def __init__(
self,
instances,
anchors, # for Feature Pyramid Networks we need 9 anchors, 3 for each scale
labels,
downsample=32, # ratio between network input's size and network output's size, 32 for YOLOv1-3
max_box_per_image=30,
batch_size=1,
# min_net_size=224,
# max_net_size=224,
shuffle=True,
jitter=True,
norm=None):
self.instances = instances
self.batch_size = batch_size
self.labels = labels
self.downsample = downsample
self.max_box_per_image = max_box_per_image
# self.min_net_size = (min_net_size // self.downsample) * self.downsample
# self.max_net_size = (max_net_size // self.downsample) * self.downsample
self.shuffle = shuffle
self.jitter = jitter
self.norm = norm
self.anchors = [
BoundBox(0, 0, anchors[2 * i], anchors[2 * i + 1])
for i in range(len(anchors) // 2)
]
self.net_h = 224
self.net_w = 224
# 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(
[
sometimes(iaa.Affine()),
# 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),
[
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.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
],
random_order=True)
],
random_order=True)
if shuffle:
np.random.shuffle(self.instances)
"y": (-0.1, 0.1)
},
rotate=(-45, 45), # rotate by -45 to +45 degrees
shear=(-5, 5),
order=[
0,
1
], # use nearest neighbour or bilinear interpolation (fast)
mode=['reflect'])),
lesssometimes(
iaa.SomeOf(
(0, 5),
[
iaa.OneOf([
iaa.GaussianBlur((0, 3.0)),
iaa.AverageBlur(k=(2, 7)),
iaa.MedianBlur(k=(3, 5)),
]),
iaa.Sharpen(alpha=(0, 1.0),
lightness=(0.75, 1.5)),
iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)),
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),
sometimes(iaa.Affine(
scale={"x": (0.65, 1.15), "y": (0.65, 1.15)}, # scale images to 80-120% of their size, individually per axis
translate_percent={"x": (-0.15, 0.15), "y": (-0.15, 0.15)}, # translate by -20 to +20 percent (per axis)
rotate=(-30, 30), # 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, 3),
[
iaa.OneOf([
iaa.GaussianBlur((0, 2.0)), # blur images with a sigma between 0 and 3.0
iaa.AverageBlur(k=(1, 5)), # blur image using local means with kernel sizes between 2 and 7
iaa.MedianBlur(k=(1, 5)), # blur image using local medians with kernel sizes between 2 and 7
]),
iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.03*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=(0.01, 0.03), per_channel=0.2),
]),
iaa.Add((-10, 10), per_channel=0.5), # change brightness of images (by -10 to 10 of original value)
iaa.ContrastNormalization((0.3, 1.0), 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
],
def black_and_white_aug():
alpha_seconds = iaa.OneOf([
iaa.Affine(rotate=(-3, 3)),
iaa.Affine(translate_percent={
"x": (0.95, 1.05),
"y": (0.95, 1.05)
}),
iaa.Affine(scale={
"x": (0.95, 1.05),
"y": (0.95, 1.05)
}),
iaa.Affine(shear=(-2, 2)),
iaa.CoarseDropout(p=0.1, size_percent=(0.08, 0.02)),
])
first_set = iaa.OneOf([
iaa.Multiply(iap.Choice([0.5, 1.5]), per_channel=True),
iaa.EdgeDetect((0.1, 1)),
])
second_set = iaa.OneOf([
iaa.AddToHueAndSaturation((-40, 40)),
iaa.ContrastNormalization((0.5, 2.0), per_channel=True)
])
color_aug = iaa.Sequential(
[
# Original Image Domain ==================================================
# Geometric Rigid
iaa.Fliplr(0.5),
iaa.OneOf([
iaa.Noop(),
iaa.Affine(rotate=90),
iaa.Affine(rotate=180),
iaa.Affine(rotate=270),
]),
iaa.OneOf([
iaa.Noop(),
iaa.Crop(percent=(0, 0.1)), # Random Crops
iaa.PerspectiveTransform(scale=(0.05, 0.15)),
]),
# Affine
sometimes(
iaa.PiecewiseAffine(
scale=(0.01, 0.07), nb_rows=(3, 6), nb_cols=(3, 6))),
fewtimes(
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)),
# Transformations outside Image domain ==============================================
# COLOR, CONTRAST, HUE
iaa.Invert(0.5, name='Invert'),
fewtimes(iaa.Add((-10, 10), per_channel=0.5, name='Add')),
fewtimes(
iaa.AddToHueAndSaturation(
(-40, 40), per_channel=0.5, name='AddToHueAndSaturation')),
# Intensity / contrast
fewtimes(
iaa.ContrastNormalization(
(0.8, 1.1), name='ContrastNormalization')),
# Add to hue and saturation
fewtimes(
iaa.Multiply(
(0.5, 1.5), per_channel=0.5, name='HueAndSaturation')),
# Noise ===========================================================================
fewtimes(
iaa.AdditiveGaussianNoise(loc=0,
scale=(0.0, 0.15 * 255),
per_channel=0.5,
name='AdditiveGaussianNoise')),
fewtimes(
iaa.Alpha(factor=(0.5, 1),
first=iaa.ContrastNormalization(
(0.5, 2.0), per_channel=True),
second=alpha_seconds,
per_channel=0.5,
name='AlphaNoise'), ),
fewtimes(
iaa.SimplexNoiseAlpha(first=first_set,
second=second_set,
per_channel=0.5,
aggregation_method="max",
sigmoid=False,
upscale_method='cubic',
size_px_max=(2, 12),
name='SimplexNoiseAlpha'), ),
fewtimes(
iaa.FrequencyNoiseAlpha(first=first_set,
second=second_set,
per_channel=0.5,
aggregation_method="max",
sigmoid=False,
upscale_method='cubic',
size_px_max=(2, 12),
name='FrequencyNoiseAlpha'), ),
# Blur
fewtimes(
iaa.OneOf([
iaa.GaussianBlur((0, 3.0)),
iaa.AverageBlur(k=(2, 7)),
iaa.MedianBlur(k=(3, 11)),
iaa.BilateralBlur(d=(3, 10),
sigma_color=(10, 250),
sigma_space=(10, 250))
],
name='Blur')),
# Regularization ======================================================================
unlikely(
iaa.OneOf([
iaa.Dropout((0.01, 0.1), per_channel=0.5, name='Dropout'),
iaa.CoarseDropout((0.03, 0.15),
size_percent=(0.02, 0.05),
per_channel=0.5,
name='CoarseDropout'),
], )),
],
random_order=True)
seq = iaa.Sequential(
[
iaa.Sequential(
[
# Texture
rarely(
iaa.Superpixels(p_replace=(0.3, 1.0),
n_segments=(500, 1000),
name='Superpixels')),
rarely(
iaa.Sharpen(alpha=(0, 0.5),
lightness=(0.75, 1.0),
name='Sharpen')),
rarely(
iaa.Emboss(
alpha=(0, 1.0), strength=(0, 1.0), name='Emboss')),
rarely(
iaa.OneOf([
iaa.EdgeDetect(alpha=(0, 0.5)),
iaa.DirectedEdgeDetect(alpha=(0, 0.5),
direction=(0.0, 1.0)),
],
name='EdgeDetect')),
rarely(
iaa.ElasticTransformation(
alpha=(0.5, 3.5),
sigma=0.25,
name='ElasticTransformation')),
],
random_order=True),
color_aug,
iaa.Grayscale(alpha=1.0, name='Grayscale')
],
random_order=False)
def activator_masks(images, augmenter, parents, default):
if 'Unnamed' not in augmenter.name:
return False
else:
return default
hooks_masks = ia.HooksImages(activator=activator_masks)
return seq, hooks_masks
def __init__(self, images,
config,
shuffle=True,
jitter=True,
norm=True):
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(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 __image_augmentation(image, markup):
"""
Искажения изображения без разметки
"""
seq = iaa.SomeOf(
(0, 5),
[
# (может очень сильно испортить текст на изображении, поэтому лучше не использовать)
# была гипотеза что включением следующей строчки увеличивается recall ценой precision
# но НЕТ - на последнем тесте это не подтвердилось, впрочем, окончательно сказать нельзя
# так что строчка оставлена, если recall низкий - можно попробовать раскомментить
# sometimes(iaa.Superpixels(p_replace=(0, 1.0), n_segments=(20, 200))),
# # convert images into their superpixel representation
# # p_replace-вероятность объединения соседних superpixel
# # n_segments-количество superpixel
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)),
])),
# добавляет черные капли на картинку
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.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))
],
random_order=True)
images_aug = Image.fromarray(
seq.augment_image(np.array(image, dtype=np.uint8)))
return images_aug, markup
def Augmentation(input_image):
with tf.Graph().as_default():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,
log_device_placement=False))
with sess.as_default():
pnet, rnet, onet = detect_face.create_mtcnn(sess, './npy')
sometimes = lambda aug: iaa.Sometimes(0.5, aug)
aug_name = input_image.split("/")[-1].split(".")[0]
minsize = 35 # minimum size of face
threshold = [0.6, 0.7, 0.7] # three steps's threshold
factor = 0.709 # scale factor
margin = 44
image_size = 200
nb_batches = 16
aug_faces = []
batches = []
seq = iaa.Sequential(
[
iaa.Fliplr(0.5),
sometimes(
iaa.CropAndPad(
percent=(-0.05, 0.1), pad_mode=ia.ALL, pad_cval=(0, 255))),
sometimes(
iaa.Affine(scale={
"x": (0.8, 1.0),
"y": (0.8, 1.0)
},
translate_percent={
"x": (-0.2, 0.2),
"y": (0, 0.2)
},
rotate=(-10, 10),
shear=(-16, 16),
order=[0, 1],
cval=(0, 255))),
iaa.SomeOf(
(0, 4),
[
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)), # sharpen images
iaa.Emboss(alpha=(0, 1.0),
strength=(0, 1.0)), # emboss images
iaa.SimplexNoiseAlpha(
iaa.OneOf([
iaa.EdgeDetect(alpha=(0.2, 0.5)),
iaa.DirectedEdgeDetect(alpha=(0.2, 0.5),
direction=(0.0, 1.0)),
])),
iaa.AdditiveGaussianNoise(
loc=0, scale=(0.0, 0.05 * 255), per_channel=0.5),
iaa.Dropout((0.01, 0.1), per_channel=0.5),
iaa.Add((-10, 10), per_channel=0.5),
iaa.AddToHueAndSaturation((-20, 20)),
iaa.ContrastNormalization((0.5, 1.5), per_channel=0.5),
iaa.Grayscale(alpha=(0.0, 1.0)),
sometimes(
iaa.ElasticTransformation(alpha=(0.5, 2), sigma=0.25)),
sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.03))),
sometimes(iaa.PerspectiveTransform(scale=(0.01, 0.1)))
],
random_order=True)
],
random_order=True)
img = misc.imread(input_image)
if img.ndim < 2:
print("Unable !")
