#锐化处理
iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)),
#边缘检测，将检测到的赋值0或者255然后叠在原图上
sometimes(
iaa.OneOf([
iaa.EdgeDetect(alpha=(0, 0.7)),
iaa.DirectedEdgeDetect(alpha=(0, 0.7),
direction=(0.0, 1.0)),
])),
# 加入高斯噪声
iaa.AdditiveGaussianNoise(
loc=0, scale=(0.0, 0.05 * 255), per_channel=0.5),
# 将1%到5%的像素设置为黑色
# 或者将3%到15%的像素用原图大小2%到5%的黑色方块覆盖
iaa.OneOf([
iaa.Dropout((0.01, 0.05), per_channel=0.5),
iaa.CoarseDropout((0.03, 0.10),
size_percent=(0.02, 0.05),
per_channel=0.2),
]),
#5%的概率反转像素的强度，即原来的强度为v那么现在的就是255-v
# 每个像素随机加减-10到10之间的数
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
def _create_augment_pipeline():
from imgaug import augmenters as iaa
# augmentors by https://github.com/aleju/imgaug
def sometimes(aug):
return iaa.Sometimes(0.5, aug)
# Define our sequence of augmentation steps that will be applied to every image
# All augmenters with per_channel=0.5 will sample one value _per image_
# in 50% of all cases. In all other cases they will sample new values
# _per channel_.
aug_pipe = iaa.Sequential(
[
# apply the following augmenters to most images
# iaa.Fliplr(0.5), # horizontally flip 50% of all images
# iaa.Flipud(0.2), # vertically flip 20% of all images
# sometimes(iaa.Crop(percent=(0, 0.1))), # crop images by 0-10% of their height/width
sometimes(
iaa.Affine(
# scale={"x": (0.8, 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=(-5, 5), # rotate by -45 to +45 degrees
# shear=(-5, 5), # shear by -16 to +16 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)
)),
# execute 0 to 5 of the following (less important) augmenters per image
# don't execute all of them, as that would often be way too strong
iaa.SomeOf(
(0, 5),
[
# sometimes(iaa.Superpixels(p_replace=(0, 1.0), n_segments=(20, 200))), # convert images into their superpixel representation
iaa.OneOf([
iaa.GaussianBlur(
(0, 3.0
)), # blur images with a sigma between 0 and 3.0
# blur image using local means with kernel sizes between 2 and 7
iaa.AverageBlur(k=(2, 7)),
# blur image using local medians with kernel sizes between 2 and 7
iaa.MedianBlur(k=(3, 11)),
]),
iaa.Sharpen(alpha=(0, 1.0),
lightness=(0.75, 1.5)), # sharpen images
# iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)), # emboss images
# search either for all edges or for directed edges
# sometimes(iaa.OneOf([
# iaa.EdgeDetect(alpha=(0, 0.7)),
# iaa.DirectedEdgeDetect(alpha=(0, 0.7), direction=(0.0, 1.0)),
# ])),
# add gaussian noise to images
iaa.AdditiveGaussianNoise(
loc=0, scale=(0.0, 0.05 * 255), per_channel=0.5),
iaa.OneOf([
# randomly remove up to 10% of the pixels
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),
]),
# iaa.Invert(0.05, per_channel=True), # invert color channels
# change brightness of images (by -10 to 10 of original value)
iaa.Add((-10, 10), per_channel=0.5),
# change brightness of images (50-150% of original value)
iaa.Multiply((0.5, 1.5), per_channel=0.5),
# improve or worsen the contrast
iaa.ContrastNormalization((0.5, 2.0), per_channel=0.5),
#iaa.Grayscale(alpha=(0.0, 1.0)),
# sometimes(iaa.ElasticTransformation(alpha=(0.5, 3.5), sigma=0.25)), # move pixels locally around (with random strengths)
# sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.05))) # sometimes move parts of the image around
],
random_order=True)
],
random_order=True)
return aug_pipe
sometimes_010(
iaa.OneOf([
iaa.LogContrast((0.8, 1.2)),
iaa.GammaContrast((0.8, 1.2)),
iaa.LinearContrast((0.8, 1.2)),
iaa.Alpha((0.0, 1.0), iaa.AllChannelsHistogramEqualization()),
iaa.Alpha((0.0, 1.0), iaa.HistogramEqualization()),
iaa.CLAHE(clip_limit=(1, 3)),
iaa.AllChannelsCLAHE(clip_limit=(1, 3)),
]))
])
dropouts = iaa.Sequential([
sometimes_010(
iaa.OneOf([
iaa.Dropout(p=0.01, per_channel=True),
iaa.Dropout(p=0.01, per_channel=False),
iaa.Cutout(fill_mode="constant",
cval=(0, 255),
size=(0.1, 0.4),
fill_per_channel=0.5),
iaa.CoarseDropout((0.0, 0.08),
size_percent=(0.02, 0.25),
per_channel=0.5),
iaa.SaltAndPepper(p=0.01, per_channel=True),
iaa.SaltAndPepper(p=0.01, per_channel=False),
iaa.AdditiveLaplaceNoise(scale=0.02 * 255, per_channel=True),
iaa.AdditiveLaplaceNoise(scale=0.02 * 255, per_channel=False),
iaa.AdditiveGaussianNoise(scale=0.02 * 255, per_channel=True),
iaa.AdditiveGaussianNoise(scale=0.02 * 255, per_channel=False),
iaa.AdditivePoissonNoise(lam=4.0, per_channel=True),
image = ia.quokka(size=(128, 128))
# Create an example segmentation map (int32, 128x128).
# Here, we just randomly place some squares on the image.
# Class 0 is the background class.
segmap = np.zeros((128, 128), dtype=np.int32)
segmap[28:71, 35:85] = 1
segmap[10:25, 30:45] = 2
segmap[10:25, 70:85] = 3
segmap[10:110, 5:10] = 4
segmap[118:123, 10:110] = 5
segmap = ia.SegmentationMapOnImage(segmap, shape=image.shape, nb_classes=1+5)
# Define our augmentation pipeline.
seq = iaa.Sequential([
iaa.Dropout([0.05, 0.2]), # drop 5% or 20% of all pixels
iaa.Sharpen((0.0, 1.0)), # sharpen the image
iaa.Affine(rotate=(-45, 45)), # rotate by -45 to 45 degrees (affects heatmaps)
iaa.ElasticTransformation(alpha=50, sigma=5) # apply water effect (affects heatmaps)
], random_order=True)
# Augment images and heatmaps.
images_aug = []
segmaps_aug = []
for _ in range(5):
seq_det = seq.to_deterministic()
images_aug.append(seq_det.augment_image(image))
segmaps_aug.append(seq_det.augment_segmentation_maps([segmap])[0])
# We want to generate an image of original input images and heatmaps before/after augmentation.
# It is supposed to have five columns: (1) original image, (2) augmented image,
def __init__(self):
configMain.__init__(self)
st = lambda aug: iaa.Sometimes(0.4, aug)
oc = lambda aug: iaa.Sometimes(0.3, aug)
rl = lambda aug: iaa.Sometimes(0.09, aug)
self.augment = iaa.Sequential(
[
rl(iaa.GaussianBlur(
(0, 1.5))), # blur images with a sigma between 0 and 1.5
rl(
iaa.AdditiveGaussianNoise(
loc=0, scale=(0.0, 0.05),
per_channel=0.5)), # add gaussian noise to images
oc(iaa.Dropout((0.0, 0.10), per_channel=0.5)
), # randomly remove up to X% of the pixels
oc(
iaa.CoarseDropout(
(0.0, 0.10), size_percent=(0.08, 0.2), per_channel=0.5)
), # randomly remove up to X% of the pixels
oc(
iaa.Add((-40, 40), per_channel=0.5)
), # change brightness of images (by -X to Y of original value)
st(iaa.Multiply((0.10, 2.5), per_channel=0.2)
), # change brightness of images (X-Y% of original value)
rl(iaa.ContrastNormalization(
(0.5, 1.5),
per_channel=0.5)), # improve or worsen the contrast
rl(iaa.Grayscale((0.0, 1))), # put grayscale
],
random_order=True # do all of the above in random order
)
self.augment_labels = True
self.augment_amount = 1 #3=max, 2=mid, 1=min
self.labels_to_augment = {
"road": True,
"buildings": True,
"grass": True,
"sky_n_zebra": True
}
# there are files with data, 200 images each, and here we select which ones to use
#5self.dataset_name = 'Carla'
#with open(os.path.join(self.save_data_stats, 'path'),'r') as f:
# path = f.read().strip()
path = '../VirtualElektraData2'
train_path = os.path.join(path, 'SeqTrain')
val_path = os.path.join(path, 'SeqVal')
print train_path, val_path
self.train_db_path = [
os.path.join(train_path, f)
for f in glob.glob1(train_path, "data_*.h5")
]
self.val_db_path = [
os.path.join(val_path, f)
for f in glob.glob1(val_path, "data_*.h5")
]
# When using data with noise, remove the recording during the first half of the noise impulse
# TODO Felipe: change to noise percentage.
self.remove_noise = False
# Speed Divide Factor
#TODO: FOr now is hardcooded, but eventually we should be able to calculate this from data at the loading time.
