631 lines
24 KiB
Python
631 lines
24 KiB
Python
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
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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, heigt, 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(heigt, 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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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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# 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
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assert resized_masks1.height == 10
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assert resized_masks1.width == 10
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assert resized_masks1.to_ndarray().shape == (1, 10, 10)
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truth1 = 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]],
|
|
np.uint8)
|
|
assert (resized_masks1.to_ndarray() == truth1).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()
|
|
|
|
|
|
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()
|