annotated test

app.py

@@ -1,4 +1,7 @@
 import gradio as gr
+import random
+
+from matplotlib import gridspec
 import matplotlib.pyplot as plt
 import numpy as np
 from PIL import Image
@@ -13,33 +16,35 @@ 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 = [
-    "road", "sidewalk", "building", "wall", "fence", "pole", "traffic light",
-    "traffic sign", "vegetation", "terrain", "sky", "person", "rider", "car",
-    "truck", "bus", "train", "motorcycle", "bicycle"
-]
+labels_list = []
+
+with open(r'labels.txt', 'r') as fp:
+    for line in fp:
+        labels_list.append(line[:-1])
 
 colormap = np.asarray(ade_palette())
 
@@ -51,7 +56,28 @@ def label_to_color_image(label):
         raise ValueError("label value too large.")
     return colormap[label]
 
-def apply_sepia(input_img, selected_labels):
+def draw_plot(pred_img, seg):
+    fig = plt.figure(figsize=(20, 15))
+
+    grid_spec = gridspec.GridSpec(1, 2, width_ratios=[6, 1])
+
+    plt.subplot(grid_spec[0])
+    plt.imshow(pred_img)
+    plt.axis('off')
+    LABEL_NAMES = np.asarray(labels_list)
+    FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1)
+    FULL_COLOR_MAP = label_to_color_image(FULL_LABEL_MAP)
+
+    unique_labels = np.unique(seg.numpy().astype("uint8"))
+    ax = plt.subplot(grid_spec[1])
+    plt.imshow(FULL_COLOR_MAP[unique_labels].astype(np.uint8), interpolation="nearest")
+    ax.yaxis.tick_right()
+    plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels])
+    plt.xticks([], [])
+    ax.tick_params(width=0.0, labelsize=25)
+    return fig
+
+def sepia(input_img):
     input_img = Image.fromarray(input_img)
 
     inputs = feature_extractor(images=input_img, return_tensors="tf")
@@ -67,45 +93,56 @@ def apply_sepia(input_img, selected_labels):
     color_seg = np.zeros(
         (seg.shape[0], seg.shape[1], 3), dtype=np.uint8
     )  # height, width, 3
-
     for label, color in enumerate(colormap):
-        if labels_list[label] in selected_labels:
-            color_seg[seg.numpy() == label, :] = color
+        color_seg[seg.numpy() == label, :] = color
 
     # Show image + mask
     pred_img = np.array(input_img) * 0.5 + color_seg * 0.5
     pred_img = pred_img.astype(np.uint8)
 
-    fig, ax = plt.subplots()
-    ax.imshow(pred_img)
-    ax.axis('off')
+    fig = draw_plot(pred_img, seg)
     return fig
 
-# Gradio Interface
-iface = gr.Interface(
-    fn=apply_sepia,
-    inputs=[
-        gr.Image(shape=(564, 846)),
-        gr.CheckboxGroup(
-            choices=[(label, label) for label in labels_list],
-            default=[labels_list[0]],
-            label="Select Labels"
-        )
-    ],
-    outputs=gr.AnnotatedImage(type="plot", loc="center", scale=True),
-    live=True,
-    examples=["city1.jpg", "city2.jpg", "city3.jpg"],
-    allow_flagging='never',
-    title="Semantic Segmentation with Color Highlighting",
-    description="Apply sepia effect to the image with color highlighting for selected labels.",
-    theme="darkpeach",
-    css="""
-    body {
-        background-color: dark;
-        color: white;
-        font-family: Arial, sans-serif;
-    }
-    """
-)
 
-iface.launch()
+demo = gr.Interface(fn=sepia,
+                    inputs=gr.Image(shape=(564,846)),
+                    outputs=['plot',gr.AnnotatedImage(
+                    color_map={
+                "road": "#CC575C",
+                "sidewalk": "#70B9D4",
+                "building": "#C4A07A",
+                "wall": "#6A87F2",
+                "fence": "#5BC0DE",
+                "pole": "#FFC0CB",
+                "traffic light": "#B0E0E6",
+                "traffic sign": "#DE3163",
+                "vegetation": "#8B4513",
+                "terrain": "#FF0000",
+                "sky": "#0000FF",
+                "person": "#FFE4B5",
+                "rider": "#800000",
+                "car": "#008000",
+                "truck": "#FF6347",
+                "bus": "#00FF00",
+                "train": "#800080",
+                "motorcycle": "#FFFF00",
+                "bicycle": "#800080"}
+        )],
+                    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()
+