def chapter_augmenters_removesaturation():
fn_start = "color/removesaturation"
aug = iaa.RemoveSaturation()
run_and_save_augseq(fn_start + ".jpg",
aug, [ia.quokka(size=(128, 128)) for _ in range(8)],
cols=4,
rows=2)
aug = iaa.RemoveSaturation(1.0)
run_and_save_augseq(fn_start + "_all.jpg",
aug, [ia.quokka(size=(128, 128)) for _ in range(8)],
cols=4,
rows=2)
def train(model):
"""Train the model."""
# Training dataset.
dataset_train = CharacterDataset()
dataset_train.load_characters("train")
dataset_train.prepare()
# Validation dataset
dataset_val = CharacterDataset()
dataset_val.load_characters("val")
dataset_val.prepare()
#Augmentation
aug = iaa.SomeOf(2, [
iaa.AdditiveGaussianNoise(scale=(0, 0.10 * 255)),
iaa.MotionBlur(),
iaa.GaussianBlur(sigma=(0.0, 2.0)),
iaa.RemoveSaturation(mul=(0, 0.5)),
iaa.GammaContrast(),
iaa.Rotate(rotate=(-45, 45)),
iaa.PerspectiveTransform(scale=(0.01, 0.15)),
iaa.JpegCompression(compression=(0, 75)),
iaa.imgcorruptlike.Spatter(severity=(1, 4)),
iaa.Rain(speed=(0.1, 0.3)),
iaa.Fog()
])
custom_callbacks = [
ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=5,
verbose=1),
EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1)
]
# *** This training schedule is an example. Update to your needs ***
# Since we're using a very small dataset, and starting from
# COCO trained weights, we don't need to train too long. Also,
# no need to train all layers, just the heads should do it.
print("Training network heads")
model.train(dataset_train,
dataset_val,
learning_rate=config.LEARNING_RATE,
epochs=100,
layers='heads',
augmentation=aug,
custom_callbacks=custom_callbacks)
def train(model):
"""Train the model."""
# Training dataset.
dataset_train = PlateDataset()
dataset_train.load_plates("train")
dataset_train.prepare()
# Validation dataset
dataset_val = PlateDataset()
dataset_val.load_plates("val")
dataset_val.prepare()
#Augmentation
aug = iaa.OneOf([
iaa.GaussianBlur(sigma=(0, 1.0)),
iaa.MotionBlur(),
iaa.RemoveSaturation((0.0, 0.5)),
iaa.GammaContrast(),
iaa.Rotate(rotate=(-45, 45)),
iaa.PerspectiveTransform(scale=(0.01, 0.15)),
iaa.SaltAndPepper(),
iaa.JpegCompression(compression=(0, 75)),
iaa.imgcorruptlike.Spatter(severity=(1, 4)),
iaa.imgcorruptlike.DefocusBlur(severity=1)
])
custom_callbacks = [
ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=5,
verbose=1),
EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1)
]
