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Commit 215b51fa authored by Lucia D. Hradecka's avatar Lucia D. Hradecka
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clean repo

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6 merge requests!71.2,!6merge all changes to 1.2.0,!5Make v1.1.0 default,!4Make v1.1.0 default,!2v1.1.0 update,!1Make v1.1.0 default
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100IterationsTest.txt deleted 100644 → 0
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Transform: AffineTransform, Size: (1, 64, 64, 64), FirstRun: 0.044, Average: 0.040, Iterations: 100, maximum: 0.046
Transform: AffineTransform, Size: (1, 128, 128, 128), FirstRun: 0.343, Average: 0.328, Iterations: 100, maximum: 0.346
Transform: AffineTransform, Size: (1, 256, 256, 256), FirstRun: 3.088, Average: 3.035, Iterations: 100, maximum: 3.110
Transform: AffineTransform, Size: (1, 512, 512, 64), FirstRun: 2.637, Average: 2.633, Iterations: 100, maximum: 2.737
Transform: AffineTransform, Size: (1, 64, 64, 64), FirstRun: 0.050, Average: 0.042, Iterations: 100, maximum: 0.050
Transform: AffineTransform, Size: (1, 128, 128, 128), FirstRun: 0.346, Average: 0.339, Iterations: 100, maximum: 0.352
Transform: AffineTransform, Size: (1, 256, 256, 256), FirstRun: 3.607, Average: 3.613, Iterations: 100, maximum: 3.689
Transform: AffineTransform, Size: (1, 512, 512, 64), FirstRun: 2.950, Average: 2.756, Iterations: 100, maximum: 3.321
Transform: Flip, Size: (1, 64, 64, 64), FirstRun: 0.007, Average: 0.001, Iterations: 100, maximum: 0.002
Transform: Flip, Size: (1, 128, 128, 128), FirstRun: 0.005, Average: 0.006, Iterations: 100, maximum: 0.007
Transform: Flip, Size: (1, 256, 256, 256), FirstRun: 0.050, Average: 0.059, Iterations: 100, maximum: 0.066
Transform: Flip, Size: (1, 512, 512, 64), FirstRun: 0.058, Average: 0.060, Iterations: 100, maximum: 0.068
Transform: GaussianBlur, Size: (1, 64, 64, 64), FirstRun: 0.009, Average: 0.001, Iterations: 100, maximum: 0.002
Transform: GaussianBlur, Size: (1, 128, 128, 128), FirstRun: 0.005, Average: 0.006, Iterations: 100, maximum: 0.007
Transform: GaussianBlur, Size: (1, 256, 256, 256), FirstRun: 0.045, Average: 0.053, Iterations: 100, maximum: 0.060
Transform: GaussianBlur, Size: (1, 512, 512, 64), FirstRun: 0.052, Average: 0.054, Iterations: 100, maximum: 0.060
Transform: GaussianNoise, Size: (1, 64, 64, 64), FirstRun: 0.016, Average: 0.008, Iterations: 100, maximum: 0.009
Transform: GaussianNoise, Size: (1, 128, 128, 128), FirstRun: 0.059, Average: 0.061, Iterations: 100, maximum: 0.062
Transform: GaussianNoise, Size: (1, 256, 256, 256), FirstRun: 0.479, Average: 0.497, Iterations: 100, maximum: 0.510
Transform: GaussianNoise, Size: (1, 512, 512, 64), FirstRun: 0.495, Average: 0.498, Iterations: 100, maximum: 0.529
Transform: HistogramEqualization, Size: (1, 64, 64, 64), FirstRun: 0.068, Average: 0.016, Iterations: 100, maximum: 0.017
Transform: HistogramEqualization, Size: (1, 128, 128, 128), FirstRun: 0.124, Average: 0.125, Iterations: 100, maximum: 0.127
