Research // Data // Statistics
Now that we have a working predictive model, we can deploy it to a simple application to test how well the eye tracker works. The plan is three-fold: Create a Predictor class that can load a trained model and make predictions Add a “tracking” mode to the data collector as a quick way to test … Read moreWebcam Eye Tracker: Eye Tracking Video Games
So far we have extracted webcam features and collected coordinate data. Now we can use that dataset to create our deep learning model with PyTorch. The following models and analyses were conducted in a Jupyter notebook, which can be found here. The problem we have is basically bounding box regression, but simplified to only 2 … Read moreWebcam Eye Tracker: Deep Learning with PyTorch
In the previous post we extracted a number of facial features from our webcam image. Now, we need a way to get the “labels”. So, we need to develop a semi-efficient data collection method to get our X-Y coordinate data. As this project is a learning project at its core, I wanted to come up … Read moreWebcam Eye Tracker: Data Collection of Screen Coordinates
Now that we have a general overview of the project, the first step in creating our eye tracker is getting video from the webcam. Following that, we need to perform face detection, alignment, and calculate various features from that video stream. Webcam video We’ll start by creating a Detector that uses OpenCV to retrieve frames … Read moreWebcam Eye Tracker: Webcam Features and Face Detection
Recently, I wanted to learn PyTorch and needed to find a project to help focus my learning. I have always been interested in the idea of creating a webcam eye tracker, so that seemed like a good project for this. Eye trackers typically rely on infrared for accurate tracking, but performing the same task using … Read moreWebcam Eye Tracker: An End-to-end Deep Learning Project
In a previous post I talked about an Overwatch dataset I’ve been collecting from my ranked games. Before running any statistical analysis on the Overwatch data, it is usually a good idea to explore and visualize the dataset. This helps us get a general sense of data patterns, which can help generate hypotheses that can … Read moreOverwatch Data Visualization
As I mentioned in a previous post, a convolutional neural network (CNN) can be used to classify colour images in much the same way as grey scale classification. The way to achieve this is by utilizing the depth dimension of our input tensors and kernels. In this example I’ll be using the CIFAR-10 dataset, which … Read moreColour image classification (CIFAR-10) using a CNN
We previously talked about Convolutional Neural Networks (CNN) and how use them to recognize handwritten digits using Lasagne. While we can manually extract kernel parameters to visualize weights and activation maps (as discussed in the previous post), the nolearn package offers an easy way to visualize different elements of CNNs. nolearn is a wrapper around Lasagne … Read moreVisualizing Convolutional Neural Networks using nolearn
Following my overview of Convolutional Neural Networks (CNN) in a previous post, now lets build a CNN model to 1) classify images of handwritten digits, and 2) see what is learned by this type of model. Handwritten digit recognition is the ‘Hello World’ example of the CNN world. I’ll be using the MNIST database of … Read moreHandwritten digit recognition with a CNN using Lasagne
Generating Python documentation for packages/modules can be quite time consuming, but there’s a way to generate it automatically from docstrings. This post is mostly a summary of the fantastic guide by Sam Nicholls found here, but with one important addition (see the section on mocking). We’ll be using the following: Sphinx – Python package for … Read moreAutomatic Python documentation with Sphinx autodoc and ReadTheDocs