self.speed_factor = 1.0 # In KM/H FOR GTA it should be maximun 30.0
# The division is made by three diferent data kinds
# in every mini-batch there will be equal number of samples with labels from each group
# e.g. for [[0,1],[2]] there will be 50% samples with labels 0 and 1, and 50% samples with label 2
self.labels_per_division = [[2], [2], [2]]
self.dataset_names = ['targets']
self.queue_capacity = 20 * self.batch_size
# TODO NOT IMPLEMENTED Felipe: True/False switches to turn data balancing on or off
self.balances_val = True
self.balances_train = True
self.augment_and_saturate_factor = True
def main():
random.seed()
ia.seed(random.randrange(10000))
bg_images = generate_data.load_dtd(dtd_dir='%s/dtd/images' % data_dir,
dump_it=False)
#bg_images = [cv2.imread('data/frilly_0007.jpg')]
background = generate_data.Backgrounds(images=bg_images)
#card_pool = pd.DataFrame()
#for set_name in fetch_data.all_set_list:
# df = fetch_data.load_all_cards_text('%s/csv/%s.csv' % (data_dir, set_name))
# card_pool = card_pool.append(df)
card_pool = fetch_data.load_all_cards_text('%s/csv/custom.csv' % data_dir)
class_ids = {}
with open('%s/obj.names' % data_dir) as names_file:
class_name_list = names_file.read().splitlines()
for i in range(len(class_name_list)):
class_ids[class_name_list[i]] = i
print(class_ids)
num_gen = 60000
num_iter = 1
for i in range(num_gen):
# Arbitrarily select top left and right corners for perspective transformation
# Since the training image are generated with random rotation, don't need to skew all four sides
skew = [[random.uniform(0, 0.25), 0], [0, 1], [1, 1],
[random.uniform(0.75, 1), 0]]
generator = ImageGenerator(background.get_random(),
class_ids,
1440,
960,
skew=skew)
out_name = ''
for _, card_info in card_pool.sample(random.randint(2, 5)).iterrows():
img_name = '%s/card_img/png/%s/%s_%s.png' % (
data_dir, card_info['set'], card_info['collector_number'],
fetch_data.get_valid_filename(card_info['name']))
out_name += '%s%s_' % (card_info['set'],
card_info['collector_number'])
card_img = cv2.imread(img_name)
if card_img is None:
fetch_data.fetch_card_image(card_info,
out_dir='%s/card_img/png/%s' %
(data_dir, card_info['set']))
card_img = cv2.imread(img_name)
if card_img is None:
print('WARNING: card %s is not found!' % img_name)
detected_object_list = generate_data.apply_bounding_box(
card_img, card_info)
card = Card(card_img, card_info, detected_object_list)
generator.add_card(card)
for j in range(num_iter):
seq = iaa.Sequential([
iaa.Multiply((0.8, 1.2)), # darken / brighten the whole image
iaa.SimplexNoiseAlpha(first=iaa.Add(random.randrange(64)),
per_channel=0.1,
size_px_max=[3, 6],
upscale_method="cubic"), # Lighting
iaa.AdditiveGaussianNoise(scale=random.uniform(0, 0.05) * 255,
per_channel=0.1), # Noises
iaa.Dropout(p=[0, 0.05], per_channel=0.1)
])
if i % 3 == 0:
generator.generate_non_obstructive()
generator.export_training_data(
visibility=0.0,
out_name='%s/train/non_obstructive_10/%s%d' %
(data_dir, out_name, j),
aug=seq)
elif i % 3 == 1:
generator.generate_horizontal_span(
theta=random.uniform(-math.pi, math.pi))
generator.export_training_data(
visibility=0.0,
out_name='%s/train/horizontal_span_10/%s%d' %
(data_dir, out_name, j),
aug=seq)
else:
generator.generate_vertical_span(
theta=random.uniform(-math.pi, math.pi))
generator.export_training_data(
visibility=0.0,
out_name='%s/train/vertical_span_10/%s%d' %
(data_dir, out_name, j),
aug=seq)
#generator.generate_horizontal_span(theta=random.uniform(-math.pi, math.pi))
#generator.render(display=True, aug=seq, debug=True)
print('Generated %s%d' % (out_name, j))
generator.img_bg = background.get_random()
pass
def GenerateRandomImgaugAugmentation(
pNbAugmentations=5, # number of augmentations
pEnableResizing=True, # enable scaling
pScaleFactor=0.5, # maximum scale factor
pEnableCropping=True, # enable cropping
pCropFactor=0.25, # maximum crop out size (minimum new size is 1.0-pCropFactor)
pEnableFlipping1=True, # enable x flipping
pEnableFlipping2=True, # enable y flipping
pEnableRotation90=True, # enable rotation
pEnableRotation=True, # enable rotation
pMaxRotationDegree=15, # maximum shear degree
pEnableShearX=True, # enable x shear
pEnableShearY=True, # enable y shear
pMaxShearDegree=15, # maximum shear degree
pEnableDropOut=True, # enable pixel dropout
pMaxDropoutPercentage=.1, # maximum dropout percentage
pEnableBlur=True, # enable gaussian blur
pBlurSigma=.25, # maximum sigma for gaussian blur
pEnableSharpness=True, # enable sharpness
pSharpnessFactor=.1, # maximum additional sharpness
pEnableEmboss=True, # enable emboss
pEmbossFactor=.1, # maximum emboss
pEnableBrightness=True, # enable brightness
pBrightnessFactor=.1, # maximum +- brightness
pEnableRandomNoise=True, # enable random noise
pMaxRandomNoise=.1, # maximum random noise strength
pEnableInvert=False, # enables color invert
pEnableContrast=True, # enable contrast change
pContrastFactor=.1, # maximum +- contrast
):
augmentationMap = []
augmentationMapOutput = []
if pEnableResizing:
if random.Random().randint(0, 1) == 1:
randomResizeX = 1 - random.Random().random() * pScaleFactor
else:
randomResizeX = 1 + random.Random().random() * pScaleFactor
if random.Random().randint(0, 1) == 1:
randomResizeY = 1 - random.Random().random() * pScaleFactor
else:
randomResizeY = 1 + random.Random().random() * pScaleFactor
aug = iaa.Resize({"height": randomResizeY, "width": randomResizeX})
augmentationMap.append(aug)
if pEnableCropping:
randomCrop2 = random.Random().random() * pCropFactor
randomCrop4 = random.Random().random() * pCropFactor
randomCrop1 = random.Random().random() * pCropFactor
randomCrop3 = random.Random().random() * pCropFactor
aug = iaa.Crop(percent=(randomCrop1, randomCrop2, randomCrop3,
randomCrop4))
augmentationMap.append(aug)
if pEnableFlipping1:
aug = iaa.Fliplr()
augmentationMap.append(aug)
if pEnableFlipping2:
aug = iaa.Flipud()
augmentationMap.append(aug)
if pEnableRotation90:
randomNumber = random.Random().randint(1, 3)
aug = iaa.Rot90(randomNumber)
augmentationMap.append(aug)
if pEnableRotation:
if random.Random().randint(0, 1) == 1:
randomRotation = random.Random().random() * pMaxRotationDegree
else:
randomRotation = -random.Random().random() * pMaxRotationDegree
aug = iaa.Rotate(randomRotation)
augmentationMap.append(aug)
if pEnableShearX:
if random.Random().randint(0, 1) == 1:
randomShearingX = random.Random().random() * pMaxShearDegree
else:
randomShearingX = -random.Random().random() * pMaxShearDegree
aug = iaa.ShearX(randomShearingX)
augmentationMap.append(aug)
if pEnableShearY:
if random.Random().randint(0, 1) == 1:
randomShearingY = random.Random().random() * pMaxShearDegree
else:
randomShearingY = -random.Random().random() * pMaxShearDegree
aug = iaa.ShearY(randomShearingY)
augmentationMap.append(aug)
if pEnableDropOut:
randomDropOut = random.Random().random() * pMaxDropoutPercentage
aug = iaa.Dropout(p=randomDropOut, per_channel=False)
augmentationMap.append(aug)
if pEnableBlur:
randomBlur = random.Random().random() * pBlurSigma
aug = iaa.GaussianBlur(randomBlur)
augmentationMap.append(aug)
if pEnableSharpness:
randomSharpness = random.Random().random() * pSharpnessFactor
aug = iaa.Sharpen(randomSharpness)
augmentationMap.append(aug)
if pEnableEmboss:
randomEmboss = random.Random().random() * pEmbossFactor
aug = iaa.Emboss(randomEmboss)
augmentationMap.append(aug)
if pEnableBrightness:
if random.Random().randint(0, 1) == 1:
randomBrightness = 1 - random.Random().random() * pBrightnessFactor
else:
randomBrightness = 1 + random.Random().random() * pBrightnessFactor
aug = iaa.Add(randomBrightness)
augmentationMap.append(aug)
if pEnableRandomNoise:
if random.Random().randint(0, 1) == 1:
randomNoise = 1 - random.Random().random() * pMaxRandomNoise
else:
randomNoise = 1 + random.Random().random() * pMaxRandomNoise
aug = iaa.MultiplyElementwise(randomNoise, per_channel=True)
augmentationMap.append(aug)
if pEnableInvert:
aug = iaa.Invert(1)
augmentationMap.append(aug)
if pEnableContrast:
if random.Random().randint(0, 1) == 1:
randomContrast = 1 - random.Random().random() * pContrastFactor
else:
randomContrast = 1 + random.Random().random() * pContrastFactor
aug = iaa.contrast.LinearContrast(randomContrast)
augmentationMap.append(aug)
widthFactor = 1
heightFactor = 1
arr = numpy.arange(0, len(augmentationMap))
numpy.random.shuffle(arr)
switchWidthHeight = False
for i in range(pNbAugmentations):
augmentationMapOutput.append(augmentationMap[arr[i]])
if arr[i] == 0:
widthFactor *= randomResizeX
heightFactor *= randomResizeY
if arr[i] == 1:
widthFactor *= (1.0 - (randomCrop2 + randomCrop4))
heightFactor *= (1.0 - (randomCrop1 + randomCrop3))
if arr[i] == 4:
if randomNumber == 1 or randomNumber == 3:
switchWidhtHeight = True
return iaa.Sequential(
augmentationMapOutput), widthFactor, heightFactor, switchWidthHeight
flip_ud = iaa.Flipud(1.0)
img = flip_ud.augment_image(image)
cv2.imwrite('dataset/transform/' + file_name + '-flip-ud' + file_extension,
img)
affine = iaa.Affine(rotate=(-90))
img = affine.augment_image(image)
cv2.imwrite('dataset/transform/' + file_name + '-affine' + file_extension,
img)
avg_blur = iaa.AverageBlur(k=(3, 3))
img = avg_blur.augment_image(image)
cv2.imwrite(
'dataset/transform/' + file_name + '-avg-blur' + file_extension, img)
dropout = iaa.Dropout(p=(0, 0.1))
img = dropout.augment_image(image)
cv2.imwrite('dataset/transform/' + file_name + '-dropout' + file_extension,
img)
def img_func(images, random_state, parents, hooks):
for img in images:
img[::4] = 0
return images
def keypoint_func(keypoints_on_images, random_state, parents, hooks):
return keypoints_on_images
def __init__(self,
list_file,
train,
transform,
device,
little_train=False,
with_file_path=False,
C=20,
test_mode=False):
print('data init')
self.train = train
self.transform = transform
self.fnames = []
self.boxes = []
self.labels = []
self.resize = 416
self.C = C
self.device = device
self._test = test_mode
self.with_file_path = with_file_path
self.img_augsometimes = lambda aug: iaa.Sometimes(0.25, aug)
self.bbox_augsometimes = lambda aug: iaa.Sometimes(0.5, aug)
self.augmentation = iaa.Sequential(
[
# augment without change bboxes
self.img_augsometimes(
iaa.SomeOf(
(1, 3),
[
iaa.Dropout([0.05, 0.2
]), # drop 5% or 20% of all pixels
iaa.Sharpen((0.1, .8)), # sharpen the image
# iaa.GaussianBlur(sigma=(2., 3.5)),
iaa.OneOf([
iaa.GaussianBlur(sigma=(2., 3.5)),
iaa.AverageBlur(k=(2, 5)),
iaa.BilateralBlur(d=(7, 12),
sigma_color=(10, 250),
sigma_space=(10, 250)),
iaa.MedianBlur(k=(3, 7)),
]),
iaa.AddElementwise((-50, 50)),
iaa.AdditiveGaussianNoise(scale=(0, 0.1 * 255)),
iaa.JpegCompression(compression=(80, 95)),
iaa.Multiply((0.5, 1.5)),
iaa.MultiplyElementwise((0.5, 1.5)),
iaa.ReplaceElementwise(0.05, [0, 255]),
# iaa.WithColorspace(to_colorspace="HSV", from_colorspace="RGB",
# children=iaa.WithChannels(2, iaa.Add((-10, 50)))),
iaa.OneOf([
iaa.WithColorspace(to_colorspace="HSV",
from_colorspace="RGB",
children=iaa.WithChannels(
1, iaa.Add((-10, 50)))),
iaa.WithColorspace(to_colorspace="HSV",
from_colorspace="RGB",
children=iaa.WithChannels(
2, iaa.Add((-10, 50)))),
]),
],
random_order=True)),
iaa.Fliplr(.5),
iaa.Flipud(.125),
# # augment changing bboxes
self.bbox_augsometimes(
iaa.Affine(
# translate_px={"x": 40, "y": 60},
scale={
"x": (0.8, 1.2),
"y": (0.8, 1.2)
},
translate_percent={
"x": (-0.1, 0.1),
"y": (-0.1, 0.1)
},
rotate=(-5, 5),
))
],
random_order=True)
# torch.manual_seed(23)
with open(list_file) as f:
lines = f.readlines()
if little_train:
lines = lines[:little_train]
for line in lines:
splited = line.strip().split()
self.fnames.append(splited[0])
self.num_samples = len(self.fnames)
def augmentation(image, mask=None):
sometimes = lambda aug: iaa.Sometimes(0.5, aug)
seq = iaa.Sequential(
[
iaa.Fliplr(0.5),
iaa.Flipud(0.2),
sometimes(
iaa.CropAndPad(
percent=(-0.05, 0.1), pad_mode=ia.ALL, pad_cval=(0, 255))),
sometimes(
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=(-45, 45),
shear=(-16, 16),
order=[0, 1],
cval=(0, 255),
mode=ia.ALL)),
# execute 0 to 5 of the following (less important) augmenters per image
iaa.SomeOf((0, 5), [
sometimes(
iaa.Superpixels(p_replace=(0, 1.0), n_segments=(20, 200))),
iaa.OneOf([
iaa.GaussianBlur((0, 3.0)),
iaa.AverageBlur(k=(2, 7)),
iaa.MedianBlur(k=(3, 11)),
]),
iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)),
iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)),
iaa.SimplexNoiseAlpha(
iaa.OneOf([
iaa.EdgeDetect(alpha=(0.5, 1.0)),
iaa.DirectedEdgeDetect(alpha=(0.5, 1.0),
direction=(0.0, 1.0)),
])),
iaa.AdditiveGaussianNoise(
loc=0, scale=(0.0, 0.05 * 255), per_channel=0.5),
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),
]),
iaa.Invert(0.05, per_channel=True),
iaa.Add((-10, 10), per_channel=0.5),
iaa.AddToHueAndSaturation((-20, 20)),
iaa.OneOf([
iaa.Multiply((0.5, 1.5), per_channel=0.5),
]),
iaa.Grayscale(alpha=(0.0, 1.0)),
sometimes(
iaa.ElasticTransformation(alpha=(0.5, 3.5), sigma=0.25)),
sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.05))),
sometimes(iaa.PerspectiveTransform(scale=(0.01, 0.1)))
],
random_order=True)
],
random_order=True)
if mask is None:
image_heavy = seq(images=image)
return image_heavy
else:
if image.ndim == 4:
mask = np.array(mask)
image_heavy, mask_heavy = seq(images=image,
segmentation_maps=mask.astype(
np.int32))
else:
image_heavy, mask_heavy = seq(images=image[np.newaxis, ...],
segmentation_maps=mask[np.newaxis,
...])
