Instance Segmentation
Run inference on instance classification models hosted on Roboflow.
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Run inference on instance classification models hosted on Roboflow.
Last updated
Was this helpful?
Retrieving JSON predictions for a local file called YOUR_IMAGE.jpg
:
base64 YOUR_IMAGE.jpg | curl -d @- \
"https://outline.roboflow.com/your-model/42?api_key=YOUR_KEY"
Inferring on an image hosted elsewhere on the web via its URL (don't forget to ):
curl -X POST "https://outline.roboflow.com/your-model/42?\
api_key=YOUR_KEY&\
image=https%3A%2F%2Fi.imgur.com%2FPEEvqPN.png"
You will need to install and . The easiest way to do this is to use the which also includes the curl
and base64
command line tools when you select "Use Git and optional Unix tools from the Command Prompt" during installation.
Then you can use the same commands as above.
const axios = require("axios");
const fs = require("fs");
const image = fs.readFileSync("YOUR_IMAGE.jpg", {
encoding: "base64"
});
axios({
method: "POST",
url: "https://outline.roboflow.com/your-model/42",
params: {
api_key: "YOUR_KEY"
},
data: image,
headers: {
"Content-Type": "application/x-www-form-urlencoded"
}
})
.then(function(response) {
console.log(response.data);
})
.catch(function(error) {
console.log(error.message);
});
const axios = require("axios");
axios({
method: "POST",
url: "https://outline.roboflow.com/your-model/42",
params: {
api_key: "YOUR_KEY",
image: "https://i.imgur.com/PEEvqPN.png"
}
})
.then(function(response) {
console.log(response.data);
})
.catch(function(error) {
console.log(error.message);
});
import UIKit
// Load Image and Convert to Base64
let image = UIImage(named: "your-image-path") // path to image to upload ex: image.jpg
let imageData = image?.jpegData(compressionQuality: 1)
let fileContent = imageData?.base64EncodedString()
let postData = fileContent!.data(using: .utf8)
// Initialize Inference Server Request with API_KEY, Model, and Model Version
var request = URLRequest(url: URL(string: "https://detect.roboflow.com/your-model/your-model-version?api_key=YOUR_APIKEY&name=YOUR_IMAGE.jpg")!,timeoutInterval: Double.infinity)
request.addValue("application/x-www-form-urlencoded", forHTTPHeaderField: "Content-Type")
request.httpMethod = "POST"
request.httpBody = postData
// Execute Post Request
URLSession.shared.dataTask(with: request, completionHandler: { data, response, error in
// Parse Response to String
guard let data = data else {
print(String(describing: error))
return
}
// Convert Response String to Dictionary
do {
let dict = try JSONSerialization.jsonObject(with: data, options: []) as? [String: Any]
} catch {
print(error.localizedDescription)
}
// Print String Response
print(String(data: data, encoding: .utf8)!)
}).resume()
import java.io.*
import java.net.HttpURLConnection
import java.net.URL
import java.nio.charset.StandardCharsets
import java.util.*
fun main() {
// Get Image Path
val filePath = System.getProperty("user.dir") + System.getProperty("file.separator") + "YOUR_IMAGE.jpg"
val file = File(filePath)
// Base 64 Encode
val encodedFile: String
val fileInputStreamReader = FileInputStream(file)
val bytes = ByteArray(file.length().toInt())
fileInputStreamReader.read(bytes)
encodedFile = String(Base64.getEncoder().encode(bytes), StandardCharsets.US_ASCII)
val API_KEY = "" // Your API Key
val MODEL_ENDPOINT = "dataset/v" // Set model endpoint (Found in Dataset URL)
// Construct the URL
val uploadURL ="https://outline.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&name=YOUR_IMAGE.jpg";
// Http Request
var connection: HttpURLConnection? = null
try {
// Configure connection to URL
val url = URL(uploadURL)
connection = url.openConnection() as HttpURLConnection
connection.requestMethod = "POST"
connection.setRequestProperty("Content-Type",
"application/x-www-form-urlencoded")
connection.setRequestProperty("Content-Length",
Integer.toString(encodedFile.toByteArray().size))
connection.setRequestProperty("Content-Language", "en-US")
connection.useCaches = false
connection.doOutput = true
//Send request
val wr = DataOutputStream(
connection.outputStream)
wr.writeBytes(encodedFile)
wr.close()
// Get Response
val stream = connection.inputStream
val reader = BufferedReader(InputStreamReader(stream))
var line: String?
