Update app.py

app.py

@@ -92,6 +92,45 @@ custom_css = """
         text-align: center;
         box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
     }
+
+    .accordion {
+        background: white;
+        border-radius: var(--border-radius);
+        margin: 10px 0;
+        box-shadow: 0 2px 4px rgba(0,0,0,0.05);
+    }
+    
+    .accordion-header {
+        padding: 15px;
+        cursor: pointer;
+        transition: background-color 0.3s ease;
+    }
+    
+    .accordion-header:hover {
+        background-color: var(--background-color);
+    }
+    
+    .result-card {
+        background: white;
+        padding: 20px;
+        border-radius: var(--border-radius);
+        margin: 10px 0;
+        box-shadow: 0 2px 4px rgba(0,0,0,0.05);
+    }
+    
+    .analysis-container {
+        display: grid;
+        grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));
+        gap: 20px;
+        margin: 20px 0;
+    }
+    
+    .chart-container {
+        height: 300px;
+        background: white;
+        border-radius: var(--border-radius);
+        padding: 15px;
+    }
 """
 
 class ImageProcessor:
@@ -110,261 +149,30 @@ class ImageProcessor:
         self.model.to(device)
         self.model.eval()
 
-    def process_image(self, image, threshold=0.5, testsize=256, enhance_contrast=False, denoise=False):
-        """增强的图像处理函数"""
-        if image is None:
-            return None, None, None, None, None, "请提供有效的图像", {}
-        
-        # 图像预处理选项
-        if denoise:
-            image = cv2.fastNlMeansDenoisingColored(image, None, 10, 10, 7, 21)
-        
-        if enhance_contrast:
-            lab = cv2.cvtColor(image, cv2.COLOR_RGB2LAB)
-            l, a, b = cv2.split(lab)
-            clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
-            l = clahe.apply(l)
-            lab = cv2.merge((l,a,b))
-            image = cv2.cvtColor(lab, cv2.COLOR_LAB2RGB)
-
-        # 保存原始图像
-        original_image = image.copy()
-        
-        # 预处理图像
-        image_pil = Image.fromarray(image).convert('RGB')
-        image_tensor = transform_image(image_pil, testsize)
-        image_tensor = image_tensor.unsqueeze(0)
-        image_tensor = image_tensor.to(device)
-        
-        # 计时
-        time_start = time.time()
-        
-        # 推理
-        with torch.no_grad():
-            x1, res, s1_sig, edg1, edg_s, s2, e2, s2_sig, e2_sig, s3, e3, s3_sig, e3_sig, s4, e4, s4_sig, e4_sig, s5, e5, s5_sig, e5_sig, sk1, sk1_sig, sk2, sk2_sig, sk3, sk3_sig, sk4, sk4_sig, sk5, sk5_sig = self.model(image_tensor)
-        
-        time_end = time.time()
-        inference_time = time_end - time_start
-        
-        # 处理输出结果
-        res = res.sigmoid().data.cpu().numpy().squeeze()
-        res = (res - res.min()) / (res.max() - res.min() + 1e-8)
-        
-        # 调整大小
-        h, w = original_image.shape[:2]
-        res_resized = cv2.resize(res, (w, h))
-        
-        # 应用阈值
-        res_vis = (res_resized * 255).astype(np.uint8)
-        
-        # 创建热力图
-        heatmap = cv2.applyColorMap(res_vis, cv2.COLORMAP_JET)
-        
-        # 二值化处理
-        _, binary_mask = cv2.threshold(res_vis, int(255 * threshold), 255, cv2.THRESH_BINARY)
-        
-        # 叠加结果
-        alpha = 0.5
-        if len(original_image.shape) == 3 and original_image.shape[2] == 3:
-            original_bgr = cv2.cvtColor(original_image, cv2.COLOR_RGB2BGR)
-        else:
-            original_bgr = cv2.cvtColor(original_image, cv2.COLOR_GRAY2BGR)
-        
-        overlayed = cv2.addWeighted(original_bgr, 1-alpha, heatmap, alpha, 0)
-        segmented = cv2.bitwise_and(original_bgr, original_bgr, mask=binary_mask)
-        
-        # 转回RGB格式
-        overlayed_rgb = cv2.cvtColor(overlayed, cv2.COLOR_BGR2RGB)
-        segmented_rgb = cv2.cvtColor(segmented, cv2.COLOR_BGR2RGB)
-        
-        # 计算统计信息
-        stats = {
