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- 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()
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