theme

app.py

@@ -1,6 +1,4 @@
 import gradio as gr
-
-
 from matplotlib import gridspec
 import matplotlib.pyplot as plt
 import numpy as np
@@ -16,28 +14,26 @@ model = TFSegformerForSemanticSegmentation.from_pretrained(
 )
 
 def ade_palette():
-
     return [
-        [204, 87, 92],  # road (Reddish)
+        [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)
+        [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)
+        [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)
-
+        [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 = []
@@ -77,7 +73,14 @@ def draw_plot(pred_img, seg):
     ax.tick_params(width=0.0, labelsize=25)
     return fig
 
-def sepia(input_img):
+def sepia(input_img, *label_buttons):
+    selected_color = None
+    for label, button_state in zip(labels_list, label_buttons):
+        if button_state:
+            label_index = labels_list.index(label)
+            selected_color = colormap[label_index]
+            break
+
     input_img = Image.fromarray(input_img)
 
     inputs = feature_extractor(images=input_img, return_tensors="tf")
@@ -87,38 +90,43 @@ def sepia(input_img):
     logits = tf.transpose(logits, [0, 2, 3, 1])
     logits = tf.image.resize(
         logits, input_img.size[::-1]
-    )  # We reverse the shape of `image` because `image.size` returns width and height.
+    )
     seg = tf.math.argmax(logits, axis=-1)[0]
 
     color_seg = np.zeros(
         (seg.shape[0], seg.shape[1], 3), dtype=np.uint8
-    )  # height, width, 3
-    for label, color in enumerate(colormap):
-        color_seg[seg.numpy() == label, :] = color
+    )
+    if selected_color:
+        label = colormap.index(selected_color)
+        color_seg[seg.numpy() == label, :] = selected_color
 
-    # Show image + mask
     pred_img = np.array(input_img) * 0.5 + color_seg * 0.5
     pred_img = pred_img.astype(np.uint8)
 
     fig = draw_plot(pred_img, seg)
     return fig
 
-demo = gr.Interface(fn=sepia,
-                    inputs=gr.Image(shape=(564,846)),
-                    outputs=['plot'],
-                    live=True,
-                    examples=["city1.jpg","city2.jpg","city3.jpg"],
-                    allow_flagging='never',
-                    title="This is a machine learning activity project at Kyunggi University.",
-                    theme="darkpeach",
-                    css="""
-                        body {
-                            background-color: dark;
-                            color: white;  /* 폰트 색상 수정 */
-                            font-family: Arial, sans-serif;  /* 폰트 패밀리 수정 */
-                        }
-                        """
-                    )
-
-
-demo.launch()
+# 라벨 버튼 생성
+label_buttons = [gr.Button(label) for label in labels_list]
+
+# Gradio 인터페이스 생성
+iface = gr.Interface(
+    fn=sepia,
+    inputs=[gr.Image(shape=(564, 846))] + label_buttons,
+    outputs="plot",
+    live=True,
+    examples=["city1.jpg", "city2.jpg", "city3.jpg"],
+    allow_flagging='never',
+    title="This is a machine learning activity project at Kyunggi University.",
+    theme="darkpeach",
+    css="""
+    body{
+        background-color: dark;
+        color: white;
+        font-family: Arial, sans-serif;
+    }
+    """
+)
+
+# Gradio 인터페이스 시작
+iface.launch()