Create aapp.py

aapp.py

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+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()