IMAGE-TO-IMAGE TRANSLATION FOR ANIMATION USING OPENCV AND GAN

The main goal is to produce an output to convert the global real image into supported impressions (Animated Image). The concept of the paper is based on one-of-a-kind photos that are turned into an art shape akin to oil. This project would use an OpenCV package in Python to create the layout and a Deep Convolutional Generative Adversarial Network (DCGAN) to generate realistic-looking photos. The system is made up of three components: (a) a generative adversarial network that has been trained to generate comic characters, (b) a cartoon model that will generate edge animated images using OpenCV, and (c) an edge detects that takes input from portraits and then generates comic characters based on the resulting edge images.