def augmentation(self, pil_img, pil_mask):
input_img = np.expand_dims(pil_img, axis=0)
input_mask = np.expand_dims(pil_mask, axis=0)
input_mask = np.expand_dims(input_mask, axis=3)
prob = random.uniform(0, 1)
if self.split == 'train' and prob > 0.5:# we do augmentation in 50% of the cases
seq = iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB", to_colorspace=self.color_map),
iaa.ChannelShuffle(0.35),
iaa.Affine(translate_percent={"x": (-0.2, 0.2), "y": (-0.2, 0.2)}),
iaa.Affine(rotate=(-180, 180)),
iaa.Affine(shear=(-16, 16)),
iaa.Fliplr(0.5),
iaa.GaussianBlur(sigma=(0, 3.0))
])
images_aug, segmaps_aug = seq(images=input_img, segmentation_maps=input_mask)
output_img = np.transpose(images_aug[0], (2, 0, 1))
output_mask = np.transpose(segmaps_aug[0], (2, 0, 1))
else:
seq = iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB", to_colorspace=self.color_map),
])
images_aug, segmaps_aug = seq(images=input_img, segmentation_maps=input_mask)
output_img = np.transpose(images_aug[0], (2, 0, 1))
output_mask = np.transpose(segmaps_aug[0], (2, 0, 1))
return output_img, output_mask
def get_seq(params):
"""
Main filters and augmentations for pilotnet data augmentation.
"""
filters = iaa.SomeOf(params.filters_repeat, [
iaa.ChangeColorspace("BGR"),
iaa.ChangeColorspace("GRAY"),
iaa.GaussianBlur(sigma=(0.0, 3.0)),
iaa.AverageBlur(k=(2, 9)),
iaa.MedianBlur(k=(3, 9)),
iaa.Add((-40, 40), per_channel=0.5),
iaa.Add((-40, 40)),
iaa.AdditiveGaussianNoise(scale=0.05 * 255, per_channel=0.5),
iaa.AdditiveGaussianNoise(scale=0.05 * 255),
iaa.Multiply((0.5, 1.5), per_channel=0.5),
iaa.Multiply((0.5, 1.5)),
iaa.MultiplyElementwise((0.5, 1.5)),
iaa.ContrastNormalization((0.5, 1.5)),
iaa.ContrastNormalization((0.5, 1.5), per_channel=0.5),
iaa.ElasticTransformation(alpha=(0, 2.5), sigma=0.25),
iaa.Sharpen(alpha=(0.6, 1.0)),
iaa.Emboss(alpha=(0.0, 0.5)),
iaa.CoarseDropout(0.2, size_percent=0.00001, per_channel=1.0),
])
affine = iaa.Affine(
rotate=(-7, 7),
scale=(0.9, 1.1),
translate_percent=dict(x=(-0.05, 0.05)),
mode="symmetric",
)
return iaa.Sequential([
filters,
affine,
])
def change_color_space(image):
x1 = int(np.random.randint(20, 50, size=1, dtype='int8'))
x2 = int(np.random.randint(60, 100, size=1, dtype='int8'))
image_aug = iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB", to_colorspace="HSV"),
iaa.WithChannels(0, iaa.Add((x1, x2))),
iaa.ChangeColorspace(from_colorspace="HSV", to_colorspace="RGB")
])(image=image)
return image_aug
def augment():
with open(CONFIG, "r") as file:
config = json.loads(file.read())
if config[ImageAugmentation.AUGMENT_DATA]:
matrix = np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]])
return iaa.Sometimes(ImageAugmentation.AUG_PERCENTAGE, [
iaa.GaussianBlur(sigma=2.0),
iaa.Sequential([iaa.Affine(rotate=45),
iaa.Sharpen(alpha=1.0)]),
iaa.WithColorspace(to_colorspace="HSV",
from_colorspace="RGB",
children=iaa.WithChannels(
0, iaa.Add((10, 50)))),
iaa.AdditiveGaussianNoise(scale=0.2 * 255),
iaa.Add(50, per_channel=True),
iaa.Sharpen(alpha=0.5),
iaa.WithChannels(0, iaa.Add((10, 100))),
iaa.WithChannels(0, iaa.Affine(rotate=(0, 45))),
iaa.Noop(),
iaa.Superpixels(p_replace=0.5, n_segments=64),
iaa.Superpixels(p_replace=(0.1, 1.0), n_segments=(16, 128)),
iaa.ChangeColorspace(from_colorspace="RGB",
to_colorspace="HSV"),
iaa.WithChannels(0, iaa.Add((50, 100))),
iaa.ChangeColorspace(from_colorspace="HSV",
to_colorspace="RGB"),
iaa.Grayscale(alpha=(0.0, 1.0)),
iaa.GaussianBlur(sigma=(0.0, 3.0)),
iaa.AverageBlur(k=(2, 11)),
iaa.AverageBlur(k=((5, 11), (1, 3))),
iaa.MedianBlur(k=(3, 11)),
iaa.Convolve(matrix=matrix),
iaa.Sharpen(alpha=(0.0, 1.0), lightness=(0.75, 2.0)),
iaa.Emboss(alpha=(0.0, 1.0), strength=(0.5, 1.5)),
iaa.EdgeDetect(alpha=(0.0, 1.0)),
iaa.DirectedEdgeDetect(alpha=(0.0, 1.0), direction=(0.0, 1.0)),
iaa.Add((-40, 40)),
iaa.Add((-40, 40), per_channel=0.5),
iaa.AddElementwise((-40, 40)),
iaa.AddElementwise((-40, 40), per_channel=0.5),
iaa.AdditiveGaussianNoise(scale=(0, 0.05 * 255)),
iaa.AdditiveGaussianNoise(scale=0.05 * 255),
iaa.AdditiveGaussianNoise(scale=0.05 * 255, per_channel=0.5),
iaa.Multiply((0.5, 1.5), per_channel=0.5),
iaa.Dropout(p=(0, 0.2)),
iaa.Dropout(p=(0, 0.2), per_channel=0.5),
iaa.CoarseDropout(0.02, size_percent=0.5),
iaa.CoarseDropout((0.0, 0.05), size_percent=(0.02, 0.25)),
iaa.CoarseDropout(0.02, size_percent=0.15, per_channel=0.5),
iaa.Invert(0.25, per_channel=0.5),
iaa.Invert(0.5),
iaa.ContrastNormalization((0.5, 1.5)),
iaa.ContrastNormalization((0.5, 1.5), per_channel=0.5),
iaa.ElasticTransformation(alpha=(0, 5.0), sigma=0.25)
])
else:
return None
def iaa_hsv_aug(hue=0, saturation=1, exposure=1):
h_l, h_t = 179 * -hue, 179 * hue
s_l, s_t = 1 / saturation if saturation else 0, saturation
v_l, v_t = 1 / exposure if exposure else 0, exposure
return iaa.Sequential([iaa.ChangeColorspace(from_colorspace="RGB", to_colorspace="HSV"),
iaa.WithChannels([0], iaa.Add((h_l, h_t))),
iaa.WithChannels([1], iaa.Multiply((s_l, s_t))),
iaa.WithChannels([2], iaa.Multiply((v_l, v_t))),
iaa.ChangeColorspace(from_colorspace="HSV", to_colorspace="RGB")])
