Python | Morphological Operations in Image Processing (Gradient) | Set-3

In the previous articles, the Opening operation and the Closing operations were specified. In this article, another morphological operation is elaborated that is Gradient. It is used for generating the outline of the image. There are two types of gradients, internal and external gradient. The internal gradient enhances the internal boundaries of objects brighter than their background and external boundaries of objects darker than their background. For binary images, the internal gradient generates a mask of the internal boundaries of the foreground image objects.

Syntax: cv2.morphologyEx(image, cv2.MORPH_GRADIENT, kernel)
Parameters: 
-> image: Input Image array. 
-> cv2.MORPH_GRADIENT: Applying the Morphological Gradient operation. 
-> kernel: Structuring element. 

Below is the Python code explaining Gradient Morphological Operation – 

Python3

# Python program to illustrate
# Gradient morphological operation
# on input frames

# organizing imports
import cv2
import numpy as np

# return video from the first webcam on your computer.
screenRead = cv2.VideoCapture(0)

# loop runs if capturing has been initialized.
while(1):
    # reads frames from a camera
    _, image = screenRead.read()

    # Converts to HSV color space, OCV reads colors as BGR
    # frame is converted to hsv
    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)

    # defining the range of masking
    blue1 = np.array([110, 50, 50])
    blue2 = np.array([130, 255, 255])

    # initializing the mask to be
    # convoluted over input image
    mask = cv2.inRange(hsv, blue1, blue2)

    # passing the bitwise_and over
    # each pixel convoluted
    res = cv2.bitwise_and(image, image, mask = mask)

    # defining the kernel i.e. Structuring element
    kernel = np.ones((5, 5), np.uint8)

    # defining the gradient function
    # over the image and structuring element
    gradient = cv2.morphologyEx(mask, cv2.MORPH_GRADIENT, kernel)

    # The mask and closing operation
    # is shown in the window
    cv2.imshow('Gradient', gradient)

    # Wait for 'a' key to stop the program
    if cv2.waitKey(1) & 0xFF == ord('a'):
        break

# De-allocate any associated memory usage
cv2.destroyAllWindows()

# Close the window / Release webcam
screenRead.release()

Result:

The output image frame shows the outline generated over the blue book and the blue object in top left.