elif img.ndim == 2:
img = facenet.to_rgb(img)
img = img[:, :, 0:3]
batches.append(np.array([img for _ in range(nb_batches)], dtype=np.uint8))
aug_images = seq.augment_images(batches[0])
for aug_img in aug_images:
bounding_boxes, _ = detect_face.detect_face(aug_img, minsize, pnet,
rnet, onet, threshold,
factor)
nrof_faces = bounding_boxes.shape[0]
if nrof_faces > 0:
det = bounding_boxes[:, 0:4]
img_size = np.asarray(img.shape)[0:2]
if nrof_faces > 1:
bounding_box_size = (det[:, 2] - det[:, 0]) * (det[:, 3] -
det[:, 1])
img_center = img_size / 2
offsets = np.vstack([
(det[:, 0] + det[:, 2]) / 2 - img_center[1],
(det[:, 1] + det[:, 3]) / 2 - img_center[0]
])
offset_dist_squared = np.sum(np.power(offsets, 2.0), 0)
index = np.argmax(bounding_box_size -
offset_dist_squared * 2.0)
det = det[index, :]
det = np.squeeze(det)
bb_temp = np.zeros(4, dtype=np.int32)
bb_temp[0] = det[0]
bb_temp[1] = det[1]
bb_temp[2] = det[2]
bb_temp[3] = det[3]
cropped_temp = aug_img[bb_temp[1]:bb_temp[3],
bb_temp[0]:bb_temp[2], :]
scaled_temp = misc.imresize(cropped_temp, (image_size, image_size),
interp='bilinear')
aug_faces.append(scaled_temp)
return aug_faces
'y': (-0.1, 0.1)
},
shear=(-10, 10),
rotate=(-30, 30))),
iaa.OneOf([
iaa.GammaContrast((0.5, 1.5)),
iaa.LinearContrast((0.5, 1.5)),
iaa.ContrastNormalization((0.70, 1.30)),
])
],
random_order=True)
Aug3 = iaa.Sequential([
iaa.Fliplr(0.5),
iaa.Sometimes(
0.5, iaa.OneOf([iaa.AverageBlur(k=(3, 5)),
iaa.MotionBlur(k=(3, 5))])),
iaa.Add((-15, 15), per_channel=0.5),
iaa.Multiply((0.8, 1.2), per_channel=0.5),
iaa.Sometimes(
0.5,
iaa.Affine(scale={
'x': (0.8, 1.2),
'y': (0.8, 1.2)
},
translate_percent={
'x': (-0.15, 0.15),
'y': (-0.15, 0.15)
},
shear=(-15, 15),
rotate=(-30, 30))),
def get_train_augmenters_seq1(mode='constant'):
# 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
# 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 -90 to +90 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=(-90, 90),
shear=(-16, 16),
order=[0, 1],
cval=0,
mode=mode)),
# In some images distort local areas with varying strength.
sometimes(
iaa.PiecewiseAffine(
scale=(0.01, 0.1), order=[0, 1], cval=0, mode=mode)),
# Execute 0 to 2 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, 2),
[
# Blur each image with varying strength using
# gaussian blur (sigma between 0 and 2.0),
# average/uniform blur (kernel size between 2x2 and 5x5)
# median blur (kernel size between 3x3 and 7x7).
iaa.OneOf([
iaa.GaussianBlur((0, 2.0), name='GaussianBlur'),
iaa.AverageBlur(k=(2, 5), name='AverageBlur'),
iaa.MedianBlur(k=(3, 7), name='MedianBlur'),
]),
# Sharpen or emboss each image, overlay the result with the original
# image using an alpha between 0 (no sharpening) and 0.25.
iaa.OneOf([
iaa.Sharpen(alpha=(0, 0.25), name='Sharpen'),
iaa.Emboss(alpha=(0, 0.25),
strength=(0, 0.75),
name='Emboss'),
]),
# Add gaussian noise to some images.
# The noise is randomly sampled per pixel.
# (i.e. brightness change).
iaa.AdditiveGaussianNoise(loc=0,
scale=(0.0, 0.05 * 255),
name='AdditiveGaussianNoise'),
# Add a value of -10 to 10 to each pixel for
# multiply them to a number between 0.8 to 1.2.
iaa.OneOf([
iaa.Add((-10, 10), name='Add_Value_to_each_Pixel'),
iaa.Multiply((0.8, 1.2), name='Change_Brightness'),
]),
# Improve or worsen the contrast of images.
iaa.ContrastNormalization(
(0.8, 1.2), name='ContrastNormalization'),
],
# do all of the above augmentations in random order
random_order=True)
],
# do all of the above augmentations in random order
random_order=True)
return seq
for i in range(len(bbs_aug.bounding_boxes)):
bb_box = bbs_aug.bounding_boxes[i]
x_min = bb_box.x1
y_min = bb_box.y1
x_max = bb_box.x2
y_max = bb_box.y2
cls_id = bb_box.label
x_cen, y_cen, w, h = xyxy2xywh(x_min, y_min, x_max, y_max)
f.write("%d %.06f %.06f %.06f %.06f\n" %
(cls_id, x_cen, y_cen, w, h))
Width = 640
Height = 640
blur = iaa.AverageBlur(k=(2, 11)) #! 2~11 random
emboss = iaa.Emboss(alpha=(1.0, 1.0), strength=(2.0, 2.0))
gray = iaa.RemoveSaturation(from_colorspace=iaa.CSPACE_BGR)
contrast = iaa.AllChannelsCLAHE(clip_limit=(10, 10), per_channel=True)
bright = iaa.MultiplyAndAddToBrightness(mul=(0.5, 1.5), add=(-30, 30))
color = iaa.pillike.EnhanceColor()
sharpen = iaa.Sharpen(alpha=(0.5, 1.0)) #! 0.5 ~ 1.0 random
edge = iaa.pillike.FilterEdgeEnhance()
augmentations = [[bright], [emboss], [color],
[edge]] #! choice augmentation ##
rotates = [[iaa.Affine(rotate=90)], [iaa.Affine(rotate=180)],
[iaa.Affine(rotate=270)]]
flip = iaa.Fliplr(1.0) #! 100% left & right
dir = "C:\\Users\\jeongseokoon\\AI-hub\\data\\original\\"
def heavy_aug_batch(self, imgpath, n):
if isinstance(imgpath, str):
img = cv2.imread(imgpath)
else:
img = imgpath
images = np.array([img for _ in range(n)], dtype=np.uint8)
sometimes = lambda aug: iaa.Sometimes(0.3, aug)
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))),
# 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),
shear=(-16, 16),
order=[0, 1],
cval=(0, 255),
mode=ia.ALL)),
#
# 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)
return seq(images=images)
#Example on how to use this code:
#import matplotlib.pyplot as plt
# (import this code)
#datagen = DataAugmentor()
#resimg = datagen.perform_rotate("C:\\Users\\Jared Habermehl\\Documents\\2020-Image-Super-Resolution\\big_im_SR.png")
#resimg = datagen.perform_flip(resimg)
#plt.axis("off")
#plt.imshow(cv2.cvtColor(resimg, cv2.COLOR_BGR2RGB))
#plt.show()
if not line:
break
line = line.strip("\n") + ".png"
lo = float(f.readline().strip("\n"))
la = float(f.readline().strip("\n"))
ret[line] = np.array([lo, la], dtype=np.float32)
return ret
imgaugment = iaa.SomeOf((0, 5), [
iaa.Noop(),
iaa.Sometimes(0.2,
iaa.CropAndPad(percent=(-0.05, 0.05)), # random crops
),
iaa.GaussianBlur(sigma=(0, 1.8)),
iaa.Sometimes(0.2,
iaa.AverageBlur(k=(1, 3))
),
iaa.Sometimes(0.05,
iaa.Sharpen(alpha=(0.0, 0.3), lightness=(0.9, 1.1))
),
iaa.ContrastNormalization((0.8, 1.22)),
iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05 * 255)),
], random_order=True)
imgflip = iaa.Fliplr(1)
def gazeImageLoader(img_list, root_dir='data/train', batch_size=BATCH_SIZE, img_size=224, train_mode=False,
imgaug=False):
gaze_label = None
if train_mode:
import imgaug as ia
NUMBER = 7 #画像1枚当たりの牌の枚数
size_x = 512
size_y = 512
dict={"0":"1m","1":"2m","2":"3m","3":"4m","4":"5m","5":"6m","6":"7m","7":"8m","8":"9m","9":"1p","10":"2p","11":"3p","12":"4p","13":"5p","14":"6p","15":"7p","16":"8p","17":"9p","18":"1s","19":"2s","20":"3s","21":"4s","22":"5s","23":"6s","24":"7s","25":"8s","26":"9s","27":"east","28":"south","29":"west","30":"north","31":"white","32":"hatsu","33":"tyun"}
