def test_AddToHueAndSaturation():
reseed()
# interestingly, when using this RGB2HSV and HSV2RGB conversion from skimage, the results
# differ quite a bit from the cv2 ones
"""
def _add_hue_saturation(img, value):
img_hsv = color.rgb2hsv(img / 255.0)
img_hsv[..., 0:2] += (value / 255.0)
return color.hsv2rgb(img_hsv) * 255
"""
def _add_hue_saturation(img, value):
img_hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
img_hsv = img_hsv.astype(np.int32)
img_hsv[..., 0] = np.mod(img_hsv[..., 0] + (value/255)*180, 180)
img_hsv[..., 1] = np.clip(img_hsv[..., 1] + value, 0, 255)
img_hsv = img_hsv.astype(np.uint8)
return cv2.cvtColor(img_hsv, cv2.COLOR_HSV2RGB)
base_img = np.zeros((2, 2, 3), dtype=np.uint8)
base_img[..., 0] += 20
base_img[..., 1] += 40
base_img[..., 2] += 60
aug = iaa.AddToHueAndSaturation(0)
observed = aug.augment_image(base_img)
expected = base_img
assert np.allclose(observed, expected)
aug = iaa.AddToHueAndSaturation(30)
observed = aug.augment_image(base_img)
expected = _add_hue_saturation(base_img, 30)
diff = np.abs(observed.astype(np.float32) - expected)
assert np.all(diff <= 3)
aug = iaa.AddToHueAndSaturation([0, 10, 20])
base_img = base_img[0:1, 0:1, :]
expected_imgs = [
iaa.AddToHueAndSaturation(0).augment_image(base_img),
iaa.AddToHueAndSaturation(10).augment_image(base_img),
iaa.AddToHueAndSaturation(20).augment_image(base_img)
]
assert not np.array_equal(expected_imgs[0], expected_imgs[1])
assert not np.array_equal(expected_imgs[1], expected_imgs[2])
assert not np.array_equal(expected_imgs[0], expected_imgs[2])
nb_iterations = 300
seen = dict([(i, 0) for i, _ in enumerate(expected_imgs)])
for _ in sm.xrange(nb_iterations):
observed = aug.augment_image(base_img)
for i, expected_img in enumerate(expected_imgs):
if np.allclose(observed, expected_img):
seen[i] += 1
assert np.sum(list(seen.values())) == nb_iterations
n_exp = nb_iterations / 3
n_exp_tol = nb_iterations * 0.1
assert all([n_exp - n_exp_tol < v < n_exp + n_exp_tol for v in seen.values()])
def test_Grayscale():
reseed()
def _compute_luminosity(r, g, b):
return 0.21 * r + 0.72 * g + 0.07 * b
base_img = np.zeros((4, 4, 3), dtype=np.uint8)
base_img[..., 0] += 10
base_img[..., 1] += 20
base_img[..., 2] += 30
aug = iaa.Grayscale(0.0)
observed = aug.augment_image(base_img)
expected = np.copy(base_img)
assert np.allclose(observed, expected)
aug = iaa.Grayscale(1.0)
observed = aug.augment_image(base_img)
luminosity = _compute_luminosity(10, 20, 30)
expected = np.zeros_like(base_img) + luminosity
assert np.allclose(observed, expected.astype(np.uint8))
aug = iaa.Grayscale(0.5)
observed = aug.augment_image(base_img)
luminosity = _compute_luminosity(10, 20, 30)
expected = 0.5 * base_img + 0.5 * luminosity
assert np.allclose(observed, expected.astype(np.uint8))
aug = iaa.Grayscale((0.0, 1.0))
base_img = np.uint8([255, 0, 0]).reshape((1, 1, 3))
base_img_float = base_img.astype(np.float64) / 255.0
base_img_gray = iaa.Grayscale(1.0).augment_image(base_img).astype(
np.float64) / 255.0
distance_max = np.linalg.norm(base_img_gray.flatten() -
base_img_float.flatten())
nb_iterations = 1000
distances = []
for _ in sm.xrange(nb_iterations):
observed = aug.augment_image(base_img).astype(np.float64) / 255.0
distance = np.linalg.norm(observed.flatten() -
base_img_float.flatten()) / distance_max
distances.append(distance)
assert 0 - 1e-4 < min(distances) < 0.1
assert 0.4 < np.average(distances) < 0.6
assert 0.9 < max(distances) < 1.0 + 1e-4
nb_bins = 5
hist, _ = np.histogram(distances,
bins=nb_bins,
range=(0.0, 1.0),
density=False)
density_expected = 1.0 / nb_bins
density_tolerance = 0.05
for nb_samples in hist:
density = nb_samples / nb_iterations
assert density_expected - density_tolerance < density < density_expected + density_tolerance
def test_BackgroundAugmenter__augment_images_worker():
reseed()
warnings.simplefilter("always")
with warnings.catch_warnings(record=True) as caught_warnings:
def gen():
yield ia.Batch(images=np.zeros((1, 4, 4, 3), dtype=np.uint8))
bl = multicore.BatchLoader(gen(), queue_size=2)
bgaug = multicore.BackgroundAugmenter(bl,
iaa.Noop(),
queue_size=1,
nb_workers=1)
queue_source = multiprocessing.Queue(2)
queue_target = multiprocessing.Queue(2)
queue_source.put(
pickle.dumps(
ia.Batch(images=np.zeros((1, 4, 8, 3), dtype=np.uint8)),
protocol=-1))
queue_source.put(pickle.dumps(None, protocol=-1))
bgaug._augment_images_worker(iaa.Add(1), queue_source, queue_target, 1)
batch_aug = pickle.loads(queue_target.get())
assert isinstance(batch_aug, ia.Batch)
assert batch_aug.images_unaug is not None
assert batch_aug.images_unaug.dtype == np.uint8
assert batch_aug.images_unaug.shape == (1, 4, 8, 3)
assert np.array_equal(batch_aug.images_unaug,
np.zeros((1, 4, 8, 3), dtype=np.uint8))
assert batch_aug.images_aug is not None
assert batch_aug.images_aug.dtype == np.uint8
assert batch_aug.images_aug.shape == (1, 4, 8, 3)
assert np.array_equal(batch_aug.images_aug,
np.zeros((1, 4, 8, 3), dtype=np.uint8) + 1)
finished_signal = pickle.loads(queue_target.get())
assert finished_signal is None
source_finished_signal = pickle.loads(queue_source.get())
assert source_finished_signal is None
assert queue_source.empty()
assert queue_target.empty()
queue_source.close()
queue_target.close()
queue_source.join_thread()
queue_target.join_thread()
bl.terminate()
bgaug.terminate()
assert len(caught_warnings) > 0
for warning in caught_warnings:
assert ("BatchLoader is deprecated" in str(warning.message)
or "BackgroundAugmenter is deprecated" in str(warning.message))
def test_BatchLoader():
reseed()
def _load_func():
for _ in sm.xrange(20):
yield ia.Batch(images=np.zeros((2, 4, 4, 3), dtype=np.uint8))
warnings.simplefilter("always")
with warnings.catch_warnings(record=True) as caught_warnings:
for nb_workers in [1, 2]:
# repeat these tests many times to catch rarer race conditions
for _ in sm.xrange(5):
loader = multicore.BatchLoader(_load_func, queue_size=2, nb_workers=nb_workers, threaded=True)
loaded = []
counter = 0
while (not loader.all_finished() or not loader.queue.empty()) and counter < 1000:
try:
batch = loader.queue.get(timeout=0.001)
loaded.append(batch)
except:
pass
counter += 1
assert len(loaded) == 20*nb_workers, \
"Expected %d to be loaded by threads, got %d for %d workers at counter %d." % (
20*nb_workers, len(loaded), nb_workers, counter
)
loader = multicore.BatchLoader(_load_func, queue_size=200, nb_workers=nb_workers, threaded=True)
loader.terminate()
assert loader.all_finished()
loader = multicore.BatchLoader(_load_func, queue_size=2, nb_workers=nb_workers, threaded=False)
loaded = []
counter = 0
while (not loader.all_finished() or not loader.queue.empty()) and counter < 1000:
try:
batch = loader.queue.get(timeout=0.001)
loaded.append(batch)
except:
pass
counter += 1
assert len(loaded) == 20*nb_workers, \
"Expected %d to be loaded by background processes, got %d for %d workers at counter %d." % (
20*nb_workers, len(loaded), nb_workers, counter
)
loader = multicore.BatchLoader(_load_func, queue_size=200, nb_workers=nb_workers, threaded=False)
loader.terminate()
assert loader.all_finished()
assert len(caught_warnings) > 0
for warning in caught_warnings:
assert "is deprecated" in str(warning.message)
def test_BackgroundAugmenter__augment_images_worker():
reseed()
warnings.simplefilter("always")
with warnings.catch_warnings(record=True) as caught_warnings:
def gen():
yield ia.Batch(images=np.zeros((1, 4, 4, 3), dtype=np.uint8))
bl = multicore.BatchLoader(gen(), queue_size=2)
bgaug = multicore.BackgroundAugmenter(bl, iaa.Noop(), queue_size=1, nb_workers=1)
queue_source = multiprocessing.Queue(2)
queue_target = multiprocessing.Queue(2)
queue_source.put(
pickle.dumps(
ia.Batch(images=np.zeros((1, 4, 8, 3), dtype=np.uint8)),
protocol=-1
)
)
queue_source.put(pickle.dumps(None, protocol=-1))
bgaug._augment_images_worker(iaa.Add(1), queue_source, queue_target, 1)
batch_aug = pickle.loads(queue_target.get())
assert isinstance(batch_aug, ia.Batch)
assert batch_aug.images_unaug is not None
assert batch_aug.images_unaug.dtype == np.uint8
assert batch_aug.images_unaug.shape == (1, 4, 8, 3)
assert np.array_equal(batch_aug.images_unaug, np.zeros((1, 4, 8, 3), dtype=np.uint8))
assert batch_aug.images_aug is not None
assert batch_aug.images_aug.dtype == np.uint8
assert batch_aug.images_aug.shape == (1, 4, 8, 3)
assert np.array_equal(batch_aug.images_aug, np.zeros((1, 4, 8, 3), dtype=np.uint8) + 1)
finished_signal = pickle.loads(queue_target.get())
assert finished_signal is None
source_finished_signal = pickle.loads(queue_source.get())
assert source_finished_signal is None
assert queue_source.empty()
assert queue_target.empty()
queue_source.close()
queue_target.close()
queue_source.join_thread()
queue_target.join_thread()
bl.terminate()
bgaug.terminate()
assert len(caught_warnings) > 0
for warning in caught_warnings:
assert "is deprecated" in str(warning.message)
def test_Snowflakes():
# rather rough test as fairly hard to test more detailed
reseed()
img = np.zeros((100, 100, 3), dtype=np.uint8)
img_aug = iaa.Snowflakes().augment_image(img)
assert 0.01 < np.average(img_aug) < 100
assert np.max(img_aug) > 100
grad_x = img_aug[:, 1:].astype(np.float32) - img_aug[:, :-1].astype(
np.float32)
grad_y = img_aug[1:, :].astype(np.float32) - img_aug[:-1, :].astype(
np.float32)
assert np.sum(np.abs(grad_x)) > 5 * img.shape[1]
assert np.sum(np.abs(grad_y)) > 5 * img.shape[0]
# test density
img_aug_undense = iaa.Snowflakes(
density=0.001, density_uniformity=0.99).augment_image(img)
img_aug_dense = iaa.Snowflakes(density=0.1,
density_uniformity=0.99).augment_image(img)
assert np.average(img_aug_undense) < np.average(img_aug_dense)
# test density_uniformity
img_aug_ununiform = iaa.Snowflakes(
density=0.4, density_uniformity=0.1).augment_image(img)
img_aug_uniform = iaa.Snowflakes(density=0.4,
density_uniformity=0.9).augment_image(img)
def _measure_uniformity(image, patch_size=5, n_patches=100):
pshalf = (patch_size - 1) // 2
grad_x = image[:, 1:].astype(np.float32) - image[:, :-1].astype(
np.float32)
grad_y = image[1:, :].astype(np.float32) - image[:-1, :].astype(
np.float32)
grad = np.abs(grad_x[1:, :] + grad_y[:, 1:])
points_y = np.random.randint(0, image.shape[0], size=(n_patches, ))
points_x = np.random.randint(0, image.shape[0], size=(n_patches, ))
stds = []
for y, x in zip(points_y, points_x):
bb = ia.BoundingBox(x1=x - pshalf,
y1=y - pshalf,
x2=x + pshalf,
y2=y + pshalf)
patch = bb.extract_from_image(grad)
stds.append(np.std(patch))
return 1 / (1 + np.std(stds))
assert _measure_uniformity(img_aug_ununiform) < _measure_uniformity(
img_aug_uniform)
def test_Fog():
# rough test as fairly hard to test more detailed
reseed()
img = np.zeros((100, 100, 3), dtype=np.uint8)
img_aug = iaa.Clouds().augment_image(img)
assert 50 < np.average(img_aug) < 255
assert np.max(img_aug) > 100
grad_x = img_aug[:, 1:].astype(np.float32) - img_aug[:, :-1].astype(np.float32)
grad_y = img_aug[1:, :].astype(np.float32) - img_aug[:-1, :].astype(np.float32)
assert np.sum(np.abs(grad_x)) > 1 * img.shape[1]
assert np.sum(np.abs(grad_y)) > 1 * img.shape[0]
def test_Fog():
# rough test as fairly hard to test more detailed
reseed()
img = np.zeros((100, 100, 3), dtype=np.uint8)
img_aug = iaa.Clouds().augment_image(img)
assert 50 < np.average(img_aug) < 255
assert np.max(img_aug) > 100
grad_x = img_aug[:, 1:].astype(np.float32) - img_aug[:, :-1].astype(np.float32)
grad_y = img_aug[1:, :].astype(np.float32) - img_aug[:-1, :].astype(np.float32)
assert np.sum(np.abs(grad_x)) > 1 * img.shape[1]
assert np.sum(np.abs(grad_y)) > 1 * img.shape[0]
def test_Snowflakes():
# rather rough test as fairly hard to test more detailed
reseed()
img = np.zeros((100, 100, 3), dtype=np.uint8)
img_aug = iaa.Snowflakes().augment_image(img)
assert 0.01 < np.average(img_aug) < 100
assert np.max(img_aug) > 100
grad_x = img_aug[:, 1:].astype(np.float32) - img_aug[:, :-1].astype(np.float32)
grad_y = img_aug[1:, :].astype(np.float32) - img_aug[:-1, :].astype(np.float32)
assert np.sum(np.abs(grad_x)) > 5 * img.shape[1]
assert np.sum(np.abs(grad_y)) > 5 * img.shape[0]
# test density
img_aug_undense = iaa.Snowflakes(density=0.001, density_uniformity=0.99).augment_image(img)
img_aug_dense = iaa.Snowflakes(density=0.1, density_uniformity=0.99).augment_image(img)
assert np.average(img_aug_undense) < np.average(img_aug_dense)
# test density_uniformity
img_aug_ununiform = iaa.Snowflakes(density=0.4, density_uniformity=0.1).augment_image(img)
img_aug_uniform = iaa.Snowflakes(density=0.4, density_uniformity=0.9).augment_image(img)
def _measure_uniformity(image, patch_size=5, n_patches=100):
pshalf = (patch_size-1) // 2
grad_x = image[:, 1:].astype(np.float32) - image[:, :-1].astype(np.float32)
grad_y = image[1:, :].astype(np.float32) - image[:-1, :].astype(np.float32)
grad = np.abs(grad_x[1:, :] + grad_y[:, 1:])
points_y = np.random.randint(0, image.shape[0], size=(n_patches,))
points_x = np.random.randint(0, image.shape[0], size=(n_patches,))
stds = []
for y, x in zip(points_y, points_x):
bb = ia.BoundingBox(x1=x-pshalf, y1=y-pshalf, x2=x+pshalf, y2=y+pshalf)
patch = bb.extract_from_image(grad)
stds.append(np.std(patch))
return 1 / (1+np.std(stds))
assert _measure_uniformity(img_aug_ununiform) < _measure_uniformity(img_aug_uniform)
def test_Fliplr():
reseed()
base_img = np.array([[0, 0, 1], [0, 0, 1], [0, 1, 1]], dtype=np.uint8)
base_img = base_img[:, :, np.newaxis]
base_img_flipped = np.array([[1, 0, 0], [1, 0, 0], [1, 1, 0]],
dtype=np.uint8)
base_img_flipped = base_img_flipped[:, :, np.newaxis]
images = np.array([base_img])
images_flipped = np.array([base_img_flipped])
keypoints = [
ia.KeypointsOnImage([
ia.Keypoint(x=0, y=0),
ia.Keypoint(x=1, y=1),
ia.Keypoint(x=2, y=2)
],
shape=base_img.shape)
]
keypoints_flipped = [
ia.KeypointsOnImage([
ia.Keypoint(x=2, y=0),
ia.Keypoint(x=1, y=1),
ia.Keypoint(x=0, y=2)
],
shape=base_img.shape)
]
# 0% chance of flip
aug = iaa.Fliplr(0)
aug_det = aug.to_deterministic()
for _ in sm.xrange(10):
observed = aug.augment_images(images)
expected = images
assert np.array_equal(observed, expected)
observed = aug_det.augment_images(images)
expected = images
assert np.array_equal(observed, expected)
observed = aug.augment_keypoints(keypoints)
expected = keypoints
assert keypoints_equal(observed, expected)
observed = aug_det.augment_keypoints(keypoints)
expected = keypoints
assert keypoints_equal(observed, expected)
# 0% chance of flip, heatmaps
aug = iaa.Fliplr(0)
heatmaps = ia.HeatmapsOnImage(np.float32([
[0, 0.5, 0.75],
[0, 0.5, 0.75],
[0.75, 0.75, 0.75],
]),
shape=(3, 3, 3))
observed = aug.augment_heatmaps([heatmaps])[0]
expected = heatmaps.get_arr()
assert observed.shape == heatmaps.shape
assert heatmaps.min_value - 1e-6 < observed.min_value < heatmaps.min_value + 1e-6
assert heatmaps.max_value - 1e-6 < observed.max_value < heatmaps.max_value + 1e-6
assert np.array_equal(observed.get_arr(), expected)
# 100% chance of flip
aug = iaa.Fliplr(1.0)
aug_det = aug.to_deterministic()
for _ in sm.xrange(10):
observed = aug.augment_images(images)
expected = images_flipped
assert np.array_equal(observed, expected)
observed = aug_det.augment_images(images)
expected = images_flipped
assert np.array_equal(observed, expected)
observed = aug.augment_keypoints(keypoints)
expected = keypoints_flipped
assert keypoints_equal(observed, expected)
observed = aug_det.augment_keypoints(keypoints)
expected = keypoints_flipped
assert keypoints_equal(observed, expected)
# 100% chance of flip, heatmaps
aug = iaa.Fliplr(1.0)
heatmaps = ia.HeatmapsOnImage(np.float32([
[0, 0.5, 0.75],
[0, 0.5, 0.75],
[0.75, 0.75, 0.75],
]),
shape=(3, 3, 3))
observed = aug.augment_heatmaps([heatmaps])[0]
expected = np.fliplr(heatmaps.get_arr())
assert observed.shape == heatmaps.shape
assert heatmaps.min_value - 1e-6 < observed.min_value < heatmaps.min_value + 1e-6
assert heatmaps.max_value - 1e-6 < observed.max_value < heatmaps.max_value + 1e-6
assert np.array_equal(observed.get_arr(), expected)
# 50% chance of flip
aug = iaa.Fliplr(0.5)
aug_det = aug.to_deterministic()
nb_iterations = 1000
nb_images_flipped = 0
nb_images_flipped_det = 0
nb_keypoints_flipped = 0
nb_keypoints_flipped_det = 0
for _ in sm.xrange(nb_iterations):
observed = aug.augment_images(images)
if np.array_equal(observed, images_flipped):
nb_images_flipped += 1
observed = aug_det.augment_images(images)
if np.array_equal(observed, images_flipped):
nb_images_flipped_det += 1
observed = aug.augment_keypoints(keypoints)
if keypoints_equal(observed, keypoints_flipped):
nb_keypoints_flipped += 1
observed = aug_det.augment_keypoints(keypoints)
if keypoints_equal(observed, keypoints_flipped):
nb_keypoints_flipped_det += 1
assert int(nb_iterations * 0.3) <= nb_images_flipped <= int(
nb_iterations * 0.7)
assert int(nb_iterations * 0.3) <= nb_keypoints_flipped <= int(
nb_iterations * 0.7)
assert nb_images_flipped_det in [0, nb_iterations]
assert nb_keypoints_flipped_det in [0, nb_iterations]
# 50% chance of flipped, multiple images, list as input
images_multi = [base_img, base_img]
aug = iaa.Fliplr(0.5)
aug_det = aug.to_deterministic()
nb_iterations = 1000
nb_flipped_by_pos = [0] * len(images_multi)
nb_flipped_by_pos_det = [0] * len(images_multi)
for _ in sm.xrange(nb_iterations):
observed = aug.augment_images(images_multi)
for i in sm.xrange(len(images_multi)):
if np.array_equal(observed[i], base_img_flipped):
nb_flipped_by_pos[i] += 1
observed = aug_det.augment_images(images_multi)
for i in sm.xrange(len(images_multi)):
if np.array_equal(observed[i], base_img_flipped):
nb_flipped_by_pos_det[i] += 1
for val in nb_flipped_by_pos:
assert int(nb_iterations * 0.3) <= val <= int(nb_iterations * 0.7)
for val in nb_flipped_by_pos_det:
assert val in [0, nb_iterations]
# test StochasticParameter as p
aug = iaa.Fliplr(p=iap.Choice([0, 1], p=[0.7, 0.3]))
seen = [0, 0]
for _ in sm.xrange(1000):
observed = aug.augment_image(base_img)
if np.array_equal(observed, base_img):
seen[0] += 1
elif np.array_equal(observed, base_img_flipped):
seen[1] += 1
else:
assert False
assert 700 - 75 < seen[0] < 700 + 75
assert 300 - 75 < seen[1] < 300 + 75
# test exceptions for wrong parameter types
got_exception = False
try:
_ = iaa.Fliplr(p="test")
except Exception:
got_exception = True
assert got_exception
# test get_parameters()
aug = iaa.Fliplr(p=0.5)
params = aug.get_parameters()
assert isinstance(params[0], iap.Binomial)
assert isinstance(params[0].p, iap.Deterministic)
assert 0.5 - 1e-4 < params[0].p.value < 0.5 + 1e-4
###################
# test other dtypes
###################
aug = iaa.Fliplr(1.0)
image = np.zeros((3, 3), dtype=bool)
image[0, 0] = True
expected = np.zeros((3, 3), dtype=bool)
expected[0, 2] = True
image_aug = aug.augment_image(image)
assert image_aug.dtype.type == image.dtype.type
assert np.all(image_aug == expected)
for dtype in [
np.uint8, np.uint16, np.uint32, np.uint64, np.int8, np.int32,
np.int64
]:
min_value, center_value, max_value = iadt.get_value_range_of_dtype(
dtype)
value = max_value
image = np.zeros((3, 3), dtype=dtype)
image[0, 0] = value
expected = np.zeros((3, 3), dtype=dtype)
expected[0, 2] = value
image_aug = aug.augment_image(image)
assert image_aug.dtype.type == dtype
assert np.array_equal(image_aug, expected)
for dtype, value in zip([np.float16, np.float32, np.float64, np.float128],
[5000, 1000**2, 1000**3, 1000**4]):
atol = 1e-9 * max_value if dtype != np.float16 else 1e-3 * max_value
image = np.zeros((3, 3), dtype=dtype)
image[0, 0] = value
expected = np.zeros((3, 3), dtype=dtype)
expected[0, 2] = value
image_aug = aug.augment_image(image)
assert image_aug.dtype.type == dtype
assert np.allclose(image_aug, expected, atol=atol)
def test_Alpha():
reseed()
base_img = np.zeros((3, 3, 1), dtype=np.uint8)
heatmaps_arr = np.float32([[0.0, 0.0, 1.0], [0.0, 0.0, 1.0],
[0.0, 1.0, 1.0]])
heatmaps_arr_r1 = np.float32([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0],
[0.0, 0.0, 1.0]])
heatmaps_arr_l1 = np.float32([[0.0, 1.0, 0.0], [0.0, 1.0, 0.0],
[1.0, 1.0, 0.0]])
heatmaps = ia.HeatmapsOnImage(heatmaps_arr, shape=(3, 3, 3))
aug = iaa.Alpha(1, iaa.Add(10), iaa.Add(20))
observed = aug.augment_image(base_img)
expected = np.round(base_img + 10).astype(np.uint8)
assert np.allclose(observed, expected)
for per_channel in [False, True]:
aug = iaa.Alpha(1,
iaa.Affine(translate_px={"x": 1}),
iaa.Affine(translate_px={"x": -1}),
per_channel=per_channel)
observed = aug.augment_heatmaps([heatmaps])[0]
assert observed.shape == heatmaps.shape
assert 0 - 1e-6 < heatmaps.min_value < 0 + 1e-6
assert 1 - 1e-6 < heatmaps.max_value < 1 + 1e-6
assert np.allclose(observed.get_arr(), heatmaps_arr_r1)
aug = iaa.Alpha(0, iaa.Add(10), iaa.Add(20))
observed = aug.augment_image(base_img)
expected = np.round(base_img + 20).astype(np.uint8)
assert np.allclose(observed, expected)
for per_channel in [False, True]:
aug = iaa.Alpha(0,
iaa.Affine(translate_px={"x": 1}),
iaa.Affine(translate_px={"x": -1}),
per_channel=per_channel)
observed = aug.augment_heatmaps([heatmaps])[0]
assert observed.shape == heatmaps.shape
assert 0 - 1e-6 < heatmaps.min_value < 0 + 1e-6
assert 1 - 1e-6 < heatmaps.max_value < 1 + 1e-6
assert np.allclose(observed.get_arr(), heatmaps_arr_l1)
aug = iaa.Alpha(0.75, iaa.Add(10), iaa.Add(20))
observed = aug.augment_image(base_img)
expected = np.round(base_img + 0.75 * 10 + 0.25 * 20).astype(np.uint8)
assert np.allclose(observed, expected)
aug = iaa.Alpha(0.75, None, iaa.Add(20))
observed = aug.augment_image(base_img + 10)
expected = np.round(base_img + 0.75 * 10 + 0.25 * (10 + 20)).astype(
np.uint8)
assert np.allclose(observed, expected)
aug = iaa.Alpha(0.75, iaa.Add(10), None)
observed = aug.augment_image(base_img + 10)
expected = np.round(base_img + 0.75 * (10 + 10) + 0.25 * 10).astype(
np.uint8)
assert np.allclose(observed, expected)
base_img = np.zeros((1, 2, 1), dtype=np.uint8)
nb_iterations = 1000
aug = iaa.Alpha((0.0, 1.0), iaa.Add(10), iaa.Add(110))
values = []
for _ in sm.xrange(nb_iterations):
observed = aug.augment_image(base_img)
observed_val = np.round(np.average(observed)) - 10
values.append(observed_val / 100)
nb_bins = 5
hist, _ = np.histogram(values,
bins=nb_bins,
range=(0.0, 1.0),
density=False)
density_expected = 1.0 / nb_bins
density_tolerance = 0.05
for nb_samples in hist:
density = nb_samples / nb_iterations
assert density_expected - density_tolerance < density < density_expected + density_tolerance
# bad datatype for factor
got_exception = False
try:
_ = iaa.Alpha(False, iaa.Add(10), None)
except Exception as exc:
assert "Expected " in str(exc)
got_exception = True
assert got_exception
# per_channel
aug = iaa.Alpha(1.0,
iaa.Add((0, 100), per_channel=True),
None,
per_channel=True)
observed = aug.augment_image(np.zeros((1, 1, 1000), dtype=np.uint8))
uq = np.unique(observed)
assert len(uq) > 1
assert np.max(observed) > 80
assert np.min(observed) < 20
aug = iaa.Alpha((0.0, 1.0), iaa.Add(100), None, per_channel=True)
observed = aug.augment_image(np.zeros((1, 1, 1000), dtype=np.uint8))
uq = np.unique(observed)
assert len(uq) > 1
assert np.max(observed) > 80
assert np.min(observed) < 20
aug = iaa.Alpha((0.0, 1.0), iaa.Add(100), iaa.Add(0), per_channel=0.5)
seen = [0, 0]
for _ in sm.xrange(200):
observed = aug.augment_image(np.zeros((1, 1, 100), dtype=np.uint8))
uq = np.unique(observed)
if len(uq) == 1:
seen[0] += 1
elif len(uq) > 1:
seen[1] += 1
else:
assert False
assert 100 - 50 < seen[0] < 100 + 50
assert 100 - 50 < seen[1] < 100 + 50
# bad datatype for per_channel
got_exception = False
try:
_ = iaa.Alpha(0.5, iaa.Add(10), None, per_channel="test")
except Exception as exc:
assert "Expected " in str(exc)
got_exception = True
assert got_exception
# propagating
aug = iaa.Alpha(0.5, iaa.Add(100), iaa.Add(50), name="AlphaTest")
def propagator(images, augmenter, parents, default):
if "Alpha" in augmenter.name:
return False
else:
return default
hooks = ia.HooksImages(propagator=propagator)
image = np.zeros((10, 10, 3), dtype=np.uint8) + 1
observed = aug.augment_image(image, hooks=hooks)
assert np.array_equal(observed, image)
# -----
# keypoints
# -----
kps = [ia.Keypoint(x=5, y=10), ia.Keypoint(x=6, y=11)]