image_heavy, mask_heavy = image_heavy[0], mask_heavy[0]
return image_heavy, mask_heavy
plt.title("masks")
plt.imshow(np.array(mask, dtype=np.float32))
# load some images and corresponding masks from the training data (different size)
x = np.array([cv2.imread("images/" + str(i) + ".png") for i in range(0, 8)])
y = np.array([cv2.imread("masks/" + str(i) + ".png") for i in range(0, 8)])
visualizeImageAndMask(x[0], y[0])
# example for having different sizes across the images.
# define the augmentation
seq1 = iaa.Sequential([iaa.Affine(rotate=(-30, 30))
]) # Augmentation for images and masks
seq2 = iaa.Sequential([iaa.Dropout([0.1, 0.5])]) # Augmentation for images
"""
Method 1
"""
# It feels clumsy cause we have to define a pipeline and calling different augmenters
# However, we can access the image directly
for img, mask in zip(x, y):
seq1.deterministic = True
image_augmented = seq1.augment_image(image=img)
final_image = seq2.augment_image(image=image_augmented)
mask_augmented = seq1.augment_image(image=mask)
visualizeImageAndMask(final_image, mask_augmented)
args = sys.argv
if len(args) != 5:
print("変数指定して。1は牌の個数、2はサイズ,3ha kosuu,4ha test kosuu")
NUMBER = int(args[1]) #画像1枚当たりの牌の枚数
size_x = int(args[2])
size_y = size_x
pic_num = int(args[3])
test_num = int(args[4])
# In[3]:
aug1 = iaa.Dropout(p=0.2)
aug2 = iaa.AverageBlur(k=(5, 15))
aug3 = iaa.Add((-40, 40), per_channel=0.5)
aug4 = iaa.ContrastNormalization((0.5, 1.5), per_channel=0.5)
aug5 = iaa.Affine(rotate=(0, 20))
# In[4]:
#画像のロバスト
def augment(img, bb, aug):
# 画像とバウンディングボックスを変換
aug_img = aug.augment_image(img)
aug_bb = aug.augment_bounding_boxes(
[bb])[0].remove_out_of_image().cut_out_of_image()
'''
def main():
'''
Dirty image augmentation to get better recognition for systems presentation.
'''
augs = [
"superpixel", "colorspace", "grayscale", "gaussian_blur",
"average_blur", "median_blur", "edge_detect", "add", "add_eltwise",
"invert", "contrast_norm", "dropout"
]
superpixel = iaa.Superpixels(p_replace=(0.4, 0.6), n_segments=(16, 64))
#colorspace = iaa.Sequential([iaa.ChangeColorspace(from_colorspace="BGR", to_colorspace="HSV"), iaa.WithChannels(0, iaa.Add(-50, 50), iaa.ChangeColorspace(from_colorspace="BGR", to_colorspace="BGR")])
grayscale = iaa.Grayscale(alpha=(0.0, 1.0))
gaussian_blur = iaa.GaussianBlur(sigma=(0.0, 3.0))
average_blur = iaa.AverageBlur(k=(2, 10))
median_blur = iaa.MedianBlur(k=(5, 11))
edge_detect = iaa.EdgeDetect(alpha=(0.0, 1.0))
add = iaa.Add((-50, 50), per_channel=0.5)
add_eltwise = iaa.AddElementwise((-50, 50), per_channel=0.5)
invert = iaa.Invert(0.25, per_channel=0.5)
contrast_norm = iaa.ContrastNormalization((0.5, 1.5), per_channel=0.5)
dropout = iaa.Dropout(p=(0, 0.3), per_channel=0.5)
image_paths = get_files("JPG")
cv_images = get_images(image_paths)
for augmentation in augs:
if augmentation == "superpixel":
aug_images = superpixel.augment_images(cv_images)
save_augmented_images("superpixel", aug_images, image_paths)
#elif augmentation == "colorspace":
# aug_images = colorspace.augment_images(cv_images)
# save_augmented_images("colorspace", aug_images, image_paths)
elif augmentation == "grayscale":
aug_images = grayscale.augment_images(cv_images)
save_augmented_images("grayscale", aug_images, image_paths)
elif augmentation == "gaussian_blur":
aug_images = gaussian_blur.augment_images(cv_images)
save_augmented_images("gaussian_blur", aug_images, image_paths)
elif augmentation == "average_blur":
aug_images = average_blur.augment_images(cv_images)
save_augmented_images("average_blur", aug_images, image_paths)
elif augmentation == "edge_detect":
aug_images = edge_detect.augment_images(cv_images)
save_augmented_images("edge_detect", aug_images, image_paths)
elif augmentation == "add":
aug_images = add.augment_images(cv_images)
save_augmented_images("add", aug_images, image_paths)
elif augmentation == "add_eltwise":
aug_images = add_eltwise.augment_images(cv_images)
save_augmented_images("add_eltwise", aug_images, image_paths)
elif augmentation == "invert":
aug_images = invert.augment_images(cv_images)
save_augmented_images("invert", aug_images, image_paths)
elif augmentation == "contrast_norm":
aug_images = contrast_norm.augment_images(cv_images)
save_augmented_images("contrast_norm", aug_images, image_paths)
elif augmentation == "dropout":
aug_images = dropout.augment_images(cv_images)
save_augmented_images("dropout", aug_images, image_paths)
from pycocotools.coco import COCO
import torch
import imgaug as ia
import imgaug.augmenters as iaa
aug_seq = iaa.Sequential([
iaa.AdditiveGaussianNoise(scale=(0, 0.025 * 255)),
iaa.MultiplyHue((0.75, 1.25)),
iaa.Add((-80, 80))
])
heavy_aug_seq = iaa.Sequential([
iaa.AdditiveGaussianNoise(scale=(0, 0.025 * 255)),
iaa.MultiplyHue((0.75, 1.25)),
iaa.Add((-80, 80)),
iaa.Dropout(p=(0, 0.3)),
iaa.imgcorruptlike.MotionBlur(severity=(1, 3)),
iaa.GammaContrast((0.0, 2.0), per_channel=True)
])
category_map = ['background', 'person']
def load_coco_json_lines(fpath, image_folder=None):
cocoGt = COCO(fpath)
records = []
for image_id in cocoGt.getImgIds():
img_file = cocoGt.loadImgs(ids=[image_id])[0]
record = {
'ID': img_file['file_name'].split('.')[0],
'height': img_file['height'],
'width': img_file['width'],
def __init__(self,
images,
config,
shuffle=True,
jitter=True,
norm=None,
flipflop=True,
shoechanger=True,
zeropad=True):
self.generator = None
self.flipflop = flipflop
self.shoechanger = shoechanger
if self.flipflop or self.shoechanger:
self.badshoes = []
for im in os.listdir('imgs/more_badshoes'):
self.badshoes.append(cv2.imread('imgs/more_badshoes/' + im))
self.zeropad = zeropad
self.images = images
self.config = config
self.shuffle = shuffle
self.jitter = jitter
self.norm = norm
self.anchors = [
BoundBox(0, 0, config['ANCHORS'][2 * i],
config['ANCHORS'][2 * i + 1])
for i in range(int(len(config['ANCHORS']) // 2))
]
### augmentors by https://github.com/aleju/imgaug
sometimes = lambda aug: iaa.Sometimes(0.5, aug)
# Define our sequence of augmentation steps that will be applied to every image
# All augmenters with per_channel=0.5 will sample one value _per image_
# in 50% of all cases. In all other cases they will sample new values
# _per channel_.
self.aug_pipe = iaa.Sequential(
[
# apply the following augmenters to most images
# iaa.Fliplr(0.5), # horizontally flip 50% of all images
# iaa.Flipud(0.2), # vertically flip 20% of all images
# sometimes(iaa.Crop(percent=(0, 0.1))), # crop images by 0-10% of their height/width
sometimes(
iaa.Affine(
# scale={"x": (0.8, 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=(-5, 5), # rotate by -45 to +45 degrees
# shear=(-5, 5), # shear by -16 to +16 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)
)),
# execute 0 to 5 of the following (less important) augmenters per image
# don't execute all of them, as that would often be way too strong
iaa.SomeOf(
(0, 5),
[
# sometimes(iaa.Superpixels(p_replace=(0, 1.0), n_segments=(20, 200))), # convert images into their superpixel representation
iaa.OneOf([
iaa.GaussianBlur(
(0, 3.0)
), # blur images with a sigma between 0 and 3.0
iaa.AverageBlur(k=(2, 7)),
# blur image using local means with kernel sizes between 2 and 7
iaa.MedianBlur(k=(3, 11)),
# blur image using local medians with kernel sizes between 2 and 7
]),
iaa.Sharpen(alpha=(0, 1.0),
lightness=(0.75, 1.5)), # sharpen images
# iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)), # emboss images
# search either for all edges or for directed edges
# sometimes(iaa.OneOf([
# iaa.EdgeDetect(alpha=(0, 0.7)),
# iaa.DirectedEdgeDetect(alpha=(0, 0.7), direction=(0.0, 1.0)),
# ])),
iaa.AdditiveGaussianNoise(
loc=0, scale=(0.0, 0.05 * 255), per_channel=0.5),
# add gaussian noise to images
iaa.OneOf([
iaa.Dropout(
(0.01, 0.1), per_channel=0.5
), # randomly remove up to 10% of the pixels
# iaa.CoarseDropout((0.03, 0.15), size_percent=(0.02, 0.05), per_channel=0.2),
]),
# iaa.Invert(0.05, per_channel=True), # invert color channels
iaa.Add((-10, 10), per_channel=0.5),
# change brightness of images (by -10 to 10 of original value)
iaa.Multiply((0.5, 1.5), per_channel=0.5),
# change brightness of images (50-150% of original value)
iaa.ContrastNormalization(
(0.5, 2.0),
per_channel=0.5), # improve or worsen the contrast
# iaa.Grayscale(alpha=(0.0, 1.0)),
# sometimes(iaa.ElasticTransformation(alpha=(0.5, 3.5), sigma=0.25)), # move pixels locally around (with random strengths)
# sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.05))) # sometimes move parts of the image around
],
random_order=True)
],
random_order=True)
if shuffle:
np.random.shuffle(self.images)
def __init__(self):
configMain.__init__(self)
st = lambda aug: iaa.Sometimes(0.2, aug)
oc = lambda aug: iaa.Sometimes(0.1, aug)
rl = lambda aug: iaa.Sometimes(0.05, aug)
self.augment = [
iaa.Sequential(
[
rl(iaa.GaussianBlur(
(0,
1.3))), # blur images with a sigma between 0 and 1.5
rl(
iaa.AdditiveGaussianNoise(
loc=0, scale=(0.0, 0.05),
per_channel=0.5)), # add gaussian noise to images
rl(iaa.Dropout((0.0, 0.10), per_channel=0.5)
), # randomly remove up to X% of the pixels
oc(
iaa.Add((-20, 20), per_channel=0.5)
), # change brightness of images (by -X to Y of original value)
st(iaa.Multiply(
(0.25, 2.5), per_channel=0.2
