Colorful Image Colorization

No algorithm description given

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 { 
 Output: {
 "output": [
 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 { 
 Output: { 
 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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This algorithm has Internet access. This is necessary for algorithms that rely on external services, however it also implies that this algorithm is able to send your input data outside of the Algorithmia platform.

Calls other algorithms

This algorithm has permission to call other algorithms. This allows an algorithm to compose sophisticated functionality using other algorithms as building blocks, however it also carries the potential of incurring additional royalty and usage costs from any algorithm that it calls.

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1. Type your input

2. See the result

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3. Use this algorithm

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/1.1.5 -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/1.1.5")
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/1.1.5");
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/1.1.5")
val result = algo.pipeJson(input)
View Scala Docs
var input = {{input | formatInput:"javascript"}};
           .then(function(output) {
View Javascript 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/1.1.5')
print algo.pipe(input)
View Python Docs

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("deeplearning/ColorfulImageColorization/1.1.5")
result <- algo$pipe(input)$result
View R Docs
require 'algorithmia'

input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('deeplearning/ColorfulImageColorization/1.1.5')
puts algo.pipe(input).result
View Ruby Docs
use algorithmia::*;

let input = {{input | formatInput:"rust"}};
let client = Algorithmia::client("YOUR_API_KEY");
let algo = client.algo("deeplearning/ColorfulImageColorization/1.1.5");
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/1.1.5") { resp, error in
View Swift Docs
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