while (reader.readLine().also { line = it } != null) {
println(line)
}
reader.close()
} catch (e: Exception) {
e.printStackTrace()
} finally {
connection?.disconnect()
}
}
main()
import java.io.BufferedReader
import java.io.DataOutputStream
import java.io.InputStreamReader
import java.net.HttpURLConnection
import java.net.URL
import java.net.URLEncoder
fun main() {
val imageURL = "https://i.imgur.com/PEEvqPN.png" // Replace Image URL
val API_KEY = "" // Your API Key
val MODEL_ENDPOINT = "dataset/v" // Set model endpoint
// Upload URL
val uploadURL = "https://outline.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&image=" + URLEncoder.encode(imageURL, "utf-8");
// Http Request
var connection: HttpURLConnection? = null
try {
// Configure connection to URL
val url = URL(uploadURL)
connection = url.openConnection() as HttpURLConnection
connection.requestMethod = "POST"
connection.setRequestProperty("Content-Type", "application/x-www-form-urlencoded")
connection.setRequestProperty("Content-Length", Integer.toString(uploadURL.toByteArray().size))
connection.setRequestProperty("Content-Language", "en-US")
connection.useCaches = false
connection.doOutput = true
// Send request
val wr = DataOutputStream(connection.outputStream)
wr.writeBytes(uploadURL)
wr.close()
// Get Response
val stream = URL(uploadURL).openStream()
val reader = BufferedReader(InputStreamReader(stream))
var line: String?
while (reader.readLine().also { line = it } != null) {
println(line)
}
reader.close()
} catch (e: Exception) {
e.printStackTrace()
} finally {
connection?.disconnect()
}
}
main()
import java.io.*;
import java.net.HttpURLConnection;
import java.net.URL;
import java.nio.charset.StandardCharsets;
import java.util.Base64;
public class InferenceLocal {
public static void main(String[] args) throws IOException {
// Get Image Path
String filePath = System.getProperty("user.dir") + System.getProperty("file.separator") + "YOUR_IMAGE.jpg";
File file = new File(filePath);
// Base 64 Encode
String encodedFile;
FileInputStream fileInputStreamReader = new FileInputStream(file);
byte[] bytes = new byte[(int) file.length()];
fileInputStreamReader.read(bytes);
encodedFile = new String(Base64.getEncoder().encode(bytes), StandardCharsets.US_ASCII);
String API_KEY = ""; // Your API Key
String MODEL_ENDPOINT = "dataset/v"; // model endpoint
// Construct the URL
String uploadURL = "https://outline.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY
+ "&name=YOUR_IMAGE.jpg";
// Http Request
HttpURLConnection connection = null;
try {
// Configure connection to URL
URL url = new URL(uploadURL);
connection = (HttpURLConnection) url.openConnection();
connection.setRequestMethod("POST");
connection.setRequestProperty("Content-Type", "application/x-www-form-urlencoded");
connection.setRequestProperty("Content-Length", Integer.toString(encodedFile.getBytes().length));
connection.setRequestProperty("Content-Language", "en-US");
connection.setUseCaches(false);
connection.setDoOutput(true);
// Send request
DataOutputStream wr = new DataOutputStream(connection.getOutputStream());
wr.writeBytes(encodedFile);
wr.close();
// Get Response
InputStream stream = connection.getInputStream();
BufferedReader reader = new BufferedReader(new InputStreamReader(stream));
String line;
while ((line = reader.readLine()) != null) {
System.out.println(line);
}
reader.close();
} catch (Exception e) {
e.printStackTrace();
} finally {
if (connection != null) {
connection.disconnect();
}
}
}
}
import java.io.BufferedReader;
import java.io.DataOutputStream;
import java.io.InputStream;
import java.io.InputStreamReader;
import java.net.HttpURLConnection;
import java.net.URL;
import java.net.URLEncoder;
import java.nio.charset.StandardCharsets;
public class InferenceHosted {
public static void main(String[] args) {
String imageURL = "https://i.imgur.com/PEEvqPN.png"; // Replace Image URL
String API_KEY = ""; // Your API Key
String MODEL_ENDPOINT = "dataset/v"; // model endpoint
