Mastering Computer Vision with TensorFlow 2.x

Image filtering with OpenCV

The image filtering concepts described previously can be better understood by applying a filter to a real image. OpenCV provides a method to do that. The OpenCV code we will use can be found at https://github.com/PacktPublishing/Mastering-Computer-Vision-with-TensorFlow-2.0/blob/master/Chapter01/Chapter1_imagefiltering.ipynb.

The important code is listed in the following snippet. After importing the image, we can add noise. Without noise, the image filtering effect can not be visualized very well. After that, we need to save the image. This is not necessary for the mean and Gaussian filter, but if we don't save the image with the median filter and import it back again, Python displays an error.

Note that we use ...