annotated test

app.py

@@ -1,11 +1,10 @@
 import gradio as gr
 import random
-
-from matplotlib import gridspec
-import matplotlib.pyplot as plt
 import numpy as np
 from PIL import Image
 import tensorflow as tf
+from matplotlib import gridspec
+import matplotlib.pyplot as plt
 from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation
 
 feature_extractor = SegformerFeatureExtractor.from_pretrained(
@@ -15,8 +14,8 @@ model = TFSegformerForSemanticSegmentation.from_pretrained(
     "nvidia/segformer-b5-finetuned-cityscapes-1024-1024"
 )
 
-def ade_palette():
 
+def ade_palette():
     return [
         [204, 87, 92],  # road (Reddish)
         [112, 185, 212],  # sidewalk (Blue)
@@ -37,17 +36,18 @@ def ade_palette():
         [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())
 
+
 def label_to_color_image(label):
     if label.ndim != 2:
         raise ValueError("Expect 2-D input label")
@@ -56,14 +56,15 @@ 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)
@@ -77,6 +78,7 @@ def draw_plot(pred_img, seg):
     ax.tick_params(width=0.0, labelsize=25)
     return fig
 
+
 def sepia(input_img):
     input_img = Image.fromarray(input_img)
 
@@ -85,9 +87,7 @@ def sepia(input_img):
     logits = outputs.logits
 
     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.
+    logits = tf.image.resize(logits, input_img.size[::-1])
     seg = tf.math.argmax(logits, axis=-1)[0]
 
     color_seg = np.zeros(
@@ -96,7 +96,6 @@ def sepia(input_img):
     for label, color in enumerate(colormap):
         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)
 
@@ -104,25 +103,75 @@ def sepia(input_img):
     return fig
 
 
+with gr.Blocks() as demo:
+    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")
 
-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 */
-                        }
-                        """
+    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"
+            }
+        )
 
-                    )
+    section_btn = gr.Button("Identify Sections")
+    selected_section = gr.Textbox(label="Selected Section")
 
 
-demo.launch()
+    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), labels_list[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, labels_list[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 labels_list[evt.index]
+
+
+    img_output.select(select_section, None, selected_section)
+
+demo.launch()