Update app.py

app.py

@@ -4,53 +4,68 @@ import cv2
 import numpy as np
 from skimage.restoration import inpaint
 
+# 邊緣檢測函式
 def edge_detection(image, threshold1, threshold2):
+    # 轉為灰階圖片
     gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+    # 使用 Canny 邊緣檢測
     edges = cv2.Canny(gray_image, threshold1, threshold2)
     return edges
 
+# 圖像分割函式
 def image_segmentation(image, compactness):
     from skimage.segmentation import slic
+    # 使用 SLIC 方法進行圖像分割
     segments = slic(image, compactness=compactness, n_segments=200)
     return color.label2rgb(segments, image, kind='avg')
 
+# 圖像修復函式
 def inpaint_image(image, mask_threshold):
+    # 轉為灰階圖片
     gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+    # 根據遮罩閾值生成遮罩
     _, mask = cv2.threshold(gray_image, mask_threshold, 255, cv2.THRESH_BINARY)
     mask = mask.astype(bool)
+    # 使用 biharmonic 方法修復圖像
     inpainted = inpaint.inpaint_biharmonic(image, mask, multichannel=True)
     return inpainted
 
+# 主應用程式
 def app():
     with gr.Blocks() as demo:
-        gr.Markdown("# 计算机视觉功能展示")
-        gr.Markdown("使用 Scikit-Image 和 OpenCV 实现的一些基本功能")
-        with gr.Tab("边缘检测"):
+        gr.Markdown("# 影像處理功能展示")
+        gr.Markdown("本應用程式展示了使用 Scikit-Image 與 OpenCV 實現的基本影像處理功能。")
+        
+        with gr.Tab("邊緣檢測"):
             with gr.Row():
-                input_image = gr.Image(label="输入图像")
-                edge_result = gr.Image(label="检测结果")
+                input_image = gr.Image(label="輸入圖片")
+                edge_result = gr.Image(label="邊緣檢測結果")
             with gr.Row():
-                threshold1 = gr.Slider(0, 255, value=100, step=1, label="阈值1")
-                threshold2 = gr.Slider(0, 255, value=200, step=1, label="阈值2")
-            edge_button = gr.Button("运行边缘检测")
+                threshold1 = gr.Slider(0, 255, value=100, step=1, label="閾值1")
+                threshold2 = gr.Slider(0, 255, value=200, step=1, label="閾值2")
+            edge_button = gr.Button("執行邊緣檢測")
             edge_button.click(edge_detection, inputs=[input_image, threshold1, threshold2], outputs=edge_result)
-        with gr.Tab("图像分割"):
+        
+        with gr.Tab("圖像分割"):
             with gr.Row():
-                input_image = gr.Image(label="输入图像")
-                seg_result = gr.Image(label="分割结果")
+                input_image = gr.Image(label="輸入圖片")
+                seg_result = gr.Image(label="分割結果")
             with gr.Row():
-                compactness = gr.Slider(0.1, 100, value=10, step=0.1, label="紧凑度")
-            seg_button = gr.Button("运行图像分割")
+                compactness = gr.Slider(0.1, 100, value=10, step=0.1, label="分割緊湊度")
+            seg_button = gr.Button("執行圖像分割")
             seg_button.click(image_segmentation, inputs=[input_image, compactness], outputs=seg_result)
-        with gr.Tab("图像修复"):
+        
+        with gr.Tab("圖像修復"):
             with gr.Row():
-                input_image = gr.Image(label="输入图像")
-                inpaint_result = gr.Image(label="修复结果")
+                input_image = gr.Image(label="輸入圖片")
+                inpaint_result = gr.Image(label="修復結果")
             with gr.Row():
-                mask_threshold = gr.Slider(0, 255, value=128, step=1, label="遮罩阈值")
-            inpaint_button = gr.Button("运行图像修复")
+                mask_threshold = gr.Slider(0, 255, value=128, step=1, label="遮罩閾值")
+            inpaint_button = gr.Button("執行圖像修復")
             inpaint_button.click(inpaint_image, inputs=[input_image, mask_threshold], outputs=inpaint_result)
+    
     return demo
 
+# 啟動應用程式
 if __name__ == "__main__":
     app().launch()