Update app.py

app.py

@@ -1,5 +1,4 @@
 import gradio as gr
-from skimage.restoration import inpaint
 from skimage import color
 import cv2
 import numpy as np
@@ -16,37 +15,33 @@ def image_segmentation(image, compactness):
     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):
-    try:
-        # 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)
-        elif image.shape[-1] == 3:
-            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-
-        # 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)
-
-        # 如果遮罩無效，返回遮罩檢查圖片
-        if not mask.any():
-            error_image = np.zeros_like(image)
-            cv2.putText(error_image, "Invalid Mask! Adjust Threshold", (10, 50),
-                        cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
-            return error_image
-
-        # 使用 biharmonic 方法修復圖像
-        inpainted = inpaint.inpaint_biharmonic(image, mask, multichannel=True)
-        return inpainted
-    except Exception as e:
-        # 如果發生錯誤，返回錯誤圖片
-        error_image = np.zeros((image.shape[0], image.shape[1], 3), dtype=np.uint8)
-        cv2.putText(error_image, f"Error: {str(e)}", (10, 50),
-                    cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
-        return error_image
+# 色彩濾鏡函式
+def color_filter(image, color_choice):
+    # 將圖像轉換為 HSV 格式
+    hsv_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
+    
+    # 定義顏色範圍（H: 色調，S: 飽和度，V: 明度）
+    if color_choice == "紅色":
+        lower_bound = np.array([0, 50, 50])  # 紅色範圍
+        upper_bound = np.array([10, 255, 255])
+    elif color_choice == "綠色":
+        lower_bound = np.array([40, 50, 50])  # 綠色範圍
+        upper_bound = np.array([80, 255, 255])
+    elif color_choice == "藍色":
+        lower_bound = np.array([100, 50, 50])  # 藍色範圍
+        upper_bound = np.array([140, 255, 255])
+    else:
+        return "未支援的顏色範圍選擇"
+    
+    # 創建遮罩，過濾選定的顏色範圍
+    mask = cv2.inRange(hsv_image, lower_bound, upper_bound)
+    
+    # 保留選定顏色，將其他部分設為灰階
+    result = image.copy()
+    gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+    result[mask == 0] = np.stack([gray_image] * 3, axis=-1)[mask == 0]
+    
+    return result
 
 # 主應用程式
 def app():
@@ -81,17 +76,17 @@ def app():
                 outputs=seg_result
             )
         
-        with gr.Tab("圖像修復"):
+        with gr.Tab("色彩濾鏡"):
             with gr.Row():
                 input_image = gr.Image(label="輸入圖片")
-                inpaint_result = gr.Image(label="修復結果或錯誤訊息")
+                filter_result = gr.Image(label="濾鏡結果")
             with gr.Row():
-                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(
-                fn=inpaint_image, 
-                inputs=[input_image, mask_threshold], 
-                outputs=inpaint_result  # 錯誤訊息或結果會顯示在此
+                color_choice = gr.Radio(["紅色", "綠色", "藍色"], label="選擇顏色", value="紅色")
+            filter_button = gr.Button("應用濾鏡")
+            filter_button.click(
+                fn=color_filter, 
+                inputs=[input_image, color_choice], 
+                outputs=filter_result
             )
     
     return demo