test_utils.py

import pytest
import numpy as np
import os
import sys
import tempfile
import torch
import torchvision.utils as utils
import unittest
from io import BytesIO
import torchvision.transforms.functional as F
from PIL import Image, __version__ as PILLOW_VERSION, ImageColor


PILLOW_VERSION = tuple(int(x) for x in PILLOW_VERSION.split('.'))

boxes = torch.tensor([[0, 0, 20, 20], [0, 0, 0, 0],
                     [10, 15, 30, 35], [23, 35, 93, 95]], dtype=torch.float)

masks = torch.tensor([
    [
        [-2.2799, -2.2799, -2.2799, -2.2799, -2.2799],
        [5.0914, 5.0914, 5.0914, 5.0914, 5.0914],
        [-2.2799, -2.2799, -2.2799, -2.2799, -2.2799],
        [-2.2799, -2.2799, -2.2799, -2.2799, -2.2799],
        [-2.2799, -2.2799, -2.2799, -2.2799, -2.2799]
    ],
    [
        [5.0914, 5.0914, 5.0914, 5.0914, 5.0914],
        [-2.2799, -2.2799, -2.2799, -2.2799, -2.2799],
        [5.0914, 5.0914, 5.0914, 5.0914, 5.0914],
        [5.0914, 5.0914, 5.0914, 5.0914, 5.0914],
        [-1.4541, -1.4541, -1.4541, -1.4541, -1.4541]
    ],
    [
        [-1.4541, -1.4541, -1.4541, -1.4541, -1.4541],
        [-1.4541, -1.4541, -1.4541, -1.4541, -1.4541],
        [-1.4541, -1.4541, -1.4541, -1.4541, -1.4541],
        [-1.4541, -1.4541, -1.4541, -1.4541, -1.4541],
        [5.0914, 5.0914, 5.0914, 5.0914, 5.0914],
    ]
], dtype=torch.float)


class Tester(unittest.TestCase):

    def test_make_grid_not_inplace(self):
        t = torch.rand(5, 3, 10, 10)
        t_clone = t.clone()

        utils.make_grid(t, normalize=False)
        self.assertTrue(torch.equal(t, t_clone), 'make_grid modified tensor in-place')

        utils.make_grid(t, normalize=True, scale_each=False)
        self.assertTrue(torch.equal(t, t_clone), 'make_grid modified tensor in-place')

        utils.make_grid(t, normalize=True, scale_each=True)
        self.assertTrue(torch.equal(t, t_clone), 'make_grid modified tensor in-place')

    def test_normalize_in_make_grid(self):
        t = torch.rand(5, 3, 10, 10) * 255
        norm_max = torch.tensor(1.0)
        norm_min = torch.tensor(0.0)

        grid = utils.make_grid(t, normalize=True)
        grid_max = torch.max(grid)
        grid_min = torch.min(grid)

        # Rounding the result to one decimal for comparison
        n_digits = 1
        rounded_grid_max = torch.round(grid_max * 10 ** n_digits) / (10 ** n_digits)
        rounded_grid_min = torch.round(grid_min * 10 ** n_digits) / (10 ** n_digits)

        self.assertTrue(torch.equal(norm_max, rounded_grid_max), 'Normalized max is not equal to 1')
        self.assertTrue(torch.equal(norm_min, rounded_grid_min), 'Normalized min is not equal to 0')

    @unittest.skipIf(sys.platform in ('win32', 'cygwin'), 'temporarily disabled on Windows')
    def test_save_image(self):
        with tempfile.NamedTemporaryFile(suffix='.png') as f:
            t = torch.rand(2, 3, 64, 64)
            utils.save_image(t, f.name)
            self.assertTrue(os.path.exists(f.name), 'The image is not present after save')

    @unittest.skipIf(sys.platform in ('win32', 'cygwin'), 'temporarily disabled on Windows')
    def test_save_image_single_pixel(self):
        with tempfile.NamedTemporaryFile(suffix='.png') as f:
            t = torch.rand(1, 3, 1, 1)
            utils.save_image(t, f.name)
            self.assertTrue(os.path.exists(f.name), 'The pixel image is not present after save')

