def chapter_augmenters_fastsnowylandscape():
fn_start = "weather/fastsnowylandscape"
image = LANDSCAPE_IMAGE
aug = iaa.FastSnowyLandscape(
lightness_threshold=140,
lightness_multiplier=2.5
)
run_and_save_augseq(
fn_start + ".jpg", aug,
[image for _ in range(4*2)], cols=4, rows=2)
aug = iaa.FastSnowyLandscape(
lightness_threshold=[128, 200],
lightness_multiplier=(1.5, 3.5)
)
run_and_save_augseq(
fn_start + "_random_choice.jpg", aug,
[image for _ in range(4*2)], cols=4, rows=2)
aug = iaa.FastSnowyLandscape(
lightness_threshold=(100, 255),
lightness_multiplier=(1.0, 4.0)
)
run_and_save_augseq(
fn_start + "_random_uniform.jpg", aug,
[image for _ in range(4*2)], cols=4, rows=2)
def get_seq(flag_normal, flag_affine, flag_noise, flag_snow, flag_cloud,
flag_fog, flag_snowflakes, flag_rain, flag_dropout):
if flag_normal:
seq_list = [
iaa.SomeOf((1, 2), [
iaa.LinearContrast((0.5, 2.0), per_channel=0.5),
iaa.Grayscale(alpha=(0.0, 1.0)),
iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)),
])
]
else:
seq_list = []
if flag_affine:
seq_list.append(
iaa.Sometimes(
0.7,
iaa.Affine(scale={
"x": (0.8, 1.2),
"y": (0.8, 1.2)
},
translate_percent={
"x": (-0.2, 0.2),
"y": (-0.2, 0.2)
},
rotate=(-25, 25),
shear=(-8, 8))))
if flag_noise:
seq_list.append(
iaa.OneOf([
iaa.GaussianBlur((0, 3.0)),
iaa.AverageBlur(k=(2, 7)),
iaa.MedianBlur(k=(3, 11)),
]))
if flag_snow:
seq_list.append(
iaa.FastSnowyLandscape(lightness_threshold=(100, 255),
lightness_multiplier=(1.0, 4.0)))
elif flag_cloud:
seq_list.append(iaa.Clouds())
elif flag_fog:
seq_list.append(iaa.Fog())
elif flag_snowflakes:
seq_list.append(
iaa.Snowflakes(flake_size=(0.2, 0.7), speed=(0.007, 0.03)))
elif flag_rain:
seq_list.append(iaa.Rain())
if flag_dropout:
seq_list.append(
iaa.OneOf([
iaa.Dropout((0.01, 0.1), per_channel=0.5),
iaa.CoarseDropout((0.03, 0.15),
size_percent=(0.02, 0.05),
per_channel=0.2),
]))
return iaa.Sequential(seq_list, random_order=True)
def test_vary_lightness_multiplier(self):
# test when varying lightness_multiplier between images
image = np.arange(0, 6 * 6 * 3).reshape((6, 6, 3)).astype(np.uint8)
image_hls = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
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)
def main():
image = imageio.imread("https://upload.wikimedia.org/wikipedia/commons/8/89/Kukle%2CCzech_Republic..jpg",
format="jpg")
augs = [
("iaa.FastSnowyLandscape(64, 1.5)", iaa.FastSnowyLandscape(64, 1.5)),
("iaa.FastSnowyLandscape(128, 1.5)", iaa.FastSnowyLandscape(128, 1.5)),
("iaa.FastSnowyLandscape(200, 1.5)", iaa.FastSnowyLandscape(200, 1.5)),
("iaa.FastSnowyLandscape(64, 2.5)", iaa.FastSnowyLandscape(64, 2.5)),
("iaa.FastSnowyLandscape(128, 2.5)", iaa.FastSnowyLandscape(128, 2.5)),
("iaa.FastSnowyLandscape(200, 2.5)", iaa.FastSnowyLandscape(200, 2.5)),
("iaa.FastSnowyLandscape(64, 3.5)", iaa.FastSnowyLandscape(64, 3.5)),
("iaa.FastSnowyLandscape(128, 3.5)", iaa.FastSnowyLandscape(128, 3.5)),
("iaa.FastSnowyLandscape(200, 3.5)", iaa.FastSnowyLandscape(200, 3.5)),
