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
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
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.