The Rantings of a Mad Computer Scientist

The research blog of Joss Whittle

Interactive Spirograph in Jupyter

Posted on by Joss Whittle


import numpy as np
%matplotlib notebook
import matplotlib.pyplot as plt
import traceback

def circle(x, y, r, rot, steps=100):
    a = rot + np.linspace(0., 2. * np.pi, steps)
    return np.append((np.cos(a) * r) + x, x), np.append((np.sin(a) * r) + y, y)

class Element:
    def __init__(self, rate, edge_radius, path_radius, path_offset, tracers):
        self.centre_x     = 0
        self.centre_y     = 0
        self.path_x       = 0
        self.path_y       = 0
        self.edge_radius  = edge_radius
        self.edge_handle, = plt.plot([],[], '-')
        self.path_radius  = path_radius
        self.path_offset  = path_offset
        self.path_handle, = plt.plot([],[], ':')
        self.tracers = tracers
        self.tracer_handles = []
        self.tracer_paths   = []
        for colour, fmt, _ in self.tracers:
            hnd, = plt.plot([],[], fmt, color=colour)
            self.tracer_handles += [ hnd ]
            self.tracer_paths   += [ [[], []] ]
        self.parent    = None
        self.children  = []
        self.rate      = rate
        self.position  = 0
        self.rotation  = 0
        self.acc_rotation = 0
    def step(self, show_elements):
        self.rotation += self.rate
        if (self.parent is not None): 
            self.acc_rotation = self.rotation + self.parent.acc_rotation
            if (self.parent.path_radius <= self.parent.edge_radius):
                offset = self.parent.path_radius - self.edge_radius
                delta  = (self.rate * self.edge_radius) / self.parent.path_radius
                offset = self.parent.path_radius + self.edge_radius
                delta  = -((self.rate * self.edge_radius) / self.parent.path_radius)
            self.position -= delta*2
            self.centre_x = self.parent.path_x + np.cos(self.position) * offset
            self.centre_y = self.parent.path_y + np.sin(self.position) * offset
        if show_elements:
        self.path_x = self.centre_x + np.cos(self.acc_rotation) * self.path_offset
        self.path_y = self.centre_y + np.sin(self.acc_rotation) * self.path_offset
        if show_elements:
        for hnd, path, (_, _, offset) in zip(self.tracer_handles, 
            path[0] += [ self.path_x + np.cos(self.acc_rotation) * offset ]
            path[1] += [ self.path_y + np.sin(self.acc_rotation) * offset ]
        for child in self.children:
    def add(self, element):
        self.children += [ element ]
        self.children[-1].parent = self
class UI:
    def __init__(self):
        self.show_elements = True
        figw, figh, figdpi = 950, 950, 50
        self.figure = plt.figure(facecolor='w', figsize=(figw/figdpi, figh/figdpi), dpi=figdpi)
        self.axis   = plt.gca()
        radius = 0.45
        num_pens = 10
        cmap = [plt.get_cmap('RdBu')(int(idx)) 
                for idx in np.linspace(64, 256-64, num_pens)]
        pens = [(colour, '-', radius-delta) 
                for colour, delta in zip(cmap, np.linspace(0.1,0.3,num_pens))]
        elem_0            = Element(0.05, radius, radius, 0.0, pens)
        self.root_element = Element(0.0, 1.0, 1.0, 0.0, [])
    def on_click(self, event):
        if not (event.inaxes == self.axis): return
            self.show_elements = not self.show_elements
        except Exception:
    def on_move(self, event):
        if not (event.inaxes == self.axis): return
        except Exception:
    def attach(self, key, func):
        self.figure.canvas.mpl_connect(key, func)

ui = UI()

def on_click(event):
    global ui
def on_move(event):
    global ui
ui.attach('button_press_event',  on_click)
ui.attach('motion_notify_event', on_move)
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CGVC 2018 Talk - A Deep Learning Approach to No-Reference Image Quality Assessment For Monte Carlo Rendered Images

Posted on by Joss Whittle

A video of my talk taken by my co-author Prof. Mark Jones on our paper "A Deep Learning Approach to No-Reference Image Quality Assessment For Monte Carlo Rendered Images" published at CGVC 2018.

In Full-Reference Image Quality Assessment (FR-IQA) images are compared with ground truth images that are known to be of high visual quality. These metrics are utilized in order to rank algorithms under test on their image quality performance. Throughout the progress of Monte Carlo rendering processes we often wish to determine whether images being rendered are of sufficient visual quality, without the availability of a ground truth image. In such cases FR-IQA metrics are not applicable and we instead must utilise No-Reference Image Quality Assessment (NR-IQA) measures to make predictions about the perceived quality of unconverged images. In this work we propose a deep learning approach to NR-IQA, trained specifically on noise from Monte Carlo rendering processes, which significantly outperforms existing NR-IQA methods and can produce quality predictions consistent with FR-IQA measures that have access to ground truth images.