Transform: HistogramEqualization, Size: (1, 256, 256, 256), FirstRun: 1.005, Average: 1.021, Iterations: 100, maximum: 1.089
Transform: HistogramEqualization, Size: (1, 512, 512, 64), FirstRun: 1.008, Average: 1.021, Iterations: 100, maximum: 1.073
Transform: Normalize, Size: (1, 64, 64, 64), FirstRun: 0.005, Average: 0.002, Iterations: 100, maximum: 0.004
Transform: Normalize, Size: (1, 128, 128, 128), FirstRun: 0.022, Average: 0.023, Iterations: 100, maximum: 0.023
Transform: Normalize, Size: (1, 256, 256, 256), FirstRun: 0.178, Average: 0.199, Iterations: 100, maximum: 0.223
Transform: Normalize, Size: (1, 512, 512, 64), FirstRun: 0.186, Average: 0.198, Iterations: 100, maximum: 0.216
Transform: NormalizeMeanStd, Size: (1, 64, 64, 64), FirstRun: 0.007, Average: 0.001, Iterations: 100, maximum: 0.001
Transform: NormalizeMeanStd, Size: (1, 128, 128, 128), FirstRun: 0.005, Average: 0.006, Iterations: 100, maximum: 0.007
Transform: NormalizeMeanStd, Size: (1, 256, 256, 256), FirstRun: 0.042, Average: 0.052, Iterations: 100, maximum: 0.063
Transform: NormalizeMeanStd, Size: (1, 512, 512, 64), FirstRun: 0.050, Average: 0.052, Iterations: 100, maximum: 0.077
Transform: RandomBrightnessContrast, Size: (1, 64, 64, 64), FirstRun: 0.007, Average: 0.001, Iterations: 100, maximum: 0.001
Transform: RandomBrightnessContrast, Size: (1, 128, 128, 128), FirstRun: 0.005, Average: 0.004, Iterations: 100, maximum: 0.007
Transform: RandomBrightnessContrast, Size: (1, 256, 256, 256), FirstRun: 0.024, Average: 0.033, Iterations: 100, maximum: 0.064
Transform: RandomBrightnessContrast, Size: (1, 512, 512, 64), FirstRun: 0.031, Average: 0.035, Iterations: 100, maximum: 0.062
Transform: RandomFlip, Size: (1, 64, 64, 64), FirstRun: 0.007, Average: 0.001, Iterations: 100, maximum: 0.002
Transform: RandomFlip, Size: (1, 128, 128, 128), FirstRun: 0.006, Average: 0.007, Iterations: 100, maximum: 0.009
Transform: RandomFlip, Size: (1, 256, 256, 256), FirstRun: 0.048, Average: 0.067, Iterations: 100, maximum: 0.087
Transform: RandomFlip, Size: (1, 512, 512, 64), FirstRun: 0.067, Average: 0.067, Iterations: 100, maximum: 0.090
Transform: RandomGamma, Size: (1, 64, 64, 64), FirstRun: 0.011, Average: 0.003, Iterations: 100, maximum: 0.004
Transform: RandomGamma, Size: (1, 128, 128, 128), FirstRun: 0.026, Average: 0.027, Iterations: 100, maximum: 0.030
Transform: RandomGamma, Size: (1, 256, 256, 256), FirstRun: 0.222, Average: 0.229, Iterations: 100, maximum: 0.247
Transform: RandomGamma, Size: (1, 512, 512, 64), FirstRun: 0.222, Average: 0.229, Iterations: 100, maximum: 0.247
Transform: RandomGaussianBlur, Size: (1, 64, 64, 64), FirstRun: 0.014, Average: 0.006, Iterations: 100, maximum: 0.007
Transform: RandomGaussianBlur, Size: (1, 128, 128, 128), FirstRun: 0.217, Average: 0.221, Iterations: 100, maximum: 0.235
Transform: RandomGaussianBlur, Size: (1, 256, 256, 256), FirstRun: 0.930, Average: 0.733, Iterations: 100, maximum: 0.971
Transform: RandomGaussianBlur, Size: (1, 512, 512, 64), FirstRun: 0.873, Average: 0.692, Iterations: 100, maximum: 0.940
Transform: RandomRotate90, Size: (1, 64, 64, 64), FirstRun: 0.009, Average: 0.002, Iterations: 100, maximum: 0.003