)), # change brightness of images (X-Y% of original value)
rl(iaa.ContrastNormalization(
(0.5, 1.5),
per_channel=0.5)), # improve or worsen the contrast
rl(iaa.Grayscale((0.0, 1))), # put grayscale
],
random_order=True # do all of the above in random order
)
] * 3
all_files = []
self.val_db_path = []
ids = [
"steer103_v5_way_v2", 'steer103_v5_way_v2_town02',
"nonoise_town03_way", "nonoise_town02_way", "nonoise_town04_way"
]
for id in ids:
all_files += glob.glob(
"/data/yang/code/aws/scratch/carla_collect/" + id +
"/*/data_*.h5")
for valid in range(1, 15, 3):
self.val_db_path += glob.glob(
"/data/yang/code/aws/scratch/carla_collect/" + id +
"/*WeatherId=" + str(valid).zfill(2) + "/data_*.h5")
self.train_db_path = list(set(all_files) - set(self.val_db_path))
self.speed_factor = 40.0 # In KM/H
# The division is made by three diferent data kinds
# in every mini-batch there will be equal number of samples with labels from each group
# e.g. for [[0,1],[2]] there will be 50% samples with labels 0 and 1, and 50% samples with label 2
self.labels_per_division = [[0, 2, 5], [3], [4]]
self.dataset_names = ['targets']
self.queue_capacity = 5 # now measured in how many batches
def _get_aug():
import imgaug as ia
from imgaug import augmenters as iaa
sometimes = lambda aug: iaa.Sometimes(0.5, aug)
return iaa.Sequential(
[
# apply the following augmenters to most images
iaa.Fliplr(0.5), # horizontally flip 50% of all images
iaa.Flipud(0.2), # vertically flip 20% of all images
# crop images by -5% to 10% of their height/width
sometimes(iaa.CropAndPad(
percent=(-0.05, 0.1),
pad_mode=ia.ALL,
pad_cval=(0, 255)
)),
sometimes(iaa.Affine(
scale={"x": (0.8, 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=(-45, 45), # rotate by -45 to +45 degrees
shear=(-16, 16), # shear by -16 to +16 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)
)),
# execute 0 to 5 of the following (less important) augmenters per image
# don't execute all of them, as that would often be way too strong
iaa.SomeOf((0, 5),
[
sometimes(iaa.Superpixels(p_replace=(0, 1.0), n_segments=(20, 200))), # convert images into their superpixel representation
iaa.OneOf([
iaa.GaussianBlur((0, 3.0)), # blur images with a sigma between 0 and 3.0
iaa.AverageBlur(k=(2, 7)), # blur image using local means with kernel sizes between 2 and 7
iaa.MedianBlur(k=(3, 11)), # blur image using local medians with kernel sizes between 2 and 7
]),
iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)), # sharpen images
iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)), # emboss images
# search either for all edges or for directed edges,
# blend the result with the original image using a blobby mask
iaa.SimplexNoiseAlpha(iaa.OneOf([
iaa.EdgeDetect(alpha=(0.5, 1.0)),
iaa.DirectedEdgeDetect(alpha=(0.5, 1.0), direction=(0.0, 1.0)),
])),
iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05*255), per_channel=0.5), # add gaussian noise to images
iaa.OneOf([
iaa.Dropout((0.01, 0.1), per_channel=0.5), # randomly remove up to 10% of the pixels
iaa.CoarseDropout((0.03, 0.15), size_percent=(0.02, 0.05), per_channel=0.2),
]),
iaa.Invert(0.05, per_channel=True), # invert color channels
iaa.Add((-10, 10), per_channel=0.5), # change brightness of images (by -10 to 10 of original value)
iaa.AddToHueAndSaturation((-20, 20)), # change hue and saturation
# either change the brightness of the whole image (sometimes
# per channel) or change the brightness of subareas
iaa.OneOf([
iaa.Multiply((0.5, 1.5), per_channel=0.5),
iaa.FrequencyNoiseAlpha(
exponent=(-4, 0),
first=iaa.Multiply((0.5, 1.5), per_channel=True),
second=iaa.ContrastNormalization((0.5, 2.0))
)
]),
iaa.ContrastNormalization((0.5, 2.0), per_channel=0.5), # improve or worsen the contrast
iaa.Grayscale(alpha=(0.0, 1.0)),
sometimes(iaa.ElasticTransformation(alpha=(0.5, 3.5), sigma=0.25)), # move pixels locally around (with random strengths)
sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.05))), # sometimes move parts of the image around
sometimes(iaa.PerspectiveTransform(scale=(0.01, 0.1)))
],
random_order=True
)
],
random_order=True
)
iaa.Pad(percent=0.125, keep_size=False),
iaa.Crop(percent=0.10, keep_size=False)
]),
iaa.ContrastNormalization((0.8, 1.2)),
iaa.Grayscale(alpha=(0.93, 1.0)),
iaa.Affine(translate_percent={
"x": (-0.2, 0.2),
"y": (-0.2, 0.2)
}),
# Flip Right and Left
iaa.Fliplr(0.5),
# Rotate 5 Degrees left and right
iaa.Affine(rotate=10),
# Similar to Cutout
iaa.Dropout(p=(0.25)),
],
name='Cutout') # apply augmenters in random order
fn = 'config/resnet18_num_0.json'
config = dict(json.load(open(fn, 'r')))
aug_func = seq_cutout.augment
BASE_DIR = 'tiny-imagenet-200'
val_df = pd.read_csv('tiny-imagenet-200/val_annotations.txt',
sep='\t',
header=None,
usecols=[0, 1],
names=['filename', 'class'])
def run_seq():
# Sometimes(0.5, ...) applies the given augmenter in 50% of all cases,
# e.g. Sometimes(0.5, GaussianBlur(0.3)) would blur roughly every second
# image.
# if not isinstance(images,list):
# images=[images]
sometimes = lambda aug: iaa.Sometimes(0.5, aug)
# Define our sequence of augmentation steps that will be applied to every image.
seq = iaa.Sequential(
[
#
# Apply the following augmenters to most images.
#
# iaa.Fliplr(0.5), # horizontally flip 50% of all images
# iaa.Flipud(0.2), # vertically flip 20% of all images
# crop some of the images by 0-10% of their height/width
# sometimes(iaa.Crop(percent=(0, 0.1))),
# sometimes(iaa.Crop(percent=(0, 0.05))),
# Apply affine transformations to some of the images
# - scale to 80-120% of image height/width (each axis independently)
# - translate by -20 to +20 relative to height/width (per axis)
# - rotate by -45 to +45 degrees
# - shear by -16 to +16 degrees
# - order: use nearest neighbour or bilinear interpolation (fast)
# - mode: use any available mode to fill newly created pixels
# see API or scikit-image for which modes are available
# - cval: if the mode is constant, then use a random brightness
# for the newly created pixels (e.g. sometimes black,
# sometimes white)
sometimes(
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=(-45, 45),
rotate=(-5, 5),
# shear=(-16, 16),
shear=(-5, 5),
order=[0, 1],
# cval=(0, 255),
cval=144, # 填充像素值
# mode=ia.ALL # 默认常数值填充边界
)),
#
# Execute 0 to 5 of the following (less important) augmenters per
# image. Don't execute all of them, as that would often be way too
# strong.
#
iaa.SomeOf(
(0, 5),
[
# Convert some images into their superpixel representation,
# sample between 20 and 200 superpixels per image, but do
# not replace all superpixels with their average, only
# some of them (p_replace).
sometimes(
iaa.Superpixels(p_replace=(0, 1.0),
n_segments=(20, 200))),
# Blur each image with varying strength using
# gaussian blur (sigma between 0 and 3.0),
# average/uniform blur (kernel size between 2x2 and 7x7)
# median blur (kernel size between 3x3 and 11x11).
iaa.OneOf([
iaa.GaussianBlur((0, 3.0)),
iaa.AverageBlur(k=(2, 7)),
iaa.MedianBlur(k=(3, 11)),
]),
# Sharpen each image, overlay the result with the original
# image using an alpha between 0 (no sharpening) and 1
# (full sharpening effect).
iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)),
# Same as sharpen, but for an embossing effect.
iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)),
# Search in some images either for all edges or for
# directed edges. These edges are then marked in a black
# and white image and overlayed with the original image
# using an alpha of 0 to 0.7.
sometimes(
iaa.OneOf([
iaa.EdgeDetect(alpha=(0, 0.7)),
iaa.DirectedEdgeDetect(alpha=(0, 0.7),
direction=(0.0, 1.0)),
])),
# Add gaussian noise to some images.
# In 50% of these cases, the noise is randomly sampled per
# channel and pixel.
# In the other 50% of all cases it is sampled once per
# pixel (i.e. brightness change).
iaa.AdditiveGaussianNoise(
loc=0, scale=(0.0, 0.05 * 255), per_channel=0.5),
# Either drop randomly 1 to 10% of all pixels (i.e. set
# them to black) or drop them on an image with 2-5% percent
# of the original size, leading to large dropped
# rectangles.
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),
]),
# Invert each image's channel with 5% probability.
# This sets each pixel value v to 255-v.
iaa.Invert(0.05,
per_channel=True), # invert color channels
# Add a value of -10 to 10 to each pixel.
iaa.Add((-10, 10), per_channel=0.5),
# Change brightness of images (50-150% of original value).
iaa.Multiply((0.5, 1.5), per_channel=0.5),
# Improve or worsen the contrast of images.
iaa.LinearContrast((0.5, 2.0), per_channel=0.5),
# Convert each image to grayscale and then overlay the
# result with the original with random alpha. I.e. remove
# colors with varying strengths.
iaa.Grayscale(alpha=(0.0, 1.0)),
# In some images move pixels locally around (with random
# strengths).
sometimes(
iaa.ElasticTransformation(alpha=(0.5, 3.5), sigma=0.25)
),
# In some images distort local areas with varying strength.
sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.05)))
],
# do all of the above augmentations in random order
random_order=True)
],
# do all of the above augmentations in random order
random_order=True)
# images_aug = seq(images=images)
return seq
def main():
parser = argparse.ArgumentParser(description="Check augmenters visually.")
