12/22/2023 0 Comments Colorize a black and white photoAs you can see in the above image, the grayscale image is a lot sharper than the color layers. That leaves only 6% of our receptors to act as sensors for colors. Science fact - 94% of the cells in our eyes determine brightness. Also, we only have to two channels to predict. This means that we can use the original grayscale image in our final prediction. L stands for lightness, and a and b for the color spectrums green–red and blue–yellow.Īs you can see below, a Lab encoded image has one layer for grayscale and have packed three color layers into two. The network is trained and tested on the same image - we’ll get back to this during the beta-version.įirst, we’ll use an algorithm to change the color channels, from RGB to Lab. The middle picture is done with our neural network and the picture to the right is the original color photo. With just 40 lines of code, we can make the following transition. This way, you can get familiar with the core syntax of our model as we add features to it. We’ll start by making a simple version of our neural network to color an image of a woman’s face. In sum, we are searching for the features that link a grid of grayscale values to the three color grids. To be more precise with our colorization task, the network needs to find the traits that link grayscale images with colored ones. If the value is 0 for all color channels, then the image pixel is black.Īs you may know, a neural network creates a relationship between an input value and output value. The value 0 means that it has no color in this layer. Just like black and white images, each layer in a color image has a value from 0 - 255. Thus, a color image encodes the color and the contrast using three layers: By adding an equal amount of red and blue, it makes the green brighter. To achieve the color white, for example, you need an equal distribution of all colors. The layers not only determine color, but also brightness. Intuitively, you might think that the plant is only present in the green layer.īut, as you see below, the leaf is present in all three channels. Imagine splitting a green leaf on a white background into the three channels. The values span from 0 - 255, from black to white.Ĭolor images consist of three layers: a red layer, a green layer, and a blue layer. Each pixel has a value that corresponds to its brightness. In this section, I’ll outline how to render an image, the basics of digital colors, and the main logic for our neural network.īlack and white images can be represented in grids of pixels. You can also check out the three versions on FloydHub and GitHub, along with code for all the experiments I ran on FloydHub’s cloud GPUs. If you want to look ahead, here’s a Jupyter Notebook with the Alpha version of our bot. To make the coloring pop, we’ll train our neural network on portraits from Unsplash. We’ll use an Inception Resnet V2 that has been trained on 1.2 million images. We’ll be able to color images the bot has not seen before.įor our “final” version, we’ll combine our neural network with a classifier. The next step is to create a neural network that can generalize - our “Beta” version. There’s not a lot of magic in this code snippet - which is helpful so that we can get familiar with the syntax. We’ll build a bare-bones 40-line neural network as an “Alpha" colorization bot. The first section breaks down the core logic. I’ll show you how to build your own colorization neural net in three steps. Yet, if you’re new to deep learning terminology, you can read my previous two posts and watch Andrej Karpathy’s lecture for more background. A face alone needs up to 20 layers of pink, green and blue shades to get it just right. In short, a picture can take up to one month to colorize. To appreciate all the hard work behind this process, take a peek at this gorgeous colorization memory lane video. Today, colorization is done by hand in Photoshop. First off, let’s look at some of the results/failures from my experiments (scroll to the bottom for the final result). I was fascinated by Amir’s neural network, so I reproduced it and documented the process. They were astonished with Amir’s deep learning bot - what could take up to a month of manual labour could now be done in just a few seconds. Ready to build, train, and deploy AI? Get started with FloydHub's collaborative AI platform for free Try FloydHub for freeĮarlier this year, Amir Avni used neural networks to troll the subreddit /r/Colorization - a community where people colorize historical black and white images manually using Photoshop.
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