def chapter_augmenters_changecolorspace():
aug = iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB", to_colorspace="HSV"),
iaa.WithChannels(0, iaa.Add((50, 100))),
iaa.ChangeColorspace(from_colorspace="HSV", to_colorspace="RGB")
])
run_and_save_augseq("changecolorspace.jpg",
aug, [ia.quokka(size=(128, 128)) for _ in range(8)],
cols=4,
rows=2)
def __init__(self):
self.contrast = iaa.ContrastNormalization((0.5, 1.5), per_channel=0.3)
self.mulaug = iaa.Multiply((0.8, 1.2), per_channel=0.3)
self.grayaug = iaa.Grayscale(alpha=(0.0, 1.0))
self.sharpen = iaa.Sharpen(alpha=(0.0, 1.0), lightness=(0.75, 2.0))
self.coloraug = iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB", to_colorspace="HSV"),
iaa.WithChannels(0, iaa.Add((-50, 50))),
iaa.WithChannels(1, iaa.Add((-50, 50))),
iaa.WithChannels(2, iaa.Add((-50, 50))),
iaa.ChangeColorspace(from_colorspace="HSV", to_colorspace="RGB")
])
def example(imgs, pieceWiseAffine, rotate):
# 옵션별로 aug 설정
if imgs.dtype != np.uint8:
imgs = imgs.astype(np.uint8)
if_ = lambda tf, t, f: t if tf else f
ia.seed(int(time.time()))
sometimes1 = lambda aug: iaa.Sometimes(0.1, aug)
sometimes3 = lambda aug: iaa.Sometimes(0.3, aug)
sometimes5 = lambda aug: iaa.Sometimes(0.5, aug)
sometimes7 = lambda aug: iaa.Sometimes(0.7, aug)
sometimes9 = lambda aug: iaa.Sometimes(0.9, aug)
seq = iaa.Sequential(
[
sometimes5(
iaa.PiecewiseAffine(
scale=if_(pieceWiseAffine, (0.03, 0.03), 0.0))),
sometimes1(iaa.ElasticTransformation(alpha=(0, 1.0), sigma=0.1)),
sometimes3(iaa.Affine(shear=(-12, 12))),
iaa.Crop(percent=(0.0, 0.25)), # 항상 하는게 좋음 (crop2 기준)
iaa.SomeOf(if_(rotate, 1, 0), [
iaa.Affine(rotate=0),
iaa.Affine(rotate=90),
iaa.Affine(rotate=180),
iaa.Affine(rotate=270)
]),
sometimes3(iaa.ContrastNormalization((0.7, 1.3), per_channel=0.5)),
sometimes1(iaa.Dropout(p=(0, 0.01), per_channel=0.5)),
sometimes1(iaa.Add((-40, 40), per_channel=0.5)),
sometimes1(iaa.Sharpen(alpha=(0.3, 0.7), lightness=(0.75, 1.25))),
sometimes1(iaa.MedianBlur(k=(3, 9))),
sometimes1(iaa.GaussianBlur(sigma=(1.0, 2.5))),
sometimes1(iaa.Grayscale(alpha=(0.1, 1.0))),
sometimes1(
iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB",
to_colorspace="HSV"),
iaa.WithChannels(0, iaa.Add((30, 70))),
iaa.ChangeColorspace(from_colorspace="HSV",
to_colorspace="RGB")
])),
],
random_order=False) # apply augmenters in random order
aug_imgs = seq.augment_images(imgs)
if aug_imgs.dtype != np.float32:
aug_imgs = aug_imgs.astype(np.float32)
return aug_imgs
def get_augmentation(noise=False,
crop=False,
flip=False,
contrast=False,
add=False,
saturation=False,
value=False):
augmenters = []
if noise:
std = 0.075
augmenters.append(iaa.AdditiveGaussianNoise(scale=(0, std)))
if crop:
augmenters.append(iaa.CropAndPad(
percent=(0, 0.2),
pad_mode='edge',
))
if flip:
augmenters.append(iaa.Fliplr(0.5))
if contrast:
augmenters.append(iaa.contrast.LinearContrast((0.7, 1.3)))
if add:
augmenters.append(iaa.Add((-0.075, 0.075)))
if saturation:
augmenters.append(
iaa.Sequential([
UInt8FromFloat32(),
iaa.ChangeColorspace(from_colorspace='RGB',
to_colorspace='HSV'),
iaa.WithChannels(1, iaa.Add((-0.15, 0.15))), # HSV
iaa.ChangeColorspace(from_colorspace='HSV',
to_colorspace='RGB'),
Float32FromUInt8(),
]))
if value:
augmenters.append(
iaa.Sequential([
UInt8FromFloat32(),
iaa.ChangeColorspace(from_colorspace='RGB',
to_colorspace='HSV'),
iaa.WithChannels(2, iaa.Add((-0.15, 0.15))), # HSV
iaa.ChangeColorspace(from_colorspace='HSV',
to_colorspace='RGB'),
Float32FromUInt8(),
]))
return iaa.Sequential([
iaa.SomeOf(max(1,
len(augmenters) // 2), augmenters),
Clip(),
])
def zanoni_aug():
return iaa.SomeOf(
(1, 4),
[
# OKS
iaa.Noop(),
iaa.Grayscale(alpha=(0.25, 1.0)),
iaa.Flipud(0.45),
iaa.Fliplr(0.45),
iaa.AddToHueAndSaturation((-40, 40)),
iaa.ContrastNormalization((0.8, 1.2), per_channel=0.33),
iaa.AdditiveGaussianNoise(scale=0.02 * 255),
iaa.GaussianBlur(sigma=(0.1, 1.0)),
iaa.OneOf([
#iaa.ChangeColorspace(from_colorspace="RGB", to_colorspace="HSV"),
iaa.ChangeColorspace(from_colorspace="RGB",
to_colorspace="BGR")
#iaa.ChangeColorspace(from_colorspace="RGB", to_colorspace="YCrCb"),
#iaa.ChangeColorspace(from_colorspace="RGB", to_colorspace="HSL")
]),
iaa.OneOf([
iaa.Emboss(alpha=(0.0, 0.33), strength=(0.02, 0.25)),
iaa.Sharpen(alpha=(0.0, 0.33), lightness=(0.02, 0.25))
]),
iaa.OneOf([
iaa.Add((-15, 15), per_channel=0.25),
iaa.Multiply((0.5, 2.0), per_channel=0.25)
])
#iaa.CoarseDropout((0.1, 0.25), size_percent=(0.5, 0.75), per_channel=0.5)
])
def saturation(origImage, value):
'''
Increases/decreeases the saturation of the image
origImage: Array of image pixels as numpy array
value: Value to add/subtract saturation by
Returns: Augmented image array with saturation changed , value
'''
aug = iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB", to_colorspace="HSV"),
iaa.WithChannels(1, iaa.Add(value)),
iaa.ChangeColorspace(from_colorspace="HSV", to_colorspace="RGB")
])
return (aug, value)
def run_and_save_augseq(filename,
augseq,
images,
cols,
rows,
quality=95,
seed=1,
image_colorspace="RGB"):
ia.seed(seed)
