def generate_lean_samples():
"""
generate lean shelf pose samples from frontal shelf pose images
:return:
"""
p = Augmentor.Pipeline(args['img_dir'])
# p.rotate(probability=1, max_left_rotation=25, max_right_rotation=25)
p.skew_left_right(probability=1, magnitude=random.randint(7, 10) / 10)
# p.skew_top_bottom(probability=1, magnitude=random.randint(6, 10) / 10)
# p.random_contrast(0.5, min_factor=0.5, max_factor=1.05)
# p.random_distortion(probability=0.2, grid_width=2, grid_height=2, magnitude=2)
# p.sample(args['sample_num'])
p.process()
def augment(sourcePath,labelPath,outputPath,size): p = Augmentor.Pipeline( source_directory=sourcePath ,output_directory=outputPath ) p.ground_truth(labelPath) p.rotate(probability=0.2, max_left_rotation=2, max_right_rotation=2) p.zoom(probability=0.2, min_factor=1.1, max_factor=1.2) p.skew(probability=0.2) p.random_distortion(probability=0.2, grid_width=100, grid_height=100, magnitude=1) p.shear(probability=0.2, max_shear_left=2, max_shear_right=2) p.crop_random(probability=0.2, percentage_area=0.8) p.flip_random(probability=0.2) p.sample(n=size)
def aug(path):
p = Augmentor.Pipeline(path)
#p.rotate(probability=0.5,max_left_rotation=25,max_right_rotation=10)
#p.skew_tilt(probability=0.1,magnitude=1)
#p.skew_corner(probability=0.01,magnitude=1)
#p.random_distortion(probability=0.01,grid_height=5,grid_width=16,magnitude=8)
p.shear(probability=0.01, max_shear_left=15, max_shear_right=15)
#p.random_erasing(probability=0.3,rectangle_area=0.5)
p.zoom(probability=0.5, min_factor=1, max_factor=1.5)
p.random_brightness(probability=0.2, min_factor=0.7, max_factor=1.2)
p.random_color(probability=0.2, min_factor=0.7, max_factor=1.2)
p.random_contrast(probability=0.2, min_factor=0.7, max_factor=1.2)
p.greyscale(probability=0.4)
p.sample(11700)
def _get_pipeline(self, path=None):
p = Augmentor.Pipeline(path)
p.crop_random(probability=0.5, percentage_area=0.7)
# p.resize(probability=1.0, width=512, height=512)
p.flip_left_right(probability=0.5)
p.rotate(probability=0.5, max_left_rotation=10, max_right_rotation=10)
p.shear(probability=0.4, max_shear_left=10, max_shear_right=10)
# p.random_distortion(probability=0.3, grid_height=5, grid_width=5, magnitude=2)
p.skew(probability=0.5)
return p
def pro_extend_earsing(imgpath):
imgpiple = p.Pipeline(imgpath)
# print(imgpath)
imgpiple.rotate(probability=0.7,
max_left_rotation=10,
max_right_rotation=10)
imgpiple.zoom(probability=0.5, min_factor=1.1, max_factor=1.5)
imgpiple.random_erasing(probability=1, rectangle_area=0.5)
# imgpiple.crop_random(probability=0.2, percentage_area=0.5)
# img = cv2.imread(imgpath)
# imgHir = cv2.flip(img,1,dst = None)
imgpiple.sample(10)
# imgpiple.process()
return 0
def test_scale_ground_truth():
standard_image_directory, ground_truth_image_directory = create_temporary_data(
)
num_samples = 10
p = Augmentor.Pipeline(standard_image_directory)
p.ground_truth(ground_truth_image_directory)
p.scale(probability=1, scale_factor=1.4)
p.sample(num_samples)
generated_files = glob.glob(
os.path.join(standard_image_directory, "output/*"))
assert (num_samples * 2) == len(generated_files)
destroy_temporary_data(standard_image_directory,
ground_truth_image_directory)
# Do the same for single image lists
standard_image_directory, ground_truth_image_directory = create_temporary_data(
)
num_samples = 10
p = Augmentor.Pipeline(standard_image_directory)
p.scale(probability=1, scale_factor=1.4)
p.sample(num_samples)
generated_files = glob.glob(
os.path.join(standard_image_directory, "output/*"))
assert num_samples == len(generated_files)
destroy_temporary_data(standard_image_directory,
