Object Detection

The object detection API locates and classifies 80 different kinds of objects in a single image.

To use this API, you need to set VISION-DETECTION=True when starting DeepStack

sudo docker run -e VISION-DETECTION=True -v localstorage:/datastore \
-p 80:5000 deepquestai/deepstack

If using the GPU Version, run

sudo docker run --rm --runtime=nvidia -e VISION-DETECTION=True -v localstorage:/datastore \
-p 80:5000 deepquestai/deepstack:gpu

Note also that you can have multiple endpoints activated, for example, both face and object detection are activated below

sudo docker run -e VISION-DETECTION=True  -e VISION-FACE=True -v localstorage:/datastore \
-p 80:5000 deepquestai/deepstack

Example

_images/test-image3.jpg
using System;
using System.IO;
using System.Net.Http;
using System.Threading.Tasks;
using Newtonsoft.Json;


namespace appone
{

class Response {

    public bool success {get;set;}
    public Object[] predictions {get;set;}

}

class Object {

    public string label {get;set;}
    public float confidence {get;set;}
    public int y_min {get;set;}
    public int x_min {get;set;}
    public int y_max {get;set;}
    public int x_max {get;set;}

}

class App {

    static HttpClient client = new HttpClient();

    public static async Task detectFace(string image_path){

        var request = new MultipartFormDataContent();
        var image_data = File.OpenRead(image_path);
        request.Add(new StreamContent(image_data),"image",Path.GetFileName(image_path));
        var output = await client.PostAsync("http://localhost:80/v1/vision/detection",request);
        var jsonString = await output.Content.ReadAsStringAsync();
        Response response = JsonConvert.DeserializeObject<Response>(jsonString);

        foreach (var user in response.predictions){

            Console.WriteLine(user.label);

        }

        Console.WriteLine(jsonString);

    }

    static void Main(string[] args){

        detectFace("test-image3.jpg").Wait();

    }

}

}

Result

dog
person
person
{'predictions': [{'x_max': 819, 'x_min': 633, 'y_min': 354, 'confidence': 99, 'label': 'dog', 'y_max': 546}, {'x_max': 601, 'x_min': 440, 'y_min': 116, 'confidence': 99, 'label': 'person', 'y_max': 516}, {'x_max': 445, 'x_min': 295, 'y_min': 84, 'confidence': 99, 'label': 'person', 'y_max': 514}], 'success': True}

We can use the coordinates returned to extract the objects

using System;
using System.IO;
using System.Net.Http;
using System.Threading.Tasks;
using Newtonsoft.Json;
using SixLabors.ImageSharp;
using SixLabors.ImageSharp.Processing;
using SixLabors.Primitives;

namespace appone
{

class Response {

public bool success {get;set;}
public Object[] predictions {get;set;}

}

class Object {

public string label {get;set;}
public float confidence {get;set;}
public int y_min {get;set;}
public int x_min {get;set;}
public int y_max {get;set;}
public int x_max {get;set;}

}

class App {

static HttpClient client = new HttpClient();

public static async Task recognizeFace(string image_path){

    var request = new MultipartFormDataContent();
    var image_data = File.OpenRead(image_path);
    request.Add(new StreamContent(image_data),"image",Path.GetFileName(image_path));
    var output = await client.PostAsync("http://localhost:80/v1/vision/detection",request);
    var jsonString = await output.Content.ReadAsStringAsync();
    Response response = JsonConvert.DeserializeObject<Response>(jsonString);

    var i = 0;

    foreach (var user in response.predictions){

        var width = user.x_max - user.x_min;
        var height = user.y_max - user.y_min;

        var crop_region = new Rectangle(user.x_min,user.y_min,width,height);

        using(var image = Image.Load(image_path)){

            image.Mutate(x => x
            .Crop(crop_region)
            );
            image.Save(user.label + i.ToString() + "_.jpg");

        }

        i++;

    }

    }

    static void Main(string[] args){

        recognizeFace("test-image3.jpg").Wait();

    }

}

}

Result

_images/image0_dog.jpg
_images/image1_person.jpg
_images/image2_person.jpg

Performance

DeepStack offers three modes allowing you to tradeoff speed for peformance. During startup, you can specify performance mode to be , “High” , “Medium” and “Low”

The default mode is “Medium”

You can speciy a different mode as seen below

sudo docker run -e MODE=High -e VISION-DETECTION=True -v localstorage:/datastore \
-p 80:5000 deepquestai/deepstack

Note the -e MODE=High above

Setting Minimum Confidence

By default, the minimum confidence for detecting objects is 0.45. The confidence ranges between 0 and 1. If the confidence level for an object falls below the min_confidence, no object is detected.

The min_confidence parameter allows you to increase or reduce the minimum confidence.

We lower the confidence allowed below.

Example

request.Add(new StringContent("0.5"),"min_confidence");

CLASSES

The following are the classes of objects DeepStack can detect in images

person,   bicycle,   car,   motorcycle,   airplane,
bus,   train,   truck,   boat,   traffic light,   fire hydrant,   stop_sign,
parking meter,   bench,   bird,   cat,   dog,   horse,   sheep,   cow,   elephant,
bear,   zebra, giraffe,   backpack,   umbrella,   handbag,   tie,   suitcase,
frisbee,   skis,   snowboard, sports ball,   kite,   baseball bat,   baseball glove,
skateboard,   surfboard,   tennis racket, bottle,   wine glass,   cup,   fork,
knife,   spoon,   bowl,   banana,   apple,   sandwich,   orange, broccoli,   carrot,
hot dog,   pizza,   donot,   cake,   chair,   couch,   potted plant,   bed, dining table,
toilet,   tv,   laptop,   mouse,   remote,   keyboard,   cell phone,   microwave,
oven,   toaster,   sink,   refrigerator,   book,   clock,   vase,   scissors,   teddy bear,
hair dryer, toothbrush.