OpenMMLab Detection Toolbox and Benchmark
https://mmdetection.readthedocs.io/
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713 lines
28 KiB
713 lines
28 KiB
# Copyright (c) OpenMMLab. All rights reserved. |
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import numpy as np |
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import pytest |
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import torch |
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from mmdet.core import BitmapMasks, PolygonMasks, mask2bbox |
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def dummy_raw_bitmap_masks(size): |
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""" |
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Args: |
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size (tuple): expected shape of dummy masks, (H, W) or (N, H, W) |
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Return: |
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ndarray: dummy mask |
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""" |
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return np.random.randint(0, 2, size, dtype=np.uint8) |
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def dummy_raw_polygon_masks(size): |
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""" |
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Args: |
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size (tuple): expected shape of dummy masks, (N, H, W) |
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Return: |
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list[list[ndarray]]: dummy mask |
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""" |
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num_obj, height, width = size |
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polygons = [] |
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for _ in range(num_obj): |
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num_points = np.random.randint(5) * 2 + 6 |
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polygons.append([np.random.uniform(0, min(height, width), num_points)]) |
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return polygons |
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def dummy_bboxes(num, max_height, max_width): |
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x1y1 = np.random.randint(0, min(max_height // 2, max_width // 2), (num, 2)) |
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wh = np.random.randint(0, min(max_height // 2, max_width // 2), (num, 2)) |
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x2y2 = x1y1 + wh |
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return np.concatenate([x1y1, x2y2], axis=1).squeeze().astype(np.float32) |
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def test_bitmap_mask_init(): |
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# init with empty ndarray masks |
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raw_masks = np.empty((0, 28, 28), dtype=np.uint8) |
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bitmap_masks = BitmapMasks(raw_masks, 28, 28) |
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assert len(bitmap_masks) == 0 |
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assert bitmap_masks.height == 28 |
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assert bitmap_masks.width == 28 |
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# init with empty list masks |
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raw_masks = [] |
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bitmap_masks = BitmapMasks(raw_masks, 28, 28) |
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assert len(bitmap_masks) == 0 |
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assert bitmap_masks.height == 28 |
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assert bitmap_masks.width == 28 |
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# init with ndarray masks contain 3 instances |
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raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) |
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bitmap_masks = BitmapMasks(raw_masks, 28, 28) |
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assert len(bitmap_masks) == 3 |
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assert bitmap_masks.height == 28 |
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assert bitmap_masks.width == 28 |
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# init with list masks contain 3 instances |
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raw_masks = [dummy_raw_bitmap_masks((28, 28)) for _ in range(3)] |
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bitmap_masks = BitmapMasks(raw_masks, 28, 28) |
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assert len(bitmap_masks) == 3 |
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assert bitmap_masks.height == 28 |
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assert bitmap_masks.width == 28 |
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# init with raw masks of unsupported type |
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with pytest.raises(AssertionError): |
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raw_masks = [[dummy_raw_bitmap_masks((28, 28))]] |
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BitmapMasks(raw_masks, 28, 28) |
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def test_bitmap_mask_rescale(): |
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# rescale with empty bitmap masks |
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raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) |