kpsoi = ia.KeypointsOnImage(kps, shape=(20, 20, 3))
aug = iaa.Alpha(1.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.deepcopy()
assert keypoints_equal([observed], [expected])
aug = iaa.Alpha(0.501, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.deepcopy()
assert keypoints_equal([observed], [expected])
aug = iaa.Alpha(0.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.shift(x=1)
assert keypoints_equal([observed], [expected])
aug = iaa.Alpha(0.499, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.shift(x=1)
assert keypoints_equal([observed], [expected])
# per_channel
aug = iaa.Alpha(1.0,
iaa.Noop(),
iaa.Affine(translate_px={"x": 1}),
per_channel=True)
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.deepcopy()
assert keypoints_equal([observed], [expected])
aug = iaa.Alpha(0.0,
iaa.Noop(),
iaa.Affine(translate_px={"x": 1}),
per_channel=True)
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.shift(x=1)
assert keypoints_equal([observed], [expected])
aug = iaa.Alpha(iap.Choice([0.49, 0.51]),
iaa.Noop(),
iaa.Affine(translate_px={"x": 1}),
per_channel=True)
expected_same = kpsoi.deepcopy()
expected_shifted = kpsoi.shift(x=1)
seen = [0, 0]
for _ in sm.xrange(200):
observed = aug.augment_keypoints([kpsoi])[0]
if keypoints_equal([observed], [expected_same]):
seen[0] += 1
elif keypoints_equal([observed], [expected_shifted]):
seen[1] += 1
else:
assert False
assert 100 - 50 < seen[0] < 100 + 50
assert 100 - 50 < seen[1] < 100 + 50
# propagating
aug = iaa.Alpha(0.0,
iaa.Affine(translate_px={"x": 1}),
iaa.Affine(translate_px={"y": 1}),
name="AlphaTest")
def propagator(kpsoi_to_aug, augmenter, parents, default):
if "Alpha" in augmenter.name:
return False
else:
return default
hooks = ia.HooksKeypoints(propagator=propagator)
observed = aug.augment_keypoints([kpsoi], hooks=hooks)[0]
assert keypoints_equal([observed], [kpsoi])
# -----
# get_parameters()
# -----
first = iaa.Noop()
second = iaa.Sequential([iaa.Add(1)])
aug = iaa.Alpha(0.65, first, second, per_channel=1)
params = aug.get_parameters()
assert isinstance(params[0], iap.Deterministic)
assert isinstance(params[1], iap.Deterministic)
assert 0.65 - 1e-6 < params[0].value < 0.65 + 1e-6
assert params[1].value == 1
# -----
# get_children_lists()
# -----
first = iaa.Noop()
second = iaa.Sequential([iaa.Add(1)])
aug = iaa.Alpha(0.65, first, second, per_channel=1)
children_lsts = aug.get_children_lists()
assert len(children_lsts) == 2
assert ia.is_iterable([lst for lst in children_lsts])
assert first in children_lsts[0]
assert second == children_lsts[1]
def test_LinearContrast():
reseed()
img = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
img = np.uint8(img)
img3d = np.tile(img[:, :, np.newaxis], (1, 1, 3))
# check basic functionality with alpha=1 or 2 (deterministic) and per_chanenl on/off (makes
# no difference due to deterministic alpha)
for per_channel in [False, 0, 0.0, True, 1, 1.0]:
for alpha in [1, 2]:
aug = iaa.LinearContrast(alpha=iap.Deterministic(alpha),
per_channel=per_channel)
img_aug = aug.augment_image(img)
img3d_aug = aug.augment_image(img3d)
assert img_aug.dtype.type == np.uint8
assert img3d_aug.dtype.type == np.uint8
assert np.array_equal(
img_aug, contrast_lib._adjust_linear(img, alpha=alpha))
assert np.array_equal(
img3d_aug, contrast_lib._adjust_linear(img3d, alpha=alpha))
# check that tuple to uniform works
aug = iaa.LinearContrast((1, 2))
assert isinstance(aug.params1d[0], iap.Uniform)
assert isinstance(aug.params1d[0].a, iap.Deterministic)
assert isinstance(aug.params1d[0].b, iap.Deterministic)
assert aug.params1d[0].a.value == 1
assert aug.params1d[0].b.value == 2
# check that list to choice works
aug = iaa.LinearContrast([1, 2])
assert isinstance(aug.params1d[0], iap.Choice)
assert all([val in aug.params1d[0].a for val in [1, 2]])
# check that per_channel at 50% prob works
aug = iaa.LinearContrast((0.5, 2.0), per_channel=0.5)
seen = [False, False]
# must not use just value 128 here, otherwise nothing will change as all values would have
# distance 0 to 128
img1000d = np.zeros((1, 1, 1000), dtype=np.uint8) + 128 + 20
for _ in sm.xrange(100):
img_aug = aug.augment_image(img1000d)
assert img_aug.dtype.type == np.uint8
l = len(set(img_aug.flatten().tolist()))
if l == 1:
seen[0] = True
else:
seen[1] = True
if all(seen):
break
assert all(seen)
# check that keypoints are not changed
kpsoi = ia.KeypointsOnImage([ia.Keypoint(1, 1)], shape=(3, 3, 3))
kpsoi_aug = iaa.LinearContrast(alpha=2).augment_keypoints([kpsoi])
assert keypoints_equal([kpsoi], kpsoi_aug)
# check that heatmaps are not changed
heatmaps = ia.HeatmapsOnImage(np.zeros((3, 3, 1), dtype=np.float32) + 0.5,
shape=(3, 3, 3))
heatmaps_aug = iaa.LinearContrast(alpha=2).augment_heatmaps([heatmaps])[0]
assert np.allclose(heatmaps.arr_0to1, heatmaps_aug.arr_0to1)
def test_SigmoidContrast():
reseed()
img = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
img = np.uint8(img)
img3d = np.tile(img[:, :, np.newaxis], (1, 1, 3))
# check basic functionality with per_chanenl on/off (makes no difference due to deterministic
# parameters)
for per_channel in [False, 0, 0.0, True, 1, 1.0]:
for gain, cutoff in itertools.product([5, 10], [0.25, 0.75]):
aug = iaa.SigmoidContrast(gain=iap.Deterministic(gain),
cutoff=iap.Deterministic(cutoff),
per_channel=per_channel)
img_aug = aug.augment_image(img)
img3d_aug = aug.augment_image(img3d)
assert img_aug.dtype.type == np.uint8
assert img3d_aug.dtype.type == np.uint8
assert np.array_equal(
img_aug,
skimage.exposure.adjust_sigmoid(img, gain=gain, cutoff=cutoff))
assert np.array_equal(
img3d_aug,
skimage.exposure.adjust_sigmoid(img3d,
gain=gain,
cutoff=cutoff))
# check that tuple to uniform works
# note that gain and cutoff are saved in inverted order in _ContrastFuncWrapper to match
# the order of skimage's function
aug = iaa.SigmoidContrast(gain=(1, 2), cutoff=(0.25, 0.75))
assert isinstance(aug.params1d[0], iap.Uniform)
assert isinstance(aug.params1d[0].a, iap.Deterministic)
assert isinstance(aug.params1d[0].b, iap.Deterministic)
assert np.allclose(aug.params1d[0].a.value, 0.25)
assert np.allclose(aug.params1d[0].b.value, 0.75)
assert isinstance(aug.params1d[1], iap.Uniform)
assert isinstance(aug.params1d[1].a, iap.Deterministic)
assert isinstance(aug.params1d[1].b, iap.Deterministic)
assert aug.params1d[1].a.value == 1
assert aug.params1d[1].b.value == 2
# check that list to choice works
# note that gain and cutoff are saved in inverted order in _ContrastFuncWrapper to match
# the order of skimage's function
aug = iaa.SigmoidContrast(gain=[1, 2], cutoff=[0.25, 0.75])
assert isinstance(aug.params1d[0], iap.Choice)
assert all([
np.allclose(val, val_choice)
for val, val_choice in zip([0.25, 0.75], aug.params1d[0].a)
])
assert isinstance(aug.params1d[1], iap.Choice)
assert all([val in aug.params1d[1].a for val in [1, 2]])
# check that per_channel at 50% prob works
aug = iaa.SigmoidContrast(gain=(1, 10),
cutoff=(0.25, 0.75),
per_channel=0.5)
seen = [False, False]
img1000d = np.zeros((1, 1, 1000), dtype=np.uint8) + 128
for _ in sm.xrange(100):
img_aug = aug.augment_image(img1000d)
assert img_aug.dtype.type == np.uint8
l = len(set(img_aug.flatten().tolist()))
if l == 1:
seen[0] = True
else:
seen[1] = True
if all(seen):
break
assert all(seen)
# check that keypoints are not changed
kpsoi = ia.KeypointsOnImage([ia.Keypoint(1, 1)], shape=(3, 3, 3))
kpsoi_aug = iaa.SigmoidContrast(gain=10,
cutoff=0.5).augment_keypoints([kpsoi])
assert keypoints_equal([kpsoi], kpsoi_aug)
# check that heatmaps are not changed
heatmaps = ia.HeatmapsOnImage(np.zeros((3, 3, 1), dtype=np.float32) + 0.5,
shape=(3, 3, 3))
heatmaps_aug = iaa.SigmoidContrast(gain=10,
cutoff=0.5).augment_heatmaps([heatmaps
])[0]
assert np.allclose(heatmaps.arr_0to1, heatmaps_aug.arr_0to1)
###################
# test other dtypes
###################
# uint, int
for dtype in [
np.uint8, np.uint16, np.uint32, np.uint64, np.int8, np.int16,
np.int32, np.int64
]:
min_value, center_value, max_value = meta.get_value_range_of_dtype(
dtype)
gains = [5, 20]
cutoffs = [0.25, 0.75]
values = [0, 100, int(center_value + 0.1 * max_value)]
tmax = 1e-8 * max_value if dtype in [np.uint64, np.int64] else 0
tolerances = [tmax, tmax, tmax]
for gain, cutoff in itertools.product(gains, cutoffs):
aug = iaa.SigmoidContrast(gain=gain, cutoff=cutoff)
for value, tolerance in zip(values, tolerances):
image = np.full((3, 3), value, dtype=dtype)
# 1/(1 + exp(gain*(cutoff - I_ij/max)))
expected = (1 / (1 + np.exp(
gain * (cutoff - image.astype(np.float128) / max_value))))
expected = (expected * max_value).astype(dtype)
image_aug = aug.augment_image(image)
assert image_aug.dtype == np.dtype(dtype)
assert len(np.unique(image_aug)) == 1
value_aug = int(image_aug[0, 0])
value_expected = int(expected[0, 0])
diff = abs(value_aug - value_expected)
assert diff <= tolerance
# float
for dtype in [np.float16, np.float32, np.float64]:
def _allclose(a, b):
atol = 1e-3 if dtype == np.float16 else 1e-8
return np.allclose(a, b, atol=atol, rtol=0)
gains = [5, 20]
cutoffs = [0.25, 0.75]
isize = np.dtype(dtype).itemsize
values = [0, 1.0, 50.0, 100**(isize - 1)]
for gain, cutoff in itertools.product(gains, cutoffs):
aug = iaa.SigmoidContrast(gain=gain, cutoff=cutoff)
for value in values:
image = np.full((3, 3), value, dtype=dtype)
expected = (1 /
(1 + np.exp(gain *
(cutoff - image.astype(np.float128))))
).astype(dtype)
image_aug = aug.augment_image(image)
assert image_aug.dtype == np.dtype(dtype)
assert _allclose(image_aug, expected)
def test_BatchLoader():
reseed()
def _load_func():
for _ in sm.xrange(20):
yield ia.Batch(images=np.zeros((2, 4, 4, 3), dtype=np.uint8))
warnings.simplefilter("always")
with warnings.catch_warnings(record=True) as caught_warnings:
for nb_workers in [1, 2]:
# repeat these tests many times to catch rarer race conditions
for _ in sm.xrange(5):
loader = multicore.BatchLoader(_load_func,
queue_size=2,
nb_workers=nb_workers,
threaded=True)
loaded = []
counter = 0
while (not loader.all_finished()
or not loader.queue.empty()) and counter < 1000:
try:
batch = loader.queue.get(timeout=0.001)
loaded.append(batch)
except:
pass
counter += 1
assert len(loaded) == 20*nb_workers, \
"Expected %d to be loaded by threads, got %d for %d workers at counter %d." % (
20*nb_workers, len(loaded), nb_workers, counter
)
loader = multicore.BatchLoader(_load_func,
queue_size=200,
nb_workers=nb_workers,
threaded=True)
loader.terminate()
assert loader.all_finished()
loader = multicore.BatchLoader(_load_func,
queue_size=2,
nb_workers=nb_workers,
threaded=False)
loaded = []
counter = 0
while (not loader.all_finished()
or not loader.queue.empty()) and counter < 1000:
try:
batch = loader.queue.get(timeout=0.001)
loaded.append(batch)
except:
pass
counter += 1
assert len(loaded) == 20*nb_workers, \
"Expected %d to be loaded by background processes, got %d for %d workers at counter %d." % (
20*nb_workers, len(loaded), nb_workers, counter
)
loader = multicore.BatchLoader(_load_func,
queue_size=200,
nb_workers=nb_workers,
threaded=False)
loader.terminate()
assert loader.all_finished()
assert len(caught_warnings) > 0
for warning in caught_warnings:
assert "BatchLoader is deprecated" in str(warning.message)
def test_MotionBlur():
reseed()
# simple scenario
aug = iaa.MotionBlur(k=3, angle=0, direction=0.0)
matrix_func = aug.matrix
matrices = [
matrix_func(np.zeros((128, 128, 3), dtype=np.uint8), 3,
ia.new_random_state(i)) for i in range(10)
]
expected = np.float32([[0, 1.0 / 3, 0], [0, 1.0 / 3, 0], [0, 1.0 / 3, 0]])
for matrices_image in matrices:
for matrix_channel in matrices_image:
assert np.allclose(matrix_channel, expected)
# 90deg angle
aug = iaa.MotionBlur(k=3, angle=90, direction=0.0)
matrix_func = aug.matrix
matrices = [
matrix_func(np.zeros((128, 128, 3), dtype=np.uint8), 3,
ia.new_random_state(i)) for i in range(10)
]
expected = np.float32([[0, 0, 0], [1.0 / 3, 1.0 / 3, 1.0 / 3], [0, 0, 0]])
for matrices_image in matrices:
for matrix_channel in matrices_image:
assert np.allclose(matrix_channel, expected)
# 45deg angle
aug = iaa.MotionBlur(k=3, angle=45, direction=0.0, order=0)
matrix_func = aug.matrix
matrices = [
matrix_func(np.zeros((128, 128, 3), dtype=np.uint8), 3,
ia.new_random_state(i)) for i in range(10)
]
expected = np.float32([[0, 0, 1.0 / 3], [0, 1.0 / 3, 0], [1.0 / 3, 0, 0]])
for matrices_image in matrices:
for matrix_channel in matrices_image:
assert np.allclose(matrix_channel, expected)
# random angle
aug = iaa.MotionBlur(k=3, angle=[0, 90], direction=0.0)
matrix_func = aug.matrix
matrices = [
matrix_func(np.zeros((128, 128, 3), dtype=np.uint8), 3,
ia.new_random_state(i)) for i in range(50)
]
expected1 = np.float32([[0, 1.0 / 3, 0], [0, 1.0 / 3, 0], [0, 1.0 / 3, 0]])
expected2 = np.float32([
[0, 0, 0],
[1.0 / 3, 1.0 / 3, 1.0 / 3],
[0, 0, 0],
])
nb_seen = [0, 0]
for matrices_image in matrices:
assert np.allclose(matrices_image[0], matrices_image[1])
assert np.allclose(matrices_image[1], matrices_image[2])
for matrix_channel in matrices_image:
if np.allclose(matrix_channel, expected1):
nb_seen[0] += 1
elif np.allclose(matrix_channel, expected2):
nb_seen[1] += 1
assert nb_seen[0] > 0
assert nb_seen[1] > 0
# 5x5
aug = iaa.MotionBlur(k=5, angle=90, direction=0.0)
matrix_func = aug.matrix
matrices = [
matrix_func(np.zeros((128, 128, 3), dtype=np.uint8), 3,
ia.new_random_state(i)) for i in range(10)
]
expected = np.float32([
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[1.0 / 5, 1.0 / 5, 1.0 / 5, 1.0 / 5, 1.0 / 5],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
])
for matrices_image in matrices:
for matrix_channel in matrices_image:
assert np.allclose(matrix_channel, expected)
# random k
aug = iaa.MotionBlur(k=[3, 5], angle=90, direction=0.0)
matrix_func = aug.matrix
matrices = [
matrix_func(np.zeros((128, 128, 3), dtype=np.uint8), 3,
ia.new_random_state(i)) for i in range(50)
]
expected1 = np.float32([
[0, 0, 0],
[1.0 / 3, 1.0 / 3, 1.0 / 3],
[0, 0, 0],
])
expected2 = np.float32([
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[1.0 / 5, 1.0 / 5, 1.0 / 5, 1.0 / 5, 1.0 / 5],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
])
nb_seen = [0, 0]
for matrices_image in matrices:
assert np.allclose(matrices_image[0], matrices_image[1])
assert np.allclose(matrices_image[1], matrices_image[2])
for matrix_channel in matrices_image:
if matrix_channel.shape == expected1.shape and np.allclose(
matrix_channel, expected1):
nb_seen[0] += 1
elif matrix_channel.shape == expected2.shape and np.allclose(
matrix_channel, expected2):
nb_seen[1] += 1
assert nb_seen[0] > 0
assert nb_seen[1] > 0
# direction 1.0
aug = iaa.MotionBlur(k=3, angle=0, direction=1.0)
matrix_func = aug.matrix
matrices = [
matrix_func(np.zeros((128, 128, 3), dtype=np.uint8), 3,
ia.new_random_state(i)) for i in range(10)
]
expected = np.float32([[0, 1.0 / 1.5, 0], [0, 0.5 / 1.5, 0],
[0, 0.0 / 1.5, 0]])
for matrices_image in matrices:
for matrix_channel in matrices_image:
assert np.allclose(matrix_channel, expected, rtol=0, atol=1e-2)
# direction -1.0
aug = iaa.MotionBlur(k=3, angle=0, direction=-1.0)
matrix_func = aug.matrix
matrices = [
matrix_func(np.zeros((128, 128, 3), dtype=np.uint8), 3,
ia.new_random_state(i)) for i in range(10)
]
expected = np.float32([[0, 0.0 / 1.5, 0], [0, 0.5 / 1.5, 0],
[0, 1.0 / 1.5, 0]])
for matrices_image in matrices:
for matrix_channel in matrices_image:
assert np.allclose(matrix_channel, expected, rtol=0, atol=1e-2)
# random direction
aug = iaa.MotionBlur(k=3, angle=[0, 90], direction=[-1.0, 1.0])
matrix_func = aug.matrix
matrices = [
matrix_func(np.zeros((128, 128, 3), dtype=np.uint8), 3,
ia.new_random_state(i)) for i in range(50)
]
expected1 = np.float32([[0, 1.0 / 1.5, 0], [0, 0.5 / 1.5, 0],
[0, 0.0 / 1.5, 0]])
expected2 = np.float32([[0, 0.0 / 1.5, 0], [0, 0.5 / 1.5, 0],
[0, 1.0 / 1.5, 0]])
nb_seen = [0, 0]
for matrices_image in matrices:
assert np.allclose(matrices_image[0], matrices_image[1])
assert np.allclose(matrices_image[1], matrices_image[2])
for matrix_channel in matrices_image:
if np.allclose(matrix_channel, expected1, rtol=0, atol=1e-2):
nb_seen[0] += 1
elif np.allclose(matrix_channel, expected2, rtol=0, atol=1e-2):
nb_seen[1] += 1
assert nb_seen[0] > 0
assert nb_seen[1] > 0
# test of actual augmenter
img = np.zeros((7, 7, 3), dtype=np.uint8)
img[3 - 1:3 + 2, 3 - 1:3 + 2, :] = 255
aug = iaa.MotionBlur(k=3, angle=90, direction=0.0)
img_aug = aug.augment_image(img)
v1 = (255 * (1 / 3))
v2 = (255 * (1 / 3)) * 2
v3 = (255 * (1 / 3)) * 3
expected = np.float32([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0],
[0, v1, v2, v3, v2, v1, 0],
[0, v1, v2, v3, v2, v1, 0],
[0, v1, v2, v3, v2, v1, 0], [0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]]).astype(np.uint8)
expected = np.tile(expected[..., np.newaxis], (1, 1, 3))
assert np.allclose(img_aug, expected)
def test_determinism():
reseed()
images = [
ia.quokka(size=(128, 128)),
ia.quokka(size=(64, 64)),
ia.imresize_single_image(skimage.data.astronaut(), (128, 256))
]
images.extend([ia.quokka(size=(16, 16))] * 20)
keypoints = [
ia.KeypointsOnImage([
ia.Keypoint(x=20, y=10), ia.Keypoint(x=5, y=5),
ia.Keypoint(x=10, y=43)], shape=(50, 60, 3))
] * 20
augs = [
iaa.Sequential([iaa.Fliplr(0.5), iaa.Flipud(0.5)]),
iaa.SomeOf(1, [iaa.Fliplr(0.5), iaa.Flipud(0.5)]),
iaa.OneOf([iaa.Fliplr(0.5), iaa.Flipud(0.5)]),
iaa.Sometimes(0.5, iaa.Fliplr(1.0)),
iaa.WithColorspace("HSV", children=iaa.Add((-50, 50))),
# iaa.WithChannels([0], iaa.Add((-50, 50))),
# iaa.Noop(name="Noop-nochange"),
# iaa.Lambda(
# func_images=lambda images, random_state, parents, hooks: images,
# func_keypoints=lambda keypoints_on_images, random_state, parents, hooks: keypoints_on_images,
# name="Lambda-nochange"
# ),
# iaa.AssertLambda(
# func_images=lambda images, random_state, parents, hooks: True,
# func_keypoints=lambda keypoints_on_images, random_state, parents, hooks: True,
# name="AssertLambda-nochange"
# ),
# iaa.AssertShape(
# (None, None, None, 3),
# check_keypoints=False,
# name="AssertShape-nochange"
# ),
iaa.Resize((0.5, 0.9)),
iaa.CropAndPad(px=(-50, 50)),
iaa.Pad(px=(1, 50)),
iaa.Crop(px=(1, 50)),
iaa.Fliplr(0.5),
iaa.Flipud(0.5),
iaa.Superpixels(p_replace=(0.25, 1.0), n_segments=(16, 128)),
# iaa.ChangeColorspace(to_colorspace="GRAY"),
iaa.Grayscale(alpha=(0.1, 1.0)),
iaa.GaussianBlur((0.1, 3.0)),
iaa.AverageBlur((3, 11)),
iaa.MedianBlur((3, 11)),
# iaa.Convolve(np.array([[0, 1, 0],
# [1, -4, 1],
# [0, 1, 0]])),
iaa.Sharpen(alpha=(0.1, 1.0), lightness=(0.8, 1.2)),
iaa.Emboss(alpha=(0.1, 1.0), strength=(0.8, 1.2)),
iaa.EdgeDetect(alpha=(0.1, 1.0)),
iaa.DirectedEdgeDetect(alpha=(0.1, 1.0), direction=(0.0, 1.0)),
iaa.Add((-50, 50)),
iaa.AddElementwise((-50, 50)),
iaa.AdditiveGaussianNoise(scale=(0.1, 1.0)),
iaa.Multiply((0.6, 1.4)),
iaa.MultiplyElementwise((0.6, 1.4)),
iaa.Dropout((0.3, 0.5)),
iaa.CoarseDropout((0.3, 0.5), size_percent=(0.05, 0.2)),
iaa.Invert(0.5),
iaa.ContrastNormalization((0.6, 1.4)),
iaa.Affine(scale=(0.7, 1.3), translate_percent=(-0.1, 0.1),
rotate=(-20, 20), shear=(-20, 20), order=ia.ALL,
mode=ia.ALL, cval=(0, 255)),
iaa.PiecewiseAffine(scale=(0.1, 0.3)),
iaa.ElasticTransformation(alpha=0.5)
]
augs_affect_geometry = [
iaa.Sequential([iaa.Fliplr(0.5), iaa.Flipud(0.5)]),
iaa.SomeOf(1, [iaa.Fliplr(0.5), iaa.Flipud(0.5)]),
iaa.OneOf([iaa.Fliplr(0.5), iaa.Flipud(0.5)]),
iaa.Sometimes(0.5, iaa.Fliplr(1.0)),
iaa.Resize((0.5, 0.9)),
iaa.CropAndPad(px=(-50, 50)),
iaa.Pad(px=(1, 50)),
iaa.Crop(px=(1, 50)),
iaa.Fliplr(0.5),
iaa.Flipud(0.5),
iaa.Affine(scale=(0.7, 1.3), translate_percent=(-0.1, 0.1),
rotate=(-20, 20), shear=(-20, 20), order=ia.ALL,
mode=ia.ALL, cval=(0, 255)),
iaa.PiecewiseAffine(scale=(0.1, 0.3)),
iaa.ElasticTransformation(alpha=(5, 100), sigma=(3, 5))
]
for aug in augs:
aug_det = aug.to_deterministic()
images_aug1 = aug_det.augment_images(images)
images_aug2 = aug_det.augment_images(images)
aug_det = aug.to_deterministic()
images_aug3 = aug_det.augment_images(images)
images_aug4 = aug_det.augment_images(images)
assert array_equal_lists(images_aug1, images_aug2), \
"Images (1, 2) expected to be identical for %s" % (aug.name,)
assert array_equal_lists(images_aug3, images_aug4), \
"Images (3, 4) expected to be identical for %s" % (aug.name,)
assert not array_equal_lists(images_aug1, images_aug3), \
"Images (1, 3) expected to be different for %s" % (aug.name,)
for aug in augs_affect_geometry:
aug_det = aug.to_deterministic()
kps_aug1 = aug_det.augment_keypoints(keypoints)
kps_aug2 = aug_det.augment_keypoints(keypoints)
aug_det = aug.to_deterministic()
kps_aug3 = aug_det.augment_keypoints(keypoints)
kps_aug4 = aug_det.augment_keypoints(keypoints)
assert keypoints_equal(kps_aug1, kps_aug2), \
"Keypoints (1, 2) expected to be identical for %s" % (aug.name,)
assert keypoints_equal(kps_aug3, kps_aug4), \
"Keypoints (3, 4) expected to be identical for %s" % (aug.name,)
assert not keypoints_equal(kps_aug1, kps_aug3), \
"Keypoints (1, 3) expected to be different for %s" % (aug.name,)
def test_FastSnowyLandscape():
reseed()
# check parameters
aug = iaa.FastSnowyLandscape(lightness_threshold=[100, 200], lightness_multiplier=[1.0, 4.0])
assert isinstance(aug.lightness_threshold, iap.Choice)
assert len(aug.lightness_threshold.a) == 2
assert aug.lightness_threshold.a[0] == 100
assert aug.lightness_threshold.a[1] == 200
assert isinstance(aug.lightness_multiplier, iap.Choice)
assert len(aug.lightness_multiplier.a) == 2
assert np.allclose(aug.lightness_multiplier.a[0], 1.0)
assert np.allclose(aug.lightness_multiplier.a[1], 4.0)
# basic functionality test
aug = iaa.FastSnowyLandscape(lightness_threshold=100, lightness_multiplier=2.0)
image = np.arange(0, 6*6*3).reshape((6, 6, 3)).astype(np.uint8)
image_hls = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
mask = (image_hls[..., 1] < 100)
expected = np.copy(image_hls).astype(np.float32)
expected[..., 1][mask] *= 2.0
expected = np.clip(np.round(expected), 0, 255).astype(np.uint8)
expected = cv2.cvtColor(expected, cv2.COLOR_HLS2RGB)
observed = aug.augment_image(image)
assert np.array_equal(observed, expected)
# test when varying lightness_threshold between images
image = np.arange(0, 6*6*3).reshape((6, 6, 3)).astype(np.uint8)
image_hls = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
class _TwoValueParam(iap.StochasticParameter):
def __init__(self, v1, v2):
super(_TwoValueParam, self).__init__()
self.v1 = v1
self.v2 = v2
def _draw_samples(self, size, random_state):
arr = np.full(size, self.v1, dtype=np.float32)
arr[1::2] = self.v2
return arr
aug = iaa.FastSnowyLandscape(lightness_threshold=_TwoValueParam(75, 125), lightness_multiplier=2.0)
mask = (image_hls[..., 1] < 75)
expected1 = np.copy(image_hls).astype(np.float64)
expected1[..., 1][mask] *= 2.0
expected1 = np.clip(np.round(expected1), 0, 255).astype(np.uint8)
expected1 = cv2.cvtColor(expected1, cv2.COLOR_HLS2RGB)
mask = (image_hls[..., 1] < 125)
expected2 = np.copy(image_hls).astype(np.float64)
expected2[..., 1][mask] *= 2.0
expected2 = np.clip(np.round(expected2), 0, 255).astype(np.uint8)
expected2 = cv2.cvtColor(expected2, cv2.COLOR_HLS2RGB)
observed = aug.augment_images([image] * 4)
assert np.array_equal(observed[0], expected1)
assert np.array_equal(observed[1], expected2)
assert np.array_equal(observed[2], expected1)
assert np.array_equal(observed[3], expected2)
# test when varying lightness_multiplier between images
aug = iaa.FastSnowyLandscape(lightness_threshold=100, lightness_multiplier=_TwoValueParam(1.5, 2.0))
mask = (image_hls[..., 1] < 100)
expected1 = np.copy(image_hls).astype(np.float64)
expected1[..., 1][mask] *= 1.5
expected1 = np.clip(np.round(expected1), 0, 255).astype(np.uint8)
expected1 = cv2.cvtColor(expected1, cv2.COLOR_HLS2RGB)
mask = (image_hls[..., 1] < 100)
expected2 = np.copy(image_hls).astype(np.float64)
expected2[..., 1][mask] *= 2.0
expected2 = np.clip(np.round(expected2), 0, 255).astype(np.uint8)
expected2 = cv2.cvtColor(expected2, cv2.COLOR_HLS2RGB)
observed = aug.augment_images([image] * 4)
assert np.array_equal(observed[0], expected1)
assert np.array_equal(observed[1], expected2)