Transform: RandomRotate90, Size: (1, 128, 128, 128), FirstRun: 0.007, Average: 0.081, Iterations: 100, maximum: 0.153
Transform: RandomRotate90, Size: (1, 256, 256, 256), FirstRun: 0.059, Average: 0.236, Iterations: 100, maximum: 0.656
Transform: RandomRotate90, Size: (1, 512, 512, 64), FirstRun: 0.405, Average: 0.189, Iterations: 100, maximum: 0.452
Transform: RandomScale, Size: (1, 64, 64, 64), FirstRun: 0.026, Average: 0.017, Iterations: 100, maximum: 0.042
Transform: RandomScale, Size: (1, 128, 128, 128), FirstRun: 0.259, Average: 0.141, Iterations: 100, maximum: 0.276
Transform: RandomScale, Size: (1, 256, 256, 256), FirstRun: 2.231, Average: 1.046, Iterations: 100, maximum: 2.180
Transform: RandomScale, Size: (1, 512, 512, 64), FirstRun: 1.857, Average: 1.024, Iterations: 100, maximum: 2.118
Transform: Scale, Size: (1, 64, 64, 64), FirstRun: 0.054, Average: 0.034, Iterations: 100, maximum: 0.047
Transform: Scale, Size: (1, 128, 128, 128), FirstRun: 0.266, Average: 0.271, Iterations: 100, maximum: 0.276
Transform: Scale, Size: (1, 256, 256, 256), FirstRun: 2.177, Average: 2.200, Iterations: 100, maximum: 2.266
Transform: Scale, Size: (1, 512, 512, 64), FirstRun: 2.199, Average: 2.196, Iterations: 100, maximum: 2.240
Transform: Scale, Size: (1, 64, 64, 64), FirstRun: 0.031, Average: 0.005, Iterations: 100, maximum: 0.006
Transform: Scale, Size: (1, 128, 128, 128), FirstRun: 0.038, Average: 0.039, Iterations: 100, maximum: 0.044
Transform: Scale, Size: (1, 256, 256, 256), FirstRun: 0.313, Average: 0.322, Iterations: 100, maximum: 0.370
Transform: Scale, Size: (1, 512, 512, 64), FirstRun: 0.322, Average: 0.325, Iterations: 100, maximum: 0.363
Transform: CenterCrop, Size: (1, 64, 64, 64), FirstRun: 0.068, Average: 0.003, Iterations: 100, maximum: 0.005
Transform: CenterCrop, Size: (1, 128, 128, 128), FirstRun: 0.029, Average: 0.031, Iterations: 100, maximum: 0.037
Transform: CenterCrop, Size: (1, 256, 256, 256), FirstRun: 0.240, Average: 0.220, Iterations: 100, maximum: 0.251
Transform: CenterCrop, Size: (1, 512, 512, 64), FirstRun: 0.263, Average: 0.255, Iterations: 100, maximum: 0.286
Transform: CenterCrop, Size: (1, 64, 64, 64), FirstRun: 0.001, Average: 0.001, Iterations: 100, maximum: 0.001
Transform: CenterCrop, Size: (1, 128, 128, 128), FirstRun: 0.004, Average: 0.005, Iterations: 100, maximum: 0.009
Transform: CenterCrop, Size: (1, 256, 256, 256), FirstRun: 0.032, Average: 0.042, Iterations: 100, maximum: 0.059
Transform: CenterCrop, Size: (1, 512, 512, 64), FirstRun: 0.037, Average: 0.038, Iterations: 100, maximum: 0.045
Transform: RandomCrop, Size: (1, 64, 64, 64), FirstRun: 0.010, Average: 0.004, Iterations: 100, maximum: 0.022
Transform: RandomCrop, Size: (1, 128, 128, 128), FirstRun: 0.029, Average: 0.031, Iterations: 100, maximum: 0.033
Transform: RandomCrop, Size: (1, 256, 256, 256), FirstRun: 0.191, Average: 0.223, Iterations: 100, maximum: 0.292
Transform: RandomCrop, Size: (1, 512, 512, 64), FirstRun: 0.259, Average: 0.243, Iterations: 100, maximum: 0.256