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bitmap_masks = BitmapMasks(raw_masks, 28, 28) |
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rescaled_masks = bitmap_masks.rescale((56, 72)) |
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assert len(rescaled_masks) == 0 |
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assert rescaled_masks.height == 56 |
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assert rescaled_masks.width == 56 |
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# rescale with bitmap masks contain 1 instances |
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raw_masks = np.array([[[1, 0, 0, 0], [0, 1, 0, 1]]]) |
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bitmap_masks = BitmapMasks(raw_masks, 2, 4) |
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rescaled_masks = bitmap_masks.rescale((8, 8)) |
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assert len(rescaled_masks) == 1 |
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assert rescaled_masks.height == 4 |
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assert rescaled_masks.width == 8 |
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truth = np.array([[[1, 1, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0], |
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[0, 0, 1, 1, 0, 0, 1, 1], [0, 0, 1, 1, 0, 0, 1, 1]]]) |
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assert (rescaled_masks.masks == truth).all() |
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def test_bitmap_mask_resize(): |
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# resize with empty bitmap masks |
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raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) |
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bitmap_masks = BitmapMasks(raw_masks, 28, 28) |
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resized_masks = bitmap_masks.resize((56, 72)) |
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assert len(resized_masks) == 0 |
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assert resized_masks.height == 56 |
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assert resized_masks.width == 72 |
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# resize with bitmap masks contain 1 instances |
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raw_masks = np.diag(np.ones(4, dtype=np.uint8))[np.newaxis, ...] |
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bitmap_masks = BitmapMasks(raw_masks, 4, 4) |
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resized_masks = bitmap_masks.resize((8, 8)) |
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assert len(resized_masks) == 1 |
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assert resized_masks.height == 8 |
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assert resized_masks.width == 8 |
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truth = np.array([[[1, 1, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0], |
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[0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 0], |
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[0, 0, 0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0], |
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[0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 1, 1]]]) |
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assert (resized_masks.masks == truth).all() |
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# resize to non-square |
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raw_masks = np.diag(np.ones(4, dtype=np.uint8))[np.newaxis, ...] |
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bitmap_masks = BitmapMasks(raw_masks, 4, 4) |
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resized_masks = bitmap_masks.resize((4, 8)) |
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assert len(resized_masks) == 1 |
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assert resized_masks.height == 4 |
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assert resized_masks.width == 8 |
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truth = np.array([[[1, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 0], |
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[0, 0, 0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1]]]) |
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assert (resized_masks.masks == truth).all() |
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def test_bitmap_mask_get_bboxes(): |
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# resize with empty bitmap masks |
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raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) |
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bitmap_masks = BitmapMasks(raw_masks, 28, 28) |
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bboxes = bitmap_masks.get_bboxes() |
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assert len(bboxes) == 0 |
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# resize with bitmap masks contain 1 instances |
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raw_masks = np.array([[[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0], |
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[0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0], |
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[0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], |
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[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, |
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0]]]) |
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bitmap_masks = BitmapMasks(raw_masks, 8, 8) |
