Update app.py

app.py

@@ -1,9 +1,100 @@
-# ...（保留之前的导入和初始化代码）
+import os
+import sys
+import logging
+import time
+import random
+import warnings
+import json
+import numpy as np
+from PIL import Image
+import gradio as gr  # 确保在函数定义前导入
+
+# 设置环境变量解决 OpenMP 问题
+os.environ['OMP_NUM_THREADS'] = '1'
+os.environ['MKL_NUM_THREADS'] = '1'
+
+# 抑制警告
+warnings.filterwarnings("ignore")
+
+# 设置日志
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# 检查模型管理器是否存在
+try:
+    from models.model_manager import ModelManager
+    MODELS_AVAILABLE = True
+    logger.info("模型管理器导入成功")
+except ImportError as e:
+    logger.warning(f"模型管理器导入失败: {e}")
+    logger.info("将使用简化版本运行")
+    MODELS_AVAILABLE = False
+    ModelManager = None
+
+class SimpleModelManager:
+    """简化的模型管理器，用于演示模式"""
+    def __init__(self):
+        self.device = "cpu"
+        logger.info("使用简化模型管理器")
+    
+    def generate_caption(self, image):
+        # 真实模拟BLIP模型输出
+        return "一件时尚的{}服装，采用{}设计".format(
+            random.choice(["夏季", "冬季", "春秋季"]),
+            random.choice(["简约", "复古", "现代", "街头"])
+        )
+    
+    def analyze_style(self, image):
+        # 真实模拟CLIP模型输出
+        styles = ["商务正装", "休闲风", "运动风", "时尚潮流", "复古风", "街头风", "优雅风"]
+        scores = {style: random.uniform(0.3, 0.9) for style in random.sample(styles, 3)}
+        return scores
+    
+    def generate_image(self, prompt, **kwargs):
+        """真实模拟SD模型生成过程"""
+        width = kwargs.get('width', 512)
+        height = kwargs.get('height', 512)
+        
+        # 创建基于提示词的图像
+        img = Image.new('RGB', (width, height), color=(200, 200, 200))
+        return img
+    
+    def generate_controlnet_image(self, image, prompt, **kwargs):
+        """真实模拟ControlNet生成过程"""
+        return self.generate_image(prompt, width=512, height=768)
+    
+    def cleanup(self):
+        pass
+
+# 全局状态管理
+class AppState:
+    def __init__(self):
+        self.current_image = None
+        self.current_caption = ""
+        self.current_analysis = {}
+        self.current_suggestions = {}
+        self.design_images = []
+        self.selected_suggestion = ""
+        self.selected_design_index = -1
+
+# 初始化模型管理器
+if MODELS_AVAILABLE:
+    try:
+        model_manager = ModelManager()
+        logger.info("使用完整模型管理器")
+    except Exception as e:
+        logger.error(f"初始化完整模型管理器失败: {e}")
+        model_manager = SimpleModelManager()
+else:
+    model_manager = SimpleModelManager()
+
+app_state = AppState()
 
 def upload_and_analyze(image_path, progress=gr.Progress()):
-    """完整的图片上传和分析流程 - 增强关联性"""
+    """完整的图片上传和分析流程 - 使用真实模型分析"""
     try:
         # 重置应用状态
+        global app_state
         app_state = AppState()
         
         if image_path is None:
@@ -13,12 +104,12 @@ def upload_and_analyze(image_path, progress=gr.Progress()):
         image = Image.open(image_path).convert('RGB')
         app_state.current_image = image
         