("iaa.FastSnowyLandscape()", iaa.FastSnowyLandscape())
]
for descr, aug in augs:
print(descr)
images_aug = aug.augment_images([image] * 64)
ia.imshow(ia.draw_grid(images_aug))
def test_zero_sized_axes(self):
shapes = [(0, 0, 3), (0, 1, 3), (1, 0, 3)]
for shape in shapes:
with self.subTest(shape=shape):
image = np.zeros(shape, dtype=np.uint8)
aug = iaa.FastSnowyLandscape(100, 1.5, from_colorspace="RGB")
image_aug = aug(image=image)
assert image_aug.dtype.name == "uint8"
assert image_aug.shape == shape
def test_basic_functionality(self):
# 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)
def test___init__(self):
# 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)
def imgaug_snowylandscape(images, val_thresh, mulfactor, base_save_path):
fastsnow = iaa.FastSnowyLandscape(val_thresh, mulfactor)
fastsnow_imgs = fastsnow.augment_images(images)
fastsnow_path = '\\snowylandscape\\'
if not os.path.exists(base_save_path + fastsnow_path):
os.mkdir(base_save_path + fastsnow_path)
name_index = 0
for img in fastsnow_imgs:
name_index += 1
imageio.imwrite(base_save_path+fastsnow_path+'img_aug_snowlandscape_'+ time.strftime('%Y%m%d_%H',time.localtime()) \
+ '_' +str(name_index)+'.jpg',img)
def test_from_colorspace(self):
# 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 create_augmenters(height, width, height_augmentable, width_augmentable,
only_augmenters):
def lambda_func_images(images, random_state, parents, hooks):
return images
def lambda_func_heatmaps(heatmaps, random_state, parents, hooks):
return heatmaps
def lambda_func_keypoints(keypoints, random_state, parents, hooks):
return keypoints
def assertlambda_func_images(images, random_state, parents, hooks):
return True
def assertlambda_func_heatmaps(heatmaps, random_state, parents, hooks):
return True
def assertlambda_func_keypoints(keypoints, random_state, parents, hooks):
return True
augmenters_meta = [
iaa.Sequential([iaa.Noop(), iaa.Noop()],
random_order=False,
name="Sequential_2xNoop"),
iaa.Sequential([iaa.Noop(), iaa.Noop()],
random_order=True,
name="Sequential_2xNoop_random_order"),
iaa.SomeOf((1, 3),
[iaa.Noop(), iaa.Noop(), iaa.Noop()],
random_order=False,
name="SomeOf_3xNoop"),
iaa.SomeOf((1, 3),
[iaa.Noop(), iaa.Noop(), iaa.Noop()],
random_order=True,
name="SomeOf_3xNoop_random_order"),
iaa.OneOf([iaa.Noop(), iaa.Noop(), iaa.Noop()], name="OneOf_3xNoop"),
iaa.Sometimes(0.5, iaa.Noop(), name="Sometimes_Noop"),
iaa.WithChannels([1, 2], iaa.Noop(), name="WithChannels_1_and_2_Noop"),
iaa.Noop(name="Noop"),
iaa.Lambda(func_images=lambda_func_images,
func_heatmaps=lambda_func_heatmaps,
func_keypoints=lambda_func_keypoints,
name="Lambda"),
iaa.AssertLambda(func_images=assertlambda_func_images,
func_heatmaps=assertlambda_func_heatmaps,
func_keypoints=assertlambda_func_keypoints,
name="AssertLambda"),
iaa.AssertShape((None, height_augmentable, width_augmentable, None),
name="AssertShape"),
iaa.ChannelShuffle(0.5, name="ChannelShuffle")
]
augmenters_arithmetic = [