# augseq may be a single seq (applied to all images) or a list (one seq per
# image).
# use type() here instead of isinstance, because otherwise Sequential is
# also interpreted as a list
if type(augseq) == list:
# one augmenter per image specified
assert len(augseq) == len(images)
images_aug = [
augseq[i].augment_image(images[i]) for i in range(len(images))
]
else:
# calling N times augment_image() is here critical for random order in
# Sequential
images_aug = [
augseq.augment_image(images[i]) for i in range(len(images))
]
if image_colorspace != "RGB":
images_aug = iaa.ChangeColorspace(
from_colorspace=image_colorspace,
to_colorspace="RGB")(images=images_aug)
save("overview_of_augmenters",
filename,
grid(images_aug, cols=cols, rows=rows),
quality=quality)
def iaa_hsv_aug(hue=0, saturation=1, exposure=1):
dhue = rand_uniform(-hue, hue) * 179
dsat = rand_scale(saturation)
dexp = rand_scale(exposure)
# h_l, h_t = 179 * -hue, 179 * hue
# s_l, s_t = 1 / saturation if saturation else 0, saturation
# v_l, v_t = 1 / exposure if exposure else 0, exposure
return iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB", to_colorspace="HSV"),
iaa.WithChannels([0], iaa.Add((dhue, dhue))),
iaa.WithChannels([1], iaa.Multiply((dsat, dsat))),
iaa.WithChannels([2], iaa.Multiply((dexp, dexp))),
iaa.ChangeColorspace(from_colorspace="HSV", to_colorspace="RGB")
])
def train(model, dataset_dir, subset):
"""Train the model."""
# Training dataset.
dataset_train = NucleusDataset()
dataset_train.load_nucleus(dataset_dir, subset)
dataset_train.prepare()
# Validation dataset
dataset_val = NucleusDataset()
dataset_val.load_nucleus(dataset_dir, "val")
dataset_val.prepare()
# Image augmentation
# http://imgaug.readthedocs.io/en/latest/source/augmenters.html
augmentation = iaa.Sequential([
iaa.OneOf([iaa.Fliplr(0.5), iaa.Flipud(0.5)]),
iaa.Sometimes(
0.75,
iaa.OneOf([
iaa.Affine(rotate=90),
iaa.Affine(rotate=180),
iaa.Affine(rotate=270)
])),
iaa.SomeOf(
(0, 3),
[
iaa.Multiply((0.8, 1.5)),
iaa.GaussianBlur(sigma=(0.0, 3.0)),
iaa.MedianBlur(k=(3, 7)),
iaa.AdditiveGaussianNoise(scale=0.02 * 255),
iaa.Sharpen(alpha=0.5),
iaa.Emboss(alpha=(0.0, 1.0), strength=(0.5, 1.5)),
iaa.Grayscale(alpha=(0.0, 1.0)),
iaa.ContrastNormalization((0.5, 1.5), per_channel=0.5),
iaa.ChangeColorspace(from_colorspace="RGB",
to_colorspace="HSV") # maybe not
])
])
# *** This training schedule is an example. Update to your needs ***
# If starting from imagenet, train heads only for a bit
# since they have random weights
print("Train network heads")
model.train(dataset_train,
dataset_val,
learning_rate=config.LEARNING_RATE,
epochs=20,
augmentation=augmentation,
layers='heads')
print("Train all layers")
model.train(dataset_train,
dataset_val,
learning_rate=config.LEARNING_RATE,
epochs=40,
augmentation=augmentation,
layers='all')
def augmentation(self, pil_img, pil_mask):
input_img = np.expand_dims(pil_img, axis=0)
input_mask = np.expand_dims(pil_mask, axis=0)
input_mask = np.expand_dims(input_mask, axis=3)
prob = random.uniform(0, 1)
if self.split == 'train' and prob > 0.5: # we do augmentation in 50% of the cases
seq = iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB",
to_colorspace=self.color_map),
iaa.ChannelShuffle(0.35),
# iaa.Cutout(nb_iterations=(1, 5), size=0.1, squared=False, fill_mode="constant", cval=0),
# iaa.CoarseDropout((0.0, 0.05), size_percent=(0.02, 0.25)),
# iaa.MultiplyAndAddToBrightness(mul=(0.5, 1.5), add=(-30, 30)),
# iaa.MultiplyHueAndSaturation((0.5, 1.5), per_channel=True),
# iaa.GammaContrast((0.5, 2.0)),
iaa.Affine(translate_percent={
"x": (-0.2, 0.2),
"y": (-0.2, 0.2)
}),
iaa.Affine(rotate=(-180, 180)),
iaa.Affine(shear=(-16, 16)),
iaa.Fliplr(0.5),
iaa.GaussianBlur(sigma=(0, 3.0))
])
images_aug, segmaps_aug = seq(images=input_img,
segmentation_maps=input_mask)
# if we would like to see the data augmentation
# segmaps_aug = np.concatenate((segmaps_aug,segmaps_aug,segmaps_aug), 3)
# seq.show_grid([images_aug[0], segmaps_aug[0]*255], cols=16, rows=8)
output_img = np.transpose(images_aug[0], (2, 0, 1))
output_mask = np.transpose(segmaps_aug[0], (2, 0, 1))
else:
seq = iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB",
to_colorspace=self.color_map),
])
images_aug, segmaps_aug = seq(images=input_img,
segmentation_maps=input_mask)
output_img = np.transpose(images_aug[0], (2, 0, 1))
output_mask = np.transpose(segmaps_aug[0], (2, 0, 1))
return output_img, output_mask
def __call__(self, sample):
image, polygon, labels = sample["image"], sample["polygon"], sample[
"labels"]
t = iaa.ChangeColorspace(from_colorspace=self.from_colorspace,
to_colorspace=self.to_colorspace)
image = np.array(image)
img = t(image=image)
image = Image.fromarray(img)
sample = {'image': image, 'polygon': polygon, 'labels': labels}
return sample
def __init__(self):
self.aug = iaa.Sequential([
iaa.Scale((224, 224)),
iaa.Sometimes(0.25, iaa.GaussianBlur(sigma=(0, 3.0))),
iaa.Affine(rotate=(-20, 20), mode='constant'),
iaa.Sometimes(0.25,
iaa.OneOf([iaa.Dropout(p=(0, 0.1)),
iaa.CoarseDropout(0.1, size_percent=0.5),
iaa.Sharpen(alpha=0.5)])),
iaa.AddToHueAndSaturation(value=(-10, 10), per_channel=True),
iaa.Sometimes(0.15,iaa.WithChannels(0, iaa.Add((10, 100)))),
iaa.Sometimes(0.05,iaa.ChangeColorspace(from_colorspace="RGB", to_colorspace="HSV"))
])
def image_aug(images):
a1 = iaa.Sometimes(0.8, iaa.Fliplr(0.5))
a2 = iaa.Sometimes(0.8, iaa.CoarseDropout(p=0.1, size_percent=0.1))
a3 = iaa.Sometimes(
0.8,
iaa.Affine(
scale={
"x": (0.80, 1.2),
"y": (0.8, 1.2)
}, # scale images to 80-120% of their size, individually per axis
translate_percent={
"x": (-0.2, 0.2),
"y": (-0.2, 0.2)
}, # translate by -20 to +20 percent (per axis)
rotate=(-30, 30), # rotate by -15 to +15 degrees
order=[0, 1
], # use nearest neighbour or bilinear interpolation (fast)
cval=(0, 255), # if mode is constant, use a cval between 0 and 255
mode=ia.
