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"""
Copyright 2021, Dana-Farber Cancer Institute and Weill Cornell Medicine
License: GNU GPL 2.0
"""
import numpy as np
import pytest
from pathml.core import HESlide, Tile, types
@pytest.fixture
def emptytiles():
slidedata = HESlide("tests/testdata/small_HE.svs")
return slidedata.tiles
@pytest.fixture
def tiles():
"""
dict of adjacent tiles
"""
tiles = []
for i in range(2):
for j in range(2):
# create tile
shape = (224, 224, 3)
coords = (224 * i, 224 * j)
name = f"{i}_{j}"
masks = {str(k): np.random.randint(2, size=shape) for k in range(2)}
labs = {
"test_string_label": "testlabel",
"test_array_label": np.array([2, 3, 4]),
"test_int_label": 3,
"test_float_label": 3.0,
}
image = np.random.random_sample(shape)
tile = Tile(
image=image,
name=name,
coords=coords,
slide_type=types.HE,
masks=masks,
labels=labs,
)
# add to dict
tiles.append(tile)
return tiles
@pytest.fixture
def tilesnonconsecutive():
"""
dict of nonconsecutive tiles
"""
tiles = []
for i in range(2):
for j in range(2):
# create tile
shape = (224, 224, 3)
coords = (224 * 2 * (i + 1), 224 * 2 * (j + 2))
name = f"{i}_{j}"
masks = {str(k): np.random.randint(2, size=shape) for k in range(2)}
labs = {
"test_string_label": "testlabel",
"test_array_label": np.array([2, 3, 4]),
"test_int_label": 3,
"test_float_label": 3.0,
}
image = np.random.randint(low=0, high=255, size=shape, dtype=np.uint8)
tile = Tile(
image=image,
name=name,
coords=coords,
slide_type=types.HE,
masks=masks,
labels=labs,
)
# add to dict
tiles.append(tile)
return tiles
@pytest.mark.parametrize(
"incorrect_input", ["string", True, 5, [5, 4, 3], {"dict": "testing"}]
)
def test_init(tiles, tilesnonconsecutive, incorrect_input):
# init from dict
slidedata = HESlide("tests/testdata/small_HE.svs", tiles=tiles)
assert (slidedata.tiles[0].image == tiles[0].image.astype(np.float16)).all()
# init len
assert len(slidedata.tiles) == 4
# incorrect input
with pytest.raises(AssertionError):
# fix obj
slidedata = HESlide("tests/testdata/small_HE.svs", tiles=incorrect_input)
# nonconsecutive tiles
slidedata = HESlide("tests/testdata/small_HE.svs", tiles=tilesnonconsecutive)
np.testing.assert_array_equal(
slidedata.tiles[(896, 1344)].image, tilesnonconsecutive[3].image
)
def test_repr(tiles):
slidedata = HESlide("tests/testdata/small_HE.svs", tiles=tiles)
assert repr(slidedata.tiles)
@pytest.mark.parametrize(
"incorrect_input", ["string", None, True, 5, [5, 4, 3], {"dict": "testing"}]
)
@pytest.mark.parametrize(
"incorrect_input2", [None, True, [5, 4, 3], {"dict": "testing"}]
)
def test_add_get(emptytiles, tileHE, incorrect_input, incorrect_input2):
# add single tile
tiles = emptytiles
tiles.add(tileHE)
# get by coords and by index
assert (tiles[(1, 3)].image == tileHE.image).all()
assert (tiles[0].image == tileHE.image).all()
assert tiles[(1, 3)].name == tileHE.name
assert tiles[(1, 3)].coords == tileHE.coords
for label in tiles[(1, 3)].labels:
if isinstance(tiles[(1, 3)].labels[label], np.ndarray):
np.testing.assert_array_equal(
tiles[(1, 3)].labels[label], tileHE.labels[label]
)
else:
assert tiles[(1, 3)].labels[label] == tileHE.labels[label]
assert tiles[(1, 3)].slide_type == tileHE.slide_type
# get masks
for mask in tiles.h5manager.h5["masks"].keys():
# masks by coords and by index
assert (tiles[(1, 3)].masks[mask] == tileHE.masks[mask]).all()
assert (tiles[0].masks[mask] == tileHE.masks[mask]).all()
# incorrect input
with pytest.raises(ValueError or KeyError):
tiles.add(incorrect_input)
with pytest.raises(KeyError or IndexError):
tiles[incorrect_input2]
# wrong shape
im = np.arange(np.product((225, 224, 3))).reshape((225, 224, 3))
wrongshapetile = Tile(image=im, coords=(4, 5), name="wrong")
with pytest.raises(ValueError):
tiles.add(wrongshapetile)
@pytest.mark.parametrize(
"incorrect_input", ["string", None, True, 5, [5, 4, 3], {"dict": "testing"}]
)
def test_remove(emptytiles, tileHE, incorrect_input):
tiles = emptytiles
tiles.add(tileHE)
tiles.remove((1, 3))
with pytest.raises(Exception):
tiles[(1, 3)]
with pytest.raises(KeyError):
tiles.remove((1, 3))
# incorrect input
with pytest.raises(KeyError):
tiles.remove(incorrect_input)
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