def test_damaged_corrupt_images(img_path):
# Truncated images should raise an exception
data = read_file(img_path)
if "corrupt34" in img_path:
match_message = "Image is incomplete or truncated"
else:
match_message = "Unsupported marker type"
with pytest.raises(RuntimeError, match=match_message):
decode_jpeg(data)
def test_damaged_images(self):
# Test image with bad Huffman encoding (should not raise)
bad_huff = read_file(os.path.join(DAMAGED_JPEG, 'bad_huffman.jpg'))
try:
_ = decode_jpeg(bad_huff)
except RuntimeError:
self.assertTrue(False)
# Truncated images should raise an exception
truncated_images = glob.glob(
os.path.join(DAMAGED_JPEG, 'corrupt*.jpg'))
for image_path in truncated_images:
data = read_file(image_path)
with self.assertRaises(RuntimeError):
decode_jpeg(data)
def test_encode_jpeg_reference(img_path):
# This test is *wrong*.
# It compares a torchvision-encoded jpeg with a PIL-encoded jpeg (the reference), but it
# starts encoding the torchvision version from an image that comes from
# decode_jpeg, which can yield different results from pil.decode (see
# test_decode... which uses a high tolerance).
# Instead, we should start encoding from the exact same decoded image, for a
# valid comparison. This is done in test_encode_jpeg, but unfortunately
# these more correct tests fail on windows (probably because of a difference
# in libjpeg) between torchvision and PIL.
# FIXME: make the correct tests pass on windows and remove this.
dirname = os.path.dirname(img_path)
filename, _ = os.path.splitext(os.path.basename(img_path))
write_folder = os.path.join(dirname, 'jpeg_write')
expected_file = os.path.join(
write_folder, '{0}_pil.jpg'.format(filename))
img = decode_jpeg(read_file(img_path))
with open(expected_file, 'rb') as f:
pil_bytes = f.read()
pil_bytes = torch.as_tensor(list(pil_bytes), dtype=torch.uint8)
for src_img in [img, img.contiguous()]:
# PIL sets jpeg quality to 75 by default
jpeg_bytes = encode_jpeg(src_img, quality=75)
assert_equal(jpeg_bytes, pil_bytes)
def test_decode_jpeg(self):
for img_path in get_images(IMAGE_ROOT, "jpg"):
img_pil = torch.from_numpy(np.array(Image.open(img_path)))
size = os.path.getsize(img_path)
img_ljpeg = decode_jpeg(
torch.from_file(img_path, dtype=torch.uint8, size=size))
norm = img_ljpeg.shape[0] * img_ljpeg.shape[1] * img_ljpeg.shape[
2] * 255
err = torch.abs(img_ljpeg.flatten().float() -
img_pil.flatten().float()).sum().float() / (norm)
self.assertLessEqual(err, 1e-2)
with self.assertRaisesRegex(
ValueError, "Expected a non empty 1-dimensional tensor."):
decode_jpeg(torch.empty((100, 1), dtype=torch.uint8))
with self.assertRaisesRegex(ValueError,
"Expected a torch.uint8 tensor."):
decode_jpeg(torch.empty((100, ), dtype=torch.float16))
with self.assertRaisesRegex(RuntimeError,
"Error while reading jpeg headers"):
decode_jpeg(torch.empty((100), dtype=torch.uint8))
def test_decode_jpeg(self):
conversion = [(None, 0), ("L", 1), ("RGB", 3)]
for img_path in get_images(IMAGE_ROOT, ".jpg"):
for pil_mode, channels in conversion:
with Image.open(img_path) as img:
is_cmyk = img.mode == "CMYK"
if pil_mode is not None:
if is_cmyk:
# libjpeg does not support the conversion
continue
img = img.convert(pil_mode)
img_pil = torch.from_numpy(np.array(img))
if is_cmyk:
# flip the colors to match libjpeg
img_pil = 255 - img_pil
img_pil = normalize_dimensions(img_pil)
data = read_file(img_path)
img_ljpeg = decode_image(data, channels=channels)
