def make(path, name):
person = Person.objects.filter(name=name)
if len(person) <= 0:
person = Person(name=name)
person.save()
else:
person = person.last()
img_raw = Image(path=path, person_id=person.id)
img_raw.save()
try:
img = cv2.imread(path)
images = np.array([img for _ in range(12)], dtype=np.uint8)
seq = iaa.Sequential([
iaa.Affine(
rotate=(0.0, 30),
translate_px=iap.RandomSign(
iap.Poisson(3)) # set seed for randomSign
)
])
images_aug = seq.augment_images(images)
store_path = os.path.join(Config.storePath,
person.name.replace(' ', ''))
for index, img in enumerate(images_aug):
img_path = "{}/{}.jpg".format(
store_path,
person.name.replace(' ', '') +
str(datetime.now().microsecond))
cv2.imwrite(img_path, img)
img = Image(path=img_path, person_id=person.id)
img.save()
except IOError:
print("Path not exists!")
def perturb_glyphs(_font, number_of_points):
poisson_distribution = iap.RandomSign(iap.Poisson(5))
for char in GlyphPerturber.characters:
if char in font.getGlyphSet():
glyph = _font.getGlyphSet().get(char)
random_indexes = random.sample(population=range(len(glyph._glyph.coordinates)),
k=min(number_of_points, len(glyph._glyph.coordinates)))
for index in random_indexes:
glyph._glyph.coordinates[index] = (glyph._glyph.coordinates[index][0] + poisson_distribution.draw_sample(),
glyph._glyph.coordinates[index][1] + poisson_distribution.draw_sample())
else:
raise LookupError(f"Given font has no glyph for the char '{char}'.")
return _font
def chapter_parameters_introduction():
ia.seed(1)
from imgaug import augmenters as iaa
from imgaug import parameters as iap
seq = iaa.Sequential([
iaa.GaussianBlur(sigma=iap.Uniform(0.0, 1.0)),
iaa.ContrastNormalization(
iap.Choice([1.0, 1.5, 3.0], p=[0.5, 0.3, 0.2])),
iaa.Affine(rotate=iap.Normal(0.0, 30),
translate_px=iap.RandomSign(iap.Poisson(3))),
iaa.AddElementwise(iap.Discretize(
(iap.Beta(0.5, 0.5) * 2 - 1.0) * 64)),
iaa.Multiply(iap.Positive(iap.Normal(0.0, 0.1)) + 1.0)
])
images = np.array([ia.quokka_square(size=(128, 128)) for i in range(16)])
images_aug = [seq.augment_image(images[i]) for i in range(len(images))]
save("parameters", "introduction.jpg", grid(images_aug, cols=4, rows=4))
def chapter_parameters_discrete():
ia.seed(1)
# -----------------------
# Binomial
# -----------------------
from imgaug import parameters as iap
params = [iap.Binomial(0.5), iap.Binomial(0.9)]
gridarr = draw_distributions_grid(params, rows=1)
save("parameters", "continuous_binomial.jpg", gridarr)
# -----------------------
# DiscreteUniform
# -----------------------
from imgaug import parameters as iap
params = [
iap.DiscreteUniform(0, 10),
iap.DiscreteUniform(-10, 10),
iap.DiscreteUniform([-10, -9, -8, -7], 10),
iap.DiscreteUniform((-10, -7), 10)
]
gridarr = draw_distributions_grid(params)
save("parameters", "continuous_discreteuniform.jpg", gridarr)
# -----------------------
# Poisson
# -----------------------
from imgaug import parameters as iap
params = [
iap.Poisson(1),
iap.Poisson(2.5),
iap.Poisson((1, 2.5)),
iap.RandomSign(iap.Poisson(2.5))
]
gridarr = draw_distributions_grid(params)
save("parameters", "continuous_poisson.jpg", gridarr)
def stochastic():
return iaa.Sequential([
iaa.GaussianBlur(
sigma=iap.Uniform(0.0, 1.0)
),
iaa.ContrastNormalization(
iap.Choice(
[1.0, 1.5, 3.0],
p=[0.5, 0.3, 0.2]
)
),
iaa.Affine(
rotate=iap.Normal(0.0, 30),
translate_px=iap.RandomSign(iap.Poisson(3))
),
iaa.AddElementwise(
iap.Discretize(
(iap.Beta(0.5, 0.5) * 2 - 1.0) * 64
)
),
iaa.Multiply(
iap.Positive(iap.Normal(0.0, 0.1)) + 1.0
)
])
def chapter_parameters_continuous():
ia.seed(1)
# -----------------------
# Normal
# -----------------------
from imgaug import parameters as iap
params = [
iap.Normal(0, 1),
iap.Normal(5, 3),
iap.Normal(iap.Choice([-3, 3]), 1),
iap.Normal(iap.Uniform(-3, 3), 1)
]
gridarr = draw_distributions_grid(params)
save("parameters", "continuous_normal.jpg", gridarr)
# -----------------------
# Laplace
# -----------------------
from imgaug import parameters as iap
params = [
iap.Laplace(0, 1),
iap.Laplace(5, 3),
iap.Laplace(iap.Choice([-3, 3]), 1),
iap.Laplace(iap.Uniform(-3, 3), 1)
]
gridarr = draw_distributions_grid(params)
save("parameters", "continuous_laplace.jpg", gridarr)
# -----------------------
# ChiSquare
# -----------------------
from imgaug import parameters as iap