iaa.Add((-10, 10), name="Add"),
iaa.AddElementwise((-10, 10), name="AddElementwise"),
#iaa.AddElementwise((-500, 500), name="AddElementwise"),
iaa.AdditiveGaussianNoise(scale=(5, 10), name="AdditiveGaussianNoise"),
iaa.AdditiveLaplaceNoise(scale=(5, 10), name="AdditiveLaplaceNoise"),
iaa.AdditivePoissonNoise(lam=(1, 5), name="AdditivePoissonNoise"),
iaa.Multiply((0.5, 1.5), name="Multiply"),
iaa.MultiplyElementwise((0.5, 1.5), name="MultiplyElementwise"),
iaa.Dropout((0.01, 0.05), name="Dropout"),
iaa.CoarseDropout((0.01, 0.05),
size_percent=(0.01, 0.1),
name="CoarseDropout"),
iaa.ReplaceElementwise((0.01, 0.05), (0, 255),
name="ReplaceElementwise"),
#iaa.ReplaceElementwise((0.95, 0.99), (0, 255), name="ReplaceElementwise"),
iaa.SaltAndPepper((0.01, 0.05), name="SaltAndPepper"),
iaa.ImpulseNoise((0.01, 0.05), name="ImpulseNoise"),
iaa.CoarseSaltAndPepper((0.01, 0.05),
size_percent=(0.01, 0.1),
name="CoarseSaltAndPepper"),
iaa.Salt((0.01, 0.05), name="Salt"),
iaa.CoarseSalt((0.01, 0.05),
size_percent=(0.01, 0.1),
name="CoarseSalt"),
iaa.Pepper((0.01, 0.05), name="Pepper"),
iaa.CoarsePepper((0.01, 0.05),
size_percent=(0.01, 0.1),
name="CoarsePepper"),
iaa.Invert(0.1, name="Invert"),
# ContrastNormalization
iaa.JpegCompression((50, 99), name="JpegCompression")
]
augmenters_blend = [
iaa.Alpha((0.01, 0.99), iaa.Noop(), name="Alpha"),
iaa.AlphaElementwise((0.01, 0.99), iaa.Noop(),
name="AlphaElementwise"),
iaa.SimplexNoiseAlpha(iaa.Noop(), name="SimplexNoiseAlpha"),
iaa.FrequencyNoiseAlpha((-2.0, 2.0),
iaa.Noop(),
name="FrequencyNoiseAlpha")
]
augmenters_blur = [
iaa.GaussianBlur(sigma=(1.0, 5.0), name="GaussianBlur"),
iaa.AverageBlur(k=(3, 11), name="AverageBlur"),
iaa.MedianBlur(k=(3, 11), name="MedianBlur"),
iaa.BilateralBlur(d=(3, 11), name="BilateralBlur"),
iaa.MotionBlur(k=(3, 11), name="MotionBlur")
]
augmenters_color = [
# InColorspace (deprecated)
iaa.WithColorspace(to_colorspace="HSV",
children=iaa.Noop(),
name="WithColorspace"),
iaa.WithHueAndSaturation(children=iaa.Noop(),
name="WithHueAndSaturation"),
iaa.MultiplyHueAndSaturation((0.8, 1.2),
name="MultiplyHueAndSaturation"),
iaa.MultiplyHue((-1.0, 1.0), name="MultiplyHue"),
iaa.MultiplySaturation((0.8, 1.2), name="MultiplySaturation"),
iaa.AddToHueAndSaturation((-10, 10), name="AddToHueAndSaturation"),
iaa.AddToHue((-10, 10), name="AddToHue"),
iaa.AddToSaturation((-10, 10), name="AddToSaturation"),
iaa.ChangeColorspace(to_colorspace="HSV", name="ChangeColorspace"),
iaa.Grayscale((0.01, 0.99), name="Grayscale"),
iaa.KMeansColorQuantization((2, 16), name="KMeansColorQuantization"),
iaa.UniformColorQuantization((2, 16), name="UniformColorQuantization")
]
augmenters_contrast = [
iaa.GammaContrast(gamma=(0.5, 2.0), name="GammaContrast"),
iaa.SigmoidContrast(gain=(5, 20),
cutoff=(0.25, 0.75),
name="SigmoidContrast"),
iaa.LogContrast(gain=(0.7, 1.0), name="LogContrast"),
iaa.LinearContrast((0.5, 1.5), name="LinearContrast"),
iaa.AllChannelsCLAHE(clip_limit=(2, 10),
