Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns

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1. Introduction This algorithm gives you the emotion in the given photo with its corresponding confidence interval. Input: (Required) Image Data API Url, Web (http/https) Url, binary image or a base64 encoded image. (Optional) Number of results (default=3, max=7) Output: A list of emotions 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/elon_musk.jpg"
} Output {
 "results":[
 [0.9136321544647217,"Neutral"],
 [0.0832965448498726,"Happy"],
 [0.0030007611494511366,"Disgust"]
 ]
} Example 2. Parameter 1: HTTP Url {
 "image": "https://s3.amazonaws.com/algorithmia-assets/algo_desc_images/deeplearning_EmotionRecognitionCNNMBP/jim_caviezel.jpg"
} Output: {
 "results":[
 [0.8319215178489686,"Neutral"],
 [0.12273486703634262,"Happy"],
 [0.024691849946975705,"Angry"]
 ]
} Example 3. Parameter 1: Base64 image {
 "image": "data:image/png;base64....",
} Output; {
 "results":[
 [0.9998809099197387,"Neutral"],
 [0.0001092417587642558,"Disgust"],
 [0.000007976091183081738,"Happy"]
 ]
} Example 4. Parameter 1: Data API Url Parameter 2: Number of results {
 "image": "data://deeplearning/example_data/elon_musk.jpg",
 "numResults": 7
} Output; {
 "results":[
 [0.9136321544647217,"Neutral"],
 [0.0832965448498726,"Happy"],
 [0.0030007611494511366,"Disgust"],
 [0.00005185294867260381,"Sad"],
 [0.000008937824532040397,"Fear"],
 [0.000007938646376715042,"Surprise"],
 [0.0000018067896689899499,"Angry"]
 ]
} 3. Credits For more information, please refer to: http://www.openu.ac.il/home/hassner/projects/cnn_emotions/ or? Gil Levi and Tal Hassner, Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns, Proc. ACM International Conference on Multimodal Interaction (ICMI), Seattle, Nov. 2015 Demo images were taken from: https://en.wikipedia.org/wiki/Elon_Musk#/media/File:Elon_Musk_2015.jpg https://gl.wikipedia.org/wiki/Jim_Caviezel#/media/File:Jim_Caviezel_SDCC_2013.jpg

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curl -X POST -d '{{input | formatInput:"curl"}}' -H 'Content-Type: application/json' -H 'Authorization: Simple YOUR_API_KEY' https://api.algorithmia.com/v1/algo/deeplearning/EmotionRecognitionCNNMBP/0.1.2
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algo auth
# Enter API Key: YOUR_API_KEY
algo run algo://deeplearning/EmotionRecognitionCNNMBP/0.1.2 -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://deeplearning/EmotionRecognitionCNNMBP/0.1.2");
AlgoResponse result = algo.pipeJson(input);
System.out.println(result.asJsonString());
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/EmotionRecognitionCNNMBP/0.1.2")
val result = algo.pipeJson(input)
System.out.println(result.asJsonString)
View Scala Docs
var input = {{input | formatInput:"javascript"}};
Algorithmia.client("YOUR_API_KEY")
           .algo("algo://deeplearning/EmotionRecognitionCNNMBP/0.1.2")
           .pipe(input)
           .then(function(output) {
             console.log(output);
           });
View Javascript Docs
var input = {{input | formatInput:"javascript"}};
Algorithmia.client("YOUR_API_KEY")
           .algo("algo://deeplearning/EmotionRecognitionCNNMBP/0.1.2")
           .pipe(input)
           .then(function(response) {
             console.log(response.get());
           });
View NodeJS Docs
import Algorithmia

input = {{input | formatInput:"python"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('deeplearning/EmotionRecognitionCNNMBP/0.1.2')
print algo.pipe(input)
View Python Docs
library(algorithmia)

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("deeplearning/EmotionRecognitionCNNMBP/0.1.2")
result <- algo$pipe(input)$result
print(result)
View R Docs
require 'algorithmia'

input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('deeplearning/EmotionRecognitionCNNMBP/0.1.2')
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/EmotionRecognitionCNNMBP/0.1.2');
let response = algo.pipe(input);
View Rust Docs
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