Colorful Image Colorization

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1. Introduction Colorful Image Colorization is an algorithm that takes in a black & white photos and returns the colorized version of it. The algorithm uses deep learning to classify objects/regions within the image and color them accordingly. Note: we've recently added batchable IO to this algorithm for interfacing with VideoTransform and other batch image processing work. Note:  When using batched images and the default 5 minute timeout, a safe maximum batch size is 20. Input: (Required) Image Data API Url, Web (http/https) Url, binary image,base64 encoded image or an array of data API URLs / web URLs. (Optional) Output image location, if input is an array, output location should be an array of equal size. Output: Output image location. Note: The first call to this algorithm will take a bit longer than sequential calls to due algorithm initialization. All following calls will be significantly faster. 2. Examples Example 1. Parameter 1: Data API Url { "image": "data://deeplearning/example_data/lincoln.jpg" } Output: { "output": "data://.algo/deeplearning/ColorfulImageColorization/temp/lincoln.png" } Example 2. Parameter 1: HTTPS Url { "image": "" } Output: { "output": "data://.algo/deeplearning/ColorfulImageColorization/temp/lincoln.png" } Example 3. Parameter 1: Base64 image { "image": "data:image/png;base64....", } Output; { "output": "data://.algo/deeplearning/ColorfulImageColorization/temp/output.png" } Example 4. Parameter 1: HTTPS Url Parameter 2: Output file save location. { "image": "data://deeplearning/example_data/lincoln.jpg", "location": "data://.algo/temp/test42.png" } Output: { "output": "data://.algo/deeplearning/ColorfulImageColorization/temp/test42.png" } Example 5. Parameter 1: An array of HTTPS Urls { "image":[ "data://deeplearning/Temp/sample_image_1.png", "data://deeplearning/Temp/sample_image_2.png", "data://deeplearning/Temp/sample_image_3.png" ] } Output: { "output": [ "data://.algo/deeplearning/ColorfulImageColorization/temp/f8ca262c-a728-4866-9215-1e1684a4b83f.png", "data://.algo/deeplearning/ColorfulImageColorization/temp/fc523e24-7db4-4f36-801d-2fbb33af6f95.png", "data://.algo/deeplearning/ColorfulImageColorization/temp/61e1a08f-6527-4d1a-b61e-299386950ede.png" ] } Example 6. Parameter 1: An array of Data API Uris Parameter 2: An array of file save paths (Data API URL's) must be the same length as parameter 1 { "image":[ "data://deeplearning/Temp/sample_image_1.png", "data://deeplearning/Temp/sample_image_2.png", "data://deeplearning/Temp/sample_image_3.png" ], "location":[ "data://.algo/temp/sample_output_1.png", "data://.algo/temp/sample_output_2.png", "data://.algo/temp/sample_output_3.png" ] } Output: { "output":[ "data://.algo/deeplearning/ColorfulImageColorization/temp/sample_output_1.png", "data://.algo/deeplearning/ColorfulImageColorization/temp/sample_output_2.png", "data://.algo/deeplearning/ColorfulImageColorization/temp/sample_output_3.png" ] } 3. Credits For more information, please refer to  or  Zhang, Richard and Isola, Phillip and Efros, Alexei A (2016). Colorful Image Colorization. arXiv preprint arXiv:1603.08511 . Demo image were retrieved from Wikipedia under Public domain:

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12 cr
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1 cr
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curl -X POST -d '{{input | formatInput:"curl"}}' -H 'Content-Type: application/json' -H 'Authorization: Simple YOUR_API_KEY'
View cURL Docs
algo auth
algo run algo://deeplearning/ColorfulImageColorization/1.1.6 -d '{{input | formatInput:"cli"}}'
View CLI Docs
import (
  algorithmia ""

input := {{input | formatInput:"go"}}

var client = algorithmia.NewClient("YOUR_API_KEY", "")
algo, _ := client.Algo("algo://deeplearning/ColorfulImageColorization/1.1.6")
resp, _ := algo.Pipe(input)
response := resp.(*algorithmia.AlgoResponse)
View Go Docs
import com.algorithmia.*;
import com.algorithmia.algo.*;

String input = "{{input | formatInput:"java"}}";
AlgorithmiaClient client = Algorithmia.client("YOUR_API_KEY");
Algorithm algo = client.algo("algo://deeplearning/ColorfulImageColorization/1.1.6");
AlgoResponse result = algo.pipeJson(input);
View Java Docs
import com.algorithmia._
import com.algorithmia.algo._

val input = {{input | formatInput:"scala"}}
val client = Algorithmia.client("YOUR_API_KEY")
val algo = client.algo("algo://deeplearning/ColorfulImageColorization/1.1.6")
val result = algo.pipeJson(input)
View Scala Docs
var input = {{input | formatInput:"javascript"}};
           .then(function(output) {
View Javascript Docs
using Algorithmia;

var input = "{{input | formatInput:"cs"}}";
var client = new Client("YOUR_API_KEY");
var algorithm = client.algo("algo://deeplearning/ColorfulImageColorization/1.1.6");
var response = algorithm.pipe<object>(input);
View .NET/C# Docs
var input = {{input | formatInput:"javascript"}};
           .then(function(response) {
View NodeJS Docs
import Algorithmia

input = {{input | formatInput:"python"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('deeplearning/ColorfulImageColorization/1.1.6')
print algo.pipe(input)
View Python Docs

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("deeplearning/ColorfulImageColorization/1.1.6")
result <- algo$pipe(input)$result
View R Docs
require 'algorithmia'

input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('deeplearning/ColorfulImageColorization/1.1.6')
puts algo.pipe(input).result
View Ruby Docs
use algorithmia::Algorithmia;

let input = {{input | formatInput:"rust"}};
let client = Algorithmia::client("YOUR_API_KEY");
let algo = client.algo("deeplearning/ColorfulImageColorization/1.1.6");
let response = algo.pipe(input);
View Rust Docs
import Algorithmia

let input = "{{input | formatInput:"swift"}}";
let client = Algorithmia.client(simpleKey: "YOUR_API_KEY")
let algo = client.algo(algoUri: "deeplearning/ColorfulImageColorization/1.1.6") { resp, error in
View Swift Docs
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