# In[2]:
aug1 = iaa.Dropout(p=0.2)
aug2 = iaa.AverageBlur(k=(5, 20))
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[3]:
#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 __init__(self, config, split, batch_size, shuffle=True, jitter=False):
'Initialization'
self.config = config
self.split = split
self.batch_size = batch_size
self.image_h = config.DATA.IMG_H
self.image_w = config.DATA.IMG_W
self.n_channels = 3 ## TODO changed to config
fold_df = pd.read_csv(self.config.FOLD_DF, engine='python')
self.dataset = fold_df.loc[fold_df['split'] == self.split].reset_index(drop=True)
if config.DEBUG:
self.fold_df = self.fold_df[:100]
print(self.split, 'set:', len(self.dataset))
self.shuffle = shuffle
self.on_epoch_end()
self.jitter = jitter
sometimes = lambda aug: iaa.Sometimes(0.5, aug)
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, 3),
[
# 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
)
def test_dtype_preservation():
reseed()
size = (4, 16, 16, 3)
images = [
np.random.uniform(0, 255, size).astype(np.uint8),
np.random.uniform(0, 65535, size).astype(np.uint16),
np.random.uniform(0, 4294967295, size).astype(np.uint32),
np.random.uniform(-128, 127, size).astype(np.int16),
np.random.uniform(-32768, 32767, size).astype(np.int32),
np.random.uniform(0.0, 1.0, size).astype(np.float32),
np.random.uniform(-1000.0, 1000.0, size).astype(np.float16),
np.random.uniform(-1000.0, 1000.0, size).astype(np.float32),
np.random.uniform(-1000.0, 1000.0, size).astype(np.float64)
]
default_dtypes = set([arr.dtype for arr in images])
# Some dtypes are here removed per augmenter, because the respective
# augmenter does not support them. This test currently only checks whether
# dtypes are preserved from in- to output for all dtypes that are supported
# per augmenter.
# dtypes are here removed via list comprehension instead of
# `default_dtypes - set([dtype])`, because the latter one simply never
# removed the dtype(s) for some reason
def _not_dts(dts):
return [dt for dt in default_dtypes if dt not in dts]
augs = [
(iaa.Add((-5, 5),
name="Add"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.AddElementwise((-5, 5), name="AddElementwise"),
_not_dts([np.uint32, np.int32, np.float64])),
(iaa.AdditiveGaussianNoise(0.01 * 255, name="AdditiveGaussianNoise"),
_not_dts([np.uint32, np.int32, np.float64])),
(iaa.Multiply((0.95, 1.05), name="Multiply"),
_not_dts([np.uint32, np.int32, np.float64])),
(iaa.Dropout(0.01, name="Dropout"),
_not_dts([np.uint32, np.int32, np.float64])),
(iaa.CoarseDropout(0.01, size_px=6, name="CoarseDropout"),
_not_dts([np.uint32, np.int32, np.float64])),
(iaa.Invert(0.01, per_channel=True, name="Invert"), default_dtypes),
(iaa.GaussianBlur(sigma=(0.95, 1.05),
name="GaussianBlur"), _not_dts([np.float16])),
(iaa.AverageBlur((3, 5), name="AverageBlur"),
_not_dts([np.uint32, np.int32, np.float16])),
(iaa.MedianBlur((3, 5), name="MedianBlur"),
_not_dts([np.uint32, np.int32, np.float16, np.float64])),
(iaa.BilateralBlur((3, 5), name="BilateralBlur"),
_not_dts([
np.uint16, np.uint32, np.int16, np.int32, np.float16, np.float64
])),
(iaa.Sharpen((0.0, 0.1), lightness=(1.0, 1.2), name="Sharpen"),
_not_dts([np.uint32, np.int32, np.float16, np.uint32])),
(iaa.Emboss(alpha=(0.0, 0.1), strength=(0.5, 1.5), name="Emboss"),
_not_dts([np.uint32, np.int32, np.float16, np.uint32])),
(iaa.EdgeDetect(alpha=(0.0, 0.1), name="EdgeDetect"),
_not_dts([np.uint32, np.int32, np.float16, np.uint32])),
(iaa.DirectedEdgeDetect(alpha=(0.0, 0.1),
direction=0,
name="DirectedEdgeDetect"),
_not_dts([np.uint32, np.int32, np.float16, np.uint32])),
(iaa.Fliplr(0.5, name="Fliplr"), default_dtypes),
(iaa.Flipud(0.5, name="Flipud"), default_dtypes),
(iaa.Affine(translate_px=(-5, 5), name="Affine-translate-px"),
_not_dts([np.uint32, np.int32])),
(iaa.Affine(translate_percent=(-0.05, 0.05),
name="Affine-translate-percent"),
_not_dts([np.uint32, np.int32])),
(iaa.Affine(rotate=(-20, 20),
name="Affine-rotate"), _not_dts([np.uint32, np.int32])),
(iaa.Affine(shear=(-20, 20),
name="Affine-shear"), _not_dts([np.uint32, np.int32])),
(iaa.Affine(scale=(0.9, 1.1),
name="Affine-scale"), _not_dts([np.uint32, np.int32])),
(iaa.PiecewiseAffine(scale=(0.001, 0.005),
name="PiecewiseAffine"), default_dtypes),
(iaa.ElasticTransformation(alpha=(0.1, 0.2),
sigma=(0.1, 0.2),
name="ElasticTransformation"),
_not_dts([np.float16])),
(iaa.Sequential([iaa.Identity(), iaa.Identity()],
name="SequentialNoop"), default_dtypes),
(iaa.SomeOf(1, [iaa.Identity(), iaa.Identity()],
name="SomeOfNoop"), default_dtypes),
(iaa.OneOf([iaa.Identity(), iaa.Identity()],
name="OneOfNoop"), default_dtypes),
(iaa.Sometimes(0.5, iaa.Identity(),
name="SometimesNoop"), default_dtypes),
(iaa.Sequential([iaa.Add(
(-5, 5)), iaa.AddElementwise((-5, 5))],
name="Sequential"),
_not_dts([np.uint32, np.int32, np.float64])),
(iaa.SomeOf(1, [iaa.Add(
(-5, 5)), iaa.AddElementwise((-5, 5))],
name="SomeOf"), _not_dts([np.uint32, np.int32,
np.float64])),
(iaa.OneOf([iaa.Add(
(-5, 5)), iaa.AddElementwise((-5, 5))],
name="OneOf"), _not_dts([np.uint32, np.int32, np.float64])),
(iaa.Sometimes(0.5, iaa.Add((-5, 5)), name="Sometimes"),
_not_dts([np.uint32, np.int32, np.float64])),
(iaa.Identity(name="Identity"), default_dtypes),
(iaa.BlendAlpha((0.0, 0.1), iaa.Identity(),
name="BlendAlphaIdentity"), default_dtypes),
(iaa.BlendAlphaElementwise(
(0.0, 0.1), iaa.Identity(),
name="BlendAlphaElementwiseIdentity"), default_dtypes),
(iaa.BlendAlphaSimplexNoise(iaa.Identity(),
name="BlendAlphaSimplexNoiseIdentity"),
default_dtypes),
(iaa.BlendAlphaFrequencyNoise(exponent=(-2, 2),
foreground=iaa.Identity(),
name="BlendAlphaFrequencyNoiseIdentity"),
default_dtypes),
(iaa.BlendAlpha((0.0, 0.1), iaa.Add(10), name="BlendAlpha"),
_not_dts([np.uint32, np.int32, np.float64])),
(iaa.BlendAlphaElementwise((0.0, 0.1),
iaa.Add(10),
name="BlendAlphaElementwise"),
_not_dts([np.uint32, np.int32, np.float64])),
(iaa.BlendAlphaSimplexNoise(iaa.Add(10),
name="BlendAlphaSimplexNoise"),
_not_dts([np.uint32, np.int32, np.float64])),
(iaa.BlendAlphaFrequencyNoise(exponent=(-2, 2),
foreground=iaa.Add(10),
name="BlendAlphaFrequencyNoise"),
_not_dts([np.uint32, np.int32, np.float64])),
(iaa.Superpixels(p_replace=0.01, n_segments=64),
_not_dts([np.float16, np.float32, np.float64])),
(iaa.Resize({
"height": 4,
"width": 4
}, name="Resize"),
_not_dts([
np.uint16, np.uint32, np.int16, np.int32, np.float32, np.float16,
np.float64
])),
(iaa.CropAndPad(px=(-10, 10), name="CropAndPad"),
_not_dts([
np.uint16, np.uint32, np.int16, np.int32, np.float32, np.float16,
np.float64
])),
(iaa.Pad(px=(0, 10), name="Pad"),