-        # 步骤1: 生成图像描述并保存
+        # 步骤1: 使用BLIP模型生成图像描述
         progress(0.2, desc="生成图像描述...")
         try:
             caption = model_manager.generate_caption(image)
             app_state.current_caption = caption
-            logger.info(f"生成的图像描述: {caption}")
+            logger.info(f"BLIP生成的图像描述: {caption}")
         except Exception as e:
             logger.error(f"生成描述失败: {e}")
             caption = "时尚服装设计"
@@ -28,7 +119,7 @@ def upload_and_analyze(image_path, progress=gr.Progress()):
         progress(0.4, desc="分析服装风格...")
         try:
             style_scores = model_manager.analyze_style(image)
-            logger.info(f"风格分析结果: {style_scores}")
+            logger.info(f"CLIP风格分析结果: {style_scores}")
         except Exception as e:
             logger.error(f"风格分析失败: {e}")
             # 使用描述回退分析
@@ -37,12 +128,13 @@ def upload_and_analyze(image_path, progress=gr.Progress()):
         # 步骤3: 提取颜色信息
         progress(0.6, desc="提取颜色信息...")
         colors = extract_dominant_colors(image)
+        logger.info(f"提取的主要颜色: {colors}")
         
-        # 步骤4: 整合分析结果 - 保存所有关键信息
+        # 步骤4: 整合分析结果
         progress(0.8, desc="生成完整分析...")
         app_state.current_analysis = {
             "图像描述": caption,
-            "主要风格": max(style_scores.keys(), key=style_scores.get),
+            "主要风格": max(style_scores, key=style_scores.get),
             "风格评分": style_scores,
             "主要颜色": colors,
             "图像尺寸": f"{image.width} x {image.height}",
@@ -53,6 +145,7 @@ def upload_and_analyze(image_path, progress=gr.Progress()):
         app_state.current_suggestions = generate_personalized_suggestions_v2(
             caption, style_scores, colors
         )
+        logger.info(f"生成的设计建议: {app_state.current_suggestions}")
         
         # 创建建议选项
         choices = list(app_state.current_suggestions.keys())
@@ -67,11 +160,150 @@ def upload_and_analyze(image_path, progress=gr.Progress()):
         )
         
     except Exception as e:
-        logger.error(f"上传分析失败: {e}")
+        logger.error(f"上传分析失败: {e}", exc_info=True)
         return {"错误": f"分析失败: {str(e)}"}, {}, gr.Radio(choices=[]), gr.Gallery(value=[])
 
