app.py

import gradio as gr
import numpy as np
from PIL import Image
import tensorflow as tf
from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation

# Segformer 모델과 관련 객체를 초기화
feature_extractor = SegformerFeatureExtractor.from_pretrained("nickmuchi/segformer-b4-finetuned-segments-sidewalk")
model = TFSegformerForSemanticSegmentation.from_pretrained("nickmuchi/segformer-b4-finetuned-segments-sidewalk", from_pt=True)

def perform_semantic_segmentation(input_img):
    input_img = Image.fromarray(input_img)

    # 이미지를 처리하고 모델에 전달
    inputs = feature_extractor(images=input_img, return_tensors="tf")
    outputs = model(**inputs)
    logits = outputs.logits

    # 모델 출력을 처리하여 시맨틱 분할 결과를 얻음
    logits = tf.transpose(logits, [0, 2, 3, 1])
    logits = tf.image.resize(logits, input_img.size[::-1])
    seg = tf.math.argmax(logits, axis=-1)[0]

    return input_img, seg.numpy()

def segformer_interface(input_image):
    original_image, segmentation_map = perform_semantic_segmentation(input_image)
    return original_image, segmentation_map

# Gradio 데모 구성
demo = gr.Interface(
    fn=segformer_interface,
    inputs=gr.Image(shape=(400, 600)),
    outputs=[gr.Image(type="plot"), gr.Image(type="plot")],  # 원본 이미지 및 시맨틱 분할 맵을 출력
    examples=["side-1.jpg", "side-2.jpg", "side-3.jpg"],
    allow_flagging='never'
)

demo.launch()