tile_grid_size_px=(3, 11),
name="AllChannelsCLAHE"),
iaa.CLAHE(clip_limit=(2, 10),
tile_grid_size_px=(3, 11),
to_colorspace="HSV",
name="CLAHE"),
iaa.AllChannelsHistogramEqualization(
name="AllChannelsHistogramEqualization"),
iaa.HistogramEqualization(to_colorspace="HSV",
name="HistogramEqualization"),
]
augmenters_convolutional = [
iaa.Convolve(np.float32([[0, 0, 0], [0, 1, 0], [0, 0, 0]]),
name="Convolve_3x3"),
iaa.Sharpen(alpha=(0.01, 0.99), lightness=(0.5, 2), name="Sharpen"),
iaa.Emboss(alpha=(0.01, 0.99), strength=(0, 2), name="Emboss"),
iaa.EdgeDetect(alpha=(0.01, 0.99), name="EdgeDetect"),
iaa.DirectedEdgeDetect(alpha=(0.01, 0.99), name="DirectedEdgeDetect")
]
augmenters_edges = [iaa.Canny(alpha=(0.01, 0.99), name="Canny")]
augmenters_flip = [
iaa.Fliplr(1.0, name="Fliplr"),
iaa.Flipud(1.0, name="Flipud")
]
augmenters_geometric = [
iaa.Affine(scale=(0.9, 1.1),
translate_percent={
"x": (-0.05, 0.05),
"y": (-0.05, 0.05)
},
rotate=(-10, 10),
shear=(-10, 10),
order=0,
mode="constant",
cval=(0, 255),
name="Affine_order_0_constant"),
iaa.Affine(scale=(0.9, 1.1),
translate_percent={
"x": (-0.05, 0.05),
"y": (-0.05, 0.05)
},
rotate=(-10, 10),
shear=(-10, 10),
order=1,
mode="constant",
cval=(0, 255),
name="Affine_order_1_constant"),
iaa.Affine(scale=(0.9, 1.1),
translate_percent={
"x": (-0.05, 0.05),
"y": (-0.05, 0.05)
},
rotate=(-10, 10),
shear=(-10, 10),
order=3,
mode="constant",
cval=(0, 255),
name="Affine_order_3_constant"),
iaa.Affine(scale=(0.9, 1.1),
translate_percent={
"x": (-0.05, 0.05),
"y": (-0.05, 0.05)
},
rotate=(-10, 10),
shear=(-10, 10),
order=1,
mode="edge",
cval=(0, 255),
name="Affine_order_1_edge"),
iaa.Affine(scale=(0.9, 1.1),
translate_percent={
"x": (-0.05, 0.05),
"y": (-0.05, 0.05)
},
rotate=(-10, 10),
shear=(-10, 10),
order=1,
mode="constant",
cval=(0, 255),
backend="skimage",
name="Affine_order_1_constant_skimage"),
# TODO AffineCv2
iaa.PiecewiseAffine(scale=(0.01, 0.05),
nb_rows=4,
nb_cols=4,
order=1,
mode="constant",
name="PiecewiseAffine_4x4_order_1_constant"),
iaa.PiecewiseAffine(scale=(0.01, 0.05),
nb_rows=4,
nb_cols=4,
order=0,
mode="constant",
name="PiecewiseAffine_4x4_order_0_constant"),
iaa.PiecewiseAffine(scale=(0.01, 0.05),
nb_rows=4,
nb_cols=4,
order=1,
mode="edge",
name="PiecewiseAffine_4x4_order_1_edge"),
iaa.PiecewiseAffine(scale=(0.01, 0.05),
nb_rows=8,
nb_cols=8,
order=1,
mode="constant",
name="PiecewiseAffine_8x8_order_1_constant"),
iaa.PerspectiveTransform(scale=(0.01, 0.05),
keep_size=False,
name="PerspectiveTransform"),
iaa.PerspectiveTransform(scale=(0.01, 0.05),
keep_size=True,
name="PerspectiveTransform_keep_size"),
iaa.ElasticTransformation(
alpha=(1, 10),
sigma=(0.5, 1.5),
order=0,
mode="constant",
cval=0,
name="ElasticTransformation_order_0_constant"),
iaa.ElasticTransformation(
alpha=(1, 10),
sigma=(0.5, 1.5),
order=1,
mode="constant",
cval=0,
name="ElasticTransformation_order_1_constant"),
iaa.ElasticTransformation(
alpha=(1, 10),
sigma=(0.5, 1.5),
order=1,
mode="nearest",
cval=0,