-            "处理分辨率": f"{w}x{h}",
-            "检测目标数量": str(len(cv2.findContours(binary_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0])),
-            "平均置信度": f"{np.mean(res_resized):.2%}",
-            "最大置信度": f"{np.max(res_resized):.2%}"
-        }
-        
-        return original_image, res_vis, heatmap, overlayed_rgb, segmented_rgb, f"推理时间: {inference_time:.4f}秒", stats
-
-# 创建处理器实例
-processor = ImageProcessor()
-
-def run_demo(input_image, threshold, enhance_contrast, denoise, show_contours):
-    """增强的主处理函数"""
-    if input_image is None:
-        return [None] * 7 + ["请上传图片"]
-    
-    # 处理图像
-    results = processor.process_image(
-        input_image, 
-        threshold=threshold/100.0,
-        enhance_contrast=enhance_contrast,
-        denoise=denoise
-    )
-    
-    original, saliency_map, heatmap, overlayed, segmented, time_info, stats = results
-    
-    # 添加轮廓显示
-    if show_contours and saliency_map is not None:
-        _, binary = cv2.threshold(saliency_map, 127, 255, cv2.THRESH_BINARY)
-        contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-        overlay_with_contours = overlayed.copy()
-        cv2.drawContours(overlay_with_contours, contours, -1, (0,255,0), 2)
-        overlayed = overlay_with_contours
-    
-    # 生成统计信息HTML
-    stats_html = "<div class='statistics-container'>"
-    for key, value in stats.items():
-        stats_html += f"<div class='statistic-card'><h4>{key}</h4><p>{value}</p></div>"
-    stats_html += "</div>"
-    
-    return original, saliency_map, heatmap, overlayed, segmented, time_info, stats_html
-
-# 创建Gradio界面
-with gr.Blocks(title="高级显著性目标检测系统", css=custom_css) as demo:
-    gr.Markdown(
-        """
-        # 🎯 智能显著性目标检测系统
-        ### 基于深度学习的图像显著性检测与分析工具
-        """
-    )
-    
-    with gr.Tabs() as tabs:
-        with gr.TabItem("🔍 主要功能"):
-            with gr.Row():
-                with gr.Column(scale=1):
-                    input_image = gr.Image(
-                        label="输入图像",
-                        type="numpy",
-                        elem_classes="input-image"
-                    )
-                    with gr.Group(elem_classes="slider-component"):
-                        threshold_slider = gr.Slider(
-                            minimum=0,
-                            maximum=100,
-                            value=50,
-                            step=1,
-                            label="检测阈值",
-                            info="调整检测的灵敏度"
-                        )
-                        enhance_contrast = gr.Checkbox(
-                            label="增强对比度",
-                            value=False
-                        )
-                        denoise = gr.Checkbox(
-                            label="降噪处理",
-                            value=False
-                        )
-                        show_contours = gr.Checkbox(
-                            label="显示轮廓",
-                            value=True
-                        )
-                    
-                    submit_btn = gr.Button(
-                        "开始检测",
-                        variant="primary",
-                        elem_classes="custom-button"
-                    )
-                
-                with gr.Column(scale=2):
-                    with gr.Tabs():
-                        with gr.TabItem("检测结果"):
-                            with gr.Row():
-                                original_output = gr.Image(
-                                    label="原始图像",
-                                    elem_classes="output-image"
-                                )
-                                saliency_output = gr.Image(
-                                    label="显著性图",
-                                    elem_classes="output-image"
-                                )
-                            with gr.Row():