# Permit a small variation on pixel values to account for implementation
# differences between Pillow and LibJPEG.
abs_mean_diff = (img_ljpeg.type(torch.float32) - img_pil).abs().mean().item()
self.assertTrue(abs_mean_diff < 2)
with self.assertRaisesRegex(RuntimeError, "Expected a non empty 1-dimensional tensor"):
decode_jpeg(torch.empty((100, 1), dtype=torch.uint8))
with self.assertRaisesRegex(RuntimeError, "Expected a torch.uint8 tensor"):
decode_jpeg(torch.empty((100, ), dtype=torch.float16))
with self.assertRaises(RuntimeError):
decode_jpeg(torch.empty((100), dtype=torch.uint8))
def test_decode_jpeg_errors():
with pytest.raises(RuntimeError, match="Expected a non empty 1-dimensional tensor"):
decode_jpeg(torch.empty((100, 1), dtype=torch.uint8))
with pytest.raises(RuntimeError, match="Expected a torch.uint8 tensor"):
decode_jpeg(torch.empty((100,), dtype=torch.float16))
with pytest.raises(RuntimeError, match="Not a JPEG file"):
decode_jpeg(torch.empty((100), dtype=torch.uint8))
def test_decode_jpeg_cuda_errors():
data = read_file(next(get_images(IMAGE_ROOT, ".jpg")))
with pytest.raises(RuntimeError, match="Expected a non empty 1-dimensional tensor"):
decode_jpeg(data.reshape(-1, 1), device='cuda')
with pytest.raises(RuntimeError, match="input tensor must be on CPU"):
decode_jpeg(data.to('cuda'), device='cuda')
with pytest.raises(RuntimeError, match="Expected a torch.uint8 tensor"):
decode_jpeg(data.to(torch.float), device='cuda')
with pytest.raises(RuntimeError, match="Expected a cuda device"):
torch.ops.image.decode_jpeg_cuda(data, ImageReadMode.UNCHANGED.value, 'cpu')
def test_decode_jpeg(self):
for img_path in get_images(IMAGE_ROOT, ".jpg"):
img_pil = torch.load(img_path.replace('jpg', 'pth'))
size = os.path.getsize(img_path)
img_ljpeg = decode_jpeg(torch.from_file(img_path, dtype=torch.uint8, size=size))
self.assertTrue(img_ljpeg.equal(img_pil))
with self.assertRaisesRegex(ValueError, "Expected a non empty 1-dimensional tensor."):
decode_jpeg(torch.empty((100, 1), dtype=torch.uint8))
with self.assertRaisesRegex(ValueError, "Expected a torch.uint8 tensor."):
decode_jpeg(torch.empty((100, ), dtype=torch.float16))
with self.assertRaises(RuntimeError):
decode_jpeg(torch.empty((100), dtype=torch.uint8))
def test_encode_jpeg(self):
for img_path in get_images(ENCODE_JPEG, ".jpg"):
dirname = os.path.dirname(img_path)
filename, _ = os.path.splitext(os.path.basename(img_path))
write_folder = os.path.join(dirname, 'jpeg_write')
expected_file = os.path.join(write_folder,
'{0}_pil.jpg'.format(filename))
img = decode_jpeg(read_file(img_path))
with open(expected_file, 'rb') as f:
pil_bytes = f.read()
pil_bytes = torch.as_tensor(list(pil_bytes), dtype=torch.uint8)
for src_img in [img, img.contiguous()]:
# PIL sets jpeg quality to 75 by default
jpeg_bytes = encode_jpeg(src_img, quality=75)
self.assertTrue(jpeg_bytes.equal(pil_bytes))
with self.assertRaisesRegex(RuntimeError,
"Input tensor dtype should be uint8"):
encode_jpeg(torch.empty((3, 100, 100), dtype=torch.float32))
with self.assertRaisesRegex(
ValueError, "Image quality should be a positive number "
"between 1 and 100"):
encode_jpeg(torch.empty((3, 100, 100), dtype=torch.uint8),
quality=-1)
with self.assertRaisesRegex(
ValueError, "Image quality should be a positive number "
"between 1 and 100"):
encode_jpeg(torch.empty((3, 100, 100), dtype=torch.uint8),
quality=101)
with self.assertRaisesRegex(
RuntimeError,
"The number of channels should be 1 or 3, got: 5"):