// Upload URL
String uploadURL = "https://outline.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&image="
+ URLEncoder.encode(imageURL, StandardCharsets.UTF_8);
// Http Request
HttpURLConnection connection = null;
try {
// Configure connection to URL
URL url = new URL(uploadURL);
connection = (HttpURLConnection) url.openConnection();
connection.setRequestMethod("POST");
connection.setRequestProperty("Content-Type", "application/x-www-form-urlencoded");
connection.setRequestProperty("Content-Length", Integer.toString(uploadURL.getBytes().length));
connection.setRequestProperty("Content-Language", "en-US");
connection.setUseCaches(false);
connection.setDoOutput(true);
// Send request
DataOutputStream wr = new DataOutputStream(connection.getOutputStream());
wr.writeBytes(uploadURL);
wr.close();
// Get Response
InputStream stream = new URL(uploadURL).openStream();
BufferedReader reader = new BufferedReader(new InputStreamReader(stream));
String line;
while ((line = reader.readLine()) != null) {
System.out.println(line);
}
reader.close();
} catch (Exception e) {
e.printStackTrace();
} finally {
if (connection != null) {
connection.disconnect();
}
}
}
}
Gemfile
source "https://rubygems.org"
gem "httparty", "~> 0.18.1"
gem "base64", "~> 0.1.0"
gem "cgi", "~> 0.2.1"
GEM
remote: https://rubygems.org/
specs:
base64 (0.1.0)
cgi (0.2.1)
httparty (0.18.1)
mime-types (~> 3.0)
multi_xml (>= 0.5.2)
mime-types (3.3.1)
mime-types-data (~> 3.2015)
mime-types-data (3.2021.0225)
multi_xml (0.6.0)
PLATFORMS
x64-mingw32
x86_64-linux
DEPENDENCIES
base64 (~> 0.1.0)
cgi (~> 0.2.1)
httparty (~> 0.18.1)
BUNDLED WITH
2.2.15
require 'base64'
require 'httparty'
encoded = Base64.encode64(File.open("YOUR_IMAGE.jpg", "rb").read)
model_endpoint = "dataset/v" # Set model endpoint
api_key = "" # Your API KEY Here
params = "?api_key=" + api_key
+ "&name=YOUR_IMAGE.jpg"
response = HTTParty.post(
"https://outline.roboflow.com/" + model_endpoint + params,
body: encoded,
headers: {
'Content-Type' => 'application/x-www-form-urlencoded',
'charset' => 'utf-8'
})
puts response
require 'httparty'
require 'cgi'
model_endpoint = "dataset/v" # Set model endpoint
api_key = "" # Your API KEY Here
img_url = "https://i.imgur.com/PEEvqPN.png" # Construct the URL
img_url = CGI::escape(img_url)
params = "?api_key=" + api_key + "&image=" + img_url
response = HTTParty.post(
"https://outline.roboflow.com/" + model_endpoint + params,
headers: {
'Content-Type' => 'application/x-www-form-urlencoded',
'charset' => 'utf-8'
})
puts response
Inferring on a Local Image
<?php
// Base 64 Encode Image
$data = base64_encode(file_get_contents("YOUR_IMAGE.jpg"));
$api_key = ""; // Set API Key
$model_endpoint = "dataset/v"; // Set model endpoint (Found in Dataset URL)
// URL for Http Request
$url = "https://outline.roboflow.com/" . $model_endpoint
. "?api_key=" . $api_key
. "&name=YOUR_IMAGE.jpg";
// Setup + Send Http request
$options = array(
'http' => array (
'header' => "Content-type: application/x-www-form-urlencoded\r\n",
'method' => 'POST',
'content' => $data
));
$context = stream_context_create($options);
$result = file_get_contents($url, false, $context);
echo $result;
?>
<?php
$api_key = ""; // Set API Key
$model_endpoint = "dataset/v"; // Set model endpoint (Found in Dataset URL)
$img_url = "https://i.imgur.com/PEEvqPN.png";
// URL for Http Request
$url = "https://outline.roboflow.com/" . $model_endpoint
. "?api_key=" . $api_key
. "&image=" . urlencode($img_url);
// Setup + Send Http request
$options = array(
'http' => array (
'header' => "Content-type: application/x-www-form-urlencoded\r\n",
'method' => 'POST'
));
$context = stream_context_create($options);
$result = file_get_contents($url, false, $context);
echo $result;
?>
Inferring on a Local Image
package main
import (
"bufio"
"encoding/base64"
"fmt"
"io/ioutil"
"os"
"net/http"
"strings"
)
func main() {
api_key := "" // Your API Key
model_endpoint := "dataset/v" // Set model endpoint
// Open file on disk.