parser.add_argument(
'--only',
default=None,
help=
"If this is set, then only the results of an augmenter with this name will be shown. Optionally, comma-separated list.",
required=False)
args = parser.parse_args()
images = [
ia.quokka_square(size=(128, 128)),
misc.imresize(data.astronaut(), (128, 128))
]
keypoints = [
ia.KeypointsOnImage([
ia.Keypoint(x=50, y=40),
ia.Keypoint(x=70, y=38),
ia.Keypoint(x=62, y=52)
],
shape=images[0].shape),
ia.KeypointsOnImage([
ia.Keypoint(x=55, y=32),
ia.Keypoint(x=42, y=95),
ia.Keypoint(x=75, y=89)
],
shape=images[1].shape)
]
bounding_boxes = [
ia.BoundingBoxesOnImage([
ia.BoundingBox(x1=10, y1=10, x2=20, y2=20),
ia.BoundingBox(x1=40, y1=50, x2=70, y2=60)
],
shape=images[0].shape),
ia.BoundingBoxesOnImage([
ia.BoundingBox(x1=10, y1=10, x2=20, y2=20),
ia.BoundingBox(x1=40, y1=50, x2=70, y2=60)
],
shape=images[1].shape)
]
# missing: InColorspace, Lambda, AssertLambda, AssertShape, Convolve
augmenters = [
iaa.Sequential([
iaa.CoarseDropout(p=0.5, size_percent=0.05),
iaa.AdditiveGaussianNoise(scale=0.1 * 255),
iaa.Crop(percent=0.1)
],
name="Sequential"),
iaa.SomeOf(2,
children=[
iaa.CoarseDropout(p=0.5, size_percent=0.05),
iaa.AdditiveGaussianNoise(scale=0.1 * 255),
iaa.Crop(percent=0.1)
],
name="SomeOf"),
iaa.OneOf(children=[
iaa.CoarseDropout(p=0.5, size_percent=0.05),
iaa.AdditiveGaussianNoise(scale=0.1 * 255),
iaa.Crop(percent=0.1)
],
name="OneOf"),
iaa.Sometimes(0.5,
iaa.AdditiveGaussianNoise(scale=0.1 * 255),
name="Sometimes"),
iaa.WithColorspace("HSV",
children=[iaa.Add(20)],
name="WithColorspace"),
iaa.WithChannels([0], children=[iaa.Add(20)], name="WithChannels"),
iaa.AddToHueAndSaturation((-20, 20),
per_channel=True,
name="AddToHueAndSaturation"),
iaa.Noop(name="Noop"),
iaa.Scale({
"width": 64,
"height": 64
}, name="Scale"),
iaa.CropAndPad(px=(-8, 8), name="CropAndPad-px"),
iaa.Pad(px=(0, 8), name="Pad-px"),
iaa.Crop(px=(0, 8), name="Crop-px"),
iaa.Crop(percent=(0, 0.1), name="Crop-percent"),
iaa.Fliplr(0.5, name="Fliplr"),
iaa.Flipud(0.5, name="Flipud"),
iaa.Superpixels(p_replace=0.75, n_segments=50, name="Superpixels"),
iaa.Grayscale(0.5, name="Grayscale0.5"),
iaa.Grayscale(1.0, name="Grayscale1.0"),
iaa.GaussianBlur((0, 3.0), name="GaussianBlur"),
iaa.AverageBlur(k=(3, 11), name="AverageBlur"),
iaa.MedianBlur(k=(3, 11), name="MedianBlur"),
iaa.BilateralBlur(d=10, name="BilateralBlur"),
iaa.Sharpen(alpha=(0.1, 1.0), lightness=(0, 2.0), name="Sharpen"),
iaa.Emboss(alpha=(0.1, 1.0), strength=(0, 2.0), name="Emboss"),
iaa.EdgeDetect(alpha=(0.1, 1.0), name="EdgeDetect"),
iaa.DirectedEdgeDetect(alpha=(0.1, 1.0),
direction=(0, 1.0),
name="DirectedEdgeDetect"),
iaa.Add((-50, 50), name="Add"),
iaa.Add((-50, 50), per_channel=True, name="AddPerChannel"),
iaa.AddElementwise((-50, 50), name="AddElementwise"),
iaa.AdditiveGaussianNoise(loc=0,
scale=(0.0, 0.1 * 255),
name="AdditiveGaussianNoise"),
iaa.Multiply((0.5, 1.5), name="Multiply"),
iaa.Multiply((0.5, 1.5), per_channel=True, name="MultiplyPerChannel"),
iaa.MultiplyElementwise((0.5, 1.5), name="MultiplyElementwise"),
iaa.Dropout((0.0, 0.1), name="Dropout"),
iaa.CoarseDropout(p=0.05,
size_percent=(0.05, 0.5),
name="CoarseDropout"),
iaa.Invert(p=0.5, name="Invert"),
iaa.Invert(p=0.5, per_channel=True, name="InvertPerChannel"),
iaa.ContrastNormalization(alpha=(0.5, 2.0),
name="ContrastNormalization"),
iaa.SaltAndPepper(p=0.05, name="SaltAndPepper"),
iaa.Salt(p=0.05, name="Salt"),
iaa.Pepper(p=0.05, name="Pepper"),
iaa.CoarseSaltAndPepper(p=0.05,
size_percent=(0.01, 0.1),
name="CoarseSaltAndPepper"),
iaa.CoarseSalt(p=0.05, size_percent=(0.01, 0.1), name="CoarseSalt"),
iaa.CoarsePepper(p=0.05, size_percent=(0.01, 0.1),
name="CoarsePepper"),
iaa.Affine(scale={
"x": (0.8, 1.2),
"y": (0.8, 1.2)
},
translate_px={
"x": (-16, 16),
"y": (-16, 16)
},
rotate=(-45, 45),
shear=(-16, 16),
order=ia.ALL,
cval=(0, 255),
mode=ia.ALL,
name="Affine"),
iaa.PiecewiseAffine(scale=0.03,
nb_rows=(2, 6),
nb_cols=(2, 6),
name="PiecewiseAffine"),
iaa.PerspectiveTransform(scale=0.1, name="PerspectiveTransform"),
iaa.ElasticTransformation(alpha=(0.5, 8.0),
sigma=1.0,
name="ElasticTransformation"),
iaa.Alpha(factor=(0.0, 1.0),
first=iaa.Add(100),
second=iaa.Dropout(0.5),
per_channel=False,
name="Alpha"),
iaa.Alpha(factor=(0.0, 1.0),
first=iaa.Add(100),
second=iaa.Dropout(0.5),
per_channel=True,
name="AlphaPerChannel"),
iaa.Alpha(factor=(0.0, 1.0),
first=iaa.Affine(rotate=(-45, 45)),
per_channel=True,
name="AlphaAffine"),
iaa.AlphaElementwise(factor=(0.0, 1.0),
first=iaa.Add(50),
second=iaa.ContrastNormalization(2.0),
per_channel=False,
name="AlphaElementwise"),
iaa.AlphaElementwise(factor=(0.0, 1.0),
first=iaa.Add(50),
second=iaa.ContrastNormalization(2.0),
per_channel=True,
name="AlphaElementwisePerChannel"),
iaa.AlphaElementwise(factor=(0.0, 1.0),
first=iaa.Affine(rotate=(-45, 45)),
per_channel=True,
name="AlphaElementwiseAffine"),
iaa.SimplexNoiseAlpha(
#first=iaa.GaussianBlur((1.0, 3.0)),
#first=iaa.MedianBlur((3, 7)),
first=iaa.EdgeDetect(1.0),
#first=iaa.Affine(rotate=-45), #(-45, 45)),
per_channel=False,
name="SimplexNoiseAlpha"),
iaa.FrequencyNoiseAlpha(
#first=iaa.GaussianBlur((1.0, 3.0)),
#first=iaa.MedianBlur((3, 7)),
first=iaa.EdgeDetect(1.0),
#first=iaa.Affine(rotate=-45), #(-45, 45)),
per_channel=False,
name="FrequencyNoiseAlpha")
]
augmenters.append(
iaa.Sequential([iaa.Sometimes(0.2, aug.copy()) for aug in augmenters],
name="Sequential"))
augmenters.append(
iaa.Sometimes(0.5, [aug.copy() for aug in augmenters],
name="Sometimes"))
for augmenter in augmenters:
if args.only is None or augmenter.name in [
v.strip() for v in args.only.split(",")
]:
print("Augmenter: %s" % (augmenter.name, ))
grid = []
for image, kps, bbs in zip(images, keypoints, bounding_boxes):
aug_det = augmenter.to_deterministic()
imgs_aug = aug_det.augment_images(
np.tile(image[np.newaxis, ...], (16, 1, 1, 1)))
kps_aug = aug_det.augment_keypoints([kps] * 16)
bbs_aug = aug_det.augment_bounding_boxes([bbs] * 16)
imgs_aug_drawn = [
kps_aug_one.draw_on_image(img_aug)
for img_aug, kps_aug_one in zip(imgs_aug, kps_aug)
]
imgs_aug_drawn = [
bbs_aug_one.draw_on_image(img_aug)
for img_aug, bbs_aug_one in zip(imgs_aug_drawn, bbs_aug)
]
grid.append(np.hstack(imgs_aug_drawn))
misc.imshow(np.vstack(grid))
def __init__(self, images,
config,
shuffle=True,
jitter=True,
norm=None):
self.generator = None
self.images = images
self.config = config
self.shuffle = shuffle
self.jitter = jitter
self.norm = norm
self.image_counter = 0
ia.seed( 1 )
sometimes = lambda aug: iaa.Sometimes(0.8, aug)
# Here the augmentation is defined but not executed.
# Define our sequence of augmentation steps that will be applied to every image
# All augmenters with per_channel=0.5 will sample one value _per image_
# in 50% of all cases. In all other cases they will sample new values
# _per channel_.
self.aug_pipe = iaa.Sequential(
[
sometimes( iaa.Affine(
#scale={"x": (0.8, 1.2), "y": (0.8, 1.2)}, # scale images to 80-120% of their size, individually per axis
translate_percent={"x": (-0.1, 0.1), "y": (-0.1, 0.1)}, # translate by -20 to +20 percent (per axis)
rotate=(-10, 10), # rotate by -45 to +45 degrees
#shear=(-5, 5), # shear by -16 to +16 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)
mode = "edge"
)),
iaa.SomeOf((0, 5),
[
#sometimes(iaa.Superpixels(p_replace=(0, 1.0), n_segments=(20, 200))), # convert images into their superpixel representation
iaa.OneOf([
iaa.GaussianBlur((0, 3.0)), # blur images with a sigma between 0 and 3.0
iaa.AverageBlur(k=(2, 7)), # blur image using local means with kernel sizes between 2 and 7
iaa.MedianBlur(k=(3, 11)), # blur image using local medians with kernel sizes between 2 and 7
]),
# iaa.CoarseSalt(0.01, size_percent=(0.002, 0.01)),
iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)), # sharpen images
#iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)), # emboss images
# search either for all edges or for directed edges
#sometimes(iaa.OneOf([
# iaa.EdgeDetect(alpha=(0, 0.7)),
# iaa.DirectedEdgeDetect(alpha=(0, 0.7), direction=(0.0, 1.0)),
#])),
iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.1*255), per_channel=0), # add gaussian noise to images
iaa.OneOf([
iaa.Dropout((0.01, 0.1), per_channel=0), # randomly remove up to 10% of the pixels
#iaa.CoarseDropout((0.03, 0.15), size_percent=(0.02, 0.05), per_channel=0.2),
]),
#iaa.Invert(0.05, per_channel=True), # invert color channels
iaa.Add((-15, 15), per_channel=0), # change brightness of images (by -10 to 10 of original value)
iaa.Multiply((0.5, 1.5), per_channel=0), # change brightness of images (50-150% of original value)
iaa.ContrastNormalization((0.5, 2.0), per_channel=0), # improve or worsen the contrast
#iaa.Grayscale(alpha=(0.0, 1.0)),
#sometimes(iaa.ElasticTransformation(alpha=(0.5, 3.5), sigma=0.25)), # move pixels locally around (with random strengths)
#sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.05))) # sometimes move parts of the image around
])
],
random_order=True
)
if shuffle: np.random.shuffle(self.images)
def test_keypoint_augmentation():
reseed()
keypoints = []
for y in range(40//5):
for x in range(60//5):
keypoints.append(ia.Keypoint(y=y*5, x=x*5))
keypoints_oi = ia.KeypointsOnImage(keypoints, shape=(40, 60, 3))
keypoints_oi_empty = ia.KeypointsOnImage([], shape=(40, 60, 3))