Transform: RandomCrop, Size: (1, 64, 64, 64), FirstRun: 0.002, Average: 0.001, Iterations: 100, maximum: 0.002
Transform: RandomCrop, Size: (1, 128, 128, 128), FirstRun: 0.004, Average: 0.005, Iterations: 100, maximum: 0.006
Transform: RandomCrop, Size: (1, 256, 256, 256), FirstRun: 0.032, Average: 0.040, Iterations: 100, maximum: 0.045
Transform: RandomCrop, Size: (1, 512, 512, 64), FirstRun: 0.036, Average: 0.038, Iterations: 100, maximum: 0.039
Transform: Resize, Size: (1, 64, 64, 64), FirstRun: 0.066, Average: 0.065, Iterations: 100, maximum: 0.068
Transform: Resize, Size: (1, 128, 128, 128), FirstRun: 0.505, Average: 0.511, Iterations: 100, maximum: 0.517
Transform: Resize, Size: (1, 256, 256, 256), FirstRun: 4.249, Average: 4.369, Iterations: 100, maximum: 5.118
Transform: Resize, Size: (1, 512, 512, 64), FirstRun: 4.287, Average: 4.259, Iterations: 100, maximum: 4.360
Transform: Resize, Size: (1, 64, 64, 64), FirstRun: 0.036, Average: 0.009, Iterations: 100, maximum: 0.011
Transform: Resize, Size: (1, 128, 128, 128), FirstRun: 0.068, Average: 0.070, Iterations: 100, maximum: 0.073
Transform: Resize, Size: (1, 256, 256, 256), FirstRun: 0.554, Average: 0.572, Iterations: 100, maximum: 0.608
Transform: Resize, Size: (1, 512, 512, 64), FirstRun: 0.560, Average: 0.568, Iterations: 100, maximum: 0.608
HowToChangeDocumentation.txt deleted 100644 → 0
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Prerequisites :
- You need to know the basics of HTML
Step 1 :
- Open the file index.html located in docs/_build/html/index.html to see what the documentation looks like in your web browser.
Step 2 :
- Open the file index.html located in docs/_build/html/index.html with your preferred text editor.
Step 3 :
- Make changes to the documentation (index.html) as desired, while ensuring that you adhere to the HTML syntax.
- You can also modify the file volumentations_biomedicine.augmentations.html if you wish to change the documentation of the functions.
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Links/resources:
- an easy Tutorial: https://www.youtube.com/watch?v=tEFkHEKypLI&t=563s
- pypi uploading packages: https://packaging.python.org/en/latest/tutorials/packaging-projects/
- pypi FAQ (directs to "Administration of projects"): https://pypi.org/help/#administration
Prerequisites:
- Have an account in PyPi
- Choose a package name that is not already taken.
- Have the code of the package directly on your local machine.
- Install Twine with : pip3 install twine
Step 1 (Library Structure) :
- Have an __init__.py in your package folder
- Have an setup.py at same level as your package
Example of how your folder should look like:
.
├── bio_volumentations (Package Folder)
│ ├── __init__.py
│ ├── 100IterationsTest.txt
│ ├── ...
└── setup.py
- Ensure that your `setup.py` does not upload a version that has already been uploaded in the past.
Setup 2 (Upload):
- When you are in the folder containing `setup.py` and your package, use the following command in your terminal to generate package files: python3 setup.py sdist bdist_wheel
Example of how your folder should look like after the command:
.
