In Matplotlib library image masking involves selectively displaying parts of an image based on a specified mask which is essentially a binary image defining regions to be shown or hidden. It allows us to apply a filter or condition to reveal or hide specific portions of an image.

Process of Image Masking

The below are the process steps for performing Image Masking.

Create a Mask

Define the criteria or pattern to create the mask. It can be based on colors, shapes, gradients or specific pixel values.

Apply the Mask

Use the mask to modify the transparency or visibility of the corresponding pixels in the original image. Pixels that correspond to areas defined in the mask as “masked” are usually hidden or made transparent while others remain visible.

Overlay or Blend

Overlay or blend the masked image with another image or a background revealing only the unmasked portions. The masked image can be superimposed on another image to combine and display the visible areas.

Types of Image Masks

The below are the types of Image masks.

Binary Masks

Consist of black and white pixels where white represents visible areas and black represents masked areas.

Grayscale Masks

Use various shades of gray to define levels of transparency or partial visibility.

Tools and Techniques for Image Masking

We can use the different tools and techniques for Image masking.

Manual Masking

Tools like Photoshop or GIMP allow users to manually create and edit masks using selection tools, brushes or layer masks.

Programmatic Masking

In programming languages like Python with libraries such as OpenCV or PIL (Pillow) we can create masks using algorithms based on color thresholds, contours or specific image features.

Key Points

• Image masking in Matplotlib involves creating a mask array with the same dimensions as the image where specific regions are marked to hide (masked) or reveal (unmasked) portions of the image.

• Masking arrays consist of boolean or numerical values where True or non-zero values indicate the regions to be displayed, and False or zero values indicate the regions to be hidden.

• Masking allows for selective visualization or manipulation of specific parts of an image based on specified conditions or criteria.

Masking the particular region of the Image

Let”s say we have an image and want to mask a particular region displaying only a portion of the image based on certain criteria.

Example

import matplotlib.pyplot as plt
import numpy as np

# Create a sample image (random pixels)
image = np.random.rand(100, 100)

# Create a mask to hide certain parts of the image
mask = np.zeros_like(image)
mask[30:70, 30:70] = 1  # Masking a square region

# Apply the mask to the image
masked_image = np.ma.masked_array(image, mask=mask)

# Display the original and masked images
plt.figure(figsize=(8, 4))
plt.subplot(1, 2, 1)
plt.imshow(image, cmap=''gray'')
plt.title(''Original Image'')
plt.axis(''off'')
plt.subplot(1, 2, 2)
plt.imshow(masked_image, cmap=''gray'')
plt.title(''Masked Image'')
plt.axis(''off'')
plt.show()

Output

Applying mask to an Image

Here this is another of masking an image using the matplotlib library.

Example

import matplotlib.pyplot as plt
import numpy as np

# Create a sample image
image_size = 100
img = np.zeros((image_size, image_size, 3), dtype=np.uint8)
img[:, :image_size // 2] = [255, 0, 0]  # Set the left half to blue

# Create a binary mask
mask = np.zeros((image_size, image_size), dtype=np.uint8)
mask[:, image_size // 4:] = 1  # Set the right quarter to 1

# Apply the mask to the image
masked_img = img * mask[:, :, np.newaxis]

# Display the original image, mask, and the masked image
plt.figure(figsize=(12, 4))
plt.subplot(131)
plt.imshow(img)
plt.title(''Original Image'')
plt.axis(''off'')
plt.subplot(132)
plt.imshow(mask, cmap=''gray'')
plt.title(''Mask'')
plt.axis(''off'')
plt.subplot(133)
plt.imshow(masked_img)
plt.title(''Masked Image'')
plt.axis(''off'')
plt.show()

Output