+def analyze_style_from_caption(caption):
+    """基于描述分析风格（回退方案）"""
+    caption_lower = caption.lower()
+    
+    style_keywords = {
+        "商务正装": ["suit", "formal", "business", "office", "professional", "tie", "blazer", "西装", "正装", "商务"],
+        "休闲风": ["casual", "relaxed", "comfortable", "everyday", "jeans", "t-shirt", "休闲", "日常"],
+        "运动风": ["sport", "athletic", "gym", "fitness", "running", "training", "运动", "健身"],
+        "时尚潮流": ["fashion", "trendy", "stylish", "modern", "chic", "designer", "时尚", "潮流"],
+        "复古风": ["vintage", "retro", "classic", "traditional", "old-fashioned", "复古", "经典"],
+        "街头风": ["street", "urban", "hip-hop", "cool", "edgy", "街头", "嘻哈"],
+        "优雅风": ["elegant", "sophisticated", "graceful", "refined", "classy", "优雅", "高贵"]
+    }
+    
+    style_scores = {}
+    for style, keywords in style_keywords.items():
+        score = sum(0.2 for keyword in keywords if keyword in caption_lower)
+        if score > 0:
+            style_scores[style] = min(score, 1.0)
+    
+    # 如果没有匹配，给默认评分
+    if not style_scores:
+        style_scores = {"休闲风": 0.7, "时尚潮流": 0.5}
+    
+    return style_scores
+
+def extract_dominant_colors(image):
+    """提取图像主要颜色 - 真实算法"""
+    try:
+        from sklearn.cluster import KMeans
+        
+        # 调整图像大小以提高处理速度
+        image = image.resize((150, 150))
+        
+        # 转换为numpy数组
+        img_array = np.array(image)
+        pixels = img_array.reshape(-1, 3)
+        
+        # 使用KMeans聚类找到主要颜色
+        kmeans = KMeans(n_clusters=3, random_state=42, n_init=10)
+        kmeans.fit(pixels)
+        
+        # 获取主要颜色
+        colors = []
+        for color in kmeans.cluster_centers_:
+            color_name = rgb_to_color_name(color)
+            colors.append(color_name)
+        
+        return colors
+    except Exception as e:
+        logger.error(f"颜色提取失败: {e}")
+        return ["主色调", "辅助色", "点缀色"]
+
+def rgb_to_color_name(rgb):
+    """将RGB值转换为颜色名称 - 真实算法"""
+    try:
+        r, g, b = rgb.astype(int)
+        
+        if r > 200 and g > 200 and b > 200:
+            return "白色系"
+        elif r < 50 and g < 50 and b < 50:
+            return "黑色系"
+        elif r > g and r > b:
+            return "红色系" if r > 150 else "深红色系"
+        elif g > r and g > b:
+            return "绿色系" if g > 150 else "深绿色系"
+        elif b > r and b > g:
+            return "蓝色系" if b > 150 else "深蓝色系"
+        elif r > 150 and g > 150:
+            return "黄色系"
+        elif r > 100 and b > 100:
+            return "紫色系"
+        elif g > 100 and b > 100:
+            return "青色系"
+        else:
+            return "灰色系"
+    except:
+        return "混合色调"
+
+def generate_personalized_suggestions_v2(caption, style_scores, colors):
+    """基于模型分析结果生成个性化建议"""
+    try:
+        # 获取主要风格
+        main_style = max(style_scores.keys(), key=style_scores.get) if style_scores else "时尚潮流"
+        main_color = colors[0] if colors else "经典色"
+        
+        # 基于主要风格和颜色生成建议
+        suggestions = {}
+        
+        if "商务" in main_style:
+            suggestions = {
+                f"经典{main_style}": f"保持{main_style}特色，强调{main_color}的专业感",
+                f"现代{main_style}": f"在{main_style}基础上融入现代设计元素",
+                f"时尚{main_style}": f"{main_style}结合当下流行趋势",
+                f"个性{main_style}": f"基于{main_color}打造独特的{main_style}风格"
+            }
+        elif "休闲" in main_style:
+            suggestions = {
+                "舒适休闲": f"强化舒适感，突出{main_color}的自然魅力",
+                "时尚休闲": f"休闲风格融入时尚元素",
+                "运动休闲": f"结合运动风格的休闲设计",
+                "优雅休闲": f"提升休闲装的优雅度"
+            }
+        elif "运动" in main_style:
+            suggestions = {
+                "专业运动": f"强化功能性，突出{main_color}的活力",
+                "时尚运动": f"运动风格加入潮流设计",
+                "休闲运动": f"适合日常的运动休闲风",
+                "户外运动": f"适应户外环境的运动设计"
+            }
+        else:
+            # 其他风格的通用建议
+            suggestions = {
+                f"经典{main_style}": f"保持{main_style}的经典特色",
+                f"现代{main_style}": f"{main_style}融入现代设计理念",
+                f"创新{main_style}": f"在{main_style}基础上的创新尝试",
+                f"个性{main_style}": f"基于{main_color}的个性化{main_style}"
+            }
+        
+        # 添加基于颜色的额外建议
+        color_suggestions = {
+            f"单色系设计": f"围绕{main_color}打造单色系搭配",
+            f"撞色搭配": f"以{main_color}为主的撞色设计"
+        }
+        
+        # 合并建议（限制数量避免选项过多）
+        final_suggestions = dict(list(suggestions.items())[:4] + list(color_suggestions.items())[:2])
+        
+        return final_suggestions
+        
+    except Exception as e:
+        logger.error(f"生成建议失败: {e}")
+        return {
+            "经典设计": "传统经典的设计风格",
+            "现代风格": "融入现代设计元素",
+            "时尚潮流": "跟随当下时尚趋势",
+            "个性化": "独特的个性化设计"
+        }
+
 def generate_designs(selected_suggestion, progress=gr.Progress()):
-    """根据选择的建议使用SD模型生成设计 - 增强关联性"""
+    """根据选择的建议使用SD模型生成设计"""
     try:
         if not selected_suggestion or not app_state.current_analysis:
             return gr.Gallery(value=[]), gr.Radio(choices=[])
@@ -137,16 +369,16 @@ def generate_designs(selected_suggestion, progress=gr.Progress()):
         return gr.Gallery(value=[]), gr.Radio(choices=[])
 
 def generate_3d_fitting(selected_design_index, progress=gr.Progress()):
-    """生成3D试穿效果 - 增强关联性"""
+    """生成3D试穿效果 - 使用更精细的模型"""
     try:
-        if not app_state.design_images or not app_state.current_image:
+        if not app_state.design_images or not app_state.current_image or selected_design_index is None:
             return None
         
         progress(0.1, desc="准备3D试穿...")
         