├── bio_volumentations
├── bio_volumentations.egg-info
├── build
├── dist
└── setup.py
- To upload the package, use this command in your terminal (in the same folder as the previous command): twine upload dist/*
- Then, enter your API Token to upload the package (You can find this in your PyPi Account settings).
- Your package is now uploaded to PyPi.
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# ============================================================================================= #
# Author: Samuel Šuľan, Lucia Hradecká, Filip Lux #
# Copyright: Lucia Hradecká : lucia.d.hradecka@gmail.com #
# Filip Lux : lux.filip@gmail.com #
# #
# MIT License. #
# #
# Permission is hereby granted, free of charge, to any person obtaining a copy #
# of this software and associated documentation files (the "Software"), to deal #
# in the Software without restriction, including without limitation the rights #
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell #
# copies of the Software, and to permit persons to whom the Software is #
# furnished to do so, subject to the following conditions: #
# #
# The above copyright notice and this permission notice shall be included in all #
# copies or substantial portions of the Software. #
# #
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR #
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, #
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE #
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER #
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, #
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE #
# SOFTWARE. #
# ============================================================================================= #
from bio_volumentations.core.composition import *
from bio_volumentations.augmentations import *
from devel import *
def get_augmentation():
return Compose([
CenterCrop( shape=(200,200,15) ,p=1),
AffineTransform( angle_limit = [(20,20), (20,20), (20,20)], scaling_coef= [1,1,3.22832576], p=1),
Pad(pad_size= [10,3,0], p = 1)
], p=1.0)
def get_augmentation_more():
return Compose([
CenterCrop( shape=(200,200,20) ,p=1),
AffineTransform( angle_limit = [(20,20), (20,20), (20,20)], scaling_coef= [1,1,3.22832576], p=1),
Pad(pad_size= [10,3,0], p = 1) ],
targets= [ ['image' , "image1"] , ['mask'], ['float_mask'] ], p=1.0)
if __name__ == '__main__':
#constants
data_samples = ["ekarev2023-crop-small-anisotropic.tif", "ekarev2023-crop-small-isotropic.tif", "brain.nii","Fluo-C3DH-H157_01_t000.tif","ekarev2023-crop2-3-zscaled.tif",
"ekarev2023-crop2-2.tif"]
number_of_sample = 1
path_to_image = "../Data_samples/" + data_samples[number_of_sample]
path_to_image1 = "../Data_samples/" + data_samples[2]
path_to_augumented_images = "D:/CBIA/demo/"
name_of_file = path_to_image.split("/")[-1]
multipleChannels = True
normalize_with_regards_to_max = False
img, maximum,minimum, affine_array = image_preparation(path_to_image ,multipleChannels)
img1, maximum1,minimum1, affine_array1 = image_preparation(path_to_image1 ,False)
mask = img[0].copy()
#or use
#img = np.random.rand(1, 128, 256, 256)
#mask = np.random.randint(0, 1, size=(128, 256, 256), dtype=np.uint8)
aug = get_augmentation()
#Categorizing data
data = {'image': img , 'mask' : mask }
#data = {'image': img , 'mask' : mask , 'image1': img1 }
#Starting transformations
aug_data = aug(**data)
#Taking data after transformations
#img, mask, img1 = aug_data['image'], aug_data['mask'] , aug_data['image1']
img, mask = aug_data['image'], aug_data['mask']
# just for saving purposes
mask = mask[np.newaxis, :]
#Saving images
image_save(path_to_augumented_images + "Image" + ".tif", img, minimum,maximum,affine_array,normalize_with_regards_to_max)
image_save(path_to_augumented_images + "mask" + ".tif", mask, minimum,maximum,affine_array,normalize_with_regards_to_max)
#image_save(path_to_augumented_images + "Image1" + ".nii", img1, minimum1,maximum1,affine_array1,normalize_with_regards_to_max)
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# coding: utf-8
# ============================================================================================= #
# Author: Samuel Šuľan, Lucia Hradecká, Filip Lux #
# Copyright: Lucia Hradecká : lucia.d.hradecka@gmail.com #
# Filip Lux : lux.filip@gmail.com #
# #
# MIT License. #
# #
# Permission is hereby granted, free of charge, to any person obtaining a copy #
# of this software and associated documentation files (the "Software"), to deal #
# in the Software without restriction, including without limitation the rights #
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell #
