> For the complete documentation index, see [llms.txt](https://docs.roboflow.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.roboflow.com/roboflow/roboflow-hi/deploy/serverless/instance-segmentation/semantic-segmentation.md). # Semantic Segmentation {% tabs %} {% tab title="cURL" %} **Linux या MacOS** नामक स्थानीय फ़ाइल के लिए JSON predictions प्राप्त करना `YOUR_IMAGE.jpg`: ```bash base64 YOUR_IMAGE.jpg | curl -d @- \\ "https://segment.roboflow.com/your-model/42?api_key=YOUR_KEY" ``` उसके URL के माध्यम से वेब पर कहीं और होस्ट की गई image पर inference करना (यह न भूलें कि [इसे URL encode करें](https://www.urlencoder.org/)): ```bash curl -X POST "https://segment.roboflow.com/your-model/42?\ api_key=YOUR_KEY&\ image=https%3A%2F%2Fi.imgur.com%2FPEEvqPN.png" ``` **Windows** आपको इंस्टॉल करना होगा [Windows के लिए curl](https://curl.se/windows/) और [Windows के लिए GNU का base64 tool](http://gnuwin32.sourceforge.net/packages/coreutils.htm). ऐसा करने का सबसे आसान तरीका है उपयोग करना [Windows के लिए git installer](https://git-scm.com/downloads) जिसमें यह भी शामिल है `curl` और `base64` command line tools, जब आप installation के दौरान "Use Git and optional Unix tools from the Command Prompt" चुनते हैं। फिर आप ऊपर दिए गए समान commands का उपयोग कर सकते हैं। {% endtab %} {% tab title="Python" %} **स्थानीय और होस्ट की गई images पर inference करें** dependencies इंस्टॉल करने के लिए, `pip install roboflow`. ```python from roboflow import Roboflow rf = Roboflow(api_key="") project = rf.workspace().project("PROJECT_ID") model = project.version(VERSION).models()[0] prediction = model.predict("YOUR_IMAGE.jpg") ``` {% endtab %} {% tab title="JavaScript" %} **Node.js** हम उपयोग कर रहे हैं [axios](https://github.com/axios/axios) इस उदाहरण में POST request करने के लिए, इसलिए पहले चलाएँ `npm install axios` ताकि dependency इंस्टॉल हो जाए। **स्थानीय image पर inference करना** ```javascript const axios = require("axios"); const fs = require("fs"); const image = fs.readFileSync("YOUR_IMAGE.jpg", { encoding: "base64" }); axios({ method: "POST", url: "https://segment.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); }); ``` **URL के माध्यम से कहीं और होस्ट की गई image पर inference करना** ```javascript const axios = require("axios"); axios({ method: "POST", url: "https://segment.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); }); ``` **Web** हमारे पास real-time on-device inference उपलब्ध है, इसके माध्यम से `roboflow.js`; देखें [दस्तावेज़ यहाँ](/roboflow/roboflow-hi/deploy/sdks/web-browser.md). {% endtab %} {% tab title="Swift/iOS" %} **Swift** **स्थानीय image पर inference करना** ```swift import UIKit // Image लोड करें और Base64 में परिवर्तित करें let image = UIImage(named: "your-image-path") // अपलोड करने के लिए image का path, उदाहरण: image.jpg let imageData = image?.jpegData(compressionQuality: 1) let fileContent = imageData?.base64EncodedString() let postData = fileContent!.data(using: .utf8) // API_KEY, Model, और Model Version के साथ Inference Server request प्रारंभ करें var request = URLRequest(url: URL(string: "https://segment.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 // Post Request निष्पादित करें URLSession.shared.dataTask(with: request, completionHandler: { data, response, error in // Response को String में पार्स करें guard let data = data else { print(String(describing: error)) return } // Response String को Dictionary में परिवर्तित करें do { let dict = try JSONSerialization.jsonObject(with: data, options: []) as? [String: Any] } catch { print(error.localizedDescription) } // String response प्रिंट करें print(String(data: data, encoding: .utf8)!) }).resume() ``` **Objective C** [Objective-C snippet का अनुरोध करने के लिए यहाँ क्लिक करें।](https://app.roboflow.com/request/snippet.inference-objc) {% endtab %} {% tab title="Android" %} **Kotlin** **स्थानीय image पर inference करना** ```kotlin import java.io.