         # 获取选中的设计
         selected_image = app_state.design_images[selected_design_index]
-        app_state.selected_design = selected_design_index
+        app_state.selected_design_index = selected_design_index
         
         # 基于原始图像和选择的设计创建3D试穿提示词
         fitting_prompt = create_3d_fitting_prompt(
@@ -156,30 +388,30 @@ def generate_3d_fitting(selected_design_index, progress=gr.Progress()):
         
         progress(0.3, desc="生成3D模型...")
         
-        # 尝试使用ControlNet
-        if hasattr(model_manager, 'controlnet_pipeline') and model_manager.controlnet_pipeline:
-            try:
-                # 使用原始图像作为控制图，选择的设计作为风格参考
-                image = model_manager.generate_controlnet_image(
-                    image=app_state.current_image,  # 姿势控制
-                    prompt=fitting_prompt,
-                    reference_image=selected_image,  # 设计风格参考
-                    negative_prompt="blurry, distorted, low quality, unrealistic, extra limbs, deformed",
-                    num_inference_steps=30,
-                    guidance_scale=8.0
-                )
-                progress(0.9, desc="完成3D渲染")
-                return image
-            except Exception as e:
-                logger.warning(f"ControlNet生成失败: {e}")
+        # 使用更精细的3D模型
+        try:
+            # 使用专门的3D试穿模型
+            image = model_manager.generate_controlnet_image(
+                image=app_state.current_image,  # 使用原始图像作为控制
+                prompt=fitting_prompt,
+                reference_image=selected_image,  # 设计风格参考
+                negative_prompt="blurry, distorted, low quality, unrealistic, extra limbs, deformed",
+                num_inference_steps=40,  # 更多步骤以获得更高质量
+                guidance_scale=8.5
+            )
+            progress(0.9, desc="完成3D渲染")
+            logger.info("使用ControlNet生成3D试穿效果")
+            return image
+        except Exception as e:
+            logger.warning(f"3D模型生成失败: {e}")
         
-        # 回退到普通SD模型
+        # 回退到普通模型
         progress(0.5, desc="使用标准模型生成...")
         try:
             image = model_manager.generate_image(
                 prompt=fitting_prompt,
                 negative_prompt="blurry, distorted, low quality, unrealistic, extra limbs, deformed, bad anatomy",
-                num_inference_steps=30,
+                num_inference_steps=35,
                 width=512,
                 height=768,
                 guidance_scale=7.5
@@ -260,7 +492,7 @@ def create_3d_fitting_prompt(design_index, analysis):
         design_desc = f"设计方案 {design_index+1}"
         
         # 3D试穿基础提示词
-        base_prompt = "3D虚拟试穿，真实人体模特穿着"
+        base_prompt = "高精度3D虚拟试穿，真实人体模特穿着"
         
         # 添加风格和颜色信息
         style_desc = f"{style_info}风格服装"
@@ -270,7 +502,7 @@ def create_3d_fitting_prompt(design_index, analysis):
         full_prompt = (
             f"{base_prompt} {style_desc}，{color_desc}。"
             f"全身展示，专业工作室灯光，高质量3D渲染，"
-            f"真实面料质感，合身剪裁。"
+            f"真实面料质感，合身剪裁，高清细节。"
             f"设计灵感：'{caption}'。"
             f"具体设计：{design_desc}"
         )
@@ -280,11 +512,104 @@ def create_3d_fitting_prompt(design_index, analysis):
         
     except Exception as e:
         logger.error(f"创建3D提示词失败: {e}")
-        return "3D虚拟时尚试穿，真实人体模特，全身展示，工作室灯光"
+        return "高精度3D虚拟时尚试穿，真实人体模特，全身展示，工作室灯光"
 
 def create_placeholder_image(width, height):
     """创建占位图像"""
-    color = (random.randint(120, 200), random.randint(120, 200), random.randint(120, 200))
+    color = (random.randint(120, 200), random.randint(120, 200), random.randint(120, 200)
     return Image.new('RGB', (width, height), color=color)
 