augs = [
iaa.Add((-5, 5), name="Add"),
iaa.AddElementwise((-5, 5), name="AddElementwise"),
iaa.AdditiveGaussianNoise(0.01*255, name="AdditiveGaussianNoise"),
iaa.Multiply((0.95, 1.05), name="Multiply"),
iaa.Dropout(0.01, name="Dropout"),
iaa.CoarseDropout(0.01, size_px=6, name="CoarseDropout"),
iaa.Invert(0.01, per_channel=True, name="Invert"),
iaa.ContrastNormalization((0.95, 1.05), name="ContrastNormalization"),
iaa.GaussianBlur(sigma=(0.95, 1.05), name="GaussianBlur"),
iaa.AverageBlur((3, 5), name="AverageBlur"),
iaa.MedianBlur((3, 5), name="MedianBlur"),
# iaa.BilateralBlur((3, 5), name="BilateralBlur"),
# WithColorspace ?
# iaa.AddToHueAndSaturation((-5, 5), name="AddToHueAndSaturation"),
# ChangeColorspace ?
# Grayscale cannot be tested, input not RGB
# Convolve ?
iaa.Sharpen((0.0, 0.1), lightness=(1.0, 1.2), name="Sharpen"),
iaa.Emboss(alpha=(0.0, 0.1), strength=(0.5, 1.5), name="Emboss"),
iaa.EdgeDetect(alpha=(0.0, 0.1), name="EdgeDetect"),
iaa.DirectedEdgeDetect(alpha=(0.0, 0.1), direction=0, name="DirectedEdgeDetect"),
iaa.Fliplr(0.5, name="Fliplr"),
iaa.Flipud(0.5, name="Flipud"),
iaa.Affine(translate_px=(-5, 5), name="Affine-translate-px"),
iaa.Affine(translate_percent=(-0.05, 0.05), name="Affine-translate-percent"),
iaa.Affine(rotate=(-20, 20), name="Affine-rotate"),
iaa.Affine(shear=(-20, 20), name="Affine-shear"),
iaa.Affine(scale=(0.9, 1.1), name="Affine-scale"),
iaa.PiecewiseAffine(scale=(0.001, 0.005), name="PiecewiseAffine"),
# iaa.PerspectiveTransform(scale=(0.01, 0.10), name="PerspectiveTransform"),
iaa.ElasticTransformation(alpha=(0.1, 0.2), sigma=(0.1, 0.2), name="ElasticTransformation"),
# Sequential
# SomeOf
# OneOf
# Sometimes
# WithChannels
# Noop
# Lambda
# AssertLambda
# AssertShape
iaa.Alpha((0.0, 0.1), iaa.Add(10), name="Alpha"),
iaa.AlphaElementwise((0.0, 0.1), iaa.Add(10), name="AlphaElementwise"),
iaa.SimplexNoiseAlpha(iaa.Add(10), name="SimplexNoiseAlpha"),
iaa.FrequencyNoiseAlpha(exponent=(-2, 2), first=iaa.Add(10),
name="SimplexNoiseAlpha"),
iaa.Superpixels(p_replace=0.01, n_segments=64),
iaa.Scale(0.5, name="Scale"),
iaa.CropAndPad(px=(-10, 10), name="CropAndPad"),
iaa.Pad(px=(0, 10), name="Pad"),
iaa.Crop(px=(0, 10), name="Crop")
]
for aug in augs:
dss = []
for i in range(10):
aug_det = aug.to_deterministic()
kp_fully_empty_aug = aug_det.augment_keypoints([])
assert kp_fully_empty_aug == []
kp_first_empty_aug = aug_det.augment_keypoints([keypoints_oi_empty])[0]
assert len(kp_first_empty_aug.keypoints) == 0
kp_image = keypoints_oi.to_keypoint_image(size=5)
kp_image_aug = aug_det.augment_image(kp_image)
kp_image_aug_rev = ia.KeypointsOnImage.from_keypoint_image(
kp_image_aug,
if_not_found_coords={"x": -9999, "y": -9999},
nb_channels=1
)
kp_aug = aug_det.augment_keypoints([keypoints_oi])[0]
ds = []
assert len(kp_image_aug_rev.keypoints) == len(kp_aug.keypoints),\
"Lost keypoints for '%s' (%d vs expected %d)" \
% (aug.name, len(kp_aug.keypoints), len(kp_image_aug_rev.keypoints))
for kp_pred, kp_pred_img in zip(kp_aug.keypoints, kp_image_aug_rev.keypoints):
kp_pred_lost = (kp_pred.x == -9999 and kp_pred.y == -9999)
kp_pred_img_lost = (kp_pred_img.x == -9999 and kp_pred_img.y == -9999)
if not kp_pred_lost and not kp_pred_img_lost:
d = np.sqrt((kp_pred.x - kp_pred_img.x) ** 2
+ (kp_pred.y - kp_pred_img.y) ** 2)
ds.append(d)
dss.extend(ds)
if len(ds) == 0:
print("[INFO] No valid keypoints found for '%s' "
"in test_keypoint_augmentation()" % (str(aug),))
assert np.average(dss) < 5.0, \
"Average distance too high (%.2f, with ds: %s)" \
% (np.average(dss), str(dss))
def train():
BATCH_SIZE = 100
network = Network()
timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H%M%S")
# create directory for saving models
os.makedirs(os.path.join('save', network.description, timestamp))
dataset = Dataset(folder='data{}_{}'.format(network.IMAGE_HEIGHT,
network.IMAGE_WIDTH),
batch_size=BATCH_SIZE)
inputs, targets = dataset.next_batch()
print(inputs.shape, targets.shape)
# augmentation_seq = iaa.Sequential([
# iaa.Crop(px=(0, 16)), # crop images from each side by 0 to 16px (randomly chosen)
# iaa.Fliplr(0.5), # horizontally flip 50% of the images
# iaa.GaussianBlur(sigma=(0, 2.0)) # blur images with a sigma of 0 to 3.0
# ])
augmentation_seq = iaa.Sequential([
iaa.Crop(
px=(0, 16), name="Cropper"
), # crop images from each side by 0 to 16px (randomly chosen)
iaa.Fliplr(0.5, name="Flipper"),
iaa.GaussianBlur((0, 3.0), name="GaussianBlur"),
iaa.Dropout(0.02, name="Dropout"),
iaa.AdditiveGaussianNoise(scale=0.01 * 255, name="GaussianNoise"),
iaa.Affine(translate_px={
"x": (-network.IMAGE_HEIGHT // 3, network.IMAGE_WIDTH // 3)
},
name="Affine")
])
# change the activated augmenters for binary masks,
# we only want to execute horizontal crop, flip and affine transformation
def activator_binmasks(images, augmenter, parents, default):
if augmenter.name in ["GaussianBlur", "Dropout", "GaussianNoise"]:
return False
else:
# default value for all other augmenters
return default
hooks_binmasks = imgaug.HooksImages(activator=activator_binmasks)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
summary_writer = tf.summary.FileWriter('{}/{}-{}'.format(
'logs', network.description, timestamp),
graph=tf.get_default_graph())
saver = tf.train.Saver(tf.global_variables(), max_to_keep=None)
test_accuracies = []
# Fit all training data
n_epochs = 500
global_start = time.time()
for epoch_i in range(n_epochs):
dataset.reset_batch_pointer()
for batch_i in range(dataset.num_batches_in_epoch()):
batch_num = epoch_i * dataset.num_batches_in_epoch(
) + batch_i + 1
augmentation_seq_deterministic = augmentation_seq.to_deterministic(
)
start = time.time()
batch_inputs, batch_targets = dataset.next_batch()
batch_inputs = np.reshape(
batch_inputs, (dataset.batch_size, network.IMAGE_HEIGHT,
network.IMAGE_WIDTH, 1))
batch_targets = np.reshape(
batch_targets, (dataset.batch_size, network.IMAGE_HEIGHT,
network.IMAGE_WIDTH, 1))
batch_inputs = augmentation_seq_deterministic.augment_images(
batch_inputs)
batch_inputs = np.multiply(batch_inputs, 1.0 / 255)
batch_targets = augmentation_seq_deterministic.augment_images(
batch_targets, hooks=hooks_binmasks)
cost, _ = sess.run(
[network.cost, network.train_op],
feed_dict={
network.inputs: batch_inputs,
network.targets: batch_targets,
network.is_training: True
})
end = time.time()
print('{}/{}, epoch: {}, cost: {}, batch time: {}'.format(
batch_num, n_epochs * dataset.num_batches_in_epoch(),
epoch_i, cost, end - start))
if batch_num % 100 == 0 or batch_num == n_epochs * dataset.num_batches_in_epoch(
):
test_inputs, test_targets = dataset.test_set
# test_inputs, test_targets = test_inputs[:100], test_targets[:100]
test_inputs = np.reshape(
test_inputs,
(-1, network.IMAGE_HEIGHT, network.IMAGE_WIDTH, 1))
test_targets = np.reshape(
test_targets,
(-1, network.IMAGE_HEIGHT, network.IMAGE_WIDTH, 1))
test_inputs = np.multiply(test_inputs, 1.0 / 255)
print(test_inputs.shape)
summary, test_accuracy = sess.run(
[network.summaries, network.accuracy],
feed_dict={
network.inputs: test_inputs,
network.targets: test_targets,
network.is_training: False
})
summary_writer.add_summary(summary, batch_num)
print('Step {}, test accuracy: {}'.format(
batch_num, test_accuracy))
test_accuracies.append((test_accuracy, batch_num))
print("Accuracies in time: ", [
test_accuracies[x][0]
for x in range(len(test_accuracies))
])
max_acc = max(test_accuracies)
print("Best accuracy: {} in batch {}".format(
max_acc[0], max_acc[1]))
print("Total time: {}".format(time.time() - global_start))
# Plot example reconstructions
n_examples = 12
test_inputs, test_targets = dataset.test_inputs[:
n_examples], dataset.test_targets[:
n_examples]
test_inputs = np.multiply(test_inputs, 1.0 / 255)
test_segmentation = sess.run(
network.segmentation_result,
feed_dict={
network.inputs:
np.reshape(test_inputs, [
n_examples, network.IMAGE_HEIGHT,
network.IMAGE_WIDTH, 1
])
})
# Prepare the plot
test_plot_buf = draw_results(test_inputs, test_targets,
test_segmentation,
test_accuracy, network,
batch_num)
# Convert PNG buffer to TF image
image = tf.image.decode_png(test_plot_buf.getvalue(),
channels=4)
# Add the batch dimension
image = tf.expand_dims(image, 0)
# Add image summary
image_summary_op = tf.summary.image("plot", image)
image_summary = sess.run(image_summary_op)
summary_writer.add_summary(image_summary)
if test_accuracy >= max_acc[0]:
checkpoint_path = os.path.join('save',
network.description,
timestamp, 'model.ckpt')
saver.save(sess,
checkpoint_path,
global_step=batch_num)
def test_unusual_channel_numbers():
reseed()
images = [
(0, create_random_images((4, 16, 16))),
(1, create_random_images((4, 16, 16, 1))),
(2, create_random_images((4, 16, 16, 2))),
(4, create_random_images((4, 16, 16, 4))),
(5, create_random_images((4, 16, 16, 5))),
(10, create_random_images((4, 16, 16, 10))),
(20, create_random_images((4, 16, 16, 20)))
]
