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Test Greedy Segmentation #13

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Feb 16, 2024
Merged

Test Greedy Segmentation #13

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14 changes: 9 additions & 5 deletions src/nuc2seg/segment.py
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Original file line number Diff line number Diff line change
Expand Up @@ -110,7 +110,7 @@ def greedy_expansion(

# Immediate neighbor flows
flow1_mask = update_mask & (flow_labels_flat != -1)
flow1_targets = np.array(flow_labels_flat[flow1_mask])
flow1_targets = flow_labels_flat[flow1_mask]
flow1_connected = (
(
pixel_labels_arr[
Expand Down Expand Up @@ -152,25 +152,29 @@ def greedy_expansion(
flow2_connected = (
(
pixel_labels_arr[
start_xy[flow2_mask, 0] - 1, start_xy[flow2_mask, 1] - 1
(start_xy[flow2_mask, 0] - 1).clip(0, x_max),
(start_xy[flow2_mask, 1] - 1).clip(0, y_max),
]
== flow2_targets
)
| (
pixel_labels_arr[
start_xy[flow2_mask, 0] + 1, start_xy[flow2_mask, 1] - 1
(start_xy[flow2_mask, 0] + 1).clip(0, x_max),
(start_xy[flow2_mask, 1] - 1).clip(0, y_max),
]
== flow2_targets
)
| (
pixel_labels_arr[
start_xy[flow2_mask, 0] - 1, start_xy[flow2_mask, 1] + 1
(start_xy[flow2_mask, 0] - 1).clip(0, x_max),
(start_xy[flow2_mask, 1] + 1).clip(0, y_max),
]
== flow2_targets
)
| (
pixel_labels_arr[
start_xy[flow2_mask, 0] + 1, start_xy[flow2_mask, 1] + 1
(start_xy[flow2_mask, 0] + 1).clip(0, x_max),
(start_xy[flow2_mask, 1] + 1).clip(0, y_max),
]
== flow2_targets
)
Expand Down
194 changes: 194 additions & 0 deletions src/nuc2seg/segment_test.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,194 @@
from nuc2seg.segment import greedy_expansion, flow_destination, greedy_cell_segmentation
import numpy as np
import pytest


def test_greedy_expansion_updates_pixel_with_distance_according_to_iter():
pixel_labels_arr = np.array(
[
[0, -1, -1, 1],
]
)

flow_labels = np.array(
[
[0, -1, 1, 1],
]
)

flow_labels2 = np.array(
[
[0, -1, 1, 1],
]
)

start_xy = np.array([[0, 0], [0, 1], [0, 2], [0, 3]])

foreground_mask = (pixel_labels_arr != 0)[start_xy[:, 0], start_xy[:, 1]]

flow_xy = np.array(
[
[0, 1],
[0, 2],
[0, 3],
[0, 3],
]
)

flow_xy2 = np.array(
[
[0, 1],
[0, 2],
[0, 3],
[0, 3],
]
)

result = greedy_expansion(
start_xy.copy(),
pixel_labels_arr.copy(),
flow_labels.copy(),
flow_labels2.copy(),
flow_xy.copy(),
flow_xy2.copy(),
foreground_mask.copy(),
max_expansion_steps=1,
)

np.testing.assert_equal(result, np.array([[0, -1, 1, 1]]))

result = greedy_expansion(
start_xy.copy(),
pixel_labels_arr.copy(),
flow_labels.copy(),
flow_labels2.copy(),
flow_xy.copy(),
flow_xy2.copy(),
foreground_mask.copy(),
max_expansion_steps=2,
)

np.testing.assert_equal(result, np.array([[0, 1, 1, 1]]))


def test_greedy_expansion_doesnt_update_pixel():
pixel_labels_arr = np.array(
[
[0, -1, 1],
]
)

flow_labels = np.array(
[
[0, -1, 1],
]
)

flow_labels2 = np.array(
[
[0, -1, 1],
]
)

start_xy = np.array([[0, 0], [0, 1], [0, 2]])

foreground_mask = (pixel_labels_arr != 0)[start_xy[:, 0], start_xy[:, 1]]

flow_xy = np.array(
[
[0, 0],
[0, 1],
[0, 2],
]
)

flow_xy2 = np.array(
[
[0, 0],
[0, 1],
[0, 2],
]
)

result = greedy_expansion(
start_xy,
pixel_labels_arr.copy(),
flow_labels,
flow_labels2,
flow_xy,
flow_xy2,
foreground_mask,
max_expansion_steps=1,
)

np.testing.assert_equal(result, np.array([[0, -1, 1]]))


@pytest.mark.parametrize(
"angle,expected_destination",
[
(-np.pi, [0, 1]),
(-3 * np.pi / 4, [0, 0]),
(-np.pi / 2, [1, 0]),
(-np.pi / 4, [2, 0]),
(0, [2, 1]),
(np.pi / 4, [2, 2]),
(np.pi / 2, [1, 2]),
(3 * np.pi / 4, [0, 2]),
(np.pi, [0, 1]),
],
)
def test_flow_destination(angle, expected_destination):
start_xy = np.array([[1, 1]])

angles = np.array([[0, 0, 0], [0, angle, 0], [0, 0, 0]])

result = flow_destination(start_xy, angles, np.sqrt(2))

np.testing.assert_equal(result, np.array([expected_destination]))


def test_greedy_cell_segmentation(mocker):
mock_dataset = mocker.Mock()
mock_dataset.labels = np.array(
[
[-1, -1, -1],
[-1, -1, -1],
[1, 1, 1],
]
)
mock_dataset.x_extent_pixels = 3
mock_dataset.y_extent_pixels = 3

mock_predictions = mocker.Mock()

mock_predictions.angles = np.array(
[
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
]
)

mock_predictions.foreground = np.array(
[
[0.1, 0.99, 0.1],
[0.1, 0.99, 0.1],
[0.1, 0.99, 0.1],
]
)

result = greedy_cell_segmentation(
mock_dataset, mock_predictions, foreground_threshold=0.5, max_expansion_steps=10
)

np.testing.assert_equal(
result,
np.array(
[
[-1, -1, -1],
[-1, 1, -1],
[1, 1, 1],
]
),
)
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