    @unittest.skipIf(sys.platform in ('win32', 'cygwin'), 'temporarily disabled on Windows')
    def test_save_image_file_object(self):
        with tempfile.NamedTemporaryFile(suffix='.png') as f:
            t = torch.rand(2, 3, 64, 64)
            utils.save_image(t, f.name)
            img_orig = Image.open(f.name)
            fp = BytesIO()
            utils.save_image(t, fp, format='png')
            img_bytes = Image.open(fp)
            self.assertTrue(torch.equal(F.to_tensor(img_orig), F.to_tensor(img_bytes)),
                            'Image not stored in file object')

    @unittest.skipIf(sys.platform in ('win32', 'cygwin'), 'temporarily disabled on Windows')
    def test_save_image_single_pixel_file_object(self):
        with tempfile.NamedTemporaryFile(suffix='.png') as f:
            t = torch.rand(1, 3, 1, 1)
            utils.save_image(t, f.name)
            img_orig = Image.open(f.name)
            fp = BytesIO()
            utils.save_image(t, fp, format='png')
            img_bytes = Image.open(fp)
            self.assertTrue(torch.equal(F.to_tensor(img_orig), F.to_tensor(img_bytes)),
                            'Pixel Image not stored in file object')

    def test_draw_boxes(self):
        img = torch.full((3, 100, 100), 255, dtype=torch.uint8)
        img_cp = img.clone()
        boxes_cp = boxes.clone()
        labels = ["a", "b", "c", "d"]
        colors = ["green", "#FF00FF", (0, 255, 0), "red"]
        result = utils.draw_bounding_boxes(img, boxes, labels=labels, colors=colors, fill=True)

        path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", "fakedata", "draw_boxes_util.png")
        if not os.path.exists(path):
            res = Image.fromarray(result.permute(1, 2, 0).contiguous().numpy())
            res.save(path)

        if PILLOW_VERSION >= (8, 2):
            # The reference image is only valid for new PIL versions
            expected = torch.as_tensor(np.array(Image.open(path))).permute(2, 0, 1)
            self.assertTrue(torch.equal(result, expected))

        # Check if modification is not in place
        self.assertTrue(torch.all(torch.eq(boxes, boxes_cp)).item())
        self.assertTrue(torch.all(torch.eq(img, img_cp)).item())

    def test_draw_boxes_vanilla(self):
        img = torch.full((3, 100, 100), 0, dtype=torch.uint8)
        img_cp = img.clone()
        boxes_cp = boxes.clone()
        result = utils.draw_bounding_boxes(img, boxes, fill=False, width=7)

        path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", "fakedata", "draw_boxes_vanilla.png")
        if not os.path.exists(path):
            res = Image.fromarray(result.permute(1, 2, 0).contiguous().numpy())
            res.save(path)

        expected = torch.as_tensor(np.array(Image.open(path))).permute(2, 0, 1)
        self.assertTrue(torch.equal(result, expected))
        # Check if modification is not in place
        self.assertTrue(torch.all(torch.eq(boxes, boxes_cp)).item())
        self.assertTrue(torch.all(torch.eq(img, img_cp)).item())

    def test_draw_invalid_boxes(self):
        img_tp = ((1, 1, 1), (1, 2, 3))
        img_wrong1 = torch.full((3, 5, 5), 255, dtype=torch.float)
        img_wrong2 = torch.full((1, 3, 5, 5), 255, dtype=torch.uint8)
        boxes = torch.tensor([[0, 0, 20, 20], [0, 0, 0, 0],
                             [10, 15, 30, 35], [23, 35, 93, 95]], dtype=torch.float)
        self.assertRaises(TypeError, utils.draw_bounding_boxes, img_tp, boxes)
        self.assertRaises(ValueError, utils.draw_bounding_boxes, img_wrong1, boxes)
        self.assertRaises(ValueError, utils.draw_bounding_boxes, img_wrong2, boxes)