_not_dts([
np.uint16, np.uint32, np.int16, np.int32, np.float32, np.float16,
np.float64
])),
(iaa.Crop(px=(0, 10), name="Crop"),
_not_dts([
np.uint16, np.uint32, np.int16, np.int32, np.float32, np.float16,
np.float64
]))
]
for (aug, allowed_dtypes) in augs:
for images_i in images:
if images_i.dtype in allowed_dtypes:
images_aug = aug.augment_images(images_i)
assert images_aug.dtype == images_i.dtype
intensity_seq = iaa.Sequential([
#iaa.Invert(0.3),
iaa.Sometimes(0.3, iaa.ContrastNormalization((0.5, 1.5))),
iaa.OneOf([
iaa.Noop(),
iaa.Sequential([
iaa.OneOf([
iaa.Add((-10, 10)),
iaa.AddElementwise((-10, 10)),
iaa.Multiply((0.95, 1.05)),
iaa.MultiplyElementwise((0.95, 1.05)),
]),
]),
iaa.OneOf([
iaa.GaussianBlur(sigma=(0.0, 1.0)),
iaa.AverageBlur(k=(2, 5)),
#iaa.MedianBlur(k=(3, 5))
])
])
], random_order=False)
tta_intensity_seq = iaa.Sequential([
iaa.Noop()
], random_order=False)
def compute_random_pad(limit=(-4,4)):
dy = IMG_TAR_SIZE - IMG_ORI_SIZE*SCALE
dy0 = dy//2 + np.random.randint(limit[0],limit[1]) # np.random.choice(dy)
dy1 = dy - dy0
dx0 = dy//2 + np.random.randint(limit[0],limit[1]) # np.random.choice(dy)
dx1 = dy - dx0
def __init__(self,
list_file,
train,
transform,
device,
little_train=False,
S=7):
print('data init')
self.train = train
self.transform = transform
self.fnames = []
self.boxes = []
self.labels = []
self.S = S
self.B = 2
self.C = 20
self.device = device
self.augmentation = iaa.Sometimes(
0.5,
iaa.SomeOf(
(1, 6),
[
iaa.Dropout([0.05, 0.2]), # drop 5% or 20% of all pixels
iaa.Sharpen((0.1, 1.0)), # 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.Fliplr(1.0),
# iaa.Flipud(1.0),
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))
torch.manual_seed(23)
with open(list_file) as f:
lines = f.readlines()
if little_train:
lines = lines[:64]
for line in lines:
splited = line.strip().split()
self.fnames.append(splited[0])
self.num_samples = len(self.fnames)
def apply_transform(matrix, image, params, cpara):
# rgb
# seq describes an object for rgb image augmentation using aleju/imgaug
seq = iaa.Sequential(
[
# blur
iaa.SomeOf((0, 2), [
iaa.GaussianBlur((0.0, 2.0)),
iaa.AverageBlur(k=(3, 7)),
iaa.MedianBlur(k=(3, 7)),
iaa.BilateralBlur(d=(1, 7)),
iaa.MotionBlur(k=(3, 7))
]),
# color
iaa.SomeOf(
(0, 2),
[
# iaa.WithColorspace(),
iaa.AddToHueAndSaturation((-15, 15)),
# iaa.ChangeColorspace(to_colorspace[], alpha=0.5),
iaa.Grayscale(alpha=(0.0, 0.2))
]),
# brightness
iaa.OneOf([
iaa.Sequential([
iaa.Add((-10, 10), per_channel=0.5),
iaa.Multiply((0.75, 1.25), per_channel=0.5)
]),
iaa.Add((-10, 10), per_channel=0.5),
iaa.Multiply((0.75, 1.25), per_channel=0.5),
iaa.FrequencyNoiseAlpha(exponent=(-4, 0),
first=iaa.Multiply(
(0.75, 1.25), per_channel=0.5),
second=iaa.LinearContrast(
(0.7, 1.3), per_channel=0.5))
]),
# contrast
iaa.SomeOf((0, 2), [
iaa.GammaContrast((0.75, 1.25), per_channel=0.5),
iaa.SigmoidContrast(
gain=(0, 10), cutoff=(0.25, 0.75), per_channel=0.5),
iaa.LogContrast(gain=(0.75, 1), per_channel=0.5),
iaa.LinearContrast(alpha=(0.7, 1.3), per_channel=0.5)
]),
],
random_order=True)
image = seq.augment_image(image)
'''
seq = iaa.Sequential([
iaa.Sometimes(0.5, iaa.CoarseDropout(p=0.2, size_percent=(0.1, 0.25))),
iaa.Sometimes(0.5, iaa.GaussianBlur(1.2*np.random.rand())),
iaa.Sometimes(0.5, iaa.Add((-25, 25), per_channel=0.3)),
iaa.Sometimes(0.5, iaa.Invert(0.2, per_channel=True)),
iaa.Sometimes(0.5, iaa.Multiply((0.6, 1.4), per_channel=0.5)),
iaa.Sometimes(0.5, iaa.Multiply((0.6, 1.4))),
iaa.Sometimes(0.5, iaa.ContrastNormalization((0.5, 2.2), per_channel=0.3))
], random_order=False)
image = seq.augment_image(image)
'''
image = cv2.warpAffine(
image,
matrix[:2, :],
dsize=(image.shape[1], image.shape[0]),
flags=params.cvInterpolation(),
borderMode=params.cvBorderMode(),
borderValue=params.cval,
)
return image
def imgAugmentor(images):
# 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.
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))),
# 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": (0.9, 1.1),
"y": (0.9, 1.1)
},
translate_percent={
"x": (-0.1, 0.1),
"y": (-0.1, 0.1)
},
rotate=(-45, 45),
shear=(-5, 5),
order=[0, 1],
cval=(0, 255),
mode=ia.ALL)),
#
# 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, 1.0)),
iaa.AverageBlur(k=(3, 5)),
iaa.MedianBlur(k=(3, 5)),
]),
# 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=(0.8, 1.2)),
# 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=(0.5, 1.0)),
iaa.DirectedEdgeDetect(alpha=(0.5, 1.0),
direction=(0.5, 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, 0.01 * 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, 0.05), per_channel=0.5),
iaa.CoarseDropout((0.01, 0.05),
size_percent=(0.01, 0.03),
per_channel=0.2),
]),
# Invert each image's channel with 5% probability.
# This sets each pixel value v to 255-v.
iaa.Invert(0.01,
per_channel=True), # invert color channels
# Add a value of -10 to 10 to each pixel.
iaa.Add((-5, 5), per_channel=0.5),
# Change brightness of images (50-150% of original value).
iaa.Multiply((0.8, 1.2), per_channel=0.5),
# Improve or worsen the contrast of images.
iaa.LinearContrast((0.8, 1.2), 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, 3.0), 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 images_aug
def simple_imgaug_example():
image_dir_path = dataset_home_dir_path + '/phenotyping/cvppp2017_lsc_lcc_challenge/package/CVPPP2017_LSC_training/training/A1'
label_dir_path = dataset_home_dir_path + '/phenotyping/cvppp2017_lsc_lcc_challenge/package/CVPPP2017_LSC_training/training/A1'
images, labels = prepare_dataset(image_dir_path, label_dir_path)
image_width, image_height = 200, 200
# FIXME [decide] >> Before or after random transformation?
# Preprocessing (normalization, standardization, etc).
images_pp = images.astype(np.float)
#images_pp /= 255.0
images_pp = standardize_samplewise(images_pp)
#images_pp = standardize_featurewise(images_pp)
if True:
augmenter = iaa.SomeOf(
(1, 2),
[
iaa.OneOf([
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 -10 to +10 percent (per axis).
rotate=(-10, 10), # Rotate by -10 to +10 degrees.
shear=(-5, 5), # Shear by -5 to +5 degrees.
#order=[0, 1], # Use nearest neighbour or bilinear interpolation (fast).
order=
0, # 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' # Use any of scikit-image's warping modes (see 2nd image from the top for examples).
),
#iaa.PiecewiseAffine(scale=(0.01, 0.05)), # Move parts of the image around. Slow.
iaa.PerspectiveTransform(scale=(0.01, 0.1)),
iaa.ElasticTransformation(
alpha=(20.0, 50.0), sigma=(6.5, 8.5)
), # Move pixels locally around (with random strengths).