name="ElasticTransformation_order_1_nearest"),
iaa.ElasticTransformation(
alpha=(1, 10),
sigma=(0.5, 1.5),
order=1,
mode="reflect",
cval=0,
name="ElasticTransformation_order_1_reflect"),
iaa.Rot90((1, 3), keep_size=False, name="Rot90"),
iaa.Rot90((1, 3), keep_size=True, name="Rot90_keep_size")
]
augmenters_pooling = [
iaa.AveragePooling(kernel_size=(1, 16),
keep_size=False,
name="AveragePooling"),
iaa.AveragePooling(kernel_size=(1, 16),
keep_size=True,
name="AveragePooling_keep_size"),
iaa.MaxPooling(kernel_size=(1, 16), keep_size=False,
name="MaxPooling"),
iaa.MaxPooling(kernel_size=(1, 16),
keep_size=True,
name="MaxPooling_keep_size"),
iaa.MinPooling(kernel_size=(1, 16), keep_size=False,
name="MinPooling"),
iaa.MinPooling(kernel_size=(1, 16),
keep_size=True,
name="MinPooling_keep_size"),
iaa.MedianPooling(kernel_size=(1, 16),
keep_size=False,
name="MedianPooling"),
iaa.MedianPooling(kernel_size=(1, 16),
keep_size=True,
name="MedianPooling_keep_size")
]
augmenters_segmentation = [
iaa.Superpixels(p_replace=(0.05, 1.0),
n_segments=(10, 100),
max_size=64,
interpolation="cubic",
name="Superpixels_max_size_64_cubic"),
iaa.Superpixels(p_replace=(0.05, 1.0),
n_segments=(10, 100),
max_size=64,
interpolation="linear",
name="Superpixels_max_size_64_linear"),
iaa.Superpixels(p_replace=(0.05, 1.0),
n_segments=(10, 100),
max_size=128,
interpolation="linear",
name="Superpixels_max_size_128_linear"),
iaa.Superpixels(p_replace=(0.05, 1.0),
n_segments=(10, 100),
max_size=224,
interpolation="linear",
name="Superpixels_max_size_224_linear"),
iaa.UniformVoronoi(n_points=(250, 1000), name="UniformVoronoi"),
iaa.RegularGridVoronoi(n_rows=(16, 31),
n_cols=(16, 31),
name="RegularGridVoronoi"),
iaa.RelativeRegularGridVoronoi(n_rows_frac=(0.07, 0.14),
n_cols_frac=(0.07, 0.14),
name="RelativeRegularGridVoronoi"),
]
augmenters_size = [
iaa.Resize((0.8, 1.2), interpolation="nearest", name="Resize_nearest"),
iaa.Resize((0.8, 1.2), interpolation="linear", name="Resize_linear"),
iaa.Resize((0.8, 1.2), interpolation="cubic", name="Resize_cubic"),
iaa.CropAndPad(percent=(-0.2, 0.2),
pad_mode="constant",
pad_cval=(0, 255),
keep_size=False,
name="CropAndPad"),
iaa.CropAndPad(percent=(-0.2, 0.2),
pad_mode="edge",
pad_cval=(0, 255),
keep_size=False,
name="CropAndPad_edge"),
iaa.CropAndPad(percent=(-0.2, 0.2),
pad_mode="constant",
pad_cval=(0, 255),
name="CropAndPad_keep_size"),
iaa.Pad(percent=(0.05, 0.2),
pad_mode="constant",
pad_cval=(0, 255),
keep_size=False,
name="Pad"),
iaa.Pad(percent=(0.05, 0.2),
pad_mode="edge",
pad_cval=(0, 255),
keep_size=False,
name="Pad_edge"),
iaa.Pad(percent=(0.05, 0.2),
pad_mode="constant",
pad_cval=(0, 255),
name="Pad_keep_size"),
iaa.Crop(percent=(0.05, 0.2), keep_size=False, name="Crop"),
iaa.Crop(percent=(0.05, 0.2), name="Crop_keep_size"),
iaa.PadToFixedSize(width=width + 10,
height=height + 10,
pad_mode="constant",
pad_cval=(0, 255),
name="PadToFixedSize"),
iaa.CropToFixedSize(width=width - 10,
height=height - 10,
name="CropToFixedSize"),