ALL # use any of scikit-image's warping modes (see 2nd image from the top for examples)
))
a4 = iaa.Sometimes(0.8, iaa.Grayscale(alpha=(0.1, 1.0)))
a5 = iaa.Sometimes(
0.8,
iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB", to_colorspace="HSV"),
iaa.WithChannels(0, iaa.Add((50, 100))),
iaa.ChangeColorspace(from_colorspace="HSV", to_colorspace="RGB")
]))
a6 = iaa.Sometimes(0.8, iaa.GaussianBlur(sigma=(0.5, 2)))
a7 = iaa.Sometimes(0.8, iaa.Sharpen(alpha=(0.5, 1.0),
lightness=(0.75, 2.0)))
a8 = iaa.Sometimes(0.8, iaa.EdgeDetect(alpha=(0.2, 0.5)))
seq = iaa.Sequential([a1, a2, a3, a4, a5, a6, a7, a8], random_order=True)
images_aug = seq.augment_images(images)
return images_aug
def random_color(data):
"""
Changing Color Randomly for Augmentation
reference : https://github.com/neptune-ml/data-science-bowl-2018/blob/master/augmentation.py
"""
s = random.randint(0, 1)
if s <= 0:
return data
aug = iaa.Sequential(
[
# Color
iaa.OneOf([
iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB",
to_colorspace="HSV"),
iaa.WithChannels(0, iaa.Add((0, 100))),
iaa.ChangeColorspace(from_colorspace="HSV",
to_colorspace="RGB")
]),
iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB",
to_colorspace="HSV"),
iaa.WithChannels(1, iaa.Add((0, 100))),
iaa.ChangeColorspace(from_colorspace="HSV",
to_colorspace="RGB")
]),
iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB",
to_colorspace="HSV"),
iaa.WithChannels(2, iaa.Add((0, 100))),
iaa.ChangeColorspace(from_colorspace="HSV",
to_colorspace="RGB")
]),
iaa.WithChannels(0, iaa.Add((0, 100))),
iaa.WithChannels(1, iaa.Add((0, 100))),
iaa.WithChannels(2, iaa.Add((0, 100)))
])
],
random_order=True)
data.img = aug.augment_image(data.img)
# data.masks = [aug.augment_image(mask) for mask in data.masks]
return data
def DA_ColorspaceWithChannel(inputimg, AUG_OPS_PARAM=[-50, -25, 25, 50]):
assert inputimg.ndim in [2, 3], "input invalid! Please check input"
AUG_OPS_NAME = ["ColorspaceWithChannels_AddC23", "ColorspaceWithChannels_AddC2",
"ColorspaceWithChannels_AddC3"]
ret = []
for i_param in np.arange(len(AUG_OPS_PARAM)):
VALUE = AUG_OPS_PARAM[i_param]
for i_name in np.arange(len(AUG_OPS_NAME)):
Name = AUG_OPS_NAME[i_name]
if Name == "ColorspaceWithChannels_AddC23":
aug = iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB", to_colorspace="HSV"),
iaa.WithChannels([1,2], iaa.Add(VALUE)),
iaa.ChangeColorspace(from_colorspace="HSV", to_colorspace="RGB")
])
augimg = aug.augment_image(inputimg)
ret.append((Name, VALUE, augimg))
elif Name == "ColorspaceWithChannels_AddC2":
aug = iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB", to_colorspace="HSV"),
iaa.WithChannels(1, iaa.Add(VALUE)),
iaa.ChangeColorspace(from_colorspace="HSV", to_colorspace="RGB")
])
augimg = aug.augment_image(inputimg)
ret.append((Name, VALUE, augimg))
elif Name == "ColorspaceWithChannels_AddC3":
aug = iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB", to_colorspace="HSV"),
iaa.WithChannels(2, iaa.Add(VALUE)),
iaa.ChangeColorspace(from_colorspace="HSV", to_colorspace="RGB")
])
augimg = aug.augment_image(inputimg)
ret.append((Name, VALUE, augimg))
else:
print "invalid ops in DA_WithChannels_Add"
assert len(ret) == 12, "DA_ColorspaceWithChannel Fails"
return ret
def color_augmentation():
return iaa.Sequential([
# Color
iaa.OneOf([
iaa.Sequential([
iaa.ChangeColorspace(
from_colorspace="RGB", to_colorspace="HSV"),
iaa.WithChannels(0, iaa.Add((0, 100))),
iaa.ChangeColorspace(from_colorspace="HSV", to_colorspace="RGB")]),
iaa.Sequential([
iaa.ChangeColorspace(
from_colorspace="RGB", to_colorspace="HSV"),
iaa.WithChannels(1, iaa.Add((0, 100))),
iaa.ChangeColorspace(from_colorspace="HSV", to_colorspace="RGB")]),
iaa.Sequential([
iaa.ChangeColorspace(
from_colorspace="RGB", to_colorspace="HSV"),
iaa.WithChannels(2, iaa.Add((0, 100))),
iaa.ChangeColorspace(from_colorspace="HSV", to_colorspace="RGB")]),
iaa.WithChannels(0, iaa.Add((0, 100))),
iaa.WithChannels(1, iaa.Add((0, 100))),
iaa.WithChannels(2, iaa.Add((0, 100)))
])
], random_order=True)
def augmenter_function(self, images, labels):
high = lambda aug: iaa.Sometimes(0.9, aug)
medium = lambda aug: iaa.Sometimes(0.6, aug)
low = lambda aug: iaa.Sometimes(0.15, aug)
if (self._augment_amount == 3): #max augmentation
road_aug = iaa.Sequential([
iaa.Multiply((0.5, 1.25)),
#texture
iaa.SomeOf(
1,
[
#iaa.AdditiveGaussianNoise(scale=(0, 0.01*255)),
iaa.AddElementwise((-20, 20)),
medium(iaa.Sharpen(alpha=(0.0, 1.0), lightness=1)),
medium(
iaa.Emboss(alpha=(0.0, 1.0), strength=(0.5, 1.5)))
])
])
building_aug = iaa.Sequential([
iaa.Multiply((0.5, 1.5), per_channel=0.8),
#texture
#iaa.SomeOf(1, [
#high(iaa.AdditiveGaussianNoise(scale=(0, 0.03*255))),
#medium(iaa.Sharpen(alpha=(0.0, 1.0), lightness=1)),
#medium(iaa.Emboss(alpha=(0.0, 1.0), strength=(0.5, 1.5)))])
])
grass_aug = iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB",
to_colorspace="HSV"),
(iaa.WithChannels(0, iaa.Add((50, -50)))),
iaa.WithChannels(2, iaa.Add((-30, 30))),
iaa.ChangeColorspace(from_colorspace="HSV",
to_colorspace="RGB"),
#texture
iaa.SomeOf(
2,
[
medium(