assert np.array_equal(observed[2], expected1)
assert np.array_equal(observed[3], expected2)
# test BGR colorspace
aug = iaa.FastSnowyLandscape(lightness_threshold=100, lightness_multiplier=2.0, from_colorspace="BGR")
image = np.arange(0, 6*6*3).reshape((6, 6, 3)).astype(np.uint8)
image_hls = cv2.cvtColor(image, cv2.COLOR_BGR2HLS)
mask = (image_hls[..., 1] < 100)
expected = np.copy(image_hls).astype(np.float32)
expected[..., 1][mask] *= 2.0
expected = np.clip(np.round(expected), 0, 255).astype(np.uint8)
expected = cv2.cvtColor(expected, cv2.COLOR_HLS2BGR)
observed = aug.augment_image(image)
assert np.array_equal(observed, expected)
def test_BoundingBoxesOnImage():
reseed()
# test height/width
bb1 = ia.BoundingBox(y1=10, x1=20, y2=30, x2=40, label=None)
bb2 = ia.BoundingBox(y1=15, x1=25, y2=35, x2=45, label=None)
bbsoi = ia.BoundingBoxesOnImage([bb1, bb2], shape=(40, 50, 3))
assert bbsoi.height == 40
assert bbsoi.width == 50
bb1 = ia.BoundingBox(y1=10, x1=20, y2=30, x2=40, label=None)
bb2 = ia.BoundingBox(y1=15, x1=25, y2=35, x2=45, label=None)
bbsoi = ia.BoundingBoxesOnImage([bb1, bb2],
shape=np.zeros((40, 50, 3),
dtype=np.uint8))
assert bbsoi.height == 40
assert bbsoi.width == 50
# empty
bb = ia.BoundingBox(y1=10, x1=20, y2=30, x2=40, label=None)
bbsoi = ia.BoundingBoxesOnImage([bb], shape=(40, 50, 3))
assert not bbsoi.empty
bbsoi = ia.BoundingBoxesOnImage([], shape=(40, 50, 3))
assert bbsoi.empty
# on()
bb1 = ia.BoundingBox(y1=10, x1=20, y2=30, x2=40, label=None)
bb2 = ia.BoundingBox(y1=15, x1=25, y2=35, x2=45, label=None)
bbsoi = ia.BoundingBoxesOnImage([bb1, bb2],
shape=np.zeros((40, 50, 3),
dtype=np.uint8))
bbsoi_projected = bbsoi.on((40, 50))
assert bbsoi_projected.bounding_boxes[0].y1 == 10
assert bbsoi_projected.bounding_boxes[0].x1 == 20
assert bbsoi_projected.bounding_boxes[0].y2 == 30
assert bbsoi_projected.bounding_boxes[0].x2 == 40
assert bbsoi_projected.bounding_boxes[1].y1 == 15
assert bbsoi_projected.bounding_boxes[1].x1 == 25
assert bbsoi_projected.bounding_boxes[1].y2 == 35
assert bbsoi_projected.bounding_boxes[1].x2 == 45
bbsoi_projected = bbsoi.on((40 * 2, 50 * 2, 3))
assert bbsoi_projected.bounding_boxes[0].y1 == 10 * 2
assert bbsoi_projected.bounding_boxes[0].x1 == 20 * 2
assert bbsoi_projected.bounding_boxes[0].y2 == 30 * 2
assert bbsoi_projected.bounding_boxes[0].x2 == 40 * 2
assert bbsoi_projected.bounding_boxes[1].y1 == 15 * 2
assert bbsoi_projected.bounding_boxes[1].x1 == 25 * 2
assert bbsoi_projected.bounding_boxes[1].y2 == 35 * 2
assert bbsoi_projected.bounding_boxes[1].x2 == 45 * 2
bbsoi_projected = bbsoi.on(np.zeros((40 * 2, 50 * 2, 3), dtype=np.uint8))
assert bbsoi_projected.bounding_boxes[0].y1 == 10 * 2
assert bbsoi_projected.bounding_boxes[0].x1 == 20 * 2
assert bbsoi_projected.bounding_boxes[0].y2 == 30 * 2
assert bbsoi_projected.bounding_boxes[0].x2 == 40 * 2
assert bbsoi_projected.bounding_boxes[1].y1 == 15 * 2
assert bbsoi_projected.bounding_boxes[1].x1 == 25 * 2
assert bbsoi_projected.bounding_boxes[1].y2 == 35 * 2
assert bbsoi_projected.bounding_boxes[1].x2 == 45 * 2
# from_xyxy_array()
bbsoi = ia.BoundingBoxesOnImage.from_xyxy_array(np.float32(
[[0.0, 0.0, 1.0, 1.0], [1.0, 2.0, 3.0, 4.0]]),
shape=(40, 50, 3))
assert len(bbsoi.bounding_boxes) == 2
assert np.allclose(bbsoi.bounding_boxes[0].x1, 0.0)
assert np.allclose(bbsoi.bounding_boxes[0].y1, 0.0)
assert np.allclose(bbsoi.bounding_boxes[0].x2, 1.0)
assert np.allclose(bbsoi.bounding_boxes[0].y2, 1.0)
assert np.allclose(bbsoi.bounding_boxes[1].x1, 1.0)
assert np.allclose(bbsoi.bounding_boxes[1].y1, 2.0)
assert np.allclose(bbsoi.bounding_boxes[1].x2, 3.0)
assert np.allclose(bbsoi.bounding_boxes[1].y2, 4.0)
assert bbsoi.shape == (40, 50, 3)
bbsoi = ia.BoundingBoxesOnImage.from_xyxy_array(np.int32([[0, 0, 1, 1],
[1, 2, 3, 4]]),
shape=(40, 50, 3))
assert len(bbsoi.bounding_boxes) == 2
assert np.allclose(bbsoi.bounding_boxes[0].x1, 0.0)
assert np.allclose(bbsoi.bounding_boxes[0].y1, 0.0)
assert np.allclose(bbsoi.bounding_boxes[0].x2, 1.0)
assert np.allclose(bbsoi.bounding_boxes[0].y2, 1.0)
assert np.allclose(bbsoi.bounding_boxes[1].x1, 1.0)
assert np.allclose(bbsoi.bounding_boxes[1].y1, 2.0)
assert np.allclose(bbsoi.bounding_boxes[1].x2, 3.0)
assert np.allclose(bbsoi.bounding_boxes[1].y2, 4.0)
assert bbsoi.shape == (40, 50, 3)
bbsoi = ia.BoundingBoxesOnImage.from_xyxy_array(np.zeros((0, 4),
dtype=np.float32),
shape=(40, 50, 3))
assert len(bbsoi.bounding_boxes) == 0
assert bbsoi.shape == (40, 50, 3)
# to_xyxy_array()
xyxy_arr = np.float32([[0.0, 0.0, 1.0, 1.0], [1.0, 2.0, 3.0, 4.0]])
bbsoi = ia.BoundingBoxesOnImage.from_xyxy_array(xyxy_arr,
shape=(40, 50, 3))
xyxy_arr_out = bbsoi.to_xyxy_array()
assert np.allclose(xyxy_arr, xyxy_arr_out)
assert xyxy_arr_out.dtype == np.float32
xyxy_arr_out = bbsoi.to_xyxy_array(dtype=np.int32)
assert np.allclose(xyxy_arr.astype(np.int32), xyxy_arr_out)
assert xyxy_arr_out.dtype == np.int32
xyxy_arr_out = ia.BoundingBoxesOnImage(
[], shape=(40, 50, 3)).to_xyxy_array(dtype=np.int32)
assert xyxy_arr_out.shape == (0, 4)
# draw_on_image()
bb1 = ia.BoundingBox(y1=10, x1=20, y2=30, x2=40, label=None)
bb2 = ia.BoundingBox(y1=15, x1=25, y2=35, x2=45, label=None)
bbsoi = ia.BoundingBoxesOnImage([bb1, bb2], shape=(40, 50, 3))
image = bbsoi.draw_on_image(np.zeros(bbsoi.shape, dtype=np.uint8),
color=[0, 255, 0],
alpha=1.0,
thickness=1,
copy=True,
raise_if_out_of_image=False)
assert np.all(image[10 - 1, 20 - 1, :] == [0, 0, 0])
assert np.all(image[10 - 1, 20 - 0, :] == [0, 0, 0])
assert np.all(image[10 - 0, 20 - 1, :] == [0, 0, 0])
assert np.all(image[10 - 0, 20 - 0, :] == [0, 255, 0])
assert np.all(image[10 + 1, 20 + 1, :] == [0, 0, 0])
assert np.all(image[30 - 1, 40 - 1, :] == [0, 0, 0])
assert np.all(image[30 + 1, 40 - 0, :] == [0, 0, 0])
assert np.all(image[30 + 0, 40 + 1, :] == [0, 0, 0])
assert np.all(image[30 + 0, 40 + 0, :] == [0, 255, 0])
assert np.all(image[30 + 1, 40 + 1, :] == [0, 0, 0])
assert np.all(image[15 - 1, 25 - 1, :] == [0, 0, 0])
assert np.all(image[15 - 1, 25 - 0, :] == [0, 0, 0])
assert np.all(image[15 - 0, 25 - 1, :] == [0, 0, 0])
assert np.all(image[15 - 0, 25 - 0, :] == [0, 255, 0])
assert np.all(image[15 + 1, 25 + 1, :] == [0, 0, 0])
assert np.all(image[35 - 1, 45 - 1, :] == [0, 0, 0])
assert np.all(image[35 + 1, 45 + 0, :] == [0, 0, 0])
assert np.all(image[35 + 0, 45 + 1, :] == [0, 0, 0])
assert np.all(image[35 + 0, 45 + 0, :] == [0, 255, 0])
assert np.all(image[35 + 1, 45 + 1, :] == [0, 0, 0])
# remove_out_of_image()
bb1 = ia.BoundingBox(y1=10, x1=20, y2=30, x2=40, label=None)
bb2 = ia.BoundingBox(y1=15, x1=25, y2=35, x2=51, label=None)
bbsoi = ia.BoundingBoxesOnImage([bb1, bb2], shape=(40, 50, 3))
bbsoi_slim = bbsoi.remove_out_of_image(fully=True, partly=True)
assert len(bbsoi_slim.bounding_boxes) == 1
assert bbsoi_slim.bounding_boxes[0] == bb1
# clip_out_of_image()
bb1 = ia.BoundingBox(y1=10, x1=20, y2=30, x2=40, label=None)
bb2 = ia.BoundingBox(y1=15, x1=25, y2=35, x2=51, label=None)
bbsoi = ia.BoundingBoxesOnImage([bb1, bb2], shape=(40, 50, 3))
eps = np.finfo(np.float32).eps
bbsoi_clip = bbsoi.clip_out_of_image()
assert len(bbsoi_clip.bounding_boxes) == 2
assert bbsoi_clip.bounding_boxes[0].y1 == 10
assert bbsoi_clip.bounding_boxes[0].x1 == 20
assert bbsoi_clip.bounding_boxes[0].y2 == 30
assert bbsoi_clip.bounding_boxes[0].x2 == 40
assert bbsoi_clip.bounding_boxes[1].y1 == 15
assert bbsoi_clip.bounding_boxes[1].x1 == 25
assert bbsoi_clip.bounding_boxes[1].y2 == 35
assert 50 - 2 * eps < bbsoi_clip.bounding_boxes[1].x2 < 50
# shift()
bb1 = ia.BoundingBox(y1=10, x1=20, y2=30, x2=40, label=None)
bb2 = ia.BoundingBox(y1=15, x1=25, y2=35, x2=51, label=None)
bbsoi = ia.BoundingBoxesOnImage([bb1, bb2], shape=(40, 50, 3))
bbsoi_shifted = bbsoi.shift(right=1)
assert len(bbsoi_shifted.bounding_boxes) == 2
assert bbsoi_shifted.bounding_boxes[0].y1 == 10
assert bbsoi_shifted.bounding_boxes[0].x1 == 20 - 1
assert bbsoi_shifted.bounding_boxes[0].y2 == 30
assert bbsoi_shifted.bounding_boxes[0].x2 == 40 - 1
assert bbsoi_shifted.bounding_boxes[1].y1 == 15
assert bbsoi_shifted.bounding_boxes[1].x1 == 25 - 1
assert bbsoi_shifted.bounding_boxes[1].y2 == 35
assert bbsoi_shifted.bounding_boxes[1].x2 == 51 - 1
# copy()
bb1 = ia.BoundingBox(y1=10, x1=20, y2=30, x2=40, label=None)
bb2 = ia.BoundingBox(y1=15, x1=25, y2=35, x2=51, label=None)
bbsoi = ia.BoundingBoxesOnImage([bb1, bb2], shape=(40, 50, 3))
bbsoi_copy = bbsoi.copy()
assert len(bbsoi.bounding_boxes) == 2
assert bbsoi_copy.bounding_boxes[0].y1 == 10
assert bbsoi_copy.bounding_boxes[0].x1 == 20
assert bbsoi_copy.bounding_boxes[0].y2 == 30
assert bbsoi_copy.bounding_boxes[0].x2 == 40
assert bbsoi_copy.bounding_boxes[1].y1 == 15
assert bbsoi_copy.bounding_boxes[1].x1 == 25
assert bbsoi_copy.bounding_boxes[1].y2 == 35
assert bbsoi_copy.bounding_boxes[1].x2 == 51
bbsoi.bounding_boxes[0].y1 = 0
assert bbsoi_copy.bounding_boxes[0].y1 == 0
# deepcopy()
bb1 = ia.BoundingBox(y1=10, x1=20, y2=30, x2=40, label=None)
bb2 = ia.BoundingBox(y1=15, x1=25, y2=35, x2=51, label=None)
bbsoi = ia.BoundingBoxesOnImage([bb1, bb2], shape=(40, 50, 3))
bbsoi_copy = bbsoi.deepcopy()
assert len(bbsoi.bounding_boxes) == 2
assert bbsoi_copy.bounding_boxes[0].y1 == 10
assert bbsoi_copy.bounding_boxes[0].x1 == 20
assert bbsoi_copy.bounding_boxes[0].y2 == 30
assert bbsoi_copy.bounding_boxes[0].x2 == 40
assert bbsoi_copy.bounding_boxes[1].y1 == 15
assert bbsoi_copy.bounding_boxes[1].x1 == 25
assert bbsoi_copy.bounding_boxes[1].y2 == 35
assert bbsoi_copy.bounding_boxes[1].x2 == 51
bbsoi.bounding_boxes[0].y1 = 0
assert bbsoi_copy.bounding_boxes[0].y1 == 10
# repr() / str()
bb1 = ia.BoundingBox(y1=10, x1=20, y2=30, x2=40, label=None)
bb2 = ia.BoundingBox(y1=15, x1=25, y2=35, x2=51, label=None)
bbsoi = ia.BoundingBoxesOnImage([bb1, bb2], shape=(40, 50, 3))
bb1_expected = "BoundingBox(x1=20.0000, y1=10.0000, x2=40.0000, y2=30.0000, label=None)"
bb2_expected = "BoundingBox(x1=25.0000, y1=15.0000, x2=51.0000, y2=35.0000, label=None)"
expected = "BoundingBoxesOnImage([%s, %s], shape=(40, 50, 3))" % (
bb1_expected, bb2_expected)
assert bbsoi.__repr__() == bbsoi.__str__() == expected
def test_Superpixels():
reseed()
def _array_equals_tolerant(a, b, tolerance):
diff = np.abs(a.astype(np.int32) - b.astype(np.int32))
return np.all(diff <= tolerance)
base_img = [[255, 255, 255, 0, 0, 0], [255, 235, 255, 0, 20, 0],
[250, 250, 250, 5, 5, 5]]
base_img = np.tile(
np.array(base_img, dtype=np.uint8)[..., np.newaxis], (1, 1, 3))
base_img_superpixels = [[251, 251, 251, 4, 4, 4], [251, 251, 251, 4, 4, 4],
[251, 251, 251, 4, 4, 4]]
base_img_superpixels = np.tile(
np.array(base_img_superpixels, dtype=np.uint8)[..., np.newaxis],
(1, 1, 3))
base_img_superpixels_left = np.copy(base_img_superpixels)
base_img_superpixels_left[:, 3:, :] = base_img[:, 3:, :]
base_img_superpixels_right = np.copy(base_img_superpixels)
base_img_superpixels_right[:, :3, :] = base_img[:, :3, :]
aug = iaa.Superpixels(p_replace=0, n_segments=2)
observed = aug.augment_image(base_img)
expected = base_img
assert np.allclose(observed, expected)
aug = iaa.Superpixels(p_replace=1.0, n_segments=2)
observed = aug.augment_image(base_img)
expected = base_img_superpixels
assert _array_equals_tolerant(observed, expected, 2)
aug = iaa.Superpixels(p_replace=1.0, n_segments=iap.Deterministic(2))
observed = aug.augment_image(base_img)
expected = base_img_superpixels
assert _array_equals_tolerant(observed, expected, 2)
aug = iaa.Superpixels(p_replace=iap.Binomial(iap.Choice([0.0, 1.0])),
n_segments=2)
observed = aug.augment_image(base_img)
assert np.allclose(observed, base_img) or _array_equals_tolerant(
observed, base_img_superpixels, 2)
aug = iaa.Superpixels(p_replace=0.5, n_segments=2)
seen = {"none": False, "left": False, "right": False, "both": False}
for _ in sm.xrange(100):
observed = aug.augment_image(base_img)
if _array_equals_tolerant(observed, base_img, 2):
seen["none"] = True
elif _array_equals_tolerant(observed, base_img_superpixels_left, 2):
seen["left"] = True
elif _array_equals_tolerant(observed, base_img_superpixels_right, 2):
seen["right"] = True
elif _array_equals_tolerant(observed, base_img_superpixels, 2):
seen["both"] = True
else:
raise Exception(
"Generated superpixels image does not match any expected image."
)
if all(seen.values()):
break
assert all(seen.values())
# test exceptions for wrong parameter types
got_exception = False
try:
_ = iaa.Superpixels(p_replace="test", n_segments=100)
except Exception:
got_exception = True
assert got_exception
got_exception = False
try:
_ = iaa.Superpixels(p_replace=1, n_segments="test")
except Exception:
got_exception = True
assert got_exception
# test get_parameters()
aug = iaa.Superpixels(p_replace=0.5,
n_segments=2,
max_size=100,
interpolation="nearest")
params = aug.get_parameters()
assert isinstance(params[0], iap.Binomial)
assert isinstance(params[0].p, iap.Deterministic)
assert isinstance(params[1], iap.Deterministic)
assert 0.5 - 1e-4 < params[0].p.value < 0.5 + 1e-4
assert params[1].value == 2
assert params[2] == 100
assert params[3] == "nearest"
###################
# test other dtypes
###################
# bool
aug = iaa.Superpixels(p_replace=1.0, n_segments=2)
img = np.array([[False, False, True, True], [False, False, True, True]],
dtype=bool)
img_aug = aug.augment_image(img)
assert img_aug.dtype == img.dtype
assert np.all(img_aug == img)
aug = iaa.Superpixels(p_replace=1.0, n_segments=1)
img = np.array([[True, True, True, True], [False, True, True, True]],
dtype=bool)
img_aug = aug.augment_image(img)
assert img_aug.dtype == img.dtype
assert np.all(img_aug)
for dtype in [np.uint8, np.uint16, np.uint32, np.int8, np.int16, np.int32]:
min_value, center_value, max_value = iadt.get_value_range_of_dtype(
dtype)
if np.dtype(dtype).kind == "i":
values = [
int(center_value),
int(0.1 * max_value),
int(0.2 * max_value),
int(0.5 * max_value), max_value - 100
]
values = [((-1) * value, value) for value in values]
else:
values = [(0, int(center_value)), (10, int(0.1 * max_value)),
(10, int(0.2 * max_value)), (10, int(0.5 * max_value)),
(0, max_value), (int(center_value), max_value)]
for v1, v2 in values:
aug = iaa.Superpixels(p_replace=1.0, n_segments=2)
img = np.array([[v1, v1, v2, v2], [v1, v1, v2, v2]], dtype=dtype)
img_aug = aug.augment_image(img)
assert img_aug.dtype == np.dtype(dtype)
assert np.array_equal(img_aug, img)
aug = iaa.Superpixels(p_replace=1.0, n_segments=1)
img = np.array([[v2, v2, v2, v2], [v1, v2, v2, v2]], dtype=dtype)
img_aug = aug.augment_image(img)
assert img_aug.dtype == np.dtype(dtype)
assert np.all(img_aug == int(np.round((7 / 8) * v2 +
(1 / 8) * v1)))
for dtype in []:
def _allclose(a, b):
atol = 1e-4 if dtype == np.float16 else 1e-8
return np.allclose(a, b, atol=atol, rtol=0)
isize = np.dtype(dtype).itemsize
for value in [0, 1.0, 10.0, 1000**(isize - 1)]:
v1 = (-1) * value
v2 = value
aug = iaa.Superpixels(p_replace=1.0, n_segments=2)
img = np.array([[v1, v1, v2, v2], [v1, v1, v2, v2]], dtype=dtype)
img_aug = aug.augment_image(img)
assert img_aug.dtype == np.dtype(dtype)
assert _allclose(img_aug, img)
aug = iaa.Superpixels(p_replace=1.0, n_segments=1)
img = np.array([[v2, v2, v2, v2], [v1, v2, v2, v2]], dtype=dtype)
img_aug = aug.augment_image(img)
assert img_aug.dtype == np.dtype(dtype)
assert _allclose(img_aug, (7 / 8) * v2 + (1 / 8) * v1)
def test_Superpixels():
reseed()
def _array_equals_tolerant(a, b, tolerance):
diff = np.abs(a.astype(np.int32) - b.astype(np.int32))
return np.all(diff <= tolerance)
base_img = [[255, 255, 255, 0, 0, 0], [255, 235, 255, 0, 20, 0],
[250, 250, 250, 5, 5, 5]]
base_img = np.tile(
np.array(base_img, dtype=np.uint8)[..., np.newaxis], (1, 1, 3))
base_img_superpixels = [[251, 251, 251, 4, 4, 4], [251, 251, 251, 4, 4, 4],
[251, 251, 251, 4, 4, 4]]
base_img_superpixels = np.tile(
np.array(base_img_superpixels, dtype=np.uint8)[..., np.newaxis],
(1, 1, 3))
base_img_superpixels_left = np.copy(base_img_superpixels)
base_img_superpixels_left[:, 3:, :] = base_img[:, 3:, :]
base_img_superpixels_right = np.copy(base_img_superpixels)
base_img_superpixels_right[:, :3, :] = base_img[:, :3, :]
aug = iaa.Superpixels(p_replace=0, n_segments=2)
observed = aug.augment_image(base_img)
expected = base_img
assert np.allclose(observed, expected)
aug = iaa.Superpixels(p_replace=1.0, n_segments=2)
observed = aug.augment_image(base_img)
expected = base_img_superpixels
assert _array_equals_tolerant(observed, expected, 2)
aug = iaa.Superpixels(p_replace=1.0, n_segments=iap.Deterministic(2))
observed = aug.augment_image(base_img)
expected = base_img_superpixels
assert _array_equals_tolerant(observed, expected, 2)
aug = iaa.Superpixels(p_replace=iap.Binomial(iap.Choice([0.0, 1.0])),
n_segments=2)
observed = aug.augment_image(base_img)
assert np.allclose(observed, base_img) or _array_equals_tolerant(
observed, base_img_superpixels, 2)
aug = iaa.Superpixels(p_replace=0.5, n_segments=2)
seen = {"none": False, "left": False, "right": False, "both": False}
for _ in sm.xrange(100):
observed = aug.augment_image(base_img)
if _array_equals_tolerant(observed, base_img, 2):
seen["none"] = True
elif _array_equals_tolerant(observed, base_img_superpixels_left, 2):
seen["left"] = True
elif _array_equals_tolerant(observed, base_img_superpixels_right, 2):
seen["right"] = True
elif _array_equals_tolerant(observed, base_img_superpixels, 2):
seen["both"] = True
else:
raise Exception(
"Generated superpixels image does not match any expected image."
)
if all(seen.values()):
break
assert all(seen.values())
# test exceptions for wrong parameter types
got_exception = False
try:
_ = iaa.Superpixels(p_replace="test", n_segments=100)
except Exception:
got_exception = True
assert got_exception
got_exception = False
try:
_ = iaa.Superpixels(p_replace=1, n_segments="test")
except Exception:
got_exception = True
assert got_exception
# test get_parameters()
aug = iaa.Superpixels(p_replace=0.5,
n_segments=2,
max_size=100,
interpolation="nearest")
params = aug.get_parameters()
assert isinstance(params[0], iap.Binomial)
assert isinstance(params[0].p, iap.Deterministic)
assert isinstance(params[1], iap.Deterministic)
assert 0.5 - 1e-4 < params[0].p.value < 0.5 + 1e-4
assert params[1].value == 2
assert params[2] == 100
assert params[3] == "nearest"
def test_determinism():
reseed()
images = [
ia.quokka(size=(128, 128)),
ia.quokka(size=(64, 64)),
ia.quokka((128, 256))
]
images.extend([ia.quokka(size=(16, 16))] * 20)
keypoints = [
ia.KeypointsOnImage([
ia.Keypoint(x=20, y=10), ia.Keypoint(x=5, y=5),
ia.Keypoint(x=10, y=43)], shape=(50, 60, 3))
] * 20
augs = [
iaa.Sequential([iaa.Fliplr(0.5), iaa.Flipud(0.5)]),
iaa.SomeOf(1, [iaa.Fliplr(0.5), iaa.Flipud(0.5)]),
iaa.OneOf([iaa.Fliplr(0.5), iaa.Flipud(0.5)]),
iaa.Sometimes(0.5, iaa.Fliplr(1.0)),
iaa.WithColorspace("HSV", children=iaa.Add((-50, 50))),
iaa.Resize((0.5, 0.9)),
iaa.CropAndPad(px=(-50, 50)),
iaa.Pad(px=(1, 50)),
iaa.Crop(px=(1, 50)),
iaa.Fliplr(0.5),
iaa.Flipud(0.5),
iaa.Superpixels(p_replace=(0.25, 1.0), n_segments=(16, 128)),
iaa.Grayscale(alpha=(0.1, 1.0)),
iaa.GaussianBlur((0.1, 3.0)),
iaa.AverageBlur((3, 11)),
iaa.MedianBlur((3, 11)),
iaa.Sharpen(alpha=(0.1, 1.0), lightness=(0.8, 1.2)),
iaa.Emboss(alpha=(0.1, 1.0), strength=(0.8, 1.2)),
iaa.EdgeDetect(alpha=(0.1, 1.0)),
iaa.DirectedEdgeDetect(alpha=(0.1, 1.0), direction=(0.0, 1.0)),
iaa.Add((-50, 50)),
iaa.AddElementwise((-50, 50)),
iaa.AdditiveGaussianNoise(scale=(0.1, 1.0)),
iaa.Multiply((0.6, 1.4)),
iaa.MultiplyElementwise((0.6, 1.4)),
iaa.Dropout((0.3, 0.5)),
iaa.CoarseDropout((0.3, 0.5), size_percent=(0.05, 0.2)),
iaa.Invert(0.5),
iaa.Affine(scale=(0.7, 1.3), translate_percent=(-0.1, 0.1),
rotate=(-20, 20), shear=(-20, 20), order=ia.ALL,
mode=ia.ALL, cval=(0, 255)),
iaa.PiecewiseAffine(scale=(0.1, 0.3)),
iaa.ElasticTransformation(alpha=10.0)
]
augs_affect_geometry = [
iaa.Sequential([iaa.Fliplr(0.5), iaa.Flipud(0.5)]),
iaa.SomeOf(1, [iaa.Fliplr(0.5), iaa.Flipud(0.5)]),
iaa.OneOf([iaa.Fliplr(0.5), iaa.Flipud(0.5)]),
iaa.Sometimes(0.5, iaa.Fliplr(1.0)),
iaa.Resize((0.5, 0.9)),
iaa.CropAndPad(px=(-50, 50)),
iaa.Pad(px=(1, 50)),
iaa.Crop(px=(1, 50)),
iaa.Fliplr(0.5),
iaa.Flipud(0.5),
iaa.Affine(scale=(0.7, 1.3), translate_percent=(-0.1, 0.1),
rotate=(-20, 20), shear=(-20, 20), order=ia.ALL,
mode=ia.ALL, cval=(0, 255)),
iaa.PiecewiseAffine(scale=(0.1, 0.3)),
iaa.ElasticTransformation(alpha=(5, 100), sigma=(3, 5))
]
for aug in augs:
aug_det = aug.to_deterministic()
images_aug1 = aug_det.augment_images(images)
images_aug2 = aug_det.augment_images(images)
aug_det = aug.to_deterministic()
images_aug3 = aug_det.augment_images(images)
images_aug4 = aug_det.augment_images(images)
assert array_equal_lists(images_aug1, images_aug2), \
"Images (1, 2) expected to be identical for %s" % (aug.name,)
assert array_equal_lists(images_aug3, images_aug4), \
"Images (3, 4) expected to be identical for %s" % (aug.name,)
assert not array_equal_lists(images_aug1, images_aug3), \
"Images (1, 3) expected to be different for %s" % (aug.name,)
for aug in augs_affect_geometry:
aug_det = aug.to_deterministic()
kps_aug1 = aug_det.augment_keypoints(keypoints)
kps_aug2 = aug_det.augment_keypoints(keypoints)
aug_det = aug.to_deterministic()
kps_aug3 = aug_det.augment_keypoints(keypoints)
kps_aug4 = aug_det.augment_keypoints(keypoints)
assert keypoints_equal(kps_aug1, kps_aug2), \
"Keypoints (1, 2) expected to be identical for %s" % (aug.name,)
assert keypoints_equal(kps_aug3, kps_aug4), \
"Keypoints (3, 4) expected to be identical for %s" % (aug.name,)
assert not keypoints_equal(kps_aug1, kps_aug3), \
"Keypoints (1, 3) expected to be different for %s" % (aug.name,)
def test_Convolve():
reseed()
img = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
img = np.uint8(img)
# matrix is None
aug = iaa.Convolve(matrix=None)
observed = aug.augment_image(img)
assert np.array_equal(observed, img)
aug = iaa.Convolve(matrix=lambda _img, nb_channels, random_state: [None])
observed = aug.augment_image(img)
assert np.array_equal(observed, img)
# matrix is [[1]]
aug = iaa.Convolve(matrix=np.float32([[1]]))
observed = aug.augment_image(img)
assert np.array_equal(observed, img)
aug = iaa.Convolve(
matrix=lambda _img, nb_channels, random_state: np.float32([[1]]))
observed = aug.augment_image(img)
assert np.array_equal(observed, img)
# matrix is [[0, 0, 0], [0, 1, 0], [0, 0, 0]]
m = np.float32([[0, 0, 0], [0, 1, 0], [0, 0, 0]])
aug = iaa.Convolve(matrix=m)
observed = aug.augment_image(img)
assert np.array_equal(observed, img)
aug = iaa.Convolve(matrix=lambda _img, nb_channels, random_state: m)
observed = aug.augment_image(img)
assert np.array_equal(observed, img)
# matrix is [[0, 0, 0], [0, 2, 0], [0, 0, 0]]
m = np.float32([[0, 0, 0], [0, 2, 0], [0, 0, 0]])
aug = iaa.Convolve(matrix=m)
observed = aug.augment_image(img)
assert np.array_equal(observed, 2 * img)
aug = iaa.Convolve(matrix=lambda _img, nb_channels, random_state: m)
observed = aug.augment_image(img)
assert np.array_equal(observed, 2 * img)
# matrix is [[0, 0, 0], [0, 2, 0], [0, 0, 0]]
# with 3 channels
m = np.float32([[0, 0, 0], [0, 2, 0], [0, 0, 0]])
img3 = np.tile(img[..., np.newaxis], (1, 1, 3))
aug = iaa.Convolve(matrix=m)
observed = aug.augment_image(img3)
assert np.array_equal(observed, 2 * img3)
aug = iaa.Convolve(matrix=lambda _img, nb_channels, random_state: m)
observed = aug.augment_image(img3)
assert np.array_equal(observed, 2 * img3)
# matrix is [[0, -1, 0], [0, 10, 0], [0, 0, 0]]
m = np.float32([[0, -1, 0], [0, 10, 0], [0, 0, 0]])
expected = np.uint8(
[[10 * 1 + (-1) * 4, 10 * 2 + (-1) * 5, 10 * 3 + (-1) * 6],
[10 * 4 + (-1) * 1, 10 * 5 + (-1) * 2, 10 * 6 + (-1) * 3],
[10 * 7 + (-1) * 4, 10 * 8 + (-1) * 5, 10 * 9 + (-1) * 6]])
aug = iaa.Convolve(matrix=m)
observed = aug.augment_image(img)
assert np.array_equal(observed, expected)
aug = iaa.Convolve(matrix=lambda _img, nb_channels, random_state: m)
observed = aug.augment_image(img)
assert np.array_equal(observed, expected)
# changing matrices when using callable
expected = []
for i in sm.xrange(5):
expected.append(img * i)
aug = iaa.Convolve(matrix=lambda _img, nb_channels, random_state: np.
