radius=12.0, noise_level=0.1, seed=42
)
for create_rois in (False, True):
for roi_geometry in sigima.enums.DetectionROIGeometry:
if (
not create_rois
and roi_geometry != sigima.enums.DetectionROIGeometry.RECTANGLE
):
continue
obj = sigima.objects.create_image("blob_dog_multi", data=data)
param = sigima.params.BlobDOGParam.create(
min_sigma=5.0,
max_sigma=20.0,
threshold_rel=0.05,
overlap=0.3,
exclude_border=True,
create_rois=create_rois,
roi_geometry=roi_geometry,
)
result = sigima.proc.image.blob_dog(obj, param)
# Apply ROIs from detection result
sigima.proc.image.apply_detection_rois(obj, result)
title = f"DoG blob detection (ROIs={create_rois}, geom={roi_geometry.name})"
guiutils.view_images_if_gui(
obj,
title=title,
results=[result],
colormap="gray",
)
if result is not None and len(result.coords) > 0:
detected_count = len(result.coords)
expected_count = len(expected_coords)
execenv.print(
f"✓ DoG multi: detected {detected_count} blobs "
f"(expected ~{expected_count}, ROIs={create_rois})"
)
# Validate coordinate format: should be [x, y, radius]
assert result.coords.shape[1] == 3, (
"Coordinates should have 3 columns [x, y, radius]"
)
# Check that all radii are positive
assert np.all(result.coords[:, 2] > 0), (
"All detected blob radii should be positive"
)
# Validate ROI creation
validate_detection_rois(obj, result.coords, create_rois, roi_geometry)
else:
execenv.print(
"✓ DoG multi: no blobs detected (acceptable for noisy case)"
)
@pytest.mark.validation
def test_image_blob_doh():
"""Blob detection using Determinant of Hessian (DoH) method validation test"""
execenv.print("Testing blob_doh detection...")
# Test with ROI creation validation
data = create_simple_blob_test_image()
for create_rois in (False, True):
for roi_geometry in sigima.enums.DetectionROIGeometry:
if (
not create_rois
and roi_geometry != sigima.enums.DetectionROIGeometry.RECTANGLE
):
continue
obj = sigima.objects.create_image("blob_doh_test", data=data)
param = sigima.params.BlobDOHParam.create(
min_sigma=1.0,
max_sigma=20.0,
threshold_rel=0.01,
overlap=0.5,
log_scale=False,
create_rois=create_rois,
roi_geometry=roi_geometry,
)
result = sigima.proc.image.blob_doh(obj, param)
sigima.proc.image.apply_detection_rois(obj, result)
assert result is not None, "Blob detection should return results"
assert len(result.coords) > 0, "Should detect at least one blob"
execenv.print(
f"✓ DoH: detected {len(result.coords)} blobs (ROIs={create_rois})"
)
# Validate ROI creation
validate_detection_rois(obj, result.coords, create_rois, roi_geometry)
@pytest.mark.validation
def test_image_blob_log():
"""Blob detection using Laplacian of Gaussian (LoG) method validation test"""
execenv.print("Testing blob_log detection...")
# Test with ROI creation validation
data = create_simple_blob_test_image()
for create_rois in (False, True):
for roi_geometry in sigima.enums.DetectionROIGeometry:
if (
not create_rois
and roi_geometry != sigima.enums.DetectionROIGeometry.RECTANGLE
):
continue
obj = sigima.objects.create_image("blob_log_test", data=data)
param = sigima.params.BlobLOGParam.create(
min_sigma=1.0,
max_sigma=20.0,
threshold_rel=0.01,
overlap=0.5,
log_scale=False,
exclude_border=False,
create_rois=create_rois,
roi_geometry=roi_geometry,
)
result = sigima.proc.image.blob_log(obj, param)
sigima.proc.image.apply_detection_rois(obj, result)
assert result is not None, "Blob detection should return results"
assert len(result.coords) > 0, "Should detect at least one blob"
execenv.print(
f"✓ LoG: detected {len(result.coords)} blobs (ROIs={create_rois})"
)
# Validate ROI creation
validate_detection_rois(obj, result.coords, create_rois, roi_geometry)
@pytest.mark.validation
@pytest.mark.skipif(not CV2_AVAILABLE, reason="OpenCV (cv2) is not available")
def test_image_blob_opencv():
"""Blob detection using OpenCV method validation test"""
execenv.print("Testing blob_opencv detection...")
