Furniture Category Recognition API

Furniture Category Recognition API - FurnishRec (also known as Furniture Category Detection API or Furniture Category Detector API) is a cross browsers REST API which get a JSON input with a still photo (as base64 encoded string), containing household furniture and returns a JSON string which contains a dominant Furniture Category of the input photo among the most used Furniture Categories and SubCategories: Baby Beds, Baby Chairs, Baldachin Beds, Bar Chairs, Bathroom Cabinets, Bed Frames, Bed Tables, Bedroom Cabinets, Benches, Camping Chairs, Camping Tables, Chair Beds, Clothes Hangers, Commode Furnitures, Decorative Mirrors, Hammocks, Kitchen Cabinets, Kitchen Corner Sofas, Kitchen Islands, Living Cabinets, Living Corner Sofas, Living Tables, Massage Armchairs, Matrimonial Beds, Meeting Tables, Nightstands, Office Chairs, Office Desks, Rocking Chairs, Round Tables, Shelves, Shoes Racks, Simple Armchairs, Simple Chairs, Simple Sofas, Single Beds, Sofa Beds, Sun Chairs, Twin Over Beds, Wobble Chairs. The recognized Furniture Categorys have confidence score, timestamp, tagId, tagName. Of course, there are some limitations in order to get a higher accuracy. We recommend properly exposed, unobstructed JPEG photos at 1920x1080 (full HD resolution) where the furniture is clear and focused. If the furniture details are too small or blured, the accuracy is lower and the AI algorithm may not classify in a proper way. We do not store pictures. Also, the quality and the angles of the camera are very important and it contribute to a higher reading accuracy. It should has varifocal lenses, high shutter speed, good infrared lighting beam, full HD resolution.

Allthough this Automatic Furniture Category Recognition API (currently we do not offer a Furniture Category Recognition sdk) is intended for software development and therefore developers, we have also here an Furniture Category Recognition online application that may be used to check the input and output JSONs of the API. The necessary steps are written below, basically for this real time Furniture Category Recognition API you send an authorized POST request in JSON format to the API endpoint and you get as JSON response the output as described below through parameters and examples.

This Furniture Category Recognition API is useful for a large number of domains like apps for: furniture e-commerce, furniture manufacturers, furniture distributors, furniture retailers etc. You own the commercial copyright of the resulted JSON with no additional fee meaning you may use it in your own apps for sale.

For using our Furniture Category Recognition API and/or APP you must create an account (free of charge, no card required), activate it from your received email, login and then start your TRIAL package with no fees as you can see at our pricing packages. After you have tested the API and/or APP and you are satisfied, you may buy a paid package. You will always see at your Admin Console page the real resources consumption in real time, your invoices, you may see/edit/delete your profile or export log consents as GDPR instructed, you may read our FAQs.

Furniture Category Recognition APP

Photo File
Image URL(*)
* Let the "NO" value of Image URL if you upload a Photo File, otherwise write the image url like http://domainname.com/image.jpg



API Endpoint (method POST):
https://www.de-vis-software.ro/furnishrec.aspx
Headers:
Authorization: Basic //Your username:password are base64 encoded string
Content-Type: application/json
Accept: application/json
JSON Request Body (change inputs here and see in real time below):
                   {
  "base64_Photo_String": "iVBORw0KGgoAAAA...base64 encoded string photo...GAAAAAElFTkSuQmCC",
  "photo_url": "NO"
}
               
JSON Response From API (change inputs here and see in real time below):
{
  "created": "2020-05-02T12:28:09.989Z",
  "predictions": [
    {
      "probability": 0.4453594,
      "tagId": "6b333d95-e461-4155-890c-9921158f7d17",
      "tagName": "Office Chairs"
    },
    {
      "probability": 0.3109611,
      "tagId": "6b333d95-e461-4155-890c-9921158f7d17",
      "tagName": "Wobble Chairs"
    }
  ]
}
JSON Response (Example) From API in case of ERROR:

 [
  {
    "cd": "1001",
    "description": "The authorization header Is either empty Or isn't Basic"
  }
]

Request Parameters Table

Parameter Name
Parameter Description
base64ImageString
This is the input photo as base64 encoded string[string] from which will be detected the Furniture Category(ies).
photo_url
This is the image url [string] used for the input photo. Its default value is NO because the above parameter base64_Photo_String is set. If this parameter is set to an image url, base64_Photo_String value must be NO.

Response Parameter Table

Parameter Name
Parameter Description
created  
This is the timestamp as  [string] at the moment that request is made.
predictions
This is a list or array which contains the parameters explained below.
probability
This is the probability score [real] of the detected Furniture Category.
tagId
This is the tagId [string] for the detected Furniture Category. Example: 6b333d95-e461-4155-890c-9921158f7d17.
tagName
This is the tagName [string] for the detected Furniture Category. Example of tagName: Baby Beds.