# copies of the Software, and to permit persons to whom the Software is #
# furnished to do so, subject to the following conditions: #
# #
# The above copyright notice and this permission notice shall be included in all #
# copies or substantial portions of the Software. #
# #
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR #
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, #
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE #
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER #
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, #
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE #
# SOFTWARE. #
# ============================================================================================= #
from bio_volumentations.core.composition import *
from bio_volumentations.augmentations import *
import numpy as np
import tifffile
import nibabel as nib
if __name__ == '__main__':
#path files
debug = False
data_samples = ["ekarev2023-crop-small-anisotropic.tif", "ekarev2023-crop-small-isotropic.tif", "brain.nii","Fluo-C3DH-H157_01_t000.tif","ekarev2023-crop2-3-zscaled.tif",
"ekarev2023-crop2-2.tif"]
number_of_sample = 0
if debug:
path_to_image = "./Data_samples/" + data_samples[number_of_sample]
path_to_augumented_images = "./data_augumented/"
else:
path_to_image = "../Data_samples/" + data_samples[number_of_sample]
path_to_augumented_images = "../data_augumented/"
path_to_augumented_images = "D:/CBIA/augumented/"
# These are functions for loading and saving images -
# They are not the perfect, feel free to change them according to your needs.
def image_to_numpy(path):
file_format = path.split(".")[-1]
if file_format == "tif":
numpy_image = tifffile.imread(path)
affine_array = None
if file_format == "nii":
img = nib.load(path)
numpy_image = img.get_fdata()
affine_array = img.affine
return numpy_image.astype(np.float32) , affine_array
def numpy_to_file(path, image, affine_array):
file_format = path.split(".")[-1]
if file_format == "tif":
if len(image.shape) == 5:
tifffile.imsave(path, image, metadata={'axes': 'TZCYX'}, imagej= True)
if len(image.shape) == 4:
tifffile.imsave(path, image, metadata={'axes': 'ZCYX'}, imagej= True)
else:
tifffile.imsave(path, image, imagej= True)
if file_format == "nii":
array_img = nib.Nifti1Image(image, affine_array)
nib.save(array_img, path)
def numpy_remove_negative(image):
negative_indices = image < 0
image[negative_indices] = 0
return image
def numpy_normalization(image):
'''
0 = min
1 = max
'''
minimun = image.min()
maximum = image.max()
if(minimun < 0):
image = image + minimun
image = image / maximum
return image, maximum, minimun
def numpy_reverse_normalization(image, minimum, maximum,normalize_with_regards_to_max):
'''
can be negative
'''
print("image.min()" + str(image.min()))
if image.min() < 0:
image = image - image.min()
print("maximum:" + str(maximum) + " image.max(): " + str(image.max()))
if image.max() == 0:
return image.astype(np.ushort)
elif normalize_with_regards_to_max:
image = image / ( image.max() / maximum)
else:
image = image * maximum
return image.astype(np.ushort)
def convention_format(image, multiple_channels = False):
shape = list(range(len(image.shape)))
shape.reverse()
if multiple_channels:
shape.insert(0, shape.pop(2))
return np.transpose(image, shape)
else:
return np.expand_dims(np.transpose(image, shape), axis=0)
def move_channels_back(image):
if image.shape[0] > 1:
shape = list(range(len(image.shape)))
shape.reverse()
shape.insert(len(image.shape) - 3, shape.pop())
return np.transpose(image, shape)
else:
shape = [i - 1 for i in range(len(image.shape))]
#shape = list(range(len(image.shape)))
shape.reverse()
shape.pop()
return np.transpose(image.squeeze(0), shape)
def image_preparation(path,multipleChannels = False):
maximum, minimum, affine_array = None, None, None
image, affine_array = image_to_numpy(path)
image = convention_format(image,multipleChannels)
image, maximum, minimum = numpy_normalization(image)
return image, maximum, minimum, affine_array
def image_save(path, image, minimum, maximum, affine_array,normalize_with_regards_to_max):
##affine_array not sure if needed for .nii file.
