Re-run step 2 and step 3 until the clusters stop changing. They’re closer to the mean of a different cluster than the cluster they’re currently in. Step 3: within each cluster, calculate the centre, and use that as the new mean.īecause the means have moved, some of the points are now in the wrong cluster. Put each point in a cluster with the closest mean, which divides the points into 3 clusters: Step 2: go through all the points, and measure the distance from the point to each of the means. Step 1: we start by picking 3 points at random. Suppose we have some points in 2D space, and we want to divide them into 3 clusters: Visual examples often help me understand something like this, and the Wikipedia article has some good illustrations. It lets you group your data into k clusters, where k is a number you can choose. There are lots of ways to find clusters k‑means is just one of them. If we group them into three clusters by saying “points that are close together are similar”, we might end up with this clustering: The squares are coloured differently to show which cluster they're in. (And conversely, points in different groups should be different.)įor example, let’s suppose our data points are positions on a 2D plane. Clustering means dividing the data points into different groups, so all of the points in each group are similar in some way. Suppose we have a collection of data points. If you already know how k‑means works, you can skip the next two sections – if not, read on, and I’ll do my best to explain. There’s an implementation of k‑means in scikit-learn, but I wanted to write my own to be sure I really knew what was going on. I’ve never used k‑means before, so I wanted to take time to understand it. I found a post by Charles Leifer addressing a similar problem that suggested using k‑means clustering, so I decided to try that. I played with a couple of simple ideas, but I didn’t get anywhere useful, so I searched for other people tackling this problem. We want to count all the different shades of green together. If we want to extract the main colours, we need to be able to group similar-looking colours together. Although there are more green-ish pixels than any other colour, there are only a few of each exact shade, so they’re low down on the colour tally. most_common ( 1 ))īut if you actually try this, you quickly discover that it usually returns something close to black or close to white – it’s not very representative! (For scanned documents it’s almost always white.) Here’s an example:Ī human looking at that photo would probably pick green as the main colour – but there are lots of different shades of green. open ( "cats.jpg" ) colors = get_colors_by_frequency ( im ) print ( colors. getdata ()) if _name_ = "_main_" : im = Image. Import collections from PIL import Image def get_colors_by_frequency ( im ): return collections. So we can find the most common colour like so: The Pillow library has a getdata() method that lets you get a list of all the colours in an image, along with their frequency. My first thought was to try a very simple approach: tally all the colours used in the image, and pick the colour that appears most often. I’ve tried my code with a few thousand images, and it picks reasonable colours each time – not always optimal, but scanning the list I didn’t see anything wildly inappropriate or unusable. I’ve come up with an approach that seems to work fairly well, which uses k‑means clustering to get the dominant images, and then compares the contrast with white to pick the best colour to use as the tint. I wanted to see if I could pick a colour from the thumbnail, and use that for the link colour – for example, taking the bright red sash from the book cover above: It’s a bit visually jarring to see the Bootstrap blue next to the large thumbnail. I can go back to the original page, or see other files with the same tags. The source URL and tags are both clickable links. When you hover over a card, it shows a little panel with some details about the file: when I saved it, how I’ve tagged it, and where I downloaded it from. (I talked about how I create the PDF thumbnails in a separate post.) If I’m looking for something with a distinctive thumbnail (say, an ebook cover), I can easily skim a grid of thumbnails to find it. Tagged with colour, images, python, python:pillowĪs part of my app for storing my electronic documents, there’s a grid view that displays big thumbnails of all my files. Widget.lenna_image_gif = tk.PhotoImage(file="lenna. Widget.lenna_image_png = tk.PhotoImage(file="lenna.png") Import tkinter as tk # either in python 2 or in python 3 Try: # In order to be able to import tkinter for png if tk.TkVersion >= 8.6) is already supported by the PhotoImage class. That being said, some image formats such as. Your actual code may return an error based on the format of the file path points to.
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