block test

app.py

@@ -1,10 +1,11 @@
 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(
@@ -39,11 +40,11 @@ def ade_palette():
     ]
 
 
-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())
 
@@ -103,75 +104,16 @@ def sepia(input_img):
     return fig
 
 
+# Gradio Blocks로 변환
 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")
-
-    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")
-
-
-    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()
+    img_input = gr.Image(shape=(564, 846))
+    img_output = gr.Image()
+
+    # 사용자 입력을 받는 부분 추가
+    input_img = gr.Image(shape=(564, 846), source=img_input)
+    input_img.click(sepia, img_input, img_output)
+
+    # 사용자 입력에 대한 결과를 출력
+    img_output.source(sepia, img_input)
+
+demo.launch()