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API Reference

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])

png

One 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"])

png

One 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"])

png

Analogous 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())

png

One 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"])

png

Analogous transform in torchaudio 🔗

spectrogram = torchaudio.transforms.Spectrogram()
masking = torchaudio.transforms.FrequencyMasking(freq_mask_param=20)
masked = masking(torch.from_numpy(img[:, :, 0]))
visualize(masked)

png

Several 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"])

png

Application 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()

png