app.py

import gradio as gr
from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation
from PIL import Image
import numpy as np


# 모델과 특징 추출기 불러오기
feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b0-finetuned-cityscapes-512-1024")
model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-cityscapes-512-1024")

def segment_image(image):
    # 이미지를 처리하고 모델에 전달하기
    inputs = feature_extractor(images=image, return_tensors="pt")
    outputs = model(**inputs)
    logits = outputs.logits

    # 결과 처리 및 NumPy 배열로 변환
    result = logits.argmax(dim=1)[0]
    result = result.cpu().detach().numpy()

    result_image = Image.fromarray(result.astype(np.uint8), mode="P").convert("RGB")

    # 결과 배열 반환
    return result_image

# Gradio 인터페이스 정의
iface = gr.Interface(
    fn=segment_image,
    inputs=gr.inputs.Image(type='pil'),
    examples = ['image1.jpg', 'image2.jpg', 'image3.jpg'],
    outputs= 'image',
    title="SegFormer Image Segmentation",
    description="Upload an image to segment it using the SegFormer model trained on Cityscapes dataset."
)

# 인터페이스 실행
iface.launch()