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app.py

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+import gradio as gr
+import numpy as np
+import cv2
+from PIL import Image
+import io
+
+class AutoWhiteBalance:
+    """自動白平衡處理類"""
+    
+    def __init__(self):
+        self.methods = {
+            "灰世界算法 (Gray World)": "gray_world",
+            "白斑算法 (White Patch)": "white_patch", 
+            "簡單平均 (Simple Average)": "simple_avg",
+            "直方圖拉伸 (Histogram Stretch)": "histogram_stretch"
+        }
+    
+    def pil_to_cv2(self, pil_image):
+        """將PIL圖像轉換為OpenCV格式"""
+        # 轉換為RGB（如果是RGBA則去除alpha通道）
+        if pil_image.mode == 'RGBA':
+            pil_image = pil_image.convert('RGB')
+        elif pil_image.mode != 'RGB':
+            pil_image = pil_image.convert('RGB')
+        
+        # 轉換為numpy array
+        image_np = np.array(pil_image)
+        # 轉換為BGR格式（OpenCV默認）
+        image_bgr = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
+        return image_bgr
+    
+    def cv2_to_pil(self, cv2_image):
+        """將OpenCV格式轉換為PIL圖像"""
+        # 轉換回RGB
+        image_rgb = cv2.cvtColor(cv2_image, cv2.COLOR_BGR2RGB)
+        # 轉換為PIL圖像
+        pil_image = Image.fromarray(image_rgb.astype(np.uint8))
+        return pil_image
+    
+    def gray_world_algorithm(self, image):
+        """灰世界算法 - 假設圖像的平均顏色應該是灰色"""
+        # 計算每個通道的平均值
+        avg_b = np.mean(image[:, :, 0])
+        avg_g = np.mean(image[:, :, 1])
+        avg_r = np.mean(image[:, :, 2])
+        
+        # 計算整體平均亮度
+        avg_gray = (avg_b + avg_g + avg_r) / 3
+        
+        # 避免除零錯誤
+        scale_b = avg_gray / max(avg_b, 1e-6)
+        scale_g = avg_gray / max(avg_g, 1e-6)
+        scale_r = avg_gray / max(avg_r, 1e-6)
+        
+        # 應用調整
+        result = image.astype(np.float32)
+        result[:, :, 0] *= scale_b
+        result[:, :, 1] *= scale_g
+        result[:, :, 2] *= scale_r
+        
+        return result
+    
+    def white_patch_algorithm(self, image):
+        """白斑算法 - 假設圖像中最亮的點應該是白色"""
+        # 找到每個通道的最大值（排除極值）
+        max_b = np.percentile(image[:, :, 0], 99)
+        max_g = np.percentile(image[:, :, 1], 99)
+        max_r = np.percentile(image[:, :, 2], 99)
+        
+        # 計算調整係數
+        scale_b = 255.0 / max(max_b, 1e-6)
+        scale_g = 255.0 / max(max_g, 1e-6)
+        scale_r = 255.0 / max(max_r, 1e-6)
+        
+        # 應用調整
+        result = image.astype(np.float32)
+        result[:, :, 0] *= scale_b
+        result[:, :, 1] *= scale_g
+        result[:, :, 2] *= scale_r
+        
+        return result
+    
+    def simple_average_algorithm(self, image):
+        """簡單平均算法 - 讓RGB三通道的平均值相等"""
+        # 計算每個通道的平均值
+        avg_b = np.mean(image[:, :, 0])
+        avg_g = np.mean(image[:, :, 1])
+        avg_r = np.mean(image[:, :, 2])
+        
+        # 以綠色通道為基準（人眼對綠色最敏感）
+        reference = avg_g
+        
+        # 計算調整係數
+        scale_b = reference / max(avg_b, 1e-6)
+        scale_g = 1.0  # 綠色通道不變
+        scale_r = reference / max(avg_r, 1e-6)
+        
+        # 應用調整
+        result = image.astype(np.float32)
+        result[:, :, 0] *= scale_b
+        result[:, :, 1] *= scale_g
+        result[:, :, 2] *= scale_r
+        
+        return result
+    
+    def histogram_stretch_algorithm(self, image):
+        """直方圖拉伸算法"""
+        result = image.astype(np.float32)
+        
+        for i in range(3):  # 對每個顏色通道
+            channel = result[:, :, i]
+            
+            # 計算1%和99%百分位數，忽略極值
+            p1 = np.percentile(channel, 1)
+            p99 = np.percentile(channel, 99)
+            
+            # 拉伸到0-255範圍
+            if p99 > p1:
+                channel = (channel - p1) * 255.0 / (p99 - p1)
+                result[:, :, i] = np.clip(channel, 0, 255)
+        
+        return result
+    
+    def preserve_image_brightness(self, original, adjusted):
+        """保持原始圖像的整體亮度"""