# Since we're using a very small dataset, and starting from
# COCO trained weights, we don't need to train too long. Also,
# no need to train all layers, just the heads should do it.
print("Training network heads")
model.train(dataset_train,
dataset_val,
learning_rate=config.LEARNING_RATE,
epochs=100,
layers='all',
augmentation=aug,
custom_callbacks=custom_callbacks)
def main():
urls = [
("https://upload.wikimedia.org/wikipedia/commons/thumb/4/43/"
"Sarcophilus_harrisii_taranna.jpg/"
"320px-Sarcophilus_harrisii_taranna.jpg"),
("https://upload.wikimedia.org/wikipedia/commons/thumb/b/ba/"
"Vincent_van_Gogh_-_Wheatfield_with_crows_-_Google_Art_Project.jpg/"
"320px-Vincent_van_Gogh_-_Wheatfield_with_crows_-_Google_Art_Project"
".jpg"),
("https://upload.wikimedia.org/wikipedia/commons/thumb/0/0c/"
"Galerella_sanguinea_Zoo_Praha_2011-2.jpg/207px-Galerella_sanguinea_"
"Zoo_Praha_2011-2.jpg"),
("https://upload.wikimedia.org/wikipedia/commons/thumb/9/96/"
"Ambrosius_Bosschaert_the_Elder_%28Dutch_-_Flower_Still_Life_-_"
"Google_Art_Project.jpg/307px-Ambrosius_Bosschaert_the_Elder_%28"
"Dutch_-_Flower_Still_Life_-_Google_Art_Project.jpg")
]
image = imageio.imread(urls[3])
aug = iaa.RemoveSaturation()
images_aug = aug(images=[image] * (5*5))
ia.imshow(ia.draw_grid(images_aug))
import imgaug.augmenters as iaa
import random
import numpy as np
import cv2
from PIL import Image
aug_transform = iaa.SomeOf((0, None), [
iaa.OneOf([
iaa.MultiplyAndAddToBrightness(mul=(0.3, 1.6), add=(-50, 50)),
iaa.MultiplyHueAndSaturation((0.5, 1.5), per_channel=True),
iaa.ChannelShuffle(0.5),
iaa.RemoveSaturation(),
iaa.Grayscale(alpha=(0.0, 1.0)),
iaa.ChangeColorTemperature((1100, 35000)),
]),
iaa.OneOf([
iaa.MedianBlur(k=(3, 7)),
iaa.BilateralBlur(
d=(3, 10), sigma_color=(10, 250), sigma_space=(10, 250)),
iaa.MotionBlur(k=(3, 9), angle=[-45, 45]),
iaa.MeanShiftBlur(spatial_radius=(5.0, 10.0),
color_radius=(5.0, 10.0)),
iaa.AllChannelsCLAHE(clip_limit=(1, 10)),
iaa.AllChannelsHistogramEqualization(),
iaa.GammaContrast((0.5, 1.5), per_channel=True),
iaa.GammaContrast((0.5, 1.5)),
iaa.SigmoidContrast(gain=(3, 10), cutoff=(0.4, 0.6), per_channel=True),
iaa.SigmoidContrast(gain=(3, 10), cutoff=(0.4, 0.6)),
iaa.HistogramEqualization(),
iaa.Sharpen(alpha=0.5)
x_min = bb_box.x1
y_min = bb_box.y1
x_max = bb_box.x2
y_max = bb_box.y2
cls_id = bb_box.label
x_cen, y_cen, w, h = xyxy2xywh(x_min, y_min, x_max, y_max)
f.write("%d %.06f %.06f %.06f %.06f\n" %
(cls_id, x_cen, y_cen, w, h))
Width = 640
Height = 640
blur = iaa.AverageBlur(k=(2, 11)) #! 2~11 random
emboss = iaa.Emboss(alpha=(1.0, 1.0), strength=(2.0, 2.0))
gray = iaa.RemoveSaturation(from_colorspace=iaa.CSPACE_BGR)
contrast = iaa.AllChannelsCLAHE(clip_limit=(10, 10), per_channel=True)
bright = iaa.MultiplyAndAddToBrightness(mul=(0.5, 1.5), add=(-30, 30))
color = iaa.pillike.EnhanceColor()
sharpen = iaa.Sharpen(alpha=(0.5, 1.0)) #! 0.5 ~ 1.0 random
edge = iaa.pillike.FilterEdgeEnhance()
augmentations = [[bright], [emboss], [color],
[edge]] #! choice augmentation ##
rotates = [[iaa.Affine(rotate=90)], [iaa.Affine(rotate=180)],
[iaa.Affine(rotate=270)]]
flip = iaa.Fliplr(1.0) #! 100% left & right
dir = "C:\\Users\\jeongseokoon\\AI-hub\\data\\original\\"
save_aug_dir = "C:\\Users\\jeongseokoon\\AI-hub\\data\\images\\" #! Absolute path
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.Identity(name="Identity"),
