26. Average Brightness

Average Brightness

Here were the steps we took to extract the average brightness of an image.

1. Convert the image to HSV color space (the Value channel is an approximation for brightness)
2. Sum up all the values of the pixels in the Value channel
3. Divide that brightness sum by the area of the image, which is just the width times the height.

This gave us one value: the average brightness or the average Value of that image.

In the next notebook, make sure to look at a variety of day and night images and see if you can think of an average brightness value that will separate the images into their respective classes!

The next step will be to feed this data into a classifier. A classifier might be as simple as a conditional statement that checks if the average brightness is above some threshold, then this image is labeled as 1 (day) and if not, it’s labeled as 0 (night).

On your own, you can choose to create more features that help distinguish these images from one another, and we’ll soon learn about testing the accuracy of a model like this.