+        try:
+            # 計算原始圖像的亮度
+            original_lab = cv2.cvtColor(original.astype(np.uint8), cv2.COLOR_BGR2LAB)
+            original_brightness = np.mean(original_lab[:, :, 0])
+            
+            # 計算調整後圖像的亮度
+            adjusted_lab = cv2.cvtColor(np.clip(adjusted, 0, 255).astype(np.uint8), cv2.COLOR_BGR2LAB)
+            adjusted_brightness = np.mean(adjusted_lab[:, :, 0])
+            
+            # 調整亮度
+            if adjusted_brightness > 1e-6:
+                brightness_ratio = original_brightness / adjusted_brightness
+                adjusted_lab[:, :, 0] = np.clip(adjusted_lab[:, :, 0] * brightness_ratio, 0, 255)
+                
+                # 轉換回BGR
+                result = cv2.cvtColor(adjusted_lab, cv2.COLOR_LAB2BGR)
+                return result.astype(np.float32)
+        except:
+            pass
+        
+        return adjusted
+    
+    def process_image(self, pil_image, method_name, strength, preserve_brightness, clip_values):
+        """處理圖像的主函數"""
+        if pil_image is None:
+            return None, "請上傳一張圖片"
+        
+        try:
+            # 轉換格式
+            cv2_image = self.pil_to_cv2(pil_image)
+            original_image = cv2_image.copy()
+            
+            # 獲取算法名稱
+            method = self.methods.get(method_name, "gray_world")
+            
+            # 選擇算法
+            if method == "gray_world":
+                adjusted = self.gray_world_algorithm(cv2_image)
+                algorithm_info = "使用灰世界算法，假設圖像的平均顏色應該是中性灰"
+            elif method == "white_patch":
+                adjusted = self.white_patch_algorithm(cv2_image)
+                algorithm_info = "使用白斑算法，假設圖像中最亮的區域應該是白色"
+            elif method == "simple_avg":
+                adjusted = self.simple_average_algorithm(cv2_image)
+                algorithm_info = "使用簡單平均算法，平衡RGB三個通道的平均值"
+            elif method == "histogram_stretch":
+                adjusted = self.histogram_stretch_algorithm(cv2_image)
+                algorithm_info = "使用直方圖拉伸算法，增強圖像對比度"
+            else:
+                adjusted = cv2_image.astype(np.float32)
+                algorithm_info = "未知算法"
+            
+            # 應用強度調整
+            if strength != 1.0:
+                adjusted = original_image.astype(np.float32) * (1.0 - strength) + adjusted * strength
+            
+            # 保持亮度
+            if preserve_brightness:
+                adjusted = self.preserve_image_brightness(original_image, adjusted)
+            
+            # 裁剪數值範圍
+            if clip_values:
+                adjusted = np.clip(adjusted, 0, 255)
+            
+            # 轉換回PIL格式
+            result_pil = self.cv2_to_pil(adjusted.astype(np.uint8))
+            
+            # 生成處理信息
+            info = f"✅ 處理完成！\n算法：{algorithm_info}\n強度：{strength:.1f}\n保持亮度：{'是' if preserve_brightness else '否'}"
+            
+            return result_pil, info
+            
+        except Exception as e:
+            return None, f"❌ 處理出錯：{str(e)}"
+
+# 創建白平衡處理器實例
+wb_processor = AutoWhiteBalance()
+
+def process_white_balance(image, method, strength, preserve_brightness, clip_values):
+    """Gradio接口函數"""
+    return wb_processor.process_image(image, method, strength, preserve_brightness, clip_values)
+
+# 創建Gradio界面
+def create_interface():
+    with gr.Blocks(
+        title="🎨 自動白平衡校正工具",
+        theme=gr.themes.Soft(),
+        css="""
+        .gradio-container {
+            max-width: 1200px !important;
+        }
+        .image-container {
+            max-height: 600px;
+        }
+        """
+    ) as demo:
+        
+        gr.Markdown("""
+        # 🎨 自動白平衡校正工具
+        
+        上傳有色偏問題的圖片，選擇適合的算法自動校正白平衡，讓圖片恢復自然的色彩！
+        
+        💡 **使用建議**：
+        - 🟡 **暖色偏（偏黃）**：推薦使用「灰世界算法」
+        - 🔵 **冷色偏（偏藍）**：推薦使用「簡單平均」或「白斑算法」
+        - 🌈 **色彩不鮮豔**：推薦使用「直方圖拉伸」
+        """)
+        
+        with gr.Row():
+            with gr.Column(scale=1):
