human

human_analysis_service.py

@@ -0,0 +1,231 @@
+import os
+import uuid
+import time
+import requests
+import numpy as np
+import json
+import torch
+import cv2
+from PIL import Image
+from dashscope import MultiModalConversation
+
+# 配置 (与 fastAPI_tarot.py 保持一致或通过参数传入)
+# 这里的常量可以根据需要调整，或者从主文件传入
+QWEN_MODEL = 'qwen-vl-max'
+
+def load_image_from_url(url: str) -> Image.Image:
+    """
+    从 URL 下载图片并转换为 RGB 格式
+    """
+    try:
+        headers = {'User-Agent': 'Mozilla/5.0'}
+        response = requests.get(url, headers=headers, stream=True, timeout=10)
+        response.raise_for_status()
+        image = Image.open(response.raw).convert("RGB")
+        return image
+    except Exception as e:
+        raise Exception(f"无法下载图片: {str(e)}")
+
+def crop_head_with_padding(image: Image.Image, box, padding_ratio=0.1) -> Image.Image:
+    """
+    根据 bounding box 裁剪图片，并添加一定的 padding
+    box格式: [x1, y1, x2, y2]
+    """
+    img_w, img_h = image.size
+    x1, y1, x2, y2 = box
+    
+    w = x2 - x1
+    h = y2 - y1
+    
+    # 计算 padding
+    pad_w = w * padding_ratio
+    pad_h = h * padding_ratio
+    
+    # 应用 padding 并确保不越界
+    new_x1 = max(0, int(x1 - pad_w))
+    new_y1 = max(0, int(y1 - pad_h))
+    new_x2 = min(img_w, int(x2 + pad_w))
+    new_y2 = min(img_h, int(y2 + pad_h))
+    
+    return image.crop((new_x1, new_y1, new_x2, new_y2))
+
+def create_highlighted_visualization(image: Image.Image, masks, output_path: str):
+    """
+    创建一个突出显示头部（Mask区域）的可视化图，背景变暗
+    """
+    # Convert PIL to numpy RGB
+    img_np = np.array(image)
+    
+    # Create darkened background (e.g., 30% brightness)
+    darkened_np = (img_np * 0.3).astype(np.uint8)
+    
+    # Combine all masks
+    if len(masks) > 0:
+        combined_mask = np.zeros(img_np.shape[:2], dtype=bool)
+        for mask in masks:
+            # Handle tensor/numpy conversions
+            if isinstance(mask, torch.Tensor):
+                m = mask.cpu().numpy().squeeze()
+            else:
+                m = mask.squeeze()
+            
+            # Ensure 2D
+            if m.ndim > 2:
+                 m = m[0]
+                 
+            # Threshold if probability or float
+            if m.dtype != bool:
+                m = m > 0.5
+                
+            # Resize mask if it doesn't match image size (rare but possible with some internal resizing)
+            if m.shape != img_np.shape[:2]:
+                 # resize to match image
+                 m = cv2.resize(m.astype(np.uint8), (img_np.shape[1], img_np.shape[0]), interpolation=cv2.INTER_NEAREST).astype(bool)
+
+            combined_mask = np.logical_or(combined_mask, m)
+            
+        # Expand mask to 3 channels for broadcasting
+        mask_3ch = np.stack([combined_mask]*3, axis=-1)
+        
+        # Composite: Original where mask is True, Darkened where False
+        result_np = np.where(mask_3ch, img_np, darkened_np)
+    else:
+        result_np = darkened_np # No masks, just dark
+        
+    # Save
+    Image.fromarray(result_np).save(output_path)
+
+def analyze_demographics_with_qwen(image_path: str) -> dict:
+    """
+    调用 Qwen-VL 模型分析人物的年龄和性别
+    """
+    try:
+        # 确保路径是绝对路径
+        abs_path = os.path.abspath(image_path)
+        file_url = f"file://{abs_path}"
+        
+        # 构造 Prompt
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"image": file_url},
+                    {"text": """请仔细观察这张图片中的人物头部/面部特写：
+1. 识别性别 (Gender)：男性/女性
+2. 预估年龄 (Age)：请给出一个合理的年龄范围，例如 "25-30岁"
+3. 简要描述：发型、发色、是否有眼镜等显著特征。
+
+请以 JSON 格式返回，包含 'gender', 'age', 'description' 字段。
+不要包含 Markdown 标记。"""}
+                ]