float32([[random_state.randint(0, 5)]]))
seen = [False] * 5
for _ in sm.xrange(200):
observed = aug.augment_image(img)
found = False
for i, expected_i in enumerate(expected):
if np.array_equal(observed, expected_i):
seen[i] = True
found = True
break
assert found
if all(seen):
break
assert all(seen)
# bad datatype for matrix
got_exception = False
try:
aug = iaa.Convolve(matrix=False)
except Exception as exc:
assert "Expected " in str(exc)
got_exception = True
assert got_exception
# get_parameters()
matrix = np.int32([[1]])
aug = iaa.Convolve(matrix=matrix)
params = aug.get_parameters()
assert np.array_equal(params[0], matrix)
assert params[1] == "constant"
# TODO add test for keypoints once their handling was improved in Convolve
###################
# test other dtypes
###################
identity_matrix = np.int64([[1]])
aug = iaa.Convolve(matrix=identity_matrix)
image = np.zeros((3, 3), dtype=bool)
image[1, 1] = True
image_aug = aug.augment_image(image)
assert image.dtype.type == np.bool_
assert np.all(image_aug == image)
for dtype in [np.uint8, np.uint16, np.int8, np.int16]:
image = np.zeros((3, 3), dtype=dtype)
image[1, 1] = 100
image_aug = aug.augment_image(image)
assert image.dtype.type == dtype
assert np.all(image_aug == image)
for dtype in [np.float16, np.float32, np.float64]:
image = np.zeros((3, 3), dtype=dtype)
image[1, 1] = 100.0
image_aug = aug.augment_image(image)
assert image.dtype.type == dtype
assert np.allclose(image_aug, image)
# ----
# non-identity matrix
# ----
matrix = np.float64([[0, 0.6, 0], [0, 0.4, 0], [0, 0, 0]])
aug = iaa.Convolve(matrix=matrix)
image = np.zeros((3, 3), dtype=bool)
image[1, 1] = True
image[2, 1] = True
expected = np.zeros((3, 3), dtype=bool)
expected[0, 1] = True
expected[2, 1] = True
image_aug = aug.augment_image(image)
assert image.dtype.type == np.bool_
assert np.all(image_aug == expected)
matrix = np.float64([[0, 0.5, 0], [0, 0.5, 0], [0, 0, 0]])
aug = iaa.Convolve(matrix=matrix)
# uint, int
for dtype in [np.uint8, np.uint16, np.int8, np.int16]:
image = np.zeros((3, 3), dtype=dtype)
image[1, 1] = 100
image[2, 1] = 100
image_aug = aug.augment_image(image)
expected = np.zeros((3, 3), dtype=dtype)
expected[0, 1] = int(np.round(100 * 0.5))
expected[1, 1] = int(np.round(100 * 0.5))
expected[2, 1] = int(np.round(100 * 0.5 + 100 * 0.5))
diff = np.abs(image_aug.astype(np.int64) - expected.astype(np.int64))
assert image_aug.dtype.type == dtype
assert np.max(diff) <= 2
# float
for dtype in [np.float16, np.float32, np.float64]:
image = np.zeros((3, 3), dtype=dtype)
image[1, 1] = 100.0
image[2, 1] = 100.0
image_aug = aug.augment_image(image)
expected = np.zeros((3, 3), dtype=dtype)
expected[0, 1] = 100 * 0.5
expected[1, 1] = 100 * 0.5
expected[2, 1] = 100 * 0.5 + 100 * 0.5
diff = np.abs(
image_aug.astype(np.float128) - expected.astype(np.float128))
assert image_aug.dtype.type == dtype
assert np.max(diff) < 1.0
# ----
# non-identity matrix, higher values
# ----
matrix = np.float64([[0, 0.5, 0], [0, 0.5, 0], [0, 0, 0]])
aug = iaa.Convolve(matrix=matrix)
# uint, int
for dtype in [np.uint8, np.uint16, np.int8, np.int16]:
_min_value, center_value, max_value = iadt.get_value_range_of_dtype(
dtype)
value = int(center_value + 0.4 * max_value)
image = np.zeros((3, 3), dtype=dtype)
image[1, 1] = value
image[2, 1] = value
image_aug = aug.augment_image(image)
expected = np.zeros((3, 3), dtype=dtype)
expected[0, 1] = int(np.round(value * 0.5))
expected[1, 1] = int(np.round(value * 0.5))
expected[2, 1] = int(np.round(value * 0.5 + value * 0.5))
diff = np.abs(image_aug.astype(np.int64) - expected.astype(np.int64))
assert image_aug.dtype.type == dtype
assert np.max(diff) <= 2
# float
for dtype, value in zip([np.float16, np.float32, np.float64],
[5000, 1000 * 1000, 1000 * 1000 * 1000]):
image = np.zeros((3, 3), dtype=dtype)
image[1, 1] = value
image[2, 1] = value
image_aug = aug.augment_image(image)
expected = np.zeros((3, 3), dtype=dtype)
expected[0, 1] = value * 0.5
expected[1, 1] = value * 0.5
expected[2, 1] = value * 0.5 + value * 0.5
diff = np.abs(
image_aug.astype(np.float128) - expected.astype(np.float128))
assert image_aug.dtype.type == dtype
assert np.max(diff) < 1.0
# assert failure on invalid dtypes
aug = iaa.Convolve(matrix=identity_matrix)
for dt in [np.uint32, np.uint64, np.int32, np.int64]:
got_exception = False
try:
_ = aug.augment_image(np.zeros((1, 1), dtype=dt))
except Exception as exc:
assert "forbidden dtype" in str(exc)
got_exception = True
assert got_exception
def test_MedianBlur():
reseed()
base_img = np.zeros((11, 11, 1), dtype=np.uint8)
base_img[3:8, 3:8, 0] = 1
base_img[4:7, 4:7, 0] = 2
base_img[5:6, 5:6, 0] = 3
blur3x3 = np.zeros_like(base_img)
blur3x3[3:8, 3:8, 0] = 1
blur3x3[4:7, 4:7, 0] = 2
blur3x3[4, 4, 0] = 1
blur3x3[4, 6, 0] = 1
blur3x3[6, 4, 0] = 1
blur3x3[6, 6, 0] = 1
blur3x3[3, 3, 0] = 0
blur3x3[3, 7, 0] = 0
blur3x3[7, 3, 0] = 0
blur3x3[7, 7, 0] = 0
blur5x5 = np.copy(blur3x3)
blur5x5[4, 3, 0] = 0
blur5x5[3, 4, 0] = 0
blur5x5[6, 3, 0] = 0
blur5x5[7, 4, 0] = 0
blur5x5[4, 7, 0] = 0
blur5x5[3, 6, 0] = 0
blur5x5[6, 7, 0] = 0
blur5x5[7, 6, 0] = 0
blur5x5[blur5x5 > 1] = 1
keypoints = [
ia.KeypointsOnImage([
ia.Keypoint(x=0, y=0),
ia.Keypoint(x=1, y=1),
ia.Keypoint(x=2, y=2)
],
shape=base_img.shape)
]
# no blur, shouldnt change anything
aug = iaa.MedianBlur(k=1)
observed = aug.augment_image(base_img)
assert np.array_equal(observed, base_img)
# k=3
aug = iaa.MedianBlur(k=3)
observed = aug.augment_image(base_img)
assert np.array_equal(observed, blur3x3)
# k=5
aug = iaa.MedianBlur(k=5)
observed = aug.augment_image(base_img)
assert np.array_equal(observed, blur5x5)
# k as (3, 5)
aug = iaa.MedianBlur(k=(3, 5))
seen = [False, False]
for i in sm.xrange(100):
observed = aug.augment_image(base_img)
if np.array_equal(observed, blur3x3):
seen[0] = True
elif np.array_equal(observed, blur5x5):
seen[1] = True
else:
raise Exception("Unexpected result in MedianBlur@1")
if all(seen):
break
assert all(seen)
# k as stochastic parameter
aug = iaa.MedianBlur(k=iap.Choice([3, 5]))
seen = [False, False]
for i in sm.xrange(100):
observed = aug.augment_image(base_img)
if np.array_equal(observed, blur3x3):
seen[0] += True
elif np.array_equal(observed, blur5x5):
seen[1] += True
else:
raise Exception("Unexpected result in MedianBlur@2")
if all(seen):
break
assert all(seen)
# keypoints shouldnt be changed
aug = iaa.MedianBlur(k=3)
aug_det = aug.to_deterministic()
observed = aug.augment_keypoints(keypoints)
expected = keypoints
assert keypoints_equal(observed, expected)
observed = aug_det.augment_keypoints(keypoints)
expected = keypoints
assert keypoints_equal(observed, expected)
def test_Alpha():
reseed()
base_img = np.zeros((3, 3, 1), dtype=np.uint8)
heatmaps_arr = np.float32([[0.0, 0.0, 1.0],
[0.0, 0.0, 1.0],
[0.0, 1.0, 1.0]])
heatmaps_arr_r1 = np.float32([[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0],
[0.0, 0.0, 1.0]])
heatmaps_arr_l1 = np.float32([[0.0, 1.0, 0.0],
[0.0, 1.0, 0.0],
[1.0, 1.0, 0.0]])
heatmaps = ia.HeatmapsOnImage(heatmaps_arr, shape=(3, 3, 3))
aug = iaa.Alpha(1, iaa.Add(10), iaa.Add(20))
observed = aug.augment_image(base_img)
expected = np.round(base_img + 10).astype(np.uint8)
assert np.allclose(observed, expected)
for per_channel in [False, True]:
aug = iaa.Alpha(1, iaa.Affine(translate_px={"x": 1}), iaa.Affine(translate_px={"x": -1}),
per_channel=per_channel)
observed = aug.augment_heatmaps([heatmaps])[0]
assert observed.shape == heatmaps.shape
assert 0 - 1e-6 < heatmaps.min_value < 0 + 1e-6
assert 1 - 1e-6 < heatmaps.max_value < 1 + 1e-6
assert np.allclose(observed.get_arr(), heatmaps_arr_r1)
aug = iaa.Alpha(0, iaa.Add(10), iaa.Add(20))
observed = aug.augment_image(base_img)
expected = np.round(base_img + 20).astype(np.uint8)
assert np.allclose(observed, expected)
for per_channel in [False, True]:
aug = iaa.Alpha(0, iaa.Affine(translate_px={"x": 1}), iaa.Affine(translate_px={"x": -1}),
per_channel=per_channel)
observed = aug.augment_heatmaps([heatmaps])[0]
assert observed.shape == heatmaps.shape
assert 0 - 1e-6 < heatmaps.min_value < 0 + 1e-6
assert 1 - 1e-6 < heatmaps.max_value < 1 + 1e-6
assert np.allclose(observed.get_arr(), heatmaps_arr_l1)
aug = iaa.Alpha(0.75, iaa.Add(10), iaa.Add(20))
observed = aug.augment_image(base_img)
expected = np.round(base_img + 0.75 * 10 + 0.25 * 20).astype(np.uint8)
assert np.allclose(observed, expected)
aug = iaa.Alpha(0.75, None, iaa.Add(20))
observed = aug.augment_image(base_img + 10)
expected = np.round(base_img + 0.75 * 10 + 0.25 * (10 + 20)).astype(np.uint8)
assert np.allclose(observed, expected)
aug = iaa.Alpha(0.75, iaa.Add(10), None)
observed = aug.augment_image(base_img + 10)
expected = np.round(base_img + 0.75 * (10 + 10) + 0.25 * 10).astype(np.uint8)
assert np.allclose(observed, expected)
base_img = np.zeros((1, 2, 1), dtype=np.uint8)
nb_iterations = 1000
aug = iaa.Alpha((0.0, 1.0), iaa.Add(10), iaa.Add(110))
values = []
for _ in sm.xrange(nb_iterations):
observed = aug.augment_image(base_img)
observed_val = np.round(np.average(observed)) - 10
values.append(observed_val / 100)
nb_bins = 5
hist, _ = np.histogram(values, bins=nb_bins, range=(0.0, 1.0), density=False)
density_expected = 1.0/nb_bins
density_tolerance = 0.05
for nb_samples in hist:
density = nb_samples / nb_iterations
assert density_expected - density_tolerance < density < density_expected + density_tolerance
# bad datatype for factor
got_exception = False
try:
_ = iaa.Alpha(False, iaa.Add(10), None)
except Exception as exc:
assert "Expected " in str(exc)
got_exception = True
assert got_exception
# per_channel
aug = iaa.Alpha(1.0, iaa.Add((0, 100), per_channel=True), None, per_channel=True)
observed = aug.augment_image(np.zeros((1, 1, 1000), dtype=np.uint8))
uq = np.unique(observed)
assert len(uq) > 1
assert np.max(observed) > 80
assert np.min(observed) < 20
aug = iaa.Alpha((0.0, 1.0), iaa.Add(100), None, per_channel=True)
observed = aug.augment_image(np.zeros((1, 1, 1000), dtype=np.uint8))
uq = np.unique(observed)
assert len(uq) > 1
assert np.max(observed) > 80
assert np.min(observed) < 20
aug = iaa.Alpha((0.0, 1.0), iaa.Add(100), iaa.Add(0), per_channel=0.5)
seen = [0, 0]
for _ in sm.xrange(200):
observed = aug.augment_image(np.zeros((1, 1, 100), dtype=np.uint8))
uq = np.unique(observed)
if len(uq) == 1:
seen[0] += 1
elif len(uq) > 1:
seen[1] += 1
else:
assert False
assert 100 - 50 < seen[0] < 100 + 50
assert 100 - 50 < seen[1] < 100 + 50
# bad datatype for per_channel
got_exception = False
try:
_ = iaa.Alpha(0.5, iaa.Add(10), None, per_channel="test")
except Exception as exc:
assert "Expected " in str(exc)
got_exception = True
assert got_exception
# propagating
aug = iaa.Alpha(0.5, iaa.Add(100), iaa.Add(50), name="AlphaTest")
def propagator(images, augmenter, parents, default):
if "Alpha" in augmenter.name:
return False
else:
return default
hooks = ia.HooksImages(propagator=propagator)
image = np.zeros((10, 10, 3), dtype=np.uint8) + 1
observed = aug.augment_image(image, hooks=hooks)
assert np.array_equal(observed, image)
# -----
# keypoints
# -----
kps = [ia.Keypoint(x=5, y=10), ia.Keypoint(x=6, y=11)]
kpsoi = ia.KeypointsOnImage(kps, shape=(20, 20, 3))
aug = iaa.Alpha(1.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.deepcopy()
assert keypoints_equal([observed], [expected])
aug = iaa.Alpha(0.501, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.deepcopy()
assert keypoints_equal([observed], [expected])
aug = iaa.Alpha(0.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.shift(x=1)
assert keypoints_equal([observed], [expected])
aug = iaa.Alpha(0.499, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.shift(x=1)
assert keypoints_equal([observed], [expected])
# per_channel
aug = iaa.Alpha(1.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}), per_channel=True)
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.deepcopy()
assert keypoints_equal([observed], [expected])
aug = iaa.Alpha(0.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}), per_channel=True)
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.shift(x=1)
assert keypoints_equal([observed], [expected])
aug = iaa.Alpha(iap.Choice([0.49, 0.51]), iaa.Noop(), iaa.Affine(translate_px={"x": 1}), per_channel=True)
expected_same = kpsoi.deepcopy()
expected_shifted = kpsoi.shift(x=1)
seen = [0, 0]
for _ in sm.xrange(200):
observed = aug.augment_keypoints([kpsoi])[0]
if keypoints_equal([observed], [expected_same]):
seen[0] += 1
elif keypoints_equal([observed], [expected_shifted]):
seen[1] += 1
else:
assert False
assert 100 - 50 < seen[0] < 100 + 50
assert 100 - 50 < seen[1] < 100 + 50
# empty keypoints
aug = iaa.Alpha(0.501, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_keypoints(ia.KeypointsOnImage([], shape=(1, 2, 3)))
assert len(observed.keypoints) == 0
assert observed.shape == (1, 2, 3)
# propagating
aug = iaa.Alpha(0.0, iaa.Affine(translate_px={"x": 1}), iaa.Affine(translate_px={"y": 1}), name="AlphaTest")
def propagator(kpsoi_to_aug, augmenter, parents, default):
if "Alpha" in augmenter.name:
return False
else:
return default
hooks = ia.HooksKeypoints(propagator=propagator)
observed = aug.augment_keypoints([kpsoi], hooks=hooks)[0]
assert keypoints_equal([observed], [kpsoi])
# -----
# polygons
# -----
ps = [ia.Polygon([(5, 5), (10, 5), (10, 10)])]
psoi = ia.PolygonsOnImage(ps, shape=(20, 20, 3))
aug = iaa.Alpha(1.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_polygons([psoi])
assert len(observed) == 1
assert len(observed[0].polygons) == 1
assert observed[0].shape == psoi.shape
assert observed[0].polygons[0].exterior_almost_equals(psoi.polygons[0])
assert observed[0].polygons[0].is_valid
aug = iaa.Alpha(0.501, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_polygons([psoi])
assert len(observed) == 1
assert len(observed[0].polygons) == 1
assert observed[0].shape == psoi.shape
assert observed[0].polygons[0].exterior_almost_equals(psoi.polygons[0])
assert observed[0].polygons[0].is_valid
aug = iaa.Alpha(0.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_polygons([psoi])
expected = psoi.shift(left=1)
assert len(observed) == 1
assert len(observed[0].polygons) == 1
assert observed[0].shape == psoi.shape
assert observed[0].polygons[0].exterior_almost_equals(expected.polygons[0])
assert observed[0].polygons[0].is_valid
aug = iaa.Alpha(0.499, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_polygons([psoi])
expected = psoi.shift(left=1)
assert len(observed) == 1
assert len(observed[0].polygons) == 1
assert observed[0].shape == psoi.shape
assert observed[0].polygons[0].exterior_almost_equals(expected.polygons[0])
assert observed[0].polygons[0].is_valid
# per_channel
aug = iaa.Alpha(1.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}), per_channel=True)
observed = aug.augment_polygons([psoi])
assert len(observed) == 1
assert len(observed[0].polygons) == 1
assert observed[0].shape == psoi.shape
assert observed[0].polygons[0].exterior_almost_equals(psoi.polygons[0])
assert observed[0].polygons[0].is_valid
aug = iaa.Alpha(0.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}), per_channel=True)
observed = aug.augment_polygons([psoi])
expected = psoi.shift(left=1)
assert len(observed) == 1
assert len(observed[0].polygons) == 1
assert observed[0].shape == psoi.shape
assert observed[0].polygons[0].exterior_almost_equals(expected.polygons[0])
assert observed[0].polygons[0].is_valid
aug = iaa.Alpha(iap.Choice([0.49, 0.51]), iaa.Noop(), iaa.Affine(translate_px={"x": 1}), per_channel=True)
expected_same = psoi.deepcopy()
expected_shifted = psoi.shift(left=1)
seen = [0, 0]
for _ in sm.xrange(200):
observed = aug.augment_polygons([psoi])[0]
if observed.polygons[0].exterior_almost_equals(expected_same.polygons[0]):
seen[0] += 1
elif observed.polygons[0].exterior_almost_equals(expected_shifted.polygons[0]):
seen[1] += 1
else:
assert False
assert 100 - 50 < seen[0] < 100 + 50
assert 100 - 50 < seen[1] < 100 + 50
# empty polygons
aug = iaa.Alpha(0.501, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_polygons(ia.PolygonsOnImage([], shape=(1, 2, 3)))
assert len(observed.polygons) == 0
assert observed.shape == (1, 2, 3)
# propagating
aug = iaa.Alpha(0.0, iaa.Affine(translate_px={"x": 1}), iaa.Affine(translate_px={"y": 1}), name="AlphaTest")
def propagator(psoi_to_aug, augmenter, parents, default):
if "Alpha" in augmenter.name:
return False
else:
return default
hooks = ia.HooksKeypoints(propagator=propagator) # no hooks for polygons yet, so we use HooksKeypoints
observed = aug.augment_polygons([psoi], hooks=hooks)[0]
assert observed.polygons[0].exterior_almost_equals(psoi.polygons[0])
# -----
# get_parameters()
# -----
first = iaa.Noop()
second = iaa.Sequential([iaa.Add(1)])
aug = iaa.Alpha(0.65, first, second, per_channel=1)
params = aug.get_parameters()
assert isinstance(params[0], iap.Deterministic)
assert isinstance(params[1], iap.Deterministic)
assert 0.65 - 1e-6 < params[0].value < 0.65 + 1e-6
assert params[1].value == 1
# -----
# get_children_lists()
# -----
first = iaa.Noop()
second = iaa.Sequential([iaa.Add(1)])
aug = iaa.Alpha(0.65, first, second, per_channel=1)
children_lsts = aug.get_children_lists()
assert len(children_lsts) == 2
assert ia.is_iterable([lst for lst in children_lsts])
assert first in children_lsts[0]
assert second == children_lsts[1]
def test_Emboss():
reseed()
base_img = [[10, 10, 10],
[10, 20, 10],
[10, 10, 15]]
base_img = np.uint8(base_img)
def _compute_embossed_base_img(img, alpha, strength):
img = np.copy(img)
base_img_embossed = np.zeros((3, 3), dtype=np.float32)
m = np.float32([[-1, 0, 0],
[0, 1, 0],
[0, 0, 1]])
strength_matrix = strength * np.float32([
[-1, -1, 0],
[-1, 0, 1],
[0, 1, 1]
])
ms = m + strength_matrix
for i in range(base_img_embossed.shape[0]):
for j in range(base_img_embossed.shape[1]):
for u in range(ms.shape[0]):
for v in range(ms.shape[1]):
weight = ms[u, v]
inputs_i = abs(i + (u - (ms.shape[0]-1)//2))
inputs_j = abs(j + (v - (ms.shape[1]-1)//2))
if inputs_i >= img.shape[0]:
diff = inputs_i - (img.shape[0]-1)
inputs_i = img.shape[0] - 1 - diff
if inputs_j >= img.shape[1]:
diff = inputs_j - (img.shape[1]-1)
inputs_j = img.shape[1] - 1 - diff
inputs = img[inputs_i, inputs_j]
base_img_embossed[i, j] += inputs * weight
return np.clip((1-alpha) * img + alpha * base_img_embossed, 0, 255).astype(np.uint8)
def _allclose(a, b):
return np.max(a.astype(np.float32) - b.astype(np.float32)) <= 2.1
aug = iaa.Emboss(alpha=0, strength=1)
observed = aug.augment_image(base_img)
expected = base_img
assert _allclose(observed, expected)
aug = iaa.Emboss(alpha=1.0, strength=1)
observed = aug.augment_image(base_img)
expected = _compute_embossed_base_img(base_img, alpha=1.0, strength=1)
assert _allclose(observed, expected)
aug = iaa.Emboss(alpha=0.5, strength=1)
observed = aug.augment_image(base_img)
expected = _compute_embossed_base_img(base_img, alpha=0.5, strength=1)
assert _allclose(observed, expected.astype(np.uint8))
aug = iaa.Emboss(alpha=0.75, strength=1)
observed = aug.augment_image(base_img)
expected = _compute_embossed_base_img(base_img, alpha=0.75, strength=1)
assert _allclose(observed, expected)
aug = iaa.Emboss(alpha=iap.Choice([0.5, 1.0]), strength=1)
observed = aug.augment_image(base_img)
expected1 = _compute_embossed_base_img(base_img, alpha=0.5, strength=1)
expected2 = _compute_embossed_base_img(base_img, alpha=1.0, strength=1)
assert _allclose(observed, expected1) or np.allclose(observed, expected2)
got_exception = False
try:
_ = iaa.Emboss(alpha="test", strength=1)
except Exception as exc:
assert "Expected " in str(exc)
got_exception = True
assert got_exception
aug = iaa.Emboss(alpha=1.0, strength=2)
observed = aug.augment_image(base_img)
expected = _compute_embossed_base_img(base_img, alpha=1.0, strength=2)
assert _allclose(observed, expected)
aug = iaa.Emboss(alpha=1.0, strength=3)
observed = aug.augment_image(base_img)
expected = _compute_embossed_base_img(base_img, alpha=1.0, strength=3)
assert _allclose(observed, expected)
aug = iaa.Emboss(alpha=1.0, strength=6)
observed = aug.augment_image(base_img)
expected = _compute_embossed_base_img(base_img, alpha=1.0, strength=6)
assert _allclose(observed, expected)
aug = iaa.Emboss(alpha=1.0, strength=iap.Choice([1.0, 1.5]))
observed = aug.augment_image(base_img)
expected1 = _compute_embossed_base_img(base_img, alpha=1.0, strength=1.0)
expected2 = _compute_embossed_base_img(base_img, alpha=1.0, strength=1.5)
assert _allclose(observed, expected1) or np.allclose(observed, expected2)
got_exception = False
try:
_ = iaa.Emboss(alpha=1.0, strength="test")
except Exception as exc:
assert "Expected " in str(exc)
got_exception = True
assert got_exception
def test_GaussianBlur():
reseed()
base_img = np.array([[0, 0, 0], [0, 255, 0], [0, 0, 0]], dtype=np.uint8)
base_img = base_img[:, :, np.newaxis]
images = np.array([base_img])
images_list = [base_img]
outer_pixels = ([], [])
for i in sm.xrange(base_img.shape[0]):
for j in sm.xrange(base_img.shape[1]):
if i != j:
outer_pixels[0].append(i)
outer_pixels[1].append(j)
keypoints = [
ia.KeypointsOnImage([
ia.Keypoint(x=0, y=0),
ia.Keypoint(x=1, y=1),
ia.Keypoint(x=2, y=2)
],
shape=base_img.shape)
]
# no blur, shouldnt change anything
aug = iaa.GaussianBlur(sigma=0)
aug_det = aug.to_deterministic()
observed = aug.augment_images(images)
expected = images
assert np.array_equal(observed, expected)
# weak blur of center pixel
aug = iaa.GaussianBlur(sigma=0.5)
aug_det = aug.to_deterministic()
# images as numpy array
observed = aug.augment_images(images)
assert 100 < observed[0][1, 1] < 255
assert (observed[0][outer_pixels[0], outer_pixels[1]] > 0).all()
assert (observed[0][outer_pixels[0], outer_pixels[1]] < 50).all()
observed = aug_det.augment_images(images)
assert 100 < observed[0][1, 1] < 255
assert (observed[0][outer_pixels[0], outer_pixels[1]] > 0).all()
assert (observed[0][outer_pixels[0], outer_pixels[1]] < 50).all()
# images as list
observed = aug.augment_images(images_list)
assert 100 < observed[0][1, 1] < 255
assert (observed[0][outer_pixels[0], outer_pixels[1]] > 0).all()
assert (observed[0][outer_pixels[0], outer_pixels[1]] < 50).all()
observed = aug_det.augment_images(images_list)
assert 100 < observed[0][1, 1] < 255
assert (observed[0][outer_pixels[0], outer_pixels[1]] > 0).all()
assert (observed[0][outer_pixels[0], outer_pixels[1]] < 50).all()
# keypoints shouldnt be changed
observed = aug.augment_keypoints(keypoints)
expected = keypoints
assert keypoints_equal(observed, expected)
observed = aug_det.augment_keypoints(keypoints)
expected = keypoints
assert keypoints_equal(observed, expected)
# varying blur sigmas
aug = iaa.GaussianBlur(sigma=(0, 1))
aug_det = aug.to_deterministic()
last_aug = None
last_aug_det = None
nb_changed_aug = 0
nb_changed_aug_det = 0
nb_iterations = 1000
for i in sm.xrange(nb_iterations):
observed_aug = aug.augment_images(images)
observed_aug_det = aug_det.augment_images(images)
if i == 0:
last_aug = observed_aug
last_aug_det = observed_aug_det
else:
if not np.array_equal(observed_aug, last_aug):
nb_changed_aug += 1
if not np.array_equal(observed_aug_det, last_aug_det):
nb_changed_aug_det += 1
last_aug = observed_aug
last_aug_det = observed_aug_det
assert nb_changed_aug >= int(nb_iterations * 0.8)
assert nb_changed_aug_det == 0
def test_Emboss():
reseed()
base_img = [[10, 10, 10], [10, 20, 10], [10, 10, 15]]
base_img = np.uint8(base_img)
def _compute_embossed_base_img(img, alpha, strength):
img = np.copy(img)
base_img_embossed = np.zeros((3, 3), dtype=np.float32)
m = np.float32([[-1, 0, 0], [0, 1, 0], [0, 0, 1]])
strength_matrix = strength * np.float32([[-1, -1, 0], [-1, 0, 1],
[0, 1, 1]])
ms = m + strength_matrix
for i in range(base_img_embossed.shape[0]):
for j in range(base_img_embossed.shape[1]):