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.Cutout(nb_iterations=1, name="Cutout-fill_constant"),
iaa.Dropout((0.01, 0.05), name="Dropout"),
iaa.CoarseDropout((0.01, 0.05), size_percent=(0.01, 0.1), name="CoarseDropout"),
iaa.Dropout2d(0.1, name="Dropout2d"),
iaa.TotalDropout(0.1, name="TotalDropout"),
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_artistic = [
iaa.Cartoon(name="Cartoon")
]
augmenters_blend = [
iaa.BlendAlpha((0.01, 0.99), iaa.Identity(), name="Alpha"),
iaa.BlendAlphaElementwise((0.01, 0.99), iaa.Identity(), name="AlphaElementwise"),
iaa.BlendAlphaSimplexNoise(iaa.Identity(), name="SimplexNoiseAlpha"),
iaa.BlendAlphaFrequencyNoise((-2.0, 2.0), iaa.Identity(), name="FrequencyNoiseAlpha"),
iaa.BlendAlphaSomeColors(iaa.Identity(), name="BlendAlphaSomeColors"),
iaa.BlendAlphaHorizontalLinearGradient(iaa.Identity(), name="BlendAlphaHorizontalLinearGradient"),
iaa.BlendAlphaVerticalLinearGradient(iaa.Identity(), name="BlendAlphaVerticalLinearGradient"),
iaa.BlendAlphaRegularGrid(nb_rows=(2, 8), nb_cols=(2, 8), foreground=iaa.Identity(), name="BlendAlphaRegularGrid"),
iaa.BlendAlphaCheckerboard(nb_rows=(2, 8), nb_cols=(2, 8), foreground=iaa.Identity(), name="BlendAlphaCheckerboard"),
# TODO BlendAlphaSegMapClassId
# TODO BlendAlphaBoundingBoxes
]
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"),
iaa.MeanShiftBlur(spatial_radius=(5.0, 40.0), color_radius=(5.0, 40.0),
name="MeanShiftBlur")
]
augmenters_collections = [
iaa.RandAugment(n=2, m=(6, 12), name="RandAugment")
]
augmenters_color = [
# InColorspace (deprecated)
iaa.WithColorspace(to_colorspace="HSV", children=iaa.Noop(), name="WithColorspace"),
iaa.WithBrightnessChannels(iaa.Identity(), name="WithBrightnessChannels"),
iaa.MultiplyAndAddToBrightness(mul=(0.7, 1.3), add=(-30, 30), name="MultiplyAndAddToBrightness"),
iaa.MultiplyBrightness((0.7, 1.3), name="MultiplyBrightness"),
iaa.AddToBrightness((-30, 30), name="AddToBrightness"),
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.RemoveSaturation((0.01, 0.99), name="RemoveSaturation"),
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"),
iaa.UniformColorQuantizationToNBits((1, 7), name="UniformQuantizationToNBits"),
iaa.Posterize((1, 7), name="Posterize")
]
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"),
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"),
iaa.WithPolarWarping(iaa.Identity(), name="WithPolarWarping"),
iaa.Jigsaw(nb_rows=(3, 8), nb_cols=(3, 8), max_steps=1, name="Jigsaw")
]
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_imgcorruptlike = [
iaa.imgcorruptlike.GaussianNoise(severity=(1, 5), name="imgcorruptlike.GaussianNoise"),
iaa.imgcorruptlike.ShotNoise(severity=(1, 5), name="imgcorruptlike.ShotNoise"),
iaa.imgcorruptlike.ImpulseNoise(severity=(1, 5), name="imgcorruptlike.ImpulseNoise"),
iaa.imgcorruptlike.SpeckleNoise(severity=(1, 5), name="imgcorruptlike.SpeckleNoise"),
iaa.imgcorruptlike.GaussianBlur(severity=(1, 5), name="imgcorruptlike.GaussianBlur"),
iaa.imgcorruptlike.GlassBlur(severity=(1, 5), name="imgcorruptlike.GlassBlur"),
iaa.imgcorruptlike.DefocusBlur(severity=(1, 5), name="imgcorruptlike.DefocusBlur"),
iaa.imgcorruptlike.MotionBlur(severity=(1, 5), name="imgcorruptlike.MotionBlur"),
iaa.imgcorruptlike.ZoomBlur(severity=(1, 5), name="imgcorruptlike.ZoomBlur"),
iaa.imgcorruptlike.Fog(severity=(1, 5), name="imgcorruptlike.Fog"),
iaa.imgcorruptlike.Frost(severity=(1, 5), name="imgcorruptlike.Frost"),