ground_truth_image_directory)
def test_crop_random_ground_truth():
standard_image_directory, ground_truth_image_directory = create_temporary_data(
)
num_samples = 10
p = Augmentor.Pipeline(standard_image_directory)
p.ground_truth(ground_truth_image_directory)
p.add_operation(CropRandom(probability=1, percentage_area=0.5))
p.sample(num_samples)
generated_files = glob.glob(
os.path.join(standard_image_directory, "output/*"))
assert (num_samples * 2) == len(generated_files)
destroy_temporary_data(standard_image_directory,
ground_truth_image_directory)
# Do the same for single images
standard_image_directory, ground_truth_image_directory = create_temporary_data(
)
num_samples = 10
p = Augmentor.Pipeline(standard_image_directory)
p.add_operation(CropRandom(probability=1, percentage_area=0.5))
p.sample(num_samples)
generated_files = glob.glob(
os.path.join(standard_image_directory, "output/*"))
assert num_samples == len(generated_files)
destroy_temporary_data(standard_image_directory,
ground_truth_image_directory)
def augment_fn(dir_str):
p = Augmentor.Pipeline(dir_str)
#p.random_brightness(probability = 0.5, min_factor = 0.6, max_factor = 1.0)
#p.random_color(probability =0.5, min_factor = 0.2, max_factor = 0.8)
#p.random_contrast(probability =0.5, min_factor = 0.5, max_factor = 0.8)
p.skew(probability=0.4, magnitude=0.2)
p.shear(probability=0.3, max_shear_left=5, max_shear_right=5)
p.rotate(probability=0.5, max_left_rotation=15, max_right_rotation=15)
p.rotate90(probability=0.5)
p.rotate180(probability=0.5)
p.rotate270(probability=0.5)
#p.random_distortion(probability = 0.2,
#grid_height = 10, grid_width =10, magnitude= 2)
p.zoom(probability=0.2, min_factor=1.1, max_factor=1.15)
p.sample(1000)
def data_augment(path_to_images_dir, data_aug_samples):
"""
data augmentation
:param path_to_images_dir:
:param data_aug_samples:
:return:
"""
p = Augmentor.Pipeline(path_to_images_dir)
p.rotate(probability=0.7, max_left_rotation=10, max_right_rotation=10)
p.zoom(probability=1, min_factor=1.1, max_factor=1.5)
p.flip_left_right(probability=0.5)
p.flip_top_bottom(probability=0.5)
p.rotate90(0.5)
p.rotate270(0.5)
p.sample(data_aug_samples)
def aug_distortion(img_path, gen_num):
'''
图像扭曲
:param img_path:
:param gen_num: 生成扭曲图像的数量
:return:
'''
p = Augmentor.Pipeline(img_path)
p.random_distortion(probability=1,
grid_width=8,
grid_height=8,
magnitude=8)
p.rotate(probability=1, max_left_rotation=5, max_right_rotation=5)
p.zoom_random(probability=0.5, percentage_area=0.9)
p.sample(gen_num)
def test_torch_transform():
torchvision = pytest.importorskip("torchvision")
red = np.zeros([10, 10, 3], np.uint8)
red[..., 0] = 255
red = Image.fromarray(red)
g = Augmentor.Operations.Greyscale(1)
p = Augmentor.Pipeline()
p.greyscale(1)
transforms = torchvision.transforms.Compose([p.torch_transform()])
assert red != transforms(red)
assert g.perform_operation(red) == transforms(red)
def augmentor(dir, out_dir):
p = Augmentor.Pipeline(dir, output_directory=out_dir)
p.rotate(probability=0.5, max_left_rotation=20, max_right_rotation=10)
p.zoom(probability=0.2, min_factor=1.0, max_factor=1.2)
p.random_distortion(probability=0.1,
grid_width=4,
grid_height=4,
magnitude=8)
p.flip_left_right(probability=0.5)
#p.flip_top_bottom(probability=0.5)#
#p.crop_random(probability=0.5, percentage_area=0.9)
p.shear(probability=0.5, max_shear_left=10, max_shear_right=10)
p.skew(probability=0.5, magnitude=1)
p.resize(probability=1, width=224, height=224)
p.sample(500)
def get_distortion_pipline(path, num):
"""
之前采用 增广策略：随机扭曲
:param path:
:param num:
:return:
"""
p = Augmentor.Pipeline(path)