f, _ := os.Open("YOUR_IMAGE.jpg")
// Read entire JPG into byte slice.
reader := bufio.NewReader(f)
content, _ := ioutil.ReadAll(reader)
// Encode as base64.
data := base64.StdEncoding.EncodeToString(content)
uploadURL := "https://outline.roboflow.com/" + model_endpoint + "?api_key=" + api_key + "&name=YOUR_IMAGE.jpg"
req, _ := http.NewRequest("POST", uploadURL, strings.NewReader(data))
req.Header.Set("Accept", "application/json")
client := &http.Client{}
resp, _ := client.Do(req)
defer resp.Body.Close()
bytes, _ := ioutil.ReadAll(resp.Body)
fmt.Println(string(bytes))
}
package main
import (
"fmt"
"net/http"
"net/url"
"io/ioutil"
)
func main() {
api_key := "" // Your API Key
model_endpoint := "dataset/v" // Set model endpoint
img_url := "https://i.ibb.co/jzr27x0/YOUR-IMAGE.jpg"
uploadURL := "https://outline.roboflow.com/" + model_endpoint + "?api_key=" + api_key + "&image=" + url.QueryEscape(img_url)
req, _ := http.NewRequest("POST", uploadURL, nil)
req.Header.Set("Accept", "application/json")
client := &http.Client{}
resp, _ := client.Do(req)
defer resp.Body.Close()
bytes, _ := ioutil.ReadAll(resp.Body)
fmt.Println(string(bytes))
}
Inferring on a Local Image
using System;
using System.IO;
using System.Net;
using System.Text;
namespace InferenceLocal
{
class InferenceLocal
{
static void Main(string[] args)
{
byte[] imageArray = System.IO.File.ReadAllBytes(@"YOUR_IMAGE.jpg");
string encoded = Convert.ToBase64String(imageArray);
byte[] data = Encoding.ASCII.GetBytes(encoded);
string API_KEY = ""; // Your API Key
string MODEL_ENDPOINT = "dataset/v"; // Set model endpoint
// Construct the URL
string uploadURL =
"https://outline.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY
+ "&name=YOUR_IMAGE.jpg";
// Service Request Config
ServicePointManager.Expect100Continue = true;
ServicePointManager.SecurityProtocol = SecurityProtocolType.Tls12;
// Configure Request
WebRequest request = WebRequest.Create(uploadURL);
request.Method = "POST";
request.ContentType = "application/x-www-form-urlencoded";
request.ContentLength = data.Length;
// Write Data
using (Stream stream = request.GetRequestStream())
{
stream.Write(data, 0, data.Length);
}
// Get Response
string responseContent = null;
using (WebResponse response = request.GetResponse())
{
using (Stream stream = response.GetResponseStream())
{
using (StreamReader sr99 = new StreamReader(stream))
{
responseContent = sr99.ReadToEnd();
}
}
}
Console.WriteLine(responseContent);
}
}
}
using System;
using System.IO;
using System.Net;
using System.Web;
namespace InferenceHosted
{
class InferenceHosted
{
static void Main(string[] args)
{
string API_KEY = ""; // Your API Key
string imageURL = "https://i.ibb.co/jzr27x0/YOUR-IMAGE.jpg";
string MODEL_ENDPOINT = "dataset/v"; // Set model endpoint
// Construct the URL
string uploadURL =
"https://outline.roboflow.com/" + MODEL_ENDPOINT
+ "?api_key=" + API_KEY
+ "&image=" + HttpUtility.UrlEncode(imageURL);
// Service Point Config
ServicePointManager.Expect100Continue = true;
ServicePointManager.SecurityProtocol = SecurityProtocolType.Tls12;
// Configure Http Request
WebRequest request = WebRequest.Create(uploadURL);
request.Method = "POST";
request.ContentType = "application/x-www-form-urlencoded";
request.ContentLength = 0;
// Get Response
string responseContent = null;