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bboxes = bitmap_masks.get_bboxes() |
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assert len(bboxes) == 1 |
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truth = np.array([[1, 1, 6, 6]]) |
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assert (bboxes == truth).all() |
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# resize to non-square |
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raw_masks = np.array([[[1, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 0], |
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[0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, |
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0]]]) |
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bitmap_masks = BitmapMasks(raw_masks, 4, 8) |
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bboxes = bitmap_masks.get_bboxes() |
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truth = np.array([[0, 0, 6, 3]]) |
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assert (bboxes == truth).all() |
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def test_bitmap_mask_flip(): |
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# flip with empty bitmap masks |
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raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) |
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bitmap_masks = BitmapMasks(raw_masks, 28, 28) |
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flipped_masks = bitmap_masks.flip(flip_direction='horizontal') |
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assert len(flipped_masks) == 0 |
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assert flipped_masks.height == 28 |
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assert flipped_masks.width == 28 |
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# horizontally flip with bitmap masks contain 3 instances |
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raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) |
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bitmap_masks = BitmapMasks(raw_masks, 28, 28) |
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flipped_masks = bitmap_masks.flip(flip_direction='horizontal') |
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flipped_flipped_masks = flipped_masks.flip(flip_direction='horizontal') |
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assert flipped_masks.masks.shape == (3, 28, 28) |
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assert (bitmap_masks.masks == flipped_flipped_masks.masks).all() |
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assert (flipped_masks.masks == raw_masks[:, :, ::-1]).all() |
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# vertically flip with bitmap masks contain 3 instances |
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raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) |
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bitmap_masks = BitmapMasks(raw_masks, 28, 28) |
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flipped_masks = bitmap_masks.flip(flip_direction='vertical') |
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flipped_flipped_masks = flipped_masks.flip(flip_direction='vertical') |
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assert len(flipped_masks) == 3 |
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assert flipped_masks.height == 28 |
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assert flipped_masks.width == 28 |
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assert (bitmap_masks.masks == flipped_flipped_masks.masks).all() |
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assert (flipped_masks.masks == raw_masks[:, ::-1, :]).all() |
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# diagonal flip with bitmap masks contain 3 instances |
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raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) |
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bitmap_masks = BitmapMasks(raw_masks, 28, 28) |
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flipped_masks = bitmap_masks.flip(flip_direction='diagonal') |
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flipped_flipped_masks = flipped_masks.flip(flip_direction='diagonal') |
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assert len(flipped_masks) == 3 |
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assert flipped_masks.height == 28 |
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assert flipped_masks.width == 28 |
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assert (bitmap_masks.masks == flipped_flipped_masks.masks).all() |
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assert (flipped_masks.masks == raw_masks[:, ::-1, ::-1]).all() |
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def test_bitmap_mask_pad(): |
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# pad with empty bitmap masks |
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raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) |
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bitmap_masks = BitmapMasks(raw_masks, 28, 28) |
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padded_masks = bitmap_masks.pad((56, 56)) |
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assert len(padded_masks) == 0 |
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assert padded_masks.height == 56 |
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assert padded_masks.width == 56 |
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# pad with bitmap masks contain 3 instances |
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raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) |