@pytest.mark.parametrize('colors', [
    None,
    ['red', 'blue'],
    ['#FF00FF', (1, 34, 122)],
])
@pytest.mark.parametrize('alpha', (0, .5, .7, 1))
def test_draw_segmentation_masks(colors, alpha):
    """This test makes sure that masks draw their corresponding color where they should"""
    num_masks, h, w = 2, 100, 100
    dtype = torch.uint8
    img = torch.randint(0, 256, size=(3, h, w), dtype=dtype)
    masks = torch.randint(0, 2, (num_masks, h, w), dtype=torch.bool)

    # For testing we enforce that there's no overlap between the masks. The
    # current behaviour is that the last mask's color will take priority when
    # masks overlap, but this makes testing slightly harder so we don't really
    # care
    overlap = masks[0] & masks[1]
    masks[:, overlap] = False

    out = utils.draw_segmentation_masks(img, masks, colors=colors, alpha=alpha)
    assert out.dtype == dtype
    assert out is not img

    # Make sure the image didn't change where there's no mask
    masked_pixels = masks[0] | masks[1]
    assert (img[:, ~masked_pixels] == out[:, ~masked_pixels]).all()

    if colors is None:
        colors = utils._generate_color_palette(num_masks)

    # Make sure each mask draws with its own color
    for mask, color in zip(masks, colors):
        if isinstance(color, str):
            color = ImageColor.getrgb(color)
        color = torch.tensor(color, dtype=dtype)

        if alpha == 1:
            assert (out[:, mask] == color[:, None]).all()
        elif alpha == 0:
            assert (out[:, mask] == img[:, mask]).all()

        interpolated_color = (img[:, mask] * (1 - alpha) + color[:, None] * alpha)
        max_diff = (out[:, mask] - interpolated_color).abs().max()
        assert max_diff <= 1


def test_draw_segmentation_masks_errors():
    h, w = 10, 10

    masks = torch.randint(0, 2, size=(h, w), dtype=torch.bool)
    img = torch.randint(0, 256, size=(3, h, w), dtype=torch.uint8)

    with pytest.raises(TypeError, match="The image must be a tensor"):
        utils.draw_segmentation_masks(image="Not A Tensor Image", masks=masks)
    with pytest.raises(ValueError, match="The image dtype must be"):
        img_bad_dtype = torch.randint(0, 256, size=(3, h, w), dtype=torch.int64)
        utils.draw_segmentation_masks(image=img_bad_dtype, masks=masks)
    with pytest.raises(ValueError, match="Pass individual images, not batches"):
        batch = torch.randint(0, 256, size=(10, 3, h, w), dtype=torch.uint8)
        utils.draw_segmentation_masks(image=batch, masks=masks)
    with pytest.raises(ValueError, match="Pass an RGB image"):
        one_channel = torch.randint(0, 256, size=(1, h, w), dtype=torch.uint8)
        utils.draw_segmentation_masks(image=one_channel, masks=masks)
    with pytest.raises(ValueError, match="The masks must be of dtype bool"):
        masks_bad_dtype = torch.randint(0, 2, size=(h, w), dtype=torch.float)
        utils.draw_segmentation_masks(image=img, masks=masks_bad_dtype)
    with pytest.raises(ValueError, match="masks must be of shape"):
        masks_bad_shape = torch.randint(0, 2, size=(3, 2, h, w), dtype=torch.bool)
        utils.draw_segmentation_masks(image=img, masks=masks_bad_shape)
    with pytest.raises(ValueError, match="must have the same height and width"):
        masks_bad_shape = torch.randint(0, 2, size=(h + 4, w), dtype=torch.bool)
        utils.draw_segmentation_masks(image=img, masks=masks_bad_shape)
    with pytest.raises(ValueError, match="There are more masks"):
        utils.draw_segmentation_masks(image=img, masks=masks, colors=[])
    with pytest.raises(ValueError, match="colors must be a tuple or a string, or a list thereof"):
        bad_colors = np.array(['red', 'blue'])  # should be a list
        utils.draw_segmentation_masks(image=img, masks=masks, colors=bad_colors)
    with pytest.raises(ValueError, match="It seems that you passed a tuple of colors instead of"):
        bad_colors = ('red', 'blue')  # should be a list
        utils.draw_segmentation_masks(image=img, masks=masks, colors=bad_colors)


if __name__ == '__main__':
    unittest.main()