

In [8]:

    
import cv2
import numpy as np

image = cv2.imread('images/input.jpg',0)

height, width = image.shape

# Extract Sobel Edges
sobel_x = cv2.Sobel(image, cv2.CV_64F, 0, 1, ksize=5)
sobel_y = cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize=5)

cv2.imshow('Original', image)
cv2.waitKey(0)
cv2.imshow('Sobel X', sobel_x)
cv2.waitKey(0)
cv2.imshow('Sobel Y', sobel_y)
cv2.waitKey(0)

sobel_OR = cv2.bitwise_or(sobel_x, sobel_y)
cv2.imshow('sobel_OR', sobel_OR)
cv2.waitKey(0)

laplacian = cv2.Laplacian(image, cv2.CV_64F)
cv2.imshow('Laplacian', laplacian)
cv2.waitKey(0)





##  Then, we need to provide two values: threshold1 and threshold2. Any gradient value larger than threshold2
# is considered to be an edge. Any value below threshold1 is considered not to be an edge. 
#Values in between threshold1 and threshold2 are either classiﬁed as edges or non-edges based on how their 
#intensities are “connected”. In this case, any gradient values below 60 are considered non-edges
#whereas any values above 120 are considered edges.


# Canny Edge Detection uses gradient values as thresholds
# The first threshold gradient
canny = cv2.Canny(image, 50, 120)
cv2.imshow('Canny', canny)
cv2.waitKey(0)

cv2.destroyAllWindows()



In [ ]:

Edge Detection & Image Gradients