image = numpy_reverse_normalization(image,minimum, maximum,normalize_with_regards_to_max)
image = move_channels_back(image)
numpy_to_file(path,image,affine_array)
def get_augmentation():
return Compose([
#RandomFlip(axes_to_choose= [1,2,3], p=1),
#RandomCrop((200,250,30), p = 1)
#RandomScale2( scale_limit= (0.9, 1.1), p = 1),
#AffineTransform( angle_limit= [(25,25), (25,25), (25,25)], border_mode="constant", scaling_coef = [1,1,3.22832576] , scale_back= False ,p=1), # True, , #
#CenterCrop( shape=(200,200,15), ignore_index= 200 ,p=1),
#Resize(shape= (300,100, 20), ignore_index= 203, p = 1)
#Scale( scale_factor = 0.5, ignore_index= 203 )
Pad(pad_size= (8,9), p = 1)
#NormalizeMeanStd( mean= [0.5, 2 ], std = 0.5 , p= 1),
#Normalize(mean = 2, std = 0, p = 1)
], p=1.0)
def get_augmentationMore():
return Compose([
RandomGamma( gamma_limit = (0.8 , 1,2) , p = 0.8),
RandomRotate90(axes=[3,3,3],p=1),
GaussianBlur(sigma = 1.2, p = 0.8),
],targets= [ ['image' , "image1"] , ['mask'], ['float_mask'] ] , p=1.0)
if __name__ == '__main__':
name_of_file = path_to_image.split("/")[-1]
multipleChannels = True
normalize_with_regards_to_max = False
path_to_image1 = "../Data_samples/" + data_samples[3]
print(path_to_image)
img, maximum,minimum, affine_array = image_preparation(path_to_image ,multipleChannels)
mask = img[0].copy()
aug = get_augmentation()
data = {'image': img, 'mask' : mask } # #, 'image1': img1
aug_data = aug(**data)
img = aug_data['image']
mask = aug_data['mask']
#image_save(path_to_augumented_images + "pad" + name_of_file, img, minimum,maximum,affine_array,normalize_with_regards_to_max)
\ No newline at end of file
tst_volumentations_speed.py deleted 100644 → 0
+ 0
− 50
View file @ 7f8be6fb
# coding: utf-8
__author__ = 'ZFTurbo: https://kaggle.com/zfturbo'
from bio_volumentations.core.composition import Compose
from bio_volumentations.augmentations.transforms import RandomAffineTransform
import time
import numpy as np
def tst_volumentations_speed():
total_volumes_to_check = 100
sizes_list = [
(1, 64, 64, 64),
# (1, 128, 128, 128),
# (1, 256, 256, 256),
# (1, 512, 512, 64),
]
for size in sizes_list:
full_list_to_check = [
RandomAffineTransform(angle_limit=[22.5, 22.5, 22.5], p=1),
]
for f in full_list_to_check:
name = f.__class__.__name__
aug1 = Compose([
f,
], p=1.0)
data = []
for i in range(total_volumes_to_check):
data.append(np.random.uniform(low=0.0, high=255, size=size))
start_time = time.time()
for i, cube in enumerate(data):
try:
cube1 = aug1(image=cube)['image']
except Exception as e:
print('Augmentation error: {}'.format(str(e)))
continue
delta = time.time() - start_time
print('Size: {} Aug: {} Time: {:.2f} sec Per sample: {:.4f} sec'.format(size, name, delta, delta / len(data)))
# print(f.__dict__)
if __name__ == '__main__':
tst_volumentations_speed()
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