using (WebResponse response = request.GetResponse())
{
using (Stream stream = response.GetResponseStream())
{
using (StreamReader sr99 = new StreamReader(stream))
{
responseContent = sr99.ReadToEnd();
}
}
}
Console.WriteLine(responseContent);
}
}
}
The hosted API inference route returns a JSON
object containing an array of predictions. Each prediction has the following properties:
x
= the horizontal center point of the detected object
y
= the vertical center point of the detected object
width
= the width of the bounding box
height
= the height of the bounding box
class
= the class label of the detected object
confidence
= the model's confidence that the detected object has the correct label and position coordinates
points
=list of points that make of the polygon outline of the object - each item in the list is an object with keys x
and y
for the horizontal and vertical coordinate of the point respectively
// an example JSON object
{
"predictions": [
{
"x": 179.2,
"y": 247,
"width": 231,
"height": 147,
"class": "A",
"confidence": 0.98,
"points": [
{
"x": 134,
"y": 314
},
{
"x": 116,
"y": 313
},
{
"x": 103,
"y": 310.1
},
{
"x": 72.7,
"y": 282
},
{
"x": 66.8,
"y": 273
},
]
}
]
}
POST
https://outline.roboflow.com/:datasetSlug/:versionNumber
You can POST a base64 encoded image directly to your model endpoint. Or you can pass a URL as the image
parameter in the query string if your image is already hosted elsewhere.
datasetSlug
string
The url-safe version of the dataset name. You can find it in the web UI by looking at the URL on the main project view or by clicking the "Get curl command" button in the train results section of your dataset version after training your model.
version
number
The version number identifying the version of of your dataset
image
string
URL of the image to add. Use if your image is hosted elsewhere. (Required when you don't POST a base64 encoded image in the request body.) Note: don't forget to URL-encode it.
overlap
number
The maximum percentage (on a scale of 0-100) that bounding box predictions of the same class are allowed to overlap before being combined into a single box. Default: 30
confidence
number
A threshold for the returned predictions on a scale of 0-100. A lower number will return more predictions. A higher number will return fewer high-certainty predictions. Default: 40
api_key
string
Your API key (obtained via your workspace API settings page)
string
A base64 encoded image. (Required when you don't pass an image URL in the query parameters).
{
"predictions": [{
"x": 234.0,
"y": 363.5,
"width": 160,
"height": 197,
"class": "hand",
"confidence": 0.943
}, {
"x": 504.5,
"y": 363.0,
"width": 215,
"height": 172,
"class": "hand",
"confidence": 0.917
}, {
"x": 1112.5,
"y": 691.0,
"width": 139,
"height": 52,
"class": "hand",
"confidence": 0.87
}, {
"x": 78.5,
"y": 700.0,
"width": 139,
"height": 34,
"class": "hand",
"confidence": 0.404
}]
}
{
"Message": "User is not authorized to access this resource"
}
We're using to perform the POST request in this example so first run npm install axios
to install the dependency.
We have realtime on-device inference available via roboflow.js
; see .
We are adding code snippets as they are requested by users. If you'd like to integrate the inference API into your Elixir app, please .