augs = [
iaa.Add((-5, 5), name="Add"),
iaa.AddElementwise((-5, 5), name="AddElementwise"),
iaa.AdditiveGaussianNoise(0.01*255, name="AdditiveGaussianNoise"),
iaa.Multiply((0.95, 1.05), name="Multiply"),
iaa.Dropout(0.01, name="Dropout"),
iaa.CoarseDropout(0.01, size_px=6, name="CoarseDropout"),
iaa.Invert(0.01, per_channel=True, name="Invert"),
iaa.ContrastNormalization((0.95, 1.05), name="ContrastNormalization"),
iaa.GaussianBlur(sigma=(0.95, 1.05), name="GaussianBlur"),
iaa.AverageBlur((3, 5), name="AverageBlur"),
iaa.MedianBlur((3, 5), name="MedianBlur"),
# iaa.BilateralBlur((3, 5), name="BilateralBlur"), # works only with 3/RGB channels
# WithColorspace ?
# iaa.AddToHueAndSaturation((-5, 5), name="AddToHueAndSaturation"), # works only with 3/RGB channels
# ChangeColorspace ?
# iaa.Grayscale((0.0, 0.1), name="Grayscale"), # works only with 3 channels
# Convolve ?
iaa.Sharpen((0.0, 0.1), lightness=(1.0, 1.2), name="Sharpen"),
iaa.Emboss(alpha=(0.0, 0.1), strength=(0.5, 1.5), name="Emboss"),
iaa.EdgeDetect(alpha=(0.0, 0.1), name="EdgeDetect"),
iaa.DirectedEdgeDetect(alpha=(0.0, 0.1), direction=0,
name="DirectedEdgeDetect"),
iaa.Fliplr(0.5, name="Fliplr"),
iaa.Flipud(0.5, name="Flipud"),
iaa.Affine(translate_px=(-5, 5), name="Affine-translate-px"),
iaa.Affine(translate_percent=(-0.05, 0.05), name="Affine-translate-percent"),
iaa.Affine(rotate=(-20, 20), name="Affine-rotate"),
iaa.Affine(shear=(-20, 20), name="Affine-shear"),
iaa.Affine(scale=(0.9, 1.1), name="Affine-scale"),
iaa.PiecewiseAffine(scale=(0.001, 0.005), name="PiecewiseAffine"),
iaa.PerspectiveTransform(scale=(0.01, 0.10), name="PerspectiveTransform"),
iaa.ElasticTransformation(alpha=(0.1, 0.2), sigma=(0.1, 0.2),
name="ElasticTransformation"),
iaa.Sequential([iaa.Add((-5, 5)), iaa.AddElementwise((-5, 5))]),
iaa.SomeOf(1, [iaa.Add((-5, 5)), iaa.AddElementwise((-5, 5))]),
iaa.OneOf([iaa.Add((-5, 5)), iaa.AddElementwise((-5, 5))]),
iaa.Sometimes(0.5, iaa.Add((-5, 5)), name="Sometimes"),
# WithChannels
iaa.Noop(name="Noop"),
# Lambda
# AssertLambda
# AssertShape
iaa.Alpha((0.0, 0.1), iaa.Add(10), name="Alpha"),
iaa.AlphaElementwise((0.0, 0.1), iaa.Add(10), name="AlphaElementwise"),
iaa.SimplexNoiseAlpha(iaa.Add(10), name="SimplexNoiseAlpha"),
iaa.FrequencyNoiseAlpha(exponent=(-2, 2), first=iaa.Add(10),
name="SimplexNoiseAlpha"),
iaa.Superpixels(p_replace=0.01, n_segments=64),
iaa.Scale({"height": 4, "width": 4}, name="Scale"),
iaa.CropAndPad(px=(-10, 10), name="CropAndPad"),
iaa.Pad(px=(0, 10), name="Pad"),
iaa.Crop(px=(0, 10), name="Crop")
]
for aug in augs:
for (nb_channels, images_c) in images:
if aug.name != "Scale":
images_aug = aug.augment_images(images_c)
assert images_aug.shape == images_c.shape
image_aug = aug.augment_image(images_c[0])
assert image_aug.shape == images_c[0].shape
else:
images_aug = aug.augment_images(images_c)
image_aug = aug.augment_image(images_c[0])
if images_c.ndim == 3:
assert images_aug.shape == (4, 4, 4)
assert image_aug.shape == (4, 4)
else:
assert images_aug.shape == (4, 4, 4, images_c.shape[3])
assert image_aug.shape == (4, 4, images_c.shape[3])
CUSTOM_FONTS = subprocess.run(
"fc-list | sed -r -e 's/^(.+): .*$/\\1/g' | grep custom",
stdout=subprocess.PIPE,
shell=True).stdout.decode("utf-8").strip().split("\n")
FONTNAMES += CUSTOM_FONTS
with open("/usr/share/dict/words") as f:
WORDS = f.read().splitlines()
AUGMENTOR = iaa.Sequential([
iaa.OneOf([iaa.GaussianBlur(
(0, 1.0)), iaa.AverageBlur(k=(1, 2))]),
iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)),
iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)),
iaa.Dropout((0.01, 0.03), per_channel=0.1),
iaa.Add((-10, 10), per_channel=0.5),
iaa.OneOf([
iaa.Multiply((0.5, 1.5), per_channel=0.5),
iaa.FrequencyNoiseAlpha(exponent=(-4, 0),
first=iaa.Multiply(
(0.5, 1.5), per_channel=True),
second=iaa.ContrastNormalization((0.5, 2.0)))
]),
],
random_order=True)
class LineState(Enum):
START = 0
HEADLINE = 1
def test_determinism():
reseed()
images = [
ia.quokka(size=(128, 128)),
ia.quokka(size=(64, 64)),
ia.imresize_single_image(skimage.data.astronaut(), (128, 256))
]
keypoints = [
ia.KeypointsOnImage([
ia.Keypoint(x=20, y=10), ia.Keypoint(x=5, y=5), ia.Keypoint(x=10, y=43)
], shape=(50, 60, 3))
]
augs = [
iaa.Sequential([iaa.Fliplr(1.0), iaa.Flipud(1.0)]),
iaa.SomeOf(1, [iaa.Fliplr(1.0), iaa.Flipud(1.0)]),
iaa.OneOf([iaa.Fliplr(1.0), iaa.Flipud(1.0)]),
iaa.Sometimes(1.0, iaa.Fliplr(1)),
iaa.WithColorspace("HSV", children=iaa.Add((-50, 50))),
iaa.WithChannels([0], iaa.Add((-50, 50))),
iaa.Noop(name="Noop-nochange"),
iaa.Lambda(
func_images=lambda images, random_state, parents, hooks: images,
func_heatmaps=lambda heatmaps, random_state, parents, hooks: heatmaps,
func_keypoints=lambda keypoints_on_images, random_state, parents, hooks: keypoints_on_images,
name="Lambda-nochange"
),
iaa.AssertLambda(
func_images=lambda images, random_state, parents, hooks: True,
func_heatmaps=lambda heatmaps, random_state, parents, hooks: True,
func_keypoints=lambda keypoints_on_images, random_state, parents, hooks: True,
name="AssertLambda-nochange"
),
iaa.AssertShape(
(None, None, None, 3),
check_keypoints=False,
name="AssertShape-nochange"
),
iaa.Scale((0.5, 0.9)),
iaa.CropAndPad(px=(-50, 50)),
iaa.Pad(px=(1, 50)),
iaa.Crop(px=(1, 50)),
iaa.Fliplr(1.0),
iaa.Flipud(1.0),
iaa.Superpixels(p_replace=(0.25, 1.0), n_segments=(16, 128)),
iaa.ChangeColorspace(to_colorspace="GRAY"),
iaa.Grayscale(alpha=(0.1, 1.0)),
iaa.GaussianBlur(1.0),
iaa.AverageBlur(5),
iaa.MedianBlur(5),
iaa.Convolve(np.array([[0, 1, 0],
[1, -4, 1],
[0, 1, 0]])),
iaa.Sharpen(alpha=(0.1, 1.0), lightness=(0.8, 1.2)),
iaa.Emboss(alpha=(0.1, 1.0), strength=(0.8, 1.2)),
iaa.EdgeDetect(alpha=(0.1, 1.0)),
iaa.DirectedEdgeDetect(alpha=(0.1, 1.0), direction=(0.0, 1.0)),
iaa.Add((-50, 50)),
iaa.AddElementwise((-50, 50)),
iaa.AdditiveGaussianNoise(scale=(0.1, 1.0)),
iaa.Multiply((0.6, 1.4)),
iaa.MultiplyElementwise((0.6, 1.4)),
iaa.Dropout((0.3, 0.5)),
iaa.CoarseDropout((0.3, 0.5), size_percent=(0.05, 0.2)),
iaa.Invert(0.5),
iaa.ContrastNormalization((0.6, 1.4)),
iaa.Affine(scale=(0.7, 1.3), translate_percent=(-0.1, 0.1),
rotate=(-20, 20), shear=(-20, 20), order=ia.ALL,
mode=ia.ALL, cval=(0, 255)),
iaa.PiecewiseAffine(scale=(0.1, 0.3)),
iaa.ElasticTransformation(alpha=0.5)
]
for aug in augs:
aug_det = aug.to_deterministic()
images_aug1 = aug_det.augment_images(images)
images_aug2 = aug_det.augment_images(images)
kps_aug1 = aug_det.augment_keypoints(keypoints)
kps_aug2 = aug_det.augment_keypoints(keypoints)
assert array_equal_lists(images_aug1, images_aug2), \
"Images not identical for %s" % (aug.name,)
assert keypoints_equal(kps_aug1, kps_aug2), \
"Keypoints not identical for %s" % (aug.name,)
def __init__(self, image):
self.img = image
# 随机通道处理,加减100以内
# self.aug_WithChannels = iaa.WithChannels((0,2), iaa.Add((-100, 100)))
# 随机裁剪和填充，percent为裁剪与填充比例，负数为放大后裁剪，正数为缩小和填充，pad_mode为填充方式，pad_cval为当空白填充时，填充像素值
self.aug_CropAndPad = iaa.CropAndPad(percent=(-0.05, 0.1),
pad_mode=ia.ALL,
pad_cval=(0, 255))
# 随机水平翻转,参数为概率
self.aug_Fliplr = iaa.Fliplr(0.5)
# 随机垂直翻转,参数为概率
self.aug_Flipud = iaa.Flipud(0.5)
# 超像素表示,p_replace被超像素代替的百分比,n_segments分割块数
self.aug_Superpixels = iaa.Superpixels(p_replace=(0, 1.0),
n_segments=(20, 200))
# 灰度化 (0.0,1.0),前者为偏彩色部分，后者为偏灰度部分，随机灰度化
self.aug_GrayScale = iaa.Grayscale(alpha=(0.0, 0.6))
# 高斯模糊
self.aug_GaussianBlur = iaa.GaussianBlur(sigma=(0, 3.0))
# 均值模糊，k为kernel size
self.aug_AverageBlur = iaa.AverageBlur(k=(2, 7))
# 中值模糊, k为kernel size
self.aug_MedianBlur = iaa.MedianBlur(k=(3, 11))
# 双边滤波,d为kernel size,sigma_color为颜色域标准差,sigma_space为空间域标准差
self.aug_BilateralBlur = iaa.BilateralBlur(sigma_color=(0, 250),
sigma_space=(0, 250),
d=(3, 7))
# 锐化
self.aug_Sharpen = iaa.Sharpen(alpha=(0.0, 1.0), lightness=(0.75, 2.0))
# 浮雕效果
self.aug_Emboss = iaa.Emboss(alpha=(0.0, 1.0), strength=(0.0, 1.5))
# 边缘检测
self.aug_EdgeDetect = iaa.EdgeDetect(alpha=(0.0, 1.0))
# 方向性边缘检测
self.aug_DirectedEdgeDetece = iaa.DirectedEdgeDetect(alpha=(0.0, 1.0),
direction=(0.0,
1.0))
# 暴力叠加像素值,每个像素统一加一个值
self.aug_Add = iaa.Add((-40, 40))
# 暴力叠加像素值，每个像素加不同的值
self.aug_AddElementwise = iaa.AddElementwise((-40, 40))
# 随机高斯加性噪声
self.aug_AdditiveGaussianNoise = iaa.AdditiveGaussianNoise(scale=(0.0,
0.1 *
255))
# 暴力乘法,每个像素统一乘以一个值
self.aug_Multiply = iaa.Multiply((0.8, 1.2))
# 暴力乘法,每个像素乘以不同值
self.aug_MultiplyElementwise = iaa.MultiplyElementwise((0.8, 1.2))
# 随机dropout像素值
self.aug_Dropout = iaa.Dropout(p=(0, 0.2))
# 随机粗dropout,2*2方块像素被dropout
self.aug_CoarseDropout = iaa.CoarseDropout(0.02, size_percent=0.5)
# 50%的图片,p概率反转颜色
self.aug_Invert = iaa.Invert(0.25, per_channel=0.5)
# 对比度归一化
self.aug_ContrastNormalization = iaa.ContrastNormalization((0.5, 1.5))
# 仿射变换
self.aug_Affine = iaa.Affine(rotate=(0, 20),
scale={
"x": (0.8, 1.2),
"y": (0.8, 1.2)
})
# 仿射变换, 局部像素仿射扭曲
self.aug_PiecewiseAffine = iaa.PiecewiseAffine(scale=(0.01, 0.05))
# 单应性变换
self.aug_PerspectiveTransform = iaa.PerspectiveTransform(scale=(0.01,
0.1))
# 弹性变换
self.aug_ElasticTransformation = iaa.ElasticTransformation(alpha=(0,
5.0),
sigma=0.25)
# 简单的加噪,小黑块
self.aug_SimplexNoiseAlpha = iaa.SimplexNoiseAlpha(
iaa.OneOf([
iaa.EdgeDetect(alpha=(0.0, 0.5)),
iaa.DirectedEdgeDetect(alpha=(0.0, 0.5), direction=(0.0, 1.0)),
]))
# 频域加噪，表现为色彩的块状变换
self.aug_FrequencyNoiseAlpha = iaa.FrequencyNoiseAlpha(
exponent=(-4, 0),
first=iaa.Multiply((0.5, 1.5), per_channel=True),
second=iaa.ContrastNormalization((0.5, 2.0)))
def test_dtype_preservation():
reseed()
size = (4, 16, 16, 3)
images = [
np.random.uniform(0, 255, size).astype(np.uint8),
np.random.uniform(0, 65535, size).astype(np.uint16),
np.random.uniform(0, 4294967295, size).astype(np.uint32),
np.random.uniform(-128, 127, size).astype(np.int16),
np.random.uniform(-32768, 32767, size).astype(np.int32),
np.random.uniform(0.0, 1.0, size).astype(np.float32),
np.random.uniform(-1000.0, 1000.0, size).astype(np.float16),
np.random.uniform(-1000.0, 1000.0, size).astype(np.float32),
np.random.uniform(-1000.0, 1000.0, size).astype(np.float64)
]
default_dtypes = set([arr.dtype for arr in images])
# Some dtypes are here removed per augmenter, because the respective
# augmenter does not support them. This test currently only checks whether
# dtypes are preserved from in- to output for all dtypes that are supported
# per augmenter.
# dtypes are here removed via list comprehension instead of
# `default_dtypes - set([dtype])`, because the latter one simply never
# removed the dtype(s) for some reason?!
def _not_dts(dts):
return [dt for dt in default_dtypes if dt not in dts]
augs = [
(iaa.Add((-5, 5), name="Add"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.AddElementwise((-5, 5), name="AddElementwise"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.AdditiveGaussianNoise(0.01*255, name="AdditiveGaussianNoise"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.Multiply((0.95, 1.05), name="Multiply"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.Dropout(0.01, name="Dropout"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.CoarseDropout(0.01, size_px=6, name="CoarseDropout"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.Invert(0.01, per_channel=True, name="Invert"), default_dtypes),
(iaa.ContrastNormalization((0.95, 1.05), name="ContrastNormalization"), default_dtypes),
(iaa.GaussianBlur(sigma=(0.95, 1.05), name="GaussianBlur"), _not_dts([np.float16])),
(iaa.AverageBlur((3, 5), name="AverageBlur"), _not_dts([np.uint32, np.int32, np.float16])),
(iaa.MedianBlur((3, 5), name="MedianBlur"), _not_dts([np.uint32, np.int32, np.float16, np.float64])),