p.zoom(probability=0.5, min_factor=1.05, max_factor=1.05)
p.random_distortion(probability=1,
grid_width=6,
grid_height=2,
magnitude=3)
p.sample(num)
return p
def augment(ims_root, max_sample, size=(112, 112)):
list_dir = os.listdir(ims_root)
for dir_n in list_dir:
data_dir = os.path.join(ims_root, dir_n)
list_im = os.listdir(data_dir)
p = Augmentor.Pipeline(data_dir)
if max_sample // 3 < len(list_im) < max_sample:
p.flip_left_right(probability=0.75)
elif len(list_im) < max_sample // 3:
p.flip_left_right(probability=0.3)
p.shear(probability=0.5, max_shear_left=20, max_shear_right=20)
p.rotate(probability=0.5, max_left_rotation=20, max_right_rotation=20)
else:
p.resize(probability=1.0, width=size[0], height=size[1])
p.sample(max_sample)
def genrate_images(source, output, number):
p = Augmentor.Pipeline(source, output_directory=output)
p.flip_random(probability=1)
p.flip_left_right(probability= 1.0)
p.flip_top_bottom(probability= 1.0)
p.crop_random(probability=1.0, percentage_area=0.90)
p.rotate_random_90(probability=0.9)
p.rotate180(probability=0.8)
p.rotate270(probability=0.6)
p.flip_random(probability=1)
p.rotate(probability=0.5, max_left_rotation=4, max_right_rotation=4)
p.shear(probability=0.6, max_shear_left=8, max_shear_right=8 )
p.zoom(probability=0.8, min_factor=1, max_factor=1.3)
p.sample(number)
shutil.rmtree(output+"/0")
def augment(input_path, n):
"""
Apply a set of augmentation operations
"""
p = Augmentor.Pipeline(input_path)
# Point to a directory containing ground truth data.
# Images with the same file names will be added as ground truth data
# and augmented in parallel to the original data.
#p.ground_truth("/path/to/ground_truth_images")
# Add operations to the pipeline as normal:
p.rotate(probability=1, max_left_rotation=5, max_right_rotation=5)
p.flip_left_right(probability=0.5)
p.zoom_random(probability=0.5, percentage_area=0.8)
p.flip_top_bottom(probability=0.5)
p.sample(n)
def test_keras_generator_from_disk():
batch_size = random.randint(1, 50)
width = 80
height = 80
tmpdir = tempfile.mkdtemp()
tmps = create_multiple_images(tmpdir, 10, (width, height))
p = Augmentor.Pipeline(tmpdir)
assert len(p.augmentor_images) == len(tmps)
p.rotate(probability=0.5, max_left_rotation=5, max_right_rotation=5)
p.flip_left_right(probability=0.333)
p.flip_top_bottom(probability=0.5)
g = p.keras_generator(batch_size=batch_size,
image_data_format="channels_last")
X, y = next(g)
assert len(X) == batch_size
assert len(X) == batch_size
assert len(X) == len(y)
assert np.shape(X) == (batch_size, width, height, 3)
# Call next() more than the total number of images in the pipeline
for i in range(20):
X, y = next(g)
assert len(X) == batch_size
assert len(X) == batch_size
assert len(X) == len(y)
assert np.shape(X) == (batch_size, width, height, 3)
g2 = p.keras_generator(batch_size=batch_size,
image_data_format="channels_first")
X2, y2 = next(g2)
assert len(X2) == batch_size
assert len(X2) == len(y2)
assert np.shape(X2) == (batch_size, 3, width, height)
# Finally remove the directory (and everything in it) as mkdtemp does
# not delete itself after closing automatically
shutil.rmtree(tmpdir)
def __init__(self, img_size=256):
super(VHFlipRotateCrop, self).__init__(img_size)
self.solt_pipeline = solt.Stream([
slt.Flip(p=self.probablity, axis=0),
slt.Flip(p=self.probablity, axis=1),
slt.Rotate(angle_range=(0, 20)),
slt.Crop(224, crop_mode="r"),
])
self.albumentations_pipeline = albu.Compose([
albu.VerticalFlip(p=self.probablity),
albu.HorizontalFlip(p=self.probablity),
albu.Rotate(limit=(0, 20),
p=self.probablity,
border_mode=cv2.BORDER_CONSTANT,
value=0),
albu.RandomCrop(height=224, width=224),
