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10/30/2017 How to colorize black & white photos with just 100 lines of neural network code Emil Wallnér Developer and writer. I explore human & machine learning. Oct 29 · 22 min read How to colorize black & white photos with just 100 lines of neural network code Earlier 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. 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. I was fascinated by Amir’s neural network, so I reproduced it and documented the process. First o, let’s look at some of the results/failures from my experiments (scroll to the bottom for the nal result). https://medium.freecodecamp.org/colorize-b-w-photos-with-a-100-line-neural-network-53d9b4449f8d 1/29

10/30/2017 How to colorize black & white photos with just 100 lines of neural network code Today, colorization is usually done by hand in Photoshop. To appreciate all the hard work behind this process, take a peek at this gorgeous colorization memory lane video: This embedded content is from a site that does not comply with the Do Not Track (DNT) setting now enabled on your browser. Please note, if you click through and view it anyway, you may be tracked by the website hosting the embed. Learn More about Medium's DNT policy In short, a picture can take up to one month to colorize. It requires extensive research. A face alone needs up to 20 layers of pink, green and blue shades to get it just right. This article is for beginners. Yet, if you’re new to deep learning terminology, you can read my previous two posts here and here, and watch Andrej Karpathy’s lecture for more background. I’ll show you how to build your own colorization neural net in three steps. The rst section breaks down the core logic. We’ll build a bare-bones 40-line neural network as an “alpha” colorization bot. There’s not a lot of magic in this code snippet. This well help us become familiar with the syntax. The next step is to create a neural network that can generalize — our “beta” version. We’ll be able to color images the bot has not seen before. For our “nal” version, we’ll combine our neural network with a classier. We’ll use an Inception Resnet V2 that has been trained on 1.2 million images. To make the coloring pop, we’ll train our neural network on portraits from Unsplash. If you want to look ahead, here’s a Jupyter Notebook with the Alpha version of our bot. You can also check out the three versions on https://medium.freecodecamp.org/colorize-b-w-photos-with-a-100-line-neural-network-53d9b4449f8d 2/29

10/30/2017 How to colorize black & white photos with just 100 lines of neural network code FloydHub and GitHub, along with code for all the experiments I ran on FloydHub’s cloud GPUs. Core logic In this section, I’ll outline how to render an image, the basics of digital colors, and the main logic for our neural network. Black and white images can be represented in grids of pixels. Each pixel has a value that corresponds to its brightness. The values span from 0– 255, from black to white. Color images consist of three layers: a red layer, a green layer, and a blue layer. This might be counter-intuitive to you. Imagine splitting a green leaf on a white background into the three channels. Intuitively, you might think that the plant is only present in the green layer. But, as you see below, the leaf is present in all three channels. The layers not only determine color, but also brightness. To achieve the color white, for example, you need an equal distribution of all colors. By adding an equal amount of red and blue, it makes the green brighter. Thus, a color image encodes the color and the contrast using three layers: https://medium.freecodecamp.org/colorize-b-w-photos-with-a-100-line-neural-network-53d9b4449f8d 3/29

10/30/2017 How to colorize black & white photos with just 100 lines of neural network code Just like black and white images, each layer in a color image has a value from 0–255. The value 0 means that it has no color in this layer. If the value is 0 for all color channels, then the image pixel is black. As you may know, a neural network creates a relationship between an input value and output value. To be more precise with our colorization task, the network needs to nd the traits that link grayscale images with colored ones. In sum, we are searching for the features that link a grid of grayscale values to the three color grids. Alpha version We’ll start by making a simple version of our neural network to color an image of a woman’s face. This way, you can get familiar with the core syntax of our model as we add features to it. With just 40 lines of code, we can make the following transition. The middle picture is done with our neural network and the picture to the right is the original color photo. The network is trained and tested on the same image — we’ll get back to this during the beta-version. https://medium.freecodecamp.org/colorize-b-w-photos-with-a-100-line-neural-network-53d9b4449f8d 4/29

10/30/2017 How to colorize black & white photos with just 100 lines of neural network code Color space First, we’ll use an algorithm to change the color channels, from RGB to Lab. L stands for lightness, and a and b for the color spectra green–red and blue–yellow. As you can see below, a Lab encoded image has one layer for grayscale, and has packed three color layers into two. This means that we can use the original grayscale image in our nal prediction. Also, we only have two channels to predict. Science fact — 94% of the cells in our eyes determine brightness. That leaves only 6% of our receptors to act as sensors for colors. As you can see in the above image, the grayscale image is a lot sharper than the color layers. This is another reason to keep the grayscale image in our nal prediction. From B&W to color Our nal prediction looks like this. We have a grayscale layer for input, and we want to predict two color layers, the ab in Lab. To create the nal color image we’ll include the L/grayscale image we used for the input. The result will be creating a Lab image. To turn one layer into two layers, we use convolutional lters. Think of them as the blue/red lters in 3D glasses. Each lter determines what we see in a picture. They can highlight or remove something to extract information out of the picture. The network can either create a new image from a lter or combine several lters into one image. https://medium.freecodecamp.org/colorize-b-w-photos-with-a-100-line-neural-network-53d9b4449f8d 5/29