for u in range(ms.shape[0]):
for v in range(ms.shape[1]):
weight = ms[u, v]
inputs_i = abs(i + (u - (ms.shape[0] - 1) // 2))
inputs_j = abs(j + (v - (ms.shape[1] - 1) // 2))
if inputs_i >= img.shape[0]:
diff = inputs_i - (img.shape[0] - 1)
inputs_i = img.shape[0] - 1 - diff
if inputs_j >= img.shape[1]:
diff = inputs_j - (img.shape[1] - 1)
inputs_j = img.shape[1] - 1 - diff
inputs = img[inputs_i, inputs_j]
base_img_embossed[i, j] += inputs * weight
return np.clip((1 - alpha) * img + alpha * base_img_embossed, 0,
255).astype(np.uint8)
def _allclose(a, b):
return np.max(a.astype(np.float32) - b.astype(np.float32)) <= 2.1
aug = iaa.Emboss(alpha=0, strength=1)
observed = aug.augment_image(base_img)
expected = base_img
assert _allclose(observed, expected)
aug = iaa.Emboss(alpha=1.0, strength=1)
observed = aug.augment_image(base_img)
expected = _compute_embossed_base_img(base_img, alpha=1.0, strength=1)
assert _allclose(observed, expected)
aug = iaa.Emboss(alpha=0.5, strength=1)
observed = aug.augment_image(base_img)
expected = _compute_embossed_base_img(base_img, alpha=0.5, strength=1)
assert _allclose(observed, expected.astype(np.uint8))
aug = iaa.Emboss(alpha=0.75, strength=1)
observed = aug.augment_image(base_img)
expected = _compute_embossed_base_img(base_img, alpha=0.75, strength=1)
assert _allclose(observed, expected)
aug = iaa.Emboss(alpha=iap.Choice([0.5, 1.0]), strength=1)
observed = aug.augment_image(base_img)
expected1 = _compute_embossed_base_img(base_img, alpha=0.5, strength=1)
expected2 = _compute_embossed_base_img(base_img, alpha=1.0, strength=1)
assert _allclose(observed, expected1) or np.allclose(observed, expected2)
got_exception = False
try:
_ = iaa.Emboss(alpha="test", strength=1)
except Exception as exc:
assert "Expected " in str(exc)
got_exception = True
assert got_exception
aug = iaa.Emboss(alpha=1.0, strength=2)
observed = aug.augment_image(base_img)
expected = _compute_embossed_base_img(base_img, alpha=1.0, strength=2)
assert _allclose(observed, expected)
aug = iaa.Emboss(alpha=1.0, strength=3)
observed = aug.augment_image(base_img)
expected = _compute_embossed_base_img(base_img, alpha=1.0, strength=3)
assert _allclose(observed, expected)
aug = iaa.Emboss(alpha=1.0, strength=6)
observed = aug.augment_image(base_img)
expected = _compute_embossed_base_img(base_img, alpha=1.0, strength=6)
assert _allclose(observed, expected)
aug = iaa.Emboss(alpha=1.0, strength=iap.Choice([1.0, 1.5]))
observed = aug.augment_image(base_img)
expected1 = _compute_embossed_base_img(base_img, alpha=1.0, strength=1.0)
expected2 = _compute_embossed_base_img(base_img, alpha=1.0, strength=1.5)
assert _allclose(observed, expected1) or np.allclose(observed, expected2)
got_exception = False
try:
_ = iaa.Emboss(alpha=1.0, strength="test")
except Exception as exc:
assert "Expected " in str(exc)
got_exception = True
assert got_exception
def test_Flipud():
reseed()
base_img = np.array([[0, 0, 1],
[0, 0, 1],
[0, 1, 1]], dtype=np.uint8)
base_img = base_img[:, :, np.newaxis]
base_img_flipped = np.array([[0, 1, 1],
[0, 0, 1],
[0, 0, 1]], dtype=np.uint8)
base_img_flipped = base_img_flipped[:, :, np.newaxis]
images = np.array([base_img])
images_flipped = np.array([base_img_flipped])
keypoints = [ia.KeypointsOnImage([ia.Keypoint(x=0, y=0), ia.Keypoint(x=1, y=1),
ia.Keypoint(x=2, y=2)], shape=base_img.shape)]
keypoints_flipped = [ia.KeypointsOnImage([ia.Keypoint(x=0, y=2), ia.Keypoint(x=1, y=1),
ia.Keypoint(x=2, y=0)], shape=base_img.shape)]
# 0% chance of flip
aug = iaa.Flipud(0)
aug_det = aug.to_deterministic()
for _ in sm.xrange(10):
observed = aug.augment_images(images)
expected = images
assert np.array_equal(observed, expected)
observed = aug_det.augment_images(images)
expected = images
assert np.array_equal(observed, expected)
observed = aug.augment_keypoints(keypoints)
expected = keypoints
assert keypoints_equal(observed, expected)
observed = aug_det.augment_keypoints(keypoints)
expected = keypoints
assert keypoints_equal(observed, expected)
# 0% chance of flip, heatmaps
aug = iaa.Flipud(0)
heatmaps = ia.HeatmapsOnImage(
np.float32([
[0, 0.5, 0.75],
[0, 0.5, 0.75],
[0.75, 0.75, 0.75],
]),
shape=(3, 3, 3)
)
observed = aug.augment_heatmaps([heatmaps])[0]
expected = heatmaps.get_arr()
assert observed.shape == heatmaps.shape
assert heatmaps.min_value - 1e-6 < observed.min_value < heatmaps.min_value + 1e-6
assert heatmaps.max_value - 1e-6 < observed.max_value < heatmaps.max_value + 1e-6
assert np.array_equal(observed.get_arr(), expected)
# 100% chance of flip
aug = iaa.Flipud(1.0)
aug_det = aug.to_deterministic()
for _ in sm.xrange(10):
observed = aug.augment_images(images)
expected = images_flipped
assert np.array_equal(observed, expected)
observed = aug_det.augment_images(images)
expected = images_flipped
assert np.array_equal(observed, expected)
observed = aug.augment_keypoints(keypoints)
expected = keypoints_flipped
assert keypoints_equal(observed, expected)
observed = aug_det.augment_keypoints(keypoints)
expected = keypoints_flipped
assert keypoints_equal(observed, expected)
# 100% chance of flip, heatmaps
aug = iaa.Flipud(1.0)
heatmaps = ia.HeatmapsOnImage(
np.float32([
[0, 0.5, 0.75],
[0, 0.5, 0.75],
[0.75, 0.75, 0.75],
]),
shape=(3, 3, 3)
)
observed = aug.augment_heatmaps([heatmaps])[0]
expected = np.flipud(heatmaps.get_arr())
assert observed.shape == heatmaps.shape
assert heatmaps.min_value - 1e-6 < observed.min_value < heatmaps.min_value + 1e-6
assert heatmaps.max_value - 1e-6 < observed.max_value < heatmaps.max_value + 1e-6
assert np.array_equal(observed.get_arr(), expected)
# 50% chance of flip
aug = iaa.Flipud(0.5)
aug_det = aug.to_deterministic()
nb_iterations = 1000
nb_images_flipped = 0
nb_images_flipped_det = 0
nb_keypoints_flipped = 0
nb_keypoints_flipped_det = 0
for _ in sm.xrange(nb_iterations):
observed = aug.augment_images(images)
if np.array_equal(observed, images_flipped):
nb_images_flipped += 1
observed = aug_det.augment_images(images)
if np.array_equal(observed, images_flipped):
nb_images_flipped_det += 1
observed = aug.augment_keypoints(keypoints)
if keypoints_equal(observed, keypoints_flipped):
nb_keypoints_flipped += 1
observed = aug_det.augment_keypoints(keypoints)
if keypoints_equal(observed, keypoints_flipped):
nb_keypoints_flipped_det += 1
assert int(nb_iterations * 0.3) <= nb_images_flipped <= int(nb_iterations * 0.7)
assert int(nb_iterations * 0.3) <= nb_keypoints_flipped <= int(nb_iterations * 0.7)
assert nb_images_flipped_det in [0, nb_iterations]
assert nb_keypoints_flipped_det in [0, nb_iterations]
# 50% chance of flipped, multiple images, list as input
images_multi = [base_img, base_img]
aug = iaa.Flipud(0.5)
aug_det = aug.to_deterministic()
nb_iterations = 1000
nb_flipped_by_pos = [0] * len(images_multi)
nb_flipped_by_pos_det = [0] * len(images_multi)
for _ in sm.xrange(nb_iterations):
observed = aug.augment_images(images_multi)
for i in sm.xrange(len(images_multi)):
if np.array_equal(observed[i], base_img_flipped):
nb_flipped_by_pos[i] += 1
observed = aug_det.augment_images(images_multi)
for i in sm.xrange(len(images_multi)):
if np.array_equal(observed[i], base_img_flipped):
nb_flipped_by_pos_det[i] += 1
for val in nb_flipped_by_pos:
assert int(nb_iterations * 0.3) <= val <= int(nb_iterations * 0.7)
for val in nb_flipped_by_pos_det:
assert val in [0, nb_iterations]
# test StochasticParameter as p
aug = iaa.Flipud(p=iap.Choice([0, 1], p=[0.7, 0.3]))
seen = [0, 0]
for _ in sm.xrange(1000):
observed = aug.augment_image(base_img)
if np.array_equal(observed, base_img):
seen[0] += 1
elif np.array_equal(observed, base_img_flipped):
seen[1] += 1
else:
assert False
assert 700 - 75 < seen[0] < 700 + 75
assert 300 - 75 < seen[1] < 300 + 75
# test exceptions for wrong parameter types
got_exception = False
try:
_ = iaa.Flipud(p="test")
except Exception:
got_exception = True
assert got_exception
# test get_parameters()
aug = iaa.Flipud(p=0.5)
params = aug.get_parameters()
assert isinstance(params[0], iap.Binomial)
assert isinstance(params[0].p, iap.Deterministic)
assert 0.5 - 1e-4 < params[0].p.value < 0.5 + 1e-4
def test_AlphaElementwise():
reseed()
base_img = np.zeros((3, 3, 1), dtype=np.uint8)
heatmaps_arr = np.float32([[0.0, 0.0, 1.0],
[0.0, 0.0, 1.0],
[0.0, 1.0, 1.0]])
heatmaps_arr_r1 = np.float32([[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0],
[0.0, 0.0, 1.0]])
heatmaps_arr_l1 = np.float32([[0.0, 1.0, 0.0],
[0.0, 1.0, 0.0],
[1.0, 1.0, 0.0]])
heatmaps = ia.HeatmapsOnImage(heatmaps_arr, shape=(3, 3, 3))
aug = iaa.AlphaElementwise(1, iaa.Add(10), iaa.Add(20))
observed = aug.augment_image(base_img)
expected = base_img + 10
assert np.allclose(observed, expected)
aug = iaa.AlphaElementwise(1, iaa.Affine(translate_px={"x": 1}), iaa.Affine(translate_px={"x": -1}))
observed = aug.augment_heatmaps([heatmaps])[0]
assert observed.shape == (3, 3, 3)
assert 0 - 1e-6 < observed.min_value < 0 + 1e-6
assert 1 - 1e-6 < observed.max_value < 1 + 1e-6
assert np.allclose(observed.get_arr(), heatmaps_arr_r1)
aug = iaa.AlphaElementwise(0, iaa.Add(10), iaa.Add(20))
observed = aug.augment_image(base_img)
expected = base_img + 20
assert np.allclose(observed, expected)
aug = iaa.AlphaElementwise(0, iaa.Affine(translate_px={"x": 1}), iaa.Affine(translate_px={"x": -1}))
observed = aug.augment_heatmaps([heatmaps])[0]
assert observed.shape == (3, 3, 3)
assert 0 - 1e-6 < observed.min_value < 0 + 1e-6
assert 1 - 1e-6 < observed.max_value < 1 + 1e-6
assert np.allclose(observed.get_arr(), heatmaps_arr_l1)
aug = iaa.AlphaElementwise(0.75, iaa.Add(10), iaa.Add(20))
observed = aug.augment_image(base_img)
expected = np.round(base_img + 0.75 * 10 + 0.25 * 20).astype(np.uint8)
assert np.allclose(observed, expected)
aug = iaa.AlphaElementwise(0.75, None, iaa.Add(20))
observed = aug.augment_image(base_img + 10)
expected = np.round(base_img + 0.75 * 10 + 0.25 * (10 + 20)).astype(np.uint8)
assert np.allclose(observed, expected)
aug = iaa.AlphaElementwise(0.75, iaa.Add(10), None)
observed = aug.augment_image(base_img + 10)
expected = np.round(base_img + 0.75 * (10 + 10) + 0.25 * 10).astype(np.uint8)
assert np.allclose(observed, expected)
base_img = np.zeros((100, 100), dtype=np.uint8)
aug = iaa.AlphaElementwise((0.0, 1.0), iaa.Add(10), iaa.Add(110))
observed = (aug.augment_image(base_img) - 10) / 100
nb_bins = 10
hist, _ = np.histogram(observed.flatten(), bins=nb_bins, range=(0.0, 1.0), density=False)
density_expected = 1.0/nb_bins
density_tolerance = 0.05
for nb_samples in hist:
density = nb_samples / observed.size
assert density_expected - density_tolerance < density < density_expected + density_tolerance
base_img = np.zeros((1, 1, 100), dtype=np.uint8)
aug = iaa.AlphaElementwise((0.0, 1.0), iaa.Add(10), iaa.Add(110), per_channel=True)
observed = aug.augment_image(base_img)
assert len(set(observed.flatten())) > 1
# propagating
aug = iaa.AlphaElementwise(0.5, iaa.Add(100), iaa.Add(50), name="AlphaElementwiseTest")
def propagator(images, augmenter, parents, default):
if "AlphaElementwise" in augmenter.name:
return False
else:
return default
hooks = ia.HooksImages(propagator=propagator)
image = np.zeros((10, 10, 3), dtype=np.uint8) + 1
observed = aug.augment_image(image, hooks=hooks)
assert np.array_equal(observed, image)
# -----
# heatmaps and per_channel
# -----
class _DummyMaskParameter(iap.StochasticParameter):
def __init__(self, inverted=False):
super(_DummyMaskParameter, self).__init__()
self.nb_calls = 0
self.inverted = inverted
def _draw_samples(self, size, random_state):
self.nb_calls += 1
h, w = size
ones = np.ones((h, w), dtype=np.float32)
zeros = np.zeros((h, w), dtype=np.float32)
if self.nb_calls == 1:
return zeros if not self.inverted else ones
elif self.nb_calls in [2, 3]:
return ones if not self.inverted else zeros
else:
assert False
aug = iaa.AlphaElementwise(
_DummyMaskParameter(inverted=False),
iaa.Affine(translate_px={"x": 1}),
iaa.Affine(translate_px={"x": -1}),
per_channel=True
)
observed = aug.augment_heatmaps([heatmaps])[0]
assert observed.shape == (3, 3, 3)
assert 0 - 1e-6 < observed.min_value < 0 + 1e-6
assert 1 - 1e-6 < observed.max_value < 1 + 1e-6
assert np.allclose(observed.get_arr(), heatmaps_arr_r1)
aug = iaa.AlphaElementwise(
_DummyMaskParameter(inverted=True),
iaa.Affine(translate_px={"x": 1}),
iaa.Affine(translate_px={"x": -1}),
per_channel=True
)
observed = aug.augment_heatmaps([heatmaps])[0]
assert observed.shape == (3, 3, 3)
assert 0 - 1e-6 < observed.min_value < 0 + 1e-6
assert 1 - 1e-6 < observed.max_value < 1 + 1e-6
assert np.allclose(observed.get_arr(), heatmaps_arr_l1)
# -----
# keypoints
# -----
kps = [ia.Keypoint(x=5, y=10), ia.Keypoint(x=6, y=11)]
kpsoi = ia.KeypointsOnImage(kps, shape=(20, 20, 3))
aug = iaa.AlphaElementwise(1.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.deepcopy()
assert keypoints_equal([observed], [expected])
aug = iaa.AlphaElementwise(0.501, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.deepcopy()
assert keypoints_equal([observed], [expected])
aug = iaa.AlphaElementwise(0.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.shift(x=1)
assert keypoints_equal([observed], [expected])
aug = iaa.AlphaElementwise(0.499, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.shift(x=1)
assert keypoints_equal([observed], [expected])
# per_channel
aug = iaa.AlphaElementwise(1.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}), per_channel=True)
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.deepcopy()
assert keypoints_equal([observed], [expected])
aug = iaa.AlphaElementwise(0.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}), per_channel=True)
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.shift(x=1)
assert keypoints_equal([observed], [expected])
"""
TODO this test currently doesn't work as AlphaElementwise augments keypoints without sampling
overlay factors per (x, y) location. (i.e. similar behaviour to Alpha)
aug = iaa.Alpha(iap.Choice([0.49, 0.51]), iaa.Noop(), iaa.Affine(translate_px={"x": 1}), per_channel=True)
expected_same = kpsoi.deepcopy()
expected_both_shifted = kpsoi.shift(x=1)
expected_first_shifted = KeypointsOnImage([kps[0].shift(x=1), kps[1]], shape=kpsoi.shape)
expected_second_shifted = KeypointsOnImage([kps[0], kps[1].shift(x=1)], shape=kpsoi.shape)
seen = [0, 0]
for _ in sm.xrange(200):
observed = aug.augment_keypoints([kpsoi])[0]
if keypoints_equal([observed], [expected_same]):
seen[0] += 1
elif keypoints_equal([observed], [expected_both_shifted]):
seen[1] += 1
elif keypoints_equal([observed], [expected_first_shifted]):
seen[2] += 1
elif keypoints_equal([observed], [expected_second_shifted]):
seen[3] += 1
else:
assert False
assert 100 - 50 < seen[0] < 100 + 50
assert 100 - 50 < seen[1] < 100 + 50
"""
# propagating
aug = iaa.AlphaElementwise(0.0, iaa.Affine(translate_px={"x": 1}), iaa.Affine(translate_px={"y": 1}),
name="AlphaElementwiseTest")
def propagator(kpsoi_to_aug, augmenter, parents, default):
if "AlphaElementwise" in augmenter.name:
return False
else:
return default
hooks = ia.HooksKeypoints(propagator=propagator)
observed = aug.augment_keypoints([kpsoi], hooks=hooks)[0]
assert keypoints_equal([observed], [kpsoi])
# -----
# polygons
# -----
ps = [ia.Polygon([(5, 5), (10, 5), (10, 10)])]
psoi = ia.PolygonsOnImage(ps, shape=(20, 20, 3))
aug = iaa.AlphaElementwise(1.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_polygons([psoi])
assert len(observed) == 1
assert len(observed[0].polygons) == 1
assert observed[0].shape == psoi.shape
assert observed[0].polygons[0].exterior_almost_equals(psoi.polygons[0])
assert observed[0].polygons[0].is_valid
aug = iaa.AlphaElementwise(0.501, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_polygons([psoi])
assert len(observed) == 1
assert len(observed[0].polygons) == 1
assert observed[0].shape == psoi.shape
assert observed[0].polygons[0].exterior_almost_equals(psoi.polygons[0])
assert observed[0].polygons[0].is_valid
aug = iaa.AlphaElementwise(0.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_polygons([psoi])
expected = psoi.shift(left=1)
assert len(observed) == 1
assert len(observed[0].polygons) == 1
assert observed[0].shape == psoi.shape
assert observed[0].polygons[0].exterior_almost_equals(expected.polygons[0])
assert observed[0].polygons[0].is_valid
aug = iaa.AlphaElementwise(0.499, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_polygons([psoi])
expected = psoi.shift(left=1)
assert len(observed) == 1
assert len(observed[0].polygons) == 1
assert observed[0].shape == psoi.shape
assert observed[0].polygons[0].exterior_almost_equals(expected.polygons[0])
assert observed[0].polygons[0].is_valid
# per_channel
aug = iaa.AlphaElementwise(1.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}), per_channel=True)
observed = aug.augment_polygons([psoi])
assert len(observed) == 1
assert len(observed[0].polygons) == 1
assert observed[0].shape == psoi.shape
assert observed[0].polygons[0].exterior_almost_equals(psoi.polygons[0])
assert observed[0].polygons[0].is_valid
aug = iaa.AlphaElementwise(0.0, iaa.Noop(), iaa.Affine(translate_px={"x": 1}), per_channel=True)
observed = aug.augment_polygons([psoi])
expected = psoi.shift(left=1)
assert len(observed) == 1
assert len(observed[0].polygons) == 1
assert observed[0].shape == psoi.shape
assert observed[0].polygons[0].exterior_almost_equals(expected.polygons[0])
assert observed[0].polygons[0].is_valid
aug = iaa.AlphaElementwise(iap.Choice([0.49, 0.51]), iaa.Noop(), iaa.Affine(translate_px={"x": 1}), per_channel=True)
expected_same = psoi.deepcopy()
expected_shifted = psoi.shift(left=1)
seen = [0, 0]
for _ in sm.xrange(200):
observed = aug.augment_polygons([psoi])[0]
if observed.polygons[0].exterior_almost_equals(expected_same.polygons[0]):
seen[0] += 1
elif observed.polygons[0].exterior_almost_equals(expected_shifted.polygons[0]):
seen[1] += 1
else:
assert False
assert 100 - 50 < seen[0] < 100 + 50
assert 100 - 50 < seen[1] < 100 + 50
# empty polygons
aug = iaa.AlphaElementwise(0.501, iaa.Noop(), iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_polygons(ia.PolygonsOnImage([], shape=(1, 2, 3)))
assert len(observed.polygons) == 0
assert observed.shape == (1, 2, 3)
# propagating
aug = iaa.AlphaElementwise(0.0, iaa.Affine(translate_px={"x": 1}), iaa.Affine(translate_px={"y": 1}), name="AlphaTest")
def propagator(psoi_to_aug, augmenter, parents, default):
if "Alpha" in augmenter.name:
return False
else:
return default
hooks = ia.HooksKeypoints(propagator=propagator) # no hooks for polygons yet, so we use HooksKeypoints
observed = aug.augment_polygons([psoi], hooks=hooks)[0]
assert observed.polygons[0].exterior_almost_equals(psoi.polygons[0])
def test_SigmoidContrast():
reseed()
img = [
[1, 2, 3],
[4, 5, 6],
[7, 8, 9]
]
img = np.uint8(img)
img3d = np.tile(img[:, :, np.newaxis], (1, 1, 3))
# check basic functionality with per_chanenl on/off (makes no difference due to deterministic
# parameters)
for per_channel in [False, 0, 0.0, True, 1, 1.0]:
for gain, cutoff in itertools.product([5, 10], [0.25, 0.75]):
aug = iaa.SigmoidContrast(gain=iap.Deterministic(gain), cutoff=iap.Deterministic(cutoff), per_channel=per_channel)
img_aug = aug.augment_image(img)
img3d_aug = aug.augment_image(img3d)
assert img_aug.dtype.type == np.uint8
assert img3d_aug.dtype.type == np.uint8
assert np.array_equal(img_aug, skimage.exposure.adjust_sigmoid(img, gain=gain, cutoff=cutoff))
assert np.array_equal(img3d_aug, skimage.exposure.adjust_sigmoid(img3d, gain=gain, cutoff=cutoff))
# check that tuple to uniform works
# note that gain and cutoff are saved in inverted order in _ContrastFuncWrapper to match
# the order of skimage's function
aug = iaa.SigmoidContrast(gain=(1, 2), cutoff=(0.25, 0.75))
assert isinstance(aug.params1d[0], iap.Uniform)
assert isinstance(aug.params1d[0].a, iap.Deterministic)
assert isinstance(aug.params1d[0].b, iap.Deterministic)
assert np.allclose(aug.params1d[0].a.value, 0.25)
assert np.allclose(aug.params1d[0].b.value, 0.75)
assert isinstance(aug.params1d[1], iap.Uniform)
assert isinstance(aug.params1d[1].a, iap.Deterministic)
assert isinstance(aug.params1d[1].b, iap.Deterministic)
assert aug.params1d[1].a.value == 1
assert aug.params1d[1].b.value == 2
# check that list to choice works
# note that gain and cutoff are saved in inverted order in _ContrastFuncWrapper to match
# the order of skimage's function
aug = iaa.SigmoidContrast(gain=[1, 2], cutoff=[0.25, 0.75])
assert isinstance(aug.params1d[0], iap.Choice)
assert all([np.allclose(val, val_choice) for val, val_choice in zip([0.25, 0.75], aug.params1d[0].a)])
assert isinstance(aug.params1d[1], iap.Choice)
assert all([val in aug.params1d[1].a for val in [1, 2]])
# check that per_channel at 50% prob works
aug = iaa.SigmoidContrast(gain=(1, 10), cutoff=(0.25, 0.75), per_channel=0.5)
seen = [False, False]
img1000d = np.zeros((1, 1, 1000), dtype=np.uint8) + 128
for _ in sm.xrange(100):
img_aug = aug.augment_image(img1000d)
assert img_aug.dtype.type == np.uint8
l = len(set(img_aug.flatten().tolist()))
if l == 1:
seen[0] = True
else:
seen[1] = True
if all(seen):
break
assert all(seen)
# check that keypoints are not changed
kpsoi = ia.KeypointsOnImage([ia.Keypoint(1, 1)], shape=(3, 3, 3))
kpsoi_aug = iaa.SigmoidContrast(gain=10, cutoff=0.5).augment_keypoints([kpsoi])
assert keypoints_equal([kpsoi], kpsoi_aug)
# check that heatmaps are not changed
heatmaps = ia.HeatmapsOnImage(np.zeros((3, 3, 1), dtype=np.float32) + 0.5, shape=(3, 3, 3))
heatmaps_aug = iaa.SigmoidContrast(gain=10, cutoff=0.5).augment_heatmaps([heatmaps])[0]
assert np.allclose(heatmaps.arr_0to1, heatmaps_aug.arr_0to1)
def test_Superpixels():
reseed()
def _array_equals_tolerant(a, b, tolerance):
diff = np.abs(a.astype(np.int32) - b.astype(np.int32))
return np.all(diff <= tolerance)
base_img = [
[255, 255, 255, 0, 0, 0],
[255, 235, 255, 0, 20, 0],
[250, 250, 250, 5, 5, 5]
]
base_img = np.tile(np.array(base_img, dtype=np.uint8)[..., np.newaxis], (1, 1, 3))
base_img_superpixels = [
[251, 251, 251, 4, 4, 4],
[251, 251, 251, 4, 4, 4],
[251, 251, 251, 4, 4, 4]
]
base_img_superpixels = np.tile(np.array(base_img_superpixels, dtype=np.uint8)[..., np.newaxis], (1, 1, 3))
base_img_superpixels_left = np.copy(base_img_superpixels)
base_img_superpixels_left[:, 3:, :] = base_img[:, 3:, :]
base_img_superpixels_right = np.copy(base_img_superpixels)
base_img_superpixels_right[:, :3, :] = base_img[:, :3, :]
aug = iaa.Superpixels(p_replace=0, n_segments=2)
observed = aug.augment_image(base_img)
expected = base_img
assert np.allclose(observed, expected)
aug = iaa.Superpixels(p_replace=1.0, n_segments=2)
observed = aug.augment_image(base_img)
expected = base_img_superpixels
assert _array_equals_tolerant(observed, expected, 2)
aug = iaa.Superpixels(p_replace=1.0, n_segments=iap.Deterministic(2))
observed = aug.augment_image(base_img)
expected = base_img_superpixels
assert _array_equals_tolerant(observed, expected, 2)
aug = iaa.Superpixels(p_replace=iap.Binomial(iap.Choice([0.0, 1.0])), n_segments=2)
observed = aug.augment_image(base_img)
assert np.allclose(observed, base_img) or _array_equals_tolerant(observed, base_img_superpixels, 2)
aug = iaa.Superpixels(p_replace=0.5, n_segments=2)
seen = {"none": False, "left": False, "right": False, "both": False}
for _ in sm.xrange(100):
observed = aug.augment_image(base_img)
if _array_equals_tolerant(observed, base_img, 2):
seen["none"] = True
elif _array_equals_tolerant(observed, base_img_superpixels_left, 2):
seen["left"] = True
elif _array_equals_tolerant(observed, base_img_superpixels_right, 2):
seen["right"] = True
elif _array_equals_tolerant(observed, base_img_superpixels, 2):
seen["both"] = True
else:
raise Exception("Generated superpixels image does not match any expected image.")