iaa.imgcorruptlike.Snow(severity=(1, 5), name="imgcorruptlike.Snow"),
iaa.imgcorruptlike.Spatter(severity=(1, 5), name="imgcorruptlike.Spatter"),
iaa.imgcorruptlike.Contrast(severity=(1, 5), name="imgcorruptlike.Contrast"),
iaa.imgcorruptlike.Brightness(severity=(1, 5), name="imgcorruptlike.Brightness"),
iaa.imgcorruptlike.Saturate(severity=(1, 5), name="imgcorruptlike.Saturate"),
iaa.imgcorruptlike.JpegCompression(severity=(1, 5), name="imgcorruptlike.JpegCompression"),
iaa.imgcorruptlike.Pixelate(severity=(1, 5), name="imgcorruptlike.Pixelate"),
iaa.imgcorruptlike.ElasticTransform(severity=(1, 5), name="imgcorruptlike.ElasticTransform")
]
augmenters_pillike = [
iaa.pillike.Solarize(p=1.0, threshold=(32, 128), name="pillike.Solarize"),
iaa.pillike.Posterize((1, 7), name="pillike.Posterize"),
iaa.pillike.Equalize(name="pillike.Equalize"),
iaa.pillike.Autocontrast(name="pillike.Autocontrast"),
iaa.pillike.EnhanceColor((0.0, 3.0), name="pillike.EnhanceColor"),
iaa.pillike.EnhanceContrast((0.0, 3.0), name="pillike.EnhanceContrast"),
iaa.pillike.EnhanceBrightness((0.0, 3.0), name="pillike.EnhanceBrightness"),
iaa.pillike.EnhanceSharpness((0.0, 3.0), name="pillike.EnhanceSharpness"),
iaa.pillike.FilterBlur(name="pillike.FilterBlur"),
iaa.pillike.FilterSmooth(name="pillike.FilterSmooth"),
iaa.pillike.FilterSmoothMore(name="pillike.FilterSmoothMore"),
iaa.pillike.FilterEdgeEnhance(name="pillike.FilterEdgeEnhance"),
iaa.pillike.FilterEdgeEnhanceMore(name="pillike.FilterEdgeEnhanceMore"),
iaa.pillike.FilterFindEdges(name="pillike.FilterFindEdges"),
iaa.pillike.FilterContour(name="pillike.FilterContour"),
iaa.pillike.FilterEmboss(name="pillike.FilterEmboss"),
iaa.pillike.FilterSharpen(name="pillike.FilterSharpen"),
iaa.pillike.FilterDetail(name="pillike.FilterDetail"),
iaa.pillike.Affine(scale=(0.9, 1.1),
translate_percent={"x": (-0.05, 0.05), "y": (-0.05, 0.05)},
rotate=(-10, 10),
shear=(-10, 10),
fillcolor=(0, 255),
name="pillike.Affine"),
]
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"),
iaa.Rain(name="Rain"),
iaa.RainLayer(density=(0.03, 0.14),
density_uniformity=(0.8, 1.0),
drop_size=(0.01, 0.02),
drop_size_uniformity=(0.2, 0.5),
angle=(-15, 15),
speed=(0.04, 0.20),
blur_sigma_fraction=(0.001, 0.001),
name="RainLayer")
]
augmenters = (
augmenters_meta
+ augmenters_arithmetic
+ augmenters_artistic
+ augmenters_blend
+ augmenters_blur
+ augmenters_collections
+ augmenters_color
+ augmenters_contrast
+ augmenters_convolutional
+ augmenters_edges
+ augmenters_flip
+ augmenters_geometric
+ augmenters_pooling
+ augmenters_imgcorruptlike
+ augmenters_pillike
+ 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 main():
try:
config_dirs_file = sys.argv[1] # directories file
config_file = sys.argv[2] # main params file
except:
print("Config file names not specified, setting them to default namess")
config_dirs_file = "config_dirs.json"
config_file = "config760.json"
print(f'USING CONFIG FILES: config dirs:{config_dirs_file} main config:{config_file}')
#print(type(feature_directory))
C = cs760.loadas_json('config760.json')
print("Running with parameters:", C)
Cdirs = cs760.loadas_json(config_dirs_file)
print("Directories:", Cdirs)
C['dirs'] = Cdirs
video_directory = C['dirs']['indir']
feature_directory = C['dirs']['outdir']
print(f'Creating feature file Dir: {feature_directory}')