iaa.KeepSizeByResize(iaa.CropToFixedSize(height=height - 10,
width=width - 10),
interpolation="nearest",
name="KeepSizeByResize_CropToFixedSize_nearest"),
iaa.KeepSizeByResize(iaa.CropToFixedSize(height=height - 10,
width=width - 10),
interpolation="linear",
name="KeepSizeByResize_CropToFixedSize_linear"),
iaa.KeepSizeByResize(iaa.CropToFixedSize(height=height - 10,
width=width - 10),
interpolation="cubic",
name="KeepSizeByResize_CropToFixedSize_cubic"),
]
augmenters_weather = [
iaa.FastSnowyLandscape(lightness_threshold=(100, 255),
lightness_multiplier=(1.0, 4.0),
name="FastSnowyLandscape"),
iaa.Clouds(name="Clouds"),
iaa.Fog(name="Fog"),
iaa.CloudLayer(intensity_mean=(196, 255),
intensity_freq_exponent=(-2.5, -2.0),
intensity_coarse_scale=10,
alpha_min=0,
alpha_multiplier=(0.25, 0.75),
alpha_size_px_max=(2, 8),
alpha_freq_exponent=(-2.5, -2.0),
sparsity=(0.8, 1.0),
density_multiplier=(0.5, 1.0),
name="CloudLayer"),
iaa.Snowflakes(name="Snowflakes"),
iaa.SnowflakesLayer(density=(0.005, 0.075),
density_uniformity=(0.3, 0.9),
flake_size=(0.2, 0.7),
flake_size_uniformity=(0.4, 0.8),
angle=(-30, 30),
speed=(0.007, 0.03),
blur_sigma_fraction=(0.0001, 0.001),
name="SnowflakesLayer")
]
augmenters = (augmenters_meta + augmenters_arithmetic + augmenters_blend +
augmenters_blur + augmenters_color + augmenters_contrast +
augmenters_convolutional + augmenters_edges +
augmenters_flip + augmenters_geometric + augmenters_pooling +
augmenters_segmentation + augmenters_size +
augmenters_weather)
if only_augmenters is not None:
augmenters_reduced = []
for augmenter in augmenters:
if any([
re.search(pattern, augmenter.name)
for pattern in only_augmenters
]):
augmenters_reduced.append(augmenter)
augmenters = augmenters_reduced
return augmenters
def test_pickleable(self):
aug = iaa.FastSnowyLandscape(lightness_threshold=(50, 150),
lightness_multiplier=(1.0, 3.0),
random_state=1)
runtest_pickleable_uint8_img(aug)
import random
from imgaug import augmenters as iaa
from imgaug.augmentables.bbs import BoundingBox, BoundingBoxesOnImage
import cv2
from verifier import fitCoords
aug = iaa.Sequential(
[
iaa.Sometimes(0.5, iaa.Crop(percent=(0.1, 0.3), keep_size=False)),
iaa.Sometimes(0.5, iaa.MotionBlur(15, random.randint(0, 360))),
iaa.OneOf([
iaa.AllChannelsCLAHE(clip_limit=10),
iaa.AdditiveGaussianNoise(scale=(10, 35)),
iaa.FastSnowyLandscape(lightness_threshold=(50, 115), from_colorspace="BGR")
]),
# iaa.Sometimes(0.25, iaa.Affine(scale={"x": (1.0, 1.2), "y": (1.0, 1.2)})),
iaa.Sometimes(0.25, iaa.Multiply((0.85, 1.15))),
iaa.Sometimes(0.25, iaa.ContrastNormalization((0.85, 1.15))),
# iaa.Affine(rotate=(0, 360))
], random_order=False
)
def customAugmentations(image, box):
y1, x1, y2, x2 = box
bb = BoundingBox(x1=x1, x2=x2, y1=y1, y2=y2)
augImage, augBox = aug(image=image, bounding_boxes=bb)
augBox = fitCoords([augBox.y1_int, augBox.x1_int, augBox.y2_int, augBox.x2_int], augImage.shape[:2])
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)