ToTensor(normalize={
"mean": [0.485, 0.456, 0.406],
"std": [0.229, 0.224, 0.225]
}),
])
self.torchvision_pipeline = tv_transforms.Compose([
tv_transforms.RandomHorizontalFlip(p=self.probablity),
tv_transforms.RandomRotation(degrees=(0, 20)),
tv_transforms.RandomCrop(224),
tv_transforms.ToTensor(),
tv_transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
_augm_ppl = augmentor.Pipeline()
_augm_ppl.flip_top_bottom(probability=self.probablity)
_augm_ppl.flip_left_right(probability=self.probablity)
_augm_ppl.rotate(probability=self.probablity,
max_left_rotation=0,
max_right_rotation=20)
_augm_ppl.crop_random(probability=1,
percentage_area=224 / float(self.img_size))
self.augmentor_pipeline = tv_transforms.Compose([
_augm_ppl.torch_transform(),
tv_transforms.transforms.ToTensor(),
tv_transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
def __init__(self,
path,
distortion=False,
flip_horizontal=False,
flip_vertical=False,
random_crop=False,
random_erasing=False,
rotate=False,
resize=False,
skew=False,
shear=False,
brightness=False,
contrast=False,
color=False,
target_width=299,
target_height=299):
self.path = path
self.categories = sorted(os.listdir(self.path))
self.categories_folder = [
os.path.abspath(os.path.join(self.path, i))
for i in self.categories
]
# options
self.distortion = distortion
self.flip_horizontal = flip_horizontal
self.flip_vertical = flip_vertical
self.random_crop = random_crop
self.random_erasing = random_erasing
self.rotate = rotate
self.resize = resize
self.skew = skew
self.shear = shear
self.brightness = brightness
self.contrast = contrast
self.color = color
self.target_width = target_width
self.target_height = target_height
# Create a pipeline for each class
self.pipelines = {}
for folder in self.categories_folder:
print("Folder %s:" % folder)
self.pipelines[os.path.split(folder)[1]] = (
Augmentor.Pipeline(folder))
print("\n----------------------------\n")
for p in self.pipelines.values():
print("Class %s has %s samples." %
(p.augmentor_images[0].class_label, len(p.augmentor_images)))
def img_distortion(img_path):
for file in os.listdir(img_path):
if file == '.DS_Store':
os.remove(img_path + '/' + file)
else:
pass
p = Augmentor.Pipeline(img_path)
p.rotate(probability=0.5, max_left_rotation=5, max_right_rotation=5)
# p.zoom(probability=0.1, min_factor=0.5, max_factor=1.0)
p.random_distortion(probability=1,
grid_width=2,
grid_height=2,
magnitude=2)
# p.flip_left_right(probability=0.2)
# p.flip_top_bottom(probability=0.2)
p.sample(2000)
def augmentate_data_from_path():
copy_covid_dataset()
input_path = Paths.DATASET_BASE_PATH + 'data/augmented-orig/'
output_path = Paths.DATASET_BASE_PATH + 'data/augmented/'
p = Augmentor.Pipeline(
source_directory=input_path,
output_directory=output_path)
p.rotate(probability=1, max_left_rotation=0.15, max_right_rotation=0.15)
p.flip_left_right(probability=0.5)
p.zoom_random(probability=0.2, percentage_area=0.8)
p.status()
p.sample(1000)
p.process()
def get_Augmentor(path,batch_size,rotate=True,color=True,flip_ud=True):
p = Augmentor.Pipeline(path, output_directory="/tmp/")
p.skew_tilt(probability=0.5, magnitude=0.5)
p.random_distortion(probability=0.5, grid_width=4, grid_height=4, magnitude=2)
if rotate:
p.rotate90(probability=0.3)
p.rotate270(probability=0.3)
p.rotate(probability=0.3, max_left_rotation=10, max_right_rotation=10)
if flip_ud:
p.flip_top_bottom(probability=0.3)
p.flip_left_right(probability=0.3)
if not color:
p.greyscale(probability=1.0)
g=p.keras_generator(batch_size=batch_size)
return g
def augment_folder(path, generated_count=50, pixels_mean=None):