# Test with ROI creation validation
data = create_simple_blob_test_image()
for create_rois in (False, True):
for roi_geometry in sigima.enums.DetectionROIGeometry:
if (
not create_rois
and roi_geometry != sigima.enums.DetectionROIGeometry.RECTANGLE
):
continue
obj = sigima.objects.create_image("blob_opencv_test", data=data)
param = sigima.params.BlobOpenCVParam.create(
min_threshold=10.0,
max_threshold=200.0,
min_repeatability=2,
min_dist_between_blobs=10.0,
filter_by_color=False,
blob_color=0,
filter_by_area=True,
min_area=10.0,
max_area=1000.0,
filter_by_circularity=False,
min_circularity=0.1,
max_circularity=1.0,
filter_by_inertia=False,
min_inertia_ratio=0.1,
max_inertia_ratio=1.0,
filter_by_convexity=False,
min_convexity=0.1,
max_convexity=1.0,
create_rois=create_rois,
roi_geometry=roi_geometry,
)
result = sigima.proc.image.blob_opencv(obj, param)
sigima.proc.image.apply_detection_rois(obj, result)
assert result is not None, "Blob detection should return results"
assert len(result.coords) > 0, "Should detect at least one blob"
execenv.print(
f"✓ OpenCV: detected {len(result.coords)} blobs (ROIs={create_rois})"
)
# Validate ROI creation
validate_detection_rois(obj, result.coords, create_rois, roi_geometry)
def test_blob_detection_consistency():
"""Test that different blob detection methods produce consistent results"""
execenv.print("Testing blob detection consistency across methods...")
# Create a simple test image with well-separated blobs
data = create_simple_blob_test_image()
# Test parameters for each method
methods_and_params = [
(
"dog",
sigima.params.BlobDOGParam.create(
min_sigma=1.0, max_sigma=20.0, threshold_rel=0.01, overlap=0.3
),
),
(
"log",
sigima.params.BlobLOGParam.create(
min_sigma=1.0, max_sigma=20.0, threshold_rel=0.01, overlap=0.3
),
),
(
"doh",
sigima.params.BlobDOHParam.create(
min_sigma=1.0, max_sigma=20.0, threshold_rel=0.01, overlap=0.3
),
),
]
results = {}
for method_name, param in methods_and_params:
obj = sigima.objects.create_image(f"blob_{method_name}_consistency", data=data)
if method_name == "dog":
result = sigima.proc.image.blob_dog(obj, param)
elif method_name == "log":
result = sigima.proc.image.blob_log(obj, param)
elif method_name == "doh":
result = sigima.proc.image.blob_doh(obj, param)
results[method_name] = result
if result is not None:
execenv.print(f"{method_name.upper()}: detected {len(result.coords)} blobs")
else:
execenv.print(f"{method_name.upper()}: no blobs detected")
# All methods should detect at least one blob (or we skip if none work)
working_methods = [
name
for name, result in results.items()
if result is not None and len(result.coords) > 0
]
if len(working_methods) == 0:
pytest.skip("No blob detection methods returned results")
for method_name, result in results.items():
if result is not None:
assert len(result.coords) > 0, (
f"{method_name} should detect at least one blob"
)
if __name__ == "__main__":
guiutils.enable_gui()
test_image_blob_dog()
test_image_blob_doh()
test_image_blob_log()
test_image_blob_opencv()
test_blob_detection_consistency()
|