Response Error Codes Table

Parameter Name
Parameter Description
cd

This is the error code which may be:

  • 1001
  • 1002
  • 1003
  • 1004
  • 1005
  • 1006
  • 1007
  • 1008
  • 1009
  • 1010
  • 1011
  • 1012
  • 1013
  • 1014
  • 1015
  • 1016
  • 2001
description

This is the description of the error code which may be:

  • 1001 - The authorization header is either empty or isn't Basic.
  • 1002 - Daily requests number exceeded in TRIAL mode!
  • 1003 - Trial expired!
  • 1004 - Requests number exceeded!
  • 1005 - Package expired!
  • 1006 - No invoice!
  • 1007 - Reader is NULL for TRIAL!
  • 1008 - Cannot Read if TRIAL exists!
  • 1009 - Error connecting to database looking for TRIAL! (and a detailed description message of the encountered error)
  • 1010 - Reader is NULL for Invoice!
  • 1011 - Cannot Read if Invoice exists!
  • 1012 - Error connecting to database! (and a detailed description message of the encountered error)
  • 1013 - Input request too long! Maximum 5 MB per request are allowed / Nothing to upload
  • 1014 - Invalid request data! (and a detailed description message of the encountered error)
  • 2001 - Invalid request data after passing to the API (and a detailed description message of the encountered error)

Source Code Examples for Using Our Furniture Category Recognition API

                       
Imports System
Imports System.Text
imports System.Collections.Generic
Imports System.Net
Imports Newtonsoft.Json

Public Class furniture_category_recognition_api
    Public Class ResponseFields
	 Public Property created As String
         Public Property predictions As New List(Of prediction)
    End Class

    Public Class prediction
	 Public Property probability As Single
         Public Property tagId As String
         Public Property tagName As String
    End Class

    Public Class ErrorFields
        Public Property cd As String
        Public Property description As String
    End Class

    Protected Sub SendRequest()
        Dim Client As WebClient = New WebClient()
        Dim credentials As String = Convert.ToBase64String(Encoding.ASCII.GetBytes("your_username:your_password"))
        Client.Headers(HttpRequestHeader.Authorization) = String.Format("Basic {0}", credentials)
        Client.Headers(HttpRequestHeader.Accept) = "application/json"
        Client.Headers(HttpRequestHeader.ContentType) = "application/json"
	Client.BaseAddress = "https://www.de-vis-software.ro/FurnishRec.aspx"
        Dim resString As String = ""

        Try
            Dim js As String = "Replace this string with your JSON Request Body string like in the example above on the website"
            Dim reqString As Byte() = Encoding.UTF8.GetBytes(js)
            Dim url As Uri = New Uri(Client.BaseAddress)
            Dim resByte As Byte() = Client.UploadData(url, "post", reqString)
            resString = Encoding.UTF8.GetString(resByte)

	    If resString.IndexOf("predictions") > 0 Then
                Dim r As ResponseFields = New ResponseFields()
                r = JsonConvert.DeserializeObject(Of ResponseFields)(resString)
                Console.Write(resString)
            Else
		Dim e As list(of ErrorFields) = New list(of ErrorFields)
		e = JsonConvert.DeserializeObject(Of list(of ErrorFields))(resString)
                Console.Write(e(0).cd)
                Console.Write(e(0).description)
            End If

            Client.Dispose()
        Catch exception As Exception
            Dim ex As System.Exception = exception
            Console.Write("ERROR: " & resString & ex.Message)
        End Try
    End Sub

    Public Shared Sub Main()
	Dim b As furniture_category_recognition_api = New  furniture_category_recognition_api
        b.SendRequest()
    End Sub
End Class



FurnishRec Online Video Presentation

Furniture Category Recognition API, FurnishRec is in the video presentation below. There are several search terms which you may use like: Furniture Category Recognition api, Furniture Category Recognition sdk, Furniture Category Detection c#, Furniture Category Recognition online, Furniture Category Recognition, automatic Furniture Category Recognition, Furniture Category Recognition python, Furniture Category Recognition python, real time Furniture Category Recognition python, python Furniture Category Recognition, image processing Furniture Category Recognition.

 



Pricing Packages

Please choose one of the below pricing packages for start using our Furniture Category Recognition API and online APP!

Start TRIAL
No catches

  • 7 days TRIAL
  • Use our cloud REST API and online APP
  • Maximum 50 requests per DAY in trial period
  • You do NOT own the commercial copyright for using the API in your apps in trial period.
  • Get Furniture Category(ies) in one photo.
  • Get confidence score for recognized Furniture Category(ies) in the photo
  • Unlimited Devices
  • Administration console
  • Support through online chat and/or tickets
  • We do NOT allow spam accounts for TRIAL



Monthly TIER
Popular

  • 80 USD per month
  • Use our cloud REST API and online APP
  • Maximum 10000 predictions(*) per month
  • Maximum 50 requests per MINUTE
  • You own the commercial copyright to use it in your apps.
  • Get Furniture Category(ies) in one photo.
  • Get confidence score for recognized Furniture Category(ies) in the photo
  • Unlimited Devices
  • Administration console
  • Premium support through online chat and/or tickets, very supportive help and quick responses.



Yearly TIER
(15% Discount)

  • 816 USD per year
  • Use our cloud REST API and online APP
  • Maximum 10000 predictions(*) per month
  • Maximum 50 requests per MINUTE
  • You own the commercial copyright to use it in your apps.
  • Get Furniture Category(ies) in one photo.
  • Get confidence score for recognized Furniture Category(ies) in the photo
  • Unlimited Devices
  • Administration console
  • Premium support through online chat and/or tickets, very supportive help and quick responses.



Note: VAT rate may be added or not, function to your country and/or if you are a taxable person or company.
* Prediction - on the input photo may exist many predictions, each of it with certain amount of probability of detected Furniture Category(ies). Even we filter the output predictions to those with probability score greater than 20%, for the input photo all predictions are counted.