* import java.net.HttpURLConnection import java.net.URL import java.nio.charset.StandardCharsets import java.util.* fun main() { // Image path प्राप्त करें val filePath = System.getProperty("user.dir") + System.getProperty("file.separator") + "YOUR_IMAGE.jpg" val file = File(filePath) // Base 64 Encode करें 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 = "" // आपकी API Key val MODEL_ENDPOINT = "dataset/v" // model endpoint सेट करें (Dataset URL में मिलता है) // URL बनाएँ val uploadURL ="https://segment.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&name=YOUR_IMAGE.jpg"; // Http Request var connection: HttpURLConnection? = null try { // URL के लिए connection कॉन्फ़िगर करें 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 // अनुरोध भेजें val wr = DataOutputStream( connection.outputStream) wr.writeBytes(encodedFile) wr.close() // Response प्राप्त करें 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() ``` **URL के माध्यम से कहीं और होस्ट की गई image पर inference करना** ```kotlin 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" // Image URL बदलें val API_KEY = "" // आपकी API Key val MODEL_ENDPOINT = "dataset/v" // model endpoint सेट करें // Upload URL val uploadURL = "https://segment.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&image=" + URLEncoder.encode(imageURL, "utf-8"); // Http Request var connection: HttpURLConnection? = null try { // URL के लिए connection कॉन्फ़िगर करें 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 // अनुरोध भेजें val wr = DataOutputStream(connection.outputStream) wr.writeBytes(uploadURL) wr.close() // Response प्राप्त करें 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() ``` **Java** **स्थानीय image पर inference करना** ```java 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 { // Image path प्राप्त करें String filePath = System.getProperty("user.dir") + System.getProperty("file.separator") + "YOUR_IMAGE.jpg"; File file = new File(filePath); // Base 64 Encode करें 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 = ""; // आपकी API Key String MODEL_ENDPOINT = "dataset/v"; // model endpoint // URL बनाएँ String uploadURL = "https://segment.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&name=YOUR_IMAGE.jpg"; // Http Request HttpURLConnection connection = null; try { // URL के लिए connection कॉन्फ़िगर करें 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); // अनुरोध भेजें DataOutputStream wr = new DataOutputStream(connection.getOutputStream()); wr.writeBytes(encodedFile); wr.close(); // Response प्राप्त करें 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(); } } } } ``` **URL के माध्यम से कहीं और होस्ट की गई image पर inference करना** ```java 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"; // Image URL बदलें String API_KEY = ""; // आपकी API Key String MODEL_ENDPOINT = "dataset/v"; // model endpoint // Upload URL String uploadURL = "https://segment.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&image=" + URLEncoder.encode(imageURL, StandardCharsets.UTF_8); // Http Request HttpURLConnection connection = null; try { // URL के लिए connection कॉन्फ़िगर करें 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); // अनुरोध भेजें DataOutputStream wr = new DataOutputStream(connection.getOutputStream()); wr.writeBytes(uploadURL); wr.close(); // Response प्राप्त करें 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(); } } } } ``` {% endtab %} {% tab title="Ruby" %} **Gemfile** {% code title="Gemfile" %} ```ruby source "https://rubygems.org" gem "httparty", "~> 0.18.1" gem "base64", "~> 0.1.0" gem "cgi", "~> 0.2.1" ``` {% endcode %} **Gemfile.lock** {% code title="Gemfile.lock" %} ```ruby 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) प्लेटफ़ॉर्म