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/tests/helpers.py
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| a | b/tests/helpers.py | ||
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| 1 | from tests.data import HEMASK |
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| 2 | from PIL import Image |
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| 3 | import numpy |
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| 4 | from openslide import AbstractSlide |
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| 5 | |||
| 6 | |||
| 7 | class FakeSlide(AbstractSlide): |
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| 8 | |||
| 9 | """ |
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| 10 | A class to mimic an openslide.OpenSlide object. |
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| 11 | |||
| 12 | Args: |
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| 13 | staining: type of staining you wanna mimic. |
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| 14 | """ |
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| 15 | |||
| 16 | def __init__(self, name="fake_slide", staining="H&E", extension=".mrxs"): |
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| 17 | """ |
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| 18 | """ |
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| 19 | self._filename = name + extension |
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| 20 | self.tissue_color = [154, 120, 156, 255] |
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| 21 | |||
| 22 | @property |
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| 23 | def level_count(self): |
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| 24 | return 11 |
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| 25 | |||
| 26 | @property |
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| 27 | def level_dimensions(self): |
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| 28 | return ( |
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| 29 | (83968, 71680), |
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| 30 | (41984, 35840), |
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| 31 | (20992, 17920), |
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| 32 | (10496, 8960), |
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| 33 | (5248, 4480), |
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| 34 | (2624, 2240), |
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| 35 | (1312, 1120), |
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| 36 | (656, 560), |
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| 37 | (328, 280), |
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| 38 | (164, 140), |
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| 39 | (82, 70), |
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| 40 | ) |
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| 41 | |||
| 42 | @property |
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| 43 | def level_downsamples(self): |
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| 44 | return ( |
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| 45 | 1.0, |
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| 46 | 2.0, |
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| 47 | 4.0, |
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| 48 | 8.0, |
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| 49 | 16.0, |
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| 50 | 32.0, |
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| 51 | 64.0, |
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| 52 | 128.0, |
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| 53 | 256.0, |
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| 54 | 512.0, |
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| 55 | 1024.0, |
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| 56 | ) |
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| 57 | |||
| 58 | @property |
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| 59 | def properties(self): |
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| 60 | """ |
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| 61 | """ |
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| 62 | return {} |
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| 63 | |||
| 64 | @property |
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| 65 | def associated_images(self): |
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| 66 | return {} |
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| 67 | |||
| 68 | def get_best_level_for_downsample(self, downsample): |
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| 69 | for k in range(1, self.level_count): |
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| 70 | if self.level_downsamples[k] > downsample: |
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| 71 | return k - 1 |
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| 72 | return self.level_count |
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| 73 | |||
| 74 | def read_region(self, location, level, size): |
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| 75 | """ |
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| 76 | """ |
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| 77 | # un pack request coordinates |
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| 78 | x, y = location |
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| 79 | dx, dy = size |
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| 80 | ds = self.level_downsamples[level] |
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| 81 | tds = self.level_downsamples[-1] |
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| 82 | dj, di = self.level_dimensions[-1] |
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| 83 | |||
| 84 | X_indices = numpy.full((dy, dx), x, dtype=float) |
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| 85 | Y_indices = numpy.full((dy, dx), y, dtype=float) |
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| 86 | # go through columns of X |
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| 87 | X_indices += (numpy.arange(dx) * ds)[None] |
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| 88 | Y_indices += (numpy.arange(dy) * ds)[:, None] |
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| 89 | # rescale x and y indices |
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| 90 | J_indices = numpy.floor(X_indices / tds).astype(int) |
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| 91 | I_indices = numpy.floor(Y_indices / tds).astype(int) |
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| 92 | # put a flag on out-of-bounds pixels |
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| 93 | J_indices[J_indices >= dj] = -1 |
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| 94 | I_indices[I_indices >= di] = -1 |
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| 95 | # compute 1D indices |
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| 96 | Indices = (I_indices * dj + J_indices) |
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| 97 | hemask = numpy.array(HEMASK) |
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| 98 | labels = numpy.where(Indices > 0, hemask[Indices], 0) |
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| 99 | numpy_img = numpy.zeros((dy, dx, 4), dtype=numpy.uint8) |
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| 100 | # out of bounds regions have val == 0 ^^ |
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| 101 | numpy_img[labels == 0] = (0, 0, 0, 0) |
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| 102 | numpy_img[labels == 1] = (255, 255, 255, 255) |
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| 103 | numpy_img[labels == 2] = self.tissue_color |
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| 104 | # get pil image |
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| 105 | return Image.fromarray(numpy_img, mode="RGBA") |