params = [
iap.ChiSquare(1),
iap.ChiSquare(3),
iap.ChiSquare(iap.Choice([1, 5])),
iap.RandomSign(iap.ChiSquare(3))
]
gridarr = draw_distributions_grid(params)
save("parameters", "continuous_chisquare.jpg", gridarr)
# -----------------------
# Weibull
# -----------------------
from imgaug import parameters as iap
params = [
iap.Weibull(0.5),
iap.Weibull(1),
iap.Weibull(1.5),
iap.Weibull((0.5, 1.5))
]
gridarr = draw_distributions_grid(params)
save("parameters", "continuous_weibull.jpg", gridarr)
# -----------------------
# Uniform
# -----------------------
from imgaug import parameters as iap
params = [
iap.Uniform(0, 1),
iap.Uniform(iap.Normal(-3, 1), iap.Normal(3, 1)),
iap.Uniform([-1, 0], 1),
iap.Uniform((-1, 0), 1)
]
gridarr = draw_distributions_grid(params)
save("parameters", "continuous_uniform.jpg", gridarr)
# -----------------------
# Beta
# -----------------------
from imgaug import parameters as iap
params = [
iap.Beta(0.5, 0.5),
iap.Beta(2.0, 2.0),
iap.Beta(1.0, 0.5),
iap.Beta(0.5, 1.0)
]
gridarr = draw_distributions_grid(params)
save("parameters", "continuous_beta.jpg", gridarr)
def chapter_parameters_special():
ia.seed(1)
# -----------------------
# Choice
# -----------------------
from imgaug import parameters as iap
params = [
iap.Choice([0, 1, 2]),
iap.Choice([0, 1, 2], p=[0.15, 0.5, 0.35]),
iap.Choice([iap.Normal(-3, 1), iap.Normal(3, 1)]),
iap.Choice([iap.Normal(-3, 1), iap.Poisson(3)])
]
gridarr = draw_distributions_grid(params)
save("parameters", "special_choice.jpg", gridarr)
# -----------------------
# Clip
# -----------------------
from imgaug import parameters as iap
params = [
iap.Clip(iap.Normal(0, 1), -2, 2),
iap.Clip(iap.Normal(0, 1), -2, None)
]
gridarr = draw_distributions_grid(params, rows=1)
save("parameters", "special_clip.jpg", gridarr)
# -----------------------
# Discretize
# -----------------------
from imgaug import parameters as iap
params = [
iap.Discretize(iap.Normal(0, 1)),
iap.Discretize(iap.ChiSquare(3))
]
gridarr = draw_distributions_grid(params, rows=1)
save("parameters", "special_discretize.jpg", gridarr)
# -----------------------
# Absolute
# -----------------------
from imgaug import parameters as iap
params = [iap.Absolute(iap.Normal(0, 1)), iap.Absolute(iap.Laplace(0, 1))]
gridarr = draw_distributions_grid(params, rows=1)
save("parameters", "special_absolute.jpg", gridarr)
# -----------------------
# RandomSign
# -----------------------
from imgaug import parameters as iap
params = [
iap.ChiSquare(3),
iap.RandomSign(iap.ChiSquare(3)),
iap.RandomSign(iap.ChiSquare(3), p_positive=0.75),
iap.RandomSign(iap.ChiSquare(3), p_positive=0.9)
]
gridarr = draw_distributions_grid(params)
save("parameters", "special_randomsign.jpg", gridarr)
# -----------------------
# ForceSign
# -----------------------
from imgaug import parameters as iap
params = [
iap.ForceSign(iap.Normal(0, 1), positive=True),
iap.ChiSquare(3) - 3.0,
iap.ForceSign(iap.ChiSquare(3) - 3.0, positive=True, mode="invert"),
iap.ForceSign(iap.ChiSquare(3) - 3.0, positive=True, mode="reroll")
]
gridarr = draw_distributions_grid(params)
save("parameters", "special_forcesign.jpg", gridarr)
def _enhance_parameter(level):
fparam = _float_parameter(level, 0.9)
return iap.Clip(
iap.Add(1.0, iap.RandomSign(fparam), elementwise=True), 0.1,
1.9)
import numpy as np
import os
import pandas as pd
import glob
import imgaug as ia
import imgaug.augmenters as iaa
import imgaug.parameters as iap
from imgaug.augmentables.kps import Keypoint, KeypointsOnImage
from imgaug.augmenters import Sequential
import sys
seq = iaa.Sequential(
[
iaa.Affine(
# rotate=iap.Normal(0.0, 20),
translate_px=iap.RandomSign(iap.Poisson(3))),
#iaa.Crop(percent=(0, 0.2)),
iaa.Multiply(iap.Positive(iap.Normal(0.0, 0.4)) + 0.8),
iaa.ContrastNormalization(iap.Uniform(0.5, 1.5))
],
random_order=True)
class DataAugmentator(object):
"""
Generates new images in form of arrays for the given parameters.
:param seq: a sequention of different augmenters applied to single augmentatiion call
:param landmarks_num: number of image's landmarks
:param batch_size: number of images that would be generated during augmentation for single image
:param img_dir: directory to images to be augmented
:param annotation_dir: directory to images' landmarks