(iaa.BilateralBlur((3, 5), name="BilateralBlur"),
_not_dts([np.uint16, np.uint32, np.int16, np.int32, np.float16, np.float64])),
# WithColorspace ?
# iaa.AddToHueAndSaturation((-5, 5), name="AddToHueAndSaturation"), # works only with RGB/uint8
# ChangeColorspace ?
# iaa.Grayscale((0.0, 0.1), name="Grayscale"), # works only with RGB/uint8
# Convolve ?
(iaa.Sharpen((0.0, 0.1), lightness=(1.0, 1.2), name="Sharpen"),
_not_dts([np.uint32, np.int32, np.float16, np.uint32])),
(iaa.Emboss(alpha=(0.0, 0.1), strength=(0.5, 1.5), name="Emboss"),
_not_dts([np.uint32, np.int32, np.float16, np.uint32])),
(iaa.EdgeDetect(alpha=(0.0, 0.1), name="EdgeDetect"),
_not_dts([np.uint32, np.int32, np.float16, np.uint32])),
(iaa.DirectedEdgeDetect(alpha=(0.0, 0.1), direction=0, name="DirectedEdgeDetect"),
_not_dts([np.uint32, np.int32, np.float16, np.uint32])),
(iaa.Fliplr(0.5, name="Fliplr"), default_dtypes),
(iaa.Flipud(0.5, name="Flipud"), default_dtypes),
(iaa.Affine(translate_px=(-5, 5), name="Affine-translate-px"), default_dtypes),
(iaa.Affine(translate_percent=(-0.05, 0.05), name="Affine-translate-percent"), default_dtypes),
(iaa.Affine(rotate=(-20, 20), name="Affine-rotate"), default_dtypes),
(iaa.Affine(shear=(-20, 20), name="Affine-shear"), default_dtypes),
(iaa.Affine(scale=(0.9, 1.1), name="Affine-scale"), default_dtypes),
(iaa.PiecewiseAffine(scale=(0.001, 0.005), name="PiecewiseAffine"), default_dtypes),
# (iaa.PerspectiveTransform(scale=(0.01, 0.10), name="PerspectiveTransform"), not_dts([np.uint32])),
(iaa.ElasticTransformation(alpha=(0.1, 0.2), sigma=(0.1, 0.2), name="ElasticTransformation"),
_not_dts([np.float16])),
(iaa.Sequential([iaa.Noop(), iaa.Noop()], name="SequentialNoop"), default_dtypes),
(iaa.SomeOf(1, [iaa.Noop(), iaa.Noop()], name="SomeOfNoop"), default_dtypes),
(iaa.OneOf([iaa.Noop(), iaa.Noop()], name="OneOfNoop"), default_dtypes),
(iaa.Sometimes(0.5, iaa.Noop(), name="SometimesNoop"), default_dtypes),
(iaa.Sequential([iaa.Add((-5, 5)), iaa.AddElementwise((-5, 5))], name="Sequential"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.SomeOf(1, [iaa.Add((-5, 5)), iaa.AddElementwise((-5, 5))], name="SomeOf"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.OneOf([iaa.Add((-5, 5)), iaa.AddElementwise((-5, 5))], name="OneOf"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.Sometimes(0.5, iaa.Add((-5, 5)), name="Sometimes"), _not_dts([np.uint32, np.int32, np.float64])),
# WithChannels
(iaa.Noop(name="Noop"), default_dtypes),
# Lambda
# AssertLambda
# AssertShape
(iaa.Alpha((0.0, 0.1), iaa.Noop(), name="AlphaNoop"), default_dtypes),
(iaa.AlphaElementwise((0.0, 0.1), iaa.Noop(), name="AlphaElementwiseNoop"), default_dtypes),
(iaa.SimplexNoiseAlpha(iaa.Noop(), name="SimplexNoiseAlphaNoop"), default_dtypes),
(iaa.FrequencyNoiseAlpha(exponent=(-2, 2), first=iaa.Noop(), name="SimplexNoiseAlphaNoop"), default_dtypes),
(iaa.Alpha((0.0, 0.1), iaa.Add(10), name="Alpha"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.AlphaElementwise((0.0, 0.1), iaa.Add(10), name="AlphaElementwise"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.SimplexNoiseAlpha(iaa.Add(10), name="SimplexNoiseAlpha"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.FrequencyNoiseAlpha(exponent=(-2, 2), first=iaa.Add(10), name="SimplexNoiseAlpha"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.Superpixels(p_replace=0.01, n_segments=64), _not_dts([np.float16, np.float32])),
(iaa.Scale({"height": 4, "width": 4}, name="Scale"),
_not_dts([np.uint16, np.uint32, np.int16, np.int32, np.float32, np.float16, np.float64])),
(iaa.CropAndPad(px=(-10, 10), name="CropAndPad"),
_not_dts([np.uint16, np.uint32, np.int16, np.int32, np.float32, np.float16, np.float64])),
(iaa.Pad(px=(0, 10), name="Pad"),
_not_dts([np.uint16, np.uint32, np.int16, np.int32, np.float32, np.float16, np.float64])),
(iaa.Crop(px=(0, 10), name="Crop"),
_not_dts([np.uint16, np.uint32, np.int16, np.int32, np.float32, np.float16, np.float64]))
]
for (aug, allowed_dtypes) in augs:
# print("aug", aug.name)
# print("allowed_dtypes", allowed_dtypes)
for images_i in images:
if images_i.dtype in allowed_dtypes:
# print("image dt", images_i.dtype)
images_aug = aug.augment_images(images_i)
assert images_aug.dtype == images_i.dtype
else:
# print("image dt", images_i.dtype, "[SKIPPED]")
pass
def example_heavy_augmentations():
print("Example: Heavy Augmentations")
import imgaug as ia
from imgaug import augmenters as iaa
# random example images
images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8)
# Sometimes(0.5, ...) applies the given augmenter in 50% of all cases,
# e.g. Sometimes(0.5, GaussianBlur(0.3)) would blur roughly every second image.
st = lambda aug: iaa.Sometimes(0.5, aug)
# Define our sequence of augmentation steps that will be applied to every image
# All augmenters with per_channel=0.5 will sample one value _per image_
# in 50% of all cases. In all other cases they will sample new values
# _per channel_.
seq = iaa.Sequential(
[
iaa.Fliplr(0.5), # horizontally flip 50% of all images
iaa.Flipud(0.5), # vertically flip 50% of all images
st(iaa.Crop(
percent=(0,
0.1))), # crop images by 0-10% of their height/width
st(iaa.GaussianBlur(
(0, 3.0))), # blur images with a sigma between 0 and 3.0
st(
iaa.AdditiveGaussianNoise(
loc=0, scale=(0.0, 0.05 * 255),
per_channel=0.5)), # add gaussian noise to images
st(iaa.Dropout(
(0.0, 0.1),
per_channel=0.5)), # randomly remove up to 10% of the pixels
st(iaa.Add(
(-10, 10), per_channel=0.5
)), # change brightness of images (by -10 to 10 of original value)
st(iaa.Multiply((0.5, 1.5), per_channel=0.5)
), # change brightness of images (50-150% of original value)
st(iaa.ContrastNormalization(
(0.5, 2.0),
per_channel=0.5)), # improve or worsen the contrast
st(iaa.Grayscale((0.0, 1.0))), # blend with grayscale image
st(
iaa.Affine(
scale={
"x": (0.8, 1.2),
"y": (0.8, 1.2)
}, # scale images to 80-120% of their size, individually per axis
translate_px={
"x": (-16, 16),
"y": (-16, 16)
}, # translate by -16 to +16 pixels (per axis)
rotate=(-45, 45), # rotate by -45 to +45 degrees
shear=(-16, 16), # shear by -16 to +16 degrees
order=[
0,
1
], # use scikit-image's interpolation orders 0 (nearest neighbour) and 1 (bilinear)
cval=(
0, 1.0
), # if mode is constant, use a cval between 0 and 1.0
mode=ia.
ALL # use any of scikit-image's warping modes (see 2nd image from the top for examples)
)),
st(iaa.ElasticTransformation(alpha=(0.5, 3.5), sigma=0.25)
) # apply elastic transformations with random strengths
],
random_order=True # do all of the above in random order
)
images_aug = seq.augment_images(images)
# -----
# Make sure that the example really does something
assert not np.array_equal(images, images_aug)
def createSequence(self):
seq_affine = []
seq_non_affine = []
seq_noise = []
def sometimes_affine(aug): return iaa.Sometimes(
self.default_param['affine_probability']/100, aug)
def sometimes_non_affine(aug): return iaa.Sometimes(
self.default_param['non_affine_probability']/100, aug)
def sometimes_noise(aug): return iaa.Sometimes(
self.default_param['noise_probability']/100, aug)
affine = {k: self.default_param[k] for k in self.default_param.keys() if (
('affine' in k) and (not 'non' in k) and self.default_param[k])}
if affine != {}:
affine_seq = {}
for a in affine:
if a == 'affine_flip_horizontal':
seq_affine.append(sometimes_affine(
iaa.Flipud(affine[a]/100)))
elif a == 'affine_flip_vertical':
seq_affine.append(sometimes_affine(
iaa.Fliplr(affine[a]/100)))
elif a == 'affine_rotate':
affine_seq['rotate'] = (-affine[a], affine[a])
elif a == 'affine_scale':
affine_seq['scale'] = {
"x": (1, 1+affine[a]/100), "y": (1, 1+affine[a]/100)}
elif a == 'affine_shear':
affine_seq['shear'] = (-affine[a], affine[a])
if affine_seq != {}:
affine_seq['mode'] = 'reflect'
seq_affine.append(sometimes_affine(iaa.Affine(**affine_seq)))
non_affine = {k: self.default_param[k] for k in self.default_param.keys() if (
('non_affine' in k) and self.default_param[k])}
if non_affine != {}:
for a in non_affine:
if a == 'non_affine_brightness':
seq_non_affine.append(sometimes_non_affine(iaa.Multiply(
(1-non_affine[a]/100, 1+non_affine[a]/100), per_channel=True)))
elif a == 'non_affine_contrast':
seq_non_affine.append(sometimes_non_affine(
iaa.ContrastNormalization((1-non_affine[a]/100, 1+non_affine[a]/100))))
elif a == 'non_affine_emboss':
seq_non_affine.append(sometimes_non_affine(iaa.Emboss(
alpha=(non_affine[a]/200, non_affine[a]/100), strength=(0, non_affine[a]/50))))
elif a == 'non_affine_grayscale':
seq_non_affine.append(sometimes_non_affine(
iaa.Grayscale(alpha=(non_affine[a]/200, non_affine[a]/100))))
elif a == 'non_affine_saturation':
seq_non_affine.append(sometimes_non_affine(
iaa.AddToHueAndSaturation((-non_affine[a], non_affine[a]))))
elif a == 'non_affine_shrpen':
seq_non_affine.append(sometimes_non_affine(iaa.Sharpen(alpha=(
non_affine[a]/200, non_affine[a]/100), lightness=(1-non_affine[a]/100, 1+non_affine[a]/100))))
noise = {k: self.default_param[k] for k in self.default_param.keys() if (
('noise' in k) and self.default_param[k])}
if noise != {}:
for a in noise:
if a == 'noise_blur':
seq_noise.append(sometimes_noise(
iaa.GaussianBlur((noise[a]/2, noise[a]))))
elif a == 'noise_dropout':
seq_noise.append(sometimes_noise(
iaa.Dropout((noise[a]/200, noise[a]/100))))
elif a == 'noise_frequency':
seq_noise.append(sometimes_noise(
iaa.FrequencyNoiseAlpha(exponent=(-noise[a], noise[a]))))
elif a == 'noise_noise':
seq_noise.append(sometimes_noise(
iaa.AdditiveGaussianNoise((255*noise[a]/200, 255*noise[a]/100))))
elif a == 'noise_salt_and_pepper':
seq_noise.append(sometimes_noise(
iaa.SaltAndPepper(p=(noise[a]/200, noise[a]/100))))
return {'affine': seq_affine, 'non_affine': seq_non_affine, 'noise': seq_noise}