if all(seen.values()):
break
assert all(seen.values())
# test exceptions for wrong parameter types
got_exception = False
try:
_ = iaa.Superpixels(p_replace="test", n_segments=100)
except Exception:
got_exception = True
assert got_exception
got_exception = False
try:
_ = iaa.Superpixels(p_replace=1, n_segments="test")
except Exception:
got_exception = True
assert got_exception
# test get_parameters()
aug = iaa.Superpixels(p_replace=0.5, n_segments=2, max_size=100, interpolation="nearest")
params = aug.get_parameters()
assert isinstance(params[0], iap.Binomial)
assert isinstance(params[0].p, iap.Deterministic)
assert isinstance(params[1], iap.Deterministic)
assert 0.5 - 1e-4 < params[0].p.value < 0.5 + 1e-4
assert params[1].value == 2
assert params[2] == 100
assert params[3] == "nearest"
###################
# test other dtypes
###################
# bool
aug = iaa.Superpixels(p_replace=1.0, n_segments=2)
img = np.array([
[False, False, True, True],
[False, False, True, True]
], dtype=bool)
img_aug = aug.augment_image(img)
assert img_aug.dtype == img.dtype
assert np.all(img_aug == img)
aug = iaa.Superpixels(p_replace=1.0, n_segments=1)
img = np.array([
[True, True, True, True],
[False, True, True, True]
], dtype=bool)
img_aug = aug.augment_image(img)
assert img_aug.dtype == img.dtype
assert np.all(img_aug)
for dtype in [np.uint8, np.uint16, np.uint32, np.int8, np.int16, np.int32]:
min_value, center_value, max_value = iadt.get_value_range_of_dtype(dtype)
if np.dtype(dtype).kind == "i":
values = [int(center_value), int(0.1 * max_value), int(0.2 * max_value),
int(0.5 * max_value), max_value-100]
values = [((-1)*value, value) for value in values]
else:
values = [(0, int(center_value)),
(10, int(0.1 * max_value)), (10, int(0.2 * max_value)), (10, int(0.5 * max_value)),
(0, max_value), (int(center_value), max_value)]
for v1, v2 in values:
aug = iaa.Superpixels(p_replace=1.0, n_segments=2)
img = np.array([
[v1, v1, v2, v2],
[v1, v1, v2, v2]
], dtype=dtype)
img_aug = aug.augment_image(img)
assert img_aug.dtype == np.dtype(dtype)
assert np.array_equal(img_aug, img)
aug = iaa.Superpixels(p_replace=1.0, n_segments=1)
img = np.array([
[v2, v2, v2, v2],
[v1, v2, v2, v2]
], dtype=dtype)
img_aug = aug.augment_image(img)
assert img_aug.dtype == np.dtype(dtype)
assert np.all(img_aug == int(np.round((7/8)*v2 + (1/8)*v1)))
for dtype in []:
def _allclose(a, b):
atol = 1e-4 if dtype == np.float16 else 1e-8
return np.allclose(a, b, atol=atol, rtol=0)
isize = np.dtype(dtype).itemsize
for value in [0, 1.0, 10.0, 1000 ** (isize - 1)]:
v1 = (-1) * value
v2 = value
aug = iaa.Superpixels(p_replace=1.0, n_segments=2)
img = np.array([
[v1, v1, v2, v2],
[v1, v1, v2, v2]
], dtype=dtype)
img_aug = aug.augment_image(img)
assert img_aug.dtype == np.dtype(dtype)
assert _allclose(img_aug, img)
aug = iaa.Superpixels(p_replace=1.0, n_segments=1)
img = np.array([
[v2, v2, v2, v2],
[v1, v2, v2, v2]
], dtype=dtype)
img_aug = aug.augment_image(img)
assert img_aug.dtype == np.dtype(dtype)
assert _allclose(img_aug, (7/8)*v2 + (1/8)*v1)
def test_FastSnowyLandscape():
reseed()
# check parameters
aug = iaa.FastSnowyLandscape(lightness_threshold=[100, 200], lightness_multiplier=[1.0, 4.0])
assert isinstance(aug.lightness_threshold, iap.Choice)
assert len(aug.lightness_threshold.a) == 2
assert aug.lightness_threshold.a[0] == 100
assert aug.lightness_threshold.a[1] == 200
assert isinstance(aug.lightness_multiplier, iap.Choice)
assert len(aug.lightness_multiplier.a) == 2
assert np.allclose(aug.lightness_multiplier.a[0], 1.0)
assert np.allclose(aug.lightness_multiplier.a[1], 4.0)
# basic functionality test
aug = iaa.FastSnowyLandscape(lightness_threshold=100, lightness_multiplier=2.0)
image = np.arange(0, 6*6*3).reshape((6, 6, 3)).astype(np.uint8)
image_hls = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
mask = (image_hls[..., 1] < 100)
expected = np.copy(image_hls).astype(np.float32)
expected[..., 1][mask] *= 2.0
expected = np.clip(expected, 0, 255).astype(np.uint8)
expected = cv2.cvtColor(expected, cv2.COLOR_HLS2RGB)
observed = aug.augment_image(image)
assert np.array_equal(observed, expected)
# test when varying lightness_threshold between images
image = np.arange(0, 6*6*3).reshape((6, 6, 3)).astype(np.uint8)
image_hls = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
class _TwoValueParam(iap.StochasticParameter):
def __init__(self, v1, v2):
super(_TwoValueParam, self).__init__()
self.v1 = v1
self.v2 = v2
def _draw_samples(self, size, random_state):
arr = np.full(size, self.v1, dtype=np.float32)
arr[1::2] = self.v2
return arr
aug = iaa.FastSnowyLandscape(lightness_threshold=_TwoValueParam(75, 125), lightness_multiplier=2.0)
mask = (image_hls[..., 1] < 75)
expected1 = np.copy(image_hls).astype(np.float64)
expected1[..., 1][mask] *= 2.0
expected1 = np.clip(expected1, 0, 255).astype(np.uint8)
expected1 = cv2.cvtColor(expected1, cv2.COLOR_HLS2RGB)
mask = (image_hls[..., 1] < 125)
expected2 = np.copy(image_hls).astype(np.float64)
expected2[..., 1][mask] *= 2.0
expected2 = np.clip(expected2, 0, 255).astype(np.uint8)
expected2 = cv2.cvtColor(expected2, cv2.COLOR_HLS2RGB)
observed = aug.augment_images([image] * 4)
assert np.array_equal(observed[0], expected1)
assert np.array_equal(observed[1], expected2)
assert np.array_equal(observed[2], expected1)
assert np.array_equal(observed[3], expected2)
# test when varying lightness_multiplier between images
aug = iaa.FastSnowyLandscape(lightness_threshold=100, lightness_multiplier=_TwoValueParam(1.5, 2.0))
mask = (image_hls[..., 1] < 100)
expected1 = np.copy(image_hls).astype(np.float64)
expected1[..., 1][mask] *= 1.5
expected1 = np.clip(expected1, 0, 255).astype(np.uint8)
expected1 = cv2.cvtColor(expected1, cv2.COLOR_HLS2RGB)
mask = (image_hls[..., 1] < 100)
expected2 = np.copy(image_hls).astype(np.float64)
expected2[..., 1][mask] *= 2.0
expected2 = np.clip(expected2, 0, 255).astype(np.uint8)
expected2 = cv2.cvtColor(expected2, cv2.COLOR_HLS2RGB)
observed = aug.augment_images([image] * 4)
assert np.array_equal(observed[0], expected1)
assert np.array_equal(observed[1], expected2)
assert np.array_equal(observed[2], expected1)
assert np.array_equal(observed[3], expected2)
# test BGR colorspace
aug = iaa.FastSnowyLandscape(lightness_threshold=100, lightness_multiplier=2.0, from_colorspace="BGR")
image = np.arange(0, 6*6*3).reshape((6, 6, 3)).astype(np.uint8)
image_hls = cv2.cvtColor(image, cv2.COLOR_BGR2HLS)
mask = (image_hls[..., 1] < 100)
expected = np.copy(image_hls).astype(np.float32)
expected[..., 1][mask] *= 2.0
expected = np.clip(expected, 0, 255).astype(np.uint8)
expected = cv2.cvtColor(expected, cv2.COLOR_HLS2BGR)
observed = aug.augment_image(image)
assert np.array_equal(observed, expected)
def test_GammaContrast():
reseed()
img = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
img = np.uint8(img)
img3d = np.tile(img[:, :, np.newaxis], (1, 1, 3))
# check basic functionality with gamma=1 or 2 (deterministic) and per_chanenl on/off (makes
# no difference due to deterministic gamma)
for per_channel in [False, 0, 0.0, True, 1, 1.0]:
for gamma in [1, 2]:
aug = iaa.GammaContrast(gamma=iap.Deterministic(gamma),
per_channel=per_channel)
img_aug = aug.augment_image(img)
img3d_aug = aug.augment_image(img3d)
assert img_aug.dtype.type == np.uint8
assert img3d_aug.dtype.type == np.uint8
assert np.array_equal(
img_aug, skimage.exposure.adjust_gamma(img, gamma=gamma))
assert np.array_equal(
img3d_aug, skimage.exposure.adjust_gamma(img3d, gamma=gamma))
# check that tuple to uniform works
aug = iaa.GammaContrast((1, 2))
assert isinstance(aug.params1d[0], iap.Uniform)
assert isinstance(aug.params1d[0].a, iap.Deterministic)
assert isinstance(aug.params1d[0].b, iap.Deterministic)
assert aug.params1d[0].a.value == 1
assert aug.params1d[0].b.value == 2
# check that list to choice works
aug = iaa.GammaContrast([1, 2])
assert isinstance(aug.params1d[0], iap.Choice)
assert all([val in aug.params1d[0].a for val in [1, 2]])
# check that per_channel at 50% prob works
aug = iaa.GammaContrast((0.5, 2.0), per_channel=0.5)
seen = [False, False]
img1000d = np.zeros((1, 1, 1000), dtype=np.uint8) + 128
for _ in sm.xrange(100):
img_aug = aug.augment_image(img1000d)
assert img_aug.dtype.type == np.uint8
l = len(set(img_aug.flatten().tolist()))
if l == 1:
seen[0] = True
else:
seen[1] = True
if all(seen):
break
assert all(seen)
# check that keypoints are not changed
kpsoi = ia.KeypointsOnImage([ia.Keypoint(1, 1)], shape=(3, 3, 3))
kpsoi_aug = iaa.GammaContrast(gamma=2).augment_keypoints([kpsoi])
assert keypoints_equal([kpsoi], kpsoi_aug)
# check that heatmaps are not changed
heatmaps = ia.HeatmapsOnImage(np.zeros((3, 3, 1), dtype=np.float32) + 0.5,
shape=(3, 3, 3))
heatmaps_aug = iaa.GammaContrast(gamma=2).augment_heatmaps([heatmaps])[0]
assert np.allclose(heatmaps.arr_0to1, heatmaps_aug.arr_0to1)
###################
# test other dtypes
###################
# uint, int
for dtype in [
np.uint8, np.uint16, np.uint32, np.uint64, np.int8, np.int16,
np.int32, np.int64
]:
min_value, center_value, max_value = meta.get_value_range_of_dtype(
dtype)
exps = [1, 2, 3]
values = [0, 100, int(center_value + 0.1 * max_value)]
tolerances = [
0, 0, 1e-8 * max_value if dtype in [np.uint64, np.int64] else 0
]
for exp in exps:
aug = iaa.GammaContrast(exp)
for value, tolerance in zip(values, tolerances):
image = np.full((3, 3), value, dtype=dtype)
expected = (((image.astype(np.float128) / max_value)**exp) *
max_value).astype(dtype)
image_aug = aug.augment_image(image)
assert image_aug.dtype == np.dtype(dtype)
assert len(np.unique(image_aug)) == 1
value_aug = int(image_aug[0, 0])
value_expected = int(expected[0, 0])
diff = abs(value_aug - value_expected)
assert diff <= tolerance
# float
for dtype in [np.float16, np.float32, np.float64]:
def _allclose(a, b):
atol = 1e-3 if dtype == np.float16 else 1e-8
return np.allclose(a, b, atol=atol, rtol=0)
exps = [1, 2]
isize = np.dtype(dtype).itemsize
values = [0, 1.0, 50.0, 100**(isize - 1)]
for exp in exps:
aug = iaa.GammaContrast(exp)
for value in values:
image = np.full((3, 3), value, dtype=dtype)
expected = (image.astype(np.float128)**exp).astype(dtype)
image_aug = aug.augment_image(image)
assert image_aug.dtype == np.dtype(dtype)
assert _allclose(image_aug, expected)
def test_determinism():
reseed()
images = [
ia.quokka(size=(128, 128)),
ia.quokka(size=(64, 64)),
ia.imresize_single_image(skimage.data.astronaut(), (128, 256))
]
images.extend([ia.quokka(size=(16, 16))] * 20)
keypoints = [
ia.KeypointsOnImage([
ia.Keypoint(x=20, y=10, vis=None, label=None), ia.Keypoint(x=5, y=5, vis=None, label=None),
ia.Keypoint(x=10, y=43, vis=None, label=None)], shape=(50, 60, 3))
] * 20
augs = [
iaa.Sequential([iaa.Fliplr(0.5), iaa.Flipud(0.5)]),
iaa.SomeOf(1, [iaa.Fliplr(0.5), iaa.Flipud(0.5)]),
iaa.OneOf([iaa.Fliplr(0.5), iaa.Flipud(0.5)]),
iaa.Sometimes(0.5, iaa.Fliplr(1.0)),
iaa.WithColorspace("HSV", children=iaa.Add((-50, 50))),
# iaa.WithChannels([0], iaa.Add((-50, 50))),
# iaa.Noop(name="Noop-nochange"),
# iaa.Lambda(
# func_images=lambda images, random_state, parents, hooks: images,
# func_keypoints=lambda keypoints_on_images, random_state, parents, hooks: keypoints_on_images,
# name="Lambda-nochange"
# ),
# iaa.AssertLambda(
# func_images=lambda images, random_state, parents, hooks: True,
# func_keypoints=lambda keypoints_on_images, random_state, parents, hooks: True,
# name="AssertLambda-nochange"
# ),
# iaa.AssertShape(
# (None, None, None, 3),
# check_keypoints=False,
# name="AssertShape-nochange"
# ),
iaa.Resize((0.5, 0.9)),
iaa.CropAndPad(px=(-50, 50)),
iaa.Pad(px=(1, 50)),
iaa.Crop(px=(1, 50)),
iaa.Fliplr(0.5),
iaa.Flipud(0.5),
iaa.Superpixels(p_replace=(0.25, 1.0), n_segments=(16, 128)),
# iaa.ChangeColorspace(to_colorspace="GRAY"),
iaa.Grayscale(alpha=(0.1, 1.0)),
iaa.GaussianBlur((0.1, 3.0)),
iaa.AverageBlur((3, 11)),
iaa.MedianBlur((3, 11)),
# iaa.Convolve(np.array([[0, 1, 0],
# [1, -4, 1],
# [0, 1, 0]])),
iaa.Sharpen(alpha=(0.1, 1.0), lightness=(0.8, 1.2)),
iaa.Emboss(alpha=(0.1, 1.0), strength=(0.8, 1.2)),
iaa.EdgeDetect(alpha=(0.1, 1.0)),
iaa.DirectedEdgeDetect(alpha=(0.1, 1.0), direction=(0.0, 1.0)),
iaa.Add((-50, 50)),
iaa.AddElementwise((-50, 50)),
iaa.AdditiveGaussianNoise(scale=(0.1, 1.0)),
iaa.Multiply((0.6, 1.4)),
iaa.MultiplyElementwise((0.6, 1.4)),
iaa.Dropout((0.3, 0.5)),
iaa.CoarseDropout((0.3, 0.5), size_percent=(0.05, 0.2)),
iaa.Invert(0.5),
iaa.ContrastNormalization((0.6, 1.4)),
iaa.Affine(scale=(0.7, 1.3), translate_percent=(-0.1, 0.1),
rotate=(-20, 20), shear=(-20, 20), order=ia.ALL,
mode=ia.ALL, cval=(0, 255)),
iaa.PiecewiseAffine(scale=(0.1, 0.3)),
iaa.ElasticTransformation(alpha=0.5)
]
augs_affect_geometry = [
iaa.Sequential([iaa.Fliplr(0.5), iaa.Flipud(0.5)]),
iaa.SomeOf(1, [iaa.Fliplr(0.5), iaa.Flipud(0.5)]),
iaa.OneOf([iaa.Fliplr(0.5), iaa.Flipud(0.5)]),
iaa.Sometimes(0.5, iaa.Fliplr(1.0)),
iaa.Resize((0.5, 0.9)),
iaa.CropAndPad(px=(-50, 50)),
iaa.Pad(px=(1, 50)),
iaa.Crop(px=(1, 50)),
iaa.Fliplr(0.5),
iaa.Flipud(0.5),
iaa.Affine(scale=(0.7, 1.3), translate_percent=(-0.1, 0.1),
rotate=(-20, 20), shear=(-20, 20), order=ia.ALL,
mode=ia.ALL, cval=(0, 255)),
iaa.PiecewiseAffine(scale=(0.1, 0.3)),
iaa.ElasticTransformation(alpha=(5, 100), sigma=(3, 5))
]
for aug in augs:
aug_det = aug.to_deterministic()
images_aug1 = aug_det.augment_images(images)
images_aug2 = aug_det.augment_images(images)
aug_det = aug.to_deterministic()
images_aug3 = aug_det.augment_images(images)
images_aug4 = aug_det.augment_images(images)
assert array_equal_lists(images_aug1, images_aug2), \
"Images (1, 2) expected to be identical for %s" % (aug.name,)
assert array_equal_lists(images_aug3, images_aug4), \
"Images (3, 4) expected to be identical for %s" % (aug.name,)
assert not array_equal_lists(images_aug1, images_aug3), \
"Images (1, 3) expected to be different for %s" % (aug.name,)
for aug in augs_affect_geometry:
aug_det = aug.to_deterministic()
kps_aug1 = aug_det.augment_keypoints(keypoints)
kps_aug2 = aug_det.augment_keypoints(keypoints)
aug_det = aug.to_deterministic()
kps_aug3 = aug_det.augment_keypoints(keypoints)
kps_aug4 = aug_det.augment_keypoints(keypoints)
assert keypoints_equal(kps_aug1, kps_aug2), \
"Keypoints (1, 2) expected to be identical for %s" % (aug.name,)
assert keypoints_equal(kps_aug3, kps_aug4), \
"Keypoints (3, 4) expected to be identical for %s" % (aug.name,)
assert not keypoints_equal(kps_aug1, kps_aug3), \
"Keypoints (1, 3) expected to be different for %s" % (aug.name,)
def test_keypoint_augmentation():
reseed()
keypoints = []
for y in sm.xrange(40 // 5):
for x in sm.xrange(60 // 5):
keypoints.append(ia.Keypoint(y=y * 5, x=x * 5))
keypoints_oi = ia.KeypointsOnImage(keypoints, shape=(40, 60, 3))
keypoints_oi_empty = ia.KeypointsOnImage([], shape=(40, 60, 3))
augs = [
iaa.Add((-5, 5), name="Add"),
iaa.AddElementwise((-5, 5), name="AddElementwise"),
iaa.AdditiveGaussianNoise(0.01 * 255, name="AdditiveGaussianNoise"),
iaa.Multiply((0.95, 1.05), name="Multiply"),
iaa.Dropout(0.01, name="Dropout"),
iaa.CoarseDropout(0.01, size_px=6, name="CoarseDropout"),
iaa.Invert(0.01, per_channel=True, name="Invert"),
iaa.GaussianBlur(sigma=(0.95, 1.05), name="GaussianBlur"),
iaa.AverageBlur((3, 5), name="AverageBlur"),
iaa.MedianBlur((3, 5), name="MedianBlur"),
iaa.Sharpen((0.0, 0.1), lightness=(1.0, 1.2), name="Sharpen"),
iaa.Emboss(alpha=(0.0, 0.1), strength=(0.5, 1.5), name="Emboss"),
iaa.EdgeDetect(alpha=(0.0, 0.1), name="EdgeDetect"),
iaa.DirectedEdgeDetect(alpha=(0.0, 0.1),
direction=0,
name="DirectedEdgeDetect"),
iaa.Fliplr(0.5, name="Fliplr"),
iaa.Flipud(0.5, name="Flipud"),
iaa.Affine(translate_px=(-5, 5), name="Affine-translate-px"),
iaa.Affine(translate_percent=(-0.05, 0.05),
name="Affine-translate-percent"),
iaa.Affine(rotate=(-20, 20), name="Affine-rotate"),
iaa.Affine(shear=(-20, 20), name="Affine-shear"),
iaa.Affine(scale=(0.9, 1.1), name="Affine-scale"),
iaa.PiecewiseAffine(scale=(0.001, 0.005), name="PiecewiseAffine"),
iaa.ElasticTransformation(alpha=(0.1, 0.2),
sigma=(0.1, 0.2),
name="ElasticTransformation"),
iaa.BlendAlpha((0.0, 0.1), iaa.Add(10), name="BlendAlpha"),
iaa.BlendAlphaElementwise((0.0, 0.1),
iaa.Add(10),
name="BlendAlphaElementwise"),
iaa.BlendAlphaSimplexNoise(iaa.Add(10), name="BlendAlphaSimplexNoise"),
iaa.BlendAlphaFrequencyNoise(exponent=(-2, 2),
foreground=iaa.Add(10),
name="BlendAlphaSimplexNoise"),
iaa.Superpixels(p_replace=0.01, n_segments=64),
iaa.Resize(0.5, name="Resize"),
iaa.CropAndPad(px=(-10, 10), name="CropAndPad"),
iaa.Pad(px=(0, 10), name="Pad"),
iaa.Crop(px=(0, 10), name="Crop")
]
for aug in augs:
dss = []
for i in sm.xrange(10):
aug_det = aug.to_deterministic()
kp_fully_empty_aug = aug_det.augment_keypoints([])
assert kp_fully_empty_aug == []
kp_first_empty_aug = aug_det.augment_keypoints(keypoints_oi_empty)
assert len(kp_first_empty_aug.keypoints) == 0
kp_image = keypoints_oi.to_keypoint_image(size=5)
kp_image_aug = aug_det.augment_image(kp_image)
kp_image_aug_rev = ia.KeypointsOnImage.from_keypoint_image(
kp_image_aug,
if_not_found_coords={
"x": -9999,
"y": -9999
},
nb_channels=1)
kp_aug = aug_det.augment_keypoints([keypoints_oi])[0]
ds = []
assert len(kp_image_aug_rev.keypoints) == len(kp_aug.keypoints), (
"Lost keypoints for '%s' (%d vs expected %d)" %
(aug.name, len(
kp_aug.keypoints), len(kp_image_aug_rev.keypoints)))
gen = zip(kp_aug.keypoints, kp_image_aug_rev.keypoints)
for kp_pred, kp_pred_img in gen:
kp_pred_lost = (kp_pred.x == -9999 and kp_pred.y == -9999)
kp_pred_img_lost = (kp_pred_img.x == -9999
and kp_pred_img.y == -9999)
if not kp_pred_lost and not kp_pred_img_lost:
d = np.sqrt((kp_pred.x - kp_pred_img.x)**2 +
(kp_pred.y - kp_pred_img.y)**2)
ds.append(d)
dss.extend(ds)
if len(ds) == 0:
print("[INFO] No valid keypoints found for '%s' "
"in test_keypoint_augmentation()" % (str(aug), ))
assert np.average(dss) < 5.0, \
"Average distance too high (%.2f, with ds: %s)" \
% (np.average(dss), str(dss))
def setUp(self):
reseed()
def test_AlphaElementwise():
reseed()
base_img = np.zeros((3, 3, 1), dtype=np.uint8)
heatmaps_arr = np.float32([[0.0, 0.0, 1.0], [0.0, 0.0, 1.0],
[0.0, 1.0, 1.0]])
heatmaps_arr_r1 = np.float32([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0],
[0.0, 0.0, 1.0]])
heatmaps_arr_l1 = np.float32([[0.0, 1.0, 0.0], [0.0, 1.0, 0.0],
[1.0, 1.0, 0.0]])
heatmaps = ia.HeatmapsOnImage(heatmaps_arr, shape=(3, 3, 3))
aug = iaa.AlphaElementwise(1, iaa.Add(10), iaa.Add(20))
observed = aug.augment_image(base_img)
expected = base_img + 10
assert np.allclose(observed, expected)
aug = iaa.AlphaElementwise(1, iaa.Affine(translate_px={"x": 1}),
iaa.Affine(translate_px={"x": -1}))
observed = aug.augment_heatmaps([heatmaps])[0]
assert observed.shape == (3, 3, 3)
assert 0 - 1e-6 < observed.min_value < 0 + 1e-6
assert 1 - 1e-6 < observed.max_value < 1 + 1e-6
assert np.allclose(observed.get_arr(), heatmaps_arr_r1)
aug = iaa.AlphaElementwise(0, iaa.Add(10), iaa.Add(20))
observed = aug.augment_image(base_img)
expected = base_img + 20
assert np.allclose(observed, expected)
aug = iaa.AlphaElementwise(0, iaa.Affine(translate_px={"x": 1}),
iaa.Affine(translate_px={"x": -1}))
observed = aug.augment_heatmaps([heatmaps])[0]
assert observed.shape == (3, 3, 3)
assert 0 - 1e-6 < observed.min_value < 0 + 1e-6
assert 1 - 1e-6 < observed.max_value < 1 + 1e-6
assert np.allclose(observed.get_arr(), heatmaps_arr_l1)
aug = iaa.AlphaElementwise(0.75, iaa.Add(10), iaa.Add(20))
observed = aug.augment_image(base_img)
expected = np.round(base_img + 0.75 * 10 + 0.25 * 20).astype(np.uint8)
assert np.allclose(observed, expected)
aug = iaa.AlphaElementwise(0.75, None, iaa.Add(20))
observed = aug.augment_image(base_img + 10)
expected = np.round(base_img + 0.75 * 10 + 0.25 * (10 + 20)).astype(
np.uint8)
assert np.allclose(observed, expected)
aug = iaa.AlphaElementwise(0.75, iaa.Add(10), None)
observed = aug.augment_image(base_img + 10)
expected = np.round(base_img + 0.75 * (10 + 10) + 0.25 * 10).astype(
np.uint8)
assert np.allclose(observed, expected)
base_img = np.zeros((100, 100), dtype=np.uint8)
aug = iaa.AlphaElementwise((0.0, 1.0), iaa.Add(10), iaa.Add(110))
observed = (aug.augment_image(base_img) - 10) / 100
nb_bins = 10
hist, _ = np.histogram(observed.flatten(),
bins=nb_bins,
range=(0.0, 1.0),
density=False)
density_expected = 1.0 / nb_bins
density_tolerance = 0.05
for nb_samples in hist:
density = nb_samples / observed.size
assert density_expected - density_tolerance < density < density_expected + density_tolerance
base_img = np.zeros((1, 1, 100), dtype=np.uint8)
aug = iaa.AlphaElementwise((0.0, 1.0),
iaa.Add(10),
iaa.Add(110),
per_channel=True)
observed = aug.augment_image(base_img)
assert len(set(observed.flatten())) > 1
# propagating
aug = iaa.AlphaElementwise(0.5,
iaa.Add(100),
iaa.Add(50),
name="AlphaElementwiseTest")
def propagator(images, augmenter, parents, default):
if "AlphaElementwise" in augmenter.name:
return False
else:
return default
hooks = ia.HooksImages(propagator=propagator)
image = np.zeros((10, 10, 3), dtype=np.uint8) + 1
observed = aug.augment_image(image, hooks=hooks)
assert np.array_equal(observed, image)
# -----
# heatmaps and per_channel
# -----
class _DummyMaskParameter(iap.StochasticParameter):
def __init__(self, inverted=False):
super(_DummyMaskParameter, self).__init__()
self.nb_calls = 0
self.inverted = inverted
def _draw_samples(self, size, random_state):
self.nb_calls += 1
h, w = size
ones = np.ones((h, w), dtype=np.float32)
zeros = np.zeros((h, w), dtype=np.float32)
if self.nb_calls == 1:
return zeros if not self.inverted else ones
elif self.nb_calls in [2, 3]:
return ones if not self.inverted else zeros
else:
assert False
aug = iaa.AlphaElementwise(_DummyMaskParameter(inverted=False),
iaa.Affine(translate_px={"x": 1}),
iaa.Affine(translate_px={"x": -1}),
per_channel=True)
observed = aug.augment_heatmaps([heatmaps])[0]
assert observed.shape == (3, 3, 3)
assert 0 - 1e-6 < observed.min_value < 0 + 1e-6
assert 1 - 1e-6 < observed.max_value < 1 + 1e-6
assert np.allclose(observed.get_arr(), heatmaps_arr_r1)
aug = iaa.AlphaElementwise(_DummyMaskParameter(inverted=True),
iaa.Affine(translate_px={"x": 1}),
iaa.Affine(translate_px={"x": -1}),
per_channel=True)
observed = aug.augment_heatmaps([heatmaps])[0]
assert observed.shape == (3, 3, 3)
assert 0 - 1e-6 < observed.min_value < 0 + 1e-6
assert 1 - 1e-6 < observed.max_value < 1 + 1e-6
assert np.allclose(observed.get_arr(), heatmaps_arr_l1)
# -----
# keypoints
# -----
kps = [ia.Keypoint(x=5, y=10), ia.Keypoint(x=6, y=11)]
kpsoi = ia.KeypointsOnImage(kps, shape=(20, 20, 3))
aug = iaa.AlphaElementwise(1.0, iaa.Noop(),
iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.deepcopy()
assert keypoints_equal([observed], [expected])
aug = iaa.AlphaElementwise(0.501, iaa.Noop(),
iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.deepcopy()
assert keypoints_equal([observed], [expected])
aug = iaa.AlphaElementwise(0.0, iaa.Noop(),
iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.shift(x=1)
assert keypoints_equal([observed], [expected])
aug = iaa.AlphaElementwise(0.499, iaa.Noop(),
iaa.Affine(translate_px={"x": 1}))
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.shift(x=1)
assert keypoints_equal([observed], [expected])
# per_channel
aug = iaa.AlphaElementwise(1.0,
iaa.Noop(),
iaa.Affine(translate_px={"x": 1}),
per_channel=True)
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.deepcopy()
assert keypoints_equal([observed], [expected])
aug = iaa.AlphaElementwise(0.0,
iaa.Noop(),
iaa.Affine(translate_px={"x": 1}),
per_channel=True)
observed = aug.augment_keypoints([kpsoi])[0]
expected = kpsoi.shift(x=1)
assert keypoints_equal([observed], [expected])
"""
TODO this test currently doesn't work as AlphaElementwise augments keypoints without sampling
overlay factors per (x, y) location. (i.e. similar behaviour to Alpha)
aug = iaa.Alpha(iap.Choice([0.49, 0.51]), iaa.Noop(), iaa.Affine(translate_px={"x": 1}), per_channel=True)
expected_same = kpsoi.deepcopy()
expected_both_shifted = kpsoi.shift(x=1)
expected_first_shifted = KeypointsOnImage([kps[0].shift(x=1), kps[1]], shape=kpsoi.shape)
expected_second_shifted = KeypointsOnImage([kps[0], kps[1].shift(x=1)], shape=kpsoi.shape)
seen = [0, 0]
for _ in sm.xrange(200):
observed = aug.augment_keypoints([kpsoi])[0]
if keypoints_equal([observed], [expected_same]):
seen[0] += 1
elif keypoints_equal([observed], [expected_both_shifted]):
seen[1] += 1
elif keypoints_equal([observed], [expected_first_shifted]):
seen[2] += 1
elif keypoints_equal([observed], [expected_second_shifted]):
seen[3] += 1
else:
assert False
assert 100 - 50 < seen[0] < 100 + 50
assert 100 - 50 < seen[1] < 100 + 50
"""
# propagating
aug = iaa.AlphaElementwise(0.0,
iaa.Affine(translate_px={"x": 1}),
iaa.Affine(translate_px={"y": 1}),
name="AlphaElementwiseTest")
def propagator(kpsoi_to_aug, augmenter, parents, default):
if "AlphaElementwise" in augmenter.name:
return False
else:
return default
hooks = ia.HooksKeypoints(propagator=propagator)
observed = aug.augment_keypoints([kpsoi], hooks=hooks)[0]
assert keypoints_equal([observed], [kpsoi])
def test_BoundingBoxesOnImage():
reseed()
# test height/width
bb1 = ia.BoundingBox(y1=10, x1=20, y2=30, x2=40, label=None)
bb2 = ia.BoundingBox(y1=15, x1=25, y2=35, x2=45, label=None)
bbsoi = ia.BoundingBoxesOnImage([bb1, bb2], shape=(40, 50, 3))
assert bbsoi.height == 40
assert bbsoi.width == 50
bb1 = ia.BoundingBox(y1=10, x1=20, y2=30, x2=40, label=None)
bb2 = ia.BoundingBox(y1=15, x1=25, y2=35, x2=45, label=None)
bbsoi = ia.BoundingBoxesOnImage([bb1, bb2], shape=np.zeros((40, 50, 3), dtype=np.uint8))
assert bbsoi.height == 40
assert bbsoi.width == 50
# empty
bb = ia.BoundingBox(y1=10, x1=20, y2=30, x2=40, label=None)
bbsoi = ia.BoundingBoxesOnImage([bb], shape=(40, 50, 3))
assert not bbsoi.empty
bbsoi = ia.BoundingBoxesOnImage([], shape=(40, 50, 3))
assert bbsoi.empty
# on()
bb1 = ia.BoundingBox(y1=10, x1=20, y2=30, x2=40, label=None)
bb2 = ia.BoundingBox(y1=15, x1=25, y2=35, x2=45, label=None)
bbsoi = ia.BoundingBoxesOnImage([bb1, bb2], shape=np.zeros((40, 50, 3), dtype=np.uint8))
bbsoi_projected = bbsoi.on((40, 50))
assert bbsoi_projected.bounding_boxes[0].y1 == 10