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bitmap_masks = BitmapMasks(raw_masks, 28, 28) |
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padded_masks = bitmap_masks.pad((56, 56)) |
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assert len(padded_masks) == 3 |
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assert padded_masks.height == 56 |
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assert padded_masks.width == 56 |
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assert (padded_masks.masks[:, 28:, 28:] == 0).all() |
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def test_bitmap_mask_crop(): |
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# crop with empty bitmap masks |
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dummy_bbox = np.array([0, 10, 10, 27], dtype=np.int) |
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raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) |
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bitmap_masks = BitmapMasks(raw_masks, 28, 28) |
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cropped_masks = bitmap_masks.crop(dummy_bbox) |
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assert len(cropped_masks) == 0 |
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assert cropped_masks.height == 17 |
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assert cropped_masks.width == 10 |
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# crop with bitmap masks contain 3 instances |
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raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) |
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bitmap_masks = BitmapMasks(raw_masks, 28, 28) |
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cropped_masks = bitmap_masks.crop(dummy_bbox) |
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assert len(cropped_masks) == 3 |
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assert cropped_masks.height == 17 |
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assert cropped_masks.width == 10 |
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x1, y1, x2, y2 = dummy_bbox |
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assert (cropped_masks.masks == raw_masks[:, y1:y2, x1:x2]).all() |
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# crop with invalid bbox |
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with pytest.raises(AssertionError): |
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dummy_bbox = dummy_bboxes(2, 28, 28) |
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bitmap_masks.crop(dummy_bbox) |
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def test_bitmap_mask_crop_and_resize(): |
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dummy_bbox = dummy_bboxes(5, 28, 28) |
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inds = np.random.randint(0, 3, (5, )) |
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# crop and resize with empty bitmap masks |
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raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) |
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bitmap_masks = BitmapMasks(raw_masks, 28, 28) |
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cropped_resized_masks = bitmap_masks.crop_and_resize( |
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dummy_bbox, (56, 56), inds) |
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assert len(cropped_resized_masks) == 0 |
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assert cropped_resized_masks.height == 56 |
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assert cropped_resized_masks.width == 56 |
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# crop and resize with bitmap masks contain 3 instances |
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raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) |
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bitmap_masks = BitmapMasks(raw_masks, 28, 28) |
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cropped_resized_masks = bitmap_masks.crop_and_resize( |
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dummy_bbox, (56, 56), inds) |
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assert len(cropped_resized_masks) == 5 |
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assert cropped_resized_masks.height == 56 |
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assert cropped_resized_masks.width == 56 |
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def test_bitmap_mask_expand(): |
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# expand with empty bitmap masks |
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raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) |
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bitmap_masks = BitmapMasks(raw_masks, 28, 28) |
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expanded_masks = bitmap_masks.expand(56, 56, 12, 14) |
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assert len(expanded_masks) == 0 |
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assert expanded_masks.height == 56 |
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assert expanded_masks.width == 56 |
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# expand with bitmap masks contain 3 instances |
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raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) |
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bitmap_masks = BitmapMasks(raw_masks, 28, 28) |
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expanded_masks = bitmap_masks.expand(56, 56, 12, 14) |
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assert len(expanded_masks) == 3 |