]),
iaa.OneOf([
iaa.GaussianBlur(sigma=(
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.MotionBlur(k=(5, 11),
angle=(0, 360),
direction=(-1.0, 1.0),
order=1),
]),
iaa.OneOf([
iaa.AdditiveGaussianNoise(
loc=0, scale=(0.1 * 255, 0.5 * 255),
per_channel=False), # Add Gaussian noise to images.
iaa.AdditiveLaplaceNoise(loc=0,
scale=(0.1 * 255, 0.4 * 255),
per_channel=False),
iaa.AdditivePoissonNoise(lam=(32, 96), per_channel=False),
iaa.CoarseSaltAndPepper(p=(0.1, 0.3),
size_percent=(0.2, 0.9),
per_channel=False),
iaa.CoarseSalt(p=(0.1, 0.3),
size_percent=(0.2, 0.9),
per_channel=False),
iaa.CoarsePepper(p=(0.1, 0.3),
size_percent=(0.2, 0.9),
per_channel=False),
iaa.CoarseDropout(p=(0.1, 0.3),
size_percent=(0.05, 0.3),
per_channel=False),
]),
iaa.OneOf([
iaa.MultiplyHueAndSaturation(mul=(-10, 10),
per_channel=False),
iaa.AddToHueAndSaturation(value=(-255, 255),
per_channel=False),
iaa.LinearContrast(
alpha=(0.5, 1.5),
per_channel=False), # Improve or worsen the contrast.
iaa.Invert(p=1,
per_channel=False), # Invert color channels.
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.
]),
],
random_order=True)
elif False:
augmenter = 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.
iaa.Sometimes(
0.5,
iaa.CropAndPad(percent=(-0.05, 0.1),
pad_mode=ia.ALL,
pad_cval=(0, 255))),
iaa.Sometimes(
0.5,
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),
[
iaa.Sometimes(
0.5,
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)),
iaa.Sometimes(
0.5,
iaa.ElasticTransformation(alpha=(0.5, 3.5),
sigma=0.25)
), # Move pixels locally around (with random strengths).
iaa.Sometimes(
0.5, iaa.PiecewiseAffine(scale=(0.01, 0.05))
), # Sometimes move parts of the image around.
iaa.Sometimes(
0.5, iaa.PerspectiveTransform(scale=(0.01, 0.1)))
],
random_order=True)
],
random_order=True)
else:
augmenter = iaa.Sequential([
iaa.SomeOf(
1,
[
#iaa.Sometimes(0.5, iaa.Crop(px=(0, 100))), # Crop images from each side by 0 to 16px (randomly chosen).
iaa.Sometimes(0.5, iaa.Crop(percent=(
0,
0.1))), # Crop images by 0-10% of their height/width.
iaa.Fliplr(0.5), # Horizontally flip 50% of the images.
iaa.Flipud(0.5), # Vertically flip 50% of the images.
iaa.Sometimes(
0.5,
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).
order=
0, # 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' # Use any of scikit-image's warping modes (see 2nd image from the top for examples).
)),
iaa.Sometimes(0.5, iaa.GaussianBlur(
sigma=(0,
3.0))) # Blur images with a sigma of 0 to 3.0.
]),
iaa.Scale(size={
'height': image_height,
'width': image_width
}) # Resize.
])
for idx in range(images.shape[0]):
images_pp[idx] = (images_pp[idx] - np.min(images_pp[idx])) / (
np.max(images_pp[idx]) - np.min(images_pp[idx])) * 255
images_pp = images_pp.astype(np.uint8)
# Test 1 (good).
augmenter_det = augmenter.to_deterministic(
) # Call this for each batch again, NOT only once at the start.
#images_aug1 = augmenter_det.augment_images(images)
images_aug1 = augmenter_det.augment_images(images_pp)
labels_aug1 = augmenter_det.augment_images(labels)
augmenter_det = augmenter.to_deterministic(
) # Call this for each batch again, NOT only once at the start.
#images_aug2 = augmenter_det.augment_images(images)
images_aug2 = augmenter_det.augment_images(images_pp)
labels_aug2 = augmenter_det.augment_images(labels)
#export_images(images, labels, './augmented1/img', '')
export_images(images_pp, labels, './augmented1/img', '')
export_images(images_aug1, labels_aug1, './augmented1/img', '_aug1')
export_images(images_aug2, labels_aug2, './augmented1/img', '_aug2')
# Test 2 (bad).
augmenter_det = augmenter.to_deterministic(
) # Call this for each batch again, NOT only once at the start.
#images_aug1 = augmenter_det.augment_images(images)
images_aug1 = augmenter_det.augment_images(images_pp)
labels_aug1 = augmenter_det.augment_images(labels)
#images_aug2 = augmenter_det.augment_images(images)
images_aug2 = augmenter_det.augment_images(images_pp)
labels_aug2 = augmenter_det.augment_images(labels)
#export_images(images, labels, './augmented2/img', '')
export_images(images_pp, labels, './augmented2/img', '')
export_images(images_aug1, labels_aug1, './augmented2/img', '_aug1')
export_images(images_aug2, labels_aug2, './augmented2/img', '_aug2')
print('*********************************', images_pp.dtype)
def get_optimistic_img_aug():
texture = iaa.OneOf([
iaa.Superpixels(p_replace=(0.1, 0.3),
n_segments=(500, 1000),
interpolation="cubic",
name='Superpixels'),
iaa.Sharpen(alpha=(0, 1.0), lightness=(0.5, 1.0), name='Sharpen'),
iaa.Emboss(alpha=(0, 1.0), strength=(0.1, 0.3), name='Emboss'),
iaa.OneOf([
iaa.EdgeDetect(alpha=(0, 0.4)),
iaa.DirectedEdgeDetect(alpha=(0, 0.7), direction=(0.0, 1.0)),
],
name='EdgeDetect'),
iaa.ElasticTransformation(alpha=(0.5, 1.0),
sigma=0.2,
name='ElasticTransformation'),
])
blur = iaa.OneOf([
iaa.GaussianBlur((1, 5.0), name='GaussianBlur'),
iaa.AverageBlur(k=(2, 15), name='AverageBlur'),
iaa.MedianBlur(k=(3, 15), name='MedianBlur'),
iaa.BilateralBlur(d=(3, 15),
sigma_color=(10, 250),
sigma_space=(10, 250),
name='BilaBlur'),
])
affine = iaa.OneOf([
iaa.Affine(rotate=(-3, 3)),
iaa.Affine(translate_percent={
"x": (0.95, 1.05),
"y": (0.95, 1.05)
}),
iaa.Affine(scale={
"x": (0.95, 1.05),
"y": (0.95, 1.05)
}),
iaa.Affine(shear=(-2, 2)),
])
factors = iaa.OneOf([
iaa.Multiply(iap.Choice([0.75, 1.25]), per_channel=False),
iaa.EdgeDetect(1.0),
])
seq = iaa.Sequential(
[
# Size and shape ==================================================
iaa.Sequential([
iaa.Fliplr(0.5),
iaa.OneOf([
iaa.Noop(),
iaa.Affine(rotate=90),
iaa.Affine(rotate=180),
iaa.Affine(rotate=270),
]),
half_times(
iaa.SomeOf(
(1, 2),
[
iaa.Crop(percent=(0.1, 0.4)), # Random Crops
iaa.PerspectiveTransform(scale=(0.10, 0.175)),
iaa.PiecewiseAffine(scale=(0.01, 0.06),
nb_rows=(3, 6),
nb_cols=(3, 6)),
])),
]),
# Texture ==================================================
sometimes(
iaa.SomeOf((1, 2), [
texture,
iaa.Alpha((0.0, 1.0), first=texture, per_channel=False)
],
random_order=True,
name='Texture')),
half_times(
iaa.SomeOf((1, 2), [
blur,
iaa.Alpha((0.0, 1.0), first=blur, per_channel=False),
iaa.Alpha(factor=(0.2, 0.8),
first=iaa.Sequential([
affine,
blur,
]),
per_channel=False),
],
random_order=True,
name='Blur')),
# Noise ==================================================
sometimes(
iaa.SomeOf(
(1, 2),
[
# Just noise
iaa.AdditiveGaussianNoise(
loc=0,
scale=(0.0, 0.15 * 255),
per_channel=False,
name='AdditiveGaussianNoise'),
iaa.SaltAndPepper(
0.05, per_channel=False, name='SaltAndPepper'),
# Regularization
iaa.Dropout(
(0.01, 0.1), per_channel=False, name='Dropout'),
iaa.CoarseDropout((0.03, 0.15),
size_percent=(0.02, 0.05),
per_channel=False,
name='CoarseDropout'),
iaa.Alpha(
factor=(0.2, 0.8),
first=texture,
second=iaa.CoarseDropout(
p=0.1, size_percent=(0.02, 0.05)),
per_channel=False,
),
# Perlin style noise
iaa.SimplexNoiseAlpha(first=factors,
per_channel=False,
aggregation_method="max",
sigmoid=False,
upscale_method='cubic',
size_px_max=(2, 12),
name='SimplexNoiseAlpha'),
iaa.FrequencyNoiseAlpha(first=factors,
per_channel=False,
aggregation_method="max",
sigmoid=False,
upscale_method='cubic',
size_px_max=(2, 12),
name='FrequencyNoiseAlpha'),
],
random_order=True,
name='Noise')),
],
random_order=False)
def activator_masks(images, augmenter, parents, default):
if 'Unnamed' not in augmenter.name:
return False
else:
return default
hooks_masks = ia.HooksImages(activator=activator_masks)
return seq, hooks_masks
new_img = Image.fromarray(new_img)
new_label = Image.fromarray(new_label)
new_inter = Image.fromarray(new_inter)
new_img.save("../data/GenData/TrainData/images/" + str("%04d" % j) +
"_flip_" + str(i) + "_.png")
new_label.save("../data/GenData/TrainData/labels/" + str("%04d" % j) +
"_flip_" + str(i) + "_.png")
new_inter.save("../data/GenData/TrainData/watershed/" +
str("%04d" % j) + "_flip_" + str(i) + "_.png")
print("Finished augmentations for rotations and cropping..")
