Filters and convolution

https://docs.opencv.org/master/d2/d96/tutorial_py_table_of_contents_imgproc.html

Simple thresholding

For every pixel, the same threshold is applaied. If the pixel is smaller then the threshold, it is set to 0, otherwise it is set to the maximum.

../_images/threshold0.png 
# Add a trackbar
import cv2 as cv
import numpy as np

img = np.fromfunction(lambda i, j: j, (50, 256), dtype='uint8')

modes = (cv.THRESH_BINARY, 
        cv.THRESH_BINARY_INV,
        cv.THRESH_TRUNC,
        cv.THRESH_TOZERO,
        cv.THRESH_TOZERO_INV)

def trackbar(x):
    """Trackbar callback function."""
    text = f'threshold={x}'
    cv.displayOverlay('window', text, 1000)
    
    ret, img1 = cv.threshold(img, x, 255, cv.THRESH_BINARY)
    ret, img2 = cv.threshold(img, x, 255, cv.THRESH_BINARY_INV)
    ret, img3 = cv.threshold(img, x, 255, cv.THRESH_TRUNC)
    ret, img4 = cv.threshold(img, x, 255, cv.THRESH_TOZERO)
    ret, img5 = cv.threshold(img, x, 255, cv.THRESH_TOZERO_INV)
    
    cv.imshow('window', np.vstack([img, img1, img2, img3, img4, img5]))

cv.imshow('window', img)
trackbar(100)
cv.createTrackbar('threshold', 'window', 100, 255, trackbar)

cv.waitKey(0)
cv.destroyAllWindows()

threshold0.py

Binary thresholding

../_images/threshold1.png 
# binary thresholding
import cv2 as cv
import numpy as np

def trackbar(x):    
    ret, img1 = cv.threshold(img, x, 255, cv.THRESH_BINARY)
    ret, img2 = cv.threshold(img, x, 255, cv.THRESH_BINARY_INV)
    cv.imshow('window', np.hstack([img, img1, img2]))

    text = f'threshold={x}, mode=BINARY, BINARY_INV'
    cv.displayOverlay('window', text, 1000)

img = cv.imread('eye.jpg')
trackbar(100)
cv.createTrackbar('threshold', 'window', 100, 255, trackbar)

cv.waitKey(0)
cv.destroyAllWindows()

threshold1.py

To zero

../_images/threshold2.png 
# threshold to zero
import cv2 as cv
import numpy as np

def trackbar(x):
    """Trackbar callback function."""
    text = f'threshold={x}, mode=TOZERO, TOZERO_INV'
    cv.displayOverlay('window', text, 1000)
    
    ret, img1 = cv.threshold(img, x, 255, cv.THRESH_TOZERO)
    ret, img2 = cv.threshold(img, x, 255, cv.THRESH_TOZERO_INV)
    cv.imshow('window', np.hstack([img, img1, img2]))

img = cv.imread('eye.jpg')
cv.imshow('window', img)
cv.createTrackbar('threshold', 'window', 100, 255, trackbar)

cv.waitKey(0)
cv.destroyAllWindows()

threshold2.py

2D convolution

https://docs.opencv.org/master/d4/d13/tutorial_py_filtering.html

../_images/convolution1.png 
# convolution
import cv2 as cv
import numpy as np

kernel = np.ones((5, 5), 'float32')/25

def trackbar(x):
    """Trackbar callback function."""
    d = 2*x + 1
    kernel = np.ones((d, d), 'float32')/(d**2)
    
    img1 = cv.filter2D(img, -1, kernel)
    cv.imshow('window', np.hstack([img, img1]))
    
    text = f'kernel=({d}x{d})'
    cv.displayOverlay('window', text)

img = cv.imread('eye.jpg')
trackbar(2)
cv.createTrackbar('threshold', 'window', 2, 7, trackbar)

cv.waitKey(0)
cv.destroyAllWindows()

convolution1.py

Morphological Transformations

Erosion

../_images/morph1.png 
# morphological transformation : erode
import cv2 as cv
import numpy as np

def trackbar(x):
    n = 2*x + 1
    kernel = np.ones((n, n), np.uint8)

    img1 = cv.erode(img, kernel, iterations=1)
    cv.imshow('window', np.hstack([img, img1]))

    text = f'erode, kernel={n}x{n}'
    cv.displayOverlay('window', text)

img = cv.imread('j.png')
trackbar(2)
cv.createTrackbar('kernel', 'window', 2, 5, trackbar)

cv.waitKey(0)
cv.destroyAllWindows()

morph1.py

Dilation

../_images/morph2.png 
# morphological transformation : dilation
import cv2 as cv
import numpy as np

def trackbar(x):
    n = 2*x + 1
    kernel = np.ones((n, n), np.uint8)

    img1 = cv.dilate(img, kernel, iterations=1)
    cv.imshow('window', np.hstack([img, img1]))

    text = f'dilate, kernel={n}x{n}'
    cv.displayOverlay('window', text)

img = cv.imread('j.png')
trackbar(2)
cv.createTrackbar('kernel', 'window', 2, 5, trackbar)

cv.waitKey(0)
cv.destroyAllWindows()

morph2.py

Opening

../_images/morph3.png 
# morphological transformation : opening
import cv2 as cv
import numpy as np

def trackbar(x):
    n = 2*x + 1
    kernel = np.ones((n, n), np.uint8)

    img1 = cv.morphologyEx(img, cv.MORPH_OPEN, kernel)
    cv.imshow('window', np.hstack([img, img1]))

    text = f'open, kernel={n}x{n}'
    cv.displayOverlay('window', text)

img = cv.imread('j.png')
trackbar(2)
cv.createTrackbar('kernel', 'window', 2, 5, trackbar)

cv.waitKey(0)
cv.destroyAllWindows()

morph3.py

Closing

../_images/morph4.png 
# morphological transformation : closing
import cv2 as cv
import numpy as np

def trackbar(x):
    n = 2*x + 1
    kernel = np.ones((n, n), np.uint8)

    img1 = cv.morphologyEx(img, cv.MORPH_CLOSE, kernel)
    cv.imshow('window', np.hstack([img, img1]))

    text = f'close, kernel={n}x{n}'
    cv.displayOverlay('window', text)

img = cv.imread('j.png')
trackbar(2)
cv.createTrackbar('kernel', 'window', 2, 5, trackbar)

cv.waitKey(0)
cv.destroyAllWindows()

morph4.py

Image gradient - Laplacian

../_images/gradient.png 
# image gradient - Laplacian
import cv2 as cv
import numpy as np

img = cv.imread('sudoku.png', cv.IMREAD_GRAYSCALE)
img1 = cv.Laplacian(img.copy(), 8)
cv.imshow('window', np.hstack([img, img1]))

cv.waitKey(0)
cv.destroyAllWindows()

gradient.py

Canny edge detection

../_images/canny.png 
# image gradient - Laplacian
import cv2 as cv
import numpy as np

img = cv.imread('sudoku.png', cv.IMREAD_GRAYSCALE)
img1 = cv.Canny(img, 100, 200)
cv.imshow('window', np.hstack([img, img1]))

cv.waitKey(0)
cv.destroyAllWindows()

canny.py