# temporary margin to make sure the object won't oversize during deformation
temp_margin = 2.0
# margin after operations = final_margin * size
final_margin = 0.05
# temporary increases size to make erosion softer
erosion_scaling = 4
# detect images
p = Augmentor.Pipeline(path, output_directory="augment")
# get dimension of first image
im_width = 100
im_height = 100
if len(p.augmentor_images) > 0:
first_path = p.augmentor_images[0].image_path
im = Image.open(first_path)
im_width = im.width
im_height = im.height
p.random_distortion(1, 2, 2, 8)
p.add_operation(
AutoresizeImage(probability=1,
max_horizontal_loss=0.4,
max_vertical_loss=0.2))
p.add_operation(
ExpandImage(probability=1,
width=int(temp_margin * im_width),
height=int(temp_margin * im_height)))
p.rotate(probability=1, max_left_rotation=7, max_right_rotation=7)
p.skew(1, 0.2)
# p.shear(1, 5, 5)
p.add_operation(AutoCropImage(probability=1, margin=final_margin))
p.resize(1, int(erosion_scaling * im_width),
int(erosion_scaling * im_height))
p.add_operation(SmoothImage(probability=1, blur=2, threshold=128))
pixels_mean_scaled = None
if pixels_mean is not None:
pixels_mean_scaled = pixels_mean * erosion_scaling * erosion_scaling * 255 # max of pickle data pixel = 1
p.add_operation(
ErosImage(probability=1,
max_erosion=3,
max_dilation=4,
pixels_mean=pixels_mean_scaled))
p.resize(1, int(im_width), int(im_height))
p.sample(generated_count)
def __getitem__(self, index):
rng = random
image1 = rng.choice(self.dataset.imgs)
# get image from same class
label = None
if index % 2 == 1:
label = 1.0
while True:
image2 = rng.choice(self.dataset.imgs)
if image1[1] == image2[1]:
break
# get image from different class
else:
label = 0.0
while True:
image2 = rng.choice(self.dataset.imgs)
if image1[1] != image2[1]:
break
image1 = Image.open(image1[0])
image2 = Image.open(image2[0])
image1 = image1.convert('L')
image2 = image2.convert('L')
# apply transformation on the fly
if self.augment:
p = Augmentor.Pipeline()
p.rotate(probability=0.5,
max_left_rotation=15,
max_right_rotation=15)
p.random_distortion(
probability=0.5,
grid_width=6,
grid_height=6,
magnitude=10,
)
trans = transforms.Compose([
p.torch_transform(),
transforms.ToTensor(),
])
else:
trans = transforms.ToTensor()
image1 = trans(image1)
image2 = transforms.ToTensor()(image2)
y = torch.from_numpy(np.array([label], dtype=np.float32))
return (image1, image2, y)
def trainingset_augmentation(data_path,
output_width,
output_height,
samples=100,
ground_truth_path=None,
output_dir='output',
ground_truth_output_dir=None):
import os
import Augmentor
# create pipeline
p = Augmentor.Pipeline(data_path, output_dir)
if ground_truth_path: p.ground_truth(ground_truth_path)
# add color operation
# p.random_contrast(probability=1, min_factor=1, max_factor=1)
# p.random_brightness(probability=1, min_factor=1, max_factor=1)
# p.random_color(probability=1, min_factor=1, max_factor=1)
# add shape operation
p.rotate(probability=1, max_left_rotation=25, max_right_rotation=25)
p.crop_random(probability=1,
percentage_area=0.5,
randomise_percentage_area=False)
p.random_distortion(probability=0.8,
grid_width=30,
grid_height=30,
magnitude=1)
p.skew(probability=0.5)
p.shear(probability=0.4, max_shear_left=5, max_shear_right=5)
p.resize(probability=1, width=output_width, height=output_height)
# generate
p.sample(samples)
# move ground truth to other folder
if ground_truth_output_dir is not None:
import shutil
from dataset import get_all_file
if not os.path.exists(ground_truth_output_dir):
os.mkdir(ground_truth_output_dir)
# read all images path
imgs_path = get_all_file(output_dir)
num = np.int32(len(imgs_path) / 2)
# move gt
gt_paths = imgs_path[num:]