# Blur = 5
blur = [
iaa.GaussianBlur(sigma=0.9),
iaa.GaussianBlur(sigma=2.9),
iaa.AverageBlur(k=7),
iaa.AverageBlur(k=9),
iaa.MedianBlur(k=7),
]
for i in tqdm(range(len(blur)), total=len(blur)):
for j, sample in tqdm(enumerate(data), total=len(data)):
img, label, inter = sample
new_img = blur[i].augment_image(img)
new_img = Image.fromarray(new_img)
label = Image.fromarray(label)
inter = Image.fromarray(inter)
new_img.save("../data/GenData/TrainData/images/" + str("%04d" % j) +
"_blur_" + str(i) + "_.png")
label.save("../data/GenData/TrainData/labels/" + str("%04d" % j) +
"_blur_" + str(i) + "_.png")
def main():
# datapath为存放训练图片的地方
datapath = '/home/zhex/data/yuncong/'
# original_file为需要被增强的
original_file = '/home/zhex/data/yuncong/UCSD_train.txt' # 需要被增强的训练真值txt
# aug_file只记录了新增的增强后图片的box，要得到原始+增强的所有label：cat original_file augfile>finalfile(txt拼接)
# aug_file输出是pdpd需要的格式，pytorch需要另行转换(可以拼接得到finalfile后直接将finalfile转换)
aug_file = 'augfile_UCSD.txt'
dict_before = readlist(original_file)
new_fp = open(aug_file, 'w')
# augscene = {'Mall': 3, 'Part_B': 10, 'Part_A': 13} # 需要哪些场景，新增几倍数量的新数据
augscene = {'UCSD': 2}
for scene in augscene:
for i in range(augscene[scene]):
for img_id in dict_before.keys():
if scene in img_id:
print(img_id)
img = Image.open(datapath + img_id)
img = np.array(img)
bbs = ia.BoundingBoxesOnImage([
ia.BoundingBox(x1=x, y1=y, x2=x + w, y2=y + h)
for [x, y, w, h] in dict_before[img_id]
],
shape=img.shape)
# 设置数据增强方式
seq = iaa.SomeOf(
(1, 3),
[
iaa.Crop(px=(0, 16)), # 裁剪
iaa.Multiply((0.7, 1.3)), # 改变色调
iaa.Affine(scale=(0.5, 0.7)), # 放射变换
iaa.GaussianBlur(sigma=(0, 1.5)), # 高斯模糊
# iaa.AddToHueAndSaturation(value=(25,-25)),
iaa.ChannelShuffle(1), # RGB三通道随机交换
iaa.ElasticTransformation(alpha=0.1),
# iaa.Grayscale(alpha=(0.2, 0.5)),
iaa.Pepper(p=0.03),
iaa.AdditiveGaussianNoise(scale=(0.03 * 255,
0.05 * 255)),
iaa.Dropout(p=(0.03, 0.05)),
iaa.Salt(p=(0.03, 0.05)),
iaa.AverageBlur(k=(1, 3)),
iaa.Add((-10, 10)),
iaa.CoarseSalt(size_percent=0.01)
])
seq_det = seq.to_deterministic(
) # 保持坐标和图像同步改变，每个batch都要调用一次，不然每次的增强都是一样的
image_aug = seq_det.augment_images([img])[0]
bbs_aug = seq_det.augment_bounding_boxes([bbs])[0]
pic_name = img_id.split('/')[-1].split('.')[0]
pic_dir = img_id.split(pic_name)[0]
if not os.path.exists(datapath + 'myaug/' + pic_dir):
os.makedirs(datapath + 'myaug/' + pic_dir)
new_img_id = 'myaug/' + pic_dir + pic_name + '_{}'.format(
i) + '.jpg'
Image.fromarray(image_aug).save(datapath + new_img_id)
new_fp = writelist(new_fp, new_img_id,
bbs_aug.bounding_boxes)
def test_many_augmenters(self):
keypoints = []
for y in sm.xrange(40 // 5):
for x in sm.xrange(60 // 5):
keypoints.append(ia.Keypoint(y=y * 5, x=x * 5))
keypoints_oi = ia.KeypointsOnImage(keypoints, shape=(40, 60, 3))
keypoints_oi_empty = ia.KeypointsOnImage([], shape=(40, 60, 3))
augs = [
iaa.Add((-5, 5), name="Add"),
iaa.AddElementwise((-5, 5), name="AddElementwise"),
iaa.AdditiveGaussianNoise(0.01 * 255,
name="AdditiveGaussianNoise"),
iaa.Multiply((0.95, 1.05), name="Multiply"),
iaa.Dropout(0.01, name="Dropout"),
iaa.CoarseDropout(0.01, size_px=6, name="CoarseDropout"),
iaa.Invert(0.01, per_channel=True, name="Invert"),
iaa.GaussianBlur(sigma=(0.95, 1.05), name="GaussianBlur"),
iaa.AverageBlur((3, 5), name="AverageBlur"),
iaa.MedianBlur((3, 5), name="MedianBlur"),
iaa.Sharpen((0.0, 0.1), lightness=(1.0, 1.2), name="Sharpen"),
iaa.Emboss(alpha=(0.0, 0.1), strength=(0.5, 1.5), name="Emboss"),
iaa.EdgeDetect(alpha=(0.0, 0.1), name="EdgeDetect"),
iaa.DirectedEdgeDetect(alpha=(0.0, 0.1),
direction=0,
name="DirectedEdgeDetect"),
iaa.Fliplr(0.5, name="Fliplr"),
iaa.Flipud(0.5, name="Flipud"),
iaa.Affine(translate_px=(-5, 5), name="Affine-translate-px"),
iaa.Affine(translate_percent=(-0.05, 0.05),
name="Affine-translate-percent"),
iaa.Affine(rotate=(-20, 20), name="Affine-rotate"),
iaa.Affine(shear=(-20, 20), name="Affine-shear"),
iaa.Affine(scale=(0.9, 1.1), name="Affine-scale"),
iaa.PiecewiseAffine(scale=(0.001, 0.005), name="PiecewiseAffine"),
iaa.ElasticTransformation(alpha=(0.1, 0.2),
sigma=(0.1, 0.2),
name="ElasticTransformation"),
iaa.BlendAlpha((0.0, 0.1), iaa.Add(10), name="BlendAlpha"),
iaa.BlendAlphaElementwise((0.0, 0.1),
iaa.Add(10),
name="BlendAlphaElementwise"),
iaa.BlendAlphaSimplexNoise(iaa.Add(10),
name="BlendAlphaSimplexNoise"),
iaa.BlendAlphaFrequencyNoise(exponent=(-2, 2),
foreground=iaa.Add(10),
name="BlendAlphaSimplexNoise"),
iaa.Superpixels(p_replace=0.01, n_segments=64),
iaa.Resize(0.5, name="Resize"),
iaa.CropAndPad(px=(-10, 10), name="CropAndPad"),
iaa.Pad(px=(0, 10), name="Pad"),
iaa.Crop(px=(0, 10), name="Crop")
]
for aug in augs:
dss = []
for i in sm.xrange(10):
aug_det = aug.to_deterministic()
kp_fully_empty_aug = aug_det.augment_keypoints([])
assert kp_fully_empty_aug == []
kp_first_empty_aug = aug_det.augment_keypoints(
keypoints_oi_empty)
assert len(kp_first_empty_aug.keypoints) == 0
kp_image = keypoints_oi.to_keypoint_image(size=5)
with assertWarns(self, iaa.SuspiciousSingleImageShapeWarning):
kp_image_aug = aug_det.augment_image(kp_image)
kp_image_aug_rev = ia.KeypointsOnImage.from_keypoint_image(
kp_image_aug,
if_not_found_coords={
"x": -9999,
"y": -9999
},
nb_channels=1)
kp_aug = aug_det.augment_keypoints([keypoints_oi])[0]
ds = []
assert len(kp_image_aug_rev.keypoints) == len(
kp_aug.keypoints), (
"Lost keypoints for '%s' (%d vs expected %d)" %
(aug.name, len(kp_aug.keypoints),
len(kp_image_aug_rev.keypoints)))
gen = zip(kp_aug.keypoints, kp_image_aug_rev.keypoints)
for kp_pred, kp_pred_img in gen:
kp_pred_lost = (kp_pred.x == -9999 and kp_pred.y == -9999)
kp_pred_img_lost = (kp_pred_img.x == -9999
and kp_pred_img.y == -9999)
if not kp_pred_lost and not kp_pred_img_lost:
d = np.sqrt((kp_pred.x - kp_pred_img.x)**2 +
(kp_pred.y - kp_pred_img.y)**2)
ds.append(d)
dss.extend(ds)
if len(ds) == 0:
print("[INFO] No valid keypoints found for '%s' "
"in test_keypoint_augmentation()" % (str(aug), ))
assert np.average(dss) < 5.0, \
"Average distance too high (%.2f, with ds: %s)" \
% (np.average(dss), str(dss))
def __init__(self):
super(ImgAugTransform, self).__init__()
from imgaug import augmenters as iaa
from imgaug import parameters as iap
self.seq = iaa.Sequential(children=[
iaa.Sequential(children=[
iaa.Sequential(children=[
iaa.OneOf(children=[
iaa.Sometimes(
p=0.95,
then_list=iaa.Affine(scale={
"x": (0.9, 1.1),
"y": (0.9, 1.1)
},
translate_percent={
"x": (-0.05, 0.05),
"y": (-0.05, 0.05)
},
rotate=(-30, 30),
shear=(-15, 15),
order=iap.Choice(
[0, 1, 3],
p=[0.15, 0.80, 0.05]),
mode="reflect",
name="Affine")),
iaa.Sometimes(p=0.05,
then_list=iaa.PerspectiveTransform(
scale=(0.01, 0.1)))