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assert expanded_masks.height == 56 |
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assert expanded_masks.width == 56 |
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assert (expanded_masks.masks[:, :12, :14] == 0).all() |
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assert (expanded_masks.masks[:, 12 + 28:, 14 + 28:] == 0).all() |
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def test_bitmap_mask_area(): |
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# area of empty bitmap mask |
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raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) |
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bitmap_masks = BitmapMasks(raw_masks, 28, 28) |
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assert bitmap_masks.areas.sum() == 0 |
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# area of bitmap masks contain 3 instances |
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raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) |
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bitmap_masks = BitmapMasks(raw_masks, 28, 28) |
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areas = bitmap_masks.areas |
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assert len(areas) == 3 |
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assert (areas == raw_masks.sum((1, 2))).all() |
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def test_bitmap_mask_to_ndarray(): |
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# empty bitmap masks to ndarray |
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raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) |
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bitmap_masks = BitmapMasks(raw_masks, 28, 28) |
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ndarray_masks = bitmap_masks.to_ndarray() |
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assert isinstance(ndarray_masks, np.ndarray) |
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assert ndarray_masks.shape == (0, 28, 28) |
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# bitmap masks contain 3 instances to ndarray |
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raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) |
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bitmap_masks = BitmapMasks(raw_masks, 28, 28) |
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ndarray_masks = bitmap_masks.to_ndarray() |
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assert isinstance(ndarray_masks, np.ndarray) |
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assert ndarray_masks.shape == (3, 28, 28) |
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assert (ndarray_masks == raw_masks).all() |
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def test_bitmap_mask_to_tensor(): |
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# empty bitmap masks to tensor |
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raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) |
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bitmap_masks = BitmapMasks(raw_masks, 28, 28) |
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tensor_masks = bitmap_masks.to_tensor(dtype=torch.uint8, device='cpu') |
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assert isinstance(tensor_masks, torch.Tensor) |
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assert tensor_masks.shape == (0, 28, 28) |
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# bitmap masks contain 3 instances to tensor |
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raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) |
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bitmap_masks = BitmapMasks(raw_masks, 28, 28) |
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tensor_masks = bitmap_masks.to_tensor(dtype=torch.uint8, device='cpu') |
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assert isinstance(tensor_masks, torch.Tensor) |
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assert tensor_masks.shape == (3, 28, 28) |
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assert (tensor_masks.numpy() == raw_masks).all() |
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def test_bitmap_mask_index(): |
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raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) |
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bitmap_masks = BitmapMasks(raw_masks, 28, 28) |
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assert (bitmap_masks[0].masks == raw_masks[0]).all() |
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assert (bitmap_masks[range(2)].masks == raw_masks[range(2)]).all() |
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def test_bitmap_mask_iter(): |
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raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) |
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bitmap_masks = BitmapMasks(raw_masks, 28, 28) |
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for i, bitmap_mask in enumerate(bitmap_masks): |
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assert bitmap_mask.shape == (28, 28) |
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assert (bitmap_mask == raw_masks[i]).all() |
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def test_polygon_mask_init(): |
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# init with empty masks |
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raw_masks = [] |
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polygon_masks = BitmapMasks(raw_masks, 28, 28) |
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assert len(polygon_masks) == 0 |
