# Semantic Segmentation {% tabs %} {% tab title="cURL" %} **Linux 또는 MacOS** 다음과 같은 로컬 파일에 대한 JSON 예측을 가져오기 `YOUR_IMAGE.jpg`: ```bash base64 YOUR_IMAGE.jpg | curl -d @- \ "https://segment.roboflow.com/your-model/42?api_key=YOUR_KEY" ``` URL을 통해 웹의 다른 곳에 호스팅된 이미지에 대해 추론하려면(잊지 말고 [URL 인코딩하세요](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** 설치해야 합니다 [Windows용 curl](https://curl.se/windows/) 와 [Windows용 GNU base64 도구](http://gnuwin32.sourceforge.net/packages/coreutils.htm). 가장 쉬운 방법은 다음을 사용하는 것입니다. [git for Windows 설치 프로그램](https://git-scm.com/downloads) 여기에도 포함됩니다. `curl` 와 `base64` 설치 중 "명령 프롬프트에서 Git 및 선택적 Unix 도구 사용"을 선택하면 명령줄 도구를 사용할 수 있습니다. 그런 다음 위와 동일한 명령을 사용할 수 있습니다. {% endtab %} {% tab title="Python" %} **로컬 및 호스팅 이미지에서 추론** 종속성을 설치하려면, `pip install roboflow`. ``` import roboflow rf = roboflow.Roboflow(api_key="") project = rf.workspace().project("PROJECT_ID") model = project.version("VERSION").model prediction = model.predict("YOUR_IMAGE.jpg") ``` {% endtab %} {% tab title="Javascript" %} **Node.js** 이 예제에서는 POST 요청을 수행하기 위해 [axios](https://github.com/axios/axios) 를 사용하므로 먼저 다음을 실행하세요 `npm install axios` 를 설치해 종속성을 추가합니다. **로컬 이미지에서 추론** ```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); }); ``` **URL을 통해 웹의 다른 곳에 호스팅된 이미지에서 추론** ```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** 다음을 통해 실시간 온디바이스 추론을 사용할 수 있습니다 `roboflow.js`; 다음을 참조하세요 [여기 문서](/roboflow/roboflow-ko/deploy/sdks/web-browser.md). {% endtab %} {% tab title="Swift/iOS" %} **Swift** **로컬 이미지에서 추론** ```swift import UIKit // 이미지 로드 및 Base64로 변환 let image = UIImage(named: "your-image-path") // 업로드할 이미지 경로 예: image.jpg let imageData = image?.jpegData(compressionQuality: 1) let fileContent = imageData?.base64EncodedString() let postData = fileContent!.data(using: .utf8) // API_KEY, Model, 및 Model Version으로 Inference Server Request 초기화 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 요청 실행 URLSession.shared.dataTask(with: request, completionHandler: { data, response, error in // 응답을 문자열로 파싱 guard let data = data else { print(String(describing: error)) return } // 응답 문자열을 Dictionary로 변환 do { let dict = try JSONSerialization.jsonObject(with: data, options: []) as? [String: Any] } catch { print(error.localizedDescription) } // 문자열 응답 출력 print(String(data: data, encoding: .utf8)!) }).resume() ``` **Objective C** [Objective-C 스니펫을 요청하려면 여기를 클릭하세요.](https://app.roboflow.com/request/snippet.inference-objc) {% endtab %} {% tab title="Android" %} **Kotlin** **로컬 이미지에서 추론** ```kotlin import java.io.* import java.net.HttpURLConnection import java.net.URL import java.nio.charset.StandardCharsets import java.util.* fun main() { // 이미지 경로 가져오기 val filePath = System.getProperty("user.dir") + System.getProperty("file.separator") + "YOUR_IMAGE.jpg" val file = File(filePath) // Base 64 인코딩 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 = "" // API 키 val MODEL_ENDPOINT = "dataset/v" // Model endpoint 설정 (Dataset URL에서 확인) // URL 구성 val uploadURL ="https://segment.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&name=YOUR_IMAGE.jpg"; // Http 요청 var connection: HttpURLConnection? = null try { // URL에 연결 구성 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 // 요청 전송 val wr = DataOutputStream( connection.outputStream) wr.writeBytes(encodedFile) wr.close() // 응답 가져오기 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() ``` **URL을 통해 웹의 다른 곳에 호스팅된 이미지에서 추론** ```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" // 이미지 URL 바꾸기 val API_KEY = "" // API 키 val MODEL_ENDPOINT = "dataset/v" // Model endpoint 설정 // 업로드 URL val uploadURL = "https://segment.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&image=" + URLEncoder.encode(imageURL, "utf-8"); // Http 요청 var connection: HttpURLConnection? = null try { // URL에 연결 구성 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 // 요청 전송 val wr = DataOutputStream(connection.outputStream) wr.writeBytes(uploadURL) wr.close() // 응답 가져오기 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** **로컬 이미지에서 추론** ```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 { // 이미지 경로 가져오기 String filePath = System.getProperty("user.dir") + System.getProperty("file.separator") + "YOUR_IMAGE.jpg"; File file = new File(filePath); // Base 64 인코딩 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 = ""; // API 키 String MODEL_ENDPOINT = "dataset/v"; // model endpoint // URL 구성 String uploadURL = "https://segment.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&name=YOUR_IMAGE.jpg"; // Http 요청 HttpURLConnection connection = null; try { // URL에 연결 구성 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); // 요청 전송 DataOutputStream wr = new DataOutputStream(connection.getOutputStream()); wr.writeBytes(encodedFile); wr.close(); // 응답 가져오기 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(); } } } } ``` **URL을 통해 웹의 다른 곳에 호스팅된 이미지에서 추론** ```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"; // 이미지 URL 바꾸기 String API_KEY = ""; // API 키 String MODEL_ENDPOINT = "dataset/v"; // model endpoint // 업로드 URL String uploadURL = "https://segment.