> 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-ko/deploy/supported-models/roboflow-2.md).

# Roboflow 2.0

Roboflow 2.0은 DeepLabv3 기반 시맨틱 세그멘테이션 모델입니다. Roboflow 플랫폼에서 Roboflow 2.0 모델을 학습하고, 당사의 [Serverless Hosted API](/roboflow/roboflow-ko/deploy/serverless-hosted-api-v2.md).

셀프 호스팅 배포는 다음을 참조하세요 [Roboflow Inference](https://inference.roboflow.com/).

{% hint style="info" %}
Roboflow 2.0에는 공개 기본 모델이 없습니다. 학습한 자체 모델은 해당 `project/version` 귀하의 [Roboflow Project](https://app.roboflow.com/).
{% endhint %}

## 코드 샘플

설치하세요. [Inference SDK](https://inference.roboflow.com/) 및 [supervision](https://supervision.roboflow.com/):

```bash
pip install inference-sdk supervision
```

학습한 Roboflow 2.0 시맨틱 세그멘테이션 모델에 대해 inference를 실행하고, 픽셀별 클래스 맵을 디코딩한 뒤, 주석이 추가된 PNG를 작성합니다. 다음을 바꾸세요 `your-project/1` 모델 URL과 버전으로. 귀하의 [Roboflow API Key](https://app.roboflow.com/settings/api) 를 `API_KEY` 환경 변수.

응답에는 다음이 포함됩니다: `segmentation_mask` (base64로 인코딩된 그레이스케일 PNG로, 각 픽셀 값이 클래스 ID이며 `0` 0은 배경)와 `class_map` 클래스 ID를 클래스 이름에 매핑하는 맵입니다. 스크립트는 이를 하나의 `sv.Detections` 클래스당 한 행씩으로 분할하므로 `sv.MaskAnnotator` 가 소스 이미지 위에 마스크를 오버레이할 수 있습니다.

```python
import base64
import os
import urllib.request

import cv2
import numpy as np
import supervision as sv
from inference_sdk import InferenceHTTPClient

image_url = "https://storage.googleapis.com/com-roboflow-marketing/notebooks/examples/bicycle.png"
image_path = "bicycle.png"
urllib.request.urlretrieve(image_url, image_path)

image = cv2.imread(image_path)
OUTPUT_PATH = "annotated.png"

client = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key=os.getenv("API_KEY"),
)
result = client.infer(image, model_id="your-project/1")
predictions = result["predictions"]

mask_bytes = base64.b64decode(predictions["segmentation_mask"])
class_map = predictions.get("class_map", {})
class_mask = cv2.imdecode(np.frombuffer(mask_bytes, np.uint8), cv2.IMREAD_GRAYSCALE)
class_mask = cv2.resize(class_mask, (image.shape[1], image.shape[0]), interpolation=cv2.INTER_NEAREST)

class_ids = [cid for cid in np.unique(class_mask).tolist() if cid != 0]
if class_ids:
    masks, xyxy, names = [], [], []
    for cid in class_ids:
        binary = class_mask == cid
        rows = np.where(np.any(binary, axis=1))[0]
        cols = np.where(np.any(binary, axis=0))[0]
        xyxy.append([cols[0], rows[0], cols[-1], rows[-1]])
        masks.append(binary)
        names.append(class_map.get(str(cid), str(cid)))

    detections = sv.Detections(
        xyxy=np.array(xyxy, dtype=np.float64),
        mask=np.array(masks),
        class_id=np.array(class_ids),
        data={"class_name": np.array(names)},
    )
    annotated = sv.MaskAnnotator().annotate(scene=image.copy(), detections=detections)
    annotated = sv.LabelAnnotator().annotate(scene=annotated, detections=detections)
else:
    annotated = image

cv2.imwrite(OUTPUT_PATH, annotated)
print(f"저장됨 {OUTPUT_PATH}")
```

{% hint style="info" %}
설정하세요 `api_url` 를 배포 대상에 맞게:

* `https://serverless.roboflow.com` Serverless Hosted API용.
* `http://localhost:9001` 로컬 [Inference](https://inference.roboflow.com/) 서버용.
* 귀하의 [Dedicated Deployment](/roboflow/roboflow-ko/deploy/dedicated-deployments.md) 개인 엔드포인트용 URL.
  {% endhint %}


---

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