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assert polygon_masks.height == 28 |
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assert polygon_masks.width == 28 |
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# init with masks contain 3 instances |
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raw_masks = dummy_raw_polygon_masks((3, 28, 28)) |
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polygon_masks = PolygonMasks(raw_masks, 28, 28) |
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assert isinstance(polygon_masks.masks, list) |
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assert isinstance(polygon_masks.masks[0], list) |
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assert isinstance(polygon_masks.masks[0][0], np.ndarray) |
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assert len(polygon_masks) == 3 |
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assert polygon_masks.height == 28 |
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assert polygon_masks.width == 28 |
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assert polygon_masks.to_ndarray().shape == (3, 28, 28) |
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# init with raw masks of unsupported type |
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with pytest.raises(AssertionError): |
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raw_masks = [[[]]] |
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PolygonMasks(raw_masks, 28, 28) |
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raw_masks = [dummy_raw_polygon_masks((3, 28, 28))] |
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PolygonMasks(raw_masks, 28, 28) |
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def test_polygon_mask_rescale(): |
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# rescale with empty polygon masks |
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raw_masks = dummy_raw_polygon_masks((0, 28, 28)) |
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polygon_masks = PolygonMasks(raw_masks, 28, 28) |
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rescaled_masks = polygon_masks.rescale((56, 72)) |
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assert len(rescaled_masks) == 0 |
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assert rescaled_masks.height == 56 |
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assert rescaled_masks.width == 56 |
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assert rescaled_masks.to_ndarray().shape == (0, 56, 56) |
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# rescale with polygon masks contain 3 instances |
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raw_masks = [[np.array([1, 1, 3, 1, 4, 3, 2, 4, 1, 3], dtype=np.float)]] |
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polygon_masks = PolygonMasks(raw_masks, 5, 5) |
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rescaled_masks = polygon_masks.rescale((12, 10)) |
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assert len(rescaled_masks) == 1 |
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assert rescaled_masks.height == 10 |
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assert rescaled_masks.width == 10 |
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assert rescaled_masks.to_ndarray().shape == (1, 10, 10) |
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truth = np.array( |
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[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], |
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[0, 0, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], |
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[0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], |
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[0, 0, 0, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0], |
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], |
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np.uint8) |
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assert (rescaled_masks.to_ndarray() == truth).all() |
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def test_polygon_mask_resize(): |
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# resize with empty polygon masks |
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raw_masks = dummy_raw_polygon_masks((0, 28, 28)) |
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polygon_masks = PolygonMasks(raw_masks, 28, 28) |
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resized_masks = polygon_masks.resize((56, 72)) |
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assert len(resized_masks) == 0 |
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assert resized_masks.height == 56 |
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assert resized_masks.width == 72 |
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assert resized_masks.to_ndarray().shape == (0, 56, 72) |
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assert len(resized_masks.get_bboxes()) == 0 |
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# resize with polygon masks contain 1 instance 1 part |
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raw_masks1 = [[np.array([1, 1, 3, 1, 4, 3, 2, 4, 1, 3], dtype=np.float)]] |
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polygon_masks1 = PolygonMasks(raw_masks1, 5, 5) |
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resized_masks1 = polygon_masks1.resize((10, 10)) |
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assert len(resized_masks1) == 1 |
|
assert resized_masks1.height == 10 |
|
assert resized_masks1.width == 10 |
|
assert resized_masks1.to_ndarray().shape == (1, 10, 10) |
|
truth1 = np.array( |
|
