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Jupyter Notebook and more for Image Morphing project of SIV course (UNITN)

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Image Morphing - SIV Project

by Davide Modolo

Presentation

The presentation aims to explain what is Image Morphing and what is it used for. It also explains how our notebook works.

Jupyter Notebook

resize.py

Since functions we use require two images of the same size, we wrote a library that edit one or both of them with a minimum (to none) information loss.

Fading

We started explaining the easier approach to image morphing: the Fading Effect. It requires two Pillow Images

Morphing Function

Function to compute the affine transformation steps. It requires two images as numpy array.

Point-picking

Function to pick by hand three points in each image in order to perform the morph. It requires two images as numpy array.

Autopick by Template-matching

Look for 3 high-similarity points and morph on them. It requires two images as numpy array.

Face detection

Using DLIB library and shape_predictor_68_face_landmarks.dat to recognise faces in images and morph. It requires two images as numpy array.

It requires shape_predictor_68_face_landmarks.dat file taken from here

Faces are taken from This Person Does Not Exist

Anaconda Environment

Since DLIB can easly bring errors, this environment ensures everything works.

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Jupyter Notebook and more for Image Morphing project of SIV course (UNITN)

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