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&image=" + URLEncoder.encode(imageURL, StandardCharsets.UTF_8); // Http 요청 HttpURLConnection connection = null; try { // URL에 연결 구성 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); // 요청 전송 DataOutputStream wr = new DataOutputStream(connection.getOutputStream()); wr.writeBytes(uploadURL); wr.close(); // 응답 가져오기 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) PLATFORMS x64-mingw32 x86_64-linux DEPENDENCIES base64 (~> 0.1.0) cgi (~> 0.2.1) httparty (~> 0.18.1) BUNDLED WITH 2.2.15 ``` {% endcode %} **로컬 이미지에서 추론** ```ruby require 'base64' require 'httparty' encoded = Base64.encode64(File.open("YOUR_IMAGE.jpg", "rb").read) model_endpoint = "dataset/v" # Model endpoint 설정 api_key = "" # 여기에 API 키 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 ``` **URL을 통해 웹의 다른 곳에 호스팅된 이미지에서 추론** ```ruby require 'httparty' require 'cgi' model_endpoint = "dataset/v" # Model endpoint 설정 api_key = "" # 여기에 API 키 img_url = "https://i.imgur.com/PEEvqPN.png" # URL 구성 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" %} **로컬 이미지에서 추론** ```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; ?> ``` **URL을 통해 웹의 다른 곳에 호스팅된 이미지에서 추론** ```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" %} **로컬 이미지에서 추론** ```go package main import ( "bufio" "encoding/base64" "fmt" "io/ioutil" "os" "net/http" "strings" ) func main() { api_key := "" // API 키 model_endpoint := "dataset/v" // model endpoint 설정 // 디스크에서 파일 열기. f, _ := os.Open("YOUR_IMAGE.jpg") // 전체 JPG를 바이트 슬라이스로 읽기. reader := bufio.NewReader(f) content, _ := ioutil.ReadAll(reader) // base64로 인코딩. 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)) } ``` **URL을 통해 웹의 다른 곳에 호스팅된 이미지에서 추론** ```go package main import ( "fmt" "net/http" "net/url" "io/ioutil" ) func main() { api_key := "" // API 키 model_endpoint := "dataset/v" // model endpoint 설정 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" %} **로컬 이미지에서 추론** ```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 = ""; // API 키 string MODEL_ENDPOINT = "dataset/v"; // model endpoint 설정 // URL 구성 string uploadURL = "https://segment.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&name=YOUR_IMAGE.jpg"; // 서비스 요청 구성 ServicePointManager.Expect100Continue = true; ServicePointManager.SecurityProtocol = SecurityProtocolType.Tls12; // 요청 구성 WebRequest request = WebRequest.Create(uploadURL); request.Method = "POST"; request.ContentType = "application/x-www-form-urlencoded"; request.ContentLength = data.Length; // 데이터 쓰기 using (Stream stream = request.GetRequestStream()) { stream.Write(data, 0, data.Length); } // 응답 가져오기 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); } } } ``` **URL을 통해 웹의 다른 곳에 호스팅된 이미지에서 추론** ```csharp using System; using System.IO; using System.Net; using System.Web; namespace InferenceHosted { class InferenceHosted { static void Main(string[] args) { string API_KEY = ""; // API 키 string imageURL = "https://i.ibb.co/jzr27x0/YOUR-IMAGE.jpg"; string MODEL_ENDPOINT = "dataset/v"; // model endpoint 설정 // URL 구성 string uploadURL = "https://segment.roboflow.com/" + MODEL_ENDPOINT + "?api_key=" + API_KEY + "&image=" + HttpUtility.UrlEncode(imageURL); // 서비스 포인트 구성 ServicePointManager.Expect100Continue = true; ServicePointManager.SecurityProtocol = SecurityProtocolType.Tls12; // Http 요청 구성 WebRequest request = WebRequest.Create(uploadURL); request.Method = "POST"; request.ContentType = "application/x-www-form-urlencoded"; request.ContentLength = 0; // 응답 가져오기 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="Elixer" %} 요청된 대로 코드 스니펫을 추가하고 있습니다. 추론 API를 Elixir 앱에 통합하고 싶다면, [여기를 클릭하여 추천을 기록해 주세요](https://app.roboflow.com/request/snippet.upload-elixir). {% endtab %} {% endtabs %} ### 응답 객체 형식 호스팅된 API 추론 경로는 다음을 반환합니다. `JSON` 배열 형태의 예측값을 포함하는 객체입니다. 각 예측에는 다음 속성이 있습니다: * `segmentation_mask` = 각 픽셀 값이 클래스 ID에 대응하는, 입력 이미지와 동일한 크기의 base64 인코딩된 단일 채널 이미지 * `class_map` = 클래스 ID를 클래스 이름에 매핑하는 객체 * `image` = 입력 이미지의 차원을 가진 객체 * height = 입력 이미지의 높이(픽셀 수) * width = 입력 이미지의 너비(픽셀 수) ```json // JSON 객체 예시 { "segmentation_mask": "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", "class_map": { "0": " 배경", "1": " 객체" }, "image": { "width": 1232, "height": 821 } } ``` ## API Reference ## 추론 API 사용하기 `POST` `https://segment.roboflow.com/:datasetSlug/:versionNumber` base64로 인코딩된 이미지를 모델 엔드포인트에 직접 POST할 수 있습니다. 또는 이미지가 이미 다른 곳에 호스팅되어 있다면 `image` 쿼리 문자열의 매개변수로 URL을 전달할 수도 있습니다. #### 경로 매개변수 | 이름 | 유형 | 설명 | | ----------- | ------ | ---------------------------------------------------------------------------------------------------------------------------------------- | | datasetSlug | string | 데이터셋 이름의 URL 안전 버전입니다. 데이터셋의 학습 결과 섹션에서 모델을 학습한 후의 버전 URL에서 확인하거나, 메인 프로젝트 보기의 URL을 살펴보거나, "Get curl command" 버튼을 클릭하면 웹 UI에서 찾을 수 있습니다. | | version | number | 데이터셋의 버전을 식별하는 버전 번호입니다. | #### 쿼리 매개변수 | 이름 | 유형 | 설명 | | ---------- | ------ | ---------------------------------------------------------------------------------------------------------------------------------------------------- | | image | string |

