Update app.py

app.py

@@ -137,6 +137,7 @@ class ImageProcessor:
     def __init__(self):
         self.model = None
         self.load_model()
+        self.last_results = None
 
     def load_model(self):
         """加载预训练的模型"""
@@ -175,4 +176,360 @@ class ImageProcessor:
             return cv2.filter2D(image, -1, kernel)
         elif filter_type == "柔化":
             return cv2.GaussianBlur(image, (5,5), 0)
-        return image
+        return image
+
+    def generate_analysis_plots(self, saliency_map):
+        """生成分析图表"""
+        # 直方图数据
+        hist_data = saliency_map.flatten()
+        fig_hist = px.histogram(hist_data, nbins=50,
+                              title="显著性分布直方图",
+                              labels={'value': '显著性值', 'count': '频率'})
+        
+        # 计算显著性统计
+        regions = np.zeros_like(saliency_map)
+        regions[saliency_map > np.mean(saliency_map)] = 1
+        
+        return fig_hist
+
+    def process_image(self, image, threshold=0.5, testsize=256, 
+                     enhance_contrast=False, denoise=False, 
+                     brightness=0, contrast=0, filter_type="无"):
+        """增强的图像处理函数"""
+        if image is None:
+            return [None] * 7 + ["请提供有效的图像"]
+
+        # 图像预处理
+        if denoise:
+            image = cv2.fastNlMeansDenoisingColored(image, None, 10, 10, 7, 21)
+        
+        # 应用亮度和对比度调整
+        image = self.adjust_brightness_contrast(image, brightness, contrast)
+        
+        # 应用滤镜
+        image = self.apply_filters(image, filter_type)
+        
+        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))
+        
+        # 生成可视化结果
+        res_vis = (res_resized * 255).astype(np.uint8)
+        heatmap = cv2.applyColorMap(res_vis, cv2.COLORMAP_JET)
+        _, binary_mask = cv2.threshold(res_vis, int(255 * threshold), 255, cv2.THRESH_BINARY)
+        
+        # 叠加结果
+        alpha = 0.5
+        original_bgr = cv2.cvtColor(original_image, cv2.COLOR_RGB2BGR)
+        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)
+        
+        # 生成分析图表
+        analysis_plot = self.generate_analysis_plots(res_resized)
+        
+        # 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%}",
+            "处理时间": f"{inference_time:.3f}秒"
+        }
+        
+        # 保存结果供后续分析
+        self.last_results = {
+            'saliency_map': res_resized,
+            'binary_mask': binary_mask,
+            'stats': stats
+        }
+        
+        return (original_image, res_vis, heatmap, overlayed_rgb, segmented_rgb, 
+                f"推理时间: {inference_time:.4f}秒", stats, analysis_plot)
+# 创建处理器实例
+processor = ImageProcessor()
+
+def run_demo(input_image, threshold, enhance_contrast, denoise, show_contours, 
+            brightness, contrast, filter_type):
+    """增强的主处理函数"""
+    if input_image is None:
+        return [None] * 8 + ["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,
+        brightness=brightness,
+        contrast=contrast,
+        filter_type=filter_type
+    )
+    
+    original, saliency_map, heatmap, overlayed, segmented, time_info, stats, analysis_plot = 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, analysis_plot)
+
+# 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):
+                    # 输入控制面板
+                    with gr.Group(elem_classes="control-panel"):
+                        input_image = gr.Image(
+                            label="输入图像",
+                            type="numpy",
+                            elem_classes="input-image"
+                        )
+                        
+                        with gr.Accordion("基础设置", open=True):
+                            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
+                            )
+                        
+                        with gr.Accordion("图像调整", open=False):
+                            brightness = gr.Slider(
+                                minimum=-100,
+                                maximum=100,
+                                value=0,
+                                step=1,
+                                label="亮度"
+                            )
+                            contrast = gr.Slider(
+                                minimum=-100,
+                                maximum=100,
+                                value=0,
+                                step=1,
+                                label="对比度"
+                            )
+                            filter_type = gr.Radio(
+                                choices=["无", "锐化", "柔化"],
+                                value="无",
+                                label="图像滤镜"
+                            )
+                        
+                        with gr.Row():
+                            submit_btn = gr.Button(
+                                "开始检测",
+                                variant="primary",
+                                elem_classes="custom-button"
+                            )
+                            reset_btn = gr.Button(
+                                "重置参数",
+                                elem_classes="custom-button"
+                            )
+                
+                with gr.Column(scale=2):
+                    # 结果显示区域
+                    with gr.Tabs():
+                        with gr.TabItem("检测结果"):
+                            with gr.Row(elem_classes="result-gallery"):
+                                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(elem_classes="result-gallery"):
+                                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.TabItem("分析报告"):
+                            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"
+                                )
+                                analysis_plot = gr.Plot(
+                                    label="显著性分布分析"
+                                )
+
+        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
+                   - 可选择是否增强对比度或进行降噪
+                   - 在图像调整面板中调整亮度、对比度和滤镜
+                3. **开始检测**: 点击"开始检测"按钮进行分析
+                4. **查看结果**: 在不同标签页中查看分析结果
+                
+                ## 功能说明
+                - **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
+                - **分析报告**: 查看详细的统计信息和分析图表
+                """
+            )
+
+        with gr.TabItem("ℹ️ 关于"):
+            gr.Markdown(
+                """
+                ## 项目信息
+                - **版本**: 2.0.0
+                - **技术架构**: PyTorch + Gradio
+                - **模型**: CyueNet
+                
+                ## 主要特点
+                - 实时图像处理和分析
+                - 多维度结果可视化
+                - 丰富的图像调整选项
+                - 详细的数据分析报告
+                
+                ## 更新日志
+                - 增加了图像预处理选项
+                - 添加了统计分析功能
+                - 优化了用户界面
+                - 提升了处理性能
+                """
+            )
+    
+    # 重置参数函数
+    def reset_params():
+        return {
+            threshold_slider: 50,
+            brightness: 0,
+            contrast: 0,
+            filter_type: "无",
+            enhance_contrast: False,
+            denoise: False,
+            show_contours: True
+        }
+    
+    # 设置事件处理
+    submit_btn.click(
+        fn=run_demo,
+        inputs=[
+            input_image,
+            threshold_slider,
+            enhance_contrast,
+            denoise,
+            show_contours,
+            brightness,
+            contrast,
+            filter_type
+        ],
+        outputs=[
+            original_output,
+            saliency_output,
+            heatmap_output,
+            overlayed_output,
+            segmented_output,
+            time_info,
+            stats_output,
+            analysis_plot
+        ]
+    )
+    
+    reset_btn.click(
+        fn=reset_params,
+        inputs=[],
+        outputs=[
+            threshold_slider,
+            brightness,
+            contrast,
+            filter_type,
+            enhance_contrast,
+            denoise,
+            show_contours
+        ]
+    )
+
+# 启动应用
+if __name__ == "__main__":
+    demo.launch(share=True)