Image Processing Using NumPy - Part 2


NumPy can be used to perform image morphological operations. Here are some examples:

  • Dilation: Dilation is an operation that expands the boundaries of objects in an image. It is often used to fill in holes or gaps in objects.
import numpy as np
import cv2

image = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)

# Create a structuring element
structuring_element = np.ones((3, 3), np.uint8)

# Dilate the image
dilated_image = cv2.dilate(image, structuring_element)

cv2.imwrite('dilated_image.jpg', dilated_image)
  • Erosion: Erosion is an operation that shrinks the boundaries of objects in an image. It is often used to remove noise or small objects from an image.
import numpy as np
import cv2

image = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)

# Create a structuring element
structuring_element = np.ones((3, 3), np.uint8)

# Erode the image
eroded_image = cv2.erode(image, structuring_element)

cv2.imwrite('eroded_image.jpg', eroded_image)
  • Opening: Opening is a morphological operation that consists of an erosion followed by a dilation. It is often used to remove small objects from an image or to smoothen the edges of objects.
import numpy as np
import cv2

image = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)

# Create a structuring element
structuring_element = np.ones((3, 3), np.uint8)

# Open the image
opened_image = cv2.morphologyEx(image, cv2.MORPH_OPEN, structuring_element)

cv2.imwrite('opened_image.jpg', opened_image)
  • Closing: Closing is a morphological operation that consists of a dilation followed by an erosion. It is often used to fill in small holes in objects or to smoothen the edges of objects.
import numpy as np
import cv2

image = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)

# Create a structuring element
structuring_element = np.ones((3, 3), np.uint8)

# Close the image
closed_image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, structuring_element)

cv2.imwrite('closed_image.jpg', closed_image)




The top hat and black hat are morphological operations that can be used to enhance or suppress features in an image.

The top hat is the difference between the original image and its opening. It is used to enhance features that are larger than the structuring element.

The black hat is the difference between the original image and its closing. It is used to enhance features that are smaller than the structuring element.

Here is an example of how to implement the top hat and black hat operations using NumPy:

import numpy as np
import cv2

image = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)

# Create a structuring element
structuring_element = np.ones((3, 3), np.uint8)

# Apply the top hat operation
top_hat = cv2.morphologyEx(image, cv2.MORPH_TOPHAT, structuring_element)

# Apply the black hat operation
black_hat = cv2.morphologyEx(image, cv2.MORPH_BLACKHAT, structuring_element)

cv2.imwrite('top_hat.jpg', top_hat)
cv2.imwrite('black_hat.jpg', black_hat)


In this example, the structuring element is a 3x3 square. The top hat image will highlight the features in the original image that are larger than 3 pixels. The black hat image will highlight the features in the original image that are smaller than 3 pixels.

The top hat and black hat operations can be used to enhance or suppress features in an image for a variety of purposes, such as edge detection, noise removal, and image segmentation.

These are just a few examples of image morphological operations that can be performed using NumPy. There are many other operations that can be performed, depending on the specific needs of the application. 

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