assert bbsoi_projected.bounding_boxes[0].x1 == 20
assert bbsoi_projected.bounding_boxes[0].y2 == 30
assert bbsoi_projected.bounding_boxes[0].x2 == 40
assert bbsoi_projected.bounding_boxes[1].y1 == 15
assert bbsoi_projected.bounding_boxes[1].x1 == 25
assert bbsoi_projected.bounding_boxes[1].y2 == 35
assert bbsoi_projected.bounding_boxes[1].x2 == 45
bbsoi_projected = bbsoi.on((40*2, 50*2, 3))
assert bbsoi_projected.bounding_boxes[0].y1 == 10*2
assert bbsoi_projected.bounding_boxes[0].x1 == 20*2
assert bbsoi_projected.bounding_boxes[0].y2 == 30*2
assert bbsoi_projected.bounding_boxes[0].x2 == 40*2
assert bbsoi_projected.bounding_boxes[1].y1 == 15*2
assert bbsoi_projected.bounding_boxes[1].x1 == 25*2
assert bbsoi_projected.bounding_boxes[1].y2 == 35*2
assert bbsoi_projected.bounding_boxes[1].x2 == 45*2
bbsoi_projected = bbsoi.on(np.zeros((40*2, 50*2, 3), dtype=np.uint8))
assert bbsoi_projected.bounding_boxes[0].y1 == 10*2
assert bbsoi_projected.bounding_boxes[0].x1 == 20*2
assert bbsoi_projected.bounding_boxes[0].y2 == 30*2
assert bbsoi_projected.bounding_boxes[0].x2 == 40*2
assert bbsoi_projected.bounding_boxes[1].y1 == 15*2
assert bbsoi_projected.bounding_boxes[1].x1 == 25*2
assert bbsoi_projected.bounding_boxes[1].y2 == 35*2
assert bbsoi_projected.bounding_boxes[1].x2 == 45*2
# from_xyxy_array()
bbsoi = ia.BoundingBoxesOnImage.from_xyxy_array(
np.float32([
[0.0, 0.0, 1.0, 1.0],
[1.0, 2.0, 3.0, 4.0]
]),
shape=(40, 50, 3)
)
assert len(bbsoi.bounding_boxes) == 2
assert np.allclose(bbsoi.bounding_boxes[0].x1, 0.0)
assert np.allclose(bbsoi.bounding_boxes[0].y1, 0.0)
assert np.allclose(bbsoi.bounding_boxes[0].x2, 1.0)
assert np.allclose(bbsoi.bounding_boxes[0].y2, 1.0)
assert np.allclose(bbsoi.bounding_boxes[1].x1, 1.0)
assert np.allclose(bbsoi.bounding_boxes[1].y1, 2.0)
assert np.allclose(bbsoi.bounding_boxes[1].x2, 3.0)
assert np.allclose(bbsoi.bounding_boxes[1].y2, 4.0)
assert bbsoi.shape == (40, 50, 3)
bbsoi = ia.BoundingBoxesOnImage.from_xyxy_array(
np.int32([
[0, 0, 1, 1],
[1, 2, 3, 4]
]),
shape=(40, 50, 3)
)
assert len(bbsoi.bounding_boxes) == 2
assert np.allclose(bbsoi.bounding_boxes[0].x1, 0.0)
assert np.allclose(bbsoi.bounding_boxes[0].y1, 0.0)
assert np.allclose(bbsoi.bounding_boxes[0].x2, 1.0)
assert np.allclose(bbsoi.bounding_boxes[0].y2, 1.0)
assert np.allclose(bbsoi.bounding_boxes[1].x1, 1.0)
assert np.allclose(bbsoi.bounding_boxes[1].y1, 2.0)
assert np.allclose(bbsoi.bounding_boxes[1].x2, 3.0)
assert np.allclose(bbsoi.bounding_boxes[1].y2, 4.0)
assert bbsoi.shape == (40, 50, 3)
bbsoi = ia.BoundingBoxesOnImage.from_xyxy_array(
np.zeros((0, 4), dtype=np.float32),
shape=(40, 50, 3)
)
assert len(bbsoi.bounding_boxes) == 0
assert bbsoi.shape == (40, 50, 3)
# to_xyxy_array()
xyxy_arr = np.float32([
[0.0, 0.0, 1.0, 1.0],
[1.0, 2.0, 3.0, 4.0]
])
bbsoi = ia.BoundingBoxesOnImage.from_xyxy_array(xyxy_arr, shape=(40, 50, 3))
xyxy_arr_out = bbsoi.to_xyxy_array()
assert np.allclose(xyxy_arr, xyxy_arr_out)
assert xyxy_arr_out.dtype == np.float32
xyxy_arr_out = bbsoi.to_xyxy_array(dtype=np.int32)
assert np.allclose(xyxy_arr.astype(np.int32), xyxy_arr_out)
assert xyxy_arr_out.dtype == np.int32
xyxy_arr_out = ia.BoundingBoxesOnImage([], shape=(40, 50, 3)).to_xyxy_array(dtype=np.int32)
assert xyxy_arr_out.shape == (0, 4)
# draw_on_image()
bb1 = ia.BoundingBox(y1=10, x1=20, y2=30, x2=40, label=None)
bb2 = ia.BoundingBox(y1=15, x1=25, y2=35, x2=45, label=None)
bbsoi = ia.BoundingBoxesOnImage([bb1, bb2], shape=(40, 50, 3))
image = bbsoi.draw_on_image(np.zeros(bbsoi.shape, dtype=np.uint8), color=[0, 255, 0], alpha=1.0, size=1,
copy=True, raise_if_out_of_image=False)
assert np.all(image[10-1, 20-1, :] == [0, 0, 0])
assert np.all(image[10-1, 20-0, :] == [0, 0, 0])
assert np.all(image[10-0, 20-1, :] == [0, 0, 0])
assert np.all(image[10-0, 20-0, :] == [0, 255, 0])
assert np.all(image[10+1, 20+1, :] == [0, 0, 0])
assert np.all(image[30-1, 40-1, :] == [0, 0, 0])
assert np.all(image[30+1, 40-0, :] == [0, 0, 0])
assert np.all(image[30+0, 40+1, :] == [0, 0, 0])
assert np.all(image[30+0, 40+0, :] == [0, 255, 0])
assert np.all(image[30+1, 40+1, :] == [0, 0, 0])
assert np.all(image[15-1, 25-1, :] == [0, 0, 0])
assert np.all(image[15-1, 25-0, :] == [0, 0, 0])
assert np.all(image[15-0, 25-1, :] == [0, 0, 0])
assert np.all(image[15-0, 25-0, :] == [0, 255, 0])
assert np.all(image[15+1, 25+1, :] == [0, 0, 0])
assert np.all(image[35-1, 45-1, :] == [0, 0, 0])
assert np.all(image[35+1, 45+0, :] == [0, 0, 0])
assert np.all(image[35+0, 45+1, :] == [0, 0, 0])
assert np.all(image[35+0, 45+0, :] == [0, 255, 0])
assert np.all(image[35+1, 45+1, :] == [0, 0, 0])
# remove_out_of_image()
bb1 = ia.BoundingBox(y1=10, x1=20, y2=30, x2=40, label=None)
bb2 = ia.BoundingBox(y1=15, x1=25, y2=35, x2=51, label=None)
bbsoi = ia.BoundingBoxesOnImage([bb1, bb2], shape=(40, 50, 3))
bbsoi_slim = bbsoi.remove_out_of_image(fully=True, partly=True)
assert len(bbsoi_slim.bounding_boxes) == 1
assert bbsoi_slim.bounding_boxes[0] == bb1
# clip_out_of_image()
bb1 = ia.BoundingBox(y1=10, x1=20, y2=30, x2=40, label=None)
bb2 = ia.BoundingBox(y1=15, x1=25, y2=35, x2=51, label=None)
bbsoi = ia.BoundingBoxesOnImage([bb1, bb2], shape=(40, 50, 3))
eps = np.finfo(np.float32).eps
bbsoi_clip = bbsoi.clip_out_of_image()
assert len(bbsoi_clip.bounding_boxes) == 2
assert bbsoi_clip.bounding_boxes[0].y1 == 10
assert bbsoi_clip.bounding_boxes[0].x1 == 20
assert bbsoi_clip.bounding_boxes[0].y2 == 30
assert bbsoi_clip.bounding_boxes[0].x2 == 40
assert bbsoi_clip.bounding_boxes[1].y1 == 15
assert bbsoi_clip.bounding_boxes[1].x1 == 25
assert bbsoi_clip.bounding_boxes[1].y2 == 35
assert 50 - 2*eps < bbsoi_clip.bounding_boxes[1].x2 < 50
# shift()
bb1 = ia.BoundingBox(y1=10, x1=20, y2=30, x2=40, label=None)
bb2 = ia.BoundingBox(y1=15, x1=25, y2=35, x2=51, label=None)
bbsoi = ia.BoundingBoxesOnImage([bb1, bb2], shape=(40, 50, 3))
bbsoi_shifted = bbsoi.shift(right=1)
assert len(bbsoi_shifted.bounding_boxes) == 2
assert bbsoi_shifted.bounding_boxes[0].y1 == 10
assert bbsoi_shifted.bounding_boxes[0].x1 == 20 - 1
assert bbsoi_shifted.bounding_boxes[0].y2 == 30
assert bbsoi_shifted.bounding_boxes[0].x2 == 40 - 1
assert bbsoi_shifted.bounding_boxes[1].y1 == 15
assert bbsoi_shifted.bounding_boxes[1].x1 == 25 - 1
assert bbsoi_shifted.bounding_boxes[1].y2 == 35
assert bbsoi_shifted.bounding_boxes[1].x2 == 51 - 1
# copy()
bb1 = ia.BoundingBox(y1=10, x1=20, y2=30, x2=40, label=None)
bb2 = ia.BoundingBox(y1=15, x1=25, y2=35, x2=51, label=None)
bbsoi = ia.BoundingBoxesOnImage([bb1, bb2], shape=(40, 50, 3))
bbsoi_copy = bbsoi.copy()
assert len(bbsoi.bounding_boxes) == 2
assert bbsoi_copy.bounding_boxes[0].y1 == 10
assert bbsoi_copy.bounding_boxes[0].x1 == 20
assert bbsoi_copy.bounding_boxes[0].y2 == 30
assert bbsoi_copy.bounding_boxes[0].x2 == 40
assert bbsoi_copy.bounding_boxes[1].y1 == 15
assert bbsoi_copy.bounding_boxes[1].x1 == 25
assert bbsoi_copy.bounding_boxes[1].y2 == 35
assert bbsoi_copy.bounding_boxes[1].x2 == 51
bbsoi.bounding_boxes[0].y1 = 0
assert bbsoi_copy.bounding_boxes[0].y1 == 0
# deepcopy()
bb1 = ia.BoundingBox(y1=10, x1=20, y2=30, x2=40, label=None)
bb2 = ia.BoundingBox(y1=15, x1=25, y2=35, x2=51, label=None)
bbsoi = ia.BoundingBoxesOnImage([bb1, bb2], shape=(40, 50, 3))
bbsoi_copy = bbsoi.deepcopy()
assert len(bbsoi.bounding_boxes) == 2
assert bbsoi_copy.bounding_boxes[0].y1 == 10
assert bbsoi_copy.bounding_boxes[0].x1 == 20
assert bbsoi_copy.bounding_boxes[0].y2 == 30
assert bbsoi_copy.bounding_boxes[0].x2 == 40
assert bbsoi_copy.bounding_boxes[1].y1 == 15
assert bbsoi_copy.bounding_boxes[1].x1 == 25
assert bbsoi_copy.bounding_boxes[1].y2 == 35
assert bbsoi_copy.bounding_boxes[1].x2 == 51
bbsoi.bounding_boxes[0].y1 = 0
assert bbsoi_copy.bounding_boxes[0].y1 == 10
# repr() / str()
bb1 = ia.BoundingBox(y1=10, x1=20, y2=30, x2=40, label=None)
bb2 = ia.BoundingBox(y1=15, x1=25, y2=35, x2=51, label=None)
bbsoi = ia.BoundingBoxesOnImage([bb1, bb2], shape=(40, 50, 3))
bb1_expected = "BoundingBox(x1=20.0000, y1=10.0000, x2=40.0000, y2=30.0000, label=None)"
bb2_expected = "BoundingBox(x1=25.0000, y1=15.0000, x2=51.0000, y2=35.0000, label=None)"
expected = "BoundingBoxesOnImage([%s, %s], shape=(40, 50, 3))" % (bb1_expected, bb2_expected)
assert bbsoi.__repr__() == bbsoi.__str__() == expected
def test_unusual_channel_numbers():
reseed()
images = [
(0, create_random_images((4, 16, 16))),
(1, create_random_images((4, 16, 16, 1))),
(2, create_random_images((4, 16, 16, 2))),
(4, create_random_images((4, 16, 16, 4))),
(5, create_random_images((4, 16, 16, 5))),
(10, create_random_images((4, 16, 16, 10))),
(20, create_random_images((4, 16, 16, 20)))
]
augs = [
iaa.Add((-5, 5), name="Add"),
iaa.AddElementwise((-5, 5), name="AddElementwise"),
iaa.AdditiveGaussianNoise(0.01*255, name="AdditiveGaussianNoise"),
iaa.Multiply((0.95, 1.05), name="Multiply"),
iaa.Dropout(0.01, name="Dropout"),
iaa.CoarseDropout(0.01, size_px=6, name="CoarseDropout"),
iaa.Invert(0.01, per_channel=True, name="Invert"),
iaa.GaussianBlur(sigma=(0.95, 1.05), name="GaussianBlur"),
iaa.AverageBlur((3, 5), name="AverageBlur"),
iaa.MedianBlur((3, 5), name="MedianBlur"),
iaa.Sharpen((0.0, 0.1), lightness=(1.0, 1.2), name="Sharpen"),
iaa.Emboss(alpha=(0.0, 0.1), strength=(0.5, 1.5), name="Emboss"),
iaa.EdgeDetect(alpha=(0.0, 0.1), name="EdgeDetect"),
iaa.DirectedEdgeDetect(alpha=(0.0, 0.1), direction=0,
name="DirectedEdgeDetect"),
iaa.Fliplr(0.5, name="Fliplr"),
iaa.Flipud(0.5, name="Flipud"),
iaa.Affine(translate_px=(-5, 5), name="Affine-translate-px"),
iaa.Affine(translate_percent=(-0.05, 0.05),
name="Affine-translate-percent"),
iaa.Affine(rotate=(-20, 20), name="Affine-rotate"),
iaa.Affine(shear=(-20, 20), name="Affine-shear"),
iaa.Affine(scale=(0.9, 1.1), name="Affine-scale"),
iaa.PiecewiseAffine(scale=(0.001, 0.005), name="PiecewiseAffine"),
iaa.PerspectiveTransform(scale=(0.01, 0.10),
name="PerspectiveTransform"),
iaa.ElasticTransformation(alpha=(0.1, 0.2), sigma=(0.1, 0.2),
name="ElasticTransformation"),
iaa.Sequential([iaa.Add((-5, 5)), iaa.AddElementwise((-5, 5))]),
iaa.SomeOf(1, [iaa.Add((-5, 5)), iaa.AddElementwise((-5, 5))]),
iaa.OneOf([iaa.Add((-5, 5)), iaa.AddElementwise((-5, 5))]),
iaa.Sometimes(0.5, iaa.Add((-5, 5)), name="Sometimes"),
iaa.Identity(name="Noop"),
iaa.BlendAlpha((0.0, 0.1), iaa.Add(10), name="BlendAlpha"),
iaa.BlendAlphaElementwise((0.0, 0.1), iaa.Add(10),
name="BlendAlphaElementwise"),
iaa.BlendAlphaSimplexNoise(iaa.Add(10), name="BlendAlphaSimplexNoise"),
iaa.BlendAlphaFrequencyNoise(exponent=(-2, 2),
foreground=iaa.Add(10),
name="BlendAlphaSimplexNoise"),
iaa.Superpixels(p_replace=0.01, n_segments=64),
iaa.Resize({"height": 4, "width": 4}, name="Resize"),
iaa.CropAndPad(px=(-10, 10), name="CropAndPad"),
iaa.Pad(px=(0, 10), name="Pad"),
iaa.Crop(px=(0, 10), name="Crop")
]
for aug in augs:
for (nb_channels, images_c) in images:
if aug.name != "Resize":
images_aug = aug.augment_images(images_c)
assert images_aug.shape == images_c.shape
image_aug = aug.augment_image(images_c[0])
assert image_aug.shape == images_c[0].shape
else:
images_aug = aug.augment_images(images_c)
image_aug = aug.augment_image(images_c[0])
if images_c.ndim == 3:
assert images_aug.shape == (4, 4, 4)
assert image_aug.shape == (4, 4)
else:
assert images_aug.shape == (4, 4, 4, images_c.shape[3])
assert image_aug.shape == (4, 4, images_c.shape[3])
def test_keypoint_augmentation():
reseed()
keypoints = []
for y in range(40//5):
for x in range(60//5):
keypoints.append(ia.Keypoint(y=y*5, x=x*5))
keypoints_oi = ia.KeypointsOnImage(keypoints, shape=(40, 60, 3))
keypoints_oi_empty = ia.KeypointsOnImage([], shape=(40, 60, 3))
augs = [
iaa.Add((-5, 5), name="Add"),
iaa.AddElementwise((-5, 5), name="AddElementwise"),
iaa.AdditiveGaussianNoise(0.01*255, name="AdditiveGaussianNoise"),
iaa.Multiply((0.95, 1.05), name="Multiply"),
iaa.Dropout(0.01, name="Dropout"),
iaa.CoarseDropout(0.01, size_px=6, name="CoarseDropout"),
iaa.Invert(0.01, per_channel=True, name="Invert"),
iaa.ContrastNormalization((0.95, 1.05), name="ContrastNormalization"),
iaa.GaussianBlur(sigma=(0.95, 1.05), name="GaussianBlur"),
iaa.AverageBlur((3, 5), name="AverageBlur"),
iaa.MedianBlur((3, 5), name="MedianBlur"),
# iaa.BilateralBlur((3, 5), name="BilateralBlur"),
# WithColorspace ?
# iaa.AddToHueAndSaturation((-5, 5), name="AddToHueAndSaturation"),
# ChangeColorspace ?
# Grayscale cannot be tested, input not RGB
# Convolve ?
iaa.Sharpen((0.0, 0.1), lightness=(1.0, 1.2), name="Sharpen"),
iaa.Emboss(alpha=(0.0, 0.1), strength=(0.5, 1.5), name="Emboss"),
iaa.EdgeDetect(alpha=(0.0, 0.1), name="EdgeDetect"),
iaa.DirectedEdgeDetect(alpha=(0.0, 0.1), direction=0, name="DirectedEdgeDetect"),
iaa.Fliplr(0.5, name="Fliplr"),
iaa.Flipud(0.5, name="Flipud"),
iaa.Affine(translate_px=(-5, 5), name="Affine-translate-px"),
iaa.Affine(translate_percent=(-0.05, 0.05), name="Affine-translate-percent"),
iaa.Affine(rotate=(-20, 20), name="Affine-rotate"),
iaa.Affine(shear=(-20, 20), name="Affine-shear"),
iaa.Affine(scale=(0.9, 1.1), name="Affine-scale"),
iaa.PiecewiseAffine(scale=(0.001, 0.005), name="PiecewiseAffine"),
# iaa.PerspectiveTransform(scale=(0.01, 0.10), name="PerspectiveTransform"),
iaa.ElasticTransformation(alpha=(0.1, 0.2), sigma=(0.1, 0.2), name="ElasticTransformation"),
# Sequential
# SomeOf
# OneOf
# Sometimes
# WithChannels
# Noop
# Lambda
# AssertLambda
# AssertShape
iaa.Alpha((0.0, 0.1), iaa.Add(10), name="Alpha"),
iaa.AlphaElementwise((0.0, 0.1), iaa.Add(10), name="AlphaElementwise"),
iaa.SimplexNoiseAlpha(iaa.Add(10), name="SimplexNoiseAlpha"),
iaa.FrequencyNoiseAlpha(exponent=(-2, 2), first=iaa.Add(10),
name="SimplexNoiseAlpha"),
iaa.Superpixels(p_replace=0.01, n_segments=64),
iaa.Resize(0.5, name="Resize"),
iaa.CropAndPad(px=(-10, 10), name="CropAndPad"),
iaa.Pad(px=(0, 10), name="Pad"),
iaa.Crop(px=(0, 10), name="Crop")
]
for aug in augs:
dss = []
for i in range(10):
aug_det = aug.to_deterministic()
kp_fully_empty_aug = aug_det.augment_keypoints([])
assert kp_fully_empty_aug == []
kp_first_empty_aug = aug_det.augment_keypoints([keypoints_oi_empty])[0]
assert len(kp_first_empty_aug.keypoints) == 0
kp_image = keypoints_oi.to_keypoint_image(size=5)
kp_image_aug = aug_det.augment_image(kp_image)
kp_image_aug_rev = ia.KeypointsOnImage.from_keypoint_image(
kp_image_aug,
if_not_found_coords={"x": -9999, "y": -9999},
nb_channels=1
)
kp_aug = aug_det.augment_keypoints([keypoints_oi])[0]
ds = []
assert len(kp_image_aug_rev.keypoints) == len(kp_aug.keypoints),\
"Lost keypoints for '%s' (%d vs expected %d)" \
% (aug.name, len(kp_aug.keypoints), len(kp_image_aug_rev.keypoints))
for kp_pred, kp_pred_img in zip(kp_aug.keypoints, kp_image_aug_rev.keypoints):
kp_pred_lost = (kp_pred.x == -9999 and kp_pred.y == -9999)
kp_pred_img_lost = (kp_pred_img.x == -9999 and kp_pred_img.y == -9999)
if not kp_pred_lost and not kp_pred_img_lost:
d = np.sqrt((kp_pred.x - kp_pred_img.x) ** 2
+ (kp_pred.y - kp_pred_img.y) ** 2)
ds.append(d)
dss.extend(ds)
if len(ds) == 0:
print("[INFO] No valid keypoints found for '%s' "
"in test_keypoint_augmentation()" % (str(aug),))
assert np.average(dss) < 5.0, \
"Average distance too high (%.2f, with ds: %s)" \
% (np.average(dss), str(dss))
def test_dtype_preservation():
reseed()
size = (4, 16, 16, 3)
images = [
np.random.uniform(0, 255, size).astype(np.uint8),
np.random.uniform(0, 65535, size).astype(np.uint16),
np.random.uniform(0, 4294967295, size).astype(np.uint32),
np.random.uniform(-128, 127, size).astype(np.int16),
np.random.uniform(-32768, 32767, size).astype(np.int32),
np.random.uniform(0.0, 1.0, size).astype(np.float32),
np.random.uniform(-1000.0, 1000.0, size).astype(np.float16),
np.random.uniform(-1000.0, 1000.0, size).astype(np.float32),
np.random.uniform(-1000.0, 1000.0, size).astype(np.float64)
]
default_dtypes = set([arr.dtype for arr in images])
# Some dtypes are here removed per augmenter, because the respective
# augmenter does not support them. This test currently only checks whether
# dtypes are preserved from in- to output for all dtypes that are supported
# per augmenter.
# dtypes are here removed via list comprehension instead of
# `default_dtypes - set([dtype])`, because the latter one simply never
# removed the dtype(s) for some reason
def _not_dts(dts):
return [dt for dt in default_dtypes if dt not in dts]
augs = [
(iaa.Add((-5, 5), name="Add"),
_not_dts([np.uint32, np.int32, np.float64])),
(iaa.AddElementwise((-5, 5), name="AddElementwise"),
_not_dts([np.uint32, np.int32, np.float64])),
(iaa.AdditiveGaussianNoise(0.01*255, name="AdditiveGaussianNoise"),
_not_dts([np.uint32, np.int32, np.float64])),
(iaa.Multiply((0.95, 1.05), name="Multiply"),
_not_dts([np.uint32, np.int32, np.float64])),
(iaa.Dropout(0.01, name="Dropout"),
_not_dts([np.uint32, np.int32, np.float64])),
(iaa.CoarseDropout(0.01, size_px=6, name="CoarseDropout"),
_not_dts([np.uint32, np.int32, np.float64])),
(iaa.Invert(0.01, per_channel=True, name="Invert"),
default_dtypes),
(iaa.GaussianBlur(sigma=(0.95, 1.05), name="GaussianBlur"),
_not_dts([np.float16])),
(iaa.AverageBlur((3, 5), name="AverageBlur"),
_not_dts([np.uint32, np.int32, np.float16])),
(iaa.MedianBlur((3, 5), name="MedianBlur"),
_not_dts([np.uint32, np.int32, np.float16, np.float64])),
(iaa.BilateralBlur((3, 5), name="BilateralBlur"),
_not_dts([np.uint16, np.uint32, np.int16, np.int32, np.float16,
np.float64])),
(iaa.Sharpen((0.0, 0.1), lightness=(1.0, 1.2), name="Sharpen"),
_not_dts([np.uint32, np.int32, np.float16, np.uint32])),
(iaa.Emboss(alpha=(0.0, 0.1), strength=(0.5, 1.5), name="Emboss"),
_not_dts([np.uint32, np.int32, np.float16, np.uint32])),
(iaa.EdgeDetect(alpha=(0.0, 0.1), name="EdgeDetect"),
_not_dts([np.uint32, np.int32, np.float16, np.uint32])),
(iaa.DirectedEdgeDetect(alpha=(0.0, 0.1), direction=0,
name="DirectedEdgeDetect"),
_not_dts([np.uint32, np.int32, np.float16, np.uint32])),
(iaa.Fliplr(0.5, name="Fliplr"), default_dtypes),
(iaa.Flipud(0.5, name="Flipud"), default_dtypes),
(iaa.Affine(translate_px=(-5, 5), name="Affine-translate-px"),
_not_dts([np.uint32, np.int32])),
(iaa.Affine(translate_percent=(-0.05, 0.05),
name="Affine-translate-percent"),
_not_dts([np.uint32, np.int32])),
(iaa.Affine(rotate=(-20, 20), name="Affine-rotate"),
_not_dts([np.uint32, np.int32])),
(iaa.Affine(shear=(-20, 20), name="Affine-shear"),
_not_dts([np.uint32, np.int32])),
(iaa.Affine(scale=(0.9, 1.1), name="Affine-scale"),
_not_dts([np.uint32, np.int32])),
(iaa.PiecewiseAffine(scale=(0.001, 0.005), name="PiecewiseAffine"),
default_dtypes),
(iaa.ElasticTransformation(alpha=(0.1, 0.2), sigma=(0.1, 0.2),
name="ElasticTransformation"),
_not_dts([np.float16])),
(iaa.Sequential([iaa.Identity(), iaa.Identity()],
name="SequentialNoop"),
default_dtypes),
(iaa.SomeOf(1, [iaa.Identity(), iaa.Identity()], name="SomeOfNoop"),
default_dtypes),
(iaa.OneOf([iaa.Identity(), iaa.Identity()], name="OneOfNoop"),
default_dtypes),
(iaa.Sometimes(0.5, iaa.Identity(), name="SometimesNoop"),
default_dtypes),
(iaa.Sequential([iaa.Add((-5, 5)), iaa.AddElementwise((-5, 5))],
name="Sequential"),
_not_dts([np.uint32, np.int32, np.float64])),
(iaa.SomeOf(1,
[iaa.Add((-5, 5)), iaa.AddElementwise((-5, 5))],
name="SomeOf"),
_not_dts([np.uint32, np.int32, np.float64])),
(iaa.OneOf([iaa.Add((-5, 5)), iaa.AddElementwise((-5, 5))],
name="OneOf"),
_not_dts([np.uint32, np.int32, np.float64])),
(iaa.Sometimes(0.5, iaa.Add((-5, 5)), name="Sometimes"),
_not_dts([np.uint32, np.int32, np.float64])),
(iaa.Identity(name="Identity"), default_dtypes),
(iaa.BlendAlpha((0.0, 0.1), iaa.Identity(), name="BlendAlphaIdentity"),
_not_dts([np.float64])), # float64 requires float128 support
(iaa.BlendAlphaElementwise((0.0, 0.1), iaa.Identity(),
name="BlendAlphaElementwiseIdentity"),
_not_dts([np.float64])), # float64 requires float128 support
(iaa.BlendAlphaSimplexNoise(iaa.Identity(),
name="BlendAlphaSimplexNoiseIdentity"),
_not_dts([np.float64])), # float64 requires float128 support
(iaa.BlendAlphaFrequencyNoise(exponent=(-2, 2),
foreground=iaa.Identity(),
name="BlendAlphaFrequencyNoiseIdentity"),
_not_dts([np.float64])),
(iaa.BlendAlpha((0.0, 0.1), iaa.Add(10), name="BlendAlpha"),
_not_dts([np.uint32, np.int32, np.float64])),
(iaa.BlendAlphaElementwise((0.0, 0.1), iaa.Add(10),
name="BlendAlphaElementwise"),
_not_dts([np.uint32, np.int32, np.float64])),
(iaa.BlendAlphaSimplexNoise(iaa.Add(10), name="BlendAlphaSimplexNoise"),
_not_dts([np.uint32, np.int32, np.float64])),
(iaa.BlendAlphaFrequencyNoise(exponent=(-2, 2),
foreground=iaa.Add(10),
name="BlendAlphaFrequencyNoise"),
_not_dts([np.uint32, np.int32, np.float64])),
(iaa.Superpixels(p_replace=0.01, n_segments=64),
_not_dts([np.float16, np.float32, np.float64])),
(iaa.Resize({"height": 4, "width": 4}, name="Resize"),
_not_dts([np.uint16, np.uint32, np.int16, np.int32, np.float32,
np.float16, np.float64])),
(iaa.CropAndPad(px=(-10, 10), name="CropAndPad"),
_not_dts([np.uint16, np.uint32, np.int16, np.int32, np.float32,
np.float16, np.float64])),
(iaa.Pad(px=(0, 10), name="Pad"),
_not_dts([np.uint16, np.uint32, np.int16, np.int32, np.float32,
np.float16, np.float64])),
(iaa.Crop(px=(0, 10), name="Crop"),
_not_dts([np.uint16, np.uint32, np.int16, np.int32, np.float32,
np.float16, np.float64]))
]
for (aug, allowed_dtypes) in augs:
for images_i in images:
if images_i.dtype in allowed_dtypes:
images_aug = aug.augment_images(images_i)
assert images_aug.dtype == images_i.dtype
def test_Fliplr():
reseed()
base_img = np.array([[0, 0, 1],
[0, 0, 1],
[0, 1, 1]], dtype=np.uint8)
base_img = base_img[:, :, np.newaxis]
base_img_flipped = np.array([[1, 0, 0],
[1, 0, 0],
[1, 1, 0]], dtype=np.uint8)
base_img_flipped = base_img_flipped[:, :, np.newaxis]
images = np.array([base_img])
images_flipped = np.array([base_img_flipped])
keypoints = [ia.KeypointsOnImage([ia.Keypoint(x=0, y=0), ia.Keypoint(x=1, y=1),
ia.Keypoint(x=2, y=2)], shape=base_img.shape)]
keypoints_flipped = [ia.KeypointsOnImage([ia.Keypoint(x=2, y=0), ia.Keypoint(x=1, y=1),
ia.Keypoint(x=0, y=2)], shape=base_img.shape)]
polygons = [ia.PolygonsOnImage(
[ia.Polygon([(0, 0), (2, 0), (2, 2)])],
shape=base_img.shape)]
polygons_flipped = [ia.PolygonsOnImage(
[ia.Polygon([(2, 0), (0, 0), (0, 2)])],
shape=base_img.shape)]
# 0% chance of flip
aug = iaa.Fliplr(0)
aug_det = aug.to_deterministic()
for _ in sm.xrange(10):
observed = aug.augment_images(images)
expected = images
assert np.array_equal(observed, expected)
observed = aug_det.augment_images(images)
expected = images
assert np.array_equal(observed, expected)
observed = aug.augment_keypoints(keypoints)
expected = keypoints
assert keypoints_equal(observed, expected)
observed = aug_det.augment_keypoints(keypoints)
expected = keypoints
assert keypoints_equal(observed, expected)
for aug_ in [aug, aug_det]:
observed = aug_.augment_polygons(polygons)
assert len(observed) == 1
assert len(observed[0].polygons) == 1
assert observed[0].shape == polygons[0].shape
assert observed[0].polygons[0].exterior_almost_equals(
polygons[0].polygons[0])
assert observed[0].polygons[0].is_valid
# 0% chance of flip, heatmaps
aug = iaa.Fliplr(0)
heatmaps = ia.HeatmapsOnImage(
np.float32([
[0, 0.5, 0.75],
[0, 0.5, 0.75],
[0.75, 0.75, 0.75],
]),
shape=(3, 3, 3)
)
observed = aug.augment_heatmaps([heatmaps])[0]
expected = heatmaps.get_arr()
assert observed.shape == heatmaps.shape
assert heatmaps.min_value - 1e-6 < observed.min_value < heatmaps.min_value + 1e-6
assert heatmaps.max_value - 1e-6 < observed.max_value < heatmaps.max_value + 1e-6
assert np.array_equal(observed.get_arr(), expected)
# 100% chance of flip
aug = iaa.Fliplr(1.0)
aug_det = aug.to_deterministic()
for _ in sm.xrange(10):
observed = aug.augment_images(images)
expected = images_flipped
assert np.array_equal(observed, expected)
observed = aug_det.augment_images(images)
expected = images_flipped
assert np.array_equal(observed, expected)
observed = aug.augment_keypoints(keypoints)
expected = keypoints_flipped
assert keypoints_equal(observed, expected)
observed = aug_det.augment_keypoints(keypoints)
expected = keypoints_flipped
assert keypoints_equal(observed, expected)
for aug_ in [aug, aug_det]:
observed = aug_.augment_polygons(polygons)
assert len(observed) == 1
assert len(observed[0].polygons) == 1
assert observed[0].shape == polygons[0].shape
assert observed[0].polygons[0].exterior_almost_equals(
polygons_flipped[0].polygons[0])
assert observed[0].polygons[0].is_valid
# 100% chance of flip, heatmaps
aug = iaa.Fliplr(1.0)
heatmaps = ia.HeatmapsOnImage(
np.float32([
[0, 0.5, 0.75],
[0, 0.5, 0.75],
[0.75, 0.75, 0.75],
]),
shape=(3, 3, 3)
)
observed = aug.augment_heatmaps([heatmaps])[0]
expected = np.fliplr(heatmaps.get_arr())
assert observed.shape == heatmaps.shape
assert heatmaps.min_value - 1e-6 < observed.min_value < heatmaps.min_value + 1e-6
assert heatmaps.max_value - 1e-6 < observed.max_value < heatmaps.max_value + 1e-6
assert np.array_equal(observed.get_arr(), expected)
# 50% chance of flip
aug = iaa.Fliplr(0.5)
aug_det = aug.to_deterministic()
nb_iterations = 1000
nb_images_flipped = 0
nb_images_flipped_det = 0
nb_keypoints_flipped = 0
nb_keypoints_flipped_det = 0
nb_polygons_flipped = 0
nb_polygons_flipped_det = 0
for _ in sm.xrange(nb_iterations):
observed = aug.augment_images(images)
if np.array_equal(observed, images_flipped):
nb_images_flipped += 1
observed = aug_det.augment_images(images)
if np.array_equal(observed, images_flipped):
nb_images_flipped_det += 1
observed = aug.augment_keypoints(keypoints)
if keypoints_equal(observed, keypoints_flipped):
nb_keypoints_flipped += 1
observed = aug_det.augment_keypoints(keypoints)
if keypoints_equal(observed, keypoints_flipped):
nb_keypoints_flipped_det += 1
observed = aug.augment_polygons(polygons)
if observed[0].polygons[0].exterior_almost_equals(
polygons_flipped[0].polygons[0]):
nb_polygons_flipped += 1
observed = aug_det.augment_polygons(polygons)
if observed[0].polygons[0].exterior_almost_equals(
polygons_flipped[0].polygons[0]):
nb_polygons_flipped_det += 1
assert int(nb_iterations * 0.3) <= nb_images_flipped <= int(nb_iterations * 0.7)
assert int(nb_iterations * 0.3) <= nb_keypoints_flipped <= int(nb_iterations * 0.7)
assert int(nb_iterations * 0.3) <= nb_polygons_flipped <= int(nb_iterations * 0.7)
