test_utils.py

import pytest
import numpy as np
import os
import sys
import tempfile
import torch
import torchvision.utils as utils

from io import BytesIO
import torchvision.transforms.functional as F
from PIL import Image, __version__ as PILLOW_VERSION, ImageColor
from _assert_utils import assert_equal


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)


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

    utils.make_grid(t, normalize=False)
    assert_equal(t, t_clone, msg='make_grid modified tensor in-place')

    utils.make_grid(t, normalize=True, scale_each=False)
    assert_equal(t, t_clone, msg='make_grid modified tensor in-place')

    utils.make_grid(t, normalize=True, scale_each=True)
    assert_equal(t, t_clone, msg='make_grid modified tensor in-place')


def test_normalize_in_make_grid():
    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)

    assert_equal(norm_max, rounded_grid_max, msg='Normalized max is not equal to 1')
    assert_equal(norm_min, rounded_grid_min, msg='Normalized min is not equal to 0')


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


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


@pytest.mark.skipif(sys.platform in ('win32', 'cygwin'), reason='temporarily disabled on Windows')
def test_save_image_file_object():
    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)
        assert_equal(F.to_tensor(img_orig), F.to_tensor(img_bytes), msg='Image not stored in file object')


@pytest.mark.skipif(sys.platform in ('win32', 'cygwin'), reason='temporarily disabled on Windows')
def test_save_image_single_pixel_file_object():
    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)
        assert_equal(F.to_tensor(img_orig), F.to_tensor(img_bytes), msg='Image not stored in file object')


def test_draw_boxes():
    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)
        assert_equal(result, expected)

    # Check if modification is not in place
    assert_equal(boxes, boxes_cp)
    assert_equal(img, img_cp)


@pytest.mark.parametrize('colors', [
    None,
    ['red', 'blue', '#FF00FF', (1, 34, 122)],
    'red',
    '#FF00FF',
    (1, 34, 122)
])
def test_draw_boxes_colors(colors):
    img = torch.full((3, 100, 100), 0, dtype=torch.uint8)
    utils.draw_bounding_boxes(img, boxes, fill=False, width=7, colors=colors)


def test_draw_boxes_vanilla():
    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)
    assert_equal(result, expected)
    # Check if modification is not in place
    assert_equal(boxes, boxes_cp)
    assert_equal(img, img_cp)


def test_draw_boxes_grayscale():
    img = torch.full((1, 4, 4), fill_value=255, dtype=torch.uint8)
    boxes = torch.tensor([[0, 0, 3, 3]], dtype=torch.int64)
    bboxed_img = utils.draw_bounding_boxes(image=img, boxes=boxes, colors=["#1BBC9B"])
    assert bboxed_img.size(0) == 3


def test_draw_invalid_boxes():
    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)
    with pytest.raises(TypeError, match="Tensor expected"):
        utils.draw_bounding_boxes(img_tp, boxes)
    with pytest.raises(ValueError, match="Tensor uint8 expected"):
        utils.draw_bounding_boxes(img_wrong1, boxes)
    with pytest.raises(ValueError, match="Pass individual images, not batches"):
        utils.draw_bounding_boxes(img_wrong2, boxes)
    with pytest.raises(ValueError, match="Only grayscale and RGB images are supported"):
        utils.draw_bounding_boxes(img_wrong2[0][:2], 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_equal(img[:, ~masked_pixels], out[:, ~masked_pixels])

    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).to(dtype)
        torch.testing.assert_close(out[:, mask], interpolated_color, rtol=0.0, atol=1.0)


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__":
    pytest.main([__file__])