iaa.AdditiveGaussianNoise(scale=(0, 0.06 * 255))),
medium(iaa.AddElementwise((-25, 25))),
medium(iaa.Sharpen(alpha=(0.0, 1.0), lightness=1)),
medium(
iaa.Emboss(alpha=(0.0, 1.0), strength=(0.5, 1.5)))
#low(iaa.Superpixels(p_replace=1, n_segments=(126, 128)))
])
])
sky_aug = iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB",
to_colorspace="HSV"),
iaa.WithChannels(0, iaa.Add((-20, 20))),
iaa.WithChannels(1, iaa.Add((0, 30))),
iaa.WithChannels(2, iaa.Add((-10, 0))),
iaa.ChangeColorspace(from_colorspace="HSV",
to_colorspace="RGB"),
#medium(iaa.WithChannels(2, iaa.Add((-40, 40)))),
#texture
#iaa.SomeOf(1, [
#medium(iaa.AddElementwise((-5, 5))),
#medium(iaa.Emboss(alpha=(0.0, 1.0), strength=(0.6, 1.4)))
#low(iaa.Superpixels(p_replace=1, n_segments=(126, 144)))])
])
elif (self._augment_amount == 2): #mid augmentation
road_aug = iaa.Sequential([
high(iaa.Multiply((0.75, 1.15))),
#texture
iaa.SomeOf(
1,
[
#iaa.AdditiveGaussianNoise(scale=(0, 0.01*255)),
iaa.AddElementwise((-8, 8)),
medium(iaa.Sharpen(alpha=(0.0, 1.0), lightness=1)),
medium(
iaa.Emboss(alpha=(0.0, 1.0), strength=(0.5, 1.5)))
])
])
building_aug = iaa.Sequential([
iaa.Multiply((0.5, 1.5), per_channel=0.8),
#texture
#iaa.SomeOf(1, [
#high(iaa.AdditiveGaussianNoise(scale=(0, 0.03*255))),
#medium(iaa.Sharpen(alpha=(0.0, 1.0), lightness=1)),
#medium(iaa.Emboss(alpha=(0.0, 1.0), strength=(0.5, 1.5)))])
])
grass_aug = iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB",
to_colorspace="HSV"),
(iaa.WithChannels(0, iaa.Add((50, -50)))),
iaa.WithChannels(2, iaa.Add((-30, 30))),
iaa.ChangeColorspace(from_colorspace="HSV",
to_colorspace="RGB"),
#texture
iaa.SomeOf(
1,
[
medium(
iaa.AdditiveGaussianNoise(scale=(0, 0.04 * 255))),
medium(iaa.AddElementwise((-11, 11))),
medium(iaa.Sharpen(alpha=(0.0, 1.0), lightness=1)),
medium(
iaa.Emboss(alpha=(0.0, 0.75), strength=(0.5, 1.5)))
#low(iaa.Superpixels(p_replace=1, n_segments=(126, 128)))
])
])
sky_aug = iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB",
to_colorspace="HSV"),
iaa.WithChannels(0, iaa.Add((-30, 30))),
iaa.WithChannels(1, iaa.Add((0, 30))),
iaa.WithChannels(2, iaa.Add((-10, 0))),
iaa.ChangeColorspace(from_colorspace="HSV",
to_colorspace="RGB"),
#medium(iaa.WithChannels(2, iaa.Add((-40, 40)))),
#texture
#iaa.SomeOf(1, [
#medium(iaa.AddElementwise((-5, 5))),
#medium(iaa.Emboss(alpha=(0.0, 1.0), strength=(0.6, 1.4)))
#low(iaa.Superpixels(p_replace=1, n_segments=(126, 144)))])
])
elif (self._augment_amount == 1): #min augmentation
road_aug = iaa.Sequential([
high(iaa.Multiply((0.9, 1.05))),
#texture
iaa.SomeOf(
1,
[
#iaa.AdditiveGaussianNoise(scale=(0, 0.01*255)),
iaa.AddElementwise((-3, 3)),
medium(iaa.Sharpen(alpha=(0.0, 1.0), lightness=1)),
medium(
iaa.Emboss(alpha=(0.0, 1.0), strength=(0.8, 1.2)))
])
])
building_aug = iaa.Sequential([
iaa.Multiply((0.5, 1.5), per_channel=0.8),
#texture
#iaa.SomeOf(1, [
#high(iaa.AdditiveGaussianNoise(scale=(0, 0.03*255))),
#medium(iaa.Sharpen(alpha=(0.0, 1.0), lightness=1)),
#medium(iaa.Emboss(alpha=(0.0, 1.0), strength=(0.5, 1.5)))])
])
grass_aug = iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB",
to_colorspace="HSV"),
iaa.WithChannels(0, iaa.Add((50, -50))),
iaa.WithChannels(2, iaa.Add((-30, 30))),
iaa.ChangeColorspace(from_colorspace="HSV",
to_colorspace="RGB"),
#texture
iaa.SomeOf(
1,
[
medium(
iaa.AdditiveGaussianNoise(scale=(0, 0.02 * 255))),
medium(iaa.AddElementwise((-10, 10))),
medium(iaa.Sharpen(alpha=(0.0, 0.5), lightness=1)),
medium(
iaa.Emboss(alpha=(0.0, 0.5), strength=(0.5, 1.5)))
#low(iaa.Superpixels(p_replace=1, n_segments=(126, 128)))
])
])
sky_aug = iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB",
to_colorspace="HSV"),
iaa.WithChannels(0, iaa.Add((-20, 20))),
iaa.WithChannels(1, iaa.Add((0, 10))),
iaa.WithChannels(2, iaa.Add((-10, 0))),
iaa.ChangeColorspace(from_colorspace="HSV",
to_colorspace="RGB"),
medium(iaa.WithChannels(2, iaa.Add((-40, 40)))),
#texture
#iaa.SomeOf(1, [
#medium(iaa.AddElementwise((-5, 5))),
#medium(iaa.Emboss(alpha=(0.0, 1.0), strength=(0.6, 1.4)))
#low(iaa.Superpixels(p_replace=1, n_segments=(126, 144)))])
])
'''fence = np.zeros_like(images)
cond = labels == 2
b = np.repeat(cond[:, :, :, np.newaxis], 3, axis=3)
fence[b[:,:,:,:,0]] = images[b[:,:,:,:,0]]'''
if self._labels_to_augment["road"] == True:
road = np.zeros_like(images)
cond = labels == 8
b = np.repeat(cond[:, :, :, np.newaxis], 3, axis=3)
road[b[:, :, :, :, 0]] = images[b[:, :, :, :, 0]]
new_road = sky_aug.augment_images(road)
images = images + new_road - road
if self._labels_to_augment["buildings"] == True:
buildings = np.zeros_like(images)
cond = labels == 1
b = np.repeat(cond[:, :, :, np.newaxis], 3, axis=3)
buildings[b[:, :, :, :, 0]] = images[b[:, :, :, :, 0]]
new_buildings = sky_aug.augment_images(buildings)
images = images + new_buildings - buildings
if self._labels_to_augment["grass"] == True:
grass = np.zeros_like(images)
cond = labels == 9
b = np.repeat(cond[:, :, :, np.newaxis], 3, axis=3)
grass[b[:, :, :, :, 0]] = images[b[:, :, :, :, 0]]
new_grass = sky_aug.augment_images(grass)
images = images + new_grass - grass