10/30/2017 How to colorize black & white photos with just 100 lines of neural network code For a convolutional neural network, each lter is automatically adjusted to help with the intended outcome. We’ll start by stacking hundreds of lters and narrow them down into two layers, the a and b layers. Before we get into detail into how it works, let’s run the code. Deploying the code on FloydHub If you are new to FloydHub, you can run their 2-min installation, watch my 5-min video tutorial or check out my step-to-step guide. It’s the best (and easiest) way to train deep learning models on cloud GPUs. Alpha version Once FloydHub is installed, use the following commands: git clone https://github.com/emilwallner/Coloring-greyscale- images-in-Keras Open the folder and initiate FloydHub. cd Coloring-greyscale-images-in-Keras/floydhub floyd init colornet The FloydHub web dashboard will open in your browser. You will be prompted to create a new FloydHub project called colornet . Once that's done, go back to your terminal and run the same init command. floyd init colornet Okay, let’s run our job: floyd run --data emilwallner/datasets/colornet/2:data --mode jupyter --tensorboard https://medium.freecodecamp.org/colorize-b-w-photos-with-a-100-line-neural-network-53d9b4449f8d 6/29

10/30/2017 How to colorize black & white photos with just 100 lines of neural network code Some quick notes about our job: • We mounted a public dataset on FloydHub (which I’ve already uploaded) at the data directory with the below line: --dataemilwallner/datasets/colornet/2:data You can explore and use this dataset (and many other public datasets) by viewing it on FloydHub • • • We enabled Tensorboard with --tensorboard We ran the job in Jupyter Notebook mode with --mode jupyter If you have GPU credit, you can also add the GPU ag --gpu to your command. This will make it approximately 50x faster Go to the Jupyter notebook. Under the Jobs tab on the FloydHub website, click on the Jupyter Notebook link and navigate to this le: floydhub/Alpha version/working_floyd_pink_light_full.ipynb Open it and click Shift+Enter on all the cells. Gradually increase the epoch value to get a feel for how the neural network learns. model.fit(x=X, y=Y, batch_size=1, epochs=1) Start with an epoch value of 1 and the increase it to 10, 100, 500, 1000 and 3000. The epoch value indicates how many times the neural network learns from the image. You will nd the image img_result.png in the main folder once you’ve trained your neural network. # Get images image = img_to_array(load_img('woman.png')) https://medium.freecodecamp.org/colorize-b-w-photos-with-a-100-line-neural-network-53d9b4449f8d 7/29

10/30/2017 How to colorize black & white photos with just 100 lines of neural network code image = np.array(image, dtype=float) # Import map images into the lab colorspace X = rgb2lab(1.0/255*image)[:,:,0] Y = rgb2lab(1.0/255*image)[:,:,1:] Y = Y / 128 X = X.reshape(1, 400, 400, 1) Y = Y.reshape(1, 400, 400, 2) model = Sequential() model.add(InputLayer(input_shape=(None, None, 1))) # Building the neural network model = Sequential() model.add(InputLayer(input_shape=(None, None, 1))) model.add(Conv2D(8, (3, 3), activation='relu', padding='same', strides=2)) model.add(Conv2D(8, (3, 3), activation='relu', padding='same')) model.add(Conv2D(16, (3, 3), activation='relu', padding='same')) model.add(Conv2D(16, (3, 3), activation='relu', padding='same', strides=2)) model.add(Conv2D(32, (3, 3), activation='relu', padding='same')) model.add(Conv2D(32, (3, 3), activation='relu', padding='same', strides=2)) model.add(UpSampling2D((2, 2))) model.add(Conv2D(32, (3, 3), activation='relu', padding='same')) model.add(UpSampling2D((2, 2))) model.add(Conv2D(16, (3, 3), activation='relu', padding='same')) model.add(UpSampling2D((2, 2))) model.add(Conv2D(2, (3, 3), activation='tanh', padding='same')) # Finish model model.compile(optimizer='rmsprop',loss='mse') #Train the neural network model.fit(x=X, y=Y, batch_size=1, epochs=3000) print(model.evaluate(X, Y, batch_size=1)) # Output colorizations output = model.predict(X) output = output * 128 canvas = np.zeros((400, 400, 3)) canvas[:,:,0] = X[0][:,:,0] canvas[:,:,1:] = output[0] imsave("img_result.png", lab2rgb(cur)) imsave("img_gray_scale.png", rgb2gray(lab2rgb(cur))) FloydHub command to run this network: https://medium.freecodecamp.org/colorize-b-w-photos-with-a-100-line-neural-network-53d9b4449f8d 8/29

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