annotated test

app.py

@@ -1,7 +1,4 @@
 import gradio as gr
-import random
-
-from matplotlib import gridspec
 import matplotlib.pyplot as plt
 import numpy as np
 from PIL import Image
@@ -16,35 +13,33 @@ 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 = []
-
-with open(r'labels.txt', 'r') as fp:
-    for line in fp:
-        labels_list.append(line[:-1])
+labels_list = [
+    "road", "sidewalk", "building", "wall", "fence", "pole", "traffic light",
+    "traffic sign", "vegetation", "terrain", "sky", "person", "rider", "car",
+    "truck", "bus", "train", "motorcycle", "bicycle"
+]
 
 colormap = np.asarray(ade_palette())
 
@@ -56,28 +51,7 @@ def label_to_color_image(label):
         raise ValueError("label value too large.")
     return colormap[label]
 
-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):
+def apply_sepia(input_img, selected_labels):
     input_img = Image.fromarray(input_img)
 
     inputs = feature_extractor(images=input_img, return_tensors="tf")
@@ -93,123 +67,45 @@ def sepia(input_img):
     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 labels_list[label] in selected_labels:
+            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 = draw_plot(pred_img, seg)
+    fig, ax = plt.subplots()
+    ax.imshow(pred_img)
+    ax.axis('off')
     return fig
 
-
-with gr.Blocks() as demo:
-    section_labels = [
-        "road",
-        "sidewalk",
-        "building",
-        "wall",
-        "fence",
-        "pole",
-        "traffic light",
-        "traffic sign",
-        "vegetation",
-        "terrain",
-        "sky",
-        "person",
-        "rider",
-        "car",
-        "truck",
-        "bus",
-        "train",
-        "motorcycle",
-        "bicycle"
-    ]
-
-    with gr.Row():
-        num_boxes = gr.Slider(1, 1, 1, step=0, label="Number of boxes")
-        num_segments = gr.Slider(0, 19, 1, step=1, label="Number of segments")
-
-    with gr.Row():
-        img_input = gr.Image()
-        img_output = 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"}
+# 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;
+    }
+    """
+)
 
-    section_btn = gr.Button("Identify Sections")
-    selected_section = gr.Textbox(label="Selected Section")
-
-
-    def section(img, num_boxes, num_segments):
-        sections = []
-
-        for a in range(num_boxes):
-            x = random.randint(0, img.shape[1])
-            y = random.randint(0, img.shape[0])
-            w = random.randint(0, img.shape[1] - x)
-            h = random.randint(0, img.shape[0] - y)
-            sections.append(((x, y, x + w, y + h), section_labels[a]))
-        for b in range(num_segments):
-            x = random.randint(0, img.shape[1])
-            y = random.randint(0, img.shape[0])
-            r = random.randint(0, min(x, y, img.shape[1] - x, img.shape[0] - y))
-            mask = np.zeros(img.shape[:2])
-            for i in range(img.shape[0]):
-                for j in range(img.shape[1]):
-                    dist_square = (i - y) ** 2 + (j - x) ** 2
-                    if dist_square < r ** 2:
-                        mask[i, j] = round((r ** 2 - dist_square) / r ** 2 * 4) / 4
-            sections.append((mask, section_labels[b + num_boxes]))
-        return (img, sections)
-
-
-    section_btn.click(section, [img_input, num_boxes, num_segments], img_output)
-
-
-    def select_section(evt: gr.SelectData):
-        return section_labels[evt.index]
-
-
-    img_output.select(select_section, None, selected_section)
-
-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()
-
+iface.launch()