],
name="Blur"),
iaa.Sometimes(p=0.01,
then_list=iaa.PiecewiseAffine(
scale=(0.0, 0.01),
nb_rows=(4, 20),
nb_cols=(4, 20),
order=iap.Choice([0, 1, 3],
p=[0.15, 0.80, 0.05]),
mode="reflect",
name="PiecewiseAffine"))
],
random_order=True,
name="GeomTransform"),
iaa.Sequential(children=[
iaa.Sometimes(p=0.75,
then_list=iaa.Add(value=(-10, 10),
per_channel=0.5,
name="Brightness")),
iaa.Sometimes(p=0.05,
then_list=iaa.Emboss(alpha=(0.0, 0.5),
strength=(0.5, 1.2),
name="Emboss")),
iaa.Sometimes(p=0.1,
then_list=iaa.Sharpen(alpha=(0.0, 0.5),
lightness=(0.5, 1.2),
name="Sharpen")),
iaa.Sometimes(p=0.25,
then_list=iaa.ContrastNormalization(
alpha=(0.5, 1.5),
per_channel=0.5,
name="ContrastNormalization"))
],
random_order=True,
name="ColorTransform"),
iaa.Sequential(children=[
iaa.Sometimes(p=0.5,
then_list=iaa.AdditiveGaussianNoise(
loc=0,
scale=(0.0, 10.0),
per_channel=0.5,
name="AdditiveGaussianNoise")),
iaa.Sometimes(p=0.1,
then_list=iaa.SaltAndPepper(
p=(0, 0.001),
per_channel=0.5,
name="SaltAndPepper"))
],
random_order=True,
name="Noise"),
iaa.OneOf(children=[
iaa.Sometimes(p=0.05,
then_list=iaa.MedianBlur(k=3,
name="MedianBlur")),
iaa.Sometimes(p=0.05,
then_list=iaa.AverageBlur(
k=(2, 4), name="AverageBlur")),
iaa.Sometimes(p=0.5,
then_list=iaa.GaussianBlur(
sigma=(0.0, 2.0), name="GaussianBlur"))
],
name="Blur"),
],
random_order=True,
name="MainProcess")
])
def test_unusual_channel_numbers():
reseed()
images = [(0, create_random_images((4, 16, 16))),
(1, create_random_images((4, 16, 16, 1))),
(2, create_random_images((4, 16, 16, 2))),
(4, create_random_images((4, 16, 16, 4))),
(5, create_random_images((4, 16, 16, 5))),
(10, create_random_images((4, 16, 16, 10))),
(20, create_random_images((4, 16, 16, 20)))]
augs = [
iaa.Add((-5, 5), name="Add"),
iaa.AddElementwise((-5, 5), name="AddElementwise"),
iaa.AdditiveGaussianNoise(0.01 * 255, name="AdditiveGaussianNoise"),
iaa.Multiply((0.95, 1.05), name="Multiply"),
iaa.Dropout(0.01, name="Dropout"),
iaa.CoarseDropout(0.01, size_px=6, name="CoarseDropout"),
iaa.Invert(0.01, per_channel=True, name="Invert"),
iaa.GaussianBlur(sigma=(0.95, 1.05), name="GaussianBlur"),
iaa.AverageBlur((3, 5), name="AverageBlur"),
iaa.MedianBlur((3, 5), name="MedianBlur"),
iaa.Sharpen((0.0, 0.1), lightness=(1.0, 1.2), name="Sharpen"),
iaa.Emboss(alpha=(0.0, 0.1), strength=(0.5, 1.5), name="Emboss"),
iaa.EdgeDetect(alpha=(0.0, 0.1), name="EdgeDetect"),
iaa.DirectedEdgeDetect(alpha=(0.0, 0.1),
direction=0,
name="DirectedEdgeDetect"),
iaa.Fliplr(0.5, name="Fliplr"),
iaa.Flipud(0.5, name="Flipud"),
iaa.Affine(translate_px=(-5, 5), name="Affine-translate-px"),
iaa.Affine(translate_percent=(-0.05, 0.05),
name="Affine-translate-percent"),
iaa.Affine(rotate=(-20, 20), name="Affine-rotate"),
iaa.Affine(shear=(-20, 20), name="Affine-shear"),
iaa.Affine(scale=(0.9, 1.1), name="Affine-scale"),
iaa.PiecewiseAffine(scale=(0.001, 0.005), name="PiecewiseAffine"),
iaa.PerspectiveTransform(scale=(0.01, 0.10),
name="PerspectiveTransform"),
iaa.ElasticTransformation(alpha=(0.1, 0.2),
sigma=(0.1, 0.2),
name="ElasticTransformation"),
iaa.Sequential([iaa.Add((-5, 5)),
iaa.AddElementwise((-5, 5))]),
iaa.SomeOf(1, [iaa.Add(
(-5, 5)), iaa.AddElementwise((-5, 5))]),
iaa.OneOf([iaa.Add((-5, 5)),
iaa.AddElementwise((-5, 5))]),
iaa.Sometimes(0.5, iaa.Add((-5, 5)), name="Sometimes"),
iaa.Identity(name="Noop"),
iaa.BlendAlpha((0.0, 0.1), iaa.Add(10), name="BlendAlpha"),
iaa.BlendAlphaElementwise((0.0, 0.1),
iaa.Add(10),
name="BlendAlphaElementwise"),
iaa.BlendAlphaSimplexNoise(iaa.Add(10), name="BlendAlphaSimplexNoise"),
iaa.BlendAlphaFrequencyNoise(exponent=(-2, 2),
foreground=iaa.Add(10),
name="BlendAlphaSimplexNoise"),
iaa.Superpixels(p_replace=0.01, n_segments=64),
iaa.Resize({
"height": 4,
"width": 4
}, name="Resize"),
iaa.CropAndPad(px=(-10, 10), name="CropAndPad"),
iaa.Pad(px=(0, 10), name="Pad"),
iaa.Crop(px=(0, 10), name="Crop")
]
for aug in augs:
for (nb_channels, images_c) in images:
if aug.name != "Resize":
images_aug = aug.augment_images(images_c)
assert images_aug.shape == images_c.shape
image_aug = aug.augment_image(images_c[0])
assert image_aug.shape == images_c[0].shape
else:
images_aug = aug.augment_images(images_c)
image_aug = aug.augment_image(images_c[0])
if images_c.ndim == 3:
assert images_aug.shape == (4, 4, 4)
assert image_aug.shape == (4, 4)
else:
assert images_aug.shape == (4, 4, 4, images_c.shape[3])
assert image_aug.shape == (4, 4, images_c.shape[3])
def data_augmentation(self, sequence):
"""
square or 16x9
"""
random.seed(self.seed)
self.inc_seed()
#square = random.randint(0,1)
square = 0
"""
hflip
"""
random.seed(self.seed)
self.inc_seed()
hflip = random.randint(0, 1)
hflip_aug = iaa.Sequential([iaa.Fliplr(1)])
hflip_det = hflip_aug.to_deterministic()
zoom_scale_rot_aug = iaa.Sequential([
iaa.Affine(scale={
"x": (0.9, 1.1),
"y": (0.9, 1.1)
},
translate_percent={
"x": (-0.1, 0.1),
"y": (-0.1, 0.1)
},
rotate=(-10, 10))
],
random_order=True)
zoom_scale_rot_det = zoom_scale_rot_aug.to_deterministic()
counter = 0
for frame in sequence:
if square:
"""
square or 16x9
"""
blank_image = np.zeros((256, 256, 3), np.uint8)
blank_image[:, :] = (79, 225, 7)
frame = cv2.resize(frame, (144, 144),
interpolation=cv2.INTER_AREA)
x_offset = y_offset = 56
blank_image[y_offset:y_offset + frame.shape[0],
x_offset:x_offset + frame.shape[1]] = frame
frame = blank_image
if hflip:
"""
hflip
"""
frame = hflip_det.augment_image(frame)
"""
Zoom & Verschiebung
"""
#frame = zoom_scale_rot_det.augment_image(frame)
"""
black Background after rotation to green
"""
#hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
#sensitivity is a int, typically set to 15 - 20
#lower_black = np.array([0, 0, 0])
#upper_black = np.array([180, 255, 10])
#mask = cv2.inRange(hsv, lower_black, upper_black)
#frame[mask>0]=(79,255,7)
"""
Background Pic or Color
"""
sequence[counter] = frame
counter = counter + 1
sometimes = lambda aug: iaa.Sometimes(0.5, aug)
seq = iaa.Sequential(
[
#
# 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, 1),
[
# 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, 0.10),
size_percent=(0.02, 0.03),
per_channel=0.2),
]),
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)),
#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)
sequence = seq(images=sequence)
return sequence
def test_determinism():
reseed()
images = [
ia.quokka(size=(128, 128)),
ia.quokka(size=(64, 64)),
ia.imresize_single_image(skimage.data.astronaut(), (128, 256))
]
images.extend([ia.quokka(size=(16, 16))] * 20)
keypoints = [
ia.KeypointsOnImage([
ia.Keypoint(x=20, y=10),
ia.Keypoint(x=5, y=5),
