Nudity Detection

No algorithm description given

Note:  There is a higher accuracy alternative to this algorithm called NudityDetectionI2v, you can check it out here: This algorithm detects nudity in pictures. For a demo of this algorithm, check out: Table of content Introduction Examples Credits Introduction The idea behind the algorithm is based primarily on observations that in general, nude images contain large amounts of skin, people have different skin tones, and skin regions in nude images are relatively close to each other. In order to make the algorithm more robust, we have incorporated face detection for skin ratio tweaking and skin color detection for limiting the generic skin color value interval.  Input: (Required): Image: You can send your input as a String (base64 encoded image, arbitrary Url pointing to an image or a data api Url pointing to an image) or pipe the image in as binary data. Output: JSON object containing values for "nude" and "confidence". Examples Example 1. Parameter 1: Safe image URL "" Output: {
 "nude": "false",
 "confidence": 0.95
} Example 2. Parameter 1: Nude image URL "" Output: {
 "nude": "true",
 "confidence": 0.93
} Credits Implementation is based on:  An Algorithm for Nudity Detection The limits that decide on the skin regions are based on the values in RGB, HSV and normalized RGB color spaces in the book  Human Computer Interaction Using Hand Gestures  by Prashan Premaratne, derived from multiple papers as described in the book. Changelog 1.1.3 - Adjusted the URL validator to urls from domains with more than 2 periods (IE: *, *, etc) 1.1.4 - Allows self-signed ssl certificates, meaning all https// requests will be treated identically to http://

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1. Type your input

2. See the result

Running algorithm...

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://sfw/NudityDetection/1.1.4 -d '{{input | formatInput:"cli"}}'
View CLI 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://sfw/NudityDetection/1.1.4");
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://sfw/NudityDetection/1.1.4")
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('sfw/NudityDetection/1.1.4')
print algo.pipe(input)
View Python Docs

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("sfw/NudityDetection/1.1.4")
result <- algo$pipe(input)$result
View R Docs
require 'algorithmia'

input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('sfw/NudityDetection/1.1.4')
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("sfw/NudityDetection/1.1.4");
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: "sfw/NudityDetection/1.1.4") { resp, error in
View Swift Docs
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