추가할 이미지의 URL입니다. 이미지가 다른 곳에 호스팅되어 있는 경우 사용하세요. (요청 본문에 base64로 인코딩된 이미지를 POST하지 않을 때 필요합니다.)

참고: URL 인코딩하는 것을 잊지 마세요.

| | confidence | number |

반환되는 예측에 대한 임계값으로, 범위는 0\~100입니다. 숫자가 낮을수록 더 많은 예측이 반환됩니다. 숫자가 높을수록 더 적은 고신뢰 예측이 반환됩니다.

기본값: 50

| | api\_key | string | API 키(워크스페이스 API 설정 페이지에서 획득) | #### 요청 본문 | 이름 | 유형 | 설명 | | -- | ------ | --------------------------------------------------------- | | | string | base64로 인코딩된 이미지입니다. (쿼리 매개변수에 이미지 URL을 전달하지 않을 때 필요합니다). | {% tabs %} {% tab title="200 JSON 형식 예측. (x,y)는 박스의" %} ``` { "segmentation_mask": "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", "class_map": { "0": " 배경", "1": " 객체" }, "image": { "width": 1232, "height": 821 } } ``` {% endtab %} {% tab title="403 api\_key가 모델에 접근하도록 권한이 없는 경우입니다." %} ``` { "Message": "사용자는 이 리소스에 접근할 권한이 없습니다" } ``` {% endtab %} {% endtabs %} --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.roboflow.com/roboflow/roboflow-ko/deploy/serverless/instance-segmentation/semantic-segmentation.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.