Canny Edges

Canny Edge Detection Quiz

Question:

Now it’s your turn! Try using Canny on your own and fiddle with the parameters for the Gaussian smoothing and Edge Detection to optimize for detecting the lane lines well without detecting a lot of other stuff. Your result should look like the example shown below.

The original image (left), and edge detection applied (right).

Start Quiz:

canny_example.py

# Do all the relevant imports
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2

# Read in the image and convert to grayscale
# Note: in the previous example we were reading a .jpg 
# Here we read a .png and convert to 0,255 bytescale
image = mpimg.imread('exit-ramp.jpg')
gray = cv2.cvtColor(image,cv2.COLOR_RGB2GRAY)

# Define a kernel size for Gaussian smoothing / blurring
kernel_size = 3 # Must be an odd number (3, 5, 7...)
blur_gray = cv2.GaussianBlur(gray,(kernel_size, kernel_size),0)

# Define our parameters for Canny and run it
low_threshold = 1
high_threshold = 10
edges = cv2.Canny(blur_gray, low_threshold, high_threshold)

# Display the image
plt.imshow(edges, cmap='Greys_r')

Solution:

Here’s how I did it…

I chose a kernelSize of 5 for Gaussian smoothing, a lowThreshold of 50 and a highThreshold of 150. These selections nicely extract the lane lines, while minimizing the edges detected in the rest of the image, producing the result shown below.

Next Concept