os.makedirs(feature_directory, exist_ok=True) #if dir already exists will continue and WILL NOT delete existing files in that directory
sometimes = lambda aug: iaa.Sometimes(C["augmentation_chance"][0], aug)
sequential_list = [iaa.Sequential([sometimes(iaa.Fliplr(1.0))]), # horizontal flip
iaa.Sequential([sometimes(iaa.Rotate(-5, 5))]), # rotate 5 degrees +/-
iaa.Sequential([sometimes(iaa.CenterCropToAspectRatio(1.15))]),
iaa.Sequential([sometimes(iaa.MultiplyBrightness((2.0, 2.0)))]), # increase brightness
iaa.Sequential([sometimes(iaa.MultiplyHue((0.5, 1.5)))]), # change hue random
iaa.Sequential([sometimes(iaa.RemoveSaturation(1.0))]), # effectively greyscale
iaa.Sequential([sometimes(iaa.pillike.FilterContour())]), # edge detection
iaa.Sequential([sometimes(iaa.AdditiveLaplaceNoise(scale=0.05*255, per_channel=True))]), # add colourful noise
iaa.Sequential([sometimes(iaa.Invert(1))]) # invert colours
]
print("Reading videos from " + video_directory)
print("Outputting features to " + feature_directory)
print("Loading pretrained CNN...")
model = hub.KerasLayer(C["module_url"]) # can be used like any other kera layer including in other layers...
print("Pretrained CNN Loaded OK")
vids = cs760.list_files_pattern(video_directory, C["vid_type"])
print(f'Processing {len(vids)} videos...')
for i, vid in enumerate(vids):
print(f'{i} Processing: {vid}')
vid_np = cs760.get_vid_frames(vid,
video_directory,
writejpgs=False,
writenpy=False,
returnnp=True)
(framecount, frameheight, framewidth, channels) = vid_np.shape
res_key = str(frameheight) + "-" + str(framewidth)
#print(vid, vid_np.shape)
outfile = os.path.splitext(vid)[0]
print(f"Vid frames, h, w, c = {(framecount, frameheight, framewidth, channels)}")
if C["crop_by_res"].get(res_key) is not None:
vid_np_top = cs760.crop_image(vid_np, C["crop_by_res"][res_key])
print(f"Cropped by resolution to {C['crop_by_res'][res_key]}")
else:
vid_np_top = cs760.crop_image(vid_np, C["crop_top"])
print(f"Cropped by default to {C['crop_top']}")
outfile_top = outfile + "__TOP.pkl"
for n in range((len(sequential_list) + 1)):
if n != 0:
vid_aug = sequential_list[n - 1](images=vid_np_top) # augments frames
if type(vid_aug) is list:
vid_aug = np.asarray(vid_aug)
batch = cs760.resize_batch(vid_aug, width=C["expect_img_size"], height=C["expect_img_size"], pad_type='L',
inter=cv2.INTER_CUBIC, BGRtoRGB=False,
simplenormalize=True,
imagenetmeansubtract=False)
temp_outfile = outfile_top[:-4] + C["augmentation_type"][n - 1] + ".pkl"
features = extract(C, model, batch)
cs760.saveas_pickle(features, os.path.join(feature_directory, temp_outfile))
else:
batch = cs760.resize_batch(vid_np_top, width=C["expect_img_size"], height=C["expect_img_size"], pad_type='L',
inter=cv2.INTER_CUBIC, BGRtoRGB=False,
simplenormalize=True,
imagenetmeansubtract=False)
features = extract(C, model, batch)
cs760.saveas_pickle(features, os.path.join(feature_directory, outfile_top))
print(f'Features output shape: {features.shape}')
if C["crop_type"] == 'B': # only for boston vids
vid_np_bot = cs760.crop_image(vid_np, C["crop_bottom"])
outfile_bot = outfile + "__BOT.pkl"
batch = cs760.resize_batch(vid_np_bot, width=C["expect_img_size"], height=C["expect_img_size"], pad_type='L',
inter=cv2.INTER_CUBIC, BGRtoRGB=False,
simplenormalize=True,
imagenetmeansubtract=False)
features = extract(C, model, batch)
cs760.saveas_pickle(features, os.path.join(feature_directory, outfile_bot))
print('Finished outputting features!!')
