Load both images as arrays scipy. Calculate the norm of the difference. Calculate some feature vector for each of them like a histogram.
Or actually not madness, but OpenCV and Python. How cool would it be to have your computer recognize the emotion on your face? You could make all sorts of things with this, from a dynamic music player that plays music fitting with what you feel, to an emotion-recognizing robot.
For this tutorial I assume that you have: The code in this tutorial is licensed under the GNU 3.
By reading on you agree to these terms. If you disagree, please navigate away from this page. I assume intermediate knowledge of Python for these tutorials. This also means you know how to interpret errors. Part of learning to program is learning to debug on your own as well. It will be updated in the near future to be cross-platform.
Citation format van Gent, P. A tech blog about fun things with Python and embedded electronics. For those interested in more background; this page has a clear explanation of what a fisher face is. I cannot distribute it so you will have to request it yourself, or of course create and use your own dataset.
It seems the dataset has been taken offline. The other option is to make one of your own or find another one. When making a set: The more data, the more variance there is for the models to extract information from. Please do not request others to share the dataset in the comments, as this is prohibited in the terms they accepted before downloading the set.
Once you have your own dataset, extract it and look at the readme. It is organised into two folders, one containing images, the other txt files with emotions encoded that correspond to the kind of emotion shown.
From the readme of the dataset, the encoding is: Organising the dataset First we need to organise the dataset. Extract the dataset and put all folders containing the txt files S, S, etc.
In the readme file, the authors mention that only a subset of the of the emotion sequences actually contain archetypical emotions.
Each image sequence consists of the forming of an emotional expression, starting with a neutral face and ending with the emotion. So, from each image sequence we want to extract two images; one neutral the first image and one with an emotional expression the last image.
Store list of sessions for current participant for files in glob. We need to find the face on each image, convert to grayscale, crop it and save the image to the dataset.
Get them from the OpenCV directory or from here and extract to the same file you have your python files. The dataset we can use will live in these folders. Because most participants have expressed more than one emotion, we have more than one neutral image of the same person. Do this by hand: Creating the training and classification set Now we get to the fun part!
The dataset has been organised and is ready to be recognized, but first we need to actually teach the classifier what certain emotions look like.
The usual approach is to split the complete dataset into a training set and a classification set.Important: The code in this tutorial is licensed under the GNU open source license and you are free to modify and redistribute the code, given that you give others you share the code with the same right, and cite my name (use citation format below).
You are not free to redistribute or modify the tutorial itself in any way. By reading on you agree to these terms. Sequence classification is a predictive modeling problem where you have some sequence of inputs over space or time and the task is to predict a category for the sequence.
What makes this problem difficult is that the sequences can vary in length, be comprised of a very large vocabulary of input. Here's what I would like to do: I'm taking pictures with a webcam at regular intervals. Sort of like a time lapse thing.
However, if nothing has . A question is infrequently answered either because few people know the answer or because it concerns an obscure, subtle point (but a point that may be crucial to you). General idea. Option 1: Load both images as arrays (regardbouddhiste.com) and calculate an element-wise (pixel-by-pixel) regardbouddhiste.comate the norm of the difference.
Option 2: Load both images. Calculate some feature vector for each of them (like a histogram). Sequence classification is a predictive modeling problem where you have some sequence of inputs over space or time and the task is to predict a category for the sequence.
What makes this problem difficult is that the sequences can vary in length, be comprised of a very large vocabulary of input.