+                # 輸入區域
+                gr.Markdown("### 📤 上傳圖片")
+                input_image = gr.Image(
+                    label="選擇要處理的圖片",
+                    type="pil",
+                    height=400
+                )
+                
+                # 參數設置
+                gr.Markdown("### ⚙️ 調整參數")
+                
+                method_dropdown = gr.Dropdown(
+                    choices=list(wb_processor.methods.keys()),
+                    value="灰世界算法 (Gray World)",
+                    label="白平衡算法",
+                    info="選擇適合的白平衡校正算法"
+                )
+                
+                strength_slider = gr.Slider(
+                    minimum=0.0,
+                    maximum=2.0,
+                    value=1.0,
+                    step=0.1,
+                    label="調整強度",
+                    info="控制校正效果的強度（0=不調整，1=標準，2=強化）"
+                )
+                
+                preserve_brightness_checkbox = gr.Checkbox(
+                    value=True,
+                    label="保持原始亮度",
+                    info="保持圖片的整體明暗度不變"
+                )
+                
+                clip_values_checkbox = gr.Checkbox(
+                    value=True,
+                    label="裁剪數值範圍",
+                    info="避免過度調整造成的色彩異常"
+                )
+                
+                # 處理按鈕
+                process_btn = gr.Button(
+                    "🚀 開始處理",
+                    variant="primary",
+                    size="lg"
+                )
+            
+            with gr.Column(scale=1):
+                # 輸出區域
+                gr.Markdown("### 📥 處理結果")
+                output_image = gr.Image(
+                    label="處理後的圖片",
+                    type="pil",
+                    height=400
+                )
+                
+                # 處理信息
+                process_info = gr.Textbox(
+                    label="處理信息",
+                    lines=4,
+                    interactive=False
+                )
+                
+                # 下載提示
+                gr.Markdown("""
+                ### 💾 保存圖片
+                右鍵點擊處理後的圖片，選擇「另存圖片為...」即可保存到本地。
+                """)
+        
+        # 示例圖片
+        gr.Markdown("### 📸 示例圖片")
+        gr.Examples(
+            examples=[
+                ["example1.jpg", "灰世界算法 (Gray World)", 1.0, True, True],
+                ["example2.jpg", "白斑算法 (White Patch)", 0.8, True, True],
+            ],
+            inputs=[input_image, method_dropdown, strength_slider, preserve_brightness_checkbox, clip_values_checkbox],
+            outputs=[output_image, process_info]
+        )
+        
+        # 綁定處理函數
+        process_btn.click(
+            fn=process_white_balance,
+            inputs=[input_image, method_dropdown, strength_slider, preserve_brightness_checkbox, clip_values_checkbox],
+            outputs=[output_image, process_info]
+        )
+        
+        # 實時預覽（可選）
+        for component in [method_dropdown, strength_slider, preserve_brightness_checkbox, clip_values_checkbox]:
+            component.change(
+                fn=process_white_balance,
+                inputs=[input_image, method_dropdown, strength_slider, preserve_brightness_checkbox, clip_values_checkbox],
+                outputs=[output_image, process_info]
+            )
+        
+        # 添加說明
+        gr.Markdown("""
+        ---
+        ### 📚 算法說明
+        
+        - **灰世界算法**：適合校正整體色偏，特別是暖色偏（偏黃/橙）
+        - **白斑算法**：適合有明顯白色或亮色區域的圖片
+        - **簡單平均**：溫和的校正方式，適合輕微色偏
+        - **直方圖拉伸**：增強對比度，讓色彩更鮮豔
+        
+        ### 🔧 技術支持
+        基於OpenCV和PIL開發，支持常見的圖片格式（JPG、PNG、WEBP等）
+        """)
+    
+    return demo
+
+# 啟動應用
+if __name__ == "__main__":
+    demo = create_interface()
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=True
+    )

requirements.txt

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+gradio>=4.0.0
+opencv-python-headless>=4.8.0
+Pillow>=9.0.0
+numpy>=1.21.0