Applying bitwise_and

In this section, we will apply bitwise_and and multiply all the bits in the black region of the image by 0000 and the white region by 1111, as shown in the following screenshot:

Fig 5.7: bitwise_and used on the black and white images

The bitwise_and conversion is as follows:

Fig 5.8: The bitwise_and conversion

Now, we will implement bitwise_and using OpenCV:

1. First, import the required libraries:

In[1]: import cv2
In[2]: import numpy as np
In[3]: import matplotlib.pyplot as plt

2. Then, write a canny edge detection function:

In[4]: def canny_edge(image):
         gray_conversion= cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
         blur_conversion = cv2.GaussianBlur(gray_conversion, (5,5),0)
         canny_conversion = cv2.Canny(blur_conversion, 50,150)
         return canny_conversion

3. Modify the region-of-interest masking function by adding bitwise_and:

In[5]: def reg_of_interest(image):
         image_height = image.shape[0]
         polygons...