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tst_volumentations_type_2.py
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tst_volumentations_type_2.py
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#=================================================================================#
# Author: Pavel Iakubovskii, ZFTurbo, ashawkey, Dominik Müller #
# Copyright: albumentations: : https://github.com/albumentations-team #
# Pavel Iakubovskii : https://github.com/qubvel #
# ZFTurbo : https://github.com/ZFTurbo #
# ashawkey : https://github.com/ashawkey #
# Dominik Müller : https://github.com/muellerdo #
# #
# Volumentations History: #
# - Original: https://github.com/albumentations-team/album #
# entations #
# - 3D Conversion: https://github.com/ashawkey/volumentations #
# - Continued Development: https://github.com/ZFTurbo/volumentations #
# - Enhancements: https://github.com/qubvel/volumentations #
# - Further Enhancements: https://github.com/muellerdo/volumentations #
# #
# 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. #
#=================================================================================#
#-----------------------------------------------------#
# Library imports #
#-----------------------------------------------------#
# External libraries
import os
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from skimage.data import cells3d
# Volumentations libraries
from volumentations import Compose
from volumentations import augmentations as ai
# -----------------------------------------------------#
# GIF Visualizer #
# -----------------------------------------------------#
def grayscale_normalization(image):
# Identify minimum and maximum
max_value = np.max(image)
min_value = np.min(image)
# Scaling
image_scaled = (image - min_value) / (max_value - min_value)
image_normalized = np.around(image_scaled * 255, decimals=0)
# Return normalized image
return image_normalized
def visualize_evaluation(index, volume, viz_path="test_volumentations"):
# Grayscale Normalization of Volume
volume_gray = grayscale_normalization(volume)
# Create a figure and two axes objects from matplot
fig = plt.figure()
img = plt.imshow(volume_gray[0, :, :], cmap='gray', vmin=0, vmax=255,
animated=True)
# Update function to show the slice for the current frame
def update(i):
plt.suptitle("Augmentation: " + str(index) + " - " + "Slice: " + str(i))
img.set_data(volume_gray[i, :, :])
return img
# Compute the animation (gif)
ani = animation.FuncAnimation(fig, update, frames=volume_gray.shape[0],
interval=5, repeat_delay=0, blit=False)
# Set up the output path for the gif
if not os.path.exists(viz_path):
os.mkdir(viz_path)
file_name = "visualization." + str(index) + ".gif"
out_path = os.path.join(viz_path, file_name)
# Save the animation (gif)
ani.save(out_path, writer='imagemagick', fps=None, dpi=None)
# Close the matplot
plt.close()
#-----------------------------------------------------#
# Albumentations Builder #
#-----------------------------------------------------#
""" Builds the albumenations augmentator by initializing all transformations.
The activated transformation and their configurations are defined as
class variables.
-> Builds a new self.operator
"""
def build(aug_flip, aug_rotate, aug_brightness, aug_contrast, aug_saturation,
aug_hue, aug_scale, aug_crop, aug_gridDistortion, aug_compression,
aug_gaussianNoise, aug_gaussianBlur, aug_downscaling, aug_gamma,
aug_elasticTransform):
# Initialize transform list
transforms = []
# Fill transform list
if aug_flip:
tf = ai.Flip(p=0.5)
transforms.append(tf)
if aug_rotate:
tf = ai.RandomRotate90(p=0.5)
transforms.append(tf)
if aug_brightness:
tf = ai.ColorJitter(contrast=0, hue=0, saturation=0,
p=0.5)
transforms.append(tf)
if aug_contrast:
tf = ai.ColorJitter(brightness=0, hue=0, saturation=0,
p=0.5)
transforms.append(tf)
if aug_saturation:
tf = ai.ColorJitter(brightness=0, contrast=0, hue=0,
p=0.5)
transforms.append(tf)
if aug_hue:
tf = ai.ColorJitter(brightness=0, contrast=0, saturation=0,
p=0.5)
transforms.append(tf)
if aug_scale:
tf = ai.RandomScale(p=0.5)
transforms.append(tf)
if aug_crop:
tf = ai.RandomCrop(shape=(30, 128, 128), p=0.5)
transforms.append(tf)
if aug_gridDistortion:
tf = ai.GridDistortion(p=0.5)
transforms.append(tf)
if aug_compression:
tf = ai.ImageCompression(p=0.5)
transforms.append(tf)
if aug_gaussianNoise:
tf = ai.GaussianNoise(p=0.5)
transforms.append(tf)
if aug_gaussianBlur:
tf = ai.GlassBlur(p=0.5)
transforms.append(tf)
if aug_downscaling:
tf = ai.Downscale(p=0.5)
transforms.append(tf)
if aug_gamma:
tf = ai.RandomGamma(p=0.5)
transforms.append(tf)
if aug_elasticTransform:
tf = ai.ElasticTransform(p=0.5)
transforms.append(tf)
# Compose transforms
return Compose(transforms)
#-----------------------------------------------------#
# Application Test #
#-----------------------------------------------------#
if __name__ == "__main__":
# Obtain 3D volume of fluorescence microscopy image of cells
data_raw = cells3d()
# Extract nuclei
data = np.reshape(data_raw[:,1,:,:], (60, 256, 256))
data = np.float32(data)
data = grayscale_normalization(data)
# Visualize original volume
visualize_evaluation("original", data)
print(data)
print("original", data.shape)
# Setup options
options = [False for x in range(15)]
options_labels = ["flip", "rotate", "brightness", "contrast", "saturation",
"hue", "scale", "crop", "grid_distortion", "compression",
"gaussian_noise", "gaussian_blur", "downscaling", "gamma",
"elastic_transform"]
# Apply each augmentation once for testing
for i in range(15):
# Active current augmentation technique
options_curr = options.copy()
options_curr[i] = True
# Initialize Volumentations
data_aug = build(*options_curr)
# Apply augmentation
img_augmented = data_aug(image=data)["image"]
# Visualize result
print(options_labels[i], img_augmented.shape)
visualize_evaluation(options_labels[i], img_augmented)