Cite as:

    author    = "Whittle, Joss and Jones, Mark W.", 
    booktitle = "Computer Graphics and Visual Computing (CGVC) 2018", 
    title     = "A Deep Learning Approach to No-Reference Image Quality Assessment For Monte Carlo Rendered Images", 
    year      = "2018", 
    month     = "Sep",
    pages     = {23--31}, 
    doi       = {10.2312/cgvc.20181204} 
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A CNC Controlled Etch-A-Sketch

Posted on by Joss Whittle

Initial work towards the creation of a CNC modification of a classic Etch-A-Sketch toy.


The stepper motors used for this project were super cheap, just 12 quid on amazon for 5 small 5v stepper motors which each came with their own control board allowing me to control them over PWM from a Raspberry Pi Model 3.

I created 3D printed parts for mounting the stepper motors to the Etch-A-Sketch and used a laser cutter to create gears out of 6mm birch plywood.

Being cheap and low powered the device suffers from being quite slow which is an issue for the Etch-A-Sketch. Slow movement tends to cause material build up on the drawing cursor making the drawn line become increasingly wide as drawing progresses.

With some software tweaking I think some of these issues can be alleviated which should allow for the project to be demonstrated at department open days for prospective students considering studying at Swansea.

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Visualizing the Growth Pattern of a Poisson Disk Sampler

Posted on by Joss Whittle

Tags: Computer Graphics

This project was inspired by the fantastic pen plotter visualizations created by Michael Fogleman.


Poisson Disk Sampling is a technique for drawing batches of blue noise distributed samples from an n-dimensional domain. The method works by selecting an initial, seed, point and proposing k (the branching factor) random points within 1 to 2 radii r of the initial point. For each of these proposal points we test whether they are closer than the threshold radius r from any of the accepted points (initially just the seed point). If the point is far enough away from any of the accepted points, it becomes accepted, and we sample k new points around it for later processing. If the point is too close to another accepted point it is immediately discarded.

By continuing the process for a given number of accepted points, until the n-dimensional space can no longer be filled without points being closer than the threshold r, or some other criteria is met we end up with a set of well distributed points that have nice mathematical properties when used in stochastic approximation methods.

An interesting observation is that the set of sample points is "grown" outwards from the seed point, and that each accepted point can trace its origin to a single parent point which spawned it. If we connect the sampled points as a tree hierarchy we can visualize the growth pattern of sample set as a tree.

The implementation I used to generate the above image used the sampling algorithm described in Fast Poisson Disk Sampling in Arbitrary Dimension, Robert Bridson 2007 which can produce batches of well distributed samples in O(n) computation time.

I have released the code for this project open source as a jupyter notebook. The main bottleneck in this code is actually the line plotting of the sample tree due to limitations of Matplotlib. With a better method of drawing the generated trees larger and deeper growth patterns could easily be visualized.

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AC-GAN, Auxiliary Classifier Generative Adversarial Networks

Posted on by Joss Whittle

Tags: Machine Learning, Computer Graphics

In this project I implemented the paper Conditional Image Synthesis With Auxiliary Classifier GANs, Odena et. al. 2016 using the Keras machine learning framework.

Generative Adversarial Networks, Goodfellow et. al. 2014 represents a training regime for teaching neural networks how to synthesize data that could plausibly have come from a distribution of real data - commonly images with a shared theme or aesthetic style such as images of celebrity faces (CelebA), of handwritten digits (MNIST), or of bedrooms (LSUN-Bedroom).

In GANs two models are trained - a generative model that progressively learns to synthesize realistic and plausible images from a random noise input (the latent vector) - and a discriminative model that learns to tell these generated (fake) images from real images sampled from the target dataset. The two models are trained in lock-step such that the generative model learns to fool the discriminator model, and the discriminator adapts to become better at not being fooled by the generator.

This forms a minimax game between the two models which converges to a Nash equilibrium. At this point the generator should be able to consistently produce convincing images that appear to be from the original dataset, but are in-fact parameterized by the latent vector fed to the generative model.

Auxiliary Classifier GANs extend the standard GAN architecture by jointly minimizing the generators ability to fool the discriminative model, with the ability of the discriminator to correctly identify which digit it was shown. This allows the generative model to be parameterized not only by a random latent vector, but also a representative encoding of which digit we would like it to synthesize.


The above image shows the result of my AC-GAN implementation trained on the MNIST dataset. On the left we see real images sampled randomly from MNIST for each of the 10 digit classes, and on the right we see images synthesized by the generative model for each class. The generated images are not sampled completely randomly, in this image I was selecting a random value of the latent vector and sweeping it from a value of 0 to 1. We can see that for each digit class the had the subtle effect of adjusting rotation and "flair" or perhaps "serif-ness", showing that the generative model has mapped the space of possible values that exist in the latent vector to different stylistic traits of the produced digits.

The results of this experiment are satisfying but not great overall. I believe the model suffers from, at least partial, "mode collapse" where the generator learns to produce a subset of possible stylistic variations convincingly and so never attempts to learn how to produce other stylistic variants.

Since the publication of Goodfellow's seminal work on GANs many variations have been proposed that attempt to solve common issues such as mode collapse and training stability.

In the future I plan to revisit this project and implement some of the newer and more advanced methods. While the code for this project is written as a jupyter notebook I do not plan to release the code as it is not very clean or well documented. I will however release well documented code when I revisit this project.

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