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title: "example xymasking" notebookName: "example_xymasking.ipynb"
Open in Google ColabRun this notebook interactively
XYMasking aug in Albumentations
This transform is a generalization of the TimeMasking and FrequencyMasking from torchaudio.
import albumentations as A
from matplotlib import pyplot as plt
/opt/homebrew/Caskroom/miniconda/base/envs/albumentations_examples/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
from .autonotebook import tqdm as notebook_tqdm
import numpy as np
import torch
import torchaudio
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
Cell In[2], line 1
----> 1 import torchaudio
2 import torch
4 import numpy as np
ModuleNotFoundError: No module named 'torchaudio'
def visualize(image):
plt.figure(figsize=(10, 5))
plt.axis("off")
plt.imshow(image)
img = np.load("../images/spectrogram.npy")
img.shape
(128, 256, 4)
visualize(img[:, :, 0])
No code provided
No code providedOne vertical stripe (time masking) with fixed width, filled with 0
params1 = {
"num_masks_x": 1,
"mask_x_length": 20,
"fill_value": 0,
}
transform1 = A.Compose([A.XYMasking(**params1, p=1)], strict=True, seed=137)
visualize(transform1(image=img[:, :, 0])["image"])
No code provided
No code providedOne vertical stripe (time masking) with randomly sampled width, filled with 0
params2 = {
"num_masks_x": 1,
"mask_x_length": (0, 20), # This line changed from fixed to a range
"fill_value": 0,
}
transform2 = A.Compose([A.XYMasking(**params2, p=1)], strict=True, seed=137)
visualize(transform2(image=img[:, :, 0])["image"])
No code provided
No code providedAnalogous transform in torchaudio¶
spectrogram = torchaudio.transforms.Spectrogram()
masking = torchaudio.transforms.TimeMasking(time_mask_param=20)
masked = masking(torch.from_numpy(img[:, :, 0]))
visualize(masked.numpy())
No code provided
No code providedOne horizontal stripe (frequency masking) with randomly sampled width, filled with 0
params3 = {
"num_masks_y": 1,
"mask_y_length": (0, 20),
"fill_value": 0,
}
transform3 = A.Compose([A.XYMasking(**params3, p=1)], strict=True, seed=137)
visualize(transform3(image=img[:, :, 0])["image"])
No code provided
No code providedAnalogous transform in torchaudio
spectrogram = torchaudio.transforms.Spectrogram()
masking = torchaudio.transforms.FrequencyMasking(freq_mask_param=20)
masked = masking(torch.from_numpy(img[:, :, 0]))
visualize(masked)
No code provided
No code providedSeveral vertical and horizontal stripes
params4 = {
"num_masks_x": (2, 4),
"num_masks_y": 5,
"mask_y_length": 8,
"mask_x_length": (10, 20),
"fill_value": 0,
}
transform4 = A.Compose([A.XYMasking(**params4, p=1)], strict=True, seed=137)
visualize(transform4(image=img[:, :, 0])["image"])
No code provided
No code providedApplication to the image with the number of channels larger than 3, and different fill values for different channels
Transform can work with any number of channels supporing image shapes of
- Grayscale: (height, width)
- RGB: (height, width, 3)
- Multichannel: (heigh, width, num_channels)
For value that is used to fill masking regions you can use:
- scalar that will be applied to every channel
- list of numbers equal to the number of channels, so that every channel will have it's own filling value
params5 = {
"num_masks_x": (2, 4),
"num_masks_y": 5,
"mask_y_length": 8,
"mask_x_length": (20, 30),
"fill_value": (0, 1, 2, 3),
}
transform5 = A.Compose([A.XYMasking(**params5, p=1)], strict=True, seed=137)
transformed = transform5(image=img)["image"]
fig, axs = plt.subplots(2, 2)
vmin = 0
vmax = 3
axs[0, 0].imshow(transformed[:, :, 0], vmin=vmin, vmax=vmax)
axs[0, 0].set_title("Channel 0")
axs[0, 0].axis("off") # Hide axes for cleaner visualization
axs[0, 1].imshow(transformed[:, :, 1], vmin=vmin, vmax=vmax)
axs[0, 1].set_title("Channel 1")
axs[0, 1].axis("off")
axs[1, 0].imshow(transformed[:, :, 2], vmin=vmin, vmax=vmax)
axs[1, 0].set_title("Channel 2")
axs[1, 0].axis("off")
axs[1, 1].imshow(transformed[:, :, 3], vmin=vmin, vmax=vmax)
axs[1, 1].set_title("Channel 3")
axs[1, 1].axis("off")
plt.tight_layout()
plt.show()
No code provided
No code provided