Morphological Operations in Image Processing (Gradient) in OpenCV

Morphological Gradient is a morphological operation that highlights the edges of structures in an image. It is the difference between dilation and erosion of an image. The result will look somewhat like an outline of the original image.

Let's explore how to perform the Morphological Gradient operation using OpenCV.

Steps to perform Morphological Gradient using OpenCV:

1. Setup:

If you haven't already, install OpenCV:

pip install opencv-python

Import necessary libraries:

import cv2
import numpy as np
import matplotlib.pyplot as plt

2. Load Image:

Load a grayscale or binary image. For clarity in this operation, I'll assume a binary image:

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

3. Define Structuring Element:

Morphological operations require a structuring element, which is a shape applied to the image. Common shapes include rectangles, ellipses, and crosses. Here, we'll use a rectangle:

kernel = np.ones((5,5), np.uint8)

This code creates a 5x5 rectangular kernel. Depending on your needs, you might adjust the kernel's size.

4. Perform Morphological Gradient:

Apply the Morphological Gradient operation using cv2.morphologyEx:

gradient_image = cv2.morphologyEx(image, cv2.MORPH_GRADIENT, kernel)

5. Display the Images:

Visualize the original and processed images:

plt.subplot(121), plt.imshow(image, cmap='gray'), plt.title('Original Image')
plt.subplot(122), plt.imshow(gradient_image, cmap='gray'), plt.title('Morphological Gradient')
plt.show()

The Morphological Gradient will display the edges or boundaries of objects in the image. It's especially effective on binary images where the distinction between the foreground (usually white) and the background (usually black) is clear.

This tutorial was a quick introduction to the Morphological Gradient operation in OpenCV. As with other morphological operations, the choice of structuring element and its size can greatly influence the result, so experimentation is often necessary to achieve the desired effect.

Morphological Gradient in OpenCV:

Description: Introduction to the morphological gradient operation.

Code:

import cv2
import numpy as np

# Read the binary image
binary_image = cv2.imread('binary_image.jpg', cv2.IMREAD_GRAYSCALE)

# Define a kernel for morphological gradient
kernel = np.ones((5, 5), np.uint8)

# Apply morphological gradient
gradient_image = cv2.morphologyEx(binary_image, cv2.MORPH_GRADIENT, kernel)

# Display the original and gradient images
cv2.imshow('Original Binary Image', binary_image)
cv2.imshow('Morphological Gradient Image', gradient_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

Dilate and Erode for Gradient in OpenCV:

Description: Demonstrates the use of dilate and erode operations to compute the gradient.

Code:

import cv2
import numpy as np

# Read the binary image
binary_image = cv2.imread('binary_image.jpg', cv2.IMREAD_GRAYSCALE)

# Define a kernel for dilation and erosion
kernel = np.ones((5, 5), np.uint8)

# Apply dilate and erode for gradient
dilated_image = cv2.dilate(binary_image, kernel)
eroded_image = cv2.erode(binary_image, kernel)
gradient_image = dilated_image - eroded_image

# Display the original and gradient images
cv2.imshow('Original Binary Image', binary_image)
cv2.imshow('Morphological Gradient Image', gradient_image)
cv2.waitKey(0)
cv2.destroyAllWindows()