encode_jpeg(torch.empty((5, 100, 100), dtype=torch.uint8))
with self.assertRaisesRegex(
RuntimeError, "Input data should be a 3-dimensional tensor"):
encode_jpeg(torch.empty((1, 3, 100, 100), dtype=torch.uint8))
with self.assertRaisesRegex(
RuntimeError, "Input data should be a 3-dimensional tensor"):
encode_jpeg(torch.empty((100, 100), dtype=torch.uint8))
def test_decode_jpeg(self):
for img_path in get_images(IMAGE_ROOT, ".jpg"):
img_pil = torch.load(img_path.replace('jpg', 'pth'))
img_pil = img_pil.permute(2, 0, 1)
data = read_file(img_path)
img_ljpeg = decode_jpeg(data)
self.assertTrue(img_ljpeg.equal(img_pil))
with self.assertRaisesRegex(RuntimeError, "Expected a non empty 1-dimensional tensor"):
decode_jpeg(torch.empty((100, 1), dtype=torch.uint8))
with self.assertRaisesRegex(RuntimeError, "Expected a torch.uint8 tensor"):
decode_jpeg(torch.empty((100, ), dtype=torch.float16))
with self.assertRaises(RuntimeError):
decode_jpeg(torch.empty((100), dtype=torch.uint8))
def test_write_jpeg_reference(img_path, tmpdir):
# FIXME: Remove this eventually, see test_encode_jpeg_reference
data = read_file(img_path)
img = decode_jpeg(data)
basedir = os.path.dirname(img_path)
filename, _ = os.path.splitext(os.path.basename(img_path))
torch_jpeg = os.path.join(tmpdir, f"{filename}_torch.jpg")
pil_jpeg = os.path.join(basedir, "jpeg_write", f"{filename}_pil.jpg")
write_jpeg(img, torch_jpeg, quality=75)
with open(torch_jpeg, "rb") as f:
torch_bytes = f.read()
with open(pil_jpeg, "rb") as f:
pil_bytes = f.read()
assert_equal(torch_bytes, pil_bytes)
def test_write_jpeg(self):
with get_tmp_dir() as d:
for img_path in get_images(ENCODE_JPEG, ".jpg"):
data = read_file(img_path)
img = decode_jpeg(data)
basedir = os.path.dirname(img_path)
filename, _ = os.path.splitext(os.path.basename(img_path))
torch_jpeg = os.path.join(d, '{0}_torch.jpg'.format(filename))
pil_jpeg = os.path.join(basedir, 'jpeg_write',
'{0}_pil.jpg'.format(filename))
write_jpeg(img, torch_jpeg, quality=75)
with open(torch_jpeg, 'rb') as f:
torch_bytes = f.read()
with open(pil_jpeg, 'rb') as f:
pil_bytes = f.read()
self.assertEqual(torch_bytes, pil_bytes)
def test_write_jpeg_reference(img_path):
# FIXME: Remove this eventually, see test_encode_jpeg_reference
with get_tmp_dir() as d:
data = read_file(img_path)
img = decode_jpeg(data)
basedir = os.path.dirname(img_path)
filename, _ = os.path.splitext(os.path.basename(img_path))
torch_jpeg = os.path.join(d, '{0}_torch.jpg'.format(filename))
pil_jpeg = os.path.join(basedir, 'jpeg_write',
'{0}_pil.jpg'.format(filename))
write_jpeg(img, torch_jpeg, quality=75)
with open(torch_jpeg, 'rb') as f:
torch_bytes = f.read()
with open(pil_jpeg, 'rb') as f:
pil_bytes = f.read()
assert_equal(torch_bytes, pil_bytes)
def test_decode_jpeg_cuda_device_param(cuda_device):
"""Make sure we can pass a string or a torch.device as device param"""
path = next(path for path in get_images(IMAGE_ROOT, ".jpg")
if "cmyk" not in path)
data = read_file(path)
decode_jpeg(data, device=cuda_device)
def test_decode_bad_huffman_images():
# sanity check: make sure we can decode the bad Huffman encoding
bad_huff = read_file(os.path.join(DAMAGED_JPEG, "bad_huffman.jpg"))
decode_jpeg(bad_huff)
def test_decode_jpeg_cuda_device_param(cuda_device):
"""Make sure we can pass a string or a torch.device as device param"""
data = read_file(next(get_images(IMAGE_ROOT, ".jpg")))
decode_jpeg(data, device=cuda_device)