iaa.Add((-10, 10), per_channel=0.5),
# 像素乘上0.5或者1.5之间的数字.
iaa.Multiply((0.8, 1.2), per_channel=0.5),
# 将整个图像的对比度变为原来的一半或者二倍
iaa.ContrastNormalization((0.5, 1.5), per_channel=0.5),
# 将RGB变成灰度图然后乘alpha加在原图上
iaa.Grayscale(alpha=(0.0, 0.2)),
#把像素移动到周围的地方。这个方法在mnist数据集增强中有见到
# sometimes(
# iaa.ElasticTransformation(alpha=(0.5, 3.5), sigma=0.25)
# ),
# 扭曲图像的局部区域
sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.05))),
iaa.GammaContrast((0.5, 1.5)),
iaa.FastSnowyLandscape(lightness_threshold=(100, 200),
lightness_multiplier=(0.5, 1.5)),
iaa.Snowflakes(),
iaa.AddToSaturation((-50, 50))
],
random_order=True # 随机的顺序把这些操作用在图像上
)
])
#seq = iaa.Grayscale(alpha=(0.1, 0.4))#以0.1-0.4的概率对图像进行灰度化
#iaa.ContrastNormalization((0.5, 2.0), per_channel=0.5),
#seq = iaa.GammaContrast((0.5,2.0))
#seq = iaa.FastSnowyLandscape(lightness_threshold=(100, 255),lightness_multiplier=(0.5, 1.5))
#seq = iaa.Fog()
#seq = iaa.Snowflakes()
#seq = iaa.Clouds()
#seq = iaa.AddToSaturation((-50,50))
seq_det = seq.to_deterministic()
iaa.BlendAlphaElementwise(
(0.0, 1.0),
foreground=iaa.Add(100),
background=iaa.Multiply(0.2)),
iaa.BlendAlphaSimplexNoise(iaa.EdgeDetect(1.0)),
iaa.BlendAlphaSimplexNoise(
iaa.EdgeDetect(1.0),
upscale_method="nearest"),
iaa.BlendAlphaSimplexNoise(
iaa.EdgeDetect(1.0),
upscale_method="linear"),
iaa.BlendAlphaSomeColors(iaa.TotalDropout(1.0)),
iaa.BlendAlphaSomeColors(
iaa.AveragePooling(7), alpha=[0.0, 1.0], smoothness=0.0),
iaa.FastSnowyLandscape(
lightness_threshold=140,
lightness_multiplier=2.5),
iaa.Clouds(),
iaa.Fog(),
iaa.Rain(speed=(0.1, 0.3)),
],
random_order=True
)
],
random_order=True
)
images_aug = seq(images=images)
i = 0
for img in images_aug:
import imgaug as ia
from imgaug import augmenters as iaa
import numpy as np
import imageio
ia.seed(1)
img = imageio.imread("test.jpg") #read you image
images = np.array(
[img for _ in range(32)], dtype=np.uint8) # 32 means creat 32 enhanced images using following methods.
seq = iaa.Sequential(
[
iaa.FastSnowyLandscape(64,1.5),
iaa.Snowflakes(0.075,0.9,0.7,0.8,30,0.03)
],
random_order=True) # apply augmenters in random order
images_aug = seq.augment_images(images)
for i in range(32):
imageio.imwrite(str(i)+'new.jpg', images_aug[i]) #write all changed images
return result
def _augment_keypoints(self, keypoints_on_images, random_state, parents,
hooks):
return keypoints_on_images
def _augment_heatmaps(self, **kwargs):
return
def get_parameters(self):
return [self.abc]
augment_object = iaa.Sequential([
iaa.Add((-20, 20)),
iaa.Sometimes(0.5, iaa.AdditiveGaussianNoise(scale=0.03 * 255)),
iaa.Sometimes(0.5, iaa.MotionBlur(angle=(0, 360))),
iaa.Sometimes(0.2, iaa.GammaContrast(gamma=(0.5, 1.44))),
iaa.Sometimes(0.1, iaa.FastSnowyLandscape(lightness_threshold=(0, 150))),
iaa.OneOf([
iaa.Sometimes(0.8, RandomShadow()),
iaa.Sometimes(0.4, RandomGravel()),
iaa.Sometimes(0.2, RandomSunFlare()),
iaa.Sometimes(0.3, RandomMotionBlur())
])
])
def augment_image(img):
return augment_object.augment_image(img)