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], |
|
[0, 0, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], |
|
[0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], |
|
[0, 0, 0, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0], |
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], |
|
np.uint8) |
|
assert (resized_masks1.to_ndarray() == truth1).all() |
|
bboxes = resized_masks1.get_bboxes() |
|
bbox_truth = np.array([[2, 2, 8, 8]]) |
|
assert (bboxes == bbox_truth).all() |
|
|
|
# resize with polygon masks contain 1 instance 2 part |
|
raw_masks2 = [[ |
|
np.array([0., 0., 1., 0., 1., 1.]), |
|
np.array([1., 1., 2., 1., 2., 2., 1., 2.]) |
|
]] |
|
polygon_masks2 = PolygonMasks(raw_masks2, 3, 3) |
|
resized_masks2 = polygon_masks2.resize((6, 6)) |
|
assert len(resized_masks2) == 1 |
|
assert resized_masks2.height == 6 |
|
assert resized_masks2.width == 6 |
|
assert resized_masks2.to_ndarray().shape == (1, 6, 6) |
|
truth2 = np.array( |
|
[[0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0], |
|
[0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]], np.uint8) |
|
assert (resized_masks2.to_ndarray() == truth2).all() |
|
|
|
# resize with polygon masks contain 2 instances |
|
raw_masks3 = [raw_masks1[0], raw_masks2[0]] |
|
polygon_masks3 = PolygonMasks(raw_masks3, 5, 5) |
|
resized_masks3 = polygon_masks3.resize((10, 10)) |
|
assert len(resized_masks3) == 2 |
|
assert resized_masks3.height == 10 |
|
assert resized_masks3.width == 10 |
|
assert resized_masks3.to_ndarray().shape == (2, 10, 10) |
|
truth3 = np.stack([truth1, np.pad(truth2, ((0, 4), (0, 4)), 'constant')]) |
|
assert (resized_masks3.to_ndarray() == truth3).all() |
|
|
|
# resize to non-square |
|
raw_masks4 = [[np.array([1, 1, 3, 1, 4, 3, 2, 4, 1, 3], dtype=np.float)]] |
|
polygon_masks4 = PolygonMasks(raw_masks4, 5, 5) |
|
resized_masks4 = polygon_masks4.resize((5, 10)) |
|
assert len(resized_masks4) == 1 |
|
assert resized_masks4.height == 5 |
|
assert resized_masks4.width == 10 |
|
assert resized_masks4.to_ndarray().shape == (1, 5, 10) |
|
truth4 = np.array( |
|
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], |
|
[0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0, 0], |
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], np.uint8) |
|
assert (resized_masks4.to_ndarray() == truth4).all() |
|
|
|
|
|
def test_polygon_mask_flip(): |
|
# flip with empty polygon masks |
|
raw_masks = dummy_raw_polygon_masks((0, 28, 28)) |
|
polygon_masks = PolygonMasks(raw_masks, 28, 28) |
|
flipped_masks = polygon_masks.flip(flip_direction='horizontal') |
|
assert len(flipped_masks) == 0 |
|
assert flipped_masks.height == 28 |
|
assert flipped_masks.width == 28 |
|
assert flipped_masks.to_ndarray().shape == (0, 28, 28) |
|
|
|
# TODO: fixed flip correctness checking after v2.0_coord is merged |
|
# horizontally flip with polygon masks contain 3 instances |
|
raw_masks = dummy_raw_polygon_masks((3, 28, 28)) |
|
polygon_masks = PolygonMasks(raw_masks, 28, 28) |
|
flipped_masks = polygon_masks.flip(flip_direction='horizontal') |
|
flipped_flipped_masks = flipped_masks.flip(flip_direction='horizontal') |
|
assert len(flipped_masks) == 3 |
|
assert flipped_masks.height == 28 |
|
assert flipped_masks.width == 28 |
|
assert flipped_masks.to_ndarray().shape == (3, 28, 28) |
|
assert (polygon_masks.to_ndarray() == flipped_flipped_masks.to_ndarray() |
|
).all() |
|
|
|
# vertically flip with polygon masks contain 3 instances |
|
raw_masks = dummy_raw_polygon_masks((3, 28, 28)) |
|
polygon_masks = PolygonMasks(raw_masks, 28, 28) |
|
flipped_masks = polygon_masks.flip(flip_direction='vertical') |
|
flipped_flipped_masks = flipped_masks.flip(flip_direction='vertical') |
|
assert len(flipped_masks) == 3 |
|
assert flipped_masks.height == 28 |
|
assert flipped_masks.width == 28 |
|
assert flipped_masks.to_ndarray().shape == (3, 28, 28) |
|
assert (polygon_masks.to_ndarray() == flipped_flipped_masks.to_ndarray() |
|
).all() |
|
|
|
# diagonal flip with polygon masks contain 3 instances |
|
raw_masks = dummy_raw_polygon_masks((3, 28, 28)) |
|
polygon_masks = PolygonMasks(raw_masks, 28, 28) |
|
flipped_masks = polygon_masks.flip(flip_direction='diagonal') |
|
flipped_flipped_masks = flipped_masks.flip(flip_direction='diagonal') |
|
assert len(flipped_masks) == 3 |
|
assert flipped_masks.height == 28 |
|
assert flipped_masks.width == 28 |
|
assert flipped_masks.to_ndarray().shape == (3, 28, 28) |
|
assert (polygon_masks.to_ndarray() == flipped_flipped_masks.to_ndarray() |
|
).all() |
|
|
|
|
|
def test_polygon_mask_crop(): |
|
dummy_bbox = np.array([0, 10, 10, 27], dtype=np.int) |
|
# crop with empty polygon masks |
|
raw_masks = dummy_raw_polygon_masks((0, 28, 28)) |
|
polygon_masks = PolygonMasks(raw_masks, 28, 28) |
|
cropped_masks = polygon_masks.crop(dummy_bbox) |
|
assert len(cropped_masks) == 0 |
|
assert cropped_masks.height == 17 |
|
assert cropped_masks.width == 10 |
|
assert cropped_masks.to_ndarray().shape == (0, 17, 10) |
|
|
|
# crop with polygon masks contain 1 instances |
|
raw_masks = [[np.array([1., 3., 5., 1., 5., 6., 1, 6])]] |
|
polygon_masks = PolygonMasks(raw_masks, 7, 7) |
|
bbox = np.array([0, 0, 3, 4]) |
|
cropped_masks = polygon_masks.crop(bbox) |
|
assert len(cropped_masks) == 1 |
|
assert cropped_masks.height == 4 |
|
assert cropped_masks.width == 3 |
|
assert cropped_masks.to_ndarray().shape == (1, 4, 3) |
|
truth = np.array([[0, 0, 0], [0, 0, 0], [0, 0, 1], [0, 1, 1]]) |
|
assert (cropped_masks.to_ndarray() == truth).all() |
|
|
|
# crop with invalid bbox |
|
with pytest.raises(AssertionError): |
|
dummy_bbox = dummy_bboxes(2, 28, 28) |
|
polygon_masks.crop(dummy_bbox) |
|
|
|
|
|
def test_polygon_mask_pad(): |
|
# pad with empty polygon masks |
|
raw_masks = dummy_raw_polygon_masks((0, 28, 28)) |