ia.Keypoint(x=10, y=43)
],
shape=(50, 60, 3))
] * 20
augs = [
iaa.Sequential([iaa.Fliplr(0.5), iaa.Flipud(0.5)]),
iaa.SomeOf(1, [iaa.Fliplr(0.5), iaa.Flipud(0.5)]),
iaa.OneOf([iaa.Fliplr(0.5), iaa.Flipud(0.5)]),
iaa.Sometimes(0.5, iaa.Fliplr(1.0)),
iaa.WithColorspace("HSV", children=iaa.Add((-50, 50))),
iaa.Resize((0.5, 0.9)),
iaa.CropAndPad(px=(-50, 50)),
iaa.Pad(px=(1, 50)),
iaa.Crop(px=(1, 50)),
iaa.Fliplr(0.5),
iaa.Flipud(0.5),
iaa.Superpixels(p_replace=(0.25, 1.0), n_segments=(16, 128)),
iaa.Grayscale(alpha=(0.1, 1.0)),
iaa.GaussianBlur((0.1, 3.0)),
iaa.AverageBlur((3, 11)),
iaa.MedianBlur((3, 11)),
iaa.Sharpen(alpha=(0.1, 1.0), lightness=(0.8, 1.2)),
iaa.Emboss(alpha=(0.1, 1.0), strength=(0.8, 1.2)),
iaa.EdgeDetect(alpha=(0.1, 1.0)),
iaa.DirectedEdgeDetect(alpha=(0.1, 1.0), direction=(0.0, 1.0)),
iaa.Add((-50, 50)),
iaa.AddElementwise((-50, 50)),
iaa.AdditiveGaussianNoise(scale=(0.1, 1.0)),
iaa.Multiply((0.6, 1.4)),
iaa.MultiplyElementwise((0.6, 1.4)),
iaa.Dropout((0.3, 0.5)),
iaa.CoarseDropout((0.3, 0.5), size_percent=(0.05, 0.2)),
iaa.Invert(0.5),
iaa.Affine(scale=(0.7, 1.3),
translate_percent=(-0.1, 0.1),
rotate=(-20, 20),
shear=(-20, 20),
order=ia.ALL,
mode=ia.ALL,
cval=(0, 255)),
iaa.PiecewiseAffine(scale=(0.1, 0.3)),
iaa.ElasticTransformation(alpha=0.5)
]
augs_affect_geometry = [
iaa.Sequential([iaa.Fliplr(0.5), iaa.Flipud(0.5)]),
iaa.SomeOf(1, [iaa.Fliplr(0.5), iaa.Flipud(0.5)]),
iaa.OneOf([iaa.Fliplr(0.5), iaa.Flipud(0.5)]),
iaa.Sometimes(0.5, iaa.Fliplr(1.0)),
iaa.Resize((0.5, 0.9)),
iaa.CropAndPad(px=(-50, 50)),
iaa.Pad(px=(1, 50)),
iaa.Crop(px=(1, 50)),
iaa.Fliplr(0.5),
iaa.Flipud(0.5),
iaa.Affine(scale=(0.7, 1.3),
translate_percent=(-0.1, 0.1),
rotate=(-20, 20),
shear=(-20, 20),
order=ia.ALL,
mode=ia.ALL,
cval=(0, 255)),
iaa.PiecewiseAffine(scale=(0.1, 0.3)),
iaa.ElasticTransformation(alpha=(5, 100), sigma=(3, 5))
]
for aug in augs:
aug_det = aug.to_deterministic()
images_aug1 = aug_det.augment_images(images)
images_aug2 = aug_det.augment_images(images)
aug_det = aug.to_deterministic()
images_aug3 = aug_det.augment_images(images)
images_aug4 = aug_det.augment_images(images)
assert array_equal_lists(images_aug1, images_aug2), \
"Images (1, 2) expected to be identical for %s" % (aug.name,)
assert array_equal_lists(images_aug3, images_aug4), \
"Images (3, 4) expected to be identical for %s" % (aug.name,)
assert not array_equal_lists(images_aug1, images_aug3), \
"Images (1, 3) expected to be different for %s" % (aug.name,)
for aug in augs_affect_geometry:
aug_det = aug.to_deterministic()
kps_aug1 = aug_det.augment_keypoints(keypoints)
kps_aug2 = aug_det.augment_keypoints(keypoints)
aug_det = aug.to_deterministic()
kps_aug3 = aug_det.augment_keypoints(keypoints)
kps_aug4 = aug_det.augment_keypoints(keypoints)
assert keypoints_equal(kps_aug1, kps_aug2), \
"Keypoints (1, 2) expected to be identical for %s" % (aug.name,)
assert keypoints_equal(kps_aug3, kps_aug4), \
"Keypoints (3, 4) expected to be identical for %s" % (aug.name,)
assert not keypoints_equal(kps_aug1, kps_aug3), \
"Keypoints (1, 3) expected to be different for %s" % (aug.name,)
def _load_augmentation_aug_all():
""" Load image augmentation model """
def sometimes(aug):
return 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='constant',
pad_cval=(0, 255))),
sometimes(
iaa.Affine(
# scale images to 80-120% of their size, individually per axis
scale={
"x": (0.8, 1.2),
"y": (0.8, 1.2)
},
# translate by -20 to +20 percent (per axis)
translate_percent={
"x": (-0.2, 0.2),
"y": (-0.2, 0.2)
},
rotate=(-45, 45), # rotate by -45 to +45 degrees
shear=(-16, 16), # shear by -16 to +16 degrees
# use nearest neighbour or bilinear interpolation (fast)
order=[0, 1],
# if mode is constant, use a cval between 0 and 255
cval=(0, 255),
# use any of scikit-image's warping modes
# (see 2nd image from the top for examples)
mode='constant')),
# 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 images into their superpixel representation
sometimes(
iaa.Superpixels(p_replace=(0, 1.0),
n_segments=(20, 200))),
iaa.OneOf([
# blur images with a sigma between 0 and 3.0
iaa.GaussianBlur((0, 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,
# blend the result with the original image using a blobby mask
iaa.BlendAlphaSimplexNoise(
iaa.OneOf([
iaa.EdgeDetect(alpha=(0.5, 1.0)),
iaa.DirectedEdgeDetect(alpha=(0.5, 1.0),
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),
]),
# invert color channels
iaa.Invert(0.05, per_channel=True),
# change brightness of images (by -10 to 10 of original value)
iaa.Add((-10, 10), per_channel=0.5),
# change hue and saturation
iaa.AddToHueAndSaturation((-20, 20)),
# 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.BlendAlphaFrequencyNoise(
exponent=(-4, 0),
foreground=iaa.Multiply(
(0.5, 1.5), per_channel=True),
background=iaa.contrast.LinearContrast((0.5, 2.0)))
]),
# improve or worsen the contrast
iaa.contrast.LinearContrast((0.5, 2.0), per_channel=0.5),
iaa.Grayscale(alpha=(0.0, 1.0)),
# move pixels locally around (with random strengths)
sometimes(
iaa.ElasticTransformation(alpha=(0.5, 3.5), sigma=0.25)
),
# sometimes move parts of the image around
sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.05))),
sometimes(iaa.PerspectiveTransform(scale=(0.01, 0.1)))
],
random_order=True)
],
random_order=True)
def Augmentors(orgimage, bbs):
sometimes = lambda aug: iaa.Sometimes(0.3, aug)
seq = iaa.Sequential(
[
# apply the following augmenters to most images
# 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
# 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(
1,
[
sometimes(
iaa.Superpixels(p_replace=(0, 1.0),
n_segments=(20, 200))
), # convert images into their superpixel representation
iaa.OneOf([
iaa.GaussianBlur(
(0, 1.5
)), # blur images with a sigma between 0 and 3.0
iaa.AverageBlur(
k=(2, 4)
), # blur image using local means with kernel sizes between 2 and 7
iaa.MedianBlur(
k=(3, 7)
), # blur image using local medians with kernel sizes between 2 and 7
]),
iaa.Sharpen(alpha=(0, 0.5),
lightness=(0.15, 1.5)), # sharpen images
iaa.Emboss(alpha=(0, 0.5),
strength=(0, 0.5)), # 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.Invert(0.05, per_channel=True), # invert color channels
iaa.Add(
(-5, 5), per_channel=0.5
), # change brightness of images (by -10 to 10 of original value)
iaa.AddToHueAndSaturation(
(-5, 5)), # 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.PerspectiveTransform(scale=(0.01, 0.1)))
],
random_order=True)
],
random_order=True)
try:
image_aug, bbs_aug = seq(image=orgimage, bounding_boxes=bbs)
return image_aug, bbs_aug
except:
print("caught")
return "caught", "caught"