assert nb_images_flipped_det in [0, nb_iterations]
assert nb_keypoints_flipped_det in [0, nb_iterations]
assert nb_polygons_flipped_det in [0, nb_iterations]
# 50% chance of flipped, multiple images, list as input
images_multi = [base_img, base_img]
aug = iaa.Fliplr(0.5)
aug_det = aug.to_deterministic()
nb_iterations = 1000
nb_flipped_by_pos = [0] * len(images_multi)
nb_flipped_by_pos_det = [0] * len(images_multi)
for _ in sm.xrange(nb_iterations):
observed = aug.augment_images(images_multi)
for i in sm.xrange(len(images_multi)):
if np.array_equal(observed[i], base_img_flipped):
nb_flipped_by_pos[i] += 1
observed = aug_det.augment_images(images_multi)
for i in sm.xrange(len(images_multi)):
if np.array_equal(observed[i], base_img_flipped):
nb_flipped_by_pos_det[i] += 1
for val in nb_flipped_by_pos:
assert int(nb_iterations * 0.3) <= val <= int(nb_iterations * 0.7)
for val in nb_flipped_by_pos_det:
assert val in [0, nb_iterations]
# test StochasticParameter as p
aug = iaa.Fliplr(p=iap.Choice([0, 1], p=[0.7, 0.3]))
seen = [0, 0]
for _ in sm.xrange(1000):
observed = aug.augment_image(base_img)
if np.array_equal(observed, base_img):
seen[0] += 1
elif np.array_equal(observed, base_img_flipped):
seen[1] += 1
else:
assert False
assert 700 - 75 < seen[0] < 700 + 75
assert 300 - 75 < seen[1] < 300 + 75
# test exceptions for wrong parameter types
got_exception = False
try:
_ = iaa.Fliplr(p="test")
except Exception:
got_exception = True
assert got_exception
# test get_parameters()
aug = iaa.Fliplr(p=0.5)
params = aug.get_parameters()
assert isinstance(params[0], iap.Binomial)
assert isinstance(params[0].p, iap.Deterministic)
assert 0.5 - 1e-4 < params[0].p.value < 0.5 + 1e-4
###################
# test other dtypes
###################
aug = iaa.Fliplr(1.0)
image = np.zeros((3, 3), dtype=bool)
image[0, 0] = True
expected = np.zeros((3, 3), dtype=bool)
expected[0, 2] = True
image_aug = aug.augment_image(image)
assert image_aug.dtype.type == image.dtype.type
assert np.all(image_aug == expected)
for dtype in [np.uint8, np.uint16, np.uint32, np.uint64, np.int8, np.int32, np.int64]:
min_value, center_value, max_value = iadt.get_value_range_of_dtype(dtype)
value = max_value
image = np.zeros((3, 3), dtype=dtype)
image[0, 0] = value
expected = np.zeros((3, 3), dtype=dtype)
expected[0, 2] = value
image_aug = aug.augment_image(image)
assert image_aug.dtype.type == dtype
assert np.array_equal(image_aug, expected)
for dtype, value in zip([np.float16, np.float32, np.float64, np.float128], [5000, 1000**2, 1000**3, 1000**4]):
atol = 1e-9 * max_value if dtype != np.float16 else 1e-3 * max_value
image = np.zeros((3, 3), dtype=dtype)
image[0, 0] = value
expected = np.zeros((3, 3), dtype=dtype)
expected[0, 2] = value
image_aug = aug.augment_image(image)
assert image_aug.dtype.type == dtype
assert np.allclose(image_aug, expected, atol=atol)
def setUp(self):
reseed()
def test_Convolve():
reseed()
img = [
[1, 2, 3],
[4, 5, 6],
[7, 8, 9]
]
img = np.uint8(img)
# matrix is None
aug = iaa.Convolve(matrix=None)
observed = aug.augment_image(img)
assert np.array_equal(observed, img)
aug = iaa.Convolve(matrix=lambda _img, nb_channels, random_state: [None])
observed = aug.augment_image(img)
assert np.array_equal(observed, img)
# matrix is [[1]]
aug = iaa.Convolve(matrix=np.float32([[1]]))
observed = aug.augment_image(img)
assert np.array_equal(observed, img)
aug = iaa.Convolve(matrix=lambda _img, nb_channels, random_state: np.float32([[1]]))
observed = aug.augment_image(img)
assert np.array_equal(observed, img)
# matrix is [[0, 0, 0], [0, 1, 0], [0, 0, 0]]
m = np.float32([
[0, 0, 0],
[0, 1, 0],
[0, 0, 0]
])
aug = iaa.Convolve(matrix=m)
observed = aug.augment_image(img)
assert np.array_equal(observed, img)
aug = iaa.Convolve(matrix=lambda _img, nb_channels, random_state: m)
observed = aug.augment_image(img)
assert np.array_equal(observed, img)
# matrix is [[0, 0, 0], [0, 2, 0], [0, 0, 0]]
m = np.float32([
[0, 0, 0],
[0, 2, 0],
[0, 0, 0]
])
aug = iaa.Convolve(matrix=m)
observed = aug.augment_image(img)
assert np.array_equal(observed, 2*img)
aug = iaa.Convolve(matrix=lambda _img, nb_channels, random_state: m)
observed = aug.augment_image(img)
assert np.array_equal(observed, 2*img)
# matrix is [[0, 0, 0], [0, 2, 0], [0, 0, 0]]
# with 3 channels
m = np.float32([
[0, 0, 0],
[0, 2, 0],
[0, 0, 0]
])
img3 = np.tile(img[..., np.newaxis], (1, 1, 3))
aug = iaa.Convolve(matrix=m)
observed = aug.augment_image(img3)
assert np.array_equal(observed, 2*img3)
aug = iaa.Convolve(matrix=lambda _img, nb_channels, random_state: m)
observed = aug.augment_image(img3)
assert np.array_equal(observed, 2*img3)
# matrix is [[0, -1, 0], [0, 10, 0], [0, 0, 0]]
m = np.float32([
[0, -1, 0],
[0, 10, 0],
[0, 0, 0]
])
expected = np.uint8([
[10*1+(-1)*4, 10*2+(-1)*5, 10*3+(-1)*6],
[10*4+(-1)*1, 10*5+(-1)*2, 10*6+(-1)*3],
[10*7+(-1)*4, 10*8+(-1)*5, 10*9+(-1)*6]
])
aug = iaa.Convolve(matrix=m)
observed = aug.augment_image(img)
assert np.array_equal(observed, expected)
aug = iaa.Convolve(matrix=lambda _img, nb_channels, random_state: m)
observed = aug.augment_image(img)
assert np.array_equal(observed, expected)
# changing matrices when using callable
expected = []
for i in sm.xrange(5):
expected.append(img * i)
aug = iaa.Convolve(matrix=lambda _img, nb_channels, random_state: np.float32([[random_state.randint(0, 5)]]))
seen = [False] * 5
for _ in sm.xrange(200):
observed = aug.augment_image(img)
found = False
for i, expected_i in enumerate(expected):
if np.array_equal(observed, expected_i):
seen[i] = True
found = True
break
assert found
if all(seen):
break
assert all(seen)
# bad datatype for matrix
got_exception = False
try:
aug = iaa.Convolve(matrix=False)
except Exception as exc:
assert "Expected " in str(exc)
got_exception = True
assert got_exception
# get_parameters()
matrix = np.int32([[1]])
aug = iaa.Convolve(matrix=matrix)
params = aug.get_parameters()
assert np.array_equal(params[0], matrix)
assert params[1] == "constant"
# TODO add test for keypoints once their handling was improved in Convolve
###################
# test other dtypes
###################
identity_matrix = np.int64([[1]])
aug = iaa.Convolve(matrix=identity_matrix)
image = np.zeros((3, 3), dtype=bool)
image[1, 1] = True
image_aug = aug.augment_image(image)
assert image.dtype.type == np.bool_
assert np.all(image_aug == image)
for dtype in [np.uint8, np.uint16, np.int8, np.int16]:
image = np.zeros((3, 3), dtype=dtype)
image[1, 1] = 100
image_aug = aug.augment_image(image)
assert image.dtype.type == dtype
assert np.all(image_aug == image)
for dtype in [np.float16, np.float32, np.float64]:
image = np.zeros((3, 3), dtype=dtype)
image[1, 1] = 100.0
image_aug = aug.augment_image(image)
assert image.dtype.type == dtype
assert np.allclose(image_aug, image)
# ----
# non-identity matrix
# ----
matrix = np.float64([
[0, 0.6, 0],
[0, 0.4, 0],
[0, 0, 0]
])
aug = iaa.Convolve(matrix=matrix)
image = np.zeros((3, 3), dtype=bool)
image[1, 1] = True
image[2, 1] = True
expected = np.zeros((3, 3), dtype=bool)
expected[0, 1] = True
expected[2, 1] = True
image_aug = aug.augment_image(image)
assert image.dtype.type == np.bool_
assert np.all(image_aug == expected)
matrix = np.float64([
[0, 0.5, 0],
[0, 0.5, 0],
[0, 0, 0]
])
aug = iaa.Convolve(matrix=matrix)
# uint, int
for dtype in [np.uint8, np.uint16, np.int8, np.int16]:
image = np.zeros((3, 3), dtype=dtype)
image[1, 1] = 100
image[2, 1] = 100
image_aug = aug.augment_image(image)
expected = np.zeros((3, 3), dtype=dtype)
expected[0, 1] = int(np.round(100 * 0.5))
expected[1, 1] = int(np.round(100 * 0.5))
expected[2, 1] = int(np.round(100 * 0.5 + 100 * 0.5))
diff = np.abs(image_aug.astype(np.int64) - expected.astype(np.int64))
assert image_aug.dtype.type == dtype
assert np.max(diff) <= 2
# float
for dtype in [np.float16, np.float32, np.float64]:
image = np.zeros((3, 3), dtype=dtype)
image[1, 1] = 100.0
image[2, 1] = 100.0
image_aug = aug.augment_image(image)
expected = np.zeros((3, 3), dtype=dtype)
expected[0, 1] = 100 * 0.5
expected[1, 1] = 100 * 0.5
expected[2, 1] = 100 * 0.5 + 100 * 0.5
diff = np.abs(image_aug.astype(np.float128) - expected.astype(np.float128))
assert image_aug.dtype.type == dtype
assert np.max(diff) < 1.0
# ----
# non-identity matrix, higher values
# ----
matrix = np.float64([
[0, 0.5, 0],
[0, 0.5, 0],
[0, 0, 0]
])
aug = iaa.Convolve(matrix=matrix)
# uint, int
for dtype in [np.uint8, np.uint16, np.int8, np.int16]:
_min_value, center_value, max_value = iadt.get_value_range_of_dtype(dtype)
value = int(center_value + 0.4 * max_value)
image = np.zeros((3, 3), dtype=dtype)
image[1, 1] = value
image[2, 1] = value
image_aug = aug.augment_image(image)
expected = np.zeros((3, 3), dtype=dtype)
expected[0, 1] = int(np.round(value * 0.5))
expected[1, 1] = int(np.round(value * 0.5))
expected[2, 1] = int(np.round(value * 0.5 + value * 0.5))
diff = np.abs(image_aug.astype(np.int64) - expected.astype(np.int64))
assert image_aug.dtype.type == dtype
assert np.max(diff) <= 2
# float
for dtype, value in zip([np.float16, np.float32, np.float64], [5000, 1000*1000, 1000*1000*1000]):
image = np.zeros((3, 3), dtype=dtype)
image[1, 1] = value
image[2, 1] = value
image_aug = aug.augment_image(image)
expected = np.zeros((3, 3), dtype=dtype)
expected[0, 1] = value * 0.5
expected[1, 1] = value * 0.5
expected[2, 1] = value * 0.5 + value * 0.5
diff = np.abs(image_aug.astype(np.float128) - expected.astype(np.float128))
assert image_aug.dtype.type == dtype
assert np.max(diff) < 1.0
# assert failure on invalid dtypes
aug = iaa.Convolve(matrix=identity_matrix)
for dt in [np.uint32, np.uint64, np.int32, np.int64]:
got_exception = False
try:
_ = aug.augment_image(np.zeros((1, 1), dtype=dt))
except Exception as exc:
assert "forbidden dtype" in str(exc)
got_exception = True
assert got_exception
def test_unusual_channel_numbers():
reseed()
images = [
(0, create_random_images((4, 16, 16))),
(1, create_random_images((4, 16, 16, 1))),
(2, create_random_images((4, 16, 16, 2))),
(4, create_random_images((4, 16, 16, 4))),
(5, create_random_images((4, 16, 16, 5))),
(10, create_random_images((4, 16, 16, 10))),
(20, create_random_images((4, 16, 16, 20)))
]
augs = [
iaa.Add((-5, 5), name="Add"),
iaa.AddElementwise((-5, 5), name="AddElementwise"),
iaa.AdditiveGaussianNoise(0.01*255, name="AdditiveGaussianNoise"),
iaa.Multiply((0.95, 1.05), name="Multiply"),
iaa.Dropout(0.01, name="Dropout"),
iaa.CoarseDropout(0.01, size_px=6, name="CoarseDropout"),
iaa.Invert(0.01, per_channel=True, name="Invert"),
iaa.ContrastNormalization((0.95, 1.05), name="ContrastNormalization"),
iaa.GaussianBlur(sigma=(0.95, 1.05), name="GaussianBlur"),
iaa.AverageBlur((3, 5), name="AverageBlur"),
iaa.MedianBlur((3, 5), name="MedianBlur"),
# iaa.BilateralBlur((3, 5), name="BilateralBlur"), # works only with 3/RGB channels
# WithColorspace ?
# iaa.AddToHueAndSaturation((-5, 5), name="AddToHueAndSaturation"), # works only with 3/RGB channels
# ChangeColorspace ?
# iaa.Grayscale((0.0, 0.1), name="Grayscale"), # works only with 3 channels
# Convolve ?
iaa.Sharpen((0.0, 0.1), lightness=(1.0, 1.2), name="Sharpen"),
iaa.Emboss(alpha=(0.0, 0.1), strength=(0.5, 1.5), name="Emboss"),
iaa.EdgeDetect(alpha=(0.0, 0.1), name="EdgeDetect"),
iaa.DirectedEdgeDetect(alpha=(0.0, 0.1), direction=0,
name="DirectedEdgeDetect"),
iaa.Fliplr(0.5, name="Fliplr"),
iaa.Flipud(0.5, name="Flipud"),
iaa.Affine(translate_px=(-5, 5), name="Affine-translate-px"),
iaa.Affine(translate_percent=(-0.05, 0.05), name="Affine-translate-percent"),
iaa.Affine(rotate=(-20, 20), name="Affine-rotate"),
iaa.Affine(shear=(-20, 20), name="Affine-shear"),
iaa.Affine(scale=(0.9, 1.1), name="Affine-scale"),
iaa.PiecewiseAffine(scale=(0.001, 0.005), name="PiecewiseAffine"),
iaa.PerspectiveTransform(scale=(0.01, 0.10), name="PerspectiveTransform"),
iaa.ElasticTransformation(alpha=(0.1, 0.2), sigma=(0.1, 0.2),
name="ElasticTransformation"),
iaa.Sequential([iaa.Add((-5, 5)), iaa.AddElementwise((-5, 5))]),
iaa.SomeOf(1, [iaa.Add((-5, 5)), iaa.AddElementwise((-5, 5))]),
iaa.OneOf([iaa.Add((-5, 5)), iaa.AddElementwise((-5, 5))]),
iaa.Sometimes(0.5, iaa.Add((-5, 5)), name="Sometimes"),
# WithChannels
iaa.Noop(name="Noop"),
# Lambda
# AssertLambda
# AssertShape
iaa.Alpha((0.0, 0.1), iaa.Add(10), name="Alpha"),
iaa.AlphaElementwise((0.0, 0.1), iaa.Add(10), name="AlphaElementwise"),
iaa.SimplexNoiseAlpha(iaa.Add(10), name="SimplexNoiseAlpha"),
iaa.FrequencyNoiseAlpha(exponent=(-2, 2), first=iaa.Add(10),
name="SimplexNoiseAlpha"),
iaa.Superpixels(p_replace=0.01, n_segments=64),
iaa.Resize({"height": 4, "width": 4}, name="Resize"),
iaa.CropAndPad(px=(-10, 10), name="CropAndPad"),
iaa.Pad(px=(0, 10), name="Pad"),
iaa.Crop(px=(0, 10), name="Crop")
]
for aug in augs:
for (nb_channels, images_c) in images:
if aug.name != "Resize":
images_aug = aug.augment_images(images_c)
assert images_aug.shape == images_c.shape
image_aug = aug.augment_image(images_c[0])
assert image_aug.shape == images_c[0].shape
else:
images_aug = aug.augment_images(images_c)
image_aug = aug.augment_image(images_c[0])
if images_c.ndim == 3:
assert images_aug.shape == (4, 4, 4)
assert image_aug.shape == (4, 4)
else:
assert images_aug.shape == (4, 4, 4, images_c.shape[3])
assert image_aug.shape == (4, 4, images_c.shape[3])
def test_Sharpen():
reseed()
def _compute_sharpened_base_img(lightness, m):
base_img_sharpened = np.zeros((3, 3), dtype=np.float32)
k = 1
# note that cv2 uses reflection padding by default
base_img_sharpened[0, 0] = (m[1, 1] + lightness) / k * 10 + 4 * (
m[0, 0] / k) * 10 + 4 * (m[2, 2] / k) * 20
base_img_sharpened[0, 2] = base_img_sharpened[0, 0]
base_img_sharpened[2, 0] = base_img_sharpened[0, 0]
base_img_sharpened[2, 2] = base_img_sharpened[0, 0]
base_img_sharpened[0, 1] = (m[1, 1] + lightness) / k * 10 + 6 * (
m[0, 1] / k) * 10 + 2 * (m[2, 2] / k) * 20
base_img_sharpened[1, 0] = base_img_sharpened[0, 1]
base_img_sharpened[1, 2] = base_img_sharpened[0, 1]
base_img_sharpened[2, 1] = base_img_sharpened[0, 1]
base_img_sharpened[
1, 1] = (m[1, 1] + lightness) / k * 20 + 8 * (m[0, 1] / k) * 10
base_img_sharpened = np.clip(base_img_sharpened, 0,
255).astype(np.uint8)
return base_img_sharpened
base_img = [[10, 10, 10], [10, 20, 10], [10, 10, 10]]
base_img = np.uint8(base_img)
m = np.float32([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]])
m_noop = np.float32([[0, 0, 0], [0, 1, 0], [0, 0, 0]])
base_img_sharpened = _compute_sharpened_base_img(1, m)
aug = iaa.Sharpen(alpha=0, lightness=1)
observed = aug.augment_image(base_img)
expected = base_img
assert np.allclose(observed, expected)
aug = iaa.Sharpen(alpha=1.0, lightness=1)
observed = aug.augment_image(base_img)
expected = base_img_sharpened
assert np.allclose(observed, expected)
aug = iaa.Sharpen(alpha=0.5, lightness=1)
observed = aug.augment_image(base_img)
expected = _compute_sharpened_base_img(0.5 * 1, 0.5 * m_noop + 0.5 * m)
assert np.allclose(observed, expected.astype(np.uint8))
aug = iaa.Sharpen(alpha=0.75, lightness=1)
observed = aug.augment_image(base_img)
expected = _compute_sharpened_base_img(0.75 * 1, 0.25 * m_noop + 0.75 * m)
assert np.allclose(observed, expected)
aug = iaa.Sharpen(alpha=iap.Choice([0.5, 1.0]), lightness=1)
observed = aug.augment_image(base_img)
expected1 = _compute_sharpened_base_img(0.5 * 1, m)
expected2 = _compute_sharpened_base_img(1.0 * 1, m)
assert np.allclose(observed, expected1) or np.allclose(observed, expected2)
got_exception = False
try:
_ = iaa.Sharpen(alpha="test", lightness=1)
except Exception as exc:
assert "Expected " in str(exc)
got_exception = True
assert got_exception
aug = iaa.Sharpen(alpha=1.0, lightness=2)
observed = aug.augment_image(base_img)
expected = _compute_sharpened_base_img(1.0 * 2, m)
assert np.allclose(observed, expected)
aug = iaa.Sharpen(alpha=1.0, lightness=3)
observed = aug.augment_image(base_img)
expected = _compute_sharpened_base_img(1.0 * 3, m)
assert np.allclose(observed, expected)
aug = iaa.Sharpen(alpha=1.0, lightness=iap.Choice([1.0, 1.5]))
observed = aug.augment_image(base_img)
expected1 = _compute_sharpened_base_img(1.0 * 1.0, m)
expected2 = _compute_sharpened_base_img(1.0 * 1.5, m)
assert np.allclose(observed, expected1) or np.allclose(observed, expected2)
got_exception = False
try:
_ = iaa.Sharpen(alpha=1.0, lightness="test")
except Exception as exc:
assert "Expected " in str(exc)
got_exception = True
assert got_exception
# this part doesnt really work so far due to nonlinearities resulting from clipping to uint8
"""
def test_dtype_preservation():
reseed()
size = (4, 16, 16, 3)
images = [
np.random.uniform(0, 255, size).astype(np.uint8),
np.random.uniform(0, 65535, size).astype(np.uint16),
np.random.uniform(0, 4294967295, size).astype(np.uint32),
np.random.uniform(-128, 127, size).astype(np.int16),
np.random.uniform(-32768, 32767, size).astype(np.int32),
np.random.uniform(0.0, 1.0, size).astype(np.float32),
np.random.uniform(-1000.0, 1000.0, size).astype(np.float16),
np.random.uniform(-1000.0, 1000.0, size).astype(np.float32),
np.random.uniform(-1000.0, 1000.0, size).astype(np.float64)
]
default_dtypes = set([arr.dtype for arr in images])
# Some dtypes are here removed per augmenter, because the respective
# augmenter does not support them. This test currently only checks whether
# dtypes are preserved from in- to output for all dtypes that are supported
# per augmenter.
# dtypes are here removed via list comprehension instead of
# `default_dtypes - set([dtype])`, because the latter one simply never
# removed the dtype(s) for some reason?!
def _not_dts(dts):
return [dt for dt in default_dtypes if dt not in dts]
augs = [
(iaa.Add((-5, 5), name="Add"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.AddElementwise((-5, 5), name="AddElementwise"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.AdditiveGaussianNoise(0.01*255, name="AdditiveGaussianNoise"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.Multiply((0.95, 1.05), name="Multiply"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.Dropout(0.01, name="Dropout"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.CoarseDropout(0.01, size_px=6, name="CoarseDropout"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.Invert(0.01, per_channel=True, name="Invert"), default_dtypes),
(iaa.ContrastNormalization((0.95, 1.05), name="ContrastNormalization"), default_dtypes),
(iaa.GaussianBlur(sigma=(0.95, 1.05), name="GaussianBlur"), _not_dts([np.float16])),
(iaa.AverageBlur((3, 5), name="AverageBlur"), _not_dts([np.uint32, np.int32, np.float16])),
(iaa.MedianBlur((3, 5), name="MedianBlur"), _not_dts([np.uint32, np.int32, np.float16, np.float64])),
(iaa.BilateralBlur((3, 5), name="BilateralBlur"),
_not_dts([np.uint16, np.uint32, np.int16, np.int32, np.float16, np.float64])),
# WithColorspace ?
# iaa.AddToHueAndSaturation((-5, 5), name="AddToHueAndSaturation"), # works only with RGB/uint8
# ChangeColorspace ?
# iaa.Grayscale((0.0, 0.1), name="Grayscale"), # works only with RGB/uint8
# Convolve ?
(iaa.Sharpen((0.0, 0.1), lightness=(1.0, 1.2), name="Sharpen"),
_not_dts([np.uint32, np.int32, np.float16, np.uint32])),
(iaa.Emboss(alpha=(0.0, 0.1), strength=(0.5, 1.5), name="Emboss"),
_not_dts([np.uint32, np.int32, np.float16, np.uint32])),
(iaa.EdgeDetect(alpha=(0.0, 0.1), name="EdgeDetect"),
_not_dts([np.uint32, np.int32, np.float16, np.uint32])),
(iaa.DirectedEdgeDetect(alpha=(0.0, 0.1), direction=0, name="DirectedEdgeDetect"),
_not_dts([np.uint32, np.int32, np.float16, np.uint32])),
(iaa.Fliplr(0.5, name="Fliplr"), default_dtypes),
(iaa.Flipud(0.5, name="Flipud"), default_dtypes),
(iaa.Affine(translate_px=(-5, 5), name="Affine-translate-px"), _not_dts([np.uint32, np.int32])),
(iaa.Affine(translate_percent=(-0.05, 0.05), name="Affine-translate-percent"), _not_dts([np.uint32, np.int32])),
(iaa.Affine(rotate=(-20, 20), name="Affine-rotate"), _not_dts([np.uint32, np.int32])),
(iaa.Affine(shear=(-20, 20), name="Affine-shear"), _not_dts([np.uint32, np.int32])),
(iaa.Affine(scale=(0.9, 1.1), name="Affine-scale"), _not_dts([np.uint32, np.int32])),
(iaa.PiecewiseAffine(scale=(0.001, 0.005), name="PiecewiseAffine"), default_dtypes),
# (iaa.PerspectiveTransform(scale=(0.01, 0.10), name="PerspectiveTransform"), not_dts([np.uint32])),
(iaa.ElasticTransformation(alpha=(0.1, 0.2), sigma=(0.1, 0.2), name="ElasticTransformation"),
_not_dts([np.float16])),
(iaa.Sequential([iaa.Noop(), iaa.Noop()], name="SequentialNoop"), default_dtypes),
(iaa.SomeOf(1, [iaa.Noop(), iaa.Noop()], name="SomeOfNoop"), default_dtypes),
(iaa.OneOf([iaa.Noop(), iaa.Noop()], name="OneOfNoop"), default_dtypes),
(iaa.Sometimes(0.5, iaa.Noop(), name="SometimesNoop"), default_dtypes),
(iaa.Sequential([iaa.Add((-5, 5)), iaa.AddElementwise((-5, 5))], name="Sequential"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.SomeOf(1, [iaa.Add((-5, 5)), iaa.AddElementwise((-5, 5))], name="SomeOf"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.OneOf([iaa.Add((-5, 5)), iaa.AddElementwise((-5, 5))], name="OneOf"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.Sometimes(0.5, iaa.Add((-5, 5)), name="Sometimes"), _not_dts([np.uint32, np.int32, np.float64])),
# WithChannels
(iaa.Noop(name="Noop"), default_dtypes),
# Lambda
# AssertLambda
# AssertShape
(iaa.Alpha((0.0, 0.1), iaa.Noop(), name="AlphaNoop"), default_dtypes),
(iaa.AlphaElementwise((0.0, 0.1), iaa.Noop(), name="AlphaElementwiseNoop"), default_dtypes),
(iaa.SimplexNoiseAlpha(iaa.Noop(), name="SimplexNoiseAlphaNoop"), default_dtypes),
(iaa.FrequencyNoiseAlpha(exponent=(-2, 2), first=iaa.Noop(), name="SimplexNoiseAlphaNoop"), default_dtypes),
(iaa.Alpha((0.0, 0.1), iaa.Add(10), name="Alpha"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.AlphaElementwise((0.0, 0.1), iaa.Add(10), name="AlphaElementwise"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.SimplexNoiseAlpha(iaa.Add(10), name="SimplexNoiseAlpha"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.FrequencyNoiseAlpha(exponent=(-2, 2), first=iaa.Add(10), name="SimplexNoiseAlpha"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.Superpixels(p_replace=0.01, n_segments=64), _not_dts([np.float16, np.float32, np.float64])),
(iaa.Resize({"height": 4, "width": 4}, name="Resize"),
_not_dts([np.uint16, np.uint32, np.int16, np.int32, np.float32, np.float16, np.float64])),
(iaa.CropAndPad(px=(-10, 10), name="CropAndPad"),
_not_dts([np.uint16, np.uint32, np.int16, np.int32, np.float32, np.float16, np.float64])),
(iaa.Pad(px=(0, 10), name="Pad"),
_not_dts([np.uint16, np.uint32, np.int16, np.int32, np.float32, np.float16, np.float64])),
(iaa.Crop(px=(0, 10), name="Crop"),
_not_dts([np.uint16, np.uint32, np.int16, np.int32, np.float32, np.float16, np.float64]))
]
for (aug, allowed_dtypes) in augs:
# print("aug", aug.name)
# print("allowed_dtypes", allowed_dtypes)
for images_i in images:
if images_i.dtype in allowed_dtypes:
# print("image dt", images_i.dtype)
images_aug = aug.augment_images(images_i)
assert images_aug.dtype == images_i.dtype
else:
# print("image dt", images_i.dtype, "[SKIPPED]")
pass
def test_Sharpen():
reseed()
def _compute_sharpened_base_img(lightness, m):
base_img_sharpened = np.zeros((3, 3), dtype=np.float32)
k = 1
# note that cv2 uses reflection padding by default
base_img_sharpened[0, 0] = (m[1, 1] + lightness)/k * 10 + 4 * (m[0, 0]/k) * 10 + 4 * (m[2, 2]/k) * 20
base_img_sharpened[0, 2] = base_img_sharpened[0, 0]
base_img_sharpened[2, 0] = base_img_sharpened[0, 0]
base_img_sharpened[2, 2] = base_img_sharpened[0, 0]
base_img_sharpened[0, 1] = (m[1, 1] + lightness)/k * 10 + 6 * (m[0, 1]/k) * 10 + 2 * (m[2, 2]/k) * 20
base_img_sharpened[1, 0] = base_img_sharpened[0, 1]
base_img_sharpened[1, 2] = base_img_sharpened[0, 1]
base_img_sharpened[2, 1] = base_img_sharpened[0, 1]
base_img_sharpened[1, 1] = (m[1, 1] + lightness)/k * 20 + 8 * (m[0, 1]/k) * 10
base_img_sharpened = np.clip(base_img_sharpened, 0, 255).astype(np.uint8)
return base_img_sharpened
base_img = [[10, 10, 10],
[10, 20, 10],
[10, 10, 10]]
base_img = np.uint8(base_img)
m = np.float32([[-1, -1, -1],
[-1, 8, -1],
[-1, -1, -1]])
m_noop = np.float32([[0, 0, 0],
[0, 1, 0],
[0, 0, 0]])
base_img_sharpened = _compute_sharpened_base_img(1, m)
aug = iaa.Sharpen(alpha=0, lightness=1)
observed = aug.augment_image(base_img)
expected = base_img
assert np.allclose(observed, expected)
aug = iaa.Sharpen(alpha=1.0, lightness=1)
observed = aug.augment_image(base_img)
expected = base_img_sharpened
assert np.allclose(observed, expected)
aug = iaa.Sharpen(alpha=0.5, lightness=1)
observed = aug.augment_image(base_img)
expected = _compute_sharpened_base_img(0.5*1, 0.5 * m_noop + 0.5 * m)
assert np.allclose(observed, expected.astype(np.uint8))
aug = iaa.Sharpen(alpha=0.75, lightness=1)
observed = aug.augment_image(base_img)
expected = _compute_sharpened_base_img(0.75*1, 0.25 * m_noop + 0.75 * m)
assert np.allclose(observed, expected)
aug = iaa.Sharpen(alpha=iap.Choice([0.5, 1.0]), lightness=1)
observed = aug.augment_image(base_img)
expected1 = _compute_sharpened_base_img(0.5*1, m)
expected2 = _compute_sharpened_base_img(1.0*1, m)
assert np.allclose(observed, expected1) or np.allclose(observed, expected2)
got_exception = False
try:
_ = iaa.Sharpen(alpha="test", lightness=1)
except Exception as exc:
assert "Expected " in str(exc)
got_exception = True
assert got_exception
aug = iaa.Sharpen(alpha=1.0, lightness=2)
observed = aug.augment_image(base_img)
expected = _compute_sharpened_base_img(1.0*2, m)
assert np.allclose(observed, expected)
aug = iaa.Sharpen(alpha=1.0, lightness=3)
observed = aug.augment_image(base_img)
expected = _compute_sharpened_base_img(1.0*3, m)
assert np.allclose(observed, expected)
aug = iaa.Sharpen(alpha=1.0, lightness=iap.Choice([1.0, 1.5]))
observed = aug.augment_image(base_img)
expected1 = _compute_sharpened_base_img(1.0*1.0, m)
expected2 = _compute_sharpened_base_img(1.0*1.5, m)
assert np.allclose(observed, expected1) or np.allclose(observed, expected2)
got_exception = False
try:
_ = iaa.Sharpen(alpha=1.0, lightness="test")
except Exception as exc:
assert "Expected " in str(exc)
got_exception = True
assert got_exception
# this part doesnt really work so far due to nonlinearities resulting from clipping to uint8
"""