if self._labels_to_augment["sky_n_zebra"] == True:
sky_n_zebra = np.zeros_like(images)
cond = labels == 0
b = np.repeat(cond[:, :, :, np.newaxis], 3, axis=3)
sky_n_zebra[b[:, :, :, :, 0]] = images[b[:, :, :, :, 0]]
new_sky_n_zebra = sky_aug.augment_images(sky_n_zebra)
images = images + new_sky_n_zebra - sky_n_zebra
#final = road + buildings + grass + sky_n_zebra + fence
#comp = np.hstack((initial,final))
#initial = buildings+grass+fence+road+sky_n_zebra
return images
def __init__(self,
src='train',
img_size=256,
validation_pct=0,
testing_pct=0,
fold_keys=None,
valid_same=True,
use_mosaics=False,
excl_keys=None):
"""
Build data processor for yielding train, valid and test data
Providing a fold_key will put all data with the given cluster in the validation set and everything else
in training. The percentages are thus ignored.
'valid_same' parameter will assure the sampling points on 'valid' mode will be the same across calls
"""
self.src_dir = os.path.join('model-data', src)
self.img_size = img_size
self.validation_pct = validation_pct
self.testing_pct = testing_pct
self.fold_keys = fold_keys if fold_keys is None or isinstance(
fold_keys, list) else [fold_keys]
self.excl_keys = excl_keys if excl_keys is None or isinstance(
excl_keys, list) else [excl_keys]
self.data_index = {'train': [], 'valid': [], 'test': []}
self.data_dist = {
'train': defaultdict(int),
'valid': defaultdict(int),
'test': defaultdict(int)
}
self.use_mosaics = use_mosaics
self._generate_data_index()
self.valid_seeds = None
if valid_same:
self.valid_seeds = np.random.rand(self.mode_size(mode='valid'), 2)
# Color augmentation
self._color_aug = iaa.Sometimes(
0.5,
iaa.Sequential([
iaa.OneOf([
iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB",
to_colorspace="HSV"),
iaa.WithChannels(0, iaa.Add((0, 100))),
iaa.ChangeColorspace(from_colorspace="HSV",
to_colorspace="RGB")
]),
iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB",
to_colorspace="HSV"),
iaa.WithChannels(1, iaa.Add((0, 100))),
iaa.ChangeColorspace(from_colorspace="HSV",
to_colorspace="RGB")
]),
iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB",
to_colorspace="HSV"),
iaa.WithChannels(2, iaa.Add((0, 100))),
iaa.ChangeColorspace(from_colorspace="HSV",
to_colorspace="RGB")
]),
iaa.WithChannels(0, iaa.Add((0, 100))),
iaa.WithChannels(1, iaa.Add((0, 100))),
iaa.WithChannels(2, iaa.Add((0, 100)))
])
],
random_order=True))
# Image augmentation (noise, blur, contrast, brightness)
self._image_aug = iaa.Sometimes(
0.5,
iaa.OneOf([
iaa.AdditiveGaussianNoise(scale=(0, 0.05 * 255)),
iaa.GaussianBlur(sigma=(0, 1.0)),
iaa.ContrastNormalization((0.9, 1.1)),
iaa.Multiply((0.8, 1.2))
]))
def load_data_aug(name,
path_to_train,
path_to_target,
size=512,
color=0,
aug=3,
nb_class=5):
imgs = []
labels = []
img_ori = Image.open(path_to_train + "or{}.png".format(name))
label_ori = Image.open(path_to_target + "col{}.png".format(name))
w, h = img_ori.size
cn = iaa.SomeOf(
(0, 1),
[
iaa.ContrastNormalization((0.7, 1.3)), # 明るさ正規化
iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB",
to_colorspace="HSV"),
iaa.WithChannels(0, iaa.Add((-10, 10))),
iaa.ChangeColorspace(from_colorspace="HSV",
to_colorspace="RGB")
]),
iaa.Grayscale(alpha=(0, 0.5))
])
cn2 = iaa.SomeOf(
(0, 1),
[
iaa.ContrastNormalization((0.7, 1.3)), # 明るさ正規化
iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB",
to_colorspace="HSV"),
iaa.WithChannels(0, iaa.Add((-10, 10))),
iaa.ChangeColorspace(from_colorspace="HSV",
to_colorspace="RGB")
]),
])
for i in range(aug):
# ランダムにcrop
lw = np.random.randint(0, (w - size))
lh = np.random.randint(0, (h - size))
img = img_ori.crop((lw, lh, lw + size, lh + size))
label = label_ori.crop((lw, lh, lw + size, lh + size))
# ランダムに反転
j = np.random.randint(3)
if j == 1:
img = ImageOps.flip(img)
label = ImageOps.flip(label)
elif j == 2:
img = ImageOps.mirror(img)
label = ImageOps.mirror(label)
if color == 0:
# そのまま
img = np.array(img, dtype=np.uint8)
if color == 1:
# gray scale
img = img.convert('L')
img = np.array(img, dtype=np.uint8)
if color == 2:
# augmentation color and gray
img = np.array(img, dtype=np.uint8)
img = cn.augment_image(img)
if color == 3:
# augmentation color
img = np.array(img, dtype=np.uint8)
img = cn2.augment_image(img)
# ランダムにratate
t = np.random.rand() * 90
rotate = iaa.Affine(rotate=(t, t))
img = rotate.augment_image(img)
label = np.array(label, dtype=np.int32)
label = rotate.augment_image(label)
label = multi_label(label, size, nb_class)
imgs.append(img)
labels.append(label)
labels = np.array(labels)
imgs = np.array(imgs, dtype=np.float64)
if color == 1:
imgs = imgs / 255.0
else:
# imgs = preprocess_input(imgs)
imgs = imgs / 255.0
return imgs, labels
def __getitem__(self, index):
"""
Returns
- path: (str) Image filename
- img: (Tensor)
- annos: (Tensor) Annotation with size [100, 5]
5 = [xc, yc, w, h, label]
"""
coco = self.coco
img_id = self.ids[index]
ann_ids = coco.getAnnIds(imgIds=img_id)
path = coco.loadImgs(img_id)[0]['file_name']
img = Image.open(os.path.join(self.root, path)).convert('RGB')
w, h = img.size
target = coco.loadAnns(ann_ids)
annos = torch.zeros(len(target), 5)