+            }
+        ]
+        
+        # 调用模型
+        response = MultiModalConversation.call(model=QWEN_MODEL, messages=messages)
+        
+        if response.status_code == 200:
+            content = response.output.choices[0].message.content[0]['text']
+            # 清理 Markdown 代码块标记
+            clean_content = content.replace("```json", "").replace("```", "").strip()
+            try:
+                result = json.loads(clean_content)
+                return result
+            except json.JSONDecodeError:
+                return {"raw_analysis": clean_content}
+        else:
+            return {"error": f"API Error: {response.code} - {response.message}"}
+            
+    except Exception as e:
+        return {"error": f"分析失败: {str(e)}"}
+
+def process_face_segmentation_and_analysis(
+    processor, 
+    image: Image.Image, 
+    prompt: str = "head", 
+    output_base_dir: str = "static/results"
+) -> dict:
+    """
+    核心处理逻辑：
+    1. SAM3 分割 (默认提示词 "head" 以包含头发)
+    2. 裁剪图片
+    3. Qwen-VL 识别性别年龄
+    4. 返回结果
+    """
+    
+    # 1. SAM3 推理
+    inference_state = processor.set_image(image)
+    output = processor.set_text_prompt(state=inference_state, prompt=prompt)
+    masks, boxes, scores = output["masks"], output["boxes"], output["scores"]
+    
+    detected_count = len(masks)
+    if detected_count == 0:
+        return {
+            "status": "success",
+            "message": "未检测到目标",
+            "detected_count": 0,
+            "results": []
+        }
+        
+    # 准备结果目录
+    request_id = f"{int(time.time())}_{uuid.uuid4().hex[:8]}"
+    output_dir = os.path.join(output_base_dir, request_id)
+    os.makedirs(output_dir, exist_ok=True)
+    
+    # --- 新增：生成背景变暗的整体可视化图 ---
+    vis_filename = f"seg_{uuid.uuid4().hex}.jpg"
+    vis_path = os.path.join(output_dir, vis_filename)
+    try:
+        create_highlighted_visualization(image, masks, vis_path)
+        full_vis_relative_path = f"results/{request_id}/{vis_filename}"
+    except Exception as e:
+        print(f"可视化生成失败: {e}")
+        full_vis_relative_path = None
+    # -------------------------------------
+
+    results = []
+    
+    # 转换 boxes 为 numpy
+    if isinstance(boxes, torch.Tensor):
+        boxes_np = boxes.cpu().numpy()
+    else:
+        boxes_np = boxes
+    
+    # 转换 scores 为 list
+    if isinstance(scores, torch.Tensor):
+        scores_list = scores.tolist()
+    else:
+        scores_list = scores if isinstance(scores, list) else [float(scores)]
+
+    for i, box in enumerate(boxes_np):
+        # 2. 裁剪 (带一点 padding 以保留完整发型)
+        # 2. 裁剪 (带一点 padding 以保留完整发型)
+        cropped_img = crop_head_with_padding(image, box, padding_ratio=0.1)
+        
+        # 保存裁剪图
+        filename = f"face_{i}.jpg"
+        save_path = os.path.join(output_dir, filename)
+        cropped_img.save(save_path)
+        
+        # 3. 识别
+        analysis = analyze_demographics_with_qwen(save_path)
+        
+        # 构造返回结果
+        # 注意：URL 生成需要依赖外部的 request context，这里只返回相对路径或文件名
+        # 由调用方组装完整 URL
+        results.append({
+            "filename": filename,
+            "relative_path": f"results/{request_id}/{filename}",
+            "analysis": analysis,
+            "score": float(scores_list[i]) if i < len(scores_list) else 0.0
+        })
+        
+    return {
+        "status": "success",
+        "message": f"成功检测并分析 {detected_count} 个人脸",
+        "detected_count": detected_count,
+        "request_id": request_id,
+        "full_visualization": full_vis_relative_path, # 返回相对路径
+        "scores": scores_list, # 返回全部分数
+        "results": results
+    }