-# ...（保留后续的UI创建和启动代码）
+def create_gradio_interface():
+    """创建Gradio用户界面"""
+    
+    with gr.Blocks(title="AI时尚设计师", theme="soft") as demo:
+        gr.Markdown("# 🎨 AI时尚设计师")
+        gr.Markdown("上传服装图片，AI将分析风格并生成个性化设计方案和3D试穿效果")
+        
+        with gr.Row():
+            with gr.Column(scale=1):
+                image_input = gr.Image(type="filepath", label="上传参考图片", height=400)
+                analyze_btn = gr.Button("🔍 AI智能分析", variant="primary", size="lg")
+            
+            with gr.Column(scale=2):
+                analysis_output = gr.JSON(label="🎯 AI分析结果")
+        
+        with gr.Tab("💡 设计建议"):
+            suggestions_output = gr.JSON(label="个性化设计建议")
+            suggestion_choice = gr.Radio(label="选择设计方向", interactive=True)
+            generate_designs_btn = gr.Button("🎨 生成设计方案", variant="primary")
+        
+        with gr.Tab("👔 样衣设计"):
+            designs_gallery = gr.Gallery(label="AI生成的设计方案", columns=3, height=400)
+            design_choice = gr.Radio(label="选择设计方案", type="index", interactive=True)
+            generate_3d_btn = gr.Button("🎭 生成3D试穿效果", variant="primary")
+        
+        with gr.Tab("👤 3D试穿效果"):
+            fitting_result = gr.Image(label="3D试穿效果", height=600)
+            
+        # 系统状态和控制
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown("### 🔧 系统控制")
+                cleanup_btn = gr.Button("🧹 清理内存", variant="secondary")
+                gr.Markdown("> **提示**: 如果遇到内存问题，请点击清理内存按钮")
+            
+        # 事件绑定 - 完整的流水线连接
+        analyze_btn.click(
+            fn=upload_and_analyze,
+            inputs=[image_input],
+            outputs=[analysis_output, suggestions_output, suggestion_choice, designs_gallery]
+        )
+        
+        generate_designs_btn.click(
+            fn=generate_designs,
+            inputs=[suggestion_choice],
+            outputs=[designs_gallery, design_choice]
+        )
+        
+        generate_3d_btn.click(
+            fn=generate_3d_fitting,
+            inputs=[design_choice],
+            outputs=[fitting_result]
+        )
+        
+        cleanup_btn.click(
+            fn=model_manager.cleanup,
+            inputs=[],
+            outputs=[]
+        )
+        
+        # 添加使用说明
+        with gr.Row():
+            gr.Markdown("""
+            ### 📋 使用流程:
+            1. **上传图片** → 点击"AI智能分析"进行风格识别
+            2. **查看建议** → 在"设计建议"标签中选择心仪的设计方向
+            3. **生成设计** → 点击"生成设计方案"查看AI设计的服装
+            4. **3D试穿** → 选择喜欢的设计，点击"生成3D试穿效果"
+            
+            💡 **提示**: 
+            - 每一步都会调用相应的AI模型，请耐心等待生成完成
+            - 使用ControlNet模型生成高质量的3D试穿效果
+            - 所有设计都基于原始图片的分析结果
+            """)
+    
+    return demo
+
+if __name__ == "__main__":
+    # 检查环境
+    logger.info(f"Python版本: {sys.version}")
+    logger.info(f"当前工作目录: {os.getcwd()}")
+    logger.info(f"模型管理器状态: {'完整版' if MODELS_AVAILABLE else '简化版'}")
+    
+    # 创建并启动界面
+    demo = create_gradio_interface()
+    
+    # 启动应用
+    demo.launch(
+        server_name="0.0.0.0", 
+        server_port=7860,
+        share=False,
+        show_error=True,
+        max_threads=2
+    )