# if self.reso is None, skip the whole image augmentation part.
# Apply image augmentations before coordinate conversion, resizing and toTensor transform.
if self.reso is not None:
#Convert image to numpy array
img = np.array(img)
# Get all the annotations (bboxes)
bboxes = []
for i in range(len(target)):
bbox = torch.Tensor(target[i]['bbox'])
bbox_x = bbox[0]
bbox_y = bbox[1]
bbox_w = bbox[2]
bbox_h = bbox[3]
bboxes.append(
BoundingBox(x1=bbox_x,
y1=bbox_y,
x2=bbox_x + bbox_w,
y2=bbox_y + bbox_h))
bbs = BoundingBoxesOnImage(bboxes, shape=img.shape)
# NOTE: resize first, otherwise rotating will crop the image (since image is not square)
# Rescale image and bounding boxes
img = ia.imresize_single_image(img, (self.reso, self.reso))
bbs = bbs.on(img)
# Define augmentations
seq = iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB",
to_colorspace="HSV"),
iaa.WithChannels(1, iaa.Add((-50, 50))), # Change saturation
iaa.WithChannels(2, iaa.Add((-50, 50))), # Change intensity
iaa.ChangeColorspace(from_colorspace="HSV",
to_colorspace="RGB"),
iaa.Sometimes(
0.5,
iaa.Affine(rotate=90)), # Rotate 90 deg. (0.5 probability)
iaa.Affine(shear=(-2, 2)), # Shear (-2 +2 degrees)
iaa.Flipud(0.5), # Flip up-down (with 0.5 probability)
iaa.Fliplr(0.5) # Flip left-right (with 0.5 probability)
])
img_aug, bbs_aug = seq(image=img, bounding_boxes=bbs)
# # DEBUG: output image
# image_after = bbs_aug.draw_on_image(img_aug)
# # Convert numpy array to PIL image
# image_after = Image.fromarray(image_after, 'RGB')
# image_after.save('debug.png')
# print(path)
# print("DEBUG IMAGE SAVED")
# print(bbox)
# print(bbs_aug)
# # sys.exit(0)
# Overwrite original image
img = img_aug
for i in range(len(target)):
# [x1, y1, w, h] => [xc, yc, w, h]
# if self.reso is not None it means we have to update the bounding boxes coordinates
if self.reso is not None:
bbox = []
bbox.append(int(bbs_aug.bounding_boxes[i].x1))
bbox.append(int(bbs_aug.bounding_boxes[i].y1))
bbox.append(
int(bbs_aug.bounding_boxes[i].x2) -
int(bbs_aug.bounding_boxes[i].x1))
bbox.append(
int(bbs_aug.bounding_boxes[i].y2) -
int(bbs_aug.bounding_boxes[i].y1))
bbox = torch.Tensor(bbox)
w = self.reso
h = self.reso
# print(bbox)
# print()
else:
bbox = torch.Tensor(target[i]['bbox'])
annos[i, 0] = (bbox[0] + bbox[2] / 2) / w
annos[i, 1] = (bbox[1] + bbox[3] / 2) / h
annos[i, 2] = bbox[2] / w
annos[i, 3] = bbox[3] / h
annos[i, 4] = self.class_map[int(target[i]['category_id'])]
#if self.transform is not None:
# img = self.transform(img)
# ndarray -> Tensor conversion
img = torch.from_numpy(img.transpose((2, 0, 1)).copy())
if isinstance(img, torch.ByteTensor):
img = img.float().div(255)
return path, img, annos
def aug_seq():