def generate_blending():
image, segmap = load_cityscapes_data()
# color car lights
ia.seed(10) # 2 = blue lights, 8 = pink, 10 = green lights
image_aug, _segmap_aug = iaa.BlendAlphaSegMapClassIds(
1,
foreground=iaa.BlendAlphaSomeColors(
iaa.AddToHueAndSaturation(value_hue=(-200, 200),
value_saturation=(-100, 100))))(
image=image,
segmentation_maps=segmap)
_save("cityscapes5-car-lights-changed.jpg", image_aug, size=0.3)
# color train
ia.seed(37)
image_aug, _segmap_aug = iaa.BlendAlphaSegMapClassIds(
2,
foreground=iaa.AddToHueAndSaturation(value_hue=(-200, 200),
value_saturation=(-100, 100)))(
image=image,
segmentation_maps=segmap)
_save("cityscapes5-train-color.jpg", image_aug, size=0.3)
# emboss street
image_aug, _segmap_aug = iaa.BlendAlphaSegMapClassIds(
4, foreground=iaa.Emboss(1.0, strength=1.0))(image=image,
segmentation_maps=segmap)
_save("cityscapes5-street-embossed.jpg", image_aug, size=0.3)
# replace street with gaussian noise
ia.seed(3)
image_aug, _segmap_aug = iaa.BlendAlphaSegMapClassIds(
4,
foreground=iaa.Sequential([
iaa.Multiply(0.0),
iaa.AdditiveGaussianNoise(loc=128, scale=40, per_channel=True)
]),
)(image=image, segmentation_maps=segmap)
_save("cityscapes5-street-gaussian-noise.jpg", image_aug, size=0.3)
# regular grid dropout
ia.seed(1)
image_aug = iaa.BlendAlphaRegularGrid(
nb_rows=(8, 12), nb_cols=(8, 12),
foreground=iaa.Multiply(0.0))(image=image)
_save("cityscapes5-regular-grid-dropout.jpg", image_aug, size=0.3)
# checkerboard dropout
ia.seed(1)
image_aug = iaa.BlendAlphaCheckerboard(
nb_rows=(8, 12), nb_cols=(8, 12),
foreground=iaa.Multiply(0.0))(image=image)
_save("cityscapes5-checkerboard-dropout.jpg", image_aug, size=0.3)
# somecolors + removesaturation
ia.seed(1)
image_gogh = imageio.imread(
os.path.join(INPUT_IMAGES_DIR,
"1280px-Vincent_Van_Gogh_-_Wheatfield_with_Crows.jpg"))
image_gogh = iaa.Resize({
"width": 256,
"height": "keep-aspect-ratio"
})(image=image_gogh)
images_aug = ([image_gogh] + iaa.BlendAlphaSomeColors(
iaa.RemoveSaturation(1.0))(images=[image_gogh] * (2 * 4 - 1)))
_save("blendalphasomecolors_removesaturation.jpg",
ia.draw_grid(images_aug, cols=4, rows=2))
transformed_image = transform(image=image)
elif augmentation == 'multiply_saturation':
transform = iaa.MultiplySaturation((0.5, 1.5))
transformed_image = transform(image=image)
elif augmentation == 'addto_saturation':
transform = iaa.AddToSaturation((-100, 100))
transformed_image = transform(image=image)
elif augmentation == 'saturate':
transform = iaa.imgcorruptlike.Saturate(severity=5)
transformed_image = transform(image=image)
elif augmentation == 'remove_saturation':
transform = iaa.RemoveSaturation()
transformed_image = transform(image=image)
elif augmentation == 'multiply_hue_and_saturation':
transform = iaa.MultiplyHueAndSaturation((0.5, 1.5), per_channel=True)
transformed_image = transform(image=image)
elif augmentation == 'brightness_contrast':
transform = RandomBrightnessContrast(always_apply=True,
brightness_limit=0.5)
transformed_image = transform(image=image)['image']
elif augmentation == 'brightness':
transform = iaa.imgcorruptlike.Brightness(severity=2)
transformed_image = transform(image=image)