From 215b51fa1c4a3adf4d07d5e63c5d3fe021a129bb Mon Sep 17 00:00:00 2001
From: "Lucia D. Hradecka" <lucia.d.hradecka@gmail.com>
Date: Fri, 12 Apr 2024 11:09:59 +0200
Subject: [PATCH] clean repo
---
100IterationsTest.txt | 88 ----------------
HowToChangeDocumentation.txt | 12 ---
HowToUploadPackage.txt | 37 -------
demo.py | 96 -----------------
devel.py | 198 -----------------------------------
tst_volumentations_speed.py | 50 ---------
6 files changed, 481 deletions(-)
delete mode 100644 100IterationsTest.txt
delete mode 100644 HowToChangeDocumentation.txt
delete mode 100644 HowToUploadPackage.txt
delete mode 100644 demo.py
delete mode 100644 devel.py
delete mode 100644 tst_volumentations_speed.py
diff --git a/100IterationsTest.txt b/100IterationsTest.txt
deleted file mode 100644
index 7aca0ad..0000000
--- a/100IterationsTest.txt
+++ /dev/null
@@ -1,88 +0,0 @@
-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
diff --git a/HowToChangeDocumentation.txt b/HowToChangeDocumentation.txt
deleted file mode 100644
index 81a71c1..0000000
--- a/HowToChangeDocumentation.txt
+++ /dev/null
@@ -1,12 +0,0 @@
-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.
\ No newline at end of file
diff --git a/HowToUploadPackage.txt b/HowToUploadPackage.txt
deleted file mode 100644
index 2e55a58..0000000
--- a/HowToUploadPackage.txt
+++ /dev/null
@@ -1,37 +0,0 @@
-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.
diff --git a/demo.py b/demo.py
deleted file mode 100644
index 7e16578..0000000
--- a/demo.py
+++ /dev/null
@@ -1,96 +0,0 @@
-# ============================================================================================= #
-# 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)
-
-
-
diff --git a/devel.py b/devel.py
deleted file mode 100644
index f20801a..0000000
--- a/devel.py
+++ /dev/null
@@ -1,198 +0,0 @@
-# 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
diff --git a/tst_volumentations_speed.py b/tst_volumentations_speed.py
deleted file mode 100644
index 07601d6..0000000
--- a/tst_volumentations_speed.py
+++ /dev/null
@@ -1,50 +0,0 @@
-# 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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