seq = iaa.Sequential(
[
iaa.Fliplr(0.4), # horizontal flips
iaa.Flipud(0.4), # vertical flips
# iaa.Crop(percent=(0, 0.1)), # random crops
# Small gaussian blur with random sigma between 0 and 0.5.
# But we only blur about 50% of all images.
# iaa.Sometimes(
# 0.3,
# iaa.GaussianBlur(sigma=(0, 0.5))
# ),
# Strengthen or weaken the contrast in each image.
iaa.LinearContrast((0.75, 1.5)),
# Add gaussian noise.
# For 50% of all images, we sample the noise once per pixel.
# For the other 50% of all images, we sample the noise per pixel AND
# channel. This can change the color (not only brightness) of the
# pixels.
# iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05*255), per_channel=0.5),
# Make some images brighter and some darker.
# In 20% of all cases, we sample the multiplier once per channel,
# which can end up changing the color of the images.
# iaa.Sometimes(
# 0.5,
# iaa.Multiply((0.8, 1.2), per_channel=0.2)
# ),
# Apply affine transformations to each image.
# Scale/zoom them, translate/move them, rotate them and shear them.
iaa.Sometimes(
0.5,
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)
) # to add shear back, remove the second ) above, and add the , back as well
),
iaa.Sometimes(
0.10,
iaa.OneOf([
iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB",
to_colorspace="HSV"),
iaa.WithChannels(0, iaa.Add((0, 50))),
iaa.ChangeColorspace(from_colorspace="HSV",
to_colorspace="RGB")
]),
iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB",
to_colorspace="HSV"),
iaa.WithChannels(1, iaa.Add((0, 50))),
iaa.ChangeColorspace(from_colorspace="HSV",
to_colorspace="RGB")
]),
iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB",
to_colorspace="HSV"),
iaa.WithChannels(2, iaa.Add((0, 50))),
iaa.ChangeColorspace(from_colorspace="HSV",
to_colorspace="RGB")
]),
iaa.WithChannels(0, iaa.Add((0, 50))),
iaa.WithChannels(1, iaa.Add((0, 50))),
iaa.WithChannels(2, iaa.Add((0, 50)))
]))
],
random_order=True) # apply augmenters in random order
return seq
#! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
import numpy as np
import cv2
from imgaug import augmenters as iaa
path = 'idcard/'
sqe_list = [
iaa.ChangeColorspace(from_colorspace="RGB", to_colorspace="HSV"),
iaa.WithChannels(0, iaa.Add((-50, 50))),
iaa.WithChannels(1, iaa.Add((-50, 50))),
iaa.WithChannels(2, iaa.Add((-50, 50))),
iaa.ChangeColorspace(from_colorspace="HSV", to_colorspace="RGB"),
iaa.Add((-80, 80), per_channel=0.5),
iaa.Multiply((0.5, 1.5), per_channel=0.5),
iaa.AverageBlur(k=((5), (1, 3))),
iaa.AveragePooling(2),
iaa.AddElementwise((-20, -5)),
iaa.AdditiveGaussianNoise(scale=(0, 0.05 * 255)),
iaa.JpegCompression(compression=(50, 99)),
iaa.MultiplyHueAndSaturation(mul_hue=(0.5, 1.5)),
iaa.WithBrightnessChannels(iaa.Add((-50, 50))),
iaa.WithBrightnessChannels(iaa.Add((-50, 50)),
to_colorspace=[iaa.CSPACE_Lab, iaa.CSPACE_HSV]),
iaa.MaxPooling(2),
iaa.MinPooling((1, 2)),
# iaa.Superpixels(p_replace=(0.1, 0.2), n_segments=(16, 128)),
iaa.Clouds(),
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
#t.RandomCrop.Both(original_size=(1918, 1280), crop_size=(config.INPUT_SIZE, config.INPUT_SIZE)),
t.ExpandDims.Mask(axis=2),
t.ImgAug.Both(iaa.Fliplr(0.5)),
t.ImgAug.Both(
iaa.Affine(scale={
'x': (0.95, 1.05),
'y': (0.95, 1.05)
},
rotate=(-16, 16),
shear=(-16, 16),
order=[0, 1],
mode='reflect'),
p=0.25,
),
t.ImgAug.Image(iaa.Sequential([
iaa.ChangeColorspace(from_colorspace='RGB', to_colorspace='HSV'),
iaa.WithChannels([0], iaa.Add((-30, 30))),
iaa.ChangeColorspace(from_colorspace='HSV', to_colorspace='RGB')
]),
p=0.25),
t.Clip.Image(),
),
'crop_fliplr': (
t.Resize.Both((config.INPUT_SIZE, config.INPUT_SIZE)),
#t.RandomCrop.Both(original_size=(1918, 1280), crop_size=(config.INPUT_SIZE, config.INPUT_SIZE)),
t.ExpandDims.Mask(axis=2),
t.ImgAug.Both(iaa.Fliplr(0.5)),
),
'pad_fliplr_affine_color': (
t.Pad.Both(size=(0, 0, 1, 1)),
t.ExpandDims.Mask(axis=2),
def gen_batch_function(data_folder, image_shape):
"""
Generate function to create batches of training data
:param data_folder: Path to folder that contains all the datasets
:param image_shape: Tuple - Shape of image
:return:
"""
sometimes = lambda aug: iaa.Sometimes(0.5, aug)
# apply to images
seq_images = iaa.Sequential(
[
# Change the colorspace from RGB to HSV, then add 0-100 to the first channel (hue),
# then convert back to RGB.
iaa.Sequential([
iaa.ChangeColorspace(from_colorspace="RGB",
to_colorspace="HSV"),
iaa.WithChannels(0, iaa.Add((0, 100))),
iaa.ChangeColorspace(from_colorspace="HSV",
to_colorspace="RGB")
]),
# Strengthen or weaken the contrast in each image.
iaa.ContrastNormalization((0.75, 1.5)),
# Multiply 50% of all images with a random value between 0.5 and 2.0
# and multiply the remaining 50% channel-wise.
iaa.Multiply((0.5, 2.0), per_channel=0.5),
# Sometimes blur image using a mean over neihbourhoods that have a random size between 1x1 and 5x5,
# or sharpen an image, then overlay the results with the original using an alpha between 0.0 and 1.0.
sometimes(
iaa.OneOf([
iaa.AverageBlur(k=(1, 5)),
iaa.Sharpen(alpha=(0.0, 1.0), lightness=(0.75, 2.0)),
])),
# Add gaussian noise.
# For 50% of all images, we sample the noise once per pixel.
# For the other 50% of all images, we sample the noise per pixel AND
# channel. This can change the color (not only brightness) of the
# pixels.
iaa.AdditiveGaussianNoise(
loc=0, scale=(0.0, 0.05 * 255), per_channel=0.5),
# Sometimes drop 2% of all pixels by converting them to black pixels,
# but do that on a lower-resolution version of the image
# that has 50% of the original size
sometimes(iaa.CoarseDropout(0.02, size_percent=0.5)),
# Sometimes sample per image a value p from the range 0<=p<=0.2
# and then drop p percent of all pixels in the image
sometimes(iaa.Dropout(p=(0, 0.2))),
],
random_order=True # apply augmenters in random order
)
# apply to both images and labels
seq_images_labels = iaa.Sequential(
[
# horizontally flip 50% of all images
iaa.Fliplr(0.5),
sometimes(
iaa.OneOf([
iaa.Affine(
scale={
'x': (0.5, 2.0),
'y': (0.5, 2.0)
},
rotate=(-15, 15),
shear=(-16, 16),
translate_percent={
"x": (-0.2, 0.2),
"y": (-0.2, 0.2)
},
mode='constant',
),
iaa.PiecewiseAffine(scale=(0.01, 0.05))
])),
],
random_order=False)
def get_batches_fn(batch_size):
"""
Create batches of training data
:param batch_size: Batch Size
:return: Batches of training data
"""
image_paths = glob(os.path.join(data_folder, 'image_2', '*.png'))
label_paths = {
re.sub(r'_(lane|road)_', '_', os.path.basename(path)): path
for path in glob(
os.path.join(data_folder, 'gt_image_2', '*_road_*.png'))
}
background_color = np.array([255, 0, 0])
random.shuffle(image_paths)
for batch_i in range(0, len(image_paths), batch_size):
images = []
gt_images = []
for image_file in image_paths[batch_i:batch_i + batch_size]:
gt_image_file = label_paths[os.path.basename(image_file)]
image = scipy.misc.imresize(scipy.misc.imread(image_file),
image_shape)
gt_image = scipy.misc.imresize(
scipy.misc.imread(gt_image_file), image_shape)
gt_bg = np.all(gt_image == background_color, axis=2)
gt_bg = gt_bg.reshape(*gt_bg.shape, 1)
gt_image = np.concatenate((gt_bg, np.invert(gt_bg)), axis=2)
images.append(image)
gt_images.append(gt_image)
# these augmentations won't affect labels
images_aug = seq_images.augment_images(images)
# make sure we apply same augmentation for both images and labels
seq_images_labels_deterministic = seq_images_labels.to_deterministic(
)
images_aug = seq_images_labels_deterministic.augment_images(
images_aug)
gt_images_aug = seq_images_labels_deterministic.augment_images(
gt_images)
yield np.array(images), np.array(gt_images)
return get_batches_fn