-                                heatmap_output = gr.Image(
-                                    label="热力图分析",
-                                    elem_classes="output-image"
-                                )
-                                overlayed_output = gr.Image(
-                                    label="叠加效果",
-                                    elem_classes="output-image"
-                                )
-                            
-                            segmented_output = gr.Image(
-                                label="目标分割",
-                                elem_classes="output-image"
-                            )
-                            
-                            with gr.Group(elem_classes="info-box"):
-                                time_info = gr.Textbox(
-                                    label="处理时间",
-                                    show_label=True
-                                )
-                                stats_output = gr.HTML(
-                                    label="统计信息"
-                                )
-
-        with gr.TabItem("使用指南"):
-            gr.Markdown(
-                """
-                ## 📖 使用说明
-                1. **上传图片**: 点击左侧"输入图像"区域上传待分析的图片
-                2. **调整参数**: 使用阈值滑块调整检测的灵敏度
-                3. **开始检测**: 点击"开始检测"按钮进行分析
-                4. **查看结果**: 系统将显示多个维度的分析结果
-                
-                ## 🎨 输出说明
-                - **显著性图**: 展示目标区域的重要性分布
-                - **热力图**: 使用色彩展示检测强度
-                - **叠加效果**: 将检测结果与原图叠加展示
-                - **目标分割**: 提取关键目标区域
-                
-                ## �� 技术特点
-                - 实时处理：快速准确的目标检测
-                - 多维分析：提供多角度的可视化结果
-                - 交互式操作：支持参数实时调整
-                """
-            )
-
-        with gr.TabItem("关于项目"):
-            gr.Markdown(
-                """
-                ## 🌟 项目信息
-                - **版本**: 1.0.0
-                - **技术架构**: PyTorch + Gradio
-                - **模型**: CyueNet
-                
-                ## 📊 性能指标
-                - 平均处理时间: <1s
-                - 准确率: >95%
-                
-                ## 🔗 相关链接
-                - [项目主页](https://github.com/your-repo)
-                - [技术文档](https://your-docs)
-                - [问题反馈](https://github.com/your-repo/issues)
-                """
-            )
-    
-    # 设置事件处理
-    submit_btn.click(
-        fn=run_demo,
-        inputs=[
-            input_image,
-            threshold_slider,
-            enhance_contrast,
-            denoise,
-            show_contours
-        ],
-        outputs=[
-            original_output,
-            saliency_output,
-            heatmap_output,
-            overlayed_output,
-            segmented_output,
-            time_info,
-            stats_output
-        ]
-    )
-
-# 启动应用
-if __name__ == "__main__":
-    demo.launch(share=True)
+    def adjust_brightness_contrast(self, image, brightness=0, contrast=0):
+        """调整图像亮度和对比度"""
+        if brightness != 0:
+            if brightness > 0:
+                shadow = brightness
+                highlight = 255
+            else:
+                shadow = 0
+                highlight = 255 + brightness
+            alpha_b = (highlight - shadow)/255
+            gamma_b = shadow
+            image = cv2.addWeighted(image, alpha_b, image, 0, gamma_b)
+        if contrast != 0:
+            f = 131*(contrast + 127)/(127*(131-contrast))
+            alpha_c = f
+            gamma_c = 127*(1-f)
+            image = cv2.addWeighted(image, alpha_c, image, 0, gamma_c)
+        return image
+
+    def apply_filters(self, image, filter_type):
+        """应用图像滤镜效果"""
+        if filter_type == "锐化":
+            kernel = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])
+            return cv2.filter2D(image, -1, kernel)
+        elif filter_type == "柔化":
+            return cv2.GaussianBlur(image, (5,5), 0)
+        return image