Update app.py

app.py

@@ -1,4 +1,5 @@
 import gradio as gr
+from skimage import color
 import cv2
 import numpy as np
 
@@ -11,22 +12,19 @@ def edge_detection(image, threshold1, threshold2):
 # 圖像分割函式
 def image_segmentation(image, compactness):
     from skimage.segmentation import slic
-    from skimage import color
     segments = slic(image, compactness=compactness, n_segments=200)
     return color.label2rgb(segments, image, kind='avg')
 
 # 顏色範圍調整函式
 def adjust_color(image, r_min, r_max, g_min, g_max, b_min, b_max):
-    # 確保圖片格式為 RGB
     if image.shape[-1] == 4:  # 如果圖片有 alpha 通道
         image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB)
     
-    # 調整 R、G、B 通道的範圍
-    image = image.astype(np.float32)  # 將圖像轉為浮點數進行處理
+    image = image.astype(np.float32)
     image[:, :, 0] = np.clip(image[:, :, 0], r_min, r_max)  # 調整 R 通道
     image[:, :, 1] = np.clip(image[:, :, 1], g_min, g_max)  # 調整 G 通道
     image[:, :, 2] = np.clip(image[:, :, 2], b_min, b_max)  # 調整 B 通道
-    image = image.astype(np.uint8)  # 轉回整數型圖像
+    image = image.astype(np.uint8)
     
     return image
 
@@ -34,11 +32,12 @@ def adjust_color(image, r_min, r_max, g_min, g_max, b_min, b_max):
 def app():
     with gr.Blocks() as demo:
         gr.Markdown("# 影像處理功能展示")
-        gr.Markdown("本應用程式展示了使用 OpenCV 實現的影像處理功能，包括邊緣檢測、圖像分割以及動態調整 RGB 範圍的功能。")
+        gr.Markdown("本應用程式展示了邊緣檢測、圖像分割和顏色範圍調整功能，每個功能附帶範例圖片和測試結果。")
         
+        # 邊緣檢測功能
         with gr.Tab("邊緣檢測"):
             with gr.Row():
-                input_image = gr.Image(label="輸入圖片")
+                input_image = gr.Image(label="輸入圖片", value="simple_plain_pikachu_by_titanplakinside_dexx027.png")  # 範例圖片
                 edge_result = gr.Image(label="邊緣檢測結果")
             with gr.Row():
                 threshold1 = gr.Slider(0, 255, value=100, step=1, label="閾值1")
@@ -49,10 +48,18 @@ def app():
                 inputs=[input_image, threshold1, threshold2], 
                 outputs=edge_result
             )
-        
+            gr.Examples(
+                examples=["simple_plain_pikachu_by_titanplakinside_dexx027.png"], 
+                inputs=[input_image], 
+                outputs=[edge_result], 
+                fn=lambda x: edge_detection(x, 100, 200),
+                label="測試範例"
+            )
+
+        # 圖像分割功能
         with gr.Tab("圖像分割"):
             with gr.Row():
-                input_image = gr.Image(label="輸入圖片")
+                input_image = gr.Image(label="輸入圖片", value="simple_plain_pikachu_by_titanplakinside_dexx027.png")  # 範例圖片
                 seg_result = gr.Image(label="分割結果")
             with gr.Row():
                 compactness = gr.Slider(0.1, 100, value=10, step=0.1, label="分割緊湊度")
@@ -62,10 +69,18 @@ def app():
                 inputs=[input_image, compactness], 
                 outputs=seg_result
             )
+            gr.Examples(
+                examples=["simple_plain_pikachu_by_titanplakinside_dexx027.png"], 
+                inputs=[input_image], 
+                outputs=[seg_result], 
+                fn=lambda x: image_segmentation(x, 10),
+                label="測試範例"
+            )
         
+        # 顏色範圍調整功能
         with gr.Tab("顏色範圍調整"):
             with gr.Row():
-                input_image = gr.Image(label="輸入圖片")
+                input_image = gr.Image(label="輸入圖片", value="simple_plain_pikachu_by_titanplakinside_dexx027.png")  # 範例圖片
                 adjusted_result = gr.Image(label="調整後的圖片")
             with gr.Row():
                 r_min = gr.Slider(0, 255, value=0, step=1, label="R 最小值")
@@ -80,6 +95,13 @@ def app():
                 inputs=[input_image, r_min, r_max, g_min, g_max, b_min, b_max],
                 outputs=adjusted_result
             )
+            gr.Examples(
+                examples=["simple_plain_pikachu_by_titanplakinside_dexx027.png"], 
+                inputs=[input_image], 
+                outputs=[adjusted_result], 
+                fn=lambda x: adjust_color(x, 50, 200, 50, 200, 50, 200),
+                label="測試範例"
+            )
     
     return demo