We will see in this article, how with the YOLO neural network we can very simply detect several objects in a photo. The objective is not to go into the details of the implementation of this neural network (much more complex than a simple sequential CNN) but rather to show how to use the implementation which was carried out in C ++ and which is called Darknet.
We will see in this article the principles of erosion and dilation of images which are widely used especially during the restoration of poor quality images.
We will see in this article how to perform some basic transformations on images with scikit-image such as rotating, and changing image scale and size.
In the previous article we saw how our digital images were built and stored. This naturally brings us to the image histograms. Of course we don’t manage an image like we do for a text . Images are in fact just matrix (like a pixel map ), so first of all we need to analyse the image, and to do that we’ll take a look on the pixel histograms.