example radio

app.py

@@ -1,39 +1,66 @@
 import gradio as gr
-import tensorflow as tf
+import random
+
+from matplotlib import gridspec
+import matplotlib.pyplot as plt
+import numpy as np
 from PIL import Image
-import requests
-
-# 모델 로드
-model = tf.saved_model.load("nvidia_segformer_b5_finetuned_cityscapes_1024")
-
-# 레이블 및 색상 정의
-label_colors = {
-    "road": [204, 87, 92],
-    "sidewalk": [112, 185, 212],
-    "building": [196, 160, 122],
-    "wall": [106, 135, 242],
-    "fence": [91, 192, 222],
-    "pole": [255, 192, 203],
-    "traffic_light": [176, 224, 230],
-    "traffic_sign": [222, 49, 99],
-    "vegetation": [139, 69, 19],
-    "terrain": [255, 0, 0],
-    "sky": [0, 0, 255],
-    "person": [255, 228, 181],
-    "rider": [128, 0, 0],
-    "car": [0, 128, 0],
-    "truck": [255, 99, 71],
-    "bus": [0, 255, 0],
-    "train": [128, 0, 128],
-    "motorcycle": [255, 255, 0],
-    "bicycle": [128, 0, 128]
-}
+import tensorflow as tf
+from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation
+
+feature_extractor = SegformerFeatureExtractor.from_pretrained(
+    "nvidia/segformer-b5-finetuned-cityscapes-1024-1024"
+)
+model = TFSegformerForSemanticSegmentation.from_pretrained(
+    "nvidia/segformer-b5-finetuned-cityscapes-1024-1024"
+)
+
+def palette():
+
+    return [
+        [204, 87, 92],  # road (Reddish)
+        [112, 185, 212],  # sidewalk (Blue)
+        [196, 160, 122],  # building (Brown)
+        [106, 135, 242],  # wall (Light Blue)
+        [91, 192, 222],  # fence (Turquoise)
+        [255, 192, 203],  # pole (Pink)
+        [176, 224, 230],  # traffic light (Light Blue)
+        [222, 49, 99],  # traffic sign (Red)
+        [139, 69, 19],  # vegetation (Brown)
+        [255, 0, 0],  # terrain (Red)
+        [0, 0, 255],  # sky (Blue)
+        [255, 228, 181],  # person (Peach)
+        [128, 0, 0],  # rider (Maroon)
+        [0, 128, 0],  # car (Green)
+        [255, 99, 71],  # truck (Tomato)
+        [0, 255, 0],  # bus (Lime)
+        [128, 0, 128],  # train (Purple)
+        [255, 255, 0],  # motorcycle (Yellow)
+        [128, 0, 128]  # bicycle (Purple)
+
+    ]
+
+labels_list = []
+
+with open(r'labels.txt', 'r') as fp:
+    for line in fp:
+        labels_list.append(line[:-1])
+colormap = np.asarray(palette())
+
+def label_to_color_image(label):
+    if label.ndim != 2:
+        raise ValueError("Expect 2-D input label")
+
+    if np.max(label) >= len(colormap):
+        raise ValueError("label value too large.")
+    return colormap[label]
 
 # Gradio 인터페이스 정의
 iface = gr.Interface(
     fn=lambda image: predict_segmentation(image, model),
     inputs="image",
-    outputs="image"
+    outputs="image",
+    examples=["city1.jpg","city2.jpg","city3.jpg"],
 )
 iface.launch()