|
polygon_masks = PolygonMasks(raw_masks, 28, 28) |
|
padded_masks = polygon_masks.pad((56, 56)) |
|
assert len(padded_masks) == 0 |
|
assert padded_masks.height == 56 |
|
assert padded_masks.width == 56 |
|
assert padded_masks.to_ndarray().shape == (0, 56, 56) |
|
|
|
# pad with polygon masks contain 3 instances |
|
raw_masks = dummy_raw_polygon_masks((3, 28, 28)) |
|
polygon_masks = PolygonMasks(raw_masks, 28, 28) |
|
padded_masks = polygon_masks.pad((56, 56)) |
|
assert len(padded_masks) == 3 |
|
assert padded_masks.height == 56 |
|
assert padded_masks.width == 56 |
|
assert padded_masks.to_ndarray().shape == (3, 56, 56) |
|
assert (padded_masks.to_ndarray()[:, 28:, 28:] == 0).all() |
|
|
|
|
|
def test_polygon_mask_expand(): |
|
with pytest.raises(NotImplementedError): |
|
raw_masks = dummy_raw_polygon_masks((0, 28, 28)) |
|
polygon_masks = PolygonMasks(raw_masks, 28, 28) |
|
polygon_masks.expand(56, 56, 10, 17) |
|
|
|
|
|
def test_polygon_mask_crop_and_resize(): |
|
dummy_bbox = dummy_bboxes(5, 28, 28) |
|
inds = np.random.randint(0, 3, (5, )) |
|
|
|
# crop and resize with empty polygon masks |
|
raw_masks = dummy_raw_polygon_masks((0, 28, 28)) |
|
polygon_masks = PolygonMasks(raw_masks, 28, 28) |
|
cropped_resized_masks = polygon_masks.crop_and_resize( |
|
dummy_bbox, (56, 56), inds) |
|
assert len(cropped_resized_masks) == 0 |
|
assert cropped_resized_masks.height == 56 |
|
assert cropped_resized_masks.width == 56 |
|
assert cropped_resized_masks.to_ndarray().shape == (0, 56, 56) |
|
|
|
# crop and resize with polygon masks contain 3 instances |
|
raw_masks = dummy_raw_polygon_masks((3, 28, 28)) |
|
polygon_masks = PolygonMasks(raw_masks, 28, 28) |
|
cropped_resized_masks = polygon_masks.crop_and_resize( |
|
dummy_bbox, (56, 56), inds) |
|
assert len(cropped_resized_masks) == 5 |
|
assert cropped_resized_masks.height == 56 |
|
assert cropped_resized_masks.width == 56 |
|
assert cropped_resized_masks.to_ndarray().shape == (5, 56, 56) |
|
|
|
|
|
def test_polygon_mask_area(): |
|
# area of empty polygon masks |
|
raw_masks = dummy_raw_polygon_masks((0, 28, 28)) |
|
polygon_masks = PolygonMasks(raw_masks, 28, 28) |
|
assert polygon_masks.areas.sum() == 0 |
|
|
|
# area of polygon masks contain 1 instance |
|
# here we hack a case that the gap between the area of bitmap and polygon |
|
# is minor |
|
raw_masks = [[np.array([1, 1, 5, 1, 3, 4])]] |
|
polygon_masks = PolygonMasks(raw_masks, 6, 6) |
|
polygon_area = polygon_masks.areas |
|
bitmap_area = polygon_masks.to_bitmap().areas |
|
assert len(polygon_area) == 1 |
|
assert np.isclose(polygon_area, bitmap_area).all() |
|
|
|
|
|
def test_polygon_mask_to_bitmap(): |
|
# polygon masks contain 3 instances to bitmap |
|
raw_masks = dummy_raw_polygon_masks((3, 28, 28)) |
|
polygon_masks = PolygonMasks(raw_masks, 28, 28) |
|
bitmap_masks = polygon_masks.to_bitmap() |
|
assert (polygon_masks.to_ndarray() == bitmap_masks.to_ndarray()).all() |
|
|
|
|
|
def test_polygon_mask_to_ndarray(): |
|
# empty polygon masks to ndarray |
|
raw_masks = dummy_raw_polygon_masks((0, 28, 28)) |
|
polygon_masks = PolygonMasks(raw_masks, 28, 28) |
|
ndarray_masks = polygon_masks.to_ndarray() |
|
assert isinstance(ndarray_masks, np.ndarray) |
|
assert ndarray_masks.shape == (0, 28, 28) |
|
|
|
# polygon masks contain 3 instances to ndarray |
|
raw_masks = dummy_raw_polygon_masks((3, 28, 28)) |
|
polygon_masks = PolygonMasks(raw_masks, 28, 28) |
|
ndarray_masks = polygon_masks.to_ndarray() |
|
assert isinstance(ndarray_masks, np.ndarray) |
|
assert ndarray_masks.shape == (3, 28, 28) |
|
|
|
|
|
def test_polygon_to_tensor(): |
|
# empty polygon masks to tensor |
|
raw_masks = dummy_raw_polygon_masks((0, 28, 28)) |
|
polygon_masks = PolygonMasks(raw_masks, 28, 28) |
|
tensor_masks = polygon_masks.to_tensor(dtype=torch.uint8, device='cpu') |
|
assert isinstance(tensor_masks, torch.Tensor) |
|
assert tensor_masks.shape == (0, 28, 28) |
|
|
|
# polygon masks contain 3 instances to tensor |
|
raw_masks = dummy_raw_polygon_masks((3, 28, 28)) |
|
polygon_masks = PolygonMasks(raw_masks, 28, 28) |
|
tensor_masks = polygon_masks.to_tensor(dtype=torch.uint8, device='cpu') |
|
assert isinstance(tensor_masks, torch.Tensor) |
|
assert tensor_masks.shape == (3, 28, 28) |
|
assert (tensor_masks.numpy() == polygon_masks.to_ndarray()).all() |
|
|
|
|
|
def test_polygon_mask_index(): |
|
raw_masks = dummy_raw_polygon_masks((3, 28, 28)) |
|
polygon_masks = PolygonMasks(raw_masks, 28, 28) |
|
# index by integer |
|
polygon_masks[0] |
|
# index by list |
|
polygon_masks[[0, 1]] |
|
# index by ndarray |
|
polygon_masks[np.asarray([0, 1])] |
|
with pytest.raises(ValueError): |
|
# invalid index |
|
polygon_masks[torch.Tensor([1, 2])] |
|
|
|
|
|
def test_polygon_mask_iter(): |
|
raw_masks = dummy_raw_polygon_masks((3, 28, 28)) |
|
polygon_masks = PolygonMasks(raw_masks, 28, 28) |
|
for i, polygon_mask in enumerate(polygon_masks): |
|
assert np.equal(polygon_mask, raw_masks[i]).all() |
|
|
|
|
|
def test_mask2bbox(): |
|
# no instance |
|
masks = torch.zeros((1, 20, 15), dtype=torch.bool) |
|
bboxes_empty_gt = torch.tensor([[0, 0, 0, 0]]).float() |
|
bboxes = mask2bbox(masks) |
|
assert torch.allclose(bboxes_empty_gt.float(), bboxes) |
|
|
|
# the entire mask is an instance |
|
bboxes_full_gt = torch.tensor([[0, 0, 15, 20]]).float() |
|
masks = torch.ones((1, 20, 15), dtype=torch.bool) |
|
bboxes = mask2bbox(masks) |
|
assert torch.allclose(bboxes_full_gt, bboxes) |
|
|
|
# a pentagon-shaped instance |
|
bboxes_gt = torch.tensor([[2, 2, 7, 6]]).float() |
|
masks = torch.zeros((1, 20, 15), dtype=torch.bool) |
|
masks[0, 2, 4] = True |
|
masks[0, 3, 3:6] = True |
|
masks[0, 4, 2:7] = True |
|
masks[0, 5, 2:7] = True |
|
bboxes = mask2bbox(masks) |
|
assert torch.allclose(bboxes_gt, bboxes)
|
|
|