for path in gt_paths:
shutil.move(path, ground_truth_output_dir)
def getdata(train_path, val_path, test_path):
# create a data generator
image_size = config.IMAGE_SIZE
datagen_batch_size = config.batch_size
p = Augmentor.Pipeline()
p.flip_left_right(probability=0.5)
p.rotate(probability=1, max_left_rotation=5, max_right_rotation=5)
p.zoom_random(probability=0.5, percentage_area=0.95)
p.random_distortion(probability=0.5,
grid_width=2,
grid_height=2,
magnitude=8)
p.random_color(probability=1, min_factor=0.8, max_factor=1.2)
p.random_contrast(probability=1, min_factor=0.8, max_factor=1.2)
p.random_brightness(probability=1, min_factor=0.8, max_factor=1.2)
p.random_erasing(probability=0.5, rectangle_area=0.2)
data_generator = ImageDataGenerator(
preprocessing_function=p.keras_preprocess_func())
# test data shouldn't be augmented
test_datagen = ImageDataGenerator()
train_it = data_generator.flow_from_directory(
train_path,
class_mode='categorical',
batch_size=datagen_batch_size,
target_size=(image_size, image_size))
# load and iterate validation dataset
val_it = data_generator.flow_from_directory(val_path,
class_mode='categorical',
batch_size=datagen_batch_size,
target_size=(image_size,
image_size))
# load and iterate test dataset
test_it = test_datagen.flow_from_directory(test_path,
class_mode='categorical',
batch_size=datagen_batch_size,
target_size=(image_size,
image_size))
return train_it, val_it, test_it
def augmentation_new_image(input_dir, output_dir, output_number):
import Augmentor # pip install Augmentor -i https://pypi.tuna.tsinghua.edu.cn/simple
p = Augmentor.Pipeline(input_dir, output_directory = output_dir)
p.zoom(probability=0.1, min_factor=1.1, max_factor=1.3)
p.flip_left_right(probability=0.1)
p.rotate(probability=0.2, max_left_rotation=15, max_right_rotation=16)
p.shear(probability=0.2, max_shear_left=10, max_shear_right=10)
p.skew(probability=0.1, magnitude=0.6)
p.skew_tilt(probability=0.2, magnitude=0.6)
p.random_distortion(probability=0.3, grid_height=4, grid_width=4, magnitude=4)
# p.random_distortion(probability=0.2, grid_height=4, grid_width=4, magnitude=4)
# p.rotate90(probability=1)
p.sample(output_number)
def pics_aug(save_img_dir):
# 确定原始图像存储路径以及掩码文件存储路径
p = Augmentor.Pipeline(save_img_dir)
p.ground_truth(aug_testlabels)
# 图像旋转： 按照概率0.8执行，最大左旋角度10，最大右旋角度10
p.rotate(probability=0.4, max_left_rotation=1, max_right_rotation=1)
# 图像左右互换： 按照概率0.5执行
p.flip_left_right(probability=0.5)
'''
# 图像放大缩小： 按照概率0.8执行，面积为原始图0.85倍
p.zoom_random(probability=0.3, percentage_area=0.9)
'''
# 最终扩充的数据样本数
p.sample(100)
'''
def augment(imgdir, sample_n):
p = Augmentor.Pipeline(imgdir)
# 加入变换操作和概率
# 旋转 & 放大 & 镜像
p.rotate(probability=0.7, max_left_rotation=10, max_right_rotation=10)
p.zoom(probability=0.5, min_factor=1.1, max_factor=1.5)
p.flip_left_right(probability=0.5)
p.random_brightness(0.8, 0.8, 1.2)
p.scale(0.4, 1.2)
p.black_and_white(0.1)
p.greyscale(0.5)
p.random_erasing(0.9, 0.5)
# p.random_distortion(0.3, 30, 30, 5)
# p.skew(0.1)
# 生成数据在 imgdir/output/ 下
p.sample(sample_n)
def generate_samples(src, dst, num_samples, seed):
p = Augmentor.Pipeline(source_directory=src, output_directory=dst)
p.set_seed(seed=seed)
p.rotate(probability=0.5, max_left_rotation=5, max_right_rotation=5)
# p.zoom(probability=0.5, min_factor=1.0, max_factor=1.2)
# p.crop_random(probability=0.1, percentage_area=0.9)
p.random_distortion(probability=0.5,
grid_height=10,
grid_width=10,
magnitude=2)
p.skew_left_right(probability=0.5, magnitude=0.1)
p.skew_top_bottom(probability=0.5, magnitude=0.1)
p.resize(probability=1.0, width=512, height=512)
p.sample(num_samples)
del p