Canny Edge detection,NMS,Double Thresholding,Project link https://lms.skill-lync.com/programs/230630/305/1910/view-grade
import numpy as np
import os
import cv2
import matplotlib.pyplot as plt
# defining the canny detector function
# here weak and strong are thresholds
# double thresholding step
def Canny_detector(img, weak = None, strong = None):
# conversion of image to grayscale
cv2.imshow('Challenge3Car', img)
cv2.waitKey(0)
cv2.imwrite('Challenge3Car.jpg', img)
# Use the cvtColor() function to grayscale the image
gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow('GrayscaleChallenge3Car', gray_image)
cv2.waitKey(0)
cv2.imwrite('GrayscaleChallenge3Car.jpg', gray_image)
# Noise reduction step
GB_image = cv2.GaussianBlur(gray_image, (5, 5), 1.4)
cv2.imshow('GaussianBlurChallenge3Car', GB_image)
cv2.waitKey(0)
cv2.imwrite('GaussianBlurChallenge3Car.jpg', GB_image)
# Calculating the gradients
gx = cv2.Sobel(np.float32(GB_image), cv2.CV_64F, 1, 0, 3)
gy = cv2.Sobel(np.float32(GB_image), cv2.CV_64F, 0, 1, 3)
cv2.imshow('SobelxChallenge3Car', gx)
cv2.waitKey(0)
cv2.imwrite('SobelxChallenge3Car.jpg', gx)
cv2.imshow('SobelyChallenge3Car', gy)
cv2.waitKey(0)
cv2.imwrite('SobelyChallenge3Car.jpg', gy)
cv2.waitKey(0)
# Conversion of Cartesian coordinates to polar
mag, ang = cv2.cartToPolar(gx, gy, angleInDegrees = True)
# setting the minimum and maximum thresholds
# for double thresholding
mag_max = np.max(mag)
if not weak:weak = mag_max * 0.1
if not strong:strong = mag_max * 0.5
# getting the dimensions of the input image
height, width = GB_image.shape
# Looping through every pixel of the grayscale
# image
for i_x in range(width):
for i_y in range(height):
grad_ang = ang[i_y, i_x]
grad_ang = abs(grad_ang-180) if abs(grad_ang)>180 else abs(grad_ang)
# selecting the neighbours of the target pixel
# according to the gradient direction
# In the x axis direction
if grad_ang<= 22.5:
neighb_1_x, neighb_1_y = i_x-1, i_y
neighb_2_x, neighb_2_y = i_x + 1, i_y
# top right (diagonal-1) direction
elif grad_ang>22.5 and grad_ang<=(22.5 + 45):
neighb_1_x, neighb_1_y = i_x-1, i_y-1
neighb_2_x, neighb_2_y = i_x + 1, i_y + 1
# In y-axis direction
elif grad_ang>(22.5 + 45) and grad_ang<=(22.5 + 90):
neighb_1_x, neighb_1_y = i_x, i_y-1
neighb_2_x, neighb_2_y = i_x, i_y + 1
# top left (diagonal-2) direction
elif grad_ang>(22.5 + 90) and grad_ang<=(22.5 + 135):
neighb_1_x, neighb_1_y = i_x-1, i_y + 1
neighb_2_x, neighb_2_y = i_x + 1, i_y-1
# Now it restarts the cycle
elif grad_ang>(22.5 + 135) and grad_ang<=(22.5 + 180):
neighb_1_x, neighb_1_y = i_x-1, i_y
neighb_2_x, neighb_2_y = i_x + 1, i_y
# Non-maximum suppression step
if width>neighb_1_x>= 0 and height>neighb_1_y>= 0:
if mag[i_y, i_x], neighb_1_x]:
mag[i_y, i_x]= 0
continue
if width>neighb_2_x>= 0 and height>neighb_2_y>= 0:
if mag[i_y, i_x], neighb_2_x]:
mag[i_y, i_x]= 0
weak_ids = np.zeros_like(GB_image)
strong_ids = np.zeros_like(GB_image)
ids = np.zeros_like(GB_image)
# double thresholding step
for i_x in range(width):
for i_y in range(height):
grad_mag = mag[i_y, i_x]
if grad_mag
mag[i_y, i_x]= 0
elif strong>grad_mag>= weak:
ids[i_y, i_x]= 1
else:
ids[i_y, i_x]= 2
# finally returning the magnitude of
# gradients of edges
return mag
frame = cv2.imread('Car_Challenge3.png')
# calling the designed function for
# finding edges
canny_img = Canny_detector(frame)
cv2.imwrite('CannyChallenge3Car.jpg', canny_img)
# Displaying the input and finaloutput image
plt.figure()
f, plots = plt.subplots(2, 1)
plots[0].imshow(frame)
plots[1].imshow(canny_img)