Smoothing the image

In this section, we will smooth the image with a Gaussian filter. Applying the GaussianBlur function from the OpenCV library reduces the noise in our image.

We will apply it using OpenCV, as follows:

1. Start by importing OpenCV and numpy:

In[1]: import cv2
In[2]: import numpy as np

2. In the next step, read the input image:

In[3]: image = cv2.imread('test_image.jpg')
In[4]: lanelines_image = np.copy(image)

3. Now, we will convert the image into grayscale:

In[5]: gray_conversion= cv2.cvtColor(lanelines_image, cv2.COLOR_RGB2GRAY)

4. Apply GaussianBlur using the OpenCV library:

In[6]: blur_conversion = cv2.GaussianBlur(gray_conversion, (5,5),0)
In[7]: cv2.imshow('input_image', blur_conversion)
In[8]: cv2.waitKey(0)
In[9]: cv2.destroyAllWindows()

The output is as follows:

Fig 5.3: Image after applying Gaussian blur

We smoothed the image and removed noise from it. In the next section, we will perform canny edge detection on the input image.

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