x64-mingw32 x86_64-linux निर्भरताएँ base64 (~> 0.1.0) cgi (~> 0.2.1) httparty (~> 0.18.1) साथ बंडल किया गया 2.2.15 ``` {% endcode %} **स्थानीय image पर inference करना** ```ruby require 'base64' require 'httparty' encoded = Base64.encode64(File.open("YOUR_IMAGE.jpg", "rb").read) model_endpoint = "dataset/v" # model endpoint सेट करें api_key = "" # आपकी API KEY यहाँ params = "?api_key=" + api_key + "&name=YOUR_IMAGE.jpg" response = HTTParty.post( "https://segment.roboflow.com/" + model_endpoint + params, body: encoded, headers: { 'Content-Type' => 'application/x-www-form-urlencoded', 'charset' => 'utf-8' }) puts response ``` **URL के माध्यम से कहीं और होस्ट की गई image पर inference करना** ```ruby require 'httparty' require 'cgi' model_endpoint = "dataset/v" # model endpoint सेट करें api_key = "" # आपकी API KEY यहाँ img_url = "https://i.imgur.com/PEEvqPN.png" # URL बनाएं img_url = CGI::escape(img_url) params = "?api_key=" + api_key + "&image=" + img_url response = HTTParty.post( "https://segment.roboflow.com/" + model_endpoint + params, headers: { 'Content-Type' => 'application/x-www-form-urlencoded', 'charset' => 'utf-8' }) puts response ``` {% endtab %} {% tab title="PHP" %} **स्थानीय image पर inference करना** ```php 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; ?> ``` **URL के माध्यम से कहीं और होस्ट की गई image पर inference करना** ```php 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; ?> ``` {% endtab %} {% tab title="Go" %} **स्थानीय image पर inference करना** ```go package main import ( "bufio" "encoding/base64" "fmt" "io/ioutil" "os" "net/http" "strings" ) func main() { api_key := "" // आपकी API Key model_endpoint := "dataset/v" // model endpoint सेट करें // डिस्क पर फ़ाइल खोलें। f, _ := os.Open("YOUR_IMAGE.jpg") // पूरे JPG को byte slice में पढ़ें। reader := bufio.NewReader(f) content, _ := ioutil.ReadAll(reader) // Base64 में encode करें। data := base64.StdEncoding.EncodeToString(content) uploadURL := "https://segment.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)) } ``` **URL के माध्यम से कहीं और होस्ट की गई image पर inference करना** ```go package main import ( "fmt" "net/http" "net/url" "io/ioutil" ) func main() { api_key := "" // आपकी API Key model_endpoint := "dataset/v" // model endpoint सेट करें img_url := "https://i.ibb.co/jzr27x0/YOUR-IMAGE.jpg" uploadURL := "https://segment.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)) } ``` {% endtab %} {% tab title=".NET" %} **स्थानीय image पर inference करना** ```csharp 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 = ""; // आपकी API Key string MODEL_ENDPOINT = "dataset/v"; // मॉडल endpoint सेट करें // URL बनाएँ string uploadURL = "https://segment.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&name=YOUR_IMAGE.jpg"; // सर्विस request कॉन्फ़िग ServicePointManager.Expect100Continue = true; ServicePointManager.SecurityProtocol = SecurityProtocolType.Tls12; // request कॉन्फ़िगर करें WebRequest request = WebRequest.Create(uploadURL); request.Method = "POST"; request.ContentType = "application/x-www-form-urlencoded"; request.ContentLength = data.Length; // डेटा लिखें using (Stream stream = request.GetRequestStream()) { stream.Write(data, 0, data.Length); } // Response प्राप्त करें 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); } } } ``` **URL के माध्यम से कहीं और होस्ट की गई image पर inference करना** ```csharp using System; using System.IO; using System.Net; using System.Web; namespace InferenceHosted { class InferenceHosted { static void Main(string[] args) { string API_KEY = ""; // आपकी API Key string imageURL = "https://i.ibb.co/jzr27x0/YOUR-IMAGE.jpg"; string MODEL_ENDPOINT = "dataset/v"; // मॉडल endpoint सेट करें // URL बनाएँ string uploadURL = "https://segment.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&image=" + HttpUtility.UrlEncode(imageURL); // सर्विस पॉइंट कॉन्फ़िग ServicePointManager.Expect100Continue = true; ServicePointManager.SecurityProtocol = SecurityProtocolType.Tls12; // Http Request कॉन्फ़िगर करें WebRequest request = WebRequest.Create(uploadURL); request.Method = "POST"; request.ContentType = "application/x-www-form-urlencoded"; request.ContentLength = 0; // Response प्राप्त करें 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); } } } ``` {% endtab %} {% tab title="एलिक्सिर" %} हम उपयोगकर्ताओं द्वारा अनुरोध किए जाने पर code snippets जोड़ रहे हैं. यदि आप अपने एलिक्सिर ऐप में inference API को integrate करना चाहते हैं, तो कृपया [अपना upvote दर्ज करने के लिए यहाँ क्लिक करें](https://app.roboflow.com/request/snippet.upload-elixir). {% endtab %} {% endtabs %} ### Response Object प्रारूप Hosted API inference route एक लौटाता है `JSON` predictions की एक array वाला एक object. प्रत्येक prediction में निम्न properties होती हैं: * `segmentation_mask` = base64-encoded single-channel image, जिसकी dimensions input image के बराबर होती हैं, जहाँ प्रत्येक pixel value एक class ID के अनुरूप होती है * `class_map` = class IDs को class names से map करने वाला object * `image` = input image dimensions वाला एक object * height = input image की height, pixels में * width = input image की width, pixels में ```json // एक उदाहरण JSON object { "segmentation_mask": "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", "class_map": { "0": " पृष्ठभूमि", "1": " वस्तु" }, "image": { "width": 1232, "height": 821 } } ``` ## API संदर्भ ## Inference API का उपयोग करना `POST` `https://segment.roboflow.com/:datasetSlug/:versionNumber` आप base64 encoded image को सीधे अपने model endpoint पर POST कर सकते हैं. या आप URL को उसी `image` parameter in the query string में पास कर सकते हैं, यदि आपकी image पहले से कहीं और hosted है. #### पथ पैरामीटर्स | नाम | प्रकार | विवरण | | ----------- | ------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | datasetSlug | string | dataset name का URL-safe version. Training के बाद अपने model को train करने के बाद dataset version के train results section में मुख्य project view पर URL देखकर या "Get curl command" बटन पर क्लिक करके आप इसे web UI में ढूँढ सकते हैं. | | संस्करण | संख्या | आपके dataset के संस्करण की पहचान करने वाला version number | #### Query Parameters | नाम | प्रकार | विवरण | | ---------- | ------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | image | string |

जोड़ने के लिए image का URL. इसका उपयोग करें यदि आपकी image कहीं और hosted है. (जब आप request body में base64 encoded image POST नहीं करते हैं, तब आवश्यक.)

नोट: इसे URL-encode करना न भूलें.

| | confidence | संख्या |

0-100 की scale पर लौटाई गई predictions के लिए threshold. कम संख्या अधिक predictions लौटाएगी. अधिक संख्या कम लेकिन अधिक निश्चित predictions लौटाएगी.

डिफ़ॉल्ट: 50

| | api\_key | string | आपकी API key (आपकी workspace API settings page के माध्यम से प्राप्त) | #### Request Body | नाम | प्रकार | विवरण | | --- | ------ | --------------------------------------------------------------------------------------------- | | | string | एक base64 encoded image. (जब आप query parameters में image URL पास नहीं करते हैं, तब आवश्यक.) | {% tabs %} {% tab title="200 JSON format predictions. (x,y) बॉक्स के" %} ``` { "segmentation_mask": "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", "class_map": { "0": " पृष्ठभूमि", "1": " वस्तु" }, "image": { "width": 1232, "height": 821 } } ``` {% endtab %} {% tab title="403 यदि आपकी api\_key model तक पहुँचने के लिए अधिकृत नहीं है." %} ``` { "Message": "उपयोगकर्ता को इस संसाधन तक पहुँचने की अनुमति नहीं है" } ``` {% endtab %} {% endtabs %} --- # Agent Instructions This documentation is published with GitBook. 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