"""
    Morphological Operations in image processing (close) set-2

    The opening operator was specified which was applying the erosion operation
    after dilation. It help in removing the internal noise in the image.
    Closing is similar to the opening operation. In closing operation, the basic
    premise is that the closing is opening performed in reverse. It is define simply as
    a dilation followed by an erosion using the same structuring element used in the
    opening operation.
"""

# Python program to illustrate
# Closing morphological operation
# on an image

# organizing imports
import cv2
import numpy as np

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

# loop runs if capturing has been initialized.
while 1:
    # reads frames from a camera
    _, image